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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Pabitha, C. Joshua, K. Paul Pradeep, S. Sophia, S. Sharma, R. Rajesh Prabhu, D. Karthik Srm Valliammai Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Scad College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Tirunelveli India MAlla Reddy Engineering College for Women Department of Computer Science and Engineering Telangana Secunderabad India Karunya Institute of Technology and Sciences Department of computer Science and Engineering Tamil Nadu Coimbatore India Alliance University Department of Computer Technology Alliance College of Engineering and Design Bangalore India Ramco Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu Rajapalaiyam India

ISBN: (纸本)9798331509828

Drones are essential for civil engineering operations like logistics and data collecting. Current autonomous drone studies mainly concerns itself with safe path planning in static scenarios;however one of the major challenges of over urban environment is often dynamic factors such as wind, building layouts, and signal coverage that can significantly impact direction stability. For autonomous navigation across dynamic wind zones, this study proposed a new Shape-aware Mesh Dual Lightweight Deep Convolutional Multi-Relational Graph Attention Network with Growth Optimizer (SAMDLDCMRGANet-GO) that leverages local data from RGB cameras and GPS. The method incorporates a Dual Aggregation Transformer for feature extraction, Shape-aware Mesh Normal Filtering for preprocessing, and the LDCMRGAN et for efficient path planning. The proposed approach demonstrates its efficacy in navigating complicated surroundings while retaining high precision in obstacle avoidance and target acquisition, achieving an impressive 99.14% recall, 99.15% accuracy, an outstanding EVA of 0.99, and a low RMSE of 0.41. © 2025 IEEE.

关键词： Mesh generation

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Secure-by-Construction Synthesis for Control Systems

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IEEE Transactions on Automatic Control 2025年第6期70卷 4170-4177页

作者： Zhong, Bingzhuo Liu, Siyuan Caccamo, Marco Zamani, Majid Thrust of Artificial Intelligence Information Hub The Thrust of Intelligent Transportation System Hub China KTH Royal Institute of Technology Division of Decision and Control Systems Stockholm Sweden Technical University of Munich TUM School of Engineering and Design Germany University of Colorado Boulder Department of Computer Science United States Ludwig Maximilian University Department of Computer Science Munich Germany

In this note, we present the synthesis of secure-by-construction controllers that address safety and security properties simultaneously in cyber-physical systems. Our focus is on studying a specific security property called opacity, which characterizes the system's ability to maintain plausible deniability of its secret behavior in the presence of an intruder. These controllers are synthesized based on a concept of so-called (augmented) control barrier functions. We propose conditions that facilitate the construction of the desired (augmented) control barrier functions and their corresponding secure-by-construction controllers. To compute these functions, we propose an iterative scheme that leverages iterative sum-of-square programming techniques. This approach enables efficient computation of these functions, particularly for polynomial systems. Finally, we validate the effectiveness of our results through a case study of a vehicle. © 1963-2012 IEEE.

关键词： Iterative methods

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Beyond Playlists: AI-Driven Emotion-Based Music Recommendation Systems 2

Beyond Playlists: AI-Driven Emotion-Based Music Recommendati...

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

作者： Shiralaskar, Hrishikesh Mendhe, Sameer Datta Meghe Institute of Higher Education md Research Department of Computer Science & Engineering Faculty of Engineering & Technology Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Department of Computer Science & Design Faculty of Engineering & Technology Maharashtra India

ISBN: (纸本)9798331505745

Music recommendation systems have evolved from simple playlist curation techniques to sophisticated AI-driven models capable of analyzing human emotions for personalized song suggestions. Emotion-based music recommendation systems integrate advanced machine learning, deep learning, and artificial intelligence techniques to detect users' emotional states through various input modes, including facial expressions, speech tone, text analysis, and physiological signals. These systems aim to enhance user experience by dynamically selecting music that aligns with real-time emotions, thereby improving mood regulation and mental well-being. This review explores the methodologies employed in emotion-based music recommendation systems, including deep learning architectures, facial recognition techniques, and sentiment analysis models. It also discusses the challenges associated with real-time emotion detection, data privacy concerns, cross-cultural differences in music perception, and system adaptability. Furthermore, the paper highlights emerging trends and future research directions, such as multimodal emotion detection, hybrid AI models, reinforcement learning, and ethical AI frameworks. Addressing these challenges and advancements will be crucial in developing more robust, accurate, and user-centric emotion-based music recommendation systems. © 2025 IEEE.

关键词： Deep learning

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Real-Time Facial Recognition System for Secure College Bus Transport using Deep Learning Techniques 3

Real-Time Facial Recognition System for Secure College Bus T...

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

作者： Kavitha, M.N. Saranya, S.S. Pragatheeswari, E. Priya, N. Kaviya Ranjith, S.R. Vanchivel, N. Rahul Kongu Engineering College Department of Computer Science and Design Tamil Nadu India Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Tamil Nadu Coimbatore India Kongu Engineering College Department of Computer Science and Engineering Tamil Nadu Erode India Kongu Engineering College Department of Csd Tamil Nadu India

ISBN: (纸本)9798331509675

The proposed system aims to enhance student transportation security through real-time face detection and recognition. Leveraging the MTCNN framework for accurate face detection and the FaceNet model for reliable face recognition, the system ensures only authorized students can board designated buses by comparing captured faces with a pre-registered database. In case of mismatches, alerts are triggered to notify drivers and authorities. Additionally, parallel implementation of Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers improves authentication accuracy. The system's deep learning-based architecture ensures robust performance under varying environmental conditions and supports continuous learning for long-term reliability. © 2025 IEEE.

关键词： Students

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Intelligent Guitar Chord Recognition Using Spectrogram-Based Feature Extraction and AlexNet Architecture for Categorization

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International Journal of Advanced computer science and Applications 2025年第4期16卷 758-767页

作者： Korade, Nilesh B. Salunke, Mahendra B. Bhosle, Amol A. Sangve, Sunil M. Joshi, Dhanashri M. Asalkar, Gayatri G. Kadu, Sujata R. Sarwade, Jayesh M. Department of Computer Engineering JSPM’s Rajarshi Shahu College of Engineering Tathawade Maharashtra Pune411033 India Department of Computer Engineering PCET’s Pimpri Chinchwad College of Engineering and Research Ravet Maharashtra Pune412101 India Department of Computer Science and Engineering School of Computing MIT Art Design and Technology University Loni Kalbhor Maharashtra Pune412201 India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Technology Bibwewadi Pune411037 India Department of Computer Engineering Terna Engineering College Maharashtra Mumbai India Department of Information Technology JSPM’s Rajarshi Shahu College of Engineering Tathawade Maharashtra Pune411033 India

Chord prediction plays a key role in the advancement of musical technological innovations, such as automatic music transcription, real-time music tutoring, and intelligent composition tools. Accurate chord prediction can assist musicians, educators, and developers in constructing tools that help in learning, playing, and composing music. Background noise and audio distortions may have an impact on chord prediction accuracy, particularly in real-world situations. Chords can have distinct voicings or finger positions on the guitar, resulting in slight variations in audio representation. This study focuses on the classification of guitar chords using techniques of deep learning. There are eight major and minor guitar chords in the dataset. They have been turned into spectrograms, chromagrams, and Mel Frequency Cepstral Coefficients (MFCC) so that features can be extracted. Various deep learning architectures, including CNN, ResNet50, AlexNet, and VGG, were employed to classify the chords. Experimental results demonstrated that the spectrogram-based AlexNet model outperforms others, achieving good accuracy and robustness in chord classification. The proposed study demonstrates the efficiency of spectrograms and advanced deep learning models for audio signal processing in music applications. By automating chord detection, this study provides beneficial resources for music learners as well as educators, enabling more efficient learning and real-time feedback during practice sessions. © (2025), (science and Information Organization). All Rights Reserved.

关键词： Deep learning

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Security Key Generation in SRAM based PUF for Cryptographic Applications 5

Security Key Generation in SRAM based PUF for Cryptographic ...

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5th IEEE International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Raikwal, Pushpa Gupta, Nipun Agarwal, Nikhil Maheshwari, Mahima Dayal, Neelam Pdpm Indian Institute of Information Technology Design and Manufacturing Department of Electronics and Communication Engineering Madhya Pradesh Jabalpur482005 India Pdpm Indian Institute of Information Technology Design and Manufacturing Department of Computer Science and Engineering Madhya Pradesh Jabalpur482005 India

ISBN: (纸本)9798350356236

This work meticulously focusses on SRAM designing and to verify its functionality as a PUF. It exploits the inherent manufacturing variations present in SRAM cells to generate unique start-up values, then utilizes them to producea cryptographic key. The design process induced mismatches in the inverters to enhance variability in the SRAM cells. Further this work focused on generating start-up values of SRAM based PUF in three methods: ideal state, practical state and by analyzing PUF Static Noise Margin (PSNM). Through these simulations, it is confirmed that the start-up values of the SRAM cells could be effectively utilized to generate a unique cryptographic key. All design and simulation tasks were conducted using Cadence Virtuoso, a sophisticated tool for integrated circuit design and verification at 90nm CMOS technology node. © 2025 IEEE.

关键词： CMOS integrated circuits

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A Unified Job Scheduler for Optimization of Different System Performance Metrics

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Concurrency and Computation: Practice and Experience 2025年第12-14期37卷

作者： Mayank, Jaishree Mondal, Arijit Department of Computer Science and Engineering Indian Institute of Information Technology Design and Manufacturing Kancheepuram India Department of Computer Science and Engineering Indian Institute of Technology Patna Bihar India

Internet-of-Things-enabled frameworks have eased the development of complex systems, but they throw a significant challenge for efficient resource utilization, thereby improving the system performance. An intelligent scheduler is essential for managing the resources and allocating the same resources to different requests or tasks. This work proposes a generic methodology to optimize system performance metrics such as throughput, utilization, and reward achieved. We present an integer linear programming formulation of the problem to find an optimal solution. We present offline heuristic methods to quickly find reasonable solutions, given the intractable nature of the problem. These heuristics yield promising outcomes, with deviations from optimal solutions below 20% in scenarios with task overlap and high utilization. In scenarios with minimal overlap and utilization, deviations remain under 10%. However, as variables and constraints increase in ILP, the demand for time and memory resources rises substantially. We conduct a comparative analysis of heuristic performance across various scenarios and large test cases. Additionally, we extend our methods to handle resources in online mode, presenting an extensive comparative study with encouraging results. © 2025 John Wiley & Sons Ltd.

关键词： Linear programming

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Dysarthric speech recognition: an investigation on using depthwise separable convolutions and residual connections

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Neural Computing and Applications 2025年第12期37卷 7991-8005页

作者： Shahamiri, Seyed Reza Mandal, Krishnendu Sarkar, Sudeshna DeepNet Discovery Network Faculty of Engineering and Design University of Auckland Private Bag 92019 Building 405 Level 6 Room 669 3 Garfton Road Auckland1142 New Zealand Computer Science and Engineering Department Indian Institute of Technology Kharagpur India

Dysarthria is a neurological condition resulting from impairments affecting muscle control involved in speech articulation, leading to reduced intelligibility or unintelligible speech, which affects communication abilities. Although Automatic Speech Recognition (ASR) technologies hold the potential to improve the lives of people with dysarthria significantly, ASR systems designed for normal speech have shown limited effectiveness when presented with impaired speech. Consequently, researchers have focused on developing ASR systems specifically tailored for dysarthria. However, progress in this area has been gradual due to the scarcity of dysarthric speech for training and the increased variability of speech among dysarthric individuals, necessitating a larger dataset of dysarthric utterances. One potential solution to enhance the robustness of dysarthric ASR is to deepen the architecture of the acoustic model, which maps the speech signal to words or phonetic units. However, deeper architectures require more training data and pose challenges in dealing with issues such as the vanishing gradient problem and representational bottlenecks in deep learning models. In this study, we expanded on our previous findings and investigated the applications of Depthwise Separable Convolution neurons and the inclusion of Residual Connections to propose a deep dysarthric acoustic model, tackling both vanishing gradients and representational bottleneck issues in dysarthric ASR. Multiple speaker-adaptive dysarthric ASRs were trained and evaluated for 15 UA-Speech dysarthric subjects, then benchmarked against the state-of-the-art and our previous dysarthric ASRs. Our proposed architectures have delivered up to 22.58% word recognition rate (WRR) improvements over the reference models. We observed an average of 10.81% better WRRs over the base traditional dysarthric ASR for all speakers. Likewise, the proposed acoustic model outperformed the state-of-the-art Transformer-based dysarthric

关键词： Convolution

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Wearable Biosensors: Transformative Innovations and Future Pathways for Continuous Health Monitoring 2

Wearable Biosensors: Transformative Innovations and Future P...

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

作者： Thute, Trupti Gudadhe, Amit Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Department of Artificial Intelligence and Data Science Maharashtra Wardha India Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Department of Computer Science and Design Maharashtra Wardha India

ISBN: (纸本)9798331505745

Wearable biosensors are revolutionizing healthcare by enabling continuous monitoring of health status, transforming it from an event-based process to an ongoing one. When integrated with AI, nanotechnology, and IoMT (Internet of Medical Things), these devices enhance precision care, telediagnosis, and chronic disease management. Advancements in electrochemical, optical, and biochemical sensors, along with innovations in energy harvesting and miniaturized flexible materials, improve both the performance and comfort of these devices. Wearable biosensors are now crucial for everyday fitness, disease prediction, and the seamless exchange of data between m-health apps and EHRs. Despite these advancements, challenges remain, such as data security, system integration, and user incentives. Addressing these concerns through enhanced security features, standardized protocols, and user-focused designs is critical. The future development of wearable biosensors is aimed at leveraging cost-effective energy sources, utilizing AI for data analysis, and improving user convenience, making significant strides in preventive and personalized healthcare. © 2025 IEEE.

关键词： mHealth

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Comprehensive Analysis of Machine Learning Techniques for Crack Detection

Comprehensive Analysis of Machine Learning Techniques for Cr...

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International Conference on Technological Innovations in Multidisciplinary Engineering and sciences, TIMES 2024

作者： Arpitha, L.M. Kumar, R. Anil Fathima, Zoya Yeshaswini, R. Dhanyashree, G. Roshini, D. M S Ramaiah Institute of Technology Karnataka Bengaluru India Department of Civil Engineering M S Ramaiah Institute of Technology Karnataka Bengaluru India TCSiON Tata Consultancy Services Karnataka Bengaluru India Department of Computer Science and Design BGS College of Engineering and Technology Karnataka Bengaluru India

ISBN: (纸本)9783031837494

Crack detection is critical to the safety and performance of engineering structures including highways, bridges, and buildings. This paper reviews the different types of image processing techniques for crack detection which includes traditional, advanced and machine learning based methods. One of the sixty works chosen and written from 1999–2023. The results indicate that machine learning is preferred simply because it can be more precise, given its automated nature. The paper reviews different methodologies applied, their performances based on the datasets employed as well as limitations governing one approach over another to realize real-time crack detection. It also highlights the need for standardized methods of categorizing and measuring cracks, as well as efficient ways to proceed in many different situations. Specifically, the study addresses potential benefits and limitations of using machine learning in infrastructure management with respect to data-driven approaches linked to big data sets as well as computing costs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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