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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Sharma, Om Chakraborty, Nilotpal India Institute of Information Technology Department of Computer Science and Engineering Assam Guwahati India

ISBN: (纸本)9798331531195

In distributed multi-robot systems, ensuring collision-free motion planning is a complex challenge, especially in dynamic environments where multiple robots are operating simultaneously. Traditional path-planning algorithms are effective for individual robot navigation but struggle when dealing with the unpredictable movements of other robots in the system. To overcome these limitations, this research focuses on advanced motion planning techniques that ensure safe and efficient robot coordination. We model the overall motion planning problem into a two-dimensional grid and our objective is to minimize both the time and the energy taken by the robots to reach their destinations from their respective sources. We enhance standard path-finding algorithms such as A∗ search, along with Deep Reinforcement Learning (DRL) with suitable modifications to adapt to real-time changes. DRL allows robots to learn from their environment and make optimal decisions, improving their ability to avoid collisions and navigate efficiently in uncertain conditions. By integrating these algorithms and conducting a comparative analysis, we propose a framework that enables robots to dynamically adjust their paths, ensuring collision-free movement and optimal coordination. © 2025 IEEE.

关键词： Robot programming

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Blockchain-Powered Financial Service Platform: Enabling P2P Payments, Crowdfunding and Loans 3

Blockchain-Powered Financial Service Platform: Enabling P2P ...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Sonawane, Swapnil S. Motwani, Dilip Vidyalankar Institute of Technology Department of Computer Engineering Mumbai India Vidyalankar Institute of Technology Department of Information Technology Mumbai India

ISBN: (纸本)9798350354348

This paper provides an in-depth analysis of a financial services platform that utilizes blockchain technology for peer-to-peer payments, crowdfunding, and loan services. By leveraging blockchain, the platform guarantees security, transparency, and efficiency in all transactions, offering a transformative alternative to traditional financial systems. It establishes a decentralized environment where users can interact and transact directly with one another, removing the need for intermediaries. Peer-to-peer payments allow for quick and effortless fund transfers between users, while crowdfunding supports the collection of capital for various projects or initiatives through numerous small contributions. Moreover, the platform includes loan services, enabling users to borrow funds in a secure and transparent manner, with smart contracts managing the loan process and ensuring compliance with repayment terms. The integration of blockchain technology on our platform aims to democratize financial services, enhance financial inclusion, and build trust within the financial system. © 2024 IEEE.

关键词： Decentralized finance

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Toward actionable testing of deep learning models

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Science China(Information Sciences) 2023年第7期66卷 300-302页

作者： Yingfei XIONG Yongqiang TIAN Yepang LIU Shing-Chi CHEUNG Key Laboratory of High Confidence Software Technologies(Peking University) Ministry of Education School of Computer Science Peking University Department of Computer Science and Engineering The Hong Kong University of Science and Technology School of Computer Science University of Waterloo Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Department of Computer Science and Engineering Southern University of Science and Technology

Deep learning has become an important computational paradigm in our daily lives with a wide range of applications,from authentication using facial recognition to autonomous driving in smart vehicles. The quality of the deep learning models, i.e., neural architectures with parameters trained over a dataset, is crucial to our daily living and economy.

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An ultra efficient 2:1 multiplexer using bar-shaped pattern in atomic silicon dangling bond technology

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Journal of Supercomputing 2024年第13期80卷 18347-18364页

作者： Rasmi, Hadi Mosleh, Mohammad Jafari Navimipour, Nima Kheyrandish, Mohammad Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou64002 Taiwan

As CMOS technology approaches its physical and technical limits, alternative technologies such as nanotechnology or quantum computing are needed to overcome the challenges of lithography, transistor scaling, interconnects, and miniaturization. This article introduces a novel nanotechnology that uses atomic-scale silicon dangling bonds (ASDB) to create high-performance, low-power, nanoscale logic circuits. DBs are atoms that can form basic logic gates on a silicon surface using a scanning tunneling microscope device. ASDB can also be an alternative to the existing complementary metal oxide semiconductor (CMOS) technology. The article also proposes a new bar-shaped pattern to design gates and logic circuits with ASDB nano tecnolgoy. The bar-shaped pattern improves the reliability of the output, reduces the area and power consumption, and solves the problem of interatomic energy effects of ASDB. The article demonstrates the efficiency of the bar-shaped pattern by implementing two-input gates such as AND, NAND, OR, NOR, XOR, XNOR, and a 2:1 multiplexer with ASDB. The article also uses a powerful tool called SiQAD to simulate and verify the performance of the proposed structures with ASDB. According to the simulation results, the proposed logic gates are more energy efficient, stable, and compact than the previous structures. They consume 35% and 24.34% less energy and have 14.18% more stability, respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Logic gates

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Novel Anchor Generation Based Residual Network for Object Tracking in Video-Surveillance Applications

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SN computer Science 2025年第2期6卷 1-12页

作者： Mohanapriya, D. Rajalakshmi, K. Helen Josephine, V.L. Panneerselvam, Karthick Department of Computer Science (SF) Sourasthra College Tamil Nadu Madurai India Department of Computer Applications CMR Institute of Technology Karnataka Bangalore India Business Analytics School of Business and Management Christ University Karnataka Bangalore India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Tamilnadu Uthupalayam 641407 India

The activity of the object in question is alerted directly upon completion of an effective object tracking. Dependent on hardware support or not, a strong object tracking protocol is required for a precise object tracking application. According to these methods, tracking an object accurately within a predetermined processing time window required a significant amount of computer complexity. In contrast, a variety of quality-degrading elements, including occlusion, shifting lighting, shadows, and so on, have an adverse effect on tracking. All of these tracking shortcomings will be fixed by a revolutionary residual network based on loss operator and anchor creation. Detection of object has concerns that rely on the process of feature extraction to afford efficient quality. For this purpose a model called ResNet has been used that comprises thirty layers and hence named as Resnet-thirty. These networks are a type of Convolutional Neural Network (CNN) that contain residual connections among various layers. The various merits of these connections is the network has the capability to learn the features of global, local and intermediate in parallel. As such, the system is robust against changes in lighting. These variations in light were understood in terms of tracking objects within a changing background. The proposed work uses MOT datasets. This dataset comprises of MOT 15, MOT16, MOT17 and MOT20. The results have been found by using these datasets. Hence, it evidently outperforms in terms of precision, recall, MOTA, IDF, MOTP, SAIDF and F1 measure to track the objects. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： computer vision Loss operator Object tracking Residual network

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Efficient Real-Time Human Activity Recognition with IoT Sensor Data Using Conditionally Parametrized Convolutions 4

Efficient Real-Time Human Activity Recognition with IoT Sens...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Lovanshi, Mayank Tiwari, Vivek Singh, Anurag Ingle, Rajesh International Institute of Information Technology Department of Computer Science Engineering Naya Raipur India ABV-Indian Institute of Information Technology Department of Computer Science Engineering Gwalior India International Institute of Information Technology Department of Electronics Communication Engineering Naya Raipur India Symbiosis Skills of Professional University Department of Computer Science Engineering Pune India

ISBN: (纸本)9798331518981

Human Activity Recognition (HAR) is a trading area in computer vision and deep learning. However, boosting the performance of deep learning models often necessitates increasing their size or capacity, which raises computational demands. The high operational costs present challenges for real-time HAR on sensor-based devices. Shallow learning approaches, while computationally efficient, generally fail to deliver comparable performance. Consequently, there is a critical need for deep learning methods that can effectively balance accuracy and computational cost, a topic that remains largely underexplored. This paper introduces a computationally efficient CNN that utilizes conditionally parametrized convolution, designed specifically for real HAR on sensor-based devices. The model is tested on the OPPORTUNITY dataset, achieving existing accuracy without increasing computational load. Through extensive ablation studies, we show that this high-capacity network surpasses baseline models while maintaining a similar level of computational efficiency. The proposed method can directly replace existing deep HAR frameworks and be easily implemented on sensor-based platforms for real-time applications. © 2024 IEEE.

关键词： Convolution

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A comparative analysis for deep-learning-based approaches for image forgery detection

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2024年第1期8卷 1-10页

作者： Ch, Ravikumar Radha, Marepalli Mahendar, Maragoni Manasa, Pinnapureddy Department of Artificial Intelligence &Data Science Chaitanya Bharathi Institute of Technology Hyderabad 500075 India Department of Computer Science and Engineering CVR College of Engineering Hyderabad 501510 India Department of Computer Science and Engineering Neil Gogte Institute of Technology Hyderabad 500039 India

The detection of counterfeit photographs is critical in the digital age because of the widespread development of digital media and its significant impact on social networks. The legitimacy of digital content is being threatened by the growing sophistication of picture counterfeiting. With the help of pre-trained VGG-16 models and deep learning techniques that integrate Error Level Analysis (ELA) and Convolutional Neural Networks (CNNs), this study presents a fresh solution to this problem. The study thoroughly assesses and contrasts these models with a dataset that has been carefully chosen to bring the presented findings into perspective. To ensure a reliable evaluation of each model's performance 5000 experiments were carried out in total. With an accuracy rate of 99.87% and an accurate identification rate of 99% of hidden forgeries, the results demonstrate the exceptional effectiveness of the ELA-CNN model. However, despite its robustness, the VGG-16 model only achieves a significantly lower accuracy rate of 97.93% and a validation rate of 75.87%. This study clarifies the relevance of deep learning in the identification of image forgeries and highlights the practical ramifications of various models. Moreover, the research recognizes its constraints, especially for highly advanced counterfeits, and proposes possible paths for enhancing the accuracy and scope of detection algorithms. In the ever-changing world of digital media, the thorough comparative analysis provided in this study offers insightful information that can direct the creation of accurate forgery detection tools, protecting digital content integrity and reducing the effects of image manipulation. © (2023), (Society of Sytematic Innovation). All Rights Reserved.

关键词： Counterfeit images deep learning ELA-CNN Image forgery detection VGG-16 model

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Single-Frame Image Based Multi-variant Bird Species Identification and Classification 5th

Single-Frame Image Based Multi-variant Bird Species Identifi...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Adhikari, Nilanjana Bhattacharya, Suman Sultana, Mahamuda Department of Computer Science and Engineering Guru Nanak Institute of Technology Kolkata700114 India

ISBN: (纸本)9789819771776

The increasing demand for advanced ornithological tools and techniques aimed at accurately identifying and classifying multiple bird species within a single-frame image has led us to develop a framework for researchers, environmentalists, and bird enthusiasts. Traditional methods often struggle to handle the complexity of diverse avian variants present in each scene, particularly when capturing images in real-world, uncontrolled environments. Single-frame images, such as those obtained through wildlife monitoring cameras or handheld devices, offer a practical and common means of avian observation. Our work centered on the automated detection and classification of a group of multi-variant bird species using a Deep Learning (DL) framework. The You Only Look Once (YOLO) version 5 networks were deployed in this study. Each object within the image frame was detected, annotated, and captured in a bounded box. Subsequently, each bounded object underwent scrutiny and recognition. The manually collected dataset comprises five mutually exclusive local bird species native to India. The developed model demonstrated satisfactory performance in terms of Precision, Recall, mAP, and F1 values, achieving scores of 0.87, 0.81, 0.82, and 0.826, respectively. This system can work as an automated bird species detection, recognition and classification tool for numerous individuals encountering difficulty recognizing multiple birds within a single image frame. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Birds

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Smart Self-Checkout System for Supermarkets 3

Smart Self-Checkout System for Supermarkets

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3rd IEEE International Conference on technology, engineering, Management for Societal Impact using Marketing, Entrepreneurship and Talent, TEMSMET 2023

作者： Chaudhari, Vrushaket Pravin Chaudhari, Rushikesh Vijay Burgul, Amey Shrikant Jain, Shantanu Ashok SCTRS Pune Institute of Computer Technology Department of Computer Engineering Maharashtra Pune India Padmabhooshan Vasantdada Patil Institute of Technology Department of Computer Engineering Maharashtra Pune India

ISBN: (纸本)9798350397819

This paper deals with the implementation of selfcheckout software application, which is a 100% digital approach to offline shopping and aims to reduce the overall time required for shopping. This approach is implemented via bar code technology, real-time database and payment gateway. This software application aims to reduce time spent in shopping along with providing better security, self-pickup option and new wish list concept. This application skims through the barcodes of the products already present on each and every product and the user can exclusively select multiple products, finally bill will be generated which will be displayed to the user along with various payment options. The study concludes that the overall time for the basic work of super markets is minimized. © 2023 IEEE.

关键词： Bar codes

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Machine Learning Models for Advancing Heart Disease Prediction and Diagnosis 3

Machine Learning Models for Advancing Heart Disease Predicti...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Reddy, Santosh Shetty, Kruthi K. Apeksha, G.S. Department of Computer Science and Engineering B N M Institute of Technology Bangalore India

ISBN: (纸本)9798331527549

Cardiovascular diseases are still among the leading causes of deaths worldwide, thus calling for early detection and precise risk assessment. Traditional methods of diagnosing cardiovascular disease often rely on standardized risk factors, which might not always catch the condition in its early stages. Machine learning and AI are revolutionizing CVD prediction by examining complex data in healthcare to find previously unknown patterns. The paper discusses advances in ML techniques such as supervised and unsupervised learning, deep learning, and ensemble methods. It emphasizes hyperparameter tuning, feature engineering, and model interpretability. AI models like Random Forest, SVM, and Neural Networks have shown better accuracy in predicting cardiovascular disease risk, enabling earlier and more personalized interventions. AI’s potential reaches from wearable devices to predictive cardiac event models, promising huge advances in cardiovascular care. Still, there are data privacy issues, model interpretability, and regulatory problems that will continue to pose significant challenges. Future advancements in multimodal imaging, precision medicine, and collaboration between clinicians and data scientists will be pivotal to the realization of AI’s full potential in transforming the prediction and prevention of CVD. © 2025 IEEE.

关键词： Artificial Intelligence Cardiovascular Diseases (CVDs) Early Detection Heart Disease Prediction Machine Learning Neural Networks Personalized Healthcare Random Forest Support Vector Machines (SVMs)

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