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International Conference on Systems, Control and Automation, ICSCA 2023

作者： Singh, Utkrisht Singh, Awadhesh Kumar Department of Computer Engineering National Institute of Technology Haryana Kurukshetra India

ISBN: (纸本)9789819773831

Sign language is a type of nonverbal communication method used by people with hearing impairments and the deaf to convey their sentiments and emotions. According to the 2011 Census of India data, there are over 50 lakh people in India who are deaf, and nearly 20 lakh people have speech impairments. In this paper, we have presented a technique to recognise the hand movements of Indian Sign Language efficiently by using a hybrid model of a strong convolutional neural network (CNN) and support vector machine (SVM). In this work, an Indian Sign Language (ISL) dataset is used to train the model. This dataset contains various two handed Indian Sign Language images. In the proposed hybrid model, CNN is used to serve as an automated feature extractor that extracts the most informative features, whereas SVM serves as a classifier. The experimental findings show the correctness of the suggested framework by reaching an identification accuracy of 99.78% for this model on the ISL dataset, and after including Gaussian and random noise to the images, the CNN-SVM model’s accuracy drops to 92.72%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Efficient Approach to Improve the Security of PDF Readers Using Transfer Learning for Few Shots Sample Scenario 2

Efficient Approach to Improve the Security of PDF Readers Us...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Bhardwaj, Sonam Dave, Mayank Dalal, Sumit School of Computer Science Engineering and Technology Bennett University Greater Noida India National Institute of Technology Department of Computer Engineering Kurukshetra India

ISBN: (纸本)9798331505745

PDF malware is a significant threat to computer security. The purpose of this study is to introduce a new approach for improving the security of PDF readers. The method utilizes transfer learning by leveraging existing models and a small number of labeled examples, even with limited training samples. The ResNet34 model has been modified for PDF malware detection by replacing the 2D and 3D channels with a 1D channel. Transfer learning is a technique that is employed to enhance the model by utilizing the labeled samples that are available in a scenario where only a few samples are provided. The evaluation of the PDFMal2022 dataset shows that our approach outperforms existing models. It achieves a detection accuracy of 91.6% on source data and 92.4% on target data. This approach effectively utilizes transfer learning and effectively mitigates the risks associated with PDF malware, thereby offering an improved security solution for PDF readers. © 2025 IEEE.

关键词： computer viruses

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Learning Non-cooperative Driving Practices and its Impact on Road Traffic Dynamics 17

Learning Non-cooperative Driving Practices and its Impact on...

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

作者： Osho Chakraborty, Suchetana Indian Institute of Technology Department of Computer Science and Engineering Jodhpur India

ISBN: (纸本)9798331531195

Cooperative driving that prioritizes global safety and efficiency over self-interest is crucial in ensuring smooth traffic flow and enhanced road safety. However, experiencing noncooperative driving is common under heterogeneous and chaotic on-road traffic conditions. While non-cooperativeness is often context-dependent and assessed during direct vehicle interactions, it can be inferred through longitudinal analysis of driving patterns from vehicular kinematics data. This behavior not only adversely affects the vehicle itself but also has a cascading impact on nearby vehicles, ultimately disrupting overall traffic flow. In this study, we quantify a vehicle's non-cooperativeness using kinematic data and further model its cascading effects on nearby cooperative vehicles. Experimental analysis was conducted using the SUMO traffic simulator and the TraCI interface to assess the impact of non-cooperativeness on overall traffic dynamics. This research is among the first to examine the cascading effects of a vehicle's non-cooperativeness on its cooperative nearby vehicles. © 2025 IEEE.

关键词： Motor transportation

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Digital Agriculture Platforms: Empowering Farmers and Enhancing Market Accessibility

Digital Agriculture Platforms: Empowering Farmers and Enhanc...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Patil, Kavita T Patil, Shruti Patil, Harshada Salunke, Madhavika Chandwadkar, Shreyash Shahade, Makarand SVKM's Institute of Technology Department of Computer Engineering Dhule India

ISBN: (纸本)9798331508685

In the present day and age of technology and digitalization our proposed platform revolutionizes the agricultural market concept by providing a direct connection between the farmers and the consumers thus reducing the middlemen and providing a clear and viable market solution. The system also benefits farmers in that it makes it easier for them to display their produce, set price and sell. By having extensive product catalogue, using multiple languages, and interacting through the website in real-time, farmers are able to directly deal with the buyers from their respective regions and they are fairly paid for their work. In turn, consumers get to enjoy new, fresh, local and affordable foods from the markets they get supplied to. The secure mode of payment and simplified interface makes it easily accessible for all clients to create a sustainable and positive impact on the economy of the agricultural sector. © 2025 IEEE.

关键词： Agricultural economics

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TruChain: A Blockchain-based Access Control to Improve the Security of Smart Water Grid Systems 17

TruChain: A Blockchain-based Access Control to Improve the S...

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

作者： Mondal, Susmita Chakraborty, Suchetana Indian Institute of Technology Department of Computer Science and Engineering Jodhpur India

ISBN: (纸本)9798331531195

Water is a significant resource in day-to-day life, and it usually requires technological association for comprehensive management. Smart Water Grids (SWG) typically use cyberphysical systems (CPS) to monitor several parameters associated with water quality and distribution, leveraging the possibility of performing remote operations. For robust architecture, remote operations rely on application-specific sensors, communication networks, edge control, and cloud computation. Needless to say, the centralized control of edge and cloud infrastructure is prone to attacks and failures, such as unauthorized access or single-point-of-failure. We propose TruChain, a Blockchain-based access control mechanism to eliminate the probability of access takeover and aggregate quorum-based validation on sensory data for transparent provenance reliability. We also provide access revocation to strengthen security by restricting access to sensitive data stored in a privately distributed cloud. We believe our solution has empirical decentralization and a verifiable authorization method by maintaining a tamper-evident, auditable trail of interactive events, building trust without third-party intervention. © 2025 IEEE.

关键词： Cloud security

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Generative Adversarial Networks (GANs) for Education: State-Of-Art and Applications

Generative Adversarial Networks (GANs) for Education: State-...

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International Conference on Power engineering and Intelligent Systems, PEIS 2024

作者： Kavyashree, K.N. Shidaganti, Ganeshayya Department of Computer Science and Engineering Ramaiah Institute of Technology Bangalore India

ISBN: (纸本)9789819767090

In the ever-changing educational landscape, technology integration has become essential to improving students’ understanding of basic topics. The COVID-19 pandemic hastened the transition to online education while posing new difficulties for teachers and students alike. The disruption of traditional classroom dynamics was exacerbated by the lack of digital innovation and interactive instructional tools, which made student participation less effective. Traditional teaching paradigms must make way for ones that encourage self-directed learning and fortify technical infrastructure in order to handle these difficulties. Generative Adversarial Networks (GANs), a cutting-edge use of deep learning, are starting to revolutionize educational technology. With the use of GANs, a range of features that are tailored to the individual learning preferences of each student may be created, thereby bridging knowledge gaps and providing a more dynamic and interesting learning environment for students. This research investigates several state-of-the-art features that leverage technology linked to GANs to enhance students’ virtual learning environments. The novel applications of generative adversarial networks are explored, with a focus on applications for student observation, academic performance, questioning and answering, and personalized learning. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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Simulating Application Driven Mobile D2D underlay Networks using Gym API 17

Simulating Application Driven Mobile D2D underlay Networks u...

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

作者： Batabyal, Suvadip National Institute of Technology Department of Computer Science & Engineering Durgapur India

ISBN: (纸本)9798331531195

Device-to-device (D2D) communication underlay cellular network improves spectral efficiency by reusing cellular resources over short and low-power links between the devices. For this, the D2D paradigm is considered to be an integral part of all kinds of future networks and is an active area of research at different levels. This requires a simple, configurable, scalable, and realistic evaluation of the protocols proposed for the D2D networking paradigm. Although there exists a few platform to evaluate a D2D underlay network, they have one or more of the following drawbacks: (i) long development time and complex environment, (ii) limited support for mobility, (iii) no support for application specific development and evaluation. In this work, we developed a framework for simulating an application-driven mobile D2D underlay network using the Gym API, where D2D connections are made spontaneously based on the application requirements. The proposed framework includes modules for generating application-specific traffic, buffer for transmission and caching of application data, and mobility management. This complements several earlier works on application (or utility) driven resource allocation in D2D underlay network. The framework is used to evaluate a D2D network using different mobility models and performance of cache based video streaming in a D2D underlay network. Finally, the framework is compared with ns-3 and OMNET++ simulators and is observed to have lower CPU utilization and run-time under the same network settings. © 2025 IEEE.

关键词： Resource allocation

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Enhancing aviation safety: Machine learning for real-time ADS-B injection detection through advanced data analysis

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Alexandria engineering Journal 2025年 126卷 262-276页

作者： Rahman, Md. Atiqur Bhuiyan, Touhid Ali, M. Ameer Department of Computer Science and Engineering East West university Dhaka Bangladesh School of Information Technology Washington University of Science and Technology Alexandria United States Department of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh

Airplanes play a critical role in global transportation, ensuring the efficient movement of people and goods. Although generally safe, aviation systems occasionally encounter incidents and accidents that underscore the need for proactive risk management. This study employs machine learning to detect abnormalities in commercial aircraft operations using Automatic Dependent Surveillance–Broadcast (ADS-B) data. Given the growing reliance on ADS-B technology, concerns regarding its susceptibility to security breaches, such as injection attacks, have intensified. To address these vulnerabilities, we propose a robust ADS-B injection detection system. Employing GridSearchCV for model optimization, it effectively identifies and categorizes injection risks. The system's performance, evaluated using the ADS-B Message Injection Attacks Dataset, achieves outstanding results, including a value of 0.9970 for the accuracy, precision, recall, and F1 score. The proposed classifier also demonstrates a higher area under the curve (0.9999), specificity (0.9956), and Cohen's kappa (0.9954) than existing approaches, while achieving a lower log loss (0.0107). This research significantly enhances aviation security by introducing a highly accurate, computationally efficient, and reliable real-time detection model for ADS-B injection attacks, ensuring the integrity and resilience of modern flight control systems. © 2025 The Authors

关键词： Risk management

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Movie Recommendation Using Web Crawling 2nd

Movie Recommendation Using Web Crawling

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2nd International Conference on Applied Algorithms, ICAA 2025

作者： Raj, Pronit Kumar, Chandrashekhar Shekhar, Harshit Kumar, Amit Paul, Kritibas Jana, Debasish Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India

ISBN: (纸本)9783031845420

In today’s digital world, streaming platforms offer a vast array of movies, making it hard for users to find content matching their preferences. This paper explores integrating real-time data from popular movie websites using advanced HTML scraping techniques and APIs. It also incorporates a recommendation system trained on a static Kaggle dataset, enhancing the relevance and freshness of suggestions. By combining content-based filtering, collaborative filtering, and a hybrid model, we create a system that utilizes both historical and real-time data for more personalized suggestions. Our methodology shows that incorporating dynamic data not only boosts user satisfaction but also aligns recommendations with current viewing trends. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Data reduction

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EssayDetect at GenAI Detection Task 2: Guardians of Academic Integrity: Multilingual Detection of AI-Generated Essays 1

EssayDetect at GenAI Detection Task 2: Guardians of Academic...

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1st Workshop on GenAI Content Detection, GenAIDetect 2025

作者： Agrahari, Shifali Jayant, Subhashi Kumar, Saurabh Singh, Sanasam Ranbir Department of Computer Science and Engineering Indian Institute of Technology Guwahati India

ISBN: (纸本)9798891762053

Detecting AI-generated text in the field of academia is becoming very prominent. This paper presents a solution for Task 2: AI vs. Human - Academic Essay Authenticity Challenge in the COLING 2025 DAIGenC Workshop1. The rise of Large Language models (LLMs) like ChatGPT has posed significant challenges to academic integrity, particularly in detecting AI-generated essays. To address this, we propose a fusion model that combines pre-trained language model embeddings with stylometric and linguistic features. Our approach, tested on both English and Arabic, utilizes adaptive training and attention mechanisms to enhance F1 scores, address class imbalance, and capture linguistic nuances across languages. This work advances multilingual solutions for detecting AI-generated text in academia. © 2025 International Conference on Computational Linguistics.

关键词： Computational linguistics

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