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MIT Art, Design and Technology School of Computing International Conference (MITADTSoCiCon)

作者： Pritam Ramesh Ahire K. Ulaga Priya Computer Science & Engineering Department Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai

ISBN: (数字)9798350362879

ISBN: (纸本)9798350362886

A comprehensive health monitoring & analyzing system that provides personalized recommendations, goal setting, and tracking based on relevant factors related to BMI & COVID-19. System aims to analyze attack obesity through prediction, prevention and ongoing monitoring through appropriate predictive analytical system. The system's Risk Assessment component categorizes individuals by their risk levels using factors like age, gender, medical history, and behavior related to pre & post covid-19 related to BMI, then relates appropriate connections in human body. The wellness solution includes features such as fitness tracking, data integration, personalized metrics, and predictive analytics to create a holistic approach for predictive analytics health management. The system healthcare monitoring and analyzing system is a groundbreaking solution that leverages predictive analytics to promote a healthy lifestyle, combat obesity, and enhance overall well-being. By considering a range of factors and employing cutting-edge technology, we empower individuals to take control of their health, achieve their fitness objectives, and mitigate health risks.

关键词： COVID-19 Obesity Pandemics Urban areas Sociology Medical services Mental health

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Deep Learning and Quantum-Enabled Cloud Platform Approach on Optimized Crop Health Predictions 5th

Deep Learning and Quantum-Enabled Cloud Platform Approach on...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Singh, Jagendra Thakur, Hardeo Kumar Sharma, Neetu Alsahlanee, Abbas Thajeel Rhaif Sahunthala, S. Shaik, Nazeer Alliance School of Advanced Computing Alliance University Bangalore India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Science and Information Technology University of Salahaddin-Erbil Erbil Iraq Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Tamil Nadu Chembarambakkam India Department of Computer Science and Engineering Srinivasa Ramanujan Institute of Technology—Autonomous Anantapur India

ISBN: (纸本)9789819763177

This study is an innovative investigation into the field of plant disease detection and plant health prediction using state-of-the-art machine learning models and Quantum Support Vector Machines (QSVM) for feature extraction. A diverse dataset of 700 images, including a variety of plant species and disease states, served as the foundation for this study. The study evaluated the performance of four machine learning models: CNN (ConvNet), RNN (Recurrent), KNN (k-Nearest Neighbors), and SVM (Support Vector Machine) for accurately identifying and classifying plant diseases. Convolutional neural network (CNN) model unexpectedly prevailed, achieving a remarkable accuracy rate of 98.77%. Accuracy, recall, and F1 score of the model further demonstrated its propensity for disease diagnosis. QSVMs were used to extract intricate features from pictures of sick leaves in order to increase the models’ precision and prognostication ability. The findings have significant impact for agriculture. And the sophisticated toolkit they offer has provided an important step forward in proactive disease management for better crop health. Carrying out an early intervention for disease can result in higher crop output, lower agricultural losses, and improved global food security. The QSVM’s application of quantum computing principles also opens up fresh frontiers for creating machine learning applications in different industries. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Advancements in Speech Recognition: Exploring Feature Extraction Techniques for Enhanced Accuracy and Human-Machine Communication

Advancements in Speech Recognition: Exploring Feature Extrac...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： R. Bhavani Kalaivani Kathirvelu Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India

Speech Recognition (SR), for all diverse languages, is still a big challenge. SR is predominantly used in telecommunication, medical documentation, car control, aviation, etc. SR involves breaking down an audio signal of spoken words into discrete units and then matching those units to words in a language model. The main target of SR is to attain healthier verbal communication amid humans and machines. Therefore, it is a good technology of the human-machine crossing point. The SR uses the Feature Extraction (FE) techniques for better accuracy. In this paper, various techniques for FE are discussed, and an overview is provided to assist in selecting the most suitable technique for achieving higher accuracy. The review highlights the importance of developing more accurate and robust SR systems to improve the user experience in various domains. Overall, it provides the manuscript deals with five different techniques, and among them, the Mel-Frequency Cepstral Coefficients (MFCC) technique was found to exhibit a high level of accuracy.

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Empowered Artificial Intelligence Approach using Intuitionistic Fuzzy based Deep Neural Network for Chronic Diseases Prediction

Empowered Artificial Intelligence Approach using Intuitionis...

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Advances in Modern Age Technologies for Health and Engineering science (AMATHE), International Conference on

作者： D. Karthik S. Arun Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

Managing the increasing prevalence of chronic illnesses is a major worldwide health concern. It's important to understand the medical problem, its stages, types, and available treatments. The most important component in the early diagnosis of chronic diseases might enhance the diagnostic procedure more successfully. Medical professionals can diagnose a process with the help of numerous artificial intelligence tools. However, ambiguity, irrelevance, and inconsistency in the disease dataset have an impact on the classification accuracy. Conventional machine learning techniques are inadequate for managing scenarios of uncertainty that arise in datasets related to chronic illnesses. To anticipate chronic diseases, each characteristic in the suggested work is represented with a hesitation index. In order to compute the hesitation degree as a more informative procedure in determining the association among features, the belongingness and non-belongingness aspects of the intuitionistic fuzzy concept are induced in the prediction process. This work effectively manages the large chronic illness dataset by developing an intuitionistic fuzzy hesitation index-based similarity measure to weed out unimportant and strongly associated characteristics and enhance the classification model. Utilizing the scored feature information, the deep neural network modifies its hyper parameter to address the overfitting issues that frequently arise in DNNs. The performance of the empowered intuitionistic fuzzy-based deep neural network (EIF-DNN) is analyzed using five distinct chronic conditions, including hypertension, diabetes, hepatitis, cancer, and stroke. Comparing the proposed EIF-DNN against the current versions of multi-layered networks, the simulation results show that it produces promising results in predicting chronic diseases at an early stage.

关键词： Training Uncertainty Accuracy Artificial neural networks Stroke (medical condition) Prediction algorithms Indexes

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A Comparative Analysis of Different CNN Architectures for Malware Classification 4

A Comparative Analysis of Different CNN Architectures for Ma...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Mahato, Anisha Majumdar, Rana Ghosh, Swarup Kr Sister Nivedita University Department of Computer Science Engineering Kolkata India Manav Rachna International Institute of Research and Studies Faridabad India

ISBN: (纸本)9798350352931

The concept of digital privacy remains a contested and often misunderstood topic in the modern, internet-dominated era. Despite numerous advancements in cybersecurity, the pervasive threats to data security continue to erode trust in digital systems. Malware, a term encompassing a wide range of malicious software designed to disrupt operations, compromise systems, or steal sensitive information, represents a major challenge in this regard. Its widespread presence has led to the perception that digital privacy is increasingly unattainable, casting a shadow over cyberspace and intensifying the urgency for robust countermeasures. This study delves into the critical task of malware classification, leveraging the Malimg dataset, a benchmark resource widely used for malware research. To tackle this problem, four cutting-edge Convolutional Neural Network (CNN) architectures-LeNet-5, AlexNet, VGG-16 and ResNet-are employed to train and test the dataset, with the objective of identifying the architecture best suited for this classification task. The models are rigorously evaluated using four essential performance metrics: Accuracy, Precision, Recall, and F1-score, which collectively provide a comprehensive measure of their effectiveness. Among these architectures, ResNet emerges as the most effective, achieving a stellar accuracy of 99.78% and excelling across all other evaluation parameters. This highlights ResNet's superior capability in accurately identifying and classifying malware, thereby offering a promising solution to mitigate the growing threat posed by malicious software in cyberspace. © 2024 IEEE.

关键词： Cybersecurity

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A Novel Approach for Credit Card Fraud Detection using Gated Recurrent Unit (GRU) Networks

A Novel Approach for Credit Card Fraud Detection using Gated...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： V. Suganthi J. Jebathangam Department of Computer Science Vels Institute of Science Technology & Advanced Studies(VISTAS) Chennai India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

Mobile payment systems have become increasingly popular with the rise of smartphones, but this has also attracted fraudsters. As fraud rates continue to rise, researchers have turned to machine learning methods to detect and analyze fraudulent transactions effectively. The purpose of this study is to propose the use of the Gated Recurrent Unit (GRU) is an effective method for analyzing and detecting patterns in sequential data, particularly for applications such as fraud detection in mobile payment systems. The GRU is chosen for its ability to capture temporal dependencies in data while being computationally efficient compared to other recurrent neural network variants like LSTM. This study will explore the implementation of GRU, evaluate its performance, and compare it with existing algorithms to highlight its advantages in real-time fraud detection scenarios. The proposed GRU method provides an efficient and effective approach for detecting fraud in credit card transactions by leveraging its ability to process and learn from sequential data. The use of GRUs can improve detection accuracy and efficiency, contributing to more robust fraud detection systems, and ultimately helping protect consumers and financial institutions from financial losses.

关键词： Accuracy Recurrent neural networks Online banking Machine learning Logic gates Credit cards Real-time systems Fraud Smart phones Long short term memory

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LSTM-MI: Revolutionizing Intrusion Detection Through Adaptive Learning and Mutual Information Analysis

LSTM-MI: Revolutionizing Intrusion Detection Through Adaptiv...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： J. Archana A.S. Aneetha Department of Computer science Vels Institute of Science Technologies and Advanced Studies (VISTAS) Chennai India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

This study introduces two innovative approaches to augment intrusion detection capabilities. The first method employs Principal Component Analysis (PCA) for dimensionality reduction, streamlining the dataset's complexity while retaining crucial features. The second method delves into feature selection using Mutual Information (MI), identifying and preserving the most informative attributes crucial for intrusion detection. These selected features are then incorporated into LSTM-based models and it evaluates the efficacy of both PCA and MI alongside LSTM, providing a comparative analysis of their impact on intrusion detection performance. Notably, the LSTM-PCA model exhibits an accuracy of 97.73%, while the LSTM-MI model achieves a higher accuracy of 98.21 %. These results underscore the significance of integrating PCA and MI in conjunction with LSTM, offering nuanced insights into their individual contributions to accurate intrusion detection. Through extensive experimentation on benchmark datasets, the proposed methodologies aim to advance the development of robust intrusion detection systems (IDS).

关键词： Dimensionality reduction Circuits Intrusion detection Benchmark testing Feature extraction Complexity theory Distributed computing

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Developing an advanced Tool for Transforming Spreadsheet Data into Interactive Dashboards with Predictive Insights 2

Developing an Advanced Tool for Transforming Spreadsheet Dat...

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2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Bir, Varnika Kumar, Tapas Tanwar, Poonam Kumar, Pradeep Department of Computer Science and Engineering Manav Rachna International Institute of Research and Studies Faridabad India School of Engineering & Technology Manav Rachna International Institute of Research and Studies Faridabad India

ISBN: (纸本)9798331541217

In today's data-driven world, organizations face the challenge of managing vast datasets. This work centres on developing an advanced tool to streamline the conversion of spreadsheet data into interactive dashboards with predictive insights. Leveraging cutting-edge technologies, the tool enables seamless data visualization and analytics, empowering users to extract meaningful insights. With integrated predictive modelling, it enhances decision-making, making it invaluable for professionals seeking efficient data-driven solutions. The tool not only visualizes data dynamically but also predicts future trends through sophisticated algorithms and machine learning. By uncovering hidden patterns, it deepens users' understanding of data dynamics. Additionally, this research meets the growing demand for intuitive, user-friendly data analytics interfaces. The tool's design ensures accessibility for users with varying technical skills, while interactive dashboards boost engagement and allow real-time exploration of complex datasets. © 2024 IEEE.

关键词： Data Analytics

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Multi-criteria rating conversion without relation loss for recommender systems

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International Journal of computers and Applications 2022年第2期44卷 130-138页

作者： Sitkrongwong, Padipat Maneeroj, Saranya Takasu, Atsuhiro Department of Informatics School of Multidisciplinary Sciences The Graduate University for Advanced Studies SOKENDAI Tokyo Japan Advanced Virtual and Intelligent Computing Center Department of Mathematics and Computer Science Faculty of Science Chulalongkorn University Bangkok Thailand National Institute of Informatics Tokyo Japan

Traditional recommender systems let users provide a single rating indicating their overall preferences toward items. Beside overall rating, multi-criteria recommender systems let users rate on multiple aspects of items with multi-criteria ratings. Most methods in recommender systems are based on collaborative filtering, which makes recommendations by exploiting ratings from neighbor users. However, most of them ignore the fact that rating behaviors of users vary due to their personal preference biases. Thus, exploiting ratings from neighbors directly might result in a poor recommendation. To solve this, the rating conversion techniques have been applied in some single criterion recommendations. For multi-criteria ratings, converting each criterion rating independently might result in loss of relation among criteria ratings. We propose a novel method that simultaneously converts all criteria ratings between users to maintain their implicit relations. The multi-criteria ratings are first normalized by variances of users and principle component analysis is applied to extract user preference patterns. Such patterns are then used for multi-criteria rating conversion. The experiment results show that our method outperforms both current single and multi-criteria rating conversion techniques with RMSEs of 1.1082 and 3.5574 on TripAdvisor and Yahoo datasets respectively, while maintaining considerably high level of prediction coverages. © 2019 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Recommender systems

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Automated Insect Detection and Classification Using Pelican Optimization Algorithm with Deep Learning on Internet of Enabled Agricultural Sector

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SN computer science 2024年第5期5卷 1-13页

作者： Assiri, Mohammed Elhameed, Elmouez Samir Abd Kumar, Arun Singla, Chinu Department of Computer Science College of Sciences and Humanities- Aflaj Prince Sattam bin Abdulaziz University Aflaj 16273 Saudi Arabia Department of Computer Science College of Post-Graduated Studies Sudan University of Science and Technology Khartoum Sudan Department of Electronics and Communication Engineering New Horizon College of engineering Bengaluru India Computer Engineering Thapar Institute of Engineering and Technology Punjab Patiala India

Recently, the combination of Deep Learning (DL) methods within the Internet of Things (IoTs) has developed in the agricultural field, especially in the domain of pest management. This study considers the implementation and development of an innovative method for Insect Detection and Classification using DL within the environment of the IoTs in agriculture. The developed system leverages advanced DL approaches for analyzing images captured by IoT-enabled devices, enabling real-time identification and categorization of insect pests. By continuously incorporating these technologies, this research goals to increase the efficiency and precision of pest monitoring, finally providing to sustainable agricultural technologies and increased crop yield. This study presents an Automated Insect Detection and Classification using Pelican Optimization Algorithm with Deep Learning (AIDC-POADL) technique on Internet of Enabled Agricultural Sector. The main aim of the AIDC-POADL system is to detect and categorize various kinds of insects exist in the agricultural field. In the primary stage, the AIDC-POADL technique involves DenseNet-121 model to learn complex features in the input images. Besides, the hyperparameter selection of the DenseNet-121 model developed by the POA. At last, multilayer perceptron (MLP) model can be applied to discriminate the insects into various classes. To validate the improved performance of the AIDC-POADL algorithm, a series of simulations were involved. The experimental outcomes stated that the AIDC-POADL technique offers enhanced recognition results over other approaches. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Agricultural region Deep learning Insect detection Internet of things Pelican optimization algorithm

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