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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Thamaraiselvi, K. Pushpalatha, A. Chidambarathanu, K. Wankhede, Jaishri P. Alagumuthukrishnan, S. Sarveshwaran, Velliangiri New Horizon College of Engineering Department of Information Science and Engineering Bangalore India Karpagam College of Engineering Department of Computer Science and Design Coimbatore India R.M.K. Engineering College Department of Computer Science and Business Systems Kavaraipettai India Cmr Institute of Technology Department of Computer Science and Engineering Hyderabad India Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India

ISBN: (纸本)9798331504403

Internet of Things (IoT) systems develop with paramount importance for security in view of the increased deployment of IoT devices over critical sectors like healthcare, industry automation, and smart cities. The risk exposure of such systems is extremely high to potent cyber threats, including those that may result in data integrity and privacy issues and operational unreliability. Security in IoT systems has thus emerged through heterogeneous integration with blockchain and artificial intelligence models. This blockchain technology provides a decentralized, immutable ledger for secure data transactions. AI models, especially those designed by anomaly detection and prediction algorithms, can identify and mitigate cyber threats. In this paper, the authors propose the SecureChainAI model that synergistically integrates artificial intelligence algorithms with blockchain to safeguard IoT environments. The two core building blocks that will make this proposed model real are CaSNet and the EchoChain algorithm. The CaSNet model integrates LSTM, RNN, and GRU techniques with novelty detection for proficient attack prediction and detection using their peculiar capabilities in sequential data handling and the identification of novel attack patterns. The EchoChain algorithm puts blockchain technology into the IoT infrastructure to provide security in communication and integrity of the values. This makes the architecture lightweight, shifting computational loads to the collector node, and hence suitable for IoT devices with low computational resources. Evaluations of the performance necessary to detect different classes of cyberattacks using SecureChainAI have improved on the detection accuracy, precision, recall, f1-score, and reduced error rates. © 2024 IEEE.

关键词： Network intrusion

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IoT-enabled Student Attendance System using RISC-V based Embedded System 22

IoT-enabled Student Attendance System using RISC-V based Emb...

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22nd International Learning and Technology Conference, L and T 2025

作者： Khan, Khadija Alzahrani, Nada Abed, Reham Razi, Iman Sofia Dhaou, Imed Ben Dar Al-Hekma University Hekma School of Engineering Computing and Design Department of Computer Science Jeddah22246-4872 Saudi Arabia

ISBN: (纸本)9798331532215

Inefficiencies, cumbersome processes, and a significant risk of errors, delays, and misuse often plague conventional methods for tracking student attendance. To address these challenges, this paper introduces a smart monitoring system driven by the Artificial Intelligence of Things (AIoT), specifically designed for resource-limited embedded processors. This research examines a real-time facial recognition-based student attendance tracking system implemented on an edge device. Additionally, it assesses the effectiveness of ARM and RISC-V microprocessors as the foundation for a reliable edge computing platform. The system employs Python's Dlib library and the ResNet model for effective facial recognition. Comparisons between the Raspberry Pi Model 4 B and the RISC-V StarFive VisionFive2 are made to determine the optimal platform for extensive deployments. The proposed edge system emphasizes security, automation, and reliability, facilitating real-time, precise attendance tracking and significantly improving operational efficiency for educators and administrators. The solution using the RISC-V processor is at least 10 times more energy-efficient than the competing solution based on the RISC processor instruction set architecture. © 2025 IEEE.

关键词： Students

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Optimizing Railway Traffic Management Using Fuzzy Logic-Based Decision-Making Systems 2

Optimizing Railway Traffic Management Using Fuzzy Logic-Base...

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Kumari, Nalli Vinaya Pradeep Ghantasala, G.S. Sharma R, Rajesh Ananthanagu, U. Vidyullatha, Pellakuri Pandiya, Dileep Kumar Jntuh Malla Reddy Engineering College for Women Department of Computer Science and Engineering Hyderabad India Alliance University Alliance College of Engineering and Design Department of Computer Science and Engineering Bengaluru India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India Zoominfo Technologies Principl Software Engineer Boston United States

ISBN: (纸本)9798350364699

Railway web page traffic manipulate is a complex and dynamic method that includes making real-time picks based mostly on various uncertain and difficult to understand factors, collectively with fluctuating passenger demand, diverse train schedules, and unpredictable weather conditions. Traditional decision-making strategies often battle to cope with the ones uncertainties, foremost to inefficiencies and potential safety risks. This paper proposes the software program of fuzzy properly judgment-based completely decision-making systems to optimize railway web page traffic manipulate. Fuzzy properly judgment, with its potential to model uncertainty and control difficult to understand data, offers a robust framework for boosting decision-making approaches in railway operations. By simulating various conditions and integrating real-time data, the proposed gadget can dynamically regulate train schedules, manipulate delays, and enhance everyday operational efficiency. The paper may present case studies demonstrating the effectiveness of fuzzy properly judgment in dealing with railway web page traffic, reducing delays, and improving passenger satisfaction. The findings advice that incorporating fuzzy properly judgment into railway web page traffic manipulates systems can purpose greater adaptive and resilient railway operations, ultimately contributing to greater steady and additional reliable rail networks. © 2024 IEEE.

关键词： Efficiency

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A Review of Advanced Technologies for Intelligent Sensing, Diagnostics, and Personalized Healthcare Solutions 4

A Review of Advanced Technologies for Intelligent Sensing, D...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Ghantasala, G. S. Pradeep Rajesh Sharma, R. Sungheetha, Akey Anitha, P. Selvalakshmi, B. Pradeepa, K. Alliance College of Engineering and Design Alliance University Department of Computer Science and Engineering Karnataka Bengaluru India Sri Sai Renganathan Engineering College Department of Ece Tamil Nadu Coimbatore India Tagore Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India St. Michael College of Engineering & Technology Department of Computer Science and Engineering Tamil Nadu Sivaganga India

ISBN: (纸本)9798331529635

Intelligent Health is a revolutionary approach to modern healthcare that uses the Internet of Things (IoT), artificial intelligence (AI), and sensor technologies to transform patient-centric solutions. This study examines how these technologies are combining to change patient monitoring, illness management, personalized treatment, and healthcare delivery. Wearable sensors, medical equipment, and IoT-enabled infrastructure allow for real-time, continuous monitoring of patient health data, enabling the early identification of anomalies and the implementation of personalized interventions. IoT platforms enable remote connectivity and data-driven insights by easily integrating sensor data into healthcare systems. AI algorithms analyses large amounts of sensor data to maximize clinical decision-making, forecast health outcomes, and extract insightful information. Intelligent health solutions offer many advantages, such as better patient outcomes and a better patient experience. However, to fully utilize these technologies, issues like data privacy, interoperability, and regulatory compliance need to be resolved. Future directions in intelligent health include the establishment of standards and regulatory frameworks, patient-centric design, integration with electronic health records, and advances in AI and machine learning. With individualized, data-driven solutions, intelligent health holds great promise for revolutionizing the delivery of healthcare and enabling people to take charge of their own health and well-being © 2024 IEEE.

关键词： Electronic health record

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Breast-NET: a lightweight DCNN model for breast cancer detection and grading using histological samples

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Neural Computing and Applications 2024年第32期36卷 20067-20087页

作者： Saha, Mousumi Chakraborty, Mainak Maiti, Suchismita Das, Deepanwita Department of Computer Science and Engineering National Institute of Technology West Bengal Durgapur713209 India School of Innovation Design and Engineering Division of Intelligent Future Technologies Malardalen University Västerås Sweden Department of Information Technology Narula Institute of Technology West Bengal Kolkata700109 India Department of Computer Science and Engineering Narula Institute of Technology West Bengal Kolkata700109 India

Breast cancer is a prevalent and highly lethal cancer affecting women globally. While non-invasive techniques like ultrasound and mammogram are used for diagnosis, histological examination after biopsy is considered the gold standard. However, manual examination of tissues for abnormality is labor-intensive, expensive, and requires prior domain knowledge. Early detection, awareness, and access to specialized medical infrastructure in resource-constrained and remote areas are significant challenges but crucial for saving lives. In recent years, deep learning-based approaches have shown promising results in breast cancer detection, facilitated by advancements in GPU memory, computation power, and the availability of digital data. Motivated by these observations, we propose the Breast-NET deep convolutional neural network model for breast cancer detection and grading using histological images. Our model’s performance is evaluated on the BreakHis dataset, and we demonstrate its generalization ability on the Invasive Ductal Carcinoma (IDC) grading and IDC datasets. Extensive experimental and statistical performance analysis, along with an ablation study, validates the efficiency of our proposed model. Furthermore, we demonstrate the effectiveness of transfer learning with seven pre-trained convolutional neural networks for breast cancer detection and grading. Experimental results show that our framework outperforms state-of-the-art approaches in terms of accuracy, space, and computational complexity for the BreakHis, IDC grading, and IDC datasets. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Grading

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Genre Classification of Movie Trailers Using Audio and Visual Features: A Comparative Study of Machine Learning Algorithms 3rd

Genre Classification of Movie Trailers Using Audio and Visua...

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3rd International Semantic Intelligence Conference, ISIC 2023

作者： Vanarote, Viresh Chandre, Pankaj Mande, Uday Shafi, Pathan Mohd Dhotre, Dhanraj Nimbalkar, Madhukar Department of Computer Science and Engineering MIT School of Computing MIT Art Design and Technology University Loni Kalbhor Pune India

ISBN: (纸本)9789819773558

Movie trailers are a crucial marketing tool for the film industry and are often used to generate audience interest and anticipation. Automatic genre classification of movie trailers can assist filmmakers in targeting their intended audience and help viewers in deciding which films to watch. This research paper aims to investigate the effectiveness of various machine learning algorithms for the classification of movie genres based on audio and visual features extracted from movie trailers. We compare the performance of several classifiers, including Support Vector Machines (SVM), Random Forest (RF), Naive Bayes (NB), and K- Nearest Neighbors (KNN) on a dataset of movie trailers belonging to five different genres—action, comedy, drama, horror, and thriller. We extract both audio and visual features from the trailers, including spectrogram features, pitch, loudness, brightness, contrast, and color histograms. We then use these features to train and evaluate the different classifiers. Moreover, we observed that combining both audio and visual features improves the overall accuracy of genre classification. Our study contributes to the field of movie genre classification by providing a comparative analysis of different machine learning algorithms for the classification of movie trailers based on both audio and visual features. The findings of this research can be applied in various domains, such as movie recommendation systems, marketing strategies, and content analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decision trees

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Knowledge-augmented Financial Market Analysis and Report Generation

Knowledge-augmented Financial Market Analysis and Report Gen...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Chen, Yuemin Wu, Feifan Wang, Jingwei Qian, Hao Liu, Ziqi Zhang, Zhiqiang Zhou, Jun Wang, Meng College of Design and Innovation Tongji University China Ant Group China School of Computer Science and Engineering Southeast University China

ISBN: (纸本)9798891761667

Crafting a convincing financial market analysis report necessitates a wealth of market information and the expertise of financial analysts, posing a highly challenging task. While large language models (LLMs) have enabled the automated generation of financial market analysis text, they still face issues such as hallucinations, errors in financial knowledge, and insufficient capability to reason about complex financial problems, which limits the quality of the generation. To tackle these shortcomings, we propose a novel task and a retrieval-augmented framework grounded in a financial knowledge graph (FKG). The proposed framework is compatible with commonly used instruction-tuning methods. Experiments demonstrate that our framework, coupled with a small-scale language model fine-tuned with instructions, can significantly enhance the logical consistency and quality of the generated analysis texts, outperforming both large-scale language models and other retrieval-augmented baselines. © 2024 Association for Computational Linguistics.

关键词： Decentralized finance

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Overcoming Spatial Constraints in VR:A Survey of Redirected Walking Techniques

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Journal of computer science & Technology 2024年第4期39卷 841-870页

作者： Jia-Hong Liu Yang-Fu Ren Qi Wen Gan Kui Huang Fiona Xiao Yu Chen Er-Xia Luo Khang Yeu Tang Yue-Yao Fu Cheng-Wei Fan Sen-Zhe Xu Song-Hai Zhang Academy of Arts and Design Tsinghua UniversityBeijing 100084China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China College of Computer Technology and Application Qinghai UniversityXining 810016China Zhili College Tsinghua UniversityBeijing 100084China School of Computer Beijing Information Science and Technology UniversityBeijing 100192China Department of Physics Tsinghua UniversityBeijing 100084China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China

As the virtual reality(VR)technology strives to provide immersive and natural user experiences,the challenge of aligning vast virtual environments with limited physical spaces remains *** survey comprehensively explores the advancements in redirected walking(RDW)techniques aimed at overcoming spatial constraints in *** addresses this by subtly manipulating users’physical movements to allow for seamless navigation within constrained *** survey delves into gain perception mechanisms,detailing how slight discrepancies between virtual and real-world movements can be utilized without user awareness,thus extending the effective navigable *** RDW control algorithms for gain-based RDW are analyzed,highlighting their implementation and effectiveness in maintaining immersion and minimizing perceptual ***,novel methods extending beyond traditional gain-based techniques are discussed,showcasing innovative approaches that further refine VR *** practical implications of RDW in enhancing safety and reducing physical collisions in VR environments are underscored,alongside its potential to improve user experience by aligning virtual exploration more closely with natural human behavior *** a thorough review of existing literature and recent advancements,this survey provides a systematic understanding for researchers,developers,and industry *** underscores the importance of RDW in the future of VR,emphasizing RDW's role in making VR more accessible and practical across various applications,from education and training to therapy and *** paper concludes with a forward-looking perspective on the continued evolution and potential of RDW in revolutionizing virtual reality experiences.

关键词： virtual reality(VR) redirected walking(RDW) redirection gain perception

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Enhancing structural health monitoring with AI-ML algorithms: a focus on crack detection and prediction

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Asian Journal of Civil engineering 2025年第5期26卷 1907-1918页

作者： Bader, Ahmad Shtayat, Amir Al-Mistarehi, Bara’ Communication and Computer Department Faculty of Engineering Jadara University Irbid Jordan Department of City Planning and Design Jordan University of Science and Technology Irbid 22110 Jordan Department of Civil Engineering Jordan University of Science and Technology Irbid Jordan

SHM is a very important process in terms of the safety and durability of infrastructure. Traditional SHM often faces problems detecting minor structural defects and handling large datasets. Therefore, certain more advanced approaches are called for. The paper discussed the applications of AI and ML algorithms, such as CatBoost and the African Vultures Optimization Algorithm, for such challenges. The research is based on a unique dataset of 8,541 rows and diverse features, developing a predictive framework that enhances crack detection and forecast capabilities. The approach mainly deals with heterogeneous data using the CatBoost algorithm, given its capability for high-accuracy predictions, while AVOA optimizes feature selection, reduces the computational cost, and guarantees no loss in model performance. This methodology has resulted in a significant enhancement of the prediction accuracy, which states the importance of AI-ML integration in SHM. The key results demonstrate the effectiveness of the model in detecting structural anomalies and crack propagation to enable proactive maintenance strategies. This study’s contributions have gone toward advancing SHM with scalable and efficient AI-ML frameworks, enabling real-time monitoring for better infrastructure management. Such development might have a transforming potential to cut down on maintenance costs and enhance operational safety, thus further encouraging sustainable infrastructure systems. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： African vultures optimization algorithm CatBoost Crack Detection Feature selection Machine learning Structural health monitoring

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Enhanced Speech Emotion and Gender Recognition Using Hybrid CNN-LSTM Models 4

Enhanced Speech Emotion and Gender Recognition Using Hybrid ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Priyadharshini, S. Sundar, G. Naveen Narmadha, D. Thangavel, Senthil Kumar Ajibesin, Adeyemi Abel Kolandapalayam Shanmugam, Selvanayaki Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Cape Peninsula University of Technology Faculty of Informatics and Design Department of Information Technology South Africa Ashland University Department of Mathematics and Computer Science United States

ISBN: (纸本)9798331523923

The current paper proposes a new approach for peripheral speech emotion analysis and gender estimation incorporating the best machine learning architectures such as CNNs and LSTMs. Its correct depiction of emotions and the variation of the gendered speech to indicate manifold signal-to-noise ratio make the approach accommodate the difficulty inherent in recognizing emotions from the recording of voices as well as predicting the gender of a speaker. It is then able to ally with preprocessing and collection of a vast range of different speech samples, feature extraction of pitch, tone and spectral data. These features serve as input to the CNNs and LSTMs: LSTMs work well at temporal regular features and the CNNs work well in feature extraction by location (spatio feature). The approach is tested on the RAVDESS and IEMOCAP datasets and establishes a near perfect match to prior approaches with reference to recognition accuracy of emotion analysis and gender prediction. Thus, applying the identified dual method approach, which could be described by such characteristics as highly accurate data pre-preprocessing, clear indication of the features of interest, a different CNN-LSTM model joint training, it offers a reasonably good platform to gain certain insights into such characteristics of the speech as our experiment demonstrates. This work offers satisfactory configurations on concerns such as human computer relation, customer relation and mental health surveillance to suggest that other applications could be achieved in future. © 2025 IEEE.

关键词： Convolutional neural networks

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