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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： M.S. Roobini M. Samuel Jebaraj P.B. Sasirudh K. Babu M. Nafees Muneera Department of Computer Science and Engineering Sathvabama Institute of Science and Technology Chennai Department of Computational Intelligence SRM Institute of Science and Technology Chennai

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Functional brain data analysis is important in diagnosing neurological disorders, yet constructing high-resolution (HR) brain graphs from low-resolution (LR) although the data presents some computational challenges. This paper introduces the Brain Graph Super-Resolution (BGSR) method that leverages generative adversarial networks (GANs) to directly generate HR graphs from LR data. By bypassing complex image processes, BGSR enhances diagnostic accuracy efficiently while retaining the features. This paper explains BGSR’ s methodology, detailing its implementation steps and emphasizing its potential to revolutionize neurological disorder diagnosis. Experimental results demonstrate BGSR's effectiveness in improving diagnostic precision compared to traditional methods. Furthermore, itdiscusses the broader implications of BGSR in advancing functional brain data analysis and suggests future research directions for further works and application in the field of medicine. BGSR is a promising tool for researchers and clinicians alike, offering insights into the minute workings of the brain and facilitating more accurate diagnosis and treatment of neurological disorders. Through its innovative approach, BGSR contributes to the ongoing efforts to unravel the complexities of the human brain and offers new avenues for understanding and addressing neurological disorders. Its streamlined methodology and enhanced diagnostic capabilities hold promise for improving patient outcomes and advancing the field of neuroscience. In the realm of neurological disorder analysis, conventional methods often grapple with issues of precision and struggle to cope with the brain data due to its complexity. This paper introduces a novel paradigm known as Brain Graph Super-Resolution (BGSR), a pioneering technique leveraging the potential of Generative Adversarial Networks (GANs) to synthesize high-resolution brain graphs directly from low-resolution data, by circumventing convoluted image processing wo

关键词： Neurological diseases Data analysis Accuracy Neuroscience Precision medicine Instruments Superresolution Generative adversarial networks Complexity theory Medical diagnostic imaging

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Detection of Military Vehicles based on Edge Devices from Unmanned Aerial Vehicle 2

Detection of Military Vehicles based on Edge Devices from Un...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Umasankar, L. Anitha, D. Trisheela, S. Al-Jawahry, Hassan M. Borra, Subba Reddy S.A.Engineering College Electrical and Electronics Engineering Tiruvallur Poonamallee India School of Computing Srm Institute of Science and Technology Department of Computational Intelligence Tamilnadu Kattankulathur India Nitte Meenakshi Institute of Technology Dept of Cse Bengaluru India The Islamic University Najaf Iraq Computer Science and Engineering Maisammaguda Hyderabad India

ISBN: (纸本)9798350382952

The recent technology in surveillance for military vehicle parts forms the basis of intelligence warfare through information realized through monitoring and tracking operations in complex situations. Therefore, the process of identifying and classifying vehicles by an aerial vehicle installed with a resource-limited device and an intelligent object detection algorithm significantly assists security agencies. It is possible to make use of this vehicle either manually or autonomously. To overcome these issues, edge device based military vehicle detection from Unmanned Aerial Vehicle (UAV) is proposed. At present, no publicly available dataset exists that includes different military classes. Therefore, the proposed approach use a dataset consisting of 6772 images labeled as Tanks, Military Trucks, Helicopters, Aircraft, Civilian Cars and Civilian Aircraft. This data set is used to train deep learning models such as Quantized SSD Mobilenetv2 and Tiny Yolo v5 that are then compared with resource limited edge devices. The results reveal that Tiny Yolo v5 outperforms other models, showing high efficiency and suitability for edge-based devices due to its lightweight design. © 2024 IEEE.

关键词： Military vehicles

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Improving Training Efficiency of Diffusion Models via Multi-Stage Framework and Tailored Multi-Decoder Architecture

Improving Training Efficiency of Diffusion Models via Multi-...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Huijie Zhang Yifu Lu Ismail Alkhouri Saiprasad Ravishankar Dogyoon Song Qing Qu Department of Electrical Engineering & Computer Science University of Michigan Department of Computational Mathematics Science & Engineering Michigan State University Department of Biomedical Engineering Michigan State University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Diffusion models, emerging as powerful deep generative tools, excel in various applications. They operate through a two-steps process: introducing noise into training samples and then employing a model to convert random noise into new samples (e.g., images). However, their remarkable generative performance is hindered by slow training and sampling. This is due to the necessity of tracking extensive forward and reverse diffusion trajectories, and employing a large model with numerous parameters across multiple timesteps (i.e., noise levels). To tackle these challenges, we present a multi-stage framework inspired by our empirical findings. These observations indicate the advantages of employing distinct parameters tailored to each timestep while retaining universal parameters shared across all time steps. Our approach involves segmenting the time interval into multiple stages where we employ custom multi-decoder U-net architecture that blends time-dependent models with a universally shared encoder. Our framework enables the efficient distribution of computational resources and mitigates inter-stage interference, which substantially improves training efficiency. Extensive numerical experiments affirm the effectiveness of our framework, showcasing significant training and sampling efficiency enhancements on three state-of-the-art diffusion models, including large-scale latent diffusion models. Furthermore, our ablation studies illustrate the impact of two important components in our framework: (i) a novel timestep clustering algorithm for stage division, and (ii) an innovative multi-decoder U-net architecture, seamlessly integrating universal and customized hyperparameters.

关键词： Training computer vision Noise computer architecture Interference Diffusion models Trajectory

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An Efficient Fusion of Infrared and Visible Images Using an Optimized Deep Learning Model with Decomposed Non-subsampled Shearlet Structural Patch Transform

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New Generation Computing 2025年第3期43卷 1-27页

作者： Murugeswari, P. Rayen, Sonia Jenifer Ramkumar, M. Sakthi, U. Department of Artificial Intelligence and Data Science Karpagam College of Engineering Coimbatore India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Electronics and Communication Engineering Department Sri Krishna College of Engineering and Technology Coimbatore India Department of Computational Intelligence Faculty of Engineering & Technology SRM Institute of Science and Technology Chennai India

Multimodal data fusion is pivotal in artificial intelligence, with infrared and visible image fusion crucial for enhancing target detection. However, low-light visible images often lack vital details, impacting fusion quality and target detection accuracy, particularly in autonomous driving applications. To address these challenges, an innovative model is proposed that introduces a normalized depth-wise crested porcupine cross-attention model to extract complementary details from visible and infrared images. The incorporation of double cross-attention effectively enhances complementary information while minimizing redundant features. Subsequently, the Decomposed Non-subsampled Shearlet Structural Patch Transform (DNSSPT) model is employed to integrate features and generate the fused image. This process involves decomposing the extracted infrared and visible images into components such as signal intensity, mean intensity, and signal structure through structural patch decomposition. Next, a membership curve is applied to precisely determine the weight of the average intensity module, minimizing artifacts while preserving the importance of infrared targets. Additionally, sharpening operations are used to improve detail layer of both the visible and infrared images, leading to a fused image with higher contrast. Through subjective and objective evaluations, the proposed model outperforms existing fusion techniques, achieving minimum processing times of 0.0521, 0.0698, 0.0681, and 0.0832 s across the TNO, MSRS, VIFB, and VOT-RGBT datasets, respectively. Additionally, it attains high detection accuracies of 94% for VOT-RGBT, 92.71% for TNO, 96% for MSRS, and 90.89% for VIFB. Furthermore, object detection experiments using roadscene dataset confirm the DNSSPT model’s effectiveness in advancing computer vision tasks, achieving an accuracy of 94.1%.

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Multiobjective adaptive car-following control of an intelligent vehicle based on receding horizon optimization

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science China(Information sciences) 2023年第4期66卷 301-302页

作者： Hongbo GAO Juping ZHU Fei ZHANG Ruidong YAN Junjie ZHOU Lei JIANG Jianqiang WANG Keqiang LI Department of Automation University of Science and Technology of China Institute of Advanced Technology University of Science and Technology of China Department of Computational Mathematics School of Mathematical Sciences Anhui University School of Traffic and Transportation Beijing Jiaotong University Autonomous Driving Business Unit Chery Automobile School of College of Computer Science and Technology Zhejiang University China North Vehicle Research Institute School of Automotive Engineering Tsinghua University

Dear editor, Vehicle control is one of the key steps of intelligent driving [1]. Algorithms based on receding horizon optimization(RHO) can predict future trajectories and handle multiobjective constraint conditions; therefore, RHO-based methods have attracted considerable attention in the field of vehicle control. Although existing methods use RHO to design controllers, they do not simultaneously meet the multiobjective optimal control performance requirements on tracking, fuel economy, and ride comfort.

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A Comprehensive Survey on Enhancing Digital Twin Security Systems with Explainable AI Techniques

A Comprehensive Survey on Enhancing Digital Twin Security Sy...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： B. Lalithadevi S. Krishnaveni Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Digital Twin (DT) technology has emerged as a cornerstone in modern engineering, enabling real-time digital replication of physical assets for monitoring, diagnostics, and optimization. However, as DTs become increasingly integrated into critical infrastructure, their security has become a paramount concern. This paper presents a comprehensive survey on the integration of Explainable Artificial Intelligence (XAI) into Digital Twin security, highlighting the potential of XAI in enhancing the transparency, interpretability, and trustworthiness of AI - driven security mechanisms in DTs. We explore various XAI techniques, including model- agnostic approaches, interpretable neural networks, and rule-based systems, detailing their potential applications in bolstering the security of Digital Twins. Furthermore, we identify several key challenges in implementing XAI for DT security, such as the need for standardized metrics for interpretability, the trade-offs between model complexity and explainability, and the potential for adversarial attacks on explainable models. Through our analysis, we highlight the critical research gaps, including the security of Digital Twin (DT) systems and explores the integration of Explainable AI (XAI) to enhance transparency, interpretability, and trustworthiness. Outcomes include detailed XAI applications for DT security, identification of limitations, and a roadmap for future research. We discuss various XAI techniques, their applications, and the current challenges in implementing XAI for DT security. Our survey identifies key research gaps and offers a roadmap for future research in this emerging field.

关键词： Surveys Measurement Explainable AI Neural networks Real-time systems Digital twins Security Internet of Things Optimization Monitoring

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Optimizing Generative AI Applications: A Comparative Study of Effective Prompting Techniques

Optimizing Generative AI Applications: A Comparative Study o...

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Shivya Jyoti Moulik Tejpal Jothi K R Department of Software Systems School of Computer Science & Engineering Vellore Institute of Technology Vellore India Department of Computational Intelligence School of Computer Science & Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

Generative Artificial Intelligence (GenAI) has emerged as a pivotal technology across various industries, driving advancements in automation, decision-making, and content generation. This paper investigates the efficacy of the prompt engineering methods such as zero-shot, one-shot, and few-shot prompting—in optimizing GenAI systems for diverse applications. Through a comprehensive literature review and an empirical survey involving 13 use cases such as chatbots, content creation, and medical decision support, we evaluate the performance of these prompting methods. The findings reveal that few-shot prompting excels in complex tasks, while zero-shot and one-shot prompting are more effective for simpler tasks. These insights provide practical guidance for leveraging GenAI across different domains, contributing to the advancement of AI-driven solutions.

关键词： Surveys Pervasive computing Industries Decision support systems Social networking (online) Large language models Chatbots Prompt engineering Human resource management Systematic literature review

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Bayes-Q-Learning Algorithm in Edge Computing for Waste Tracking

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Intelligent Automation & Soft Computing 2023年第5期36卷 2425-2440页

作者： D.Palanikkumar R.Ramesh Kumar Mehedi Masud Mrim M.Alnfiai Mohamed Abouhawwash Department of Computer Science and Engineering Dr.NGP Institute of TechnologyCoimbatore641048India Department of Information Technology Sri Krishna College of TechnologyCoimbatore641042India Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

The major environmental hazard in this pandemic is the unhygienic dis-posal of medical *** wastage is not properly managed it will become a hazard to the environment and *** medical wastage is a major issue in the city,municipalities in the aspects of the environment,and *** efﬁcient supply chain with edge computing technology is used in managing medical *** supply chain operations include processing of waste collec-tion,transportation,and disposal of *** research works have been applied to improve the management of *** main issues in the existing techniques are ineffective and expensive and centralized edge computing which leads to failure in providing security,trustworthiness,and *** over-come these issues,in this paper we implement an efﬁcient Naive Bayes classiﬁer algorithm and Q-Learning algorithm in decentralized edge computing technology with a binary bat optimization algorithm(NBQ-BBOA).This proposed work is used to track,detect,and manage medical *** minimize the transferring cost of medical wastage from various nodes,the Q-Learning algorithm is *** accuracy obtained for the Naïve Bayes algorithm is 88%,the Q-Learning algo-rithm is 82%and NBQ-BBOA is 98%.The error rate of Root Mean Square Error(RMSE)and Mean Error(MAE)for the proposed work NBQ-BBOA are 0.012 and 0.045.

关键词： Binary bat algorithm naïve bayes supply chain edge medical wastage

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Improved Metaheuristics with Deep Autoencoders for COVID-19 Detection and Classification 2

Improved Metaheuristics with Deep Autoencoders for COVID-19 ...

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2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Assudani, Manish K Nirmala, B. Kanimozhi, N. Chandre, Shanker Sunalini, K.K. Gopatoti, Anandbabu G H Raisoni University Department of Computer Science and Engineering Maharashtra Amravati India Sri Ramachandra Institute of Higher Education and Research Sri Ramachandra Faculty of Engineering and Technology Department of Computer Science and Engineering Chennai India School of Computing SRM Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Chennai India SR University Department of Computer Science & Artificial Intelligence Telangana Warangal India Koneru Lakshmaiah Education Foundation Department of English Andhrapradesh Guntur India Hindusthan College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350397635

A healthcare monitoring system requires the support of current technologies like big data, artificial intelligence (AI), and machine learning (ML), particularly during the outbreak of COVID19. This pandemic caused millions of death worldwide. ML and AI are used to detect and combat COVID19 in the initial phase. Deep learning refers to a subdivision of ML that utilizes NN for making predictions and learning from data. In the context of COVID19 detection, deep learning (DL) methods can be trained for examining medical images like CT scans and chest X-rays and identify the presence of virus. This study presents an improved whale optimization algorithm with deep autoencoder (IWOA-DAE) algorithm for COVID-19 detection and classification. The presented IWOA-DAE technique makes use of DL model with metaheuristic hyperparameter optimizer for COVID-19 classification. To accomplish this, the IWOA-DAE technique employs bilateral filtering (BF) process to eliminate noisy data. Next, the IWOA-DAE technique uses NASNetLarge model for feature vector generation purposes. For COVID-19 detection, the IWOA-DAE technique uses deep autoencoder (DAE) model with IWOA based optimal hyperparameter adjustment process. To highlight the improved performance of the IWOA-DAE algorithm, a wide-ranging simulation analysis takes place. The simulation outcomes highlighted the enhanced performance of the IWOA-DAE technique over other methods. © 2023 IEEE.

关键词： COVID-19

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Deep learning-based crack detection and prediction for structural health monitoring

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Discover Applied sciences 2025年第7期7卷 1-26页

作者： Roy, Satyabrata Yogi, Biswarup Majumdar, Raj Ghosh, Pritha Das, Sourav Kumar Department of Computer Science and Engineering Manipal University Jaipur Jaipur India Department of Computational Sciences Brainware University KolkataBarasat India Department of Civil Engineering Manipal University Jaipur Jaipur India

This study describes a system for real-time crack detection and estimation in constructions, using a proposed model based on CNN, VGG16, U-Net, and Swin Transformer architectures. This model classifies and segments structural cracks irrespective of their severity with 98.88% precision. Preprocessing techniques such as oversampling and class under sampling mitigate imbalance, while augmentation aids generalization, was followed. Detection performance is improved by fine-tuning VGG16 using transfer learning. Robust segmentation and identification are provided by U-Net and Swin Transformer. A focus on a visual model indicates cracks and inspects the possibility of damage to the structure in the future by employing bounding boxes. Recurrent structure problems, validated by RGB histograms and confusion matrices, are justified and analyzed. Improving the precision detection of the system contributes to preventive maintenance, which significantly reduces the cost of repairs and increases safety. Using deep learning models within the framework enables effective and automated structural health monitoring. This approach allows for the monitoring of cracks, damage progression, and other relevant details and serves as a critical step toward infrastructure management, protection, and risk evaluation for disaster prevention and mitigation.

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