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Pioneering Developments in Computer Science & Digital Technologies (IC2SDT), International Conference on

作者： S. Subashree Remya Rose S A. S. Valarmathy Princy Joseph P. Jasmin Dennis S. Ravi Department of Computer Science and Engineering E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India Department of Artificial Intelligence and Machine Learning R.M.D. Engineering College Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering CMR College of Engineering and Technology Hyderabad India Department of Electronics and Communication Engineering Vi Institute of Technology Chengalpattu Tamil Nadu India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350365016

ISBN: (纸本)9798350365023

Fake face recognition has emerged as a critical area of research due to the proliferation of synthetic media and its potential misuse in various domains. This study presents a thorough evaluation of a novel Fake Face Recognition Scheme utilizing artificial intelligence Oriented learning Scheme (AIOLS) coupled with Generative Adversarial Network (GAN)-GoogleNet integration. The aim is to discern authentic facial images from synthetic or tampered ones with high accuracy. In this research, we employed a dataset comprising a diverse range of authentic and synthetic facial images. The proposed scheme incorporates the power of GANs for generating synthetic facial images and leverages GoogleNet, a state-of-the-art deep convolutional neural network, for discriminative feature extraction. The integration within the AIOLS framework facilitates efficient learning and adaptation to complex patterns present in fake facial images. The evaluation of our scheme was conducted through rigorous experimentation on the dataset, employing various performance metrics. Notably, the accuracy obtained was measured at an impressive 96.7%, signifying the effectiveness of our approach in distinguishing between real and fake facial images. Additionally, other metrics such as precision, recall, and F1-score were also computed to provide a comprehensive assessment of the scheme's performance.

关键词： Measurement Accuracy Image recognition Face recognition Authentication learning (artificial intelligence) Media Generative adversarial networks Feature extraction Convolutional neural networks

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IoT Security Based on machine learning

IoT Security Based on Machine Learning

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International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： Rama Krishna Vanakamamidi L. Ramalingam N. Abirami S. Priyanka C. Sasi Kumar S. Murugan HCL Technology LTD Vijayawada Andhra Pradesh India Department of Artificial Intelligence and Machine Learning Chennai Institute of Technology (Autonomous) Chennai Tamil Nadu India Department of Information Technology R.M.D. Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Department of Computer Science and Engineering Shree Sathyam College of Engineering and Technology Salem Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

The protection of user privacy and mitigation of threats like spoofing, DoS, jamming, and eavesdropping are essential for the Internets of Things (IoT) to fulfill its promise of bringing improved and intelligent services to users via the integration of diverse devices into networks. machine learning (ML) approaches, including supervised learning, unsupervised learning, and Reinforcement learning (RL), are explored as potential solutions for securing the IoT. This article focuses on the use of ML in IoT authentications, access controls, secured offloading, and malware detection strategies to safeguard sensitive information. The challenges of incorporating these ML-based security strategies into real-world IoT systems are also discussed. A complete analysis of the threats facing IoT networks is provided, as the methods already in use to protect against them and the need for securing such networks. Then, highlight the areas where these security solutions are lacking and might benefit from ML and DL methods. Finally, go through in-depth the current ML and DL methods for dealing with various IoT network security issues. Several potential research possibilities for IoT securities based on ML and DL are also discussed.

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Improving EFL Students' Reading Skills Using Graph Attention Networks and intelligence-Based Tasks

Improving EFL Students' Reading Skills Using Graph Attention...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Radha Ranjan Arpita Goswami Benzigar M Julius Irudayasamy K. Lokeswar Reddy M V Rathnamma Department of Law Amity Law School Amity University Patna India Department of Humanities School of Liberal Study Kalinga Institute of Industrial Technology (KIIT) Deemed to be University Bhubaneswar India Department of Arts and Humanities Christ Academy Institute for Advanced Studies Bangalore India Department of English Language and Literature CAAS Dhofar University Salalah Oman Department of Artificial Intelligence and Machine Learning Annamacharya University Rajampet India Department of Computer Science and Engineering KLM College of Engineering for Women Kadapa India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Reading is an engaging practice that helps language learners understand texts. Develop and prioritize basic skills like reading strategies to improve performance and learn new languages. Reading and reading methods will be defined, distinct reading techniques reviewed, successful reading models established, and essential reading principles investigated. The suggested approach involves preprocessing, feature extraction, and model training. PREP techniques such stop word reduction and Porter stemming address data sparsity. Latent Dirichlet Allocation (LDA) selects features, and Hierarchical Generative Adversarial Network trains models. When compared to BERT and GAN, the recommended model wins. The model's 95.39% accuracy is impressive. This proposed shows that the suggested technique improves reading comprehension and text processing precision. Superior preprocessing, feature selection, and hierarchical GAN-based training make it superior to state-of-the-art methods.

关键词： Training Accuracy Inspection Generative adversarial networks Feature extraction Resource management Proposals Integrated circuit modeling Pupils Text processing

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Development of Secure Hardware Architectures for Preventing Side-Channel Attacks and Protecting Sensitive Data in VLSI Systems

Development of Secure Hardware Architectures for Preventing ...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： Varuna Kumara S. Jayakumar D. Leela Rani Ramya Maranan D. Raja Ramesh M. Deivakani Department of Artificial Intelligence and Machine Learning Moodlakatte Institute of Technology Kundapura Kundapura Karnataka India Department of Electronics and Communication Engineering Sri Sairam College of Engineering Anekal Bengaluru India Department of Electronics and Communication Engineering School of Engineering Mohan Babu University Tirupati Andhra Pradesh India Department of Research and Innovation Saveetha School of Engineering Chennai Tamil Nadu India Department of Electronics and Communication Engineering MVGR College of Engineering (A) Chintalavalasa Vizianagaram India Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul Tamil Nadu India

This research paper has been developed by targeting to develop the secure hardware architecture for preventing the potential threats like side channel attack and protecting sensitive data in VLSI systems. In this context, major of the key security frameworks like information theory, cryptography, online threats protector has been implemented in this study. It has been found, on the basis of extensive simulations, that the innovative design effectively protects against side-channel assaults and cuts the chance of mistakes by half, from 16 to 1. This concept has the potential to deliver considerable advantages for a variety of different sectors, including the Internet of Things, the military, and aerospace. In order to improve the design's overall performance and investigate the ways in which it could be applicable to other VLSI systems, more study could be carried out.

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A Super-Wideband Multiband MIMO Antenna for Microwave and Millimeter Wave Wireless Applications

A Super-Wideband Multiband MIMO Antenna for Microwave and Mi...

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Wireless Antenna and Microwave Symposium (WAMS), IEEE

作者： Manish Sharma Kanhaiya Sharma Rana Gill Ganga Prasad Pandey Shailaja Salagrama Om Mishra Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Commuter Science & Engineering Symbiosis Institute of Technology Symbiosis International (Deemed) University Pune Department of Electronics & Communication Engineering University Centre for Research & Development Chandigarh University Gharuan Mohali Punjab India Information & Communication Technology School of Technology Pandit Deendayal Energy University Gandhinagar India Department of Information Technology University of Cumberland’s Williamsburg Kentucky USA Department of Artificial Intelligence & Machine Learning Symbiosis Institute of Technology Symbiosis International (Deemed) University Pune

This communication reports a multi-super-wideband microwave-access antenna that achieves a bandwidth of 8.56GHz-39.02GHz and finds applications for X-band, Ku-band in Microwave and 28.0GHz & 38.0GHz for millimeter-Wave band 5G infrastructure. The patch which radiates electromagnetic energy consists of a hexagon shape with partial rectangular ground printed on Rogers-Substrate with a thin thickness of 0.254mm. The proposed work with a single element occupies a very compact space of 120 mm 2 and the MIMO SWB counterpart occupies a space of 240 mm 2 . The proposed antenna releasing EM wave offers an average peak realized gain of 4.74dBi and directivity of 4.09dB. The proposed antenna also offers desirable 2-D radiation patterns at 10.60GHz, 16.0GHz, 28.0GHz, and 38.0GHz respectively. The MIMO SWB configuration with an edge-to-edge spacing of 2.50mm achieves isolation of more than 12.50dB with diversity parameters ECC SWB <0.12, DG SWB >9.995dB, TARC SWB SWB<0.30b/s/Hz. The antenna radiating technology achieving several useable bands is useful in mobile networks for achieving information and communication system.

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Predictive Modeling of Dental Health Outcomes Based on Fluoride Concentrations using AI

Predictive Modeling of Dental Health Outcomes Based on Fluor...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Swathi Pai M Annepu Arudra Dankan Gowda V Parashuram Shankar Vadar Mahadeo Ramchandra Jadhav Mandeep Kaur Department of Artificial intelligence and machine learning NITTE (Deemed to be University) NMAM Institute of Technology Nitte Karnataka India Department of Computer Science & Engineering Rajiv Gandhi Institute of Technology Bangalore Karnataka India Department of Electronics & Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka India Yashwantrao Chavan School of Rural Development Shivaji University Kolhapur Maharashtra India Department of Applied Science Bharati Vidyapeeth College of Engineering Navi Mumbai India Department of Electronics and Communication Engineering Punjabi University Patiala Punjab India

Oral health is a crucial aspect of general health, and the presence of fluoride in drinking water has been consistently linked to its improvement. This work utilizes deep learning and machine learning approaches to develop prediction models that can estimate oral health consequences based on fluoride concentrations. Our analysis is based on a comprehensive dataset that includes fluoride levels and oral health indicators from several geographic locations. The dataset covers a broad range of demographic and environmental factors. The study involves a thorough data pretreatment procedure, which includes activities such as data cleaning, standardization, and feature engineering. All of these processes contribute to improving and making the input variables more relevant. The approach used encompasses a range of machine learning algorithms, including as neural networks, decision trees, and ensemble approaches, which are used to create prediction models. Thoroughly adjusting hyperparameters and using cross-validation methods are used to maximize the effectiveness of the model.

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Quantum LSTM Model for Estimation of Energy Expenditure in Human Aging using Wearable IoT Healthcare technology

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IEEE Internet of Things Journal 2025年

作者： Tran, Bao-Nhi Dang Fahim, Muhammad McNiven, Bradley D. E. Guizani, Mohsen Shin, Hyundong Duong, Trung Q. Memorial University Faculty of Engineering and Applied Science St. John'sNLA1B 3X5 Canada Queen's University Belfast United Kingdom Mohammad Bin Zayed University of Artificial Intelligence Machine Learning Department Abu Dhabi United Arab Emirates Kyung Hee University Department of Electronic Engineering Yongin-si Gyeonggi-do17104 Korea Republic of Memorial University Faculty of Engineering and Applied Science St. John'sNLA1C 5S7 Canada Queen's University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom

Physical activity energy expenditure (PAEE) offers significant benefits for general healthcare monitoring and has the potential to promote healthy and active aging for elderly individuals. With recent advancements in quantum information and computation, quantum machine learning (QML) has emerged as a tool capable of improving upon the measurement of PAEE. In this paper, we propose a hybrid QML model to predict PAEE which consists of a classical long short-term memory (LSTM) model integrated with a variational quantum circuit (VQC). This model, which we refer to as the enhanced quantum long short-term memory linear (eQLSTML), was subsequently trained and tested using the publicly available GOTOV Human Physical Activity and Energy Expenditure Dataset for Older Individuals. In particular, we study the proposed eQLSTML model with different gate choices in the quantum circuit along with various embedding and layering techniques. Our results indicate our model to be superior in both performance comparisons and prediction when compared to traditional machine learning methods currently employed. Our findings indicate that combining QML approaches with wearable IoT healthcare devices provides a new avenue for personalized healthcare monitoring and an effective method for promoting healthy aging. © 2025 IEEE.

关键词： Quantum electronics

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Empirical Analysis of Prediction of Crime and Forecasting With IGWO-SVM Approach

Empirical Analysis of Prediction of Crime and Forecasting Wi...

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International Conference on Edge Computing and Applications (ICECAA)

作者： R Vasuki Neha Ganvir Rohita Sharma K Senthil Kumar SVHN Krishna Kumari Kamlesh Singh Department of Computer Application Mannar Thirumalai Naicker College Madurai Tamilnadu India KJ College of Engineering and Management Pune Maharashtra India Fine Arts Lovely Professional University Punjab India Department of and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai Tamil Nadu India Department of CSE (Artificial Intelligence and Machine Learning) Vardhaman College of Engineering Telangana India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India

In order to understand the nuances of the crime data from recent years, in-depth analysis is necessary. Involved parties in law enforcement will benefit from this procedure in terms of making arrests and guiding efforts to reduce crime. Strategically or tactically, it benefits from having a better understanding of where, when, and how crimes will occur in the future. However, given the rising incidence of crime in the modern world, developing an effective method for predicting future criminal behavior is a difficult issue. As a result, it is crucial to develop a strategy for predicting criminal activity in order to prevent further crimes and lower crime rates. Researchers are currently attempting to predict criminal behavior based on a variety of factors. SVM, CNN and ANN are just few of the prediction approaches that can be used to assess a model's accuracy. Although their studies have yielded encouraging findings, there are still major blind spots that preclude an accurate prediction of crime hubs. There have been many earlier publications of academic papers on this subject. So, the results are compiled and conducted a comprehensive assessment of all of them in this report. Proposed goal is to catalog the various forms the crime prediction method has taken and the ways in which it might be improved in the future. Preprocessing, feature extraction, and model training are the three main components of the proposed method. Preprocessing includes activities including data cleansing, integration, transformation, reduction, and discretization. It uses principal component analysis to glean features. The model is trained with IGWO-SVM. When compared to two existing models, the proposed model performs exceptionally well.

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Advancements in Immunization Management for Personalized Vaccine Scheduling with IoT and machine learning

Advancements in Immunization Management for Personalized Vac...

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International Conference on Communications and Signal Processing

作者： M. Madheswaran R. Dhanalakshmi G. Ramasubramanian S. Aghalya S Raju P. Thirumaraiselvan Department of Electronics and Communication Engineering Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering St. Joseph’s College of Engineering Chennai Tamil Nadu India Department of Information Technology Mahendra Engineering College (Autonomous) Namakkal Tamil Nadu India Department of Electronics and Communication Engineering Adhiparasakthi Engineering College Melmaruvathur Tamil Nadu India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

Immunization is essential to public health; currently, comprehensive vaccine schedules may not sufficiently address individual health profiles. The Internet of Things (IoT) collects real-time health data, and decision trees (DTs) develop personalized vaccination schedules. Wearable health monitors and electronic health records provide continuous health data and information. DTs are customizable machine learning (ML) algorithms that assess this data and create immunization regimens. Based on age, vaccination history, underlying health issues, and real-time health measurements, DTs provides individualized vaccine recommendations. More vaccination compliance and greater health needs alignment are the system’s main benefits. Based on real-time data, this technique recommends vaccination administration timings. Optimization of vaccination systems reduces vaccine-preventable illness transmission. Integrating IoT data, preprocessing, and vaccination scheduling using DTs are covered in this work. It emphasizes data security and ethics while managing sensitive health information during privacy and security issues. Personal vaccination scheduling using IoT and DTs advances public health. Regulations that protect privacy and provide equal access to personalized vaccination schedules are essential as the system matures. This novel method might save lives, save healthcare costs, and improve vaccination systems.

关键词： Schedules Ethics machine learning algorithms Costs machine learning Real-time systems Vaccines

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Better Together? The Role of Explanations in Supporting Novices in Individual and Collective Deliberations about AI

arXiv

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arXiv 2024年

作者： Schmude, Timothée Koesten, Laura Möller, Torsten Tschiatschek, Sebastian University of Vienna Faculty of Computer Science Research Network Data Science Doctoral School Computer Science Austria Mohamed bin Zayed University of Artificial Intelligence Department of Human-Computer Interaction United Arab Emirates University of Vienna Faculty of Computer Science Research Group Visualization and Data Analysis Austria AIT Austrian Institute of Technology GmbH Center for Technology Experience Austria University of Vienna Faculty of Computer Science Research Network Data Science Research Group Visualization and Data Analysis Austria University of Vienna Faculty of Computer Science Research Network Data Science Research Group Data Mining and Machine Learning Austria

Deploying AI systems in public institutions can have far-reaching consequences for many people, making it a matter of public interest. Providing opportunities for stakeholders to come together, understand these systems, and debate their merits and harms is thus essential. Explainable AI often focuses on individuals, but deliberation benefits from group settings, which are underexplored. To address this gap, we present findings from an interview study with 8 focus groups and 12 individuals. Our findings provide insight into how explanations support AI novices in deliberating alone and in groups. Participants used modular explanations with four information categories to solve tasks and decide about an AI system’s deployment. We found that the explanations supported groups in creating shared understanding and in finding arguments for and against the system’s deployment. In comparison, individual participants engaged with explanations in more depth and performed better in the study tasks, but missed an exchange with others. Based on our findings, we provide suggestions on how explanations should be designed to work in group settings and describe their potential use in real-world contexts. With this, our contributions inform XAI research that aims to enable AI novices to understand and deliberate AI systems in the public sector. Copyright © 2024, The Authors. All rights reserved.

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