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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Prabakaran, Manjula Zakaria, Jesmin Kannan, S. Santhiya, P. Rajasekar, P. Rathnamma, M.V. Andhra Pradesh India NSHM Knowledge Campus West Bengal Durgapur India Dhanalakshmi Srinivasan University School of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Trichy India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Klm College of Engineering for Women Department of Cse Andhra Pradesh Kadapa India

ISBN: (纸本)9798331509675

CT of the urinary system, abdomen, and kidneys appears to improve kidney stone screening and identification. Segmentation and categorization are essential for kidney stone identification and management. This work developed a semi-automatic kidney stone detection system using geometric concepts and image processing. The main steps include model training, feature extraction, segmentation, embossing, and preprocessing. Preprocessing involves trimming the input image to remove unnecessary kidney regions to focus on critical CT scan locations. Relief embossing enhanced image details. Segmentation accurately classified veins and blood arteries to define kidney stone boundaries. CNN-RELM was our training model. Compared to CNN and ELM models, the recommended system performed better. Its 93.87% kidney stone detection and localization accuracy prove its reliability. The semi-automatic system's reliable kidney stone detection and segmentation method may improve clinical decision-making. Combining CNN-RELM modeling and enhanced image processing could lead to medical imaging applications. © 2025 IEEE.

关键词： computerized tomography

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Dual-level Mixup for Graph Few-shot Learning with Fewer Tasks 34

Dual-level Mixup for Graph Few-shot Learning with Fewer Task...

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34th ACM Web Conference, WWW 2025

作者： Liu, Yonghao Li, Mengyu Giunchiglia, Fausto Huang, Lan Li, Ximing Feng, Xiaoyue Guan, Renchu College of Computer Science and Technology Jilin University Changchun China Department of Information Engineering and Computer Science University of Trento Trento Italy Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education China

ISBN: (纸本)9798400712746

Graph neural networks have been demonstrated as a powerful paradigm for effectively learning graph-structured data on the web and mining content from it. Current leading graph models require a large number of labeled samples for training, which unavoidably leads to overfitting in few-shot scenarios. Recent research has sought to alleviate this issue by simultaneously leveraging graph learning and meta-learning paradigms. However, these graph meta-learning models assume the availability of numerous meta-training tasks to learn transferable meta-knowledge. Such assumption may not be feasible in the real world due to the difficulty of constructing tasks and the substantial costs involved. Therefore, we propose a SiMple yet effectIve approach for graph few-shot Learning with fEwer tasks, named SMILE. We introduce a dual-level mixup strategy, encompassing both within-task and across-task mixup, to simultaneously enrich the available nodes and tasks in meta-learning. Moreover, we explicitly leverage the prior information provided by the node degrees in the graph to encode expressive node representations. Theoretically, we demonstrate that SMILE can enhance the model generalization ability. Empirically, SMILE consistently outperforms other competitive models by a large margin across all evaluated datasets with in-domain and cross-domain settings. Our anonymous code can be found here. © 2025 Copyright held by the owner/author(s).

关键词： Few-shot learning Graph neural network Node classification

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Chaos-Based Novel Watermarked Satellite Image Encryption Scheme

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computer Modeling in engineering & sciences 2025年第4期143卷 1049-1070页

作者： Mohamed Medani Yahia Said Nashwan Adnan Othman Farrukh Yuldashev Mohamed Kchaou Faisal Khaled Aldawood Bacha Rehman Applied College of Muhayil Aseer King Khalid UniversityAbha62529Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border UniversityArar73213Saudi Arabia Department of Computer Engineering College of EngineeringKnowledge UniversityErbil44001Iraq Department of Computer Engineering Al-Kitab UniversityAltun Kupri36001Iraq Department of Informatics and Its Teaching Methods Tashkent State Pedagogical UniversityTashkent100070Uzbekistan Department of Industrial Engineering College of EngineeringUniversity of BishaBishaP.O.Box 001Saudi Arabia Department of Computer Science Solent UniversitySouthamptonSO140YNUK

Satellite images are widely used for remote sensing and defence applications,however,they are subject to a variety of *** ensure the security and privacy of these images,theymust be watermarked and encrypted before ***,this paper proposes a novel watermarked satellite image encryption scheme based on chaos,Deoxyribonucleic Acid(DNA)sequence,and hash *** watermark image,DNA sequence,and plaintext image are passed through the Secure Hash Algorithm(SHA-512)to compute the initial condition(keys)for the Tangent-Delay Ellipse Reflecting Cavity Map(TD-ERCS),Henon,and Duffing chaotic maps,*** bitwise XOR and substitution,the TD-ERCS map encrypts the watermark *** ciphered watermark image is embedded in the plaintext *** embedded plaintext image is permuted row-wise and column-wise using the Henon chaotic *** permuted image is then bitwise XORed with the values obtained from the Duffing *** additional security,the XORed image is substituted through a dynamic *** evaluate the efficiency and performance of the proposed algorithm,several tests are performed which prove its resistance to various types of attacks such as brute-force and statistical attacks.

关键词： DNA sequence TD-ERCS chaoticmap henon chaoticmap duffing chaotic system SHA-512 encryption technique watermark embedding

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Gupacker: Generalized Unpacking Framework for Android Malware

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4338-4352页

作者： Zheng, Tao Hou, Qiyu Chen, Xingshu Ren, Hao Li, Meng Li, Hongwei Shen, Changxiang Sichuan University School of Cyber Science and Engineering Chengdu610065 China Sichuan University School of Cyber Science and Engineering Cyber Science Research Institute Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Intelligent Interconnected Systems Laboratory of Anhui Province School of Computer Science and Information Engineering Hefei230002 China University of Padua Department of Mathematics HIT Center Padua35131 Italy University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Sichuan University Cyber Science Research Institute Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China

Android malware authors often use packers to evade analysis. Although many unpacking tools have been proposed, they face two significant challenges: 1) They are easily impeded by anti-analysis techniques employed by packers, preventing efficient collection of hidden Dex data. 2) They are typically designed to unpack a specific packer and cannot handle malware packed with mixed packers. Consequently, many packed malware samples evade detection. To bridge this gap, we propose Gupacker , a novel generalized unpacking framework. Gupacker offers a generic solution for first-generation holistic packer by customizing the Android system source code. It identifies the type of packer and selects an appropriate unpacking function, constructs a deeper active call chain to achieve generic unpacking of second-generation function extraction packers, and uses JNI function and instruction monitoring to handle third-generation virtual obfuscation packer. On this basis, we counteract a diverse array of anti-analysis techniques. We conduct extensive experiments on 5K packed Android malware samples, comparing Gupacker with 2 commercial and 4 state-of-the-art academic unpacking tools. The results demonstrate that Gupacker significantly improves the efficiency of Android malware unpacking with acceptable system overhead. We analyze real packed applications based on Gupacker and found several are second-packed by attackers, including WPS for Android, with tens of millions of users. We receive and responsibly report 13 0day vulnerabilities and also assist in the remediation of all vulnerabilities. © 2005-2012 IEEE.

关键词： Android malware

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Blockchain-Based IoMT Security: BSHS-EODL Method for Secure Data Storage and Analysis 3

Blockchain-Based IoMT Security: BSHS-EODL Method for Secure ...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Rajak, Biru Shailaja, M. Al-Dabagh, Mustafa Zuhaer Nayef Mohammed, Ali Ali Saber Alazzam, Malik Bader Ponni Valavan, M. Asansol Girls' College Kazi Nazrul University Department of Computer Science Asansol India Hyderabad India College of Science Knowledge University Department of Computer Science Erbil Iraq Al Kitab University Department of Computer Engineering Kirkuk Iraq Jadara University Faculty of Information Technology Irbid Jordan K. Ramakrishnan College of Engineering Department of Ai & Ds Trichy India

ISBN: (纸本)9798331508456

The integration of the Internet of Medical Things (IoMT) has revolutionized healthcare by improving patient care and system efficiency. However, this progress introduces significant concerns about data security and privacy, particularly with medical data. Traditional methods like Decision Tree, Random Forest, and Naïve Bayes face limitations in scalability, accuracy for large datasets, and energy efficiency. To address these challenges, this research introduces a novel Blockchain-based IoMT framework, BSHS-EODL (Blockchain-Secured Healthcare System with Edge-Oriented Deep Learning). The framework employs Long Short-Term Memory (LSTM) networks for precise healthcare data analysis, optimized using the Grey Wolf Optimization (GWO) algorithm for hyperparameter tuning. The proposed system achieved outstanding results, including an accuracy of 99.42%, precision of 99.51%, recall rate of 99.47%, and F1 score of 99.39%, outperforming conventional methods. Moreover, BSHS-EODL demonstrated remarkable efficiency, this approach not only enhances healthcare data security and diagnostic accuracy The system is implemented using Python integrated with MATLAB. © 2025 IEEE.

关键词： Patient treatment

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Real-Time Myanmar Hand Gestures Recognition Using Deep Learning Models 8th

Real-Time Myanmar Hand Gestures Recognition Using Deep Learn...

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8th International Conference on Intelligent Technologies, ICIT 2023

作者： Kyaw, Nwe Ni Mitra, Pabitra Sinha, G.R. Department of Computer Science and Engineering Indian Institute of Technology Kharagpur West Bengal721302 India Indian Knowledge System Lab International Institute of Information Technology Bangalore Karnataka Bengaluru560100 India

ISBN: (纸本)9789819738588

The proposed system for recognizing Myanmar sign language between individuals who are deaf. The aim of this study is to develop deep learning models for the purpose of accurately identifying dynamic hand gesture images depicting 14 classes of Myanmar words and numbers. The present system utilizes a new dataset comprising 7834 images for 14 classes obtained through Webcam. The objective of this study is to enhance the robustness of the new model. Next, we proceed to evaluate the proposed model by comparing them to those of existing convolutional neural network models. These models are generally recognized as effective deep-learning approaches for recognition tasks. The proposed model exhibits a performance level of 96% for both validation and testing accuracies. Furthermore, the experimental finding demonstrates that the model, convolutional layers, achieved an accuracy of 100% in recognizing Myanmar Sign Language (MSL). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Improving Multi-Factor Authentication Security with Deep Learning-Based User Behaviour Analysis 3

Improving Multi-Factor Authentication Security with Deep Lea...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Alnafea, Fahad Sulaiman Mohammed Sengar, Alok Singh Hamid, Noor Kaylan Selladurai, Kuttimarks Muthu Hassan, Sharmeen Izzat Saravanakumar, R. College of Business and Economics Qassim University Department of Accounting Buraydah Saudi Arabia Haridwar University Department of Computer Science & Engineering Roorkee India Al Kitab University Department of Computer Education Kirkuk Iraq Shivajirao S. Jondhle College of Engineering and Technology Asangaon India College of Engineering Knowledge University Department of Computer Engineering Erbil Iraq Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Ece Chennai India

ISBN: (纸本)9798331508456

With growing frequency and sophistication of cyber threats, there is an urgent need for more adaptive, secure multi-factor authentication systems. Traditional approaches often tend to make static factor-based authentication systems that just ignore the dynamic nature of user behavior and are constantly at risk from evolved attacks against authentication mechanisms. This paper presents a novel hybrid deep learning approach - that of long short-term memory networks and convolutional neural networks combined in an attempt to address these disadvantages and enhance the security of an MFA system. The approach uses LSTM to analyze sequential data on user behavior, hence recognizing long-term dependencies like login pattern and session durations. On the other hand, CNN layers will classify the attribute by learning the spatial hierarchy thus identifying small variations that can mark emerging threats to security. This method, in contrast to others, analyzes statically or all at once, providing a dynamic check into the temporal as well as spatial aspects of user interactions;thus, there is more force in its frame for anomaly detection. Experimental tests further verify the excellent performance of the system, with results indicating an accuracy of 98.2%, precision of 99.9%, recall of 99.5%, and an F1 score of 99%. The new strategy exploits MFA frameworks more effectively in minimizing their vulnerabilities against authentication attacks, while developing a dynamic and robust solution that caters to the shift in the ever-changing landscape within cybersecurity, thus advancing the possibility of practice securely. © 2025 IEEE.

关键词： Deep learning

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Accurate, Secure, and Efficient Semi-Constrained Navigation Over Encrypted City Maps

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2642-2658页

作者： Li, Meng Chen, Yifei Gao, Jianbo Wu, Jingyu Zhang, Zijian He, Jialing Zhu, Liehuang Conti, Mauro Lin, Xiaodong Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei230002 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230002 China Hefei University of Technology Anhui Province Key Laboratory of Industry Safety and Emergency Technology Intelligent Interconnected Systems Laboratory of Anhui Province Hefei230002 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100811 China University of Science and Technology of China School of Computer Science and Technology Hefei230026 China Beijing Institute of Technology Southeast Institute of Information Technology Fujian Putian351100 China Chongqing University College of Computer Science Chongqing400044 China University of Padua Department of Mathematics HIT Center Padua35131 Italy Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft2628 CD Netherlands University of Guelph School of Computer Science GuelphONN1G 2W1 Canada

Navigation services enable users to find the shortest path from a starting point S to a destination D, reducing time, gas, and traffic congestion. Still, navigation users risk the exposure of their sensitive location data. Our motivation arises from how users can accurately, securely, and efficiently navigate from S to Dwhile passing through k unordered stops, i.e., midway locations with a non-fixed visiting order. In this work, we formally define Semi-Constrained Navigation (SCN) and present a novel scheme Hermestoachieveaccurate,secure, andefficient SCN. Specifically, weproposeadivide-and-conquerapproachtostrikeagoodbalance betweenaccuracyandefficiency. It recursively depth-first-searches the whole area (a navigation tree) and invokes five carefully-crafted strategies stop-by-stop to compute three subpaths in three se quential subareas. We construct a path-distance oracle to encrypt the road graph and securely implement the strategies by using homomorphic encryption and garble circuits. We formally prove the security in the random oracle model and analyze the search complexity to be less than O(k2). We experiment over a real-world city map and compare with six baselines. Results show that path search with k = 4 among N = 1000 intersections requires 5.58 seconds with a 3.2% distance deviation rate and an 82.5% path similarity. © 2004-2012 IEEE.

关键词： Traffic congestion

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Hybrid Classification Model Development for Enhancement the Data Driven Based Human Falling Detection Performance 3rd

Hybrid Classification Model Development for Enhancement the ...

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3rd International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Ahmed, Saadaldeen Rashid Fadel, Hala Adnan. Ismael, Omar Ayad Sulaiman, Sazan Kamal Mahdi, Nidhal Raoof Ahmed, NoorUlhuda S. Abubaker, Brwa Abdulrahman Hussain, Abadal-Salam T. Artificial Intelligence Engineering Department College of Engineering Alayan University Nasiriyah Iraq Department of Laser Optoelectronic Engineering/Al-Ma’moon University College/Al-Washash Baghdad Iraq Department of Computer Science College of Sciences University of Diyala Diyala Iraq Department of Computer Engineering College of Engineering Knowledge University Erbil44001 Iraq Medical Laboratories Techniques Al Hikma University College Baghdad Iraq College of Medicine University of Al-Ameed PO Box 198 Karbala Iraq Computer Science Department Bayan University Kurdistan Erbil Iraq Department of Medical Instrumentation Techniques Engineering Technical Engineering College Al-Kitab University Altun Kupri Kirkuk Iraq

ISBN: (纸本)9789819624676

It is thus important to identify the cause of a fall in order to be able to protect the lives of people, especially the elderly and hospitalized patients who are most likely to fall. Three fall detection models are compared in this paper: These are the auto-encoder (AEC), the feed forward neural network (FFNN), and the particle swarm optimization-feed forward neural network (PSO-FFNN). The idea is to assess how well these models identify falls, perform, and are suitable for deployment in real-life scenarios. This study was based on data from 17 candidates, and the four classes of falls that were considered here include: Based on this dataset, the models were trained and evaluated with the emphasis made on the correct classification of falls and their differentiation from other movements. From the results presented, all the three models show encouraging signs in fall detection. The PSO-FFNN model, with 98% of accurate optimal rate, however, gave the highest accuracy level. By this result, it is clear that the PSO-FFNN model is highly reliable and accurate in detecting falls. The objective of this research was to develop more precise and credible forecasts of various types of motion turbulence employing the autoencoder’s unsupervised learning and FFFNN’s partitioned classification approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Unsupervised learning

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Innovative Approaches to the Use of Multifunctional Materials in Building Envelopes 7th

Innovative Approaches to the Use of Multifunctional Material...

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7th International Conference on Construction, Real Estate, Infrastructure, and Project Management, ICCRIP 2023

作者： Kumari, Soni Suresh Kumar, R. Abhishek, Kumar Pathak, B.N. Nijhawan, Ginni Khayoon, Ali Talib Rajalakshmi, B. Kalra, Ravi Department of Mechanical Engineering GLA University UP Mathura India Department of Civil Engineering Institute of Aeronautical Engineering Hyderabad500043 India Gujarat Ahmedabad380026 India Department of Mechanical Engineering IMS Engineering College Ghaziabad India Lovely Professional University Panjab Phagwara India College of Engineering Technology National University of Science and Technology Dhi Qar Iraq Department of Computer Science New Horizon College of Engineering Bangalore India Lloyd Institute of Engineering and Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India

ISBN: (纸本)9789819649013

Multifunctional materials have become a hot topic in building envelope design due to their ability to simultaneously perform several functions such as structural support, insulation, energy harvesting, and storage. This paper reviews recent developments in the use of multifunctional materials in building envelopes, including their composition, properties, and potential applications. The paper also examines innovative approaches to integrating these materials into building envelopes to enhance their performance and sustainability. The potential benefits and challenges of using multifunctional materials in building envelopes are discussed, along with future directions for research in this field. However, there are also challenges associated with the use of these materials, including their high cost, complexity of integration, and potential durability issues, which require further research and development. Overall, this review provides insights into the current state and potential of multifunctional materials in building envelopes, highlighting opportunities for future research and application. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Thermal comfort

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