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International Conference on Recent Developments in Cyber Security, ReDCySec 2023

作者： Chourasiya, Shraddha Indurkar, Ayush Ghagare, Apoorva Potphode, Kaushal Sayam, Varun Gaikwad, Dikshant Department of Computer Science Engineering Jhulelal Institute of Technology Maharashtra Nagpur India

ISBN: (纸本)9789819998104

The traceback problem is widely acknowledged as one of the most challenging issues in the field of information security, and it is crucial to find effective solutions to hold attackers accountable for their actions. This research paper aims to provide a comprehensive introduction to the intricate issue of traceback in network forensics. To fully comprehend the problem, the paper delves into the unique characteristics of computer, network, and software forensics. By exploring the distinctive features and challenges of each forensic subfield, a comprehensive understanding of the broader context in which traceback techniques are utilized is established. Furthermore, the paper analyses various traceback mechanisms and categorizes them based on their distinct features and modes of operation. In conclusion, the study proposes a systematic classification system for all traceback techniques, evaluating and integrating their respective advantages. This offers valuable insights for digital forensics investigations and brings us closer to identifying the actual perpetrator. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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EnviroTech: Revolutionizing Waste Management with IoT-Driven Automated Segregation and Alerts

EnviroTech: Revolutionizing Waste Management with IoT-Driven...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Thiyaga Vinnarasu, V. Bobby Farel, P. Balakrishnan, Mathiarasi Hindustan Institute Of Technology And Science Department Of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350364828

The swift rise in urbanization has resulted in growing difficulties with garbage management, calling for creative approaches to lessen its negative effects on the environment. The system accomplishes real-time garbage recognition and segregation into biodegradable and non-biodegradable categories on a Raspberry Pi 4. It is built using the OpenCV library and the MobileNetV3 algorithm. The system uses a mix of sensors, load cells, and cameras to measure garbage, weigh, and keep an eye on levels in different bins. The utilization of OpenCV improves computational performance and makes it appropriate for deployment on edge devices, while the inclusion of MobileNetV3 guarantees high accuracy of 96.32 % in waste classification. A deodorizing capability is integrated for biodegradable trash to reduce offensive odours, improving user experience, and promoting environmentally friendly waste management practices. The non-biodegradable garbage bin also has a fire detection sensor built in, which allows for early fire detection and notifies authorities of the situation. Thorough statistics illustrate the usefulness and effectiveness of the waste segregation system, including accuracy measurements, real-world testing results, and computational efficiency studies. Through its scalable and flexible approach to supporting sustainable urban environments, this research advances smart waste management systems. © 2024 IEEE.

关键词： Object detection

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Enhancing Diagnostic Precision: Comparative Analysis of BERT and Machine Learning Algorithms for Multi-Disease Prediction from Symptom Narratives 3

Enhancing Diagnostic Precision: Comparative Analysis of BERT...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Kumar, Y Udit Kumar, K Pradeep Vijayarani, J. Hindustan Institute of Technology and Science Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350375190

This study examines the prediction of diseases through the analysis of 1,200 symptom narratives across 24 different diseases. Employing Huggingface's 'bert-base-cased' transformer model, renowned for its ability in natural language understanding, this research study capitalizes on its extensive training on diverse English language corpora. Utilizing the BERT model's advanced capabilities in next sentence prediction and masked language modeling, the study aims to deduce probable diseases from intricate symptom descriptions. Additionally, the research rigorously evaluates established machine learning models such as AdaBoostClassifier, SVM, and RandomForest, juxtaposing their predictive efficacy against the more sophisticated BERT model. The overarching objective of this comparative analysis is to develop a comprehensive predictive model for multiple diseases, transcending conventional methods focused on individual disease detection. © 2024 IEEE.

关键词： Forecasting

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Image Processing Based Proposed System for Malnutrition Using Deep Learning 7

Image Processing Based Proposed System for Malnutrition Usin...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Mary Posonia, A. Priyadarshi, Hariom Jai Adithya, B. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350372816

In many nations, malnutrition is the primary cause of death for children under five. An inventive way to handle the problem of hunger in a non-invasive and effective way is through the use of deep learning in image processing for malnutrition diagnosis. The system trains a deep learning model, such as a convolutional neural network, on a huge dataset of photographs of people with various degrees of malnutrition in order to find patterns and features in the photos that indicate malnutritionFor ease of usage by medical practitioners, the trained model is then made available on a web-based platform or mobile device. The suggested system may supplement current practices and offer more data for a thorough evaluation, especially in environments with limited resources. © 2024 IEEE.

关键词： Convolutional neural networks

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Privacy-Enhanced Federated Expanded Graph Learning for Secure QoS Prediction

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IEEE Transactions on Services Computing 2025年第3期18卷 1641-1654页

作者： Zou, Guobing Yan, Zhi Hu, Shengxiang Gan, Yanglan Zhang, Bofeng Chen, Yixin Shanghai University School of Computer Engineering and Science Shanghai200444 China Donghua University School of Computer Science and Technology Shanghai201620 China Shanghai Polytechnic University School of Computer and Information Engineering Shanghai201209 China Washington University Department of Computer Science and Engineering St. LouisMO63130 United States

Current state-of-the-art QoS prediction methods face two main limitations. Firstly, most existing QoS prediction approaches are centralized, gathering all user-service invocation QoS records for training and optimization, which causes privacy breaches. While some federated learning-based methods consider user privacy in a distributed way, they either directly upload local trained parameters or use simple encryption for global aggregation at the central server, thus failing to truly protect user privacy. Secondly, existing federated learning-based methods neglect distributed user-service topology and latent behavior-attribute correlations, compromising QoS prediction accuracy. To address these limitations, we propose a novel framework named Privacy-Enhanced Federated Expanded Graph Learning (PE-FGL) for secure QoS prediction. It first conducts user-service expansion on the invocation graph with advanced privacy-preserving techniques, upgrading first-order local QoS invocations to high-order interaction relationships. Then, it extracts hybrid features from the expanded invocation graph via deep learning and graph residual learning. Finally, a two-layer secure mechanism of federated parameters aggregation is designed to enable collaborative learning among users through local parameter segmentation and global aggregation, achieving effective and secure QoS prediction. Extensive experiments on WS-DREAM demonstrate effective QoS prediction across multiple metrics while preserving privacy in user-service invocations. © 2025 IEEE.

关键词： Federated learning

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Human-Centered Coordination for Robot-Assisted Equipment Transport

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ASME Letters in Dynamic Systems and Control 2025年第2期5卷 021002页

作者： Fernandez, Joshua Cosby, Austin Mazumdar, Anirban Department of Mechanical Engineering Georgia Institute of Technology AtlantaGA30363 United States Department of Computer Science Mississippi State University MS39762 United States

This work explores how to use an unmanned ground vehicle (UGV) to offload the physical burdens of equipment from humans. This work formulates dynamic alignment following and compares it to position-based following techniques. We describe the control strategies of both following methods and implement them in a dynamic simulation and a physical prototype. We test the performance of the two following methods and show that dynamic alignment following can reduce robot positional error and interaction force between the human and the robot. We then analyze the energetics and the performance of the human-UGV team for candidate transportation tasks. The presence of the robot can make some tasks take longer to perform. Nonetheless, the results show that for the candidate tasks, the robot can reduce human average metabolic power and average overall task energy. © 2024 by ASME.

关键词： Ground vehicles

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Diagnosis Of Cardio Vascular Diseases Using Retinal Fundus Scans Via Deep Learning

Diagnosis Of Cardio Vascular Diseases Using Retinal Fundus S...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering Science, AMATHE 2024

作者： Pryadarshne, D. Madireddy, Sai Ruthvik Vaishnnave, M.P. Paul, Mercy Sathyabama Institute Of Science And Technology Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350371567

Cardiovascular disease (CVD) remains a leading global health concern, necessitating early and accurate detection methods for effective intervention. This study presents a groundbreaking approach to CVD detection leveraging deep learning techniques applied to retinal fundus scans. Retinal fundus scans offer a non-invasive, cost-effective, and easily accessible source of information for potential CVD risk assessment. The utilization of deep learning algorithms, such as Convolution Neural Networks (CNNs), has demonstrated remarkable promise in identifying subtle pathological changes within the retinal vasculature and optic nerve. This study reviews the current state of research on CVD risk prediction and highlights the potential of retinal fundus scans as a valuable diagnostic tool. The integration of deep learning methods facilitates the automatic extraction of intricate features from retinal images, enabling the development of accurate and efficient CVD detection models. By examining retinal vasculature, microaneurysms, hemorrhages, and other relevant features, these models can aid in early risk stratification and timely intervention. Furthermore, this study delves into the challenges and future prospects of this novel approach, emphasizing the importance of large, diverse datasets, model interpretability, and the integration of clinical data to enhance predictive accuracy. As the field of deep learning continues to advance, this innovative strategy has the potential to revolutionize cardiovascular disease detection, enabling proactive healthcare measures and ultimately improving patient outcomes. © 2024 IEEE.

关键词： Diagnosis

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A Comparative Evaluation of Lightweight Cryptographic Algorithms for Secure Data Transmission in IoT Network 3

A Comparative Evaluation of Lightweight Cryptographic Algori...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Verma, Abhay Thokchom, Surmila National Institute of Technology Department of Computer Science and Engineering Meghalaya Shillong India

ISBN: (纸本)9798350372120

Internet of Things (IoT), in a blend with advanced computer vision techniques, models new challenges for end users in securing images and videos. Securing images becomes one of the crucial aspects in the enormous and growing domain of digital transfer. One of the well-known methods for preserving the privacy of photos on an unconstrained public medium is image encryption. IoT devices generally have constrained resources due to low computational potential, bounded memory, and limited power supply. This exhibits the demand for lightweight security schemes instead of traditional cryptographic algorithms to facilitate image security in an intelligent environment. This paper implemented three well-known lightweight encryption algorithms on the actual IoT, such as the Raspberry Pi 3 device, to capture and encrypt images and transmit the encrypted binary over the network. PRESENT, SIMON & XTEA lightweight algorithms are implemented on the Raspberry device. A comparison is made among those to find efficiency in terms of encryption time, PSNR, MSE, and SSIM over the live environment. On comparison over the number of factors, the SIMON algorithm was found to be the most efficient of the other two. © 2024 IEEE.

关键词： Internet of things

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StrokeAlert: Early Detection of Minor Strokes 4

StrokeAlert: Early Detection of Minor Strokes

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Vasa, Shruti Savani, Simoni Thaker, Anmol Bhangale, Pradnya Kj Somaiya Institute of Technology Department of Computer Engineering Maharashtra Mumbai India

ISBN: (纸本)9798350364729

Stroke is a critical global health issue, with minor strokes often going undetected due to subtle symptoms. These minor events are key indicators of potential major cerebrovascular issues and, if untreated, can lead to severe health deterioration. This paper presents StrokeAlert, a mobile application for the early detection of minor strokes using the FAST criteria (Face, Arms, Speech, Time). The app employs image analysis, speech recognition and arm weakness detection technologies to detect stroke symptoms in real time using deep learning algorithms. Testing demonstrated an 83.84% accuracy in facial drooping detection, 96.33% in speech difficulty evaluation and 88% in arm weakness detection. StrokeAlert offers an accessible, user-friendly solution that could significantly enhance early intervention and reduce stroke-related complications. © 2024 IEEE.

关键词： Electronic health record

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Financial Zen: Achieving Market Harmony through Reinforcement Learning and Algorithmic Trading 2

Financial Zen: Achieving Market Harmony through Reinforcemen...

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2nd International Conference on Data Science and Information System, ICDSIS 2024

作者： Sowmya, M.R. Chhetri, Diwash Khatri Chai, Kumar Rohit Ashraf, Suhail Nitte Meenakshi Institute Of Technology Department Of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350381665

Algorithmic trading, utilizing reinforcement learning, has attracted significant attention for its adaptability and potential in present financial markets. This research combines Deep Deterministic Policy Gradients (DDPG), Advantage Actor-Critic (AAC), and Proximal Policy Optimization (PPO) algorithms for a comprehensive trading system. To address memory constraints, a load-on-demand technique manages large datasets. Performance is evaluated across 30 Dow Jones stocks, compared to the Dow Jones Industrial Average (DJIA) and a minimum-variance portfolio. Results show the integrated approach outperforms traditional strategies in annualized return, Sharpe ratio, and maximum drawdown. This study contributes to algo-trading literature by leveraging multiple reinforcement learning algorithms, offering benefits for investors in dynamic markets. © 2024 IEEE.

关键词： Reinforcement learning

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