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

作者： Madasanal, Kartik Gudnavar, Anand Nagarmunoli, Sreedevi Madagouda, Basavaraj Naregal, Keerti Visvesaraya Technological University Jain College of Engineering and Research Department of Computer Science and Engineering Belagavi India Graphic Era University Department of Computer Science and Engineering Dehradun India S.G.Balekundri Institute of Technology Visvesaraya Technological University Department of Computer Science and Engineering Belagavi India

ISBN: (纸本)9798331513894

This paper develops a Smart Public Transportation System using RFID technology, IoT integration, and a MERN-based web application for increasing the efficiency, accessibility, and user experience of urban transit systems. It addresses problems such as unreliable bus tracking, lack of support for multilingualism, and ineffective communication between operators and passengers. The key hardware components include RFID tags for bus identification, and IoT-enabled bus stations with RFID scanners, speakers, GSM modules for data transmission, and LCD displays. These ensure accurate real-time updates about the bus locations. On the software side, it is supported by a MERN stack, including MongoDB, ***, ***, and ***, providing a responsive web interface that's complemented by *** for dynamic route visualization and WebSockets for live updates. Multilingual support through i18next makes it inclusive for various user groups. The system is efficient in that it only updates when RFID scanners capture bus tags at certain checkpoints, thereby reducing server load much more than GPS-based systems. Additional features include route tracking, interactive maps, ticket pricing, and anonymous passenger chats. Crowd-sourced feedback mechanisms and an admin panel for bus management ensure the reliability and scalability of the system. This work shows the feasibility of cost-effective, scalable, and user-centric modernization of public transportation systems, paving the way for further enhancements such as integrated payment systems and AI-based route optimization. © 2025 IEEE.

关键词： Liquid crystal displays

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Contract-Based Incentive Mechanisms for UAV Networks in Covert Communications

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IEEE Transactions on Vehicular technology 2025年第5期74卷 7378-7391页

作者： Tian, Wen Fan, Hao Liu, Guangjie Du, Miao Liu, Yinqiu Niyato, Dusit Han, Zhu Nanjing University of Information Science and Technology School of Electronic and Information Engineering Nanjing University of Information Science and Technology Key Laboratory of Intelligent Support Technology In Complex Environment Ministry of Education Nanjing210044 China Southeat University School of Computer Science and Engineering China Southeast University Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

Covert communication is considered a promising technology for hiding transmission processes and activities from malicious eavesdropping. With the development of detection technology, the traditional point-to-point covert communication method cannot effectively guarantee the reliability of covert communication. In order to solve this problem, unmanned aerial vehicle (UAV) are widely adopted in covert communication as common relay equipment. However, the UAV maximizes its benefits by taking advantage of the information asymmetry between itself and the covert communication sender. Therefore, it is necessary to encourage UAV-assisted potential users (UPU) to transmit covert data honestly by designing an information asymmetry-based incentive mechanism. In this paper, we propose a UAV-assisted contract-theoretic (UACT) model to improve the covert communication performance, where UPU honestly transmits covert data and the throughput of UACT can be increased. Specifically, we first divide UPU into finite types and model the data transfer contract between the sender, UPU, and receiver. Then, the contract feasibility of UACT is derived with sufficient and necessary conditions. Finally, we analyze the contract optimization in the continuous case of UPUs. Numerical simulations show that the UACT can incentivize UPUs to transmit covert data through UAV assistance and make covert communication performance close to the information symmetry case. © 1967-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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LegalAI Across Languages: A Comprehensive Survey on Tasks, Methodologies and Challenges

LegalAI Across Languages: A Comprehensive Survey on Tasks, M...

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AIML-Applications for engineering & technology (ICAET), International Conference on

作者： Anjali Deshpande Pratiksha Deshmukh Dept. of Computer Science and Engineering COEP Technological University Pune Dept. of AI & DS PICT Pune Dept. of Computer Science and Engineering School of Computational Sciences COEP Technological University Pune

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

Natural Language Processing (NLP) is revolutionizing the legal domain, enabling tasks such as predicting legal outcomes, summarizing complex documents, identifying key entities, and assessing bail risks. This survey paper presents a comprehensive review of the existing research on NLP tasks applied to legal documents and judgments across various languages, with a special emphasis on low-resource languages. Despite the extensive work done in widely spoken languages like English, there remains a critical need to extend these advancements to languages with limited resources and data availability. This paper categorizes and analyzes the NLP tasks in the legal domain based on the languages they target. It highlights the methodologies employed, datasets utilized, and the performance of different models across diverse languages. Furthermore, it identifies the gaps in current research and proposes potential directions for future work, particularly in improving the accessibility and applicability of NLP solutions for legal texts in underrepresented languages. By providing a comprehensive overview of advancements and obstacles, this survey seeks to assist future research endeavors that bridge the disparity between high-resource and low-resource languages in legalAI applications.

关键词： Surveys Law Reviews Semantics Focusing Named entity recognition Linguistics Natural language processing Labeling Global communication

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Feature Contrast Difference and Enhanced Network for RGB-D Indoor Scene Classification in Internet of Things

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IEEE Internet of Things Journal 2025年第11期12卷 17610-17621页

作者： Zhou, Wujie Jian, Bitao Liu, Yuanyuan Zhejiang University of Science and Technology School of Information and Electronic Engineering Hangzhou310023 China China University of Geosciences School of Computer Science and Technology Wuhan430074 China Nanyang Technological University College of Computing and Data Science 308232 Singapore

The era of smart connectivity spawned by the Internet of Things (IoT) has made the need to achieve environmental perception and understanding of different scenarios increasingly urgent. Among the many scenarios, indoor scene classification has attracted much attention because of its its relevance to the daily lives of people, ranging from comfort regulation in living spaces to the optimal allocation of resources in offices, and a variety of approaches for this task have emerged. However, increasing accuracy remains a crucial objective due to the complexity and disorder of indoor scenes. Therefore, we propose a feature contrast difference and enhanced network for RGB-D indoor scene classification, FCDENet. First, the RGB and Depth images express different information. Therefore, we built a feature contrast difference module for the first two low-level features to extend the receptive fields of the different features, utilizing differential contrast to complement each other. Second, the high-level feature semantic information is abstract. Therefore, we introduced information cluster blocks, which are used to aggregate feature points with similar attributes into compact clusters after being parsed by an initial frequency transform, enabling instantiated representations of the semantic information. Finally, to further enhance the integrated features, we introduced a wavelet transform block in the cross-layer decoding process. In contrast to conventional decoding methods, we employed a wavelet transform for initial denoising cross-layer features and used multiple pooling structures to supplement local information, gradually weighting to achieve higher prediction accuracy. Extensive experiments on two typical indoor datasets, NYUDv2 and SUN RGB-D, show that our results exhibit excellent performance. In addition, to better demonstrate the reliability of the method, we conducted generalizability experiments on other datasets, and the proposed method provides a robust soluti

关键词： Wavelet decomposition

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Leveraging BERT, DistilBERT, and TinyBERT for Rumor Detection

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IEEE Access 2025年 13卷 72918-72929页

作者： Qazi, Aijazahamed Goudar, R.H. Patil, Rudragoud Hukkeri, Geetabai S. Kulkarni, Dhanashree Angadi Institute of Technology and Management Department of Artificial Intelligence and Data Science Belagavi591108 India Visvesvaraya Technological University Department of Computer Science and Engineering Belagavi590018 India KLS Gogte Institute of Technology Department of Computer Science and Engineering Belagavi590008 India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Computer Science and Engineering Karnataka Manipal576104 India Angadi Institute of Technology and Management Department of Computer Science and Engineering Belagavi591108 India

The rapid spread of false information on social media has become a major challenge in today’s digital world. This has created a need for an effective rumor detection system that can identify and control the spread of false information in real-time. The proposed work introduces a rumor detection system by integrating transformer-based models such as BERT, DistilBERT, and TinyBERT with traditional Machine Learning (ML) techniques. The classifiers include Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF) and Naïve Bayes (NB) help in categorizing content as either rumor or non-rumor based on the patterns. The proposed work evaluated BERT, DistilBERT, TinyBERT combined with ML models (SVM, DT, RF, NB) across PHEME dataset using 70:30, 60:40, and 80:20 splits. Overall, BERT + DT and TinyBERT + SVM provided significant results, with BERT + RF and DistilBERT + NB demonstrating better classification capabilities across various events and split ratios on the dataset. © 2013 IEEE.

关键词： Tweets

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Minimum Initial Marking Estimation in Labeled Petri Nets Using Minimum Token Number Prediction

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IEEE Transactions on Automatic Control 2025年第5期70卷 3296-3302页

作者： Yue, Hao Xu, Yakun Hu, Hesuan Wu, Weimin Li, Lingxi College of Computer Science and Technology Qingdao266580 China Xidian University School of Electro-Mechanical Engineering Xi'an710071 China Nanyang Technological University School of Computer Science and Engineering College of Engineering 639798 Singapore Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China Zhejiang University Institute of Cyber-Systems and Control Hangzhou310027 China Purdue University Elmore Family School of Electrical and Computer Engineering College of Engineering IndianapolisIN46202 United States

This article proposes an approach to addressing the problem of minimum initial marking (MuIM) estimation for labeled Petri nets (LPNs). We introduce the important concept of a label synthesis net for LPNs and develop a method for predicting the minimum number of tokens. By using this prediction method, we develop an algorithm that has polynomial complexity in the length of the observed label sequence for estimating MuIMs. An illustrative example is provided to show the effectiveness and efficiency of our proposed approach. Moreover, experimental results demonstrate its advantage over existing work. Finally, we provide a comparison of some representative studies in the literature for MuIM estimation. © 1963-2012 IEEE.

关键词： Discrete event simulation

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A Novel Pyra-Cryptosystem Relying Secure Light-Weight Data Exchange, Peer-to-Peer Authentication, and Secure Communication Session Key Management Approach-Based Model for Internet of Things Network Security

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SN computer Science 2025年第1期6卷 1-25页

作者： Attkan, Ankit Ahlawat, Priyanka Ranga, Virender Department of Computer Engineering NIT Kurukshetra Kurukshetra 136119 India Department of Information Technology Delhi Technological University Delhi 110042 India

The Internet of Things has such a huge domain of applications that it is very common to locate any IoT device, when we look around the technological things working around us. The diversification of multi-purpose devices and intersection of the technology with heterogeneous cyber-physical devices in a computer network, all have some sort of common Internet based mode for communication and data transfer. This data can be sensitive, confidential, or even useless depending on the nature of protection it requires from the attacker’s target point of view. Just like the IoT heterogeneous devices, there is a variety of technological specifications and hardware configurations around millions of devices working over the internet and constituting the cyberspace domain. IoT networks are implemented in, but not restricted to the areas such as military, agriculture, disaster management, healthcare, daily care, finance management, education, surveillance, automobiles, chemical engineering, robotics, biochemistry etc. All such mentioned fields generate a huge amount of data which is required to be protected against the network attacks. This can be done by boosting the security of the communication channel while keeping the things light weight to deal with the problem of limited resource devices and their scarcity of computational power as well. We propose a light-weight secure session key (Session-S_KEY) generation model that relies upon the pyramid structure oriented Pyra-cryptosystem that boosts the cryptographic strength of the IoT network. For mutually authenticating the peer-to-peer devices, we utilize the newly proposed MDSKMA: Mobile Node Mutual authentication and Session key Agreement approached Mutual Authentication mechanism for IoT network. Based on experiments and simulations using hardware (Raspberry Pi), software (NS-3, Miracle) and tools (Proverif), we reached promising results of security and attack deterrence using our proposed Pyra-cryptosystem based sec

关键词： Cryptographic security model Data security Digital signatures IoT security Light-weight cryptosystem Mobile computing security Mutual authentication Number theory Parallel hash Pyra-cryptosystem Pyramid key component Resource constrained networks Shared session keys (Session-SKEY) Switchable security Wireless sensor networks

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Enhancing object detection for visually impaired integrating YOLOv8 with spiking EfficientDet

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Multimedia Tools and Applications 2025年 1-29页

作者： Biradar, Vinod Gull, Karuna C. Department of Computer Science and Engineering KLE College of Engineering & Technology Karnataka Chikodi591201 India Department of Computer Science and Engineering S. G. Balekundri Institute of Technology Belagavi Visvesvaraya Technological University Karnataka Belagavi590018 India

Object detection is a significant method in computer vision, mainly for detecting objects belonging to different classes in an image. Its applications are widely spread to video surveillance, human tracking, autonomous vehicles, and assistive technologies for sight-impaired individuals. However, existing methods suffer from accurately identifying an object and its respective classes due to misidentifying or failing to identify smaller objects. Therefore, this research proposes a new object detection model called You Only Look Once version 8 with Spiking EfficientDet (Yv8SED) that improves detection accuracy, reducing wrong classification with particular emphasis on smaller objects. This model is beneficial in assistive devices for the navigation and safety of a visually-impaired person. The Yv8SED method provides excellent object detection performance with reduced time and cost, making it an efficient methodology for different object detection tasks. The segmentation process is improved through SegNet, which efficiently separates the object from the image. Moreover, the Hippopotamus Optimization Algorithm (HOA) is used to optimize error parameters in the proposed method, thereby improving efficiency and robustness in the object detection process. Experimental results proved the efficiency of the proposed method, with mAP at 95% and mean Average Recall (mAR) at 93% on the COCO dataset and mAP and mAR at 95% and 98%, respectively, on the VOC dataset. These results verify the efficacy and reliability of the proposed method for object detection, particularly for enhancing assistive technologies for the blind and visually impaired. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Object detection

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File Prefetching Accuracy Enhancement Using Deep Learning Approaches 4th

File Prefetching Accuracy Enhancement Using Deep Learning Ap...

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4th International Conference on Advanced Network Technologies and Intelligent Computing, ANTIC 2024

作者： Jamadar, Amaan Mulik, Vaishnavi Joshi, Anvay Joshi, Amit D. Department of Computer Science and Engineering School of Computational Sciences COEP Technological University Pune India

ISBN: (纸本)9783031837951

Efficient file prefetching is essential for optimizing input-output performance in modern operating systems. The readahead mechanism in Linux plays a crucial role in this process by predicting future file access patterns and pre-loading data. However, existing heuristics often struggle to adapt dynamically to varying workloads or other machine learning based approaches might struggle with system-level tasks like readahead because it does not capture temporal features effectively. Current readahead algorithms lack the ability to predict real-time changes in file access patterns, leading to inefficiencies in file system performance, especially under diverse workloads. In this approach, temporal and non-temporal features are utilized, applying models such as Extreme Gradient Boosting and Long Short-Term Memory networks for readahead size prediction. This approach compared the performance of Long Short-Term Memory and Extreme Gradient Boosting for system-level predictive tasks. Long Short-Term Memory outperformed Extreme Gradient Boosting in sequential data handling, achieving higher accuracy but at the cost of greater computational overhead and latency. Extreme Gradient Boosting, though faster and more resource-efficient, struggled with capturing long-term dependencies, still giving an accuracy similar to Long Short-Term Memory. This approach demonstrates the potential for machine learning models to enhance file prefetching mechanisms in Linux, offering a more adaptive and efficient solution for handling diverse file access patterns. Long Short-Term Memory and Extreme Gradient Boosting models have demonstrated impressive performance metrics, with Long Short-Term Memory achieving an R2 value of 0.9886 and Extreme Gradient Boosting reaching 0.9785. These results indicate that both models significantly outperform Kernel Machine Learning’s accuracy of 95.5%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Linux

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Enhanced Alzheimer's Disease Prediction Through Integration of Protein-Protein Interaction Data and Meta-Learning

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IEEE Access 2025年 13卷 68422-68438页

作者： Thattil, Hansa J. Arunkumar, M.N. Antony, Francis APJ Abdul Kalam Technological University Research Scholar Computer Science and Engineering Kerala Thiruvananthapuram695016 India Federal Institute of Science and Technology Ernakulam Department of Computer Science and Engineering Kerala Angamaly683577 India KPIT Technologies Kerala Kakkanad682037 India

Association between proteins and diseases have been widely studied to understand disease triggers and identify potential therapeutic targets. Deciphering the complexity of gene networks is crucial for understanding diseases. Node embedding offers a powerful approach, revealing latent patterns that can predict novel gene-disease associations. In this study, we applied a meta-learning approach to predict protein-disease associations with a focus on Alzheimer's disease. Our approach utilized curated data from the Disease Gene Network Database (DisGeNET), a database of gene-disease associations, and protein-protein interaction data from the Search Tool for Retrieval of Interacting Genes/Proteins (STRING) dataset. We generated embeddings for Protein-Protein Interactions(PPI) using the node2vec algorithm, capturing interaction patterns in a low-dimensional space. HybridBoost, a two-layered model architecture, was then employed, with random forest and CatBoost classifiers in the first layer and CatBoost as the meta-learner in the second layer. Our model achieved an accuracy of 96% and with precision, recall, and F1 scores also outperforming individual models like random forest, support vector machine, AdaBoost, and CatBoost. These results demonstrate the effectiveness of the proposed meta-learning model in predicting protein-disease associations for Alzheimer's disease. © 2025 IEEE.

关键词： Embeddings

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