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1st Asian Conference on Communication and Networks, ASIANComNet 2024

作者： Naveen Raja, S.M. Shajin Nargunam, A. Noorul Islam Centre for Higher Education Kumaracoil Department of Information Technology Thuckalay India Noorul Islam Centre for Higher Education Kumaracoil Department of Computer Science and Engineering Thuckalay India

ISBN: (纸本)9798350367003

Wheat is one of the crucial cereal crops globally, and its productivity is affected by various diseases. Therefore, the timely and precise detection and classification of these diseases are important. The advancements in deep learning (DL), especially convolutional neural networks, have observed significant evolution in wheat disease identification. This article provides a comprehensive overview of research works, which effectively utilized DL for wheat disease prediction and classification. The paper begins by introducing the importance of wheat disease management and the challenges associated with traditional disease identification methods. The survey further explores the general architecture of the DL-based wheat disease prediction framework the study highlights the significance of DL models in achieving accurate and effective disease classification. In addition, it demonstrates the challenges associated with different DL models relative to wheat disease identification. Furthermore, it examines the research works related to wheat disease detection using DL models and highlights the limitations of those methods. Finally, the survey provides the future research scope for an improved disease identification model. © 2024 IEEE.

关键词： Convolutional neural networks

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Towards Efficient Deep Hashing Retrieval: Condensing Your Data via Feature-Embedding Matching

Towards Efficient Deep Hashing Retrieval: Condensing Your Da...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Feng, Tao Zhang, Jie Liu, Huashan Wang, Zhijie Pang, Shengyuan College of Information Science and Technology Donghua University Shanghai China Department of Computer Science ETH Zurich Zurich Switzerland College of Electrical Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798350368741

Deep hashing retrieval has gained widespread use in big data retrieval due to its robust feature extraction and efficient hashing process. However, training advanced deep hashing models has become more expensive due to complex optimizations and large datasets. Coreset selection and Dataset Condensation lower overall training costs by reducing the volume of training data without significantly compromising model accuracy for classification task. In this paper, we explore the effect of mainstream dataset condensation methods for deep hashing retrieval and propose IEM (information-intensive feature-Embedding Matching), which is centered on distribution matching and incorporates model and data augmentation techniques to further enhance the feature of hashing space. Extensive experiments demonstrate the superior performance and efficiency of our approach. © 2025 IEEE.

关键词： Dataset Condensation Deep Hashing Retrieval

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ACDV: Adaptive Content Delivery for Vehicular Digital Twin Networks

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IEEE Transactions on Vehicular technology 2025年第5期74卷 7084-7098页

作者： Zheng, Jinkai Luan, Tom H. Li, Guanjie Yin, Zhisheng Wu, Yuan Dong, Mianxiong Dongguan University of Technology School of Electrical Engineering and Intelligentization Dongguan523808 China Xi'an Jiaotong University School of Cyber Science and Engineering Xi'an710049 China Xidian University School of Cyber Engineering Xi'an710071 China Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore Xidian University State Key Laboratory of Isn Xi'an710071 China University of Macau State Key Laboratory of Internet of Things for Smart City 999078 China University of Macau Department of Computer Information Science 999078 China Muroran Institute of Technology Department of Sciences and Informatics Muroran05-08585 Japan

Digital Twins (DTs), serving personalized cloud-based digital assistants, hold great promise for supporting infotainment applications of the Internet of Vehicles (IoVs), in which personalized content such as high-definition maps, system updates, and social video streaming can be delivered from individualized DTs to vehicular users (VUEs) to enhance the intelligence of IoV services and improve their driving experience. This paper proposes a novel content caching framework tailored to DT-enabled IoVs, where DTs selectively cache on-demand contents on edge devices (i.e., cellular base stations and roadside units) managed by the edge service manager (ESM) along the VUE's trip to avoid backbone congestion yet save download time. However, ESMs are inherently selfish and reluctant to contribute resources to cache contents without benefits. Furthermore, considering the diverse trajectories of vehicles, it is inefficient to cache data along a single path. To address these challenges, we propose ACDV, which first models the interactions between the ESMs and DTs as a Stackelberg game to incentivize ESMs to actively participate in the content caching process. We then deploy a Markov model to predict the VUE distribution, which enables DTs to cache contents with a focus on predicted trajectories. To save storage costs, we derive an upper bound of content sizes that the VUE can download within a limited network connection time. Considering the lack of network information and users' private utility model in practical scenarios, we further develop a learning-based algorithm to find the optimal pricing scheme and content size strategies of ESMs and DTs. Through extensive simulations, we show that our proposal can effectively find the optimal strategy and achieve a fast convergence speed and high-level performance compared to the baselines. © 1967-2012 IEEE.

关键词： Markov processes

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Investigating Various Autonomous Vehicle Technologies for Automated Steering 15

Investigating Various Autonomous Vehicle Technologies for Au...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Patil, Swati B. Kannagi, A. Daivagna, Umesh M. Mathur, Mala Singh, K. Ranjith Sathya, T. Department of Information Technology Vishwakarma Institute of Technology Pune India Karnataka Bangalore India Department of uGDX ATLAS SkillTech University Maharastra Mumbai India Department of Civil Engineering Vivekananda Global University Jaipur India Department of Computer Science Karpagam Academy of Higher Education Coimbatore India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India

ISBN: (纸本)9798350370249

this paper investigates the ability for using diverse self-sufficient vehicle technology for computerized steering. The studies specializes in two precise technology, Optical waft Estimation (OFE) and Inertial size devices (IMUs), to assess their suitable levels of accuracy and reliability for controlling steering in real-international scenarios. For each era, the paper offers a top level view of its operating precept and a dialogue of its modern-day competencies, detailing its benefits and limitations. The experimental effects of each era are offered to evaluate its steerage overall performance in various riding situations, and an ensemble device combining the 2 sensors is used to demonstrate potential with progressed accuracy and reliability. Ultimately, the paper delves into the consequences of self reliant car technology for automated steerage and offers pointers and avenues for in addition studies. ©2024 IEEE.

关键词： Autonomous vehicles

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Spoofing Face Detection Using Novel Edge-Net Autoencoder for Security

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Intelligent Automation & Soft Computing 2023年第3期35卷 2773-2787页

作者： Amal H.Alharbi S.Karthick K.Venkatachalam Mohamed Abouhawwash Doaa Sami Khafaga Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Electrical Electronics and Communication Engineering GITAM School of TechnologyGITAM Deemed to be UniversityBengaluru CampusKarnataka560065India Department of Computer Science and Engineering CHRIST(Deemed to be University)Bangalore560074India Department of Mathematics Faculty of ScienceMansoura UniversityMansoura 35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Recent security applications in mobile technologies and computer sys-tems use face recognition for high-end *** numerous security tech-niques,face recognition is considered a high-security *** fuse and carry out face identiﬁcation as an access authority into these ***,face identiﬁcation authentication is sensitive to attacks with a 2-D photo image or captured video to access the system as an authorized *** the existing spooﬁng detection algorithm,there was some loss in the recreation of *** research proposes an unobtrusive technique to detect face spooﬁng attacks that apply a single frame of the sequenced set of frames to overcome the above-said *** research offers a novel Edge-Net autoencoder to select convoluted and dominant features of the input diffused ***,this pro-posed method is tested with the Cross-ethnicity Face Anti-spooﬁng(CASIA),Fetal alcohol spectrum disorders(FASD)*** database has three models of attacks:distorted photographs in printed form,photographs with removed eyes portion,and video *** images are taken with three different quality cameras:low,average,and high-quality real and spoofed *** extensive experimental study was performed with CASIA-FASD,3 Diagnostic Machine Aid-Digital(DMAD)dataset that proved higher results when compared to existing algorithms.

关键词： Image processing edge detection edge net auto-encoder face authentication digital security

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Fisherman Location Store, Updating, and Alert System using Cloud AWS with Satellite

Fisherman Location Store, Updating, and Alert System using C...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Kiruthiga Devi, M. Parthasarathy, R. Unnamalai, K. Perinba Athiban, S. Manikandan, K. Ramya, N. Dr. MGR Educational and Research Institute Chennai Department of Information Technology India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Adarsh College for Women Department of Computer Science Anna India VIT Artificial Intelligence and Robotics Chennai India Sona College of Technology Department of Biomedical Engineering Salem India Sri Sai Ram Engineering College Department of CSE Chennai India

ISBN: (纸本)9798331522988

he successful demonstration of their location tracking alert system for fishermen, Satellites will employ Amazon Web Services (AWS). Using cloud-based computing technology, the activities and whereabouts of fishermen are monitored in real time through this system. The ACS sat will send a vast amount of information in short always-shrinking cycle and store data very efficiently, only the location data for live updates on every moves by the fishermen. Here using this project we are stressing on the need to analyze data and push alerts according protocols, for timely raise alarm which definitely saves Fishermen life. The aim is to develop a Real Time Location Tracking and Monitoring GIS Application, which can trace the fishermen real-time location in marine activities. Analyze location information with the best edge of the sword such as cloud computing and AI /ML. The GIS App will have a simple user interface along with extensive monitoring abilities. Building a GIS App to aid marine safety by improving the tracking and monitoring of fishing operations using cloud technology. In the end, with these benchmarks in tow this could mark a step change and benchmark for maritime operations;as we finally get, safety and productivity combined. © 2024 IEEE.

关键词： Benchmarking

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Menu Knowledge based Propagation Graph Attention Network using Consumer Interaction and Behavior

Menu Knowledge based Propagation Graph Attention Network usi...

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2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Singh, Mahesh Kumar Bhardwaj, Mukesh Kumar Anthwal, Aarti Riya Das, Vanshita Department of Computer Science and Engineering Dronacharya Group of Institutions Uttar Pradesh Greater Noida India Department of Information Technology Dronacharya Group of Institutions Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350355338

In the era of large volumes of data (big data), suggestions on various methods are crucial, as they utilize vast amounts of data and consumer behavior information to suggest menus based on eating preferences. A novel approach to personalized menu suggestion is proposed, based on a project where the attention of a graph is diverted toward the Neural Network. The network captures the structural message of parts in the menu knowledge graph by fine-grained representation of consumers and their recipes. It uses a bidirectional dissemination approach to various sets of the consumer and system, learns topological adjacent structure features, and designs an attention aggregator to produce a refined embedding. The core product of the things predicts the probability of consumer-menu interactions. Experimental results show that CGRS outperforms baseline advanced suggestion models. © 2024 IEEE.

关键词： Knowledge graph

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Differential evolution variants for optic disc localisation in eye fundus images using entropy measure

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International Journal of Intelligent Systems Technologies and Applications 2023年第2期21卷 129-152页

作者： Zhang, Chen Kumar, B. Vinoth Zhang, Siwen Prakash, J. Wen, Shiman Joel, Bineeshia Li, Wenjin Shanghai Chest Hospital Shanghai China Department of Information Technology PSG College of Technology Tamil Nadu Coimbatore India Faculty of Life Sciences and Medicine King's College London United Kingdom Department of Computer Science and Engineering PSG College of Technology Tamil Nadu Coimbatore India Shanghai University of Medicine and Health Sciences Shanghai China School of Health Science and Engineering University of Shanghai for Science and Technology China

Retinal images are broadly used to expose several retinopathies like glaucoma, optic neuritis, macular degeneration related to age, and diabetic retinopathy. computer-aided retinal image diagnoses efficiently facilitate clinicians by automating the load screening process to diagnose these conditions of which optic disc (OD) segmentation is the primary phase and categorised as a critical search problem. The proposed approach incorporates the preliminary processing of retinal fundus images and optical disc localisation using three variants of differential evolution (DE) for this application: standard differential evolution (SDE), cellular differential evolution (CDE), and unified differential evolution (UDE). To localise optic disc effectively, Entropy of an image region is proposed as a fitness function. The performance of the suggested technique is examined on two datasets: DIARETDB0 and DIARETDB1. The results suggested that the proposed methodology has achieved a 99.23% detection accuracy and demonstrates the dominance of localising the optic disc over the others. © 2023 Inderscience Enterprises Ltd.

关键词： Entropy

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Phonocardiographic Signal Analysis for the Detection of Cardiovascular Diseases 11th

Phonocardiographic Signal Analysis for the Detection of Card...

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11th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2023

作者： Gupta, Deena Nath Anand, Rohit Ahamad, Shahanawaj Patil, Trupti Dhabliya, Dharmesh Gupta, Ankur CDAC Maharashtra Mumbai India New Delhi110020 India College of Computer Science and Engineering University of Hail Hail City Saudi Arabia Department of Engineering and Technology Bharati Vidyapeeth Deemed to be University Maharashtra Navi Mumbai India Department of Information Technology Vishwakarma Institute of Information Technology Maharashtra Pune India Department of Computer Science and Engineering Vaish College of Engineering Haryana Rohtak India

ISBN: (纸本)9789819967056

Under this work, we propose a novel method for analyzing heart sounds to find cardiovascular illnesses. The phonocardiography (PCG) signal is divided into four pieces by the heart sound segmentation process: S1, systolic interval, S2, and diastolic interval. One may argue that it is an essential stage in the automated analysis of PCG signals. Mechanical activity of the circulatory system is conveyed via heart sounds. This data comprises the subject’s precise physiological condition as well as any short-term variations connected to the respiratory cycle. This paper’s focus is on an issue that is currently open: how to analyze noises and extract physiological state changes while keeping short-term variability. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Phonocardiography

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AI based Real-Time Traffic Signal Control System using Machine Learning 4

AI based Real-Time Traffic Signal Control System using Machi...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Heltin Genitha, C. Abishek Danny, S. Hepsi Ajibah, A.S. Aravint, S. Angeline Valentina Sweety, A. St. Joseph's College of Engineering Department of Information Technology Chennai India St. Joseph's Institute of Technology Department of Computer Science and Engineering Chennai Chennai India

ISBN: (纸本)9798350300093

This study presents a novel system that utilizes computer vision and machine learning approaches to address the problem of traffic congestion in urban areas. The proposed system leverages the advanced object detection algorithm, You Only Look Once (YOLO), to detect and track vehicles in live camera footage from traffic junctions. The system then calculates the traffic density in real-time by analyzing the number and speed of vehicles passing through the intersection. The proposed system utilizes an intelligent algorithm that optimizes traffic flow by switching traffic lights based on the calculated traffic density. This approach reduces congestion and minimizes delays, resulting in faster transit times and reduced fuel consumption and air pollution. To assess the performance of the proposed system, experiments are carried on real-world traffic data. The results demonstrate that the system can accurately detect and track vehicles with high precision and recall rates. The real-time traffic density calculations produced by the system were found to be highly reliable, and the traffic light switching algorithm led to a significant reduction in traffic congestion and improved traffic flow. The proposed system has several advantages over traditional traffic management systems, including lower implementation and maintenance costs, improved accuracy and efficiency, and the ability to adapt to changing traffic conditions in real-time. © 2023 IEEE.

关键词： Object detection

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