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1st International Conference on Computational science and Technology, ICCST 2022

作者： Sampath, R. Baskar, M. Devi, V. Brindha Sri Sai Ram Institute of Technology Department of Information Technology KattankulathurTamilnadu Chengalpattu603203 India School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computer Science and Engineering KattankulathurTamilnadu Chengalpattu603203 India

ISBN: (纸本)9781665476553

Alzheimer's disease (AD) is a kind of Dementia. It affects the brain functions, thinking ability and creates memory loss. Each stage of AD is worsening the symptoms and also affects the patient's daily activities. The current diagnosis AD detection process is not providing an accurate report for the early stages of AD. Therefore, accurate Alzheimer detection is an open challenge for the researchers. In this research, a deep learning model is introduced to improve Alzheimer's stage diagnosis. The Hybrid deep model is designed to detect abnormal changes in brain (Magnetic Resonance Imaging) MRI scans. It utilizes the multi-class log loss (MCLL) function as the objective function to reduce the error rate in detecting AD stages. The MCLL approach computes the variations in actual and detected AD stages of each biomarkers features of input MRI images to identify loss rate. It helps to reduce the loss rate in AD stages detection. Moreover, the Hybrid deep learning model for Alzheimer stages detection (HDLMASD)system analysis the images deeply to provide accurate biomarkers detection with the help of all essential biomarkers processing steps. The efficiency of the Alzheimer stages diagnosis system is evaluated with various evaluation metrics. Finally, a comparative analysis is made with multiple present diagnosis systems to verify the performance of the AD detection system. The performance analysis proves that the AD stages diagnosis approach accuracy rate is improved up to 0.47% and the prediction error rate reduced up to 0.49% than comparison approaches. © 2022 IEEE.

关键词： Biomarkers

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Mathematical Modelling of Dengue Transmission with Adaptive Fuzzy Backstepping Control and RK-5 Numerical Method

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International Journal of applied and Computational Mathematics 2025年第3期11卷 1-29页

作者： Vijayalakshmi, G.M. Ariyanatchi, M. Akgül, Ali Department of Mathematics Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Tamilnadu Avadi 600062 India Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Tamilnadu Chennai India Department of Mathematics Art and Science Faculty Siirt University Siirt 56100 Turkey Department of Computer Engineering Biruni University Istanbul Topkapı 34010 Turkey Department of Mathematics Mathematics Research Center Near East University Near East Boulevard Mersin 10 Nicosia 99138 Turkey

The aim of this research is to create an adaptive nonlinear fuzzy backstepping robust controller for a dengue disease outbreak model. We use an SIR-SI compartmental mathematical model to analyse the diffusion mechanism of dengue disease. A nonlinear adaptive controller regulates prevention strategies for susceptible human beings and enhances hospitalization-based treatment for the infected people. Besides, the spraying of chemical insecticides will reduce the infected mosquito population. Adaptation laws are used to derive fuzzy system parameters, enhancing the system's adaptability. Then we construct a suitable Lyapunov function, which guarantees convergence, robustness, and stability for a closed-loop system. Finally, we plot all the system's control variables graphically and calculate their numerical approximations using the Runge–Kutta technique of fifth order. These simulations demonstrate that the proposed adaptive controllers can effectively control the dengue disease and ensure the stability of the system. Hence, we conclude that adaptive backstepping control measures can protect more people from dengue infection. © The Author(s), under exclusive licence to Springer Nature India Private Limited 2025.

关键词： Adaptive back stepping Dengue model Fuzzy system Lyapunov function Runge–Kutta method

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Mitigating Credit Card Fraud: A Machine Learning Perspective

Mitigating Credit Card Fraud: A Machine Learning Perspective

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Recent Trends in Microelectronics, Automation, computing and Communications Systems (ICMACC), International Conference on

作者： Shivam Bhushan Indrajeet Gupta Angayarkanni S A Balakiruthiga B Pandimurugan V Rajaram V Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur India Associate Professor Computer Science and engineering Acharya Institute of Technology Bangalore India

ISBN: (数字)9798350366570

ISBN: (纸本)9798350366587

In this day and age of online and digital transactions, credit cards have come up as a popular option of payment. This has led to the increase in the fraudulent credit card transactions and as a result the banks and the customers have to bear the loss. It is very vital for the credit card companies to identify and stop the fraud transactions. The main aim of this project is to create a model that can distinguish fraudulent credit card transactions from real ones. The proposed model uses the transaction patterns of previous credit card transaction data which contains both genuine and fraud data to train itself using suitable ML algorithms and identify the fraudulent transactions and nip it in the bud. We will be using using SMOTE to process the data considering the imbalance between legitimate and fraudulent transactions, hyperparameter tuning the model for increased effectiveness and using random forest classifier and decision tree classifier to compare results. By using the above mentioned techniques we saw an increase in accuracy and recall. These models offer a scaleable solution to the problem of real time fraud detection in an ever growing and vast financial landscape

关键词： Machine learning algorithms Accuracy Credit cards Data models Real-time systems Fraud Microelectronics Random forests Tuning Testing

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Leveraging Resource-Aware Deep Collaborative Learning Towards Secure B5G-Driven IoT-Fog-Based Consumer Electronic Systems

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IEEE Transactions on Consumer Electronics 2024年 1-1页

作者： Tripathy, Subhranshu Sekhar Bebortta, Sujit Chakraborty, Chinmay Senapati, Dilip Pani, Subhendu Kumar Guduri, Manisha School of Computer Engineering KIIT Deemed to be University Bhubaneswar India Department of Computer Science Ravenshaw University Cuttack Odisha India Department of Electronics and Communication Engineering Birla Institute of Technology Jharkhand India Department of Computer Science University of Delhi Delhi India Kausalya Ganga Bhubaneswar Odisha India School of Computing and Informatics University of Louisiana at Lafayette LA USA

The integration of Fog computing and 5G communication may enhance Cyber Physical Systems (CPSs) for effective time identification of cyber attacks among consumer electronics. An unsupervised Intrusion Detection System (IDS) based on Generative Adversarial Networks (GANs) and Recurrent Neural Networks (RNNs) is presented in this study. This system is tailored to the resource limitations of consumer electronics within CPSs. By leveraging the processing power of fog nodes and the low-latency capabilities of 5G networks, cyber attacks can be swiftly identified. The use of a trained Long Short-Term Memory (LSTM) network encoder improves detection rates by enhancing reconstruction loss computation. Experimental results demonstrate that this approach, implemented through distributed fog computing infrastructure, offers better detection rates with a 15.2% reduction in detection latency and a 24.2% decrease in overall energy consumption compared to baseline methods. This innovative system could serve as an effective alternative for securing consumer electronic devices integrated into CPSs. IEEE

关键词： Fog computing

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Rainfall Prediction in Diverse Indian Regions Using Machine Learning Approaches

Rainfall Prediction in Diverse Indian Regions Using Machine ...

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International Conference on computing and Machine Learning, CML 2024

作者： Darji, Mohini Dave, Jaykumar A. Sankalchand Patel University Gujarat Visnagar India Devang Patel Institute of Advance Technology and Research Charotar University of Science and Technology Anand Changa India Department of Computer Engineering Aditya Silver Oak Institute of Technology Silver Oak University Gujarat Ahmedabad India

ISBN: (纸本)9789819765874

Agriculture has an irreplaceable important position in poor countries such as India. The availability of precipitation is fundamental for farming and has an impact on water resources, agricultural ecology, and hydrology. Today, rain forecasting is as much of a key concern as other predictions in the future. Accurate rainfall forecasting accurately enlightens people with useful knowledge, preparing for probable future occurrences that may be dangerous to their crop yields and protecting against the influence of adverse weather on them. This research aims to assess the efficiency of experimental models in predicting weather phenomena, XGBoost with extreme gradient boosting technology, Multilayer Perceptrons (MLPs), Convolution Neural Networks (CNNs), and Long Short Term Memory (LSTM) networks. The Rajasthan and Kerala region dataset was collected from the National Aeronautics and Space Administration (NASA) from 1980 to 2022. Using a monthly climate dataset from Rajasthan and Kerala states, the evaluation set for these frameworks is partitioned into 80:20 for training and testing. The root-mean-square error (RMSE) evaluation method measures how effectively a model predicts an outcome from combined mathematics and statistics. The LSTM model outperformed the other three models, and its RMSE values are as follows: 0.056 for Rajasthan;and 0.041 for Kerala. These experimental results show the LSTM model's strong predictive capabilities for rainfall. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Mean square error

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Single-and Multiuser Virtual Reality Training for Forklift Driving 2

Single-and Multiuser Virtual Reality Training for Forklift D...

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2nd IEEE International Conference on Cognitive Aspects of Virtual Reality, CVR 2023

作者： Froland, Tord Hettervik Ravyse, Werner Luimula, Mika Heldal, Ilona Western Norway University of Applied Sciences Chemistry and Biomedical Laboratory Sciences Department of Safety Bergen Norway School of ICT: Futuristic Interactive Technologies Research Group Turku University of Applied Sciences Turku Finland Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Bergen Norway

ISBN: (纸本)9798350338638

Hands-on skills training is a constant requirement in a multitude of practical work settings and virtual reality (VR) is often found to be a valuable training opportunity to safely prepare students for their future work environments. One of the main benefits of utilizing VR-based training is providing high grades of presence and involvement. Although the user experiences of students are a key factor that impacts the use of a VR simulation, it becomes more challenging to decide whether single-or multi-user experiences should be planned and developed. This paper presents the results from a between-subjects case study comparing the user experiences of single-and multi-user VR training for forklift driving. Findings highlight the importance of considering various aspects of VR technology to design VR training environments for high user experiences, including the impact of age, group dynamics, and freedom of movement in the environment. The benefits and challenges of single-and multi-user VR training for high experiences and implications for utilizing serious games in VR-based training are discussed. © 2023 IEEE.

关键词： Virtual reality

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Comparative Study of Consumer Purchasing and Decision Pattern Analysis using Pincer Search Based Data Mining Method 13

Comparative Study of Consumer Purchasing and Decision Patter...

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13th International Conference on computing Communication and Networking Technologies, ICCCNT 2022

作者： Bhargavi, Mopuru Sinha, Anurag Desai, Jay Garg, Namit Bhatnagar, Yash Mishra, Pawan Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Vaddeswaram India Department of Computer Science IGNOU Delhi India Department of CSE Devang Patel Institute of Advance Technology and Research Gujrat India Department of Mathematics and Computing Indian Institute of Technology Kanpur India Department of Biotechnology and Biochemical Engineering Indian Institute of Technology Delhi India Department of Electronics and Communication University of Allahabad U.P. Prayagraj India

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

Big data analysis is now widely used as a result of the Internet's quick development. researchers have focused a lot of attention on data mining because it is essential for obtaining potentially valuable information from big data. Python is a popular programming language used in data mining. Python is regarded as an indispensable tool for data mining because of its robust scientific calculation capabilities and rich database. There have been many consumer purchasing behaviour studies that have been presented and applied to actual issues. In-depth consumer behaviour analysis will likely benefit from the use of data mining techniques. Both benefits and drawbacks of the data mining approach exist, though. To mine databases effectively, it is crucial to choose the right techniques. This paper aims to apply several methods, such as pincer search-based association rule generation, to enhance conventional data mining analysis. The a priori algorithm uses a bottom-up, breadth-first search approach. The computation begins with the lowest set of frequent item sets and advances until it reaches the greatest frequent item set. The greatest size of the frequent item collection is equivalent to the number of database passes. The algorithm must go through numerous iterations as a result of any one of the frequent item sets getting longer, which lowers performance. To overcome this challenge, this paper aims to propose pincer searching-based association rule generation based on big data mining and classification algorithms for effective accuracy. The wholesale data market is used for simulation purposes. © 2022 IEEE.

关键词： Data mining

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Fair Rank Aggregation

arXiv

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arXiv 2023年

作者： Chakraborty, Diptarka Das, Syamantak Khan, Arindam Subramanian, Aditya School of Computing National University of Singapore Singapore Department of Computer Science and Engineering Indraprastha Institute of Information Technology Delhi India Department of Computer Science and Automation Indian Institute of Science Bengaluru India

Ranking algorithms find extensive usage in diverse areas such as web search, employment, college admission, voting, etc. The related rank aggregation problem deals with combining multiple rankings into a single aggregate ranking. However, algorithms for both these problems might be biased against some individuals or groups due to implicit prejudice or marginalization in the historical data. We study ranking and rank aggregation problems from a fairness or diversity perspective, where the candidates (to be ranked) may belong to different groups and each group should have a fair representation in the final ranking. We allow the designer to set the parameters that define fair representation. These parameters specify the allowed range of the number of candidates from a particular group in the top-k positions of the ranking. Given any ranking, we provide a fast and exact algorithm for finding the closest fair ranking for the Kendall tau metric under block-fairness. We also provide an exact algorithm for finding the closest fair ranking for the Ulam metric under strict-fairness, when there are only O(1) number of groups. Our algorithms are simple, fast, and might be extendable to other relevant metrics. We also give a novel meta-algorithm for the general rank aggregation problem under the fairness framework. Surprisingly, this meta-algorithm works for any generalized mean objective (including center and median problems) and any fairness criteria. As a byproduct, we obtain 3-approximation algorithms for both center and median problems, under both Kendall tau and Ulam metrics. Furthermore, using sophisticated techniques we obtain a (3 − Ε)-approximation algorithm, for a constant Ε > 0, for the Ulam metric under strong fairness. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Advancing Color Perception: An IoT-driven Pedagogical System for Visually Impaired Children

Advancing Color Perception: An IoT-driven Pedagogical System...

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Advances in Signal Processing, Power, Communication, and computing (ASPCC), IEEE International Conference on

作者： Prasandika Balage Nuwan Jayawardene School of Computer Science and Engineering University of Westminster London United Kingdom Department of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (数字)9798350355093

ISBN: (纸本)9798350355109

Visual impairment is characterized by the loss of vision, encompassing both complete blindness and partial vision loss. Studies reveal a notable prevalence of visual impairment among school-aged children. The significance of colors in acquiring and discerning visual information is crucial, aiding children in effectively perceiving and comprehending their surroundings. This study presents an Internet of Things (IoT) system designed for teaching colors to visually impaired children. The system achieved an accuracy rate surpassing 77.8%, and it is operated effectively regardless of surrounding lighting conditions. Given that the target users of the system are visually impaired children, this research was conducted in close alignment with Human-computer Interaction (HCI) principles.

关键词： Human computer interaction Visualization Accuracy Visual impairment Education Lighting Color Blindness Signal processing Internet of Things

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Implementing Blockchain Based DApp for Secure Sharing of Students' Credentials 9

Implementing Blockchain Based DApp for Secure Sharing of Stu...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Sree Divya, K. Swetha, G. Charyulu, Gomatam Mohana Madhuri, P. Shyamala Srividya, M. Mutar, Modhi Lafta Saikumar, K. Madanapalle Institute of Technology And Science Department of Computer Science & Technology Andhra Pradesh Madanapalle India Cvr College of Engineering Department of Cse Telangana Hyderabad India Vfstr Deemed To Be University School of Applied Sciences And Humanities Dept of English AP Vadlamudi India Vishnu Institute of Technology Department of Cse Bhimavaram Andhra Pradesh Vishnupur534202 India Mlr Institute of Technology Department of Computer Science And Information Technology Telangana Hyderabad India Al-Ayen University Technical Engineering College Thi-Qar64001 Iraq Koneru Lakshmaiah Education Foundation Dept of Ece Green Fields Andhra Pradesh Vaddeswaram522302 India

ISBN: (纸本)9798350394474

When a student excels in a course, the school rewards them with a course credential. These credentials are crucial for students when presenting their academic achievements to potential employers or government agencies for employment or further education. However, with the current reliance on computer systems to store this information, there is a risk of data theft or loss in case of device malfunctions. Obtaining government approval for these credentials is a time-consuming process, as manual verification is required to cross-reference the candidate's information with the school records. Additionally, the existing methods for preventing the creation and alteration of counterfeit graduation papers are insufficient. The use of blockchain technology in this process can address these challenges by creating a decentralized system. Blockchain ensures security through cryptographic links between blocks, and the entire chain is shared among all local nodes, making the system less vulnerable to centralization. The proposed decentralized certificate verification system incorporates essential features to create a Decentralized Application (DAPP) in the blockchain era. This design aims to streamline the verification and issuance of certificates, addressing the prevalent issue of fraudulent statements. The system is comprehensive, providing solutions for the entire Validation Issuance, Confirmation, and Certification Cycle while considering the needs of all stakeholders. © 2024 IEEE.

关键词： Smart contract

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