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

作者： Sahana Lokesh, R. Siddaganga Institute of Technology Department of Computer Science and Engineering Karnataka Tumakuru India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505745

Large Language Models (LLMs) have emerged as strategic in the promotion of natural language processing (NLP) to the extent of having machines translate languages as well as making logical deductions. The role of the open-source team in advancing this progress is that LLMs are now available for development and research. This paper offers an elaborate literature review on some of the most widely used open-source LLMs and their architecture, training approach, and evaluation results. Certainly, the mathematical aspects behind these models are explored, the comparison of the models is given and the problems and perspectives of development of the models are described. © 2025 IEEE.

关键词： Natural language processing systems

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TrackNCount - Intelligent Vehicle Tracking, Counting, and Speed Estimation using YOLOv8 and DeepSORT Algorithms 6

TrackNCount - Intelligent Vehicle Tracking, Counting, and Sp...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Sathiyaprasad, B. Reddy, Paluru Ambareesh Ram Venkatesh, Digumalla Manisri Department of Computer Science and Engineering Sathyabama institute of science and technology Chennai India

ISBN: (纸本)9798331522667

For effective city planning and traffic control, it is now crucial to develop some effective monitoring systems for vehicle traffic in order to address this quickly growing tendency within a city. As a result, the TrackNCount project combines DeepSORT with YOLOv8 for object identification to create a smart system for tracking, counting, and speed estimation of vehicles. The idea behind this design is to use sophisticated computer vision techniques in conjunction with efficient algorithms to handle real-world scenarios. Setting up a reliable procedure that recognizes cars, gives them distinct IDs, and records their movements through video frames is our first priority. The system computes vehicle speeds using the Euclidean distance formula, manages item trajectories using double-ended queues, and detects entering and outgoing vehicles based on directional thresholds. Motion trails and bounding boxes are two further visual aids that improve the results' readability. With the added benefit of simple speed estimation feature integration, the model provides exceptional accuracy in real-time vehicle monitoring and counting. Experiments conducted on a variety of traffic scenarios consistently showed that this system can deliver precise vehicle counts in designated directions and speed calculations with a small margin of error. The findings show that TrackNCount is a viable and scalable solution for data-driven city planning and intelligent traffic management. © 2025 IEEE.

关键词： Street traffic control

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Deep analysis of MFCC and MEL spectrogram features to recognize and classify stuttered speech

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Multimedia Tools and Applications 2025年 1-20页

作者： Banerjee, Nilanjan Sethi, Nilambar Borah, Samarjeet Department of Computer Science and Engineering GIET University Odisha India Department of Computer Applications Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim India

Human thoughts, feelings, and ideas are expressed through speech. Stuttering, also referred to as stammering, is an impediment to speech that affects millions of people across the globe. Stuttering speech recognition is a good deal of research within the realm of speech signal processing. Classification of eight different types of stuttering including fluent, interjections, broken words, prolongations, word repetitions, phrase–repetitions, part-word repetitions, and sound repetitions is the main motivation in this research. Stuttering speech recognition is becoming easier with the advancement of machine learning and deep learning. The present study focuses on the efficacy of stuttering speech recognition through the application of machine learning & deep learning. Analysis in this study began with traditional machine learning algorithms, such as Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). It is seen, feature extraction is an essential component in achieving accurate classification for machine learning algorithms. MFCC & MEL spectrogram features are extracted for this experiment. Furthermore, Delta2 MFCC and Delta2 Mel Spectrogram are derived by second-order derivatives on MFCC & Mel Spectrogram features. Therefore, a variety of these features are employed as input to the model. In comparison with all the aforesaid traditional machine learning models, the KNN model results in the highest accuracy of 69.1% based on MFCC-40 input features. To improve accuracy, we move to a deep learning approach. In this experiment, the convolutional neural network (CNN) model produces better outcomes than traditional machine learning. The study presents a comprehensive analysis of the CNN model with MFCC & MEL spectrogram features, as well as its efficiency. It is noted that MFCC-40 input features in CNN model results in the highest accuracy of 89% after 400 epochs. © The Author(s), under exclusive licence to Springer Science+Bus

关键词： Feature Selection

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Enhancing Security in Logic-Locked Systems Through UART-Based Key Authentication

Enhancing Security in Logic-Locked Systems Through UART-Base...

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2025 IEEE International Conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Jaisachin, B. SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512965

The security of integrated circuits is crucial due to their wide range of applications in modern gadgets. An unauthorized access (i.e., third-party access) or a security breach in the logic-locked system can pose a great threat to user privacy, thus leading to data theft and misusing the user data. Several logic locking methods have been proposed, but with the advancements of technology, the latest attack techniques can easily crack those existing locking techniques. Thus, in this research paper we have proposed a sensor-based methodology to detect the unauthorized access with microswitch and UART. In a globalized world where the security of logic-locked systems is really important, this paper empathizes with UART-based mechanisms as a practical solution. By focusing on the existing logic locking methodologies, this paper contributes to the ongoing security efforts for a secured logic locked system. © 2025 IEEE.

关键词： computer circuits

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Multi-View Test-Time Adaptation for Semantic Segmentation in Clinical Cataract Surgery

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IEEE Transactions on Medical Imaging 2025年第5期44卷 2307-2318页

作者： Li, Heng Ou, Mingyang Li, Haojin Qiu, Zhongxi Niu, Ke Fu, Huazhu Liu, Jiang Southern University of Science and Technology Research Institute of Trustworthy Autonomous Systems Shenzhen518055 China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China Beijing Information Science and Technology University Computer School Beijing100192 China Institute of High Performance Computing Agency for Science Technology and Research Fusionopolis Way 138632 Singapore Southern University of Science and Technology Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Shenzhen518055 China

Cataract surgery, a widely performed operation worldwide, is incorporating semantic segmentation to advance computer-assisted intervention. However, the tissue appearance and illumination in cataract surgery often differ among clinical centers, intensifying the issue of domain shifts. While domain adaptation offers remedies to the shifts, the necessity for data centralization raises additional privacy concerns. To overcome these challenges, we propose a Multi-view Test-time Adaptation algorithm (MUTA) to segment cataract surgical scenes, which leverages multi-view learning to enhance model training within the source domain and model adaptation within the target domain. In the training phase, the segmentation model is equipped with multi-view decoders to boost its robustness against variations in cataract surgery. During the inference phase, test-time adaptation is implemented using multi-view knowledge distillation, enabling model updates in clinics without data centralization or privacy concerns. We conducted experiments in a simulated cross-center scenario using several cataract surgery datasets to evaluate the effectiveness of MUTA. Through comparisons and investigations, we have validated that MUTA effectively learns a robust source model and adapts the model to target data during the practical inference phase. © 1982-2012 IEEE.

关键词： Semantic Segmentation

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Tolerant Testing and Distance Estimation for Distributions Under Memory Constraints 50th

Tolerant Testing and Distance Estimation for Distributions...

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50th International Conference on Current Trends in Theory and Practice of computer Science, SOFSEM 2025

作者： Roy, Sampriti Vasudev, Yadu Department of Computer Science and Engineering Indian Institute of Technology Madras Chennai India

ISBN: (纸本)9783031826962

We investigate tolerant testing and distance estimation problems in distribution testing when the amount of memory is limited. In particular, our aim is to provide a good estimate of the distance measure (in total variation distance) using constant memory words. Though the problems are well understood in the standard access model with an Ω(nlogn) lower bound, it has not been explored under memory constraints. We propose algorithms that require O(nϵ7) queries for estimating distance to a known distribution and O(nϵ6) queries (where ϵ∈(0,1) is an error parameter) for estimating distance to uniformity that use only O(1) memory words. Our algorithms match the Ω(nϵ4) lower bound established by [19] in terms of the dependence on n. Additionally, we present an (ϵ,cϵ) tolerant closeness tester in terms of 2 distance for all c≥3, which requires O(n3mϵ2) samples and O(m) bits of memory for logn/ϵ4≤m≤n/ϵ2. Furthermore, we outline an O(n2ϵ12mϵ24) trade-off for (ϵ1,ϵ2) tolerant closeness testing problem (0121 distance, where logn≤m≤nlogn is the available memory in bits. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Distribution testing Property testing Streaming

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Systematic Selection of a Suitable Data Imputation Technique Based on Data Characteristics

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SN computer Science 2025年第5期6卷 1-19页

作者： Sebastian, Anu Maria Peter, David Artificial Intelligence Lab Department of Computer Science Cochin University of Science and Technology Kerala Cochin India Department of Computer Science and Engineering Indian Institute of Information Technology Kottayam India

Analyzing incomplete data is one of the prime concerns in data analysis. Discarding the missing records or values might result in inaccurate analysis outcomes or loss of helpful information, especially when the size of the data is small. A preferable alternative is to substitute the missing values using imputation such that the substituted values are very close to the actual missing values and this is a challenging task. In spite of the existence of many imputation algorithms, there is no universal imputation algorithm that can yield the best values for imputing all types of datasets. This is mainly because of the dependence of the imputation algorithm on the inherent properties of the data. These properties include type of data distribution, data size, dimensionality, presence of outliers, data dependency among the attributes, and so on. In the literature, there exists no straightforward method for determining a suitable imputation algorithm based on the data characteristics. The existing practice is to conduct exhaustive experimentation using the available imputation techniques with every dataset and this requires a lot of time and effort. Moreover, the current approaches for checking the suitability of imputations cannot be done when the ground truth data is not available. In this paper, we propose a new method for the systematic selection of a suitable imputation algorithm based on the inherent properties of the dataset which eliminates the need for exhaustive experimentation. Our method determines the imputation technique which consistently gives lower errors while imputing datasets with specific properties. Also, our method is particularly useful when the real-world data do not have the ground truth for missing data to check the imputation performance and suitability. Once the suitability of a DI technique is established based on the data properties, this selection will remain valid for another dataset with similar properties. Thus, our method can save time an

关键词： Data analysis Data distribution Data imputation Data preprocessing Imputation performance Machine learning Missing value handling

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SPVPC: Smart Contract Based Publicly Verifiable Polynomial Computations 18th

SPVPC: Smart Contract Based Publicly Verifiable Polynomial C...

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18th International Conference on Network and System Security, NSS 2024

作者： Chakraborty, Partha Sarathi Tripathy, Somanath Department of Computer Science and Engineering Indian Institute of Technology Patna Patna India

ISBN: (纸本)9789819635306

The desire to delegate computation to the cloud arises from the proliferation of small, computational restrained devices. However, to ensure the security and privacy of the stored data in the cloud, data owners always encrypt their sensitive data before outsourcing it. Nonetheless, computation outsourcing proves beneficial only when the returned result is trustworthy. Moreover, it must ensure the security and confidentiality of the computation and data during execution. Thus, the challenge lies in efficiently verifying the correctness of computation results generated over encrypted data and maintaining practical computational time for verifying results. Blockchain emerges as a trustworthy entity, enabling fairness and transparency in the scheme. We propose a secure and efficient smart contract based publicly verifiable polynomial computation with an incentive mechanism to protect confidentiality. In the proposed scheme, the smart contract is a verifier that ensures trusted verification and manages incentive transfer. We demonstrate the proposed scheme’s effectiveness through security proofs and performance analysis, significantly reducing verifier computational costs and enhancing overall efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Polynomials

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Natural Language Processing in Geographic Information Systems 3

Natural Language Processing in Geographic Information System...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kushmitha, S. Kathrine, Jaspher W. Abisha, M.B. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798331527549

The integration of Geographic Information Systems (GIS) with Natural Language Processing (NLP) has transformed how spatial data is accessed and utilized. By enabling GIS interfaces to interpret natural language queries, NLP enhances user experience, democratizes geospatial data, and improves query accuracy. This paper reviews state-of-the-art methodologies for geo-parsing, geo-coding, and spatial query interpretation, addressing challenges like geographic ambiguity, imprecise spatial relationships, and scalability limitations. Key contributions include a systematic literature review identifying research gaps such as insufficient context-awareness and a lack of annotated datasets, alongside case studies demonstrating the benefits of NLP integration in applications like disaster management and urban planning. Future opportunities are explored in leveraging deep learning, multimodal data integration, and adaptive user interfaces to further enhance GIS accessibility and usability. Future opportunities are explored in leveraging deep learning for spatial reasoning, integrating multimodal data sources for enhanced decision-making, and developing adaptive user interfaces tailored for diverse audiences. Furthermore, the paper highlights the potential of NLP in generating geo-descriptions for distributed sensor networks, supporting accessibility for impaired users, and enhancing real-time data analysis in smart city initiatives. Addressing these gaps and building scalable frameworks will pave the way for GIS systems that are not only powerful but also inclusive, fostering global accessibility and broader adoption of geospatial technologies. © 2025 IEEE.

关键词： Structured Query Language

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ClipArtGAN: An Application of Pix2Pix Generative Adversarial Network for Clip Art Generation

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Multimedia Tools and Applications 2025年 1-25页

作者： Elnabawy, Reham H. Abdennadher, Slim Hellwich, Olaf Eldawlatly, Seif Technische Universität Berlin Berlin Germany Computer Science and Engineering Department Faculty of Media Engineering and Technology German University in Cairo Cairo Egypt Computer Science Department Faculty of Informatics and Computer Science German International University New Administrative Capital Egypt Computer and Systems Engineering Department Faculty of Engineering Ain Shams University 1 El-sarayat st. Abbassia Cairo Egypt Computer Science and Engineering Department The American University in Cairo Cairo Egypt School of Information Technology New Giza University Giza Egypt

An image can convey a thousand words. This statement emphasizes the importance of illustrating ideas visually rather than writing them down. Although detailed image representation is typically instructive, there are several contexts where simplification of image representations may be more informative. One way to provide an abstract representation of an object is via clip art, which is a simple graphic illustration of an object that can be included in documents or presentations. Clip art representation can be utilized in graphic design for concepts expression and daily life objects illustration. Furthermore, clip art had shown remarkable advantages when compared to real images in terms of editing and manipulation since clip art geometry can be easily edited in addition to the feasibility of storing it in a resolution-independent manner. However, it can be difficult to automatically create clip arts that resemble any object. Here, we propose a novel deep learning model, named ClipArtGAN, that utilizes Generative Adversarial Networks (GANs) to convert a photo of an object to its corresponding clip art. To our knowledge, this is the first GAN model based on Pix2Pix architecture that is capable of automatically generating clip art for any arbitrary object. Three training datasets were collected to train and compare two GAN models based on Pix2Pix and CycleGAN architectures. Our results demonstrate that the mean accuracy obtained using ClipArtGAN utilizing Pix2Pix outperforms that of CycleGAN by 58% and that of Google Images-based search by 54%. These results indicate the efficacy of ClipArtGAN in generating representative clip art images. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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