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15th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2025

作者： Dudam, Nerajdattu Chato, Lina Motupalli, Nagamani Kashyap, Kristipati Thoyajaksha Sane, Madhusree Bollina, Mounika University of South Dakota Dept. Computer Science Vermillion United States

ISBN: (纸本)9798331507695

An automatic diagnosis of brain tumors based on Deep Learning (DL) is a promising research field to enhance the diagnosis and prognosis of brain tumors. This paper presents an exploration into the use of Convolutional Neural Networks (CNNs) and Vision Transformers (ViT) for brain tumor classification, specifically identifying glioma, meningioma, pituitary tumors, and no tumor from brain Magnetic Reasons Imaging (MRI) scans. We implemented different approaches, such as 1) developing a custom CNN model from scratch, 2) leveraging pre-trained models based on Transfer Learning (TL) (including VGG16 and ResNet50), 3) developing a custom ViT model from scratch, and 4) leveraging pre-trained model in vision transformers, such as ViT B16. A publicly available labeled dataset is used to develop and test our proposed classification Deep Learning models. Our findings reveal that both custom-trained and pre-trained CNN and ViT models achieve high accuracy. The model ViT B16 based on transferred learning achieves the best accuracy of 97.6%, which is slightly better than the CNN models. To enhance the accuracy of the prediction model and reduce the misclassifications, ensemble model based on the combination of ViT B16 and CNN model;this approach increases the accuracy to 98%. © 2025 IEEE.

关键词： Convolutional neural networks

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Lightweight Attention Based Deep CNN Framework for Human Facial Emotion Detection from Video Sequences

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SN computer science 2025年第1期6卷 1-12页

作者： Kant, Krishna Shah, Dipti B. Computer Science Dept. P.G Department of Computer Science and Technology Sardar Patel University V.V. Nagar Gujrat Anand 388120 India

Emotions with their intensities are associated with the action of humans which decides the behaviour of an *** recent research has gained enormous attention in the domain of emotion detection due to automatic facial emotion detection. The prime goal of facial emotion recognition (FER) is intended to map different facial expressions from video sequences to specific emotional states. Human facial emotion recognition (HFER) has captivated the attention of researchers for its excellent real time application which serves the society. This research work has proposed a novel lightweight attention based deep convolution neural framework for human facial emotion detection from video sequences on CK + dataset. A dense convolutional network (DenseNet-201) has been applied to strengthen feature propagation to find a solution to the vanishing-gradient problem, and significantly lower the parameter *** provide the model for better visual perceptibility, an attention block is applied between the convolution and dense layer. The proposed model achieves good accuracy of 98.8% while using less memory and processing in comparison to state-of-the-art approaches to reach good performance on CK + data set. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Attention network Deep convolution neural network DensNet-201 Emotion detection Human face detection Intensity Video surveillance

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Benefits of Machine Learning Explanations: Improved Learning in an AI-assisted Sequence Prediction Task 25

Benefits of Machine Learning Explanations: Improved Learning...

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30th International Conference on Intelligent User Interfaces, IUI 2025

作者： Liang, Yu Collaris, Dennis Willemsen, Martijn C. Van Wijk, Jarke J. Human-Technology Interaction Eindhoven University of Technology Eindhoven Netherlands Dept. Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands Jheronimus Academy of Data Science Den Bosch Netherlands

ISBN: (纸本)9798400713064

Research in Explainable AI (XAI) has shown that explanations can improve users' understanding of AI models, improve user performance and potentially reduce overreliance on AI predictions. However, this is mostly evaluated by static rather than dynamic measures, and the role of XAI on learning over trials is rarely studied. In this study, we use a context-free sequence prediction task, in which 458 participants predict the next symbol in a fixed sequence (with some noise) over 80 trials. We compare performance with AI and XAI advice against no AI support, and subsequently we test for learning by taking away the AI support after 40 trials (i.e., a reversal study design). Our results show that users learn faster with XAI than with AI without explanations or no AI and are better able to recover in performance from the removal of AI. However, the benefits of XAI on learning are much smaller for more difficult tasks. This work demonstrates the benefits of repeated measures user studies and multilevel modeling to better understand learning processes in XAI. It also shows the potential of AI explanations to help users to learn and poses XAI design suggestions to support learning in human-AI collaboration. © 2025 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Federated Learning under Attack: Game-Theoretic Mitigation of data Poisoning 18

Federated Learning under Attack: Game-Theoretic Mitigation o...

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18th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2025

作者： De Santis, Marco Esposito, Christian University of Salerno Dept. Computer Science Fisciano Italy

ISBN: (纸本)9798331534677

Federated Learning (FL) is vulnerable to various attacks, including data poisoning and model poisoning, which can degrade the quality of the global model. Current solutions are based on cryptographic primitives or byzantine aggregation but suffer from performance and/or quality worsening and are even ineffective in case of data poisoning. This work proposes a game-theoretic solution to identify malicious weights and differentiate between benign and compromised updates. We use the Prisoner's Dilemma and Signaling Games to model interactions between local learners and the aggregator, allowing a precise evaluation of the legitimacy of shared weights. Upon detecting an attack, the system activates a rollback mechanism to restore the model to a safe state. The proposed approach enhances FL robustness by mitigating attack impacts while preserving the global model's generalization capabilities. © 2025 IEEE.

关键词： Federated learning

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A Hybrid Approach Using Cryptography and K-Least Significant Bit Steganography Algorithm in Cloud Computing 1

A Hybrid Approach Using Cryptography and K-Least Significant...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Goyal, Anshika Gupta, Bhumika VMSB Uttarakhand Technical University Dept. of Computer Science & Engineering Dehradun India G.B. Pant Institute of Engineering & Technology Dept. of Computer Science & Engineering Pauri India

ISBN: (纸本)9798331518578

The exponential growth of communication technologies has broadened the cloud computing ecosystem horizon to meet main communication needs. However, in-parallel upsurge in online attacks has alarmed industries to ensure optimal data security. The combination of steganography and cryptosystems has been recognized as an enhanced security model for data transfer among the main security systems now in use. In this paper, a hybrid approach using cryptography and K- Least Significant Bit Steganography algorithm in Cloud Computing (HYBC-KLSBS-CC) are proposed. Initially, input text data is retrieved from cloud storage and compressed using Huffman Compression Coding (HCC) to reduce data size. The compressed data is then encrypted with a Hybrid Cryptography Algorithm (Hyb-MRFC-ALC), combining Modified Random Fibonacci Cryptography and Asymmetric Lightweight Cryptography to ensure robust encryption. Finally, encrypted data is embedded within an image using the K-Least Significant Bit (KLSB) steganography technique, providing additional layer of security. The proposed method enhances data confidentiality, integrity, and storage efficiency in cloud environments. The proposed HYBC-KLSBS-CC approach attains 26.89%, 29.49% and 18.64% lower PSNR and 26.89%, 29.49% and 18.64% lower MSE when analyzed through existing techniques like data security dependent on cryptography steganography related with OTP procedure and Huffman coding in the cloud environment (CS-OTP-CE), a novel hybrid cryptographic framework for secure data storage in cloud computing: Integrating AES-OTP and RSA with adaptive key management and Time-Limited access control (AES-OTP-RSA-CC) and Crypto-Stegno dependent model for securing medical information on IOMT platform (CS-SMI-IOMT). © 2025 IEEE.

关键词： Cloud platforms

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HPC master design: experience from Pisa 33

HPC master design: experience from Pisa

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33rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2025

作者： Danelutto, Marco Univ. of Pisa Dept. of Computer Science Italy

ISBN: (纸本)9798331524937

HPC is a widely used term, often referred to the applications, architectures and programming models and tools targeting highly parallel machines such as those of the *** lists. Recent advances in computing hardware resources require application of HPC techniques when using much smaller machines. Indeed, proper parallel programming tools and applications are needed also to exploit parallel hardware resources in personal computers (laptops, desktops, servers). This paper outlines key challenges in designing master's degree programs in HPC and shares lessons learned from various experiences in developing and implementing such programs in Italy and Europe. © 2025 IEEE.

关键词： Parallel programming

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An efficient conference key agreement protocol suited for resource constrained devices

An efficient conference key agreement protocol suited for re...

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作者： Pundir, Manmohan Kumar, Abhimanyu Dept. of Computer Science and Engg. NIT Uttarakhand Uttarakhand Srinagar Garhwal246174 India Dept. of Computer Science and Engg. NIT Patna Bihar Patna800005 India

Conference key agreement (CKA) is essential for securing communication in group-oriented scenarios like multi-party messaging and collaborative environments. While elliptic curve cryptography (ECC) offers efficiency and strong security, ECC-based CKA protocols often rely on expensive pairings, making them computationally impractical for deployment over the resource limited devices. This paper introduces a novel CKA approach using ECC without requiring pairing computations, thus addressing scalability and efficiency challenges. The proposed protocol employs scalar point multiplications over a prime field elliptic curve group, enabling secure and efficient CKA operations with reduced computational overhead. Compared to existing ECC-based key agreement protocols, it minimizes user-level computation and enhances performance in computational efficiency, communication overhead, and security strength. Particularly suitable for resource-constrained environments like IoT and edge computing, where computational resources are limited yet secure group communication is crucial. © 2024 Elsevier Inc.

关键词： Authentication Protocol

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LegalAI Across Languages: A Comprehensive Survey on Tasks, Methodologies and Challenges 1

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

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1st International Conference on AIML-Applications for Engineering and Technology, ICAET 2025

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

ISBN: (纸本)9798350355611

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. © 2025 IEEE.

关键词： Risk assessment

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Framework for Cloud-Based data Management in Autonomous Vehicles

Framework for Cloud-Based Data Management in Autonomous Vehi...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Pallapothu, Harshith Gopi Krishna, G. Sunil, Digvijay Teja Yadav, S. Nagasundari, S. Pes University Dept. of Computer Science & Engineering Bengaluru India Research Center for Information Security Forensics and Cyber Resilience Pes University Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331529833

The increasing adoption of autonomous vehicles has driven the need for robust data management solutions that support real-time operations and ensure vehicle safety and efficiency. This work introduces a cloud-based framework for management of sensor data from autonomous vehicles, focusing on optimizing payload transmission rates. The framework leverages AWS IoT Core and AWS IoT Analytics to ensure efficient data flow from vehicle sensors to cloud storage. This framework demonstrates its potential for scalable deployment in real-world autonomous vehicle networks, contributing to the evolution of connected vehicle technologies and intelligent transport systems. This framework ensures reliable data transmission up to 250 payloads per second, beyond which data loss occurs. © 2025 IEEE.

关键词： Cloud platforms

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Integrated and Intelligent Elder-Friendly E-commerce Framework using NLP Techniques

Integrated and Intelligent Elder-Friendly E-commerce Framewo...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Sivasanjith, G. Mohan Prabhu, N. Hemaswathi, S. Rajkumar, P. Rajeshkannan, C. Dhivya, R. Kare Dept. of Information Technology Krishnankovil India Artificial Intelligence and Data Science Indra Ganesan College of Engineering Tiruchirappalli India Mount Zion College of Engg. and Tech. Dept. of Computer Science and Engg. Pudukottai India Suguna College of Engineering Dept. of Computer Science and Engg. Coimbatore India M.Kumarasamy College of Engineering Dept. of Information Technology Karur India

ISBN: (纸本)9798331529833

All over the world, the population of elderly people is increasing a lot. There needs to be special attention to the welfare of the elderly person to make them confident in their independent quality of living. In recent years, researchers have shown much interest in smart home automation for people who live alone without any human assistance. However, the purchase of medical drugs, diapers, etc., outside the house has to be fulfilled by their family members or caretakers. In most cases, it is not feasible, as the family members may stay long away from the elders. Purchasing online is easy;however, analyzing the best product requires knowledge in each e-commerce application for better quality at a low cost. This paper proposes an integrated and intelligent e-commerce framework to avoid the drawback of purchasing items online in a single window using the NLP technique. The proposed framework provides offers, discounts, star ratings, and product reviews by analyzing other customers' review data and purchase patterns. It provides interactive data analysis at a single window for the ease of elderly persons. The performance of the proposed framework was evaluated by 120 elderly persons using a simple questionnaire with a statistical analysis of a sample purchase. © 2025 IEEE.

关键词： Recommender systems

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