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IEEE Conference on Vehicular Technology (VTC)

作者： Ram Mohan Chowdary Kota Jaishree Mayank Amab Sarkar Department of Computer Science and Engineering Indian Institute of Information Technology Design and Manufacturing Chennai India Department of Computer Science and Engineering Advanced Technology Development Centre Indian Institute of Technology Kharagpur India

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

Time Sensitive Ethernet is quickly emerging to be the preferred choice as the backbone network for in-vehicle communication, due to its high bandwidth, reliability, scalability, backward compatibility, and support for diverse traffic types. However, individual real-time control subsystems which may need to communicate via this backbone network, may be driven by non-Ethernet protocols like CAN, FlexRay, etc. Beyond ad hoc approaches, this highlights the need for a systematic message scheduling mechanism that enables seamless real-time message transmission over heterogeneous network domains. In this context, this work proposes a formal SMT (Satisfiability Modulo Theories) formulation for statically scheduling a set of persistent, periodic real-time messages. While the sources and destinations of these messages are in different CAN-network domains, the message transmissions must be conducted via an intermediate Time Sensitive Networking (TSN; IEEE802.1Q) backbone. Although, this formal strategy achieves high resource utilization while guaranteeing end-to-end timeliness, it is computationally exponential in nature with overheads becoming prohibitively expensive even for moderate problem sizes. Hence, we propose a lower overhead scheduling strategy called CAN-THER which can deliver efficient and quick solutions even for large problem sizes. The proposed scheduling strategies have been analyzed and compared using extensive simulation based experiments. Results reveal that CAN-THER is able to achieve performance that is up to 90% of the SMT formulation, while producing solutions at speeds that are approximately 10 4 times faster for problems having up to 12 CAN flows and 15 TSN switches.

关键词： Schedules Systematics Processor scheduling Scalability Ethernet Routing Real-time systems Timing Resource management Reliability

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Internet of Things Assisted Remote Health and Safety Monitoring Scheme Using Intelligent Sensors 2

Internet of Things Assisted Remote Health and Safety Monitor...

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

作者： Ramkumar, V. Shanthi, S. Kumar, K. Suresh Kanageswari, S. Mahalakshmi, S. Anusuya, R. R.M.K. Engineering College Department of ECE Chennai India Care College of Engineering Department of ECE Tiruchirappalli India Panimalar Engineering College Department of Mba Chennai India Loyola College Department of Computer Science Chennai India R.M.K Engineering College Department of Computer Science and Design Chennai India Modern Institute of Technology and Research Centre Department of Cse Rajasthan Alwar India

ISBN: (纸本)9798350315905

The Internet of Things (IoT) in telemedicine creates an enormous data volume that must be processed, analyzed, and stored. Risk to one's health is no longer mitigated by one's chronological age, thanks to today's hectic schedules and unexpected ways of living. The Internet of Things has made it easier to provide continuous remote health checks on patients outside of the hospital or clinic, or even in their own homes. Detectors record data on a variety of patient health factors, and the Internet of Things saves and displays this data on a website that also allows for remote monitoring. When the potential of IoT technology is taken into consideration, the challenges that patients with physical instability have while trying to regularly contact a doctor in person can be circumvented. The microcontroller's role in this project is that of a data relay. This system presents a new method for health service activities by using sensors to monitor a person's vitals and the network to alert concerned family members or physicians in the case of an *** attached buzzer in the controller will sound when there is a change in detector output, alerting the career. A microcontroller is used to monitor the patient's condition;this controller communicates with an LCD display and a wireless LAN to send out alert messages. The patient's heart rate, blood pressure, and temperature will all be displayed in the cloud in instantaneously, and a warning will be sent to the physician via the Internet of Things if the sensor recognizes an abnormality. In order to comprehend how the sensors worked, a comprehensive analysis of the data was collected with regard to fluctuations in physical and environmental activity. © 2023 IEEE.

关键词： Blood pressure

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Adaptive Behavioral Authentication for Fraud Detection: Leveraging Real-Time User Behavior to Enhance Financial Security

Adaptive Behavioral Authentication for Fraud Detection: Leve...

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Data, Computation and Communication (ICDCC), International Conference on

作者： Pankaj Chandre Smita Gumaste Aditi Wangikar Suruchi Deshmukh Department of Computer Science and Engineering MIT School of Computing MIT Art Design and Technology University Pune India

ISBN: (数字)9798331532956

ISBN: (纸本)9798331532963

This paper presents a comprehensive analysis of adaptive behavioral authentication systems designed for fraud detection in financial services. These systems leverage real-time user behavior, such as typing patterns, mouse movements, and geolocation data, to continuously monitor and assess authentication risks. A layered approach integrates behavioral analysis with traditional credentials, providing enhanced security against evolving fraud techniques. The proposed system illustrates the interaction between users, the authentication system, a behavioral engine, and fraud detection models, enabling dynamic decision-making processes. The proposed framework enhances fraud detection by ensuring robust monitoring without compromising user experience. Future work aims to address challenges in data privacy, ethical considerations, and system adaptability to emerging financial technologies like decentralized finance (DeFi).

关键词： Ethics Adaptive systems Geology Authentication Finance Real-time systems User experience Fraud Security Monitoring

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Fine- Tuning Pre-trained Models for Aspect-Based Sentiment Analysis of Amazon Food Reviews

Fine- Tuning Pre-trained Models for Aspect-Based Sentiment A...

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2025 International Conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Sridhar, D. Manikandan, K.B. Padma Priya, R.S. Pradeepa, D. Vasumathi, B. Shankar, R. Alliane University Alliance College of Engineering and Design Central Campus Karnataka Bangalore India Hyderabad India Vignan's Foundation for Science Technology and Research Department of Information Technology and Computer Applications Guntur India Reva University School of Computer Science and Applications Bangalore India Brainware University Department of Computational Sciences West Bengal India Bharathiar University Research in Commerce Coimbatore India

ISBN: (纸本)9798331513894

The process of classifying consumer responses to a particular product review as neutral, negative, or positive is known as sentiment analysis. Product owners can utilize sentiment analysis for assistance in them decide whether to continue producing their product, increase production, or eliminate production altogether. As a result, they will gain a deeper comprehension of how customers feel about their products. The method used to determine the opinions expressed in the review regarding a specific feature or aspect of the product is called Aspect Based Sentiment Analysis, or ABSA. Numerous machine learning and natural language processing models are currently available for ABSA. The inability to precisely identify and categories the views expressed towards particular features is a major drawback of the aspect-based sentiment analysis (ABSA) techniques presently in usage for Amazon food assessment tasks. The Hybrid Contextual Aspect-Based Sentiment Analyzer (HCABSA), a hybridized framework for aspect-based sentiment analysis, is developed in this research to accurately assess the sentiment of Amazon food reviews through a number of processes. This research starts by collecting dataset for Amazon food reviews from Kaggle, preprocessing the data to eliminate noise and irrelevant information, and then using POS tagging to identify the specific aspects and sentiment words related to those aspects, which are nouns and adjectives in the review using the Lexicon based approach. In the following stage, the frequency-based candidate extraction method is utilized to pick the characteristics that are used to determine the product's internal and exterior features. The raw data is then transformed into features that the machine learning models can access using the Bag of Words feature extraction process. The Gradient Boosting Decision Tree technique is then used to categories the sentiment as either positive or negative. Using performance metrics like accuracy, precision, recall,

关键词： Decision trees

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A Deep Q-Learning Approach to Intrusion Detection and Prevention Systems: Enhancing Cybersecurity through Intelligent Adaptation

A Deep Q-Learning Approach to Intrusion Detection and Preven...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Pankaj Chandre Manoj Bapurao Shinde Shruti Vilas Tilwant Arun Bhau Ghandat Bhagyashree D Shendkar Prashant Dhotre Department of Computer Science and Engineering MIT School of Computing MIT Art Design and Technology University Pune India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

A Deep Q-Learning approach to Intrusion Detection and Prevention Systems (IDPS) offers a cutting-edge solution for enhancing cybersecurity by leveraging intelligent machine learning models. This method dynamically adapts to evolving threats, learning optimal defense strategies through a reward-punishment system. The presented architecture integrates sensor-based traffic monitoring, feature extraction, and real-time decision-making to strengthen network resilience. The system leverages sensor nodes to monitor traffic and uses traffic analyzers to extract critical features from network data. A Deep Q-Learning model is employed to analyze these features, dynamically adjusting security policies by learning through reward and punishment mechanisms. The system interacts with network infrastructure, such as firewalls, routers, and switches, to mitigate threats in real time. Threat intelligence is continuously updated in a dedicated database, while an action executor enforces security measures and policies. This intelligent adaptation ensures proactive threat prevention and network resilience.

关键词： Adaptation models Q-learning Prevention and mitigation Intrusion detection Machine learning Network security Feature extraction Real-time systems Monitoring Resilience

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The Role of Non-Invasive Neural Devices in Modern Neurorehabilitation and Mental Health Treatment 8

The Role of Non-Invasive Neural Devices in Modern Neurorehab...

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8th International Conference on Trends in Electronics and Informatics, ICOEI 2025

作者： Pawar, Adesh Gourshettiwar, Palash Gote, Pradnyawant M. Choudhary, Vishal Kumar Yesankar, Prajyot Khadse, Shrikant Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Maharashtra Wardha India Department of Computer Science and Design Faculty of Engineering and Technology Maharashtra Wardha India Department of Artificial Intelligence &Machine Learning Faculty of Engineering and Technology Maharashtra Wardha India

ISBN: (纸本)9798331544607

Modern neurorehabilitation and mental health treatment have received transformative tools through non-invasive neural devices that offer effective and patient-friendly alternatives to traditional interventions. Technologies in this regard include transcranial magnetic stimulation, transcranial direct current stimulation, EEG-based neurofeedback, and functional near-infrared spectroscopy, where advances in neuroscience and bioengineering are utilised in modulating brain activity without risks associated with surgical procedures. Their applications in neurorehabilitation range from post-stroke motor recovery to cognitive rehabilitation and treatment of mental health disorders, including depression, anxiety, and PTSD. Focusing on personalised medicine, machine learning, and big data could be used in non-invasive neural devices to fine-tune outcomes tailored to an individual's needs. These are seminal challenges, such as accessibility, variability in patient responses, and regulatory hurdles, in the face of these devices offering much promise regarding enhancing the quality of life and improving cognitive functions, besides providing sustainable, long-term mental health. This paper underlines their potential and discusses future directions in integration. © 2025 IEEE.

关键词： Personalized medicine

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Adapting Deep Learning Transformer Model for Depression-Specific Named Entity Recognition: A Transfer Learning Approach

Adapting Deep Learning Transformer Model for Depression-Spec...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： Dipti Pawar Shraddha Phansalkar Department of Computer Science Engineering MIT School of Computing MIT Art Design Technology University Pune India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Named Entity Recognition (NER) is essential in the biomedical domain, particularly in mental health studies focused on disorders like depression. It helps extract structured information from unstructured text, enabling accurate recognition of key entities such as symptoms, disorder, antidepressants, brands, and therapies. This step is crucial for recommending diagnoses, personalizing treatments, and advancing research. Traditional methods, like rule-based or dictionary-driven approaches, often fall short in flexibility and scalability, as they rely heavily on predefined lexicons and corpora. The proposed work presents an advanced deep learning model aimed at improving NER performance on a customized dataset. Our model focuses on identifying critical entities disorders, brands, antidepressants, and therapies by addressing the limitations of conventional techniques. It uses a hybrid approach that combines a BERT-based model, fine-tuned through transfer learning, with a knowledge-based dictionary (MedDRA). This integration allows for enhanced semantic enrichment, improving the identification and categorization of biomedical entities. By leveraging BERT’s contextual capabilities alongside the structured knowledge from MedDRA, the model delivers better accuracy and classification in mental health data. Preliminary results show high confidence levels, with substantial improvements in key metrics like precision, recall, and F1 score, further advancing NER's role in mental health research.

关键词： Deep learning Adaptation models Accuracy Biological system modeling Computational modeling Transfer learning Antidepressants Medical treatment Mental health Named entity recognition

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Enhanced Food Classification System Using YOLO Models For Object Detection Algorithm 14

Enhanced Food Classification System Using YOLO Models For Ob...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Sudharson, S. Kokil, Priyanka Annamalai, R. Sai Manoj, N.V. Computer Science and Engineering Vellore Institute of Technology Chennai India Electronics and Communication Engineering Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai600127 India Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai601103 India

ISBN: (纸本)9798350335095

Food classification is crucial for the food industry and has a significant impact on public health and nutrition. Deep learning techniques such as YOLOv5 and YOLOv7 have recently shown significant improvement in food categorization accuracy. The results of this study's analysis of both models is evaluated on a manually labeled dataset, evaluating their accuracy, recall, and mAP at the different intersections over union (IoU) criteria. The accuracy, recall, and mAP of both YOLOv5 and YOLOv7 were evaluated at various IoU criteria, and the results showed that YOLOv5 outperformed YOLOv7 in all three metrics. Specifically, YOLOv5 achieved an accuracy score of 0.851 and a recall score of 0.836, whereas YOLOv7 scored 0.67 and 0.675, respectively. Furthermore, YOLOv5's mAP score at the 0.5 IoU threshold was 0.892, while YOLOv7 scored 0.669. At the 0.5-0.95 IoU threshold, YOLOv5's mAP was 0.644 and YOLOv7's was 0.426. This study demonstrates that YOLOv5 is a superior deep learning model for food categorization tasks, with potential implications for improving consumer nutrition and food safety in the food industry. © 2023 IEEE.

关键词： Object detection

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Optimisation of alloy composition for highly-formable magnesium sheet

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International Journal of Minerals,Metallurgy and Materials 2022年第7期29卷 1388-1395页

作者： Zhuoran Zeng Mingzhe Bian Shiwei Xu Weineng Tang Chris Davies Nick Birbilis Jianfeng Nie College of Engineering and Computer Science Australian National UniversityACT.2601Australia Department of Materials Science and Engineering Monash UniversityVIC 3800Australia State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan UniversityChangsha 410082China Research Institute(R&D Center) Baosteel Group CorporationShanghai 201999China Department of Mechanical and Aerospace Engineering Monash UniversityVIC 3800Australia

The effectiveness of Ca or Gd addition on ductility and formability of Mg-Zn-Zr based dilute alloys in deep drawing has not been systematically compared *** this study,formable Mg-Zn-Gd-Zr and Mg-Zn-Ca-Zr sheet alloys are produced by hot *** sheets have similarly weakened basal texture,but the sheet of the Mg-Zn-Gd-Zr alloys has higher ductility and formability than that of Mg-Zn-Ca-Zr *** combined addition of 0.2wt%Ca and 0.4wt%Gd to the Mg-1Zn-0.5Zr(wt%)alloy leads to a Mg-1Zn-0.4Gd-0.2Ca-0.5Zr alloy that has even better ductility,and its formability during deep drawing is comparable to the benchmark Al6016 *** increase in Ca content from 0.2wt%to 0.5wt%leads to decreased sheet ductility and formability,predominantly due to grain boundary embrittlement.

关键词： magnesium sheet alloy texture microstructure formability solute segregation

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Implementing Lightweight Post-Quantum Signatures for Enhanced Security in Green IoE

Implementing Lightweight Post-Quantum Signatures for Enhance...

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

作者： H. Manoj T. Gadiyar Sivakumar KK Nainee Patel Shadaksharaiah C Amit Lathigara Vanguri Smitha Department of Information Science and Engineering Canara Engineering College Benjanapadavu India School of Liberal Arts and Sciences Mohan Babu University Tirupati India Department of Computer Science & Engineering IES Insititute of Technology and Management IES University Bhopal India Department of Computer Science and Design Bapuji Institute of Engineering & Technology (BIET) Davangere India Department of Computer Science and Engineering School of Engineering RK University Rajkot India Department of Computer Science and Engineering (AI&ML) CMR Engineering College Hyderabad India

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

Joining people, processes, data, and gadgets in a sustainable way is called the environmentally friendly Internet of Everything (Green IoE). Traditional encryption techniques, however, face serious security threats from the advent of quantum computing. Discrete logarithmic and factorisation issues make current cryptography systems like RSA and ECC susceptible to quantum attacks. In order to improve Green IoE security without sacrificing energy efficiency, this article suggests using lightweight post-quantum signature methods. The promising members are the ones derived from the lattice, hash, or multivariate quadratic theory when it comes to the choice between security and performance. This research identifies how it is possible to integrate quantum-safe security for Green IoE applications without eradicating the low power requirements inherent in these systems. It is by combining these little post-quantum signatures into Green IoE that it becomes possible to come up with a safe system that meets the set environmental standards. Realizing the need to construct a dependable safety system with Green IoE in the post-quantum period, the research evaluates the strength and speed of the proposed cryptography algorithms.

关键词： Accuracy Quantum computing Green products Stability criteria Robustness Timing Cryptography Sustainable development Streams Standards

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