Scandinavian Journal of Information Systems

Using pre-existing datasets to combine published information with new metrics would help researchers construct a broader picture of chromatin in disease

Moataz Dowaidar — Tue, 02 May 2023 00:00:00 +0000

Using pre-existing datasets to combine published information with new metrics would help researchers construct a broader picture of chromatin in disease. A computational biology goal is the near-real-time integration of epigenomic data sets, irrespective of the laboratory they were generated in—similar to a blood pressure, ECG or troponin test. In addition, epigenome modeling must become dynamic, considering cell-to-cell variability and changes over time due to normal physiological or pathological stressors. Probabilistic modeling and machine learning can help such model creation, while finding (and quantifying) previously identified developing chromatin properties that match heart health changes. A 3D genome representation, for example, may reveal a structural or accessibility attribute connected to health or disease that no single epigenomic test alone can discover. Such strategies can expand basic knowledge of biology and illness.

Incorporating wet and dry lab training components to teach schemes to foster the formation of more diverse technical repertoires. Data mining and fresh data collection will revolutionize how we handle chromatin challenges in coming years. Knowing how computers solve problems (as opposed to how people do) and how to computationally phrase questions would create a shared vocabulary that completes tasks. Team members don't need all the big data skills, but a collaborative attitude is important for effective large-scale epigenomic research. UCLA's QCBio Collaboratory is a great platform for teaching non-programmers and facilitating cooperation to resolve biological issues.

It also encourages the use of open source technology by making genomics datasets available to non-experts.There are already many bioinformatics tools—and others will be developed to introduce new understanding—but basic knowledge of how computers work and how to answer big-data questions will continue to empower scientists to test the most meaningful hypotheses with appropriate tools to reveal new insights about cardiac biology.

Secure-Medishare: A Comprehensive Secure Medical Data-Sharing System Using Blockchain, Watermarking, Steganography, And Optimized Hybrid Cryptography

Walzade Arti Krushnarao and Dr. Sharanbasappa Gandage — Thu, 15 Jun 2023 00:00:00 +0000

Medical data plays a crucial role in healthcare, enabling accurate diagnosis, treatment planning, and research. However, the secure sharing of sensitive medical data and images remains a significant challenge. Existing techniques often fall short in terms of protecting data integrity, confidentiality, and authenticity. To address these limitations, this paper introduces SECURE-MEDISHARE, a novel secure medical data-sharing system that integrates blockchain technology, watermarking, steganography, and enhanced cryptography.The proposed SECURE-MEDISHARE system aims to provide robust security mechanisms for medical data sharing. Unlike centralized systems, which are susceptible to single points of failure and unauthorized access, SECURE-MEDISHARE utilizes blockchain technology to ensure decentralized and tamper-resistant storage and sharing of medical data. SECURE-MEDISHARE employs watermarking for data integrity and authentication and steganography for confidential transmission of metadata, ensuring authenticity, privacy, and confidentiality of medical data. Furthermore, an optimized hybrid cryptography technique is implemented to secure the transmission and storage of medical data, safeguarding confidentiality and privacy.SECURE-MEDISHARE offers several advantages over existing techniques. It provides enhanced security and privacy protection, efficient data sharing and retrieval, and improved trust among healthcare providers. The system ensures the integrity and authenticity of medical data, preventing unauthorized modifications or tampering. Additionally, the decentralized nature of blockchain technology reduces the risk of data breaches and single points of failure. Experimental results show that SECURE-MEDISHARE generates hashes quickly, taking only 65 milliseconds for 100 blocks. Optimized hybrid cryptography used in SECURE-MEDISHARE also outperforms other cryptography combinations, with encryption and decryption times of 5.635 seconds for 96-bit data. These findings highlight the efficiency and effectiveness of SECURE-MEDISHARE for secure medical data and image sharing. The experimental evaluation confirms that SECURE-MEDISHARE is a reliable and robust solution for secure medical data sharing in healthcare environments.

Enhanced Ensemble Fusion Model For Stress Classification And Prediction

Suryavanshi Prashant Maharudra and Dr. Sharanbasappa Gandage — Thu, 15 Jun 2023 00:00:00 +0000

Stress has become a common phenomenon in modern society, and it has been identified as a major factor that affects people's health and well-being. Stress can be caused by various factors, such as work pressure, financial difficulties, relationship problems, and health issues. Prolonged exposure to stress can lead to physical and mental health problems, including anxiety, depression, cardiovascular diseases, and obesity. Accurate stress classification and prediction can help individuals and organizations identify the sources and levels of stress and take appropriate measures to manage stress and prevent negative outcomes. By identifying individuals who are at risk of stress, proactive interventions can be initiated to prevent negative outcomes. Additionally, stress classification and prediction can be useful for designing effective stress management programs and policies that can improve the well-being and productivity of individuals and organizations.Existing systems for stress classification and prediction have limitations in terms of accuracy and efficiency. To overcome these limitations, this paper proposes an Enhanced Ensemble Fusion (EEF)model that combines three ensemble classifiers, namely stacking, bagging, and boosting, using a blending classifier. The EEF model is composed of several classifiers, including the stacking classifier, the bagging classifier, and the boosting classifier, each using an Enhanced J48, Enhanced SVM, and Enhanced Naive Bayes classifier. An Enhanced Logistic Regression classifier is used as a meta-classifier for the stacking classifier. The model was evaluated on a Swell-EDA dataset and WESAD-EDA dataset, and the results show that it outperformed existing systems in terms of accuracy and robustness. The Enhanced Ensemble Fusion Model achieved anaccuracyof 72.86% for WESAD-EDA dataset and 50% for Swell-EDA datasetwhich is significantly higher than the accuracy of individual classifiers and existing ensemble methods. The proposed model provides a promising approach for stress classification and prediction, which can be useful in various applications, such as healthcare, human resources, and education.

Oe-Mdl: Optimized Ensemble Machine And Deep Learning For Fake News Detection

Raut Rahul Ganpat and Dr. Sonawane Vijay Ramnath — Thu, 15 Jun 2023 00:00:00 +0000

The proliferation of fake news in today's digital era has raised concerns about the credibility and trustworthiness of online information. The detection of fake news has become an essential task to protect individuals, organizations, and societies from misinformation and its potential consequences. Existing fake news detection techniques, including rule-based, supervised machine learning, and NLP approaches, have limitations. Rule-based methods lack adaptability, supervised machine learning struggles with generalization, and NLP techniques face challenges in capturing context and nuanced language. To overcome these limitations, this paper introduces the Optimized Ensemble Machine and Deep Learning (OE-MDL) algorithm for effectively detecting fake news. The proposed OE-MDL algorithm addresses the disadvantages of existing techniques by offering several improvements. Firstly, preprocessing methods are employed, including lowercase conversion, tokenization, stop word removal, word stemming, lemmatization, and spell-checking. Additionally, n-grams generation and the computation of term frequency-inverse document frequency (TF-IDF) scores are utilized. By considering a broad range of linguistic and statistical features, OE-MDL aims to capture the nuanced signals that differentiate fake news from real news. Furthermore, OE-MDL combines optimized machine learning (OML) and optimizeddeep learning (ODL) phases for improved classification accuracy and robustness. In the OML phase, base classifiers like optimizedRandomForest, optimizedJ48, optimizedSMO, optimizedNaiveBayes, and optimizedIBk are stacked with anoptimizedMultilayer Perceptron as the Meta classifier. This stacked classifier serves as the base for a bagging classifier, which becomes the classifier for an AdaBoostM1 boosting classifier. In the ODL phase, a Dl4jMlpClassifier serves as the base for a bagging classifier, which becomes the classifier for an AdaBoostM1 boosting classifier. The OML and ODL classifiers are combined using a blending classifier with weighted voting to classify the training set. The trained blending classifier predicts the authenticity of news articles in the testing set. The experimental results demonstrate that the OE-MDL algorithm outperforms existing techniques with the highest accuracy (84.27%), precision (74.17%), recall (85.18%), and F1-Score (79.29%), offering an effective solution to combat the spread of fake news.

Controllability Problems And Stability Problems Of Nonlinear Discrete Dynamical Systems By Using Functional Analytic Techniques

Udit Kumar Patel and Dr. Anjna Rajoria — Thu, 15 Jun 2023 00:00:00 +0000

infinite-dimensional system that may be used to represent propagation and transport processes as well as population dynamics (reproduction, development, and extinction). There will always be a delay in economic systems since choices and effects are separated by a non-zero time. In communication networks, the start and delivery of data is also accompanied by a non-zero time interval. The delay might be caused by a model simplification in some situations. Such systems are distinguished by the fact that their energetic may be characterized using discrepancy equations that incorporate data about the system's history. There are numerous approaches to express such systems numerically

A Machine Learning Model for Automating Irrigation System

Khadri S S and Dr. Arpana Bharani — Thu, 15 Jun 2023 00:00:00 +0000

The India most people are dependent on agriculture-based businesses. It also contributes to our economy. Automation in agriculture will help improve crop production quality and quantity. Additionally, help to minimize the resources required. But automation in agriculture requires a significant amount of investment. Therefore, in order to minimize the automation cost of the agricultural irrigation system a Machine Learning (ML) technique is proposed. The aim of the proposed ML model is to reduce the sensor implementation cost and running cost of the complete remote sensor network. Therefore, we considered a dataset of soil moisture and temperature dataset where the predictable is an irrigation treatment type. The dataset is pre-processed first to transform data for learning, then the k-means clustering is applied for behavioral data analysis. This process groups the similar sensor reading and enhances the learning performance regarding the accuracy and learning time. Finally, two machine learning techniques have been implemented to train and predict the irrigation treatment. This technique minimizes the expenses of implementing the sensors on the ground and their execution and maintenance costs. However, the entire system’s performance depends on the accuracy of the prediction model, therefore in near future, we need to work on improving the prediction accuracy of the proposed model.

Detection and Prevention of Wormhole Attack in Mobile Ad-Hoc Networks Using Trust Tunneling

Versha Matre and Dr. Sharanbasappa Gandage — Thu, 15 Jun 2023 00:00:00 +0000

Wireless networks are susceptible to multiple attacks, including an attack known as the wormhole attack. The wormhole attack is actually strong, and preventing the attack has proven to be actually sensitive. A strategic placement of the wormhole can perform in a significant breakdown in communication across a wireless network. In similar attacks two or another malicious colluding nodes produce an advanced- situation virtual tunnel in the network, which is placed to transport packets between the tunnel endpoints. These tunnels emulate shorter links in the network and so act as advantage to unknowing network nodes which by default seek low-lying routes. This work present a novel trust-based trick for connecting and separating nodes that produce a wormhole in the network without engaging any cryptographic measure. With the support of extended simulations, we substantiate that our device functions effectively in the sight of malicious colluding nodes and doesn't assess any inessential stipulations upon the network joint and assignment aspect.

Argumentative Texts As A Strategy For Critical Thinking Development In Engineering Students

Eli Romeo Carrillo Vásquez — Thu, 15 Jun 2023 00:00:00 +0000

The Future of Jobs report (World Economic Forum, 2020) reveals that the analysis capacity and critical thinking will be one of the most requested work skills by the year 2025, in addition to being the one presenting the biggest relative growth in recent years.

Additionally, critical thinking (CP) is one of the soft skills that are most in demand in the labor market (Rebele & Pierre, 2019). This evidences the urgency of the development of critical thinking in the professional formative stage of students (Davies, 2013).

Likewise, critical thinking is widely recognized as one of the hallmarks of high-quality education (Kölbel & Jentges, 2017), so it has been reported that CP is directly related to educational performance (Enríquez Canto et al., 2021)

A Study On The Fault Recognition Utilizing Svm Classifier

Manidipa Acharjya and Dr. Rahul Mishra — Thu, 15 Jun 2023 00:00:00 +0000

WSNs should focus on fault-tolerant event detection and change detection. According to recent articles, 1.21 percent and 2.12 percent of work has been done in detection and estimation, wireless radio, and link characteristics, respectively. The goal of this research is to contribute to the event region detection in WSN by using the suggested technique to optimize the network's fault tolerance capacity. This research is being expanded to investigate the effect of a noisy communication channel on event detection. The research contributes to the detection of non-binary fault-tolerant event regions by taking into account symmetric and non-symmetric sensor fault probability. Because mushrooms are sensitive to temperature fluctuations, this research could be applied to temperature monitoring in mushroom cultivation. For detecting systematic errors, the third section proposes a sequential change detection algorithm. This work will be useful in time-critical applications, particularly in industrial applications where data must be handled before processing.

Enhancing Performance Parameters for Smart Video Surveillance Application with AIoT via Collaborative Cloud and Edge Computing

Ms. Trupti K. Wable, Dr. Rahul Mishra — Thu, 15 Jun 2023 00:00:00 +0000

The traditional cloud-based paradigm is under tremendous pressure on network bandwidth and communication latency, which is why a newly emerging paradigm of computing paradigm is involved. As a result, AIoT applications can be implemented in a cloud-based environment, where model building and model abuse are embedded in the cloud and edges, respectively. However, engineers still face the challenge of building AIoT systems in practice due to the natural diversity of IoT devices, diminishing accuracy of trained models, security and privacy issues, etc. In this paper, I want to introduce the development of an industrial edge- cloud based collaboration platform aimed at facilitating the implementation of AIoT applications. In addition, a land use case was filed in this paper, which proved the effectiveness of the AIoT application building on the platform. In this paper we simply do the comparatively study of edge system for surveillance and cloud-edge system for surveillance and measure various parameter using both system and conclude which system is best.

The New Workplace and Digital Education: Generation Z and Millennials

Ms. Shubhika Gaur, Dr. Garima Srivastava, Ms. Apoorva Kapoor — Thu, 15 Jun 2023 00:00:00 +0000

There is a clear mismatch between individuals' skills and those that businesses are sourcing in today's business environment. The workforce ought to still be in a position to complement the value of technology, despite the fact that business leaders ought to ensure that automation, digitization, and the extraction of the value of data—such as through artificial intelligence—are fundamental priorities for the organization. The way knowledge is acquired has changed as a result of technology's growing popularity. This is especially crucial for Generation Z and Millennials, who are currently adjusting to the new workplace and largely acquiring knowledge online. The COVID-19 pandemic, on the other hand, has upset the balance of this new work that combines business, digital technologies, and innovative work practices. Millennials and Generation Z must embrace technology and upgrade training programs if they are to successfully transition.

As a result, the best method for harnessing the transition to the new workplace through digital learning has been identified in this paper. Additionally, this paper has evaluated how markets change or evolve over time. The current COVID-19 pandemic has been noted to have an impact on the labor markets today and in the future. Their implications would include varying employment markets, shifting demands and supplies for skills, and demographic trends (Millennials and Generation Z).

Facial Action Recognition Using Multikernel Learning to Combine Heterogeneous Features

Bombale Girisha Ramhari, Dr. Rais Abdul Hamid Khan — Thu, 15 Jun 2023 00:00:00 +0000

Facial action recognition is a challenging problem because facial expressions can be ambiguous and appear differently on different individuals. In this paper, we propose a novel approach based on multikernel learning to identify facial actions. The approach is a combination of heterogeneous features derived from local binary pattern (LBP), histograms of oriented gradients (HOG), and deep convolutional neural networks (CNNs) features. The proposed method is evaluated on the Cohn-Kanade (CK+) and BU3DFE datasets. Our experimental results demonstrate that the proposed method substantially outperforms the state-of-the-art single and multi-kernel approaches with respect to accuracy. The experiments also show that the proposed method can exploit the complementarity of different features, resulting in improved recognition performance. This is evidenced by the significant gains over the single-kernel approaches.

Common Fixed Point Theorem in ϵ- Chainable Fuzzy Metric Space Using Absorbing Maps

Sonal Dixit and Dr. Animesh Kumar Gupta — Fri, 23 Jun 2023 00:00:00 +0000

We prove some common fixed point theorems for set – valued and single – valued mappings in fuzzy metric and fuzzy 2 – metric space Also generalization and extension of Known results are there by obtained.

Impact of HRM Practices on Organizational Performance - A Study of Indian IT Sector

Yashaswee Dash and Sanjita Lenka — Sun, 25 Jun 2023 00:00:00 +0000

The study intends to investigate the way organizational performance is related with human resource management practices in the Information Technology (IT) sector in Odisha, India with a special focus on performance appraisal, training practices, and recruitment & selection. A number of studies are available exploring this research topic in western countries, but limited researches are found concerning India's HRM landscape. Therefore, this article aims to examine the relationship between HR practices and organizational performance in Indian context. Structural equation modeling is used to elicit the relationship clearly. The findings of this study indicate a positive association between performance appraisal and training practices with organizational performance. These results suggest that effective performance appraisal systems and robust training practices have a beneficial impact on organizational performance in the IT sector in India and more particularly in Odisha. This information can assist organizations in understanding the importance of implementing sound HRM practices to enhance their overall performance and competitiveness in the market. IT industry in India is well-known for its significant impact on the country's economy and high levels of employee engagement. Examining the influence of HRM practices on organizational performance within this specific context can provide valuable insights for both researchers and practitioners.

Speech Recognition based detection of Real-time Objects with Tensor Flow

Vijay Karnatak, Satwik Teotia, Renu Yadav — Sun, 25 Jun 2023 00:00:00 +0000

A system for persons who are blind or visually impaired that runs on Android. For users who are visually impaired, it offers object detection in the close vicinity. This technique aids those who are blind in recognising everyday objects such as a chair, table, phone, etc. in their environment. This system helps the blind person buy at the supermarket by using an RGB colour camera to capture images of the immediate area and deep learning to recognise the type and placement of objects in front of the blind person. This method builds two sets of picture databases with a combined total of nine categories, each with an object detection system based on an SSD network as well as an object classification system based on a MobileNets network, to train a neural network. In order to install an object categorization network on an Android phone, this system is constructed of an Android - TensorFlow interface. The Android terminal now has a voice announcement feature that notifies the blind person in real time when an object is recognised. For persons with vision impairments, this system has been shown to be more effective.

Artificial Intelligence-Driven Innovations in Agriculture

Kasidit Chueinwittaya, C.Vijai, Worakamol Wisetsri* — Tue, 18 Jul 2023 00:00:00 +0000

According to a have a glance at the Food and Agriculture Organization of the UN, the world is projected to realize nine billion by 2050. Speedy world growth, shortage of agricultural land, shortage of seasoner resources, erratic weather changes, and marketplace wants square measure the first factors. of these can cause large food shortages in the future. it's terribly difficult to fulfill our demands in our current agricultural system. A solution to this can be expected in artificial intelligence technology. This artificial intelligence technology that we have is going to bring a huge change in the agriculture sector. Artificial intelligence in agriculture is sure to not only create employment opportunities for crores of people but also bring about a huge agricultural revolution in the future. This article examines the use of artificial intelligence in agriculture and agribusiness and analyzes the challenges of using robots and drones and artificial intelligence technology and various weeding and crop monitoring systems.

Triple-Entry Accounting With Blockchain Technology

Kasidit Chueinwittaya, C.Vijai, Worakamol Wisetsri* — Tue, 18 Jul 2023 00:00:00 +0000

Although double-entry accounting has been used for more than 600 years this days’ era of disruptive technological alternate using blockchain and FinTech has caused the emergence of some other promising accounting method: triple-entry accounting. Blockchain changes the dynamics of business processes and since the accounting department must intervene all company processes, it is to know the effect of blockchain in different business processes. Triple-entry accounting with Blockchain, while it is well made, basically can improve accounting.

Classification of Indian Music using intrinsic mode functions based features

*Saurabh Sarkar, Sandeep Singh Solanki, Soubhik Chakraborty — Wed, 02 Aug 2023 00:00:00 +0000

Indian music is very rich in emotional content and technicalities. It is also contrasting having multiple genres which may be broadly classified as Classical (Hindustani or Carnatic) music, Semi-classical music (including Ghazals) and Light (including folk) music. The so called “rasa” or emotional content in Indian music is of much significance, e.g. selection of the right kind of music for therapeutic intervention by the music therapist who might be an expert in western art music (WAM) but could be new to Indian music. This paper presents a novel classification technique based on empirical mode decomposition (EMD). In this work, two genres of Indian music, Classical and Semi-Classical, are considered. There is a significant increase in classification accuracy as compared to previous works involving generic audio features.

Occupational Alteration: Examination Of Fingerprints And Psychological Behaviour On Carpenters

Mansi Singh , Dr. Sahil Sharma — Wed, 02 Aug 2023 00:00:00 +0000

Fingerprint alteration is a major concern with their basic need in the crime world which holds the individuality of people to being individual. It holds the pattern with their unique ridges and makes the two fingerprints identical. Forensic science relies heavily on the accurate examination and analysis of fingerprints for crime scene investigations. However, the occupational alterations experienced by carpenters who are involved in this process, along with their psychological behavior, can have implications for the reliability & effectiveness of their work. Carpenters, due to the nature of their occupation, may experience alterations to their fingerprints caused by the repetitive friction and pressure exerted on their hands while handling tools and materials. These alterations can affect the ridge patterns and quality of the fingerprints. This abstract aims to explore the relationship between occupational alterations, psychological behavior, and the role of carpenters in fingerprint examination within the field of forensic science.

Energy-Efficient Routing Protocol for Next-Generation Application in the Internet of Things and Wireless Sensor Networks

Avneesh Gour, Dr. Nishant Kumar Pathak — Tue, 15 Aug 2023 00:00:00 +0000

Our Invention “Energy-Efficient Routing Protocol for Next-Generation Application in the Internet of Things and Wireless Sensor Networks” is a the fact that the majority of sensor nodes in wireless sensor networks (WSNs) are powered by energy-constrained batteries is one of the main problems with these networks, as it significantly affects the effectiveness, dependability, and durability of the system. As a result, several clustering strategies have been created to improve WSNs' energy efficiency. The use of multiple-input multiple-output (MIMO) antennas is required for fifth-generation (5G) transmissions in many Internet of Things (IoT) applications in order to provide enhanced capacity in a multipath spectrum environment. We think that balancing the energy usage per unit area is preferable to using a single sensor that can provide greater load balancing use. In 5G networks, the IoT devices are enmeshed with a variety of MIMO transmission interfaces. An efficient clustering strategy for quickly growing IoT systems is both absent and urgently required to handle a range of user situations now that MIMO is more frequently available on IoT devices. The quality of experience (QoE) for transmitting in clusters for IoT networks is the main goal of the intelligent MIMO-based 5G balanced energy-efficient protocol that we suggested in this research. In compared to the current protocols, the suggested protocol uses 30% less energy while improving network longevity and energy efficiency.

Social Media and Focus-group Based Purchase-Intention Determinants of Automobile Industry In India

Anuradha Banerjee, Basav Roychoudhury, Bidyut Jyoti Gogoi — Tue, 15 Aug 2023 00:00:00 +0000

The current research is of exploratory nature that finds out and analyzes antecedents of automobile purchase intention (API) of Indian customers in recent times. The pandemic of Covid has recently calm down and markets are working hard towards prosperity. Many customers have deferred their purchase plans and many are looking for more price-efficient alternatives. However, there are others who are looking for safe and personal mobility to maintain social distancing. In this context, it is worthwhile to study the influencers of API of customers. Based on price value of the vehicle a customer already owns, components of his next choice of vehicle is determined here by extracting and analyzing social media review comments. Subsequently these are compared with responses of prospective buyers which were obtained by conducting focus group interviews where participants belong to various price ranges expressed in millions or M in short. The price ranges are obtained through cluster analysis of responses found from social media. For focus group interviews, we reached out to respondents belonging to three major cities of India - Chennai, Kolkata and Mumbai. Responses of the participants have been analyzed both qualitatively and quantitatively. Qualitative analysis helped us to extract names of the antecedents where quantitative analysis estimates their overall impact on API. Function approximation is applied to quantify the antecedents separately for focus group and social media. Values of antecedents have been mathematically expressed as function of purchase price of the vehicle a customer already owns. This information is extremely useful for vehicle manufacturers as well as salespersons. Using the technique proposed here, a vehicle manufacturer is able to figure out expectations of existing customers in terms of vehicle functionalities whereas sales persons are able to identify automobile product features they should accentuate while demonstrating a vehicle to a potential customer

Constructing A Conceptual Framework For Implementing Assistive Learning Technology To Enhance Word Problem Solving Skills In Children With Autism

ZAREENA ROSLI, FAAIZAH SHAHBODIN, CHE KU NURAINI CHE KU MOHD — Fri, 25 Aug 2023 00:00:00 +0000

Mathematical learning difficulties are the most "resistant" within intervention programmes for autistic children. Nonetheless, mathematics cannot be eliminated from the curriculum because it develops skills that will facilitate the child's social adaptation. The essence of mathematics education lies in problem-solving, as it instructs students on when to employ their acquired mathematical knowledge in practical situations, rather than simply following computational procedures. Understanding mathematics word problems may be challenging for many autisms spectrum disorder (ASD) children because it also requires literary skills. The main aim of word problem comprehension is to foster a clear understanding of the meaning behind mathematical word problems. To achieve this, it is crucial for individuals to visualize the events described in the narrative text, which contributes to the development of an accurate scenario model. However, supporting children with Autism Spectrum Disorder (ASD) in mastering word problem comprehension poses significant challenges due to various academic and cognitive factors they encounter. This study focuses on utilizing virtual reality learning technologies to explore and identify the foundational concepts that underpin word problem comprehension in autistic children. The ultimate goal is to assess how effectively these children can learn to solve word problems, leading to the development of a clear research objective. By investigating the potential integration of virtual reality into word problem learning, this research aims to provide valuable insights into supporting students in grasping word problem situations. Successfully completing this project will pave the way for incorporating more technology into special education, benefiting learners with special needs

Comparative Analysis Of Cyclostationary Feature Detection Techniques For Spectrum Sensing In Cognitive Radio Networks

Mr. Haribhau Shinde, Dr. Sandeep Garg — Wed, 30 Aug 2023 00:00:00 +0000

Modern wireless communication systems place a great deal of emphasis on the efficient use of radio frequency spectrum. Cognitive Radio networks (CR) have emerged as a promising solution to address spectrum scarcity by enabling opportunistic access to underutilized spectrum bands. In order to avoid interference, spectrum sensing, an integral part of CR, is based on detecting the presence of primary users. As part of this study, we present a comprehensive comparative analysis of the effectiveness of cyclostationary feature detection techniques for the detection of spectrum features in cognitive radio networks. Cyclostationary analysis takes advantage of the periodic properties of signals and noises to enhance the detection capability. In this paper, prominent cyclostationary-based methods, such as cyclic autocorrelation, cyclic periodogram and spectral correlation density, are examined in detail to determine their performance, robustness, and computational complexity. As a result of real-world scenarios and simulation results, it is possible to gain insight into the strengths and limitations of these techniques, thereby assisting in the selection of the optimal spectrum sensing strategy for cognitive radio networks

Artificial Neural Network Data Subscription System for Prediction by Level5 Algorithm

Binu C T and Dr.Mohan Kumar.S, Dr.Chitra Ravi — Tue, 05 Sep 2023 00:00:00 +0000

Artificial Neural Network is a techhnology which helps to get future data and for decision making.The existing algorithm in the system is slower in execution timeThe proposed Level5 algorithm is much faster and 99.9% accurate.There are different ANN methods named feed forward,recurrent network,back propagation,counter propagation,cascading Neural Network,Hopfield Network and perception Neural Network.These methods input may vary based on past,present and future data.The proposed Level5 Algorithm have data,code for past present and future of data and priority as input and obtain most accurate and faster result.

Hindi/Marathi-BART: An Efficient Sequence-to-Sequence Model for Abstractive Text Summarization for Hindi and Marathi language

Neha Rane, Dr. Sharvari S. Govilkar — Tue, 05 Sep 2023 00:00:00 +0000

The efficacy of neural sequence-to-sequence models for text summarization has been dramatically enhanced by attention-based architectures. Despite the fact that these models are effective at summarizing English documents, they are not readily transferable to other languages, leaving room for development. This paper presents Hindi/Marathi-BART, a sequence-to-sequence model designed particularly for the Hindi and Marathi languages based on the BART architecture. The model is pre-trained on a large corpus of Hindi and Marathi texts to acquire language-specific features and then fine-tuned for abstractive summarization using benchmark datasets. Despite having substantially fewer parameters, Hindi/Marathi-BART outperforms other cutting-edge models in terms of ROUGE scores, as demonstrated by experimental results. Although the ROUGE score is commonly used to evaluate the quality of automatically generated summaries, it does not adequately convey the semantic similarity between automatically generated and reference summaries. To circumvent this limitation, we augment ROUGE with BERTScore, a more sophisticated evaluator that measures the token-level semantic similarity between the generated and reference summaries. Using Hindi/Marathi-BART can facilitate the development of natural language processing (NLP) applications for these two languages. We will disseminate the model to the research community in an effort to stimulate additional investigation and application.

Segmentation Method to Obtain Required Area of Interest Using Deep Learning

Shreedevi P, H S Mohana — Tue, 05 Sep 2023 00:00:00 +0000

Segmentation is very important in the area of the analysis of the image or image in the videos. Crowd behavior analysis is very valuable economically and has numerous potential applications, including intelligent video surveillance, virtual reality, public safety, and other areas. In densely populated areas, humans might be regarded as a collection of several groups with similar motion characteristics. Groups are essential components of a crowd. A quick crowd segmentation strategy based on crowd spatiotemporal linkages is presented to get fundamental crowd interactions for subsequent crowd behavior analysis. The processing of medical images, face identification, pedestrian detection, and other applications make extensive use of image segmentation technologies. Segmentation is on Region-base, edge detection, segmentation based on clustering, segmentation based on weakly-supervised learning in CNN, etc. are some of the current picture segmentation techniques. This approach takes into account both pedestrian spatial information and crowd motion data. The technique may be used on different crowd scenarios and can get the same segmentation results in less time. The effectiveness and efficiency of the suggested approach for crowd segmentation are shown by extensive experiment results on videos of real-world crowd scenarios.

The Effect Of Organizational And Occupational Stress On The Health And Performance Of Police Officers

Anup Athawale, Dr. Sunowar Ameer — Sun, 10 Sep 2023 00:00:00 +0000

The research explores the effect of organizational and occupational stress factors on the physical and mental well-being of police officers. Furthermore, it examines potential connections between heightened stress levels among officers and incidents of custodial violence, analyzing resulting behavioral changes, decision-making patterns, and the use of force during such situations. The research methodology adopted for the paper is qualitative descriptive analysis and reviewing relevant literature. This study aims to provide insights into stress-related challenges faced by police officers. The findings will contribute to evidence-based interventions, support mechanisms, and potential reforms within law enforcement. Future research directions could delve into specialized stress management techniques, innovative training programs, and structural enhancements to alleviate stress-related risks and promote a more effective and healthier police force.

Research on the effects of online learning on students' learning process and results Coursera courses at FPT University

Tran Khai Minh Phap — Sun, 10 Sep 2023 00:00:00 +0000

Up to now, Vietnam has experienced four outbreaks of COVID-19 disease in most provinces’ city across the country. Like other countries, the COVID-19 pandemic has not only had a strong impact on socio-economic activities, but also greatly affect educational activities in Vietnam. Specifically, around March Until April 2020, when the first outbreak of the epidemic broke out in the country, all schools were forced to close and All students must leave school to prevent epidemics, according to Directive No. 16/CT-TTg of the government. According to the system As of April 2020, all 63 provinces and cities have allowed students to stay at home. Up until now, due to complicated developments Due to the complexities of the epidemic, the government of Vietnam has repeatedly implemented social distancing measures across the province. city or even on a national scale. In that context, to prevent the spread of the disease COVID-19, while maintaining the quality of teaching and completing the program on schedule, ensuring students' learning student; many schools have applied online teaching to most students. all levels of education.

Intelligent Sentimental Analysis of Learning Quotient of Students in Education 4.0

Prof.(Dr.) Vivek Kumar, Prof.(Dr.) Tanupriya Choudhury, Neena Bansal — Sun, 10 Sep 2023 00:00:00 +0000

Our Research “Intelligent Sentimental Analysis of Learning Quotient of Students In Education 4.0” is a the "Conversation" part of feeling investigation denotes a crucial stage where the outcomes and their suggestions are analyzed, giving an exhaustive translation of the opinion examination results. This portion rises above the domain of crude information and measurements, diving into the importance, setting, and more extensive ramifications of the feelings communicated inside the text-based corpus. Inside this talk, the opinion appropriation is analyzed considering the particular setting and area, revealing insight into any startling patterns, inclinations, or examples. Positive, negative, and unbiased opinion predominance is investigated to recognize whether feelings line up with assumptions and to uncover potential elements adding to feeling varieties.The exhibition measurements of feeling investigation models are fundamentally assessed to understand their exactness and constraints. Inconsistencies between feeling arrangements and genuine opinions are investigated, directing refinement systems. Bits of knowledge into the models' assets and shortcomings enable choices with respect to additional streamlining or the reception of elective procedures.

Evaluating the Efficacy and Security of Steganography Techniques in Cloud Computing

Apeksha Dave, Dr. Sandeep Singh Rajpoot — Wed, 20 Sep 2023 00:00:00 +0000

The way data is handled, accessed, and stored has been completely transformed by cloud computing. But maintaining the security and privacy of data in cloud settings is still a major challenge. This work gives a thorough investigation that tackles the goals of creating a reliable steganographic method, measuring its efficacy, gauging its influence on cloud computing performance, and looking into potential flaws and remedies. Assessing how steganography affects cloud computing system performance is the goal here. To examine the impact of steganography on overall system performance, performance assessments and benchmarks are carried out. In order to imitate real-world situations, several file formats and sizes are employed. It looks at potential weaknesses in the suggested steganographic technique and looks into mitigation strategies for these weaknesses. The analysis of security flaws, including attacks and detection methods, results in the development of suitable countermeasures. The results of this study help steganography-based data security in cloud computing settings develop. The study sheds light on the advantages, disadvantages, and possible areas for development of the suggested steganographic approach. The performance assessment and vulnerability analysis provide insight into how steganography affects cloud computing systems and aid in the creation of efficient defences.

Automated Robotic Placement of Secured Object in Appropriate Location Using ML

Madhurjya Baruah, Biplab Kumar Sarkar, Reena Singh — Wed, 20 Sep 2023 00:00:00 +0000

The convergence of robotics, automation, and machine learning has ushered in a new era of precision and efficiency in object placement processes. This research investigates the development and implementation of an advanced automated robotic system capable of securely placing objects in designated locations, revolutionizing industries such as manufacturing, logistics, and healthcare. The keywords for this research are: automated robotics, secured object placement, machine learning, computer vision, motion planning, and precision. Our system integrates state-of-the-art machine learning techniques, including computer vision for object recognition and motion planning for path optimization. Reinforcement learning is employed for fine-tuning robotic control, ensuring both accuracy and efficiency in object placement. Through rigorous experimentation, this paper showcases the system's remarkable capabilities, addressing challenges and paving the way for practical applications across a multitude of domains. This research not only promises to enhance automation processes but also underscores the potential for significant advancements in secure, precise, and reliable object manipulation using modern machine learning techniques

The Return of In-person Classes in the State-Run Basic Education Institutions in the Philippines: Viewpoints of Teachers Working in Remote Locations

Wed, 20 Sep 2023 00:00:00 +0000

Remote schools in the Philippines continue to experience a lack of teaching resources and instructors are continuously challenged to provide quality basic education in the countryside. Teachers with passion and dedication are required in remote institutions to provide the services that are sorely needed. During the resumption of classes, the purpose of this study was to characterize and analyze the transitional experiences of teachers assigned to remote locations. The study utilized phenomenological research methods using in-depth interviews and focus group discussions, this study discovered that assigning teachers far from their hometowns is difficult and not simple not only for them but also for teachers across the nation. The findings revealed that teachers' actual experiences included traveling by sea, riding a motorcycle or "habal-habal" that requires payment, cultural differences, no internet connection or cellular data, and system and curriculum adjustments. Teachers in this study are looking forward to a much better assignment in the future, despite the rewarding experiences they have had serving a disadvantaged community.

The Role of Machine Learning and Deep Learning Approaches in the field of HealthCare

Thu, 28 Sep 2023 00:00:00 +0000

Healthcare and medical science are the most happening area in the last few decades. There are lot many technological innovations carried out in this field as per the need of the current era. Healthcare has gained a prominent place in human lives. Predictive and analysis tools are urgently needed as a result of the development of technology in health care and medical sciences, which produce vast amounts of data that are beyond the capacity of humans. Their findings will open up a variety of research opportunities while also fostering improved judgments and decision-making in medical and healthcare companies. This leads to addressing the prominent use of advanced technologies namely, artificial intelligence and its subsets which imitate human intellect, evaluate data, and produce truthful outcomes. One of them is machine learning, which uses predetermined computer learning patterns for a certain dataset to achieve novel outcomes with previously undiscovered data.

As machines learn how to optimize themselves to deliver amazing results, this requires the least amount of human interaction. However, it is not without flaws, as these algorithms cannot learn deep patterns and do not learn with accuracy. To address these issues, deep learning algorithms have been developed. There are numerous layers or levels of abstraction in a deep learning environment, which aids in identifying the intricacy of the patterns hidden in the data. This paper is constructed with the goal to scrutinize the place of ML and DL in the healthcare and medical industries. Also, it provides an overview of medical imaging in the machine and deep learning techniques used for analyzing different diseases.

Road safety measures and the development of Motor Vehicle law in India - A Descriptive Analysis

Dr. Amit Singh, Nidhi Shanker — Tue, 03 Oct 2023 00:00:00 +0000

Road safety measures have been an ever-growing concern among both Indian union and state governments in an effort to reduce road accidents, but no significant results have been obtained as of yet. To thrive economically, socially, and culturally, transportation is essential. In any country, but especially one still in its developmental stages like India, the road transport industry is crucial. The importance of India's road transport sector cannot be overstated. The Indian economy relies heavily on road transport. The internal road network, which stands inclusive of state as well as national highways in addition to crucial and utmost important district and village roads, efficiently transports people and commodities throughout the country. Therefore, traffic regulations and enforcement are crucial components of traffic management that must be implemented everywhere. Road safety measures are an essential part of road engineering, traffic management, vehicle regulations, driver behaviour, environmental protection, and the laws governing these areas. In this study, several regulatory issues related to the country's motor vehicle law regime are examined, along with other elements that influence it.

Simulation And Modelling Of Modified Upfc In Power Systems

Shailendra Shrivastava, Prof. (Dr.) Annapurna Bhargava — Tue, 10 Oct 2023 00:00:00 +0000

To ensure the stability and security of an electric power transmission system while optimizing its capacity, there is a need for an efficient and reliable power flow controller. Currently, the most promising technology for managing power flow is the Unified Power Flow Controller (UPFC). The UPFC has the capability to simultaneously regulate various parameters of the transmission lines, including impedance, voltage, and phase angle. Although conventional UPFC algorithms are logic-based, the approach remains firmly rooted in logical principles. In the proposed model, termed "collaboration," fuzzy logic is integrated with the fundamental design of the flow controller. Comparing the outcomes of neuro-fuzzy controllers with conventional controllers, it is evident that there is less overshooting with the former. In simulations, UPFC consistently enhances results in both transient and typical scenarios compared to alternative control systems.

Obstacle Detection using Holoentropy-based Single Shot MultiBox Detector for aiding visually impaired persons

Anamika Maurya, Dr. Prabhat Verma — Mon, 16 Oct 2023 00:00:00 +0000

People suffering from vision loss and sightlessness, face number of challenges, such as independent mobility and finding obstacles. Specifically, evading obstacles is a complex task for blind people because they greatly depend on their sense of touch and hearing. In such instance, they can observe the impediments at short distance only and they are failed to identify the obstacles, which are hard to determine. Hence, there arises a need for developing obstacle detection techniques to enable easy navigation of the visually impaired persons. Hence, this research work devises an efficient obstacle detection technique using Holoentropy-based Single Shot MultiBox Detector (HeSSD) for aiding visually impaired persons. Here, the input videos are extracted into various frames, and the obstacles are recognized and detected by using developed HeSSD method, where the matching process is carried by Holoentropy approach. Furthermore, the experimental result establishes that proposed HeSSDmethod attained highest accuracy of 0.80, MAP of 0.77 and recall values of 0.78.

Smart Heating of Greenhouse using Sensors Coupled with Earth Tube Heat Exchanger under Cold Arid Region of Ladakh

Sonam Angchuk, Aleem Ali, D. Namgyal — Mon, 16 Oct 2023 00:00:00 +0000

Protected structures such as greenhouses, trenches in the challenging terrain of Ladakh, India represent a lifeline for producing green vegetables, especially during the harsh winter months in this region. To address the temperature control challenges, this research paper explores the innovative use of Earth Tube Heat Exchangers (ETHE) to harness the Earth's stored thermal energy for efficient heating and cooling within a greenhouse. In this paper, the ETHE system was rigorously evaluated during the peak winter months of December 2021, January 2022, and February 2022, specifically during night-time when temperatures plummeted to their lowest levels. The findings reveal a substantial difference in temperature profiles among various monitored parameters, specifically, the mean outlet temperature of the ETHE system, the temperature within the control greenhouse, the temperature within a non-ETHE equipped greenhouse (check greenhouse) and the ambient temperature over these three critical months averaged at 8.8°C, 0.2°C, -3.9°C, and -11.9°C, respectively. This remarkable disparity demonstrates the ETHE system's ability to maintain a consistent and favourable climate within the greenhouse, with an impressive temperature differential of approximately 4°C compared to a non-ETHE equipped greenhouse. The implementation of an automated sensor-based control system for the blower operation has further enriched the findings, significantly reducing the need for human intervention, saving valuable labor time, and optimizing energy consumption. These results underscore the potential of ETHE systems as a sustainable solution for greenhouse temperature management in challenging agricultural environments.

Method And Process of Energy Cloud Clustering Mechanism Using Cloud Computing Models

Sonam Chikara, Dr. Nishant Kumar Pathak — Mon, 16 Oct 2023 00:00:00 +0000

The increasing global demand for energy and the urgent need for sustainable resource management have prompted the development of innovative solutions. This research introduces an Energy Cloud Clustering Mechanism (ECCM) that leverages cloud computing models to optimize energy resource management. ECCM employs advanced data analytics and machine learning techniques to categorize diverse energy resources, including renewables (e.g., solar, wind, and hydroelectric), fossil fuels, and energy storage systems. By creating intelligent clusters based on resource characteristics and geographical distribution, ECCM enhances the efficiency of energy allocation and distribution. The scalability and adaptability offered by cloud computing ensure ECCM's responsiveness to changing energy supply and demand patterns, enhancing the resilience and sustainability of the energy ecosystem. This paper details ECCM's architecture, components, and practical applications, accompanied by case studies and simulation results, demonstrating its potential to revolutionize energy management for a greener and more sustainable future.

Reinforcement Machine Learning-based Improved protocol for Mobile Ad-Hoc Networks

Biradar Ashwini Vishwanathrao and Dr. Pradnya Ashish Vikhar — Thu, 19 Oct 2023 00:00:00 +0000

Mobile Ad-Hoc Networks (MANETs) serve as a vital communication infrastructure in scenarios where fixed infrastructure is absent or impractical. Energy efficiency is a critical concern in such networks, given the inherent constraints of battery-powered mobile devices. This research introduces a groundbreaking approach—a Reinforcement Machine Learning-based Improved Energy Efficient AODV (Ad-Hoc On-Demand Distance Vector) Protocol (RML-EEAODV)—to address the pressing need for enhanced energy utilization in MANETs. RML-EEAODV combines the strengths of reinforcement machine learning with the AODV routing protocol to create an intelligent and adaptive energy-efficient routing solution. In the current situation, the most significant challenge in MANET is to reduce energy consumption and overhead of each node, and to give a better packet delivery ratio. Reinforcement Machine learning can be used to improve routing decisions of AODV protocol by encouraging nodes to updates a state information database of intermediate nodes along routes to destinations. state information is used for taking forwarding decision to find guaranteed QoS routes. Our proposed solution RML-EEAODV is highly efficient in terms of energy consumption, network overhead and Gives the acceptable level of packet delivery ratio.

Analysis of Proactive Routing Protocols in MANET using Modified DVRA

G. K. Srikanth and Dr. Pramod Pandurang Jadhav — Thu, 19 Oct 2023 00:00:00 +0000

In this paper, we proposed a new modified distance vector routing (MDVR) based proactive routing algorithm for MANET, has been designed, implemented, and simulated in the laboratory. The count-to-infinity (CTI) problem in the DVR has been avoided and faster convergence has been achieved through some key modifications in the DVR algorithm rather than using sequence numbers which is used by several well-known routing algorithms under the same category. Each node in the MANET efficiently monitors its dynamic neighborhood using a novel state diagram approach and handles the losses and gains of neighbors in an intelligent and proactive manner. With a datagram approach to routing, based on three different types of packets, namely NNT, DVRT, and LEAVE, NMMDVR has avoided packet looping and reduced the loss of routes to a considerable extent. In the paper, we propose a new proactive routing algorithm for MANETs called Neighbourhood Management based Modified Distance Vector Routing for MANET or NMMDVR in short. It is based on some important modifications of the well-known and widely-used DVR algorithm (DVRA), which are incorporated in the algorithm Modified DVRA. These modifications allow the NMMDVR to avoid routing loops without the use of any sequence numbers (a concept used by several well-known routing algorithms for MANET) and offer reliable communication, even under the condition of high mobility.

Review on Cost Overrun Analysis Using Artificial Neural Network in Residential Construction Projects

Ankita Yadav, Jaydeep Pipaliya — Thu, 19 Oct 2023 00:00:00 +0000

Cost overrun in residential construction projects is a prevalent issue that adversely affects project stakeholders and the construction industry as a whole. The objective of this research is to develop a robust predictive model using Artificial Neural Network to anticipate and mitigate cost overruns in residential construction projects. The anticipated outcomes of this research include the development of an accurate cost overrun prediction tool that can assist project stakeholders in proactively managing and controlling construction budgets. By identifying potential cost overrun risks early in the project lifecycle, decision-makers can make informed choices to mitigate these risks and improve project cost management. The findings of this research may inform industry best practices and contribute to the sustainable and efficient execution of residential construction projects. In conclusion, this research project endeavours to leverage Artificial Neural Networks to address the challenge of cost overrun in residential construction projects, ultimately fostering more reliable and cost-effective project delivery in the construction industry.

Review Paper on Risk Management in PPP Highway Construction

Deep Modi, Jaydeep Pipaliya, Dixit Patel — Thu, 19 Oct 2023 00:00:00 +0000

The paper explores the value of PPPs in meeting infrastructure demands and emphasises the need of effective risk management to avoid project failures. It looks at how PPPs have changed over time in India as well as other PPP types such Build-Operate-Transfer (BOT), Build-Own-Operate (BOO), Design-Build-Finance-Operate (DBFO), and Design-Build-Finance-Operate (DBFM). The advantages of PPPs are examined, with a focus on their capacity to draw in private funding, improve project efficiency, and encourage innovation. The study focuses on categorising risks, such as those associated with traffic revenues, land acquisition, and cost overruns. For PPP highway projects, critical success factors (CSFs) are identified. These CSFs include political stability, macroeconomic factors, contractual agreements, the availability of trained labour, and more. Numerous risk assessment techniques are described, emphasising their use in measuring risks and assisting in decision-making. These include the Delphi method, Analytic Hierarchy Process, fuzzy set theory, and Monte Carlo simulation. The report provides a thorough toolset for overcoming obstacles and succeeding in PPP highway building projects, highlighting the significance of educated decision-making and holistic project management.

Review on LEAN principles to eliminate obstacles in the Indian Construction Industry

Karan R Jumani, Jaydeep Pipaliya — Thu, 19 Oct 2023 00:00:00 +0000

Lean construction is a result of attempts to adapt and implement the Japanese Lean production concept in the building sector. Lean building is a convergence of concepts such as waste removal, flattening of organizational structures, resource efficiency, and cooperative supply chains. Lean Construction is currently being discussed in India in light of the adoption of Lean Production in the manufacturing sector and the growth of the discipline in nations like Brazil, Denmark, and the USA. The study's goal is to identify the obstacles to the effective adoption of lean construction in the Indian construction industry.According to the findings, "their lack of a long-term philosophy," "the absence of a lean culture in their organizations," "the use of multi-layer subcontracting," and other factors are among the most significant obstacles to the adoption of lean principles, according to Indian building professionals.

Building the Future with AI: An In-Depth Review of Artificial Intelligence in Construction Sector

Kaushal G Chauhan, Jaydeep Pipaliya — Thu, 19 Oct 2023 00:00:00 +0000

The construction industry plays a vital role in the economic development of any country. The construction industry stands as a cornerstone of economic progress in nations across the globe. With the steady march of technological innovation, this industry finds itself amidst a remarkable evolution. Among the forefront advancements, artificial intelligence (AI) has emerged as a transformative force. With the advent of new technologies, the construction industry is experiencing a significant transformation, and one of the most prominent technological advancements is artificial intelligence (AI). AI has the potential to revolutionize various aspects of the construction industry, from project planning and design to construction management and operation. This review paper provides an overview of the Benefits and challenges of artificial intelligence in the construction industry. It explores the current AI adoption in construction projects and discusses the potential benefits and challenges associated with its implementation. The practical impact it has had on project efficiency, cost reduction, and safety improvement.

A Secure Protocol for information Storage on cloud Database

Amit B. Waghmare, Dr. Pradnya Ashish Vikhar — Thu, 19 Oct 2023 00:00:00 +0000

Cloud technology has opened up new opportunities for storing and sharing data. It enables individuals and businesses to store their data securely and access it from anywhere in the world. However, existing cloud storage solutions are vulnerable to malicious attacks. This paper presents a secure protocol for information storage on cloud. The protocol is based on a three-layer architecture that comprises a transport layer, an application layer and a security layer. The transport layer handles the data transfer between the cloud provider and a user's device. The application layer contains a set of functions and services that enable users to manage and access their data stored on the cloud. Finally, the security layer provides enhanced security features that protect the data stored on the cloud, while also ensuring data integrity and confidentiality. The protocol is designed to be secure, scalable, and easy to use. Furthermore, the protocol can be extended to support additional security features, such as the use of encryption, two-factor authentication, and the provision of audit logs. This paper provides a detailed explanation of the protocol and its security features.

Text Classification and Clustering of Twitter Data for Business Analytics

Sharad Maruti Rokade, Dr. Kailash Patidar — Thu, 19 Oct 2023 00:00:00 +0000

In the era of social media dominance, Twitter has emerged as a powerful platform for users to express their opinions, share information, and engage with brands. The vast amount of textual data generated on Twitter presents both opportunities and challenges for businesses looking to leverage this information for effective decision-making. Text classification and clustering techniques can provide valuable insights by organizing, analyzing, and categorizing this data in a meaningful way. Text classification involves assigning predefined categories or labels to tweets, enabling businesses to understand sentiments, opinions, or topics associated with their brand or products. By applying sentiment analysis algorithms, businesses can determine the sentiment expressed in tweets, helping them gauge customer satisfaction, identify areas of improvement, or evaluate the impact of marketing campaigns. Text clustering, on the other hand, enables the identification of patterns or groups within the Twitter data without pre-defined categories. It allows businesses to discover natural groupings of tweets based on their content, allowing them to gain insights into emerging trends, customer segments, or communities of interest. These clusters can be used to personalize marketing strategies, recommend products, or target specific customer groups.

Analysis of Permanent Magnet Synchronous Motor (PMSM) under Vibration Effect using Analytical Magnetic Field Calculations

Mahesh M.Bulhe, Manjusha Deshmukh*, Zunzarrao V.Thorat* — Fri, 20 Oct 2023 00:00:00 +0000

In recent years Permanent Magnet Synchronous Motor (PMSM) drives are gradually replacing induction motors drives in engineering fields. This is becoming a trend due to some key features of PM motors, including compactness, efficiency, and robustness, reliability, and shape adaptation to the working environment. Based on analytical magnetic field computation techniques, the analytical solutions for electromagnetic vibration sources such as torque ripple, cogging torque, and radial force density have been developed. By applying FFT analysis to these vibration sources, the specific harmonic orders and the corresponding frequencies that affect vibration of the PMSM’s stator have been investigated. It is observed that results are in good agreement with the predicted values.

Review Paper on Quality Management Practices for Construction Project Delivery

Paul Kwapong* and Jaydeep Pipaliya — Sat, 21 Oct 2023 00:00:00 +0000

The construction industry's primary purpose is to guarantee that construction projects are performed quickly and effectively within the limitations of highest quality, specified time frame, and lowest viable cost. This review examines the notion of Quality Management, its advantages, and its relationship to effective project delivery in construction organizations. The studies that were examined focused on quality practices and adoption in the construction sector by applying various Quality Management practices. According to Quality Management practices research, it is critical for construction organizations to support the development of quality management systems in all parts of their operations by creating a flexible and welcoming organizational climate. Digital quality management practices should be used in order to compete in a world of fast technology changes. These quality management practices are critical in guiding organizations towards Customer Satisfaction and project success.

Safety Stock For Fluctuations Force Management System Using Regression Algorithm

A.Anitha, Dr.P.Kavitha, S.Anitha, V.Kaviya, T.Jayasuja — Tue, 24 Oct 2023 00:00:00 +0000

The Inventory Management System is a real- time force database able of handling large supplies of an association. This can be used to track the force of a single store, or to manage the distribution of stock between several stores of a larger ballot. still, the system simply records deals and restocking data and provides announcement of low stock at any position at a specified interval. The thing is to reduce the strain of shadowing rather than to handle all store conservation force operation is a large exploration field with high practical applicability. It includes numerous functions, similar as demand planning, force controlling and force planning. It determines all decision- related data which have an influence on force position( Pfohl, 2000). force optimization refers to the reduction of stock in storages, product and the entire force chain while icing high material vacuity( ten Hompel and Weidenblut, 2011). likewise, it's across- sectional function that optimizes force and material inflow within all functional areas. force operation also includes integration and collaboration functions, since upstream and downstream processes are coordinated( Stadtler, 2002).

An Artificial Intelligence Approach using Internet-of-Things for Forest Fire Management System

Prof Dr R K Vaithiyanathan — Mon, 06 Nov 2023 00:00:00 +0000

Prediction of forest fires is an important part of forest fire management. It is crucial in terms of resource allocation, mitigation, and recovery. Fires are dangerous because of the fuel they consume. There will always be fire where there is gasoline. Using data to anticipate how things burn and fuel model conditions is the most valuable use of Artificial Intelligence (AI) in battling wildfires. In order to aid in the fight against wildfires, AI can assist in anticipating how big fires will develop and where they will spread. Forest fire prediction, prevention, and mitigation can be aided by computer vision technology, data analysis and prediction, as well as machine learning and deep learning. Forest fires can be predicted using atmospheric data and deep learning. Environmental conditions, as well as data analysis of the surrounding trees and leaves that serve as the fuel for any forest fire, can be examined using AI to evaluate and predict the likelihood of a forest fire with a good degree of accuracy. The concepts of employing AI in forest fire prediction, prevention, and mitigation are presented in this study.

An Unified Effect On Triangular Fuzzy Linear Fractional Programming Problem

A.Anna Sheela — Fri, 10 Nov 2023 00:00:00 +0000

The linear fractional programming (LFP) problem has attracted the attention of many researchers as it applies to many important fields such as production planning, finance and business planning, healthcare and hospital planning. Several methods have been proposed to solve this problem such as the variable transformation method [5] and the updated objective function method [3]. The first method transforms the (LFP) problem into an equivalent linear programming problem, while the second method solves a sequence of linear programming problems in response to an update of the local gradient of the fractional objective function at consecutive points. Several topics on the concept of duality and the optimal conditions for (LFP) were also discussed by [11] and [12] respectively. Recent research on the theory and methods of fractional programming can be found in [2],[1]. Loganathan.T et al., proposed a solution approach to fully fuzzy linear fractional programming problems in [7]. Jervin used two square determinant approaches for simplex method in [6].In addition, the fuzzy concepts are taken into account for this paper. The topic of fuzzy subsets was introduced in 1965[10]. Researches across the world developed various concepts bridging fuzzy with most of the area in Mathematics and introduced Fuzzy Real line [8], fuzzy topology [9], fuzzy trigonometry [4], etc. Later, fuzzy numbers were defined and were found to have more application developments than fuzzy subsets. Fuzzy numbers have been used to obtain better results in problems where in decision making and analysis are involved. Fuzzy number, which is an extension of real numbers, has its own properties which can be related to theory of numbers.

The paper is organized as follows: section 2 deals with some preliminary definitions and the

Existing fuzzy sets and fuzzy numbers operations are given. An Algorithm is developed for solving Triangular fuzzy linear fractional programming problem and to strengthen our procedure, the Numerical example is given in section 3. Finally, concluding remarks are given in section 4.

Energy efficient resource allocation for big data processing in Heterogeneous Cloud Computing Centers

Sameerana C P, Dr Madhu B K — Fri, 10 Nov 2023 00:00:00 +0000

In order to effectively analyse large data, this study provides a novel technique for dealing with the challenges posed by resource allocation in HCCCs. In order to optimise resource allocation, the suggested technique makes use of a hybrid algorithm called the Artificial Bee Colony with Gravitational Optimisation Algorithm (ABC-GOA). This algorithm places an emphasis on energy saving as well as performance. The efficacy of the ABC-GOA algorithm has been shown via rigorous testing and research. It has outperformed standard resource allocation strategies in terms of energy efficiency, processing time, and system utilisation. This novel technique shows potential for boosting energy-efficient resource allocation in HCCC, contributing to lower operating costs and environmental impact, and consequently redefining the landscape of big data processing in heterogeneous cloud computing environments. HCCC is a hybrid cloud computing centre.

Challenge and propose strategies to strengthen the digital infrastructure and technological capabilities needed for the sharing economy to thrive

Ha Huy Hoang — Sat, 11 Nov 2023 00:00:00 +0000

The sharing economy has emerged as a disruptive and transformative phenomenon on a global scale, with notable effects on various industries, including Vietnam. This economic model has gained significant traction and reshaped economic activities within the country. However, the sharing economy in Vietnam encounters distinct challenges that require careful analysis and effective solutions. The objective of this master's thesis is to identify, analyze, and propose solutions to address these challenges. Through an extensive review of relevant literature and examination of case studies, this research aims to shed light on three prominent obstacles: regulatory constraints, lack of trust, and conflicts with traditional businesses. Furthermore, this study explores potential solutions, including policy recommendations, trust-building mechanisms, and fostering dialogue and collaboration between sharing economy platforms and traditional businesses. The findings of this research contribute to a comprehensive understanding of the sharing economy landscape in Vietnam and offer valuable insights for policymakers, businesses, and entrepreneurs seeking to navigate and nurture the growth of this emerging sector.

Social Media Link Prediction For Friend Recommendation Using Graph Mining

Sat, 11 Nov 2023 00:00:00 +0000

Social media plays a vital role in today's interconnected world, and establishes a nexus in various ways. It breaks down geographical barriers and enables individuals to interact with others regardless of their location. Moreover, it is an essential tool for businesses and professionals. It facilitates networking, brand promotion, and customer engagement. It also serves as a platform for job seekers and recruiters to connect. During crises such as natural disasters or emergencies, social media can be a lifeline for affected communities.

In recommendation systems, link prediction helps improve user experience by suggesting connections or content that users are likely to find relevant. This is widely used in platforms like Netflix, Amazon, and social media to suggest friends, products, or content. It is vital for understanding and analysing the dynamics of social networks and also identify potential connections, friendships, or collaborations that are likely to form in the future.Graph mining for link prediction is valuable in applications where understanding the potential relationships between entities is crucial.

In this research there is an overview of Graphs, node, vertex, edge, path, Data format & Limitations. Exploratory data analysis is used to help identify obvious errors, as well as for better understanding of patterns within the data, detect outliers or anomalous events and find relations among the variables. In addition, to get optimal recommendations Feature engineering is used on Graphs with similarity measures including Jaccard & Cosine Similarities, PageRank, Shortest Path, Connected components, Adar index, Kartz Centrality, HITS Score, Weight features. Finally missing links are predicted by training a model.

Causes Of Tax Evasion In Ghana And Its Effects On The Economy

Felix Ahima-Adonteng — Tue, 21 Nov 2023 00:00:00 +0000

For decades, tax system and taxation academics have struggled to understand the elements that influence taxpayer noncompliance. Because tax revenues are viewed as an important source of cash in paying government expenditures, numerous experimental and survey results concluded by tax scholars have revealed characteristics of noncompliant taxpayers. Globalization has created a great demand for a variety of public services, prompting governments to raise taxes to fund these programs. The burden of taxes is shifted to taxpayers as a result of the government's and taxpayers' need for public service. Because of taxpayer noncompliance, the gap between tax receipts and tax expenditures is widening, resulting in unbalanced government budgets. Understanding the causes of tax evasion necessitates investigation into the motivations of tax evaders. The study's major goal is to look into the causes of tax evasion in Ghana and how it affects the economy. Based on research of various literatures, we have determined that insufficient government tax incentives, poverty or a lack of cash, a lack of or weak tax law, and insufficient tax education are among the causes of tax evasion in the country.

AI Based Mock Interviewer with Question, Answer and Feedback

Tue, 21 Nov 2023 00:00:00 +0000

Nowadays, technology is everywhere, and it’s constantly providing us with ways to work more efficiently and save time. One such programme or tool is the Smart Interviewing System, which automates the conventional interviewing process utilizing contemporary NLP methods and deep learning technologies. Interviewers and HR management staff employed by various orga- nizations who conduct technology-related interviews will primarily benefit from the solution. System works by figuring patterns from human audio and writing. It converts human voice into computer understandable inputs which are used in our AI Based Interviewer. AI based Interviewer is unable to identify cheating and the questions are only asked from the dataset on which the model is trained.

Analysis of the Factors Impeding Efficient Financial Management Practices of District Assemblies in Ghana

Alexander Owiredu — Tue, 21 Nov 2023 00:00:00 +0000

The Metropolitan, Municipal and District Assemblies (MMDA’s) are core drivers of the Ghanaian local government. There have been evidences of poor management of financial capital among District Assemblies in Ghana. The present study therefore seeks to investigate factors impeding efficient financial management practices of District Assemblies in Ghana. Keywords and phrases including, financial management practices, barriers, challenges, working capital, financial analysis, budgeting, accountability and District Assemblies in Ghana were employed to search relevant literature via google scholar, SCOPUS, NCBI, Science direct, Research gate and Pubmed. 20 out 55 articles searched for that met specified inclusion and exclusion criteria were selected. Predominant challenges identified were political interferences, inadequate ICT logistics and Inadequate staff and training in relevant financial expertise/reforms. The study recommends addressing these challenges to promote efficient financial management practices in District Assemblies in Ghana.

Utilization of Technology in the Ghanaian Hospitality Industry: Prospects and Challenges

Carolyn Odonkor — Tue, 21 Nov 2023 00:00:00 +0000

Technology and digitalization have been of immerse benefits to the hospitality industry. Ghana’s hospitality industry is a cash-cow and the effective utilization of ICT in this sector can maximize income generation. The present study therefore, seeks to investigate the utilization of technology in Ghana’s hospitality industry and associated challenges. In this study, the common digitalization tools used by the country’s hospitality industry were explored. It was identified that most ICT innovations used by hotels and guesthouses are basic and not up to the standards of modern technology trends in the global hospitality industry. The paper identified certain challenges and when these are resolved there could be boosts in the utilization of modern technology in Ghana’s hospitality industry.

Content Indexing with Automatic Image Captioning using Contextual Information Fusion

Tue, 21 Nov 2023 00:00:00 +0000

Dealing with and retrieving collections of photos presents a big challenge in the modern world. Effective image organization and retrieval depend on effective content indexing. In order to achieve content indexing, this research study offers a technique that blends automatic image captioning with information fusion. Convolutional Neural Networks (CNNs) are used in our suggested strategy to extract characteristics from photos by utilizing cutting-edge machine learning techniques. The Bidirectional Long Short Term Memory (bi LSTM) network uses these features as input to create captions for the images. These captions, together with other data like user-generated tags or metadata, let us understand the image's content completely. This information fusion greatly enhances picture indexing's accuracy and relevance, making it more suited to users' demands and flexible across applications. We demonstrate the efficacy of our methodology through experiments on image datasets by displaying considerable improvements in precision and recall rates for content indexing. The advancement of content management systems, multimedia retrieval methods, and user-centric methods of organizing photos through this research will lead to more effective searching and exploration experiences.

Weed Detection using Image Processing

Tue, 21 Nov 2023 00:00:00 +0000

In today’s farming world, it’s crucial to grow crops efficiently and without harming the environment. One big problem is dealing with weeds that can hurt crops and lead to more pesticide use. This research explores a new way to solve this problem using computers and pictures. Traditionally, people used expensive and not-so-environmentally-friendly methods to control weeds. But now, we can use technology like high-quality cameras on drones and satellites to take pictures of fields. Then, we use smart computer programs to figure out which plants are crops and which ones are weeds. This helps us target the weeds more precisely without harming the crops. A multiclass setup is used to detect and categorize corn and soybeans in images. Diverse datasets with annotations are collected. Convolutional Neural Networks (CNNs) extract features from images, and a neural network model, like UNet, classifies them as “corn”, “soybean” or “neither”. The model is pre-trained on large datasets and fine-tuned for agriculture. Training and validation sets assess its performance. Real-time deployment aids farmers, and postprocessing refines results. Periodic retraining adapts to changing conditions. This approach enhances precision agriculture. By bringing together technology and farming, we’re creating a new way to fight weeds. This research aims to contribute to the conversation about making farming more precise and ecofriendly. It’s all about using technology to make farming better for the environment and more efficient. In simple terms, this research is about using computers and advanced cameras to help farmers grow crops better and control weeds more effectively while taking care of the environment.

LipRead: An Empirical Examination of Lip-Reading Patterns in Sentence Comprehension

Manish Bishnoi, Sushama A Shirke, Mohammad Ashraf, Naveen Jhajhriya, Rupender — Tue, 21 Nov 2023 00:00:00 +0000

In the intriguing domain of sentence-level lip reading, a narrative unfolds—a story in the ever-evolving intersection of computer vision and NLP. This abstract unveils the essential facets and recent progress in this captivating narrative. Traditionally focused on isolated word recognition, the research community now aspires to bridge the chasm between individual words and the comprehension of entire sentences. This transition holds great promise, extending the applications to diverse domains, from enhancing accessibility for the hearing-impaired to fortifying security measures and enabling more profound human-robot interactions.

The modern-day protagonists of our narrative, Deep Learning Models, come to the fore. CNN , RNN, and Transformer-based models excel in capturing the intricate temporal and spatial complexities inherent in lip movement data, pushing the boundaries of transcription accuracy.

Nevertheless, the journey is fraught with challenges. Variations in speech rates, fluctuating lighting conditions, and the rich diversity among speakers introduce hurdles. Ethical considerations, encompassing privacy and bias mitigation, loom large as our pursuit of accuracy navigates the intricacies of its course.

This narrative of sentence-level lip reading is one of immense potential. With advancements in data collection, feature extraction, multimodal fusion, and deep learning, the prospects for accuracy and utility soar, benefitting not only those with hearing impairments but society as a whole. The narrative continues, with each chapter drawing us closer to the ultimate aspiration: a world where spoken words are unveiled through the eloquent choreography of the lips.

Role of Social Support Systems in Childbirth- Evidence from Sub-Saharan Africa

*Patience Naa Adaku Bortie and Ebenezer Ato Ewusie — Tue, 21 Nov 2023 00:00:00 +0000

Social support is vital in Sub-Saharan Africa, promoting maternal and child health. It involves mutual assistance, reducing anxiety during pregnancy, and improving obstetric outcomes. As a result, the goal of this study is to examine the functions of social support networks in childbirth, particularly in Sub-Saharan Africa, in order to gain a better understanding of how to enhance mother and child health in Africa. This review was conducted using an electronic search engine to search for articles that described social support systems and their roles and which were published up to August 2023. After screening, a total of 17 articles were included. The contents on social support systems were thematically categorized into four major systems: (1) Family, (2) Individual, (3) Professional and (4) Community. The sub-themes of social support systems observed from the reviewed papers were mostly the family of the mother (30.44%), the husband or the baby’s father (23.90%), Community members, neighbours, and friends of the mother (19.57%) and the family of the husband/baby’s father (10.88%). The least common type of social support sources was from Health workers (6.53%) and Traditional Birth Attendants (2.17%), "Self" (2.17%), Religious counsellors/members (2.17%) and colleagues at work (2.17%). The most stated social supports were Emotional Support (24.51%), Instrumental support (20.73%), financial support (16.97%), Informational Support (15.08%) and the least stated social supports were Material support (9.53%), Spiritual/Religious support (5.65%), Physical Support (5.65%) and Relationship Support (1.88%).

Sub-Saharan Africa faces significant challenges in providing social support for mothers during childbirth. Family networks, spousal support, and various entities like friends, community, healthcare providers, and traditional birth attendants play crucial roles. However, professional health practitioners are underrepresented in social support contexts, highlighting potential gaps in healthcare training and patient relations.

General Quantum Secret Sharing Based on Three-Qubit using Monotone Span Program

Hardeep, Manoj Kumar , Anand Chauhan and Yogendra Kumar Rajoria* — Tue, 21 Nov 2023 00:00:00 +0000

In this research, we present a QSSS established on MSP, in which MSP defines the access structure. The key benefit of MSP is that it can choose the authorization structure more flexibly and practically. Our scheme has a general access structure, which means that each qualifying set might have a varied number of participants. Along with more information, high-dimensional quantum states have another advantage for quantum communication: they are more resilient to ambient or eavesdropping-related noise.

A Conceptual Framework for Cloud Load Balancing of data in Remote Health Monitoring Systems

Vidya.R, Prof.(Dr.) Gladston Raj S — Fri, 24 Nov 2023 00:00:00 +0000

Cloud load balancing is essential for improving the efficiency and dependability of remote health monitoring systems. Remote health monitoring systems have become essential in today's interconnected society since they allow for the monitoring and enhancement of healthcare results. These systems consistently gather and send an extensive quantity of data from diverse sensors and devices, such as wearables and IoT devices, to healthcare practitioners or centralized databases for analysis and decision-making. Cloud load balancing is the process of evenly distributing incoming data traffic among different resources or servers in a cloud environment. Monitoring devices manage the large volumes of data processed and acquired by sophisticated medical sensors, while simultaneously ensuring performance factors such as throughput and latency. Load balancing is utilized to effectively manage large amounts of data through the use of centralized and distributed methods. Through the integration of cloud load balancing into remote health monitoring systems, healthcare organizations can guarantee the efficient distribution of data processing and analysis across numerous servers. This prevents any single server from becoming overwhelmed, thereby ensuring the system's high availability and responsiveness. Furthermore, cloud load balancing helps optimize the utilization of computing resources, ensuring that each server handles an appropriate amount of workload. This not only improves the performance and response time of the system but also enhances scalability, allowing the system to handle increasing data loads with more patients and devices. Hence, it is necessary to allocate the burden of the intelligent operational devices to avoid any possible lack of response. This study presents a cloud-based framework that aims to equally divide the burden among fog nodes. The system is designed to cater to the communication and processing requirements of intelligent real-time applications.

Cultural Historical Activity Theory: A Framework for Understanding and Transforming Teaching and Learning in the Classroom

*Dr. Tibi Thomas R.S, Dr. D. Radharamanan Pillai — Fri, 24 Nov 2023 00:00:00 +0000

Cultural Historical Activity Theory (CHAT) stands as a profound socio-cultural framework for comprehending and revolutionizing the dynamics of pedagogy and learning within the classroom milieu. CHAT, rooted in the seminal work of Vygotsky and subsequently expanded upon by scholars such as Leontiev and Engeström, underscores the paramount significance of the socio-cultural context in the genesis and evolution of human activity, which, in turn, encompasses the intricate processes of knowledge acquisition and skill development. This theoretical perspective prominently accents the mediating role of tools, artifacts, and symbols, thus elucidating their pivotal function in scaffolding human activity and fostering learning experiences.