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Intelligent Control, computing and Communications (IC3), International Conference on

作者： M. Karthikeyan Pravin Ramdas Kshirsagar Tan Kuan Tak Keshav Lalit Kamal Upreti Rituraj Jain Dept. of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai India Department of Electronics Engg. J.D College of Engg & Mgmt. Nagpur MS India Engineering Cluster Singapore Institute of Technology Singapore Dept. of Computer Science & Engineering Manipal Institute of Technology Manipal Karnataka India Department of Computer Science Christ University Delhi NCR Ghaziabad India Department of Info. Tech. Marwadi University Rajkot Gujarat India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

computer vision-based surveillance is very important today's security systems to detect, track and regulate the security much better than standard cameras. However, like any other performance measurement systems they have potential pitfalls and technical, ethical, and legal implications must be well understood. The continuous rise in connection and interaction implies that safety of the public especially when navigating roads or operating in public domains is paramount. The conventional approaches to accident identification include observation or reporting from witnesses and always record slow and imprecise outcomes. With the improvement of AI and computer visions, especially with deep learning models such as YOLO, accident detection is changing. YOLO v10 which is incorporated in the surveillance systems, performs real time video analysis to provide object and pattern recognition of accidents including car accidents and incidents involving the pedestrians. When applied to the initial set of annotated accident images, the fine-tuning of the YOLO v10 model enhances its detection capability. The system is in watching the video frames that contain aberrations and issues and alarms are issued when the accidents happen and relayed to the monitoring stations or emergency departments for proper response. The optimized YOLOv10 here delivers a meaningful testing score of 72.3% mAP to outperform the regular YOLOv10 efficiency in incident detection.

关键词： YOLO Deep learning computer vision Surveillance Computational modeling Streaming media Cameras Real-time systems Time factors Accidents

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Explainable AI in 6G O-RAN: A Tutorial and Survey on Architecture, Use Cases, Challenges, and Future Research

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IEEE Communications Surveys and Tutorials 2024年

作者： Brik, Bouziane Chergui, Hatim Zanzi, Lanfranco Devoti, Francesco Ksentini, Adlen Siddiqui, Muhammad Shuaib Costa-Perez, Xavier Verikoukis, Christos University of Sharjah Computer Science Department College of Computing and Informatics United Arab Emirates 2CAT Foundation Barcelona Spain NEC Laboratories Europe Heidelberg69115 Germany EURECOM France NEC Laboratories Europe ICREA Barcelona08034 Spain University of Patras ATHENA/ISI IQUADRAT Informatica Barcelona08006 Spain

The recent o-ran specifications promote the evolution of ranran architecture by function disaggregation, adoption of open interfaces, and instantiation of a hierarchical closed-loop control architecture managed by ric entities. This paves the road to novel data-driven network management approaches based on programmable logic. Aided by ai and ml, novel solutions targeting traditionally unsolved ran management issues can be devised. Nevertheless, the adoption of such smart and autonomous systems is limited by the current inability of human operators to understand the decision process of such ai/ml solutions, affecting their trust in such novel tools. xai aims at solving this issue, enabling human users to better understand and effectively manage the emerging generation of artificially intelligent schemes, reducing the human-To-machine barrier. In this survey, we provide a summary of the xai methods and metrics before studying their deployment over the o-ran Alliance ran architecture along with its main building blocks. We then present various use-cases and discuss the automation of xai pipelines for o-ran as well as the underlying security aspects. We also review some projects/standards that tackle this area. Finally, we identify different challenges and research directions that may arise from the heavy adoption of ai/ml decision entities in this context, focusing on how xai can help to interpret, understand, and improve trust in o-ran operational networks. © 1998-2012 IEEE.

关键词： Information management

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AdCare-VLM: Leveraging Large Vision Language Model (LVLM) to Monitor Long-Term Medication Adherence and Care

arXiv

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arXiv 2025年

作者： Jabin, Md. Asaduzzaman Jiang, Hanqi Li, Yiwei Kaggwa, Patrick Douglass, Eugene Sekandi, Juliet N. Liu, Tianming School of Electrical and Computer Engineering College of Engineering University of Georgia AthensGA30602 United States School of Computing College of Engineering University of Georgia AthensGA30602 United States Department of Epidemiology and Biostatistics College of Public Health University of Georgia AthensGA30602 United States Department of Pharmaceutical and Biomedical Sciences College of Pharmacy University of Georgia AthensGA30602 United States Global Health Institute College of Public Health University of Georgia AthensGA30602 United States

Chronic diseases, including diabetes, hypertension, asthma, HIV/AIDS, epilepsy, and tuberculosis, necessitate rigorous adherence to medication to avert disease progression, manage symptoms, and decrease mortality rates. Adherence is frequently undermined by factors including patient behavior, caregiver support, elevated medical costs, and insufficient healthcare infrastructure. We propose AdCare-VLM, a specialized Video-LLaVA-based multimodal large vision language model (LVLM) aimed at visual question answering (VQA) concerning medication adherence through patient videos. We employ a private dataset comprising 806 custom-annotated tuberculosis (TB) medication monitoring videos, which have been labeled by clinical experts, to fine-tune the model for adherence pattern detection. We present LLM-TB-VQA, a detailed medical adherence VQA dataset that encompasses positive, negative, and ambiguous adherence cases. Our method identifies correlations between visual features—such as the clear visibility of the patient’s face, medication, water intake, and the act of ingestion—and their associated medical concepts in captions. This facilitates the integration of aligned visual-linguistic representations and improves multimodal interactions. Experimental results indicate that our method surpasses parameter-efficient fine-tuning (PEFT) enabled VLM models, such as LLaVA-V1.5 and Chat-UniVi, with absolute improvements ranging from 3.1% to 3.54% across pre-trained, regular, and low-rank adaptation (LoRA) configurations. Comprehensive ablation studies and attention map visualizations substantiate our approach, enhancing interpretability. © 2025, CC BY-NC-ND.

关键词： Fetal monitoring

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3DSC2-UNet: Advancing Breast Cancer Segmentation Techniques Using the 3D Skipped Convolution Capsule-UNet

3DSC2-UNet: Advancing Breast Cancer Segmentation Techniques ...

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Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT), International Conference on

作者： Parthasarathy Ramadass C. Sharmila Rakesh Kumar Mahendran Hema V E. Kavitha R Sanmugasundaram Department of Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information and Technology Dr. M.G.R. Educational and Research Institute Chennai India Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering School of Computing Sathyabama Institute of Science and Technology Chennai Department of Physics Dr.M.G.R Educational and Research Institute Chennai Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540692

Breast cancer (BC) persists as a substantial global health hurdle, demanding ongoing progress in the realms of detection, diagnosis, and therapeutic approaches. This thorough examination delves into cutting-edge technologies and methodologies that have surfaced in the quest for enhanced results in BC management. Yet, investigations employing solitary imaging modalities have showcased restricted clinical effectiveness owing to their relatively diminished diagnostic precision and dependability when compared to multimodal systems. As a result, our aim is to create a 3D Skipped Convolution Capsule UNet designed for the segmentation of breast tumors, employing both mammogram and ultrasound imaging modalities. For the integration of these modalities, we utilize the Dynamic Fusion Net (DF-Net), incorporating two modules named the Molecular-Level Aligner Module and Biphasic Sequential Integration Module (BSInU). The process of segmentation takes place within our innovative framework, 3DSC2-UNet, encompassing four primary elements: (a) Graphical feature extraction, (b) Convolutional Capsule (ConvCaps) encoder module, (c) Synaptic skip connection, and (d) Convolutional decoder (Conv Decoder). The overall accuracy attained is 99.97%, accompanied by a Dice Similarity Coefficient (DSC) of 0.89 and an Average Surface Distance (ASD) of 2.5mm across the mammogram dataset. Moreover, the precision stands at 99.65%, accompanied by a Dice Similarity Coefficient (DSC) of 0.88 and an Average Surface Distance (ASD) of 2.4mm across the ultrasound dataset. The suggested 3DSC2-UNet, harnessing both mammogram and ultrasound, showcases exceptional performance in accuracy, ASD, and DSC metrics. This model holds the capability to diminish unwarranted biopsy procedures, thereby advancing its prospects for clinical implementation.

关键词： Image segmentation Solid modeling Ultrasonic imaging Three-dimensional displays Accuracy Convolution Imaging

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Efficient Grey Wolf: High-Performance Optimization for Reduced Memory Usage and Accelerated Convergence 5

Efficient Grey Wolf: High-Performance Optimization for Reduc...

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5th International Conference on Consumer Electronics and computer engineering, ICCECE 2025

作者： Tan, Lianghao Peng, Zhuo Liu, Xiaoyi Wu, Weixi Liu, Dong Zhao, Ruobing Jiang, Huangqi W. P. Carey School of Business Arizona State University Tempe United States Ira A. Fulton Schools of Engineering Arizona State University Tempe United States Tandon School of Engineering New York University New York United States Yale University Department of Computer Science New Haven United States Lamar University Department of Industrial and Systems Engineering Beaumont United States College of Computing Georgia Institute of Technology Atlanta United States

ISBN: (纸本)9798331533113

This paper presents an efficient Grey Wolf Optimizer (EGWO) designed to address the limitations of the standard Grey Wolf Optimizer (GWO), focusing on reducing memory usage and accelerating convergence. The proposed method integrates Sinusoidal Mapping for enhanced population diversity and a Transverse Longitudinal Crossover strategy to balance global exploration and local exploitation. These innovations improve search efficiency and optimization precision while maintaining a lightweight computational footprint. Experimental evaluations on 16 benchmark functions demonstrate EGWO's superior performance in convergence speed, solution accuracy, and robustness. Its application to hyperparameter tuning of a Random Forest model for a housing price dataset confirms its practical utility, further supported by SHAP-based interpretability analysis. © 2025 IEEE.

关键词： Crossover Strategy Grey Wolf Optimizer Lightweight Optimization Random Forest SHAP Analysis Sinusoidal Mapping

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Distance-based mutual congestion feature selection with genetic algorithm for high-dimensional medical datasets

arXiv

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arXiv 2024年

作者： Nematzadeh, Hossein Mani, Joseph Nematzadeh, Zahra Akbari, Ebrahim Mohamad, Radziah Department of Mathematics and Computer Science Modern College of Business and Science Oman Muscat Oman Otolaryngology Department School of Medicine Emory University AtlantaGA30322 United States Department of Computer Engineering Sari Branch Islamic Azad University Sari Iran Faculty of Computing Universiti Teknologi Malaysia Skudai81310 Malaysia

Feature selection poses a challenge in small-sample high-dimensional datasets, where the number of features exceeds the number of observations, as seen in microarray, gene expression, and medical datasets. There isn't a universally optimal feature selection method applicable to any data distribution, and as a result, the literature consistently endeavors to address this issue. One recent approach in feature selection is termed frequency-based feature selection. However, existing methods in this domain tend to overlook feature values, focusing solely on the distribution in the response variable. In response, this paper introduces the Distance-based Mutual Congestion (DMC) as a filter method that considers both the feature values and the distribution of observations in the response variable. DMC sorts the features of datasets, and the top 5% are retained and clustered by KMeans to mitigate multicollinearity. This is achieved by randomly selecting one feature from each cluster. The selected features form the feature space, and the search space for the Genetic Algorithm with Adaptive Rates (GAwAR) will be approximated using this feature space. GAwAR approximates the combination of the top 10 features that maximizes prediction accuracy within a wrapper scheme. To prevent premature convergence, GAwAR adaptively updates the crossover and mutation rates. The hybrid DMC-GAwAR is applicable to binary classification datasets, and experimental results demonstrate its superiority over some recent works. The implementation and corresponding data are available at https://***/hnematzadeh/DMC-GAwAR © 2024, CC BY-NC-ND.

关键词： Feature Selection

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BSH-Det3D: Improving 3D Object Detection with BEV Shape Heatmap

BSH-Det3D: Improving 3D Object Detection with BEV Shape Heat...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： You Shen Yunzhou Zhang Yanmin Wu Zhenyu Wang Linghao Yang Sonya Coleman Dermot Kerr College of Information Science and Engineering Northeastern University Shenyang China School of Electronic and Computer Engineering Peking University Shenzhen China Department of Electronic and Computer Engineering Faculty of Robotics and Engineering North-eastern University Technical University in Munich Germany Shenyang China Department of Electronic and Computer Engineering Technical University Munich Germany School of Computing Engineering and Intelligent Systems Ulster University N. Ireland UK

The progress of LiDAR-based 3D object detection has significantly enhanced developments in autonomous driving and robotics. However, due to the limitations of LiDAR sensors, object shapes suffer from deterioration in occluded and distant areas, which creates a fundamental challenge to 3D perception. Existing methods estimate specific 3D shapes and achieve remarkable performance. However, these methods rely on extensive computation and memory, causing imbalances between accuracy and real-time performance. To tackle this challenge, we propose a novel LiDAR-based 3D object detection model named BSH-Det3D, which applies an effective way to enhance spatial features by estimating complete shapes from a bird's eye view (BEV). Specifically, we design the Pillar-based Shape Completion (PSC) module to predict the probability of occupancy whether a pillar contains object shapes. The PSC module generates a BEV shape heatmap for each scene. After integrating with heatmaps, BSH-Det3D can provide additional information in shape deterioration areas and generate high-quality 3D proposals. We also design an attention-based densification fusion module (ADF) to adaptively associate the sparse features with heatmaps and raw points. The ADF module integrates the advantages of points and shapes knowledge with negligible overheads. Extensive experiments on the KITTI benchmark achieve state-of-the-art (SOTA) performance in terms of accuracy and speed, demonstrating the efficiency and flexibility of BSH-Det3D. The source code is available on https://***/mystorm16/BSH-Det3D.

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Revolutionizing carotid pulse analysis: a novel approach with custom designed neck band

Revolutionizing carotid pulse analysis: a novel approach wit...

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7th IET Smart Cities Symposium (SCS 2023)

作者： Pradeep Surya Dadi R. Deepa S. Johnpaul M. Sadish Sendil R. Surendran Department of Mechatronics Engineering Chennai Institute of Technology Chennai India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India Department of Information Technology Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai India Department of Emerging Technologies Guru Nanak Institute of Technology Telegana India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

Healthcare could undergo a major transformation with the incorporation of non-invasive sensors into wearable technology. This is due to these sensors' ability to provide instant monitoring of vital signs. Could be rewritten as Photoplethysmography (PPG) is a non-invasive technique that uses a small LED and photo sensor to detect the carotid pulse. Transcranial Doppler (TCD) is another non-invasive technique that measures the velocity of blood flow in the carotid artery using high frequency sound waves. This pulse is an essential indicator of blood pressure and heart rate. The primary goal of our research is to create a neck band equipped with both PPG and TCD sensors to enable comprehensive and precise carotid pulse monitoring. This paper will detail the designing, developing, and testing stages of the neck band, as well as the integration of sensors. Additionally, we will present data acquisition, data processing, and analysis. Lastly, the demonstration of the neck band's effectiveness in a controlled environment will be discussed.

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An Effective Diagnosis of Alzheimer’s Disease with the Use of Deep Learning based CNN Model

An Effective Diagnosis of Alzheimer’s Disease with the Use ...

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International Conference on ent computing and Control Systems (ICICCS)

作者： K C Sharmili G P Suja E. Pandian Md. Abul Ala Walid Sripriya Arunachalam G.Charles Babu Computer Science Stella Maris College 17 Cathedral Road Chennai Computer science Muslim Arts College Thiruvithancode Master of Computer Application Sri Muthukumaran Institute of Technology Mangadu Chennai Department of Computer Science and Engineering Khulna University of Engineering & Technology (KUET) Khulna Computer Applications-PG School of Computing Sciences VELS Institute of Science Technology & Advanced Studies - VISTAS Velan Nagar P.V. Vaithiyalingam Road Pallavaram Chennai Department of Computer Science and Engineering GRIET Hyderabad

In recent years, Alzheimer’s disease has become a major health concern. Over 45 million individuals worldwide suffer from this illness. Alzheimer’s disease is a neurodegenerative illness of unidentified etiology and process that causes cognitive deterioration, and it mostly affects the elderly. Dementia, the gradual and irreversible loss of brain cells, is the leading cause of Alzheimer’s disease. Those with this illness lost the ability to think, read, and do other basic tasks. A machine learning system may aid in this situation by making accurate predictions about the onset of illness. Dementia diagnosis in a broad patient population is the major objective. This research presents the findings and analysis of many Machine Learning models for identifying dementia. To alleviate this obstacle and help in the diagnosis of AD will create a deep-learning architecture that uses stacked auto-encoders and a softmax output layer. The proposed technique can analyze numerous classes in a single setting, unlike earlier procedures, and only needs a small number of labeled training samples and basic domain expertise to get started. In the present studies, a substantial improvement in performance when it came to classifying all diagnostic subgroups. Using the proposed technique can run these time series through a Convolutional Neural Network (CNN) and ResNet50 model to extract the distinguishing patterns across stages. With an F1-score of 0.99 and an accuracy of 99.91 percent, the CNN-based technique outperformed the standard feature-based classifiers by a significant margin.

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An Exploratory Study of Diverse Models and Datasets for Transfer Learning based Image Classification on Sparse Data

An Exploratory Study of Diverse Models and Datasets for Tran...

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Moratuwa engineering Research Conference (MERCon)

作者： Jathurshan Pathmarasa Udaya Wijenayake Ruchire Eranga Wijesinghe Bhagya Nathali Silva Department of Computer Engineering Faculty of Engineering University of Sri Jayewardenepura Nugegoda Sri Lanka Department of Electrical and Electronic Engineering Faculty of Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Center for Excellence in Informatics Electronics & Transmission (CIET) Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331529048

ISBN: (纸本)9798331529055

Transfer learning is a powerful technique for image classification, especially when dealing with limited data. However, selection of the best transfer learning approach and model remains challenging, since the strategy is highly influenced by the scenario. This work conducted a thorough analysis of four transfer learning approaches for binary classification, and multiclass classification using four pretrained models i.e. MobileNetV2, MobileNetV3Large, EfficientNetB0, and EfficientNetB5. The test accuracy was measured with respect to the sample size considering the effects of data augmentation and fine tuning. The key finding of the study revealed that data augmentation has improved the test accuracy by up to $11.8 \%$. Moreover, the lightweight models marked a breakthrough by outperforming large scale models’ accuracy for sparse data settings when fine tuning (FT) and data augmentation (DA) are applied appropriately. Hence, this work provides a comprehensive overview on selecting different transfer learning approaches for sparse data classification by exploring diverse models and datasets, while discussing the consequences and challenges for future real-world applications.

关键词： Knowledge engineering Accuracy Transfer learning Optical wavelength conversion Data augmentation Feature extraction Size measurement Data models Tuning Image classification

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