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2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Ramachandran, R. Hema Priya, K. Malathy, K. Sivagamidevi, G. Sudhakar, C. Venkata Indhumathi, V. Rajalakshmi Engineering College Department of Biomedical Engineering Chennai India Easwari Engineering College Department of Computer Science and Engineering Chennai India S.A. Engineering College Department of Master of Computer Applications Chennai India Dhanalakshmi College of Engineering Dept of Ai & Ds Chennai India SreeVidyanikethan Engineering College Department of ECE Andhra Pradesh Tirupati India Panimalar Institute of Technology Department of It Chennai India

ISBN: (纸本)9798350315905

The component of a system charged with ensuring the security of its users is among its most crucial parts. It has been demonstrated that using a password or login that is too basic makes you vulnerable to hackers and does not ensure a high level of security. Authentication using biometric methods can be achieved in several ways. Some of the most advanced and reliable are facial recognition technology and iris recognition. Because it relies heavily on detection of patterns, it is able to reliably identify the rightful owner of an Iris scan. Accuracy as well as effectiveness have both been greatly enhanced in the resultant recognition system. Security breaches and other identification scams are on the rise, making it all the more crucial to implement a robust biometric system. The option that has gained a lot of attention is biometric identification. The iris's potential as a biometric has gained traction in recent years. This quantifiable quality ensures great productivity and precision, which is what triggered the phenomenon. We present a complete ResNet50-based deep learning system for iris identification in this study. Utilizing only a small number of training photos from every class, we train our algorithm on a popular identification of iris dataset, achieving significant gains over prior methods. In addition, we introduce a visualization method that may identify key features of iris pictures that have a significant bearing on the precision of recognition. We anticipate that this approach will be utilized extensively in the future to improve the scalability and precision of various biometrics identification jobs © 2023 IEEE.

关键词： Biometrics

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Machine Learning Classifiers for Predicting the Risk of Cardiovascular Disease in Healthcare 4.0

Machine Learning Classifiers for Predicting the Risk of Card...

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Communication, Control and Intelligent Systems (CCIS)

作者： Palak Kumari Ashish Kumar Kakali Chatterjee Master of Computer Applications Patna Women’s College Patna India Dept. of Computer Science and Engineering National Institute of Technology Patna Patna India

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528218

Cardiovascular disease (CVD) generic phrase used to describe heart or blood vessel illness. Conditions that impact the body’s, brain’s, or heart’s blood flow. Reduced blood flow can result from factors such as blood clots, leading to heart attack, angina, or stroke. Other potential meanings of CVD. These models offer key benefits like clear interpretability and the ability to analyze complex patterns, significantly enhancing the accuracy of cardiovascular disease risk predictions. The demerits of using SVM, LR, K-NN, and MLP for CVD prediction include inter-pretability issues, overfitting risks, data requirements, feature selection challenges, scalability concerns, algorithm complexity, and class imbalance. Collaboration between machine learning experts and healthcare professionals is essential for developing accurate and reliable CVD prediction models that enhance patient care. CVD is a significant health threat, often leading to fatal complications. Early detection is crucial for effective intervention. This study employs SVM (accuracy is 0.84%), LR (accuracy is 0.86%), K-NN (accuracy is 0.94%), and MLP (accuracy is 0.79%) machine learning techniques to enhance CVD detection by addressing outliers and null values, contributing to advancing automatic detection initiatives.

关键词： Support vector machines Accuracy Scalability Machine learning Medical services Predictive models Stroke (medical condition) Prediction algorithms Reliability Blood flow

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Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments 6

Deep Reinforcement Learning-based Obstacle Avoidance for Rob...

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6th IEEE International Conference on Civil Aviation Safety and Information technology, ICCASIT 2024

作者： Li, Keqin Chen, Jiajing Yu, Dezhi Dajun, Tao Qiu, Xinyu Lian, Jieting Ji, Ryan Zhang, Shengyuan Wan, Zhenyu Sun, Baiwei Hong, Bo Ni, Fanghao Han, Jiaqi Ama University Department of Computer Science Quezon Philippines Courant Institute New York University 251 Mercer St New YorkNY10012 United States University of California Berkeley University Avenue and Oxford St School of Information BerkeleyCA94720 United States Carnegie Mellon University Master of Science in Mechanical Engineering School of Engineering 5000 Forbes Avenue PittsburghPA United States Northeastern University College of Engineering Seattle United States School of Professional Studies New York University Master of Science in Database Technology 50 West 4th Street New YorkNY United States Cornell University College of Engineering 616 Thurston Ave IthacaNY14853 United States Cornell University Master of Professional Science in Applied Statistics-Data Science College of Computing and Information Science 616 Thurston Ave IthacaNY United States Independent Researcher Beijing China University of California Irvine Master of Computer Science Donald Bren School of Information and Computer Sciences 510 Aldrich Hall IrvineCA United States Northern Arizona University School of Informatics Computing and Cyber Systems FlagstaffAZ United States Shanghai Institute of Technology Shanghai China

ISBN: (纸本)9798350389418

This paper proposes a deep reinforcement learning based on the warehouse environment, the mobile robot obstacle avoidance Algorithm. Firstly, the value function network is improved based on the pedestrian interaction. We can learn to obtain the relative importance of the current state and the historical trajectory state as well as the joint impact on the robot's obstacle avoidance strategy. Secondly, the reward function of reinforcement learning is designed based on the spatial behaviour of pedestrians, and the robot is punished for the state where the angle changes too much, so as to achieve the requirement of comfortable obstacle avoidance. Finally, the feasibility and effectiveness of the deep reinforcement learning-based mobile robot obstacle avoidance algorithm in the warehouse environment in the complex environment of the warehouse are verified through simulation experiments. © 2024 IEEE.

关键词： Deep reinforcement learning

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Hybrid optimal feature selection-based iterative deep convolution learning for COVID-19 classification system

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computers in Biology and Medicine 2024年 181卷 109031-109031页

作者： Santosh Kumar Patra, P. Tripathy, Biswajit Research Scholar Department of Computer Science and Engineering Biju Patnaik University of Technology Odisha Rourkela769015 India Professor Master of Computer Applications Einstein College of Computer Application and Management Odisha Khurda752060 India

The COVID-19 pandemic has necessitated the development of innovative and efficient methods for early detection and diagnosis. Integrating Internet of Things (IoT) devices and applications in healthcare has facilitated various functions. This work aims to employ practical artificial intelligence (AI) approaches to extract meaningful information from the vast amount of IoT data to perform disease prediction tasks. However, traditional AI methods need help in feature analysis due to the complexity and scale of IoT data. So, this work implements the optimal iterative COVID-19 classification network (OICC-Net) using machine learning optimization and deep learning approaches. Initially, the preprocessing operation normalizes the dataset with uniform values. Here, random forest infused particle swarm-based black widow optimization (RFI-PS-BWO) algorithm was used to get the disease-specific patterns from SARS-CoV-2 (SC2), and other disease classes, where patterns of the SC2 virus are very similar to those of other virus classes. In addition, an iterative deep convolution learning (IDCL) feature selection method is used to distinguish features from the RFI-PS-BWO data. This iterative process enhances the performance of feature selection by providing improved representation and reducing the dimensionality of the input data. Then, a one-dimensional convolutional neural network (1D-CNN) was employed to classify and identify the extracted features from SC2 with no virus classes. The 1D-CNN model is trained using a large dataset of COVID-19 samples, enabling it to learn intricate patterns and make accurate predictions. It was tested and found that the proposed OICC-Net system is more accurate than current methods, with a score of 99.97 % for F1-score, 100 % for sensitivity, 100 % for specificity, 99.98 % for precision, and 99.99 % for recall. © 2024 Elsevier Ltd

关键词： COVID-19 pandemic Internet of things Remote patient monitoring Machine learning optimization Particle swarm optimization Black widow optimization Iterative deep convolution learning

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Sustainable and Green IoT Solutions for Environmental Monitoring 9

Sustainable and Green IoT Solutions for Environmental Monito...

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9th International Conference on science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Banu, E. Afreen Venkateswaran, N. Mageshwari, P.S. Latha Elangovan, M. Pandey, Rakesh Kumar Department of Computer Science Engineering Chennai Tamil Nadu Thandalam602105 India Panimalar Engineering College Department of Master of Business Administration Tamil Nadu Chennai600123 India Tamil Nadu Kavaraipettai601206 India Sns College of Technology Department of Aerospace Engineering Tamil Nadu Coimbatore641035 India Mats University Department of Civil Engineering Chhattisgarh Raipur493441 India

ISBN: (纸本)9798350365092

In response to growing environmental enterprises, this exploration paper investigates the development and perpetuation of sustainable and green Internet of Effects (IoT) results for environmental monitoring. With the rapidfire expansion of IoT technologies, an occasion arises to work on these advancements for the benefit of environmental conservation and sustainability sweats. This study explores the design and deployment of IoT-grounded detector networks able to cover colorful environmental parameters, such as air quality, water quality, and biodiversity, in a manner that minimizes energy consumption and environmental impact. By employing energy-effective detector bumps, renewable energy sources, and eco-friendlyaccouterments, the proposed results aim to achieve a balance between technological invention and environmental responsibility. Through case studies and performance evaluations, the efficacity and feasibility of these sustainable IoT results are examined, pressing their eventuality to grease data-driven decisions- timber and promote sustainable practices in environmental operation. The findings of this exploration contribute to the ongoing conversation on sustainable development and emphasise the role of IoT in fostering a greener and more flexible future. © 2024 IEEE.

关键词： Sensor networks

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Fake News Detection Review: Concepts, Tools, Research GAPS, and Possible solutions

Fake News Detection Review: Concepts, Tools, Research GAPS, ...

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International Conference on Network Applications Protocols and Services (NETAPPS)

作者： Sapana Yakkundi Rudragoud Patil Swetha Indudhar Goudar Dept. of Computer Science and Engineering KLS Gogte Institute of Technology Belagavi Karnataka India Department of Computer Science and Engineering KLS Gogte Institute of Technology Belagavi Karnataka India Department. of Master of Computer Applications KLS Gogte Institute of Technology Belagavi Karnataka India

ISBN: (数字)9798331519513

ISBN: (纸本)9798331519520

The increasing number of social platforms means that there is a rapid expansion of online data. However, not all of this data can be trusted, as some users intentionally manipulate information to spread false news for personal or political reasons. Fake news can significantly impact public perception and societal outcomes. Its widespread influence was especially noticeable during the 2016 United States election cycle, leading to heated discussions about its role and consequences. Given the changing nature of news, real-time data processing has become essential for effectively identifying and countering misinformation. This research provides a comprehensive examination of the datasets, tools, and techniques utilized for detecting and combating fake news. Many researchers have developed models based on specific datasets, limiting their scope to particular domains. A thorough review of advanced models for real-time fake news detection highlights existing research gaps. The analysis of the review reveals that numerous datasets used in the literature are outdated, and there is a methodological challenge associated with the use of traditional machine learning approaches. With the continuous evolution of news, there is an urgent demand for utilizing sophisticated deep learning models for the automated, real-time verification of misinformation. This research presents promising approaches to tackle the challenge of detecting fake news.

关键词： Accuracy Protocols Limiting Reviews Social networking (online) Voting Real-time systems Data models Internet Fake news

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BERT in Cognition for Learner

BERT in Cognition for Learner

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International Conference on Computing and Networking technology (ICCNT)

作者： Souvik Ganguly Priyanka Chatterjee Kailash Pati Mandal School of Computer Science Inspiria Knowledge Campus Siliguri India Master in Computer Application Dr. B.C. Roy Engineering College Durgapur India Computer Science and Engineering National Institute of Technology Durgapur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Profound subject knowledge is necessary for education to spark creative or formative projects and boost individual’s dynamism. Acquiring a magnificent education is crucial because it permits us to live completely, make wise decisions, and overcome obstacles in life. A learner’s academic journey is continued in early education, which also permits them to become more adept at discriminating between inequity and fairness and equips them for a broader spectrum of activities in higher education. Alongside superficial constraints like threats or financial crises, learners must compose a plethora of delightful and demanding activities in addition to their studies. Therefore, learners can experience moments of cognitive disorientation and progressively acquire cognitive ailments such as stress or depression, to name a couple. Thus, this research suggests a novel Bi-directional Encoder Representations from Transformer (BERT) method for ascertaining learners’ cognitive declines. This research utilizes the BERT-Base architecture, and the hyper-tuning of the hyperparameters is carried out by the innovative Tweaked Shuffled Frog Leaping Algorithm technique. Although there is a high need for language models these days, there are currently no specific language models that can reliably specify a learner’s cognitive deficits according to psychological inqueries. Thus, this work offers a novel strategy known as CogniBERT in response to this. Our approach confounds some of the weaknesses of the BERT methodology by choosing the right scaling factors during training. In terms of training, validation, and testing accuracies, the experimental result surpasses the other approaches, obtaining $\mathbf{9 9. 5 6 \%}, 98.97 \%$, and $99.29 \%$, respectively, when the suggested method is contrasted with a few state-of-the-art methods.

关键词： Training Psychology Bidirectional control Human factors computer architecture Depression Transformers Encoding Cognition Testing

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A Survey on Privacy-Preserving Federated Learning in Internet of Things (IoT)

A Survey on Privacy-Preserving Federated Learning in Interne...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： S. Raghav B. Vamsi Krishna Vanipriya Ch Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Master of Computer Applications Sir M Visvesvaraya Institute of Technology Bengaluru India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

This survey article provides a comprehensive overview of Privacy-preserving Federated Learning (FL) in the Internet of Things (IoT), focusing on secure collaborative learning with distributed data. In response to the proliferation of IoT devices generating vast amounts of sensitive information, the survey delves into state-of-the-art methodologies and technologies aimed at preserving user privacy during collaborative model training. The review encompasses cryptographic techniques, secure aggregation protocols, and differential privacy mechanisms, addressing the unique challenges posed by decentralized and heterogeneous IoT environments. Through an analysis of communication overhead, resource constraints, and device diversity, the survey offers a nuanced understanding of the current landscape, including optimization strategies and emerging trends. Serving as a valuable resource for researchers and practitioners, this survey not only synthesizes existing knowledge but also identifies research gaps and proposes potential directions for advancing secure collaborative learning in IoT through Privacy-Preserving Federated Learning.

关键词： Surveys Training Differential privacy Federated learning Reviews Scalability Malware Blockchains Internet of Things Optimization

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Unlocking the Experimental Possibilities of a Logical Wireless Power Transfer System Design for Charging Electric Vehicles Through High Intensity Concentration

Unlocking the Experimental Possibilities of a Logical Wirele...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Rani, A. Jaya Mabel Selvi, D.M. Kalai Saranya, D. Rathika, S. Sharma, V. Rama Krishna Shobana, D. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.D Engineering College Department of Cse Tamil Nadu Chennai India St Joseph's College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Tamil Nadu Chennai India Mlr Institute of Technology Department of Ece Hyderabad India Rajalakshmi Engineering College Department of Mechatronics Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350375237

An exciting new direction in electric vehicle (EV) energy supply system development is the wireless power transfer (WPT) method. The WPT outshines plug-in hybrid electrical charging systems in many respects, including its compact battery size, low weight, rapid charging capabilities (charging-in-motion services), and extensive range while on the go. After establishing the framework and categorization of the WPT system, this article goes on to examine the fundamentals of the most well-known WPT method, electromechanical reactive resonance transmission. Through the use of wireless technology, known as wireless power transfer (WPT), electric energy may be sent wirelessly from an energy source to a load. With their numerous benefits over their wired counterparts - including the elimination of exposed cables, simplified charging, and unfaltering transfer of power even in harsh environments - wireless power transceivers (WPTs) are finding widespread usage in industrial settings. Several businesses are considering using WPTs to power the electric vehicle (EV)'s internal batteries, and they're working to enhance and advance the many related topologies. The low-cost inductive connection of two coils, the transmitter and receiver coils, allows for WPT to function. The transmitter coils for electric car charging applications are buried under the road, while the receiver coils are installed inside the vehicle. In this study, a novel approach to power transfer is presented, HIWPT. To assess its efficacy, it is cross-validated with an existing technique, CPTS, which stands for Conventional Power Transfer System. Regarding moderately high transfer of power usage, such as charging electric vehicles, capacitive WPT of the resonance type is typically preferred because to its higher efficiency. While designing the WPT system's power supply stages, we took into account the following factors(i) the values and ratings of the capacitors that render the coil-coupling resonance;(ii) the ele

关键词： Energy transfer

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Understanding the Potential of AI-Powered Image Recognition Systems for Real-Time Applications 2

Understanding the Potential of AI-Powered Image Recognition ...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Kamalraj, R. Gajendran, P. Rathika, S. Dhabliya, Dharmesh Jakhar, Sunil Kumar Savita Department of Computer Science and Information Technology Karnataka Bangalore India Karpagam College of Engineering Department of Electrical and Electronics Engineering Coimbatore641032 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Vivekananda Global University Department of Mechanical Engineering Jaipur India School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350308846

This paper investigates the potential of synthetic Intelligence (AI)-powered image popularity systems for actual-time packages. It explores how these structures can beautify actual-time operations' efficiency, accuracy, and productivity. The paper examines the accuracy, velocity, and effectiveness of image reputation technologies, which include convolutional neural networks (CNNs), switch studying, and item detection. These technologies can create fashions to detect and classify gadgets in real-time and for self-reliant selectionmaking. Additionally, the paper will speak about the security threats associated with the lengthy-time utilization of these structures. Examples of AI-powered image reputation systems for numerous actual-time programs can also be included. Sooner or later, the paper will offer an end, which considers the potential pitfalls of using AI-powered image reputation systems for actual-time packages. © 2023 IEEE.

关键词： Accuracy Packages Programs Reputation Utilization

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