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3rd International Conference on Intelligent Systems, Advanced computing, and Communication, ISACC 2025

作者： Ilambirai, R.C. Barkathulla, A. Mahalakshmi, G. Babu, J. Jagan Prabu, K. Srinivasan, C. SRM Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.D Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Gojan School of Business and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331523893

The crucial agricultural practice of precision pruning fruit trees greatly impacts fruit production yield and quality. Traditional pruning techniques take a lot of time and money because they need skilled workers and are labor-intensive. It suggests a novel cloud-based AI robotics system intended to precisely prune fruit trees to address these issues. This system uses cutting-edge robotics fitted with Convolutional Neural Networks (CNNs) to carry out precise and effective tasks related to pruning. Cloud computing integration makes it possible to process and make real-time decisions, improving the system's flexibility in responding to changing tree conditions and pruning needs. The system's primary parts are robotic arms for accurate cuts, various sensors for analyzing tree structures, and CNN models for maximizing pruning techniques. Early field trials show noticeable gains in operational efficiency and pruning accuracy compared to conventional techniques. This study demonstrates how cloud-integrated AI robotics can transform agricultural practices and improve fruit farming's sustainability and productivity. © 2025 IEEE.

关键词： Robotic arms

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Laryngeal Leukoplakia Classification Via Dense Multiscale Feature Extraction in White Light Endoscopy Images 48

Laryngeal Leukoplakia Classification Via Dense Multiscale Fe...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： You, Zhenzhen Yan, Yan Shi, Zhenghao Zhao, Minghua Yan, Jing Liu, Haiqin Hei, Xinhong Ren, Xiaoyong Xi'An University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi'an China Xi'An Jiaotong University Second Affiliated Hospital of Medical College Department of Otorhinolaryngology Xi'an China

ISBN: (纸本)9781728163277

Laryngeal leukoplakia classification is challenging using white light endoscopy images. Relevant research focus on normal tissues versus non normal tissues, cancer versus non cancer classification. The objective of this paper is to classify laryngeal leukoplakia in white light endoscopy images into six classes: normal tissues, inflammatory keratosis, mild dysplasia, moderate dysplasia, severe dysplasia and squamous cell carcinoma. We proposed a dense multiscale convolutional neural network including parallel multiscale convolution, dense convolution and recurrent convolution in favor of extracting dense multiscale features of laryngeal leukoplakia for fine classification. The proposed network achieved an overall accuracy of 0.8958 for the six-class classification. It has high sensitivity and specificity for each class which are, respectively, 1.0000 and 0.9394 for normal tissues, 0.6667 and 1.0000 for inflammatory keratosis, 0.8889 and 0.9744 for mild dysplasia and moderate dysplasia, 0.7500 and 1.0000 for severe dysplasia, 1.0000 and 0.9767 for squamous cell carcinoma. The experimental results show that our proposed model is superior to the state-of-the-art deep learning-based models. © 2023 IEEE.

关键词： Endoscopy

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Real-time Agrochemical Management Solutions using Cloud computing and K-Nearest Neighbors Algorithm 8

Real-time Agrochemical Management Solutions using Cloud Comp...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Sivanesan, T.M. Mohankumar, N. Shibu, N.V. Nandagopal, V. Rajasekaran, B. Srinivasan, S. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Nagpur Campus Maharastra Pune India Srm Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur Tamil Nadu Chennai India Mohan Babu University School of Engineering Department of Electrical and Electronics Engineering Andhra Pradesh Tirupathi India Vinayaka Missions Kirupananda Variyar Engineering College Department of Electronics and Communication Engineering Tamil Nadu Salem India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350367904

Optimizing pesticides to ensure crop health and productivity while limiting environmental effect is one of the many issues faced by modern agriculture. In this research, it provides a cloud-based, K-Nearest Neighbors (KNN) algorithm-based real-time agrochemical management system. The proposed method allows for effective data processing and analysis by using the computing power of cloud platforms. Environmental factors, crop type, soil properties, and historical data are used to inform the KNN algorithm's prediction of appropriate pesticide application rates. To provide the most current insights and suggestions, the system incorporates real-time data from a variety of sources, including sensors, satellites, and weather predictions. Enhanced crop yields, decreased costs, and less environmental impact may result from farmers making informed decisions about the use of agrochemicals via continuous monitoring and analysis. The system can be easily deployed and adjusted to many agricultural contexts because of the scalability and flexibility of cloud computing. This method provides a viable strategy for improving pesticide management practices, which in turn promotes sustainable farming and increases food safety. © 2024 IEEE.

关键词： Smart agriculture

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Human Orientation Estimation Under Partial Observation

Human Orientation Estimation Under Partial Observation

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

作者： Jieting Zhao Hanjing Ye Yu Zhan Hao Luan Hong Zhang Department of Electronic and Electrical Engineering SUSTech Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology (SUSTech) Department of Computer Science School of Computing National University of Singapore

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Reliable Human Orientation Estimation (HOE) from a monocular image is critical for autonomous agents to understand human intention. Significant progress has been made in HOE under full observation. However, the existing methods easily make a wrong prediction under partial observation and give it an unexpectedly high confidence. To solve the above problems, this study first develops a method called Part-HOE that estimates orientation from the visible joints of a target person so that it is able to handle partial observation. Subsequently, we introduce a confidence-aware orientation estimation method, enabling more accurate orientation estimation and reasonable confidence estimation under partial observation. The effectiveness of our method is validated on both public and custom-built datasets, and it shows great accuracy and reliability improvement in partial observation scenarios. In particular, we show in real experiments that our method can benefit the robustness and consistency of the Robot Person Following (RPF) task.

关键词： Accuracy Filtering Estimation Robustness Autonomous agents Intelligent robots

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Hybrid Signal Integrity Modeling and Analysis of Heterogeneous Integrated System With Neuromorphic Darwin Chip

IEEE Transactions on Signal and Power Integrity

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IEEE Transactions on Signal and Power Integrity 2024年 3卷 37-46页

作者： Chen, Quankun Ma, Hanzhi Li, Da Tao, Tuomin Tan, Shurun Liu, En-Xiao Schutt-Aine, Jose Li, Er-Ping Zhejiang University Key Laboratory of Advanced Micro/Nano Electronic Devices and Smart Systems and Applications College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang University University of Illinois at Urbana-Champaign Institute Haining314400 China A∗star Institute of High Performance Computing Singapore138632 Singapore University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering UrbanaIL61820 United States

This article introduces a comprehensive approach for designing and analyzing signal integrity in heterogeneous integrated systems that incorporate neuromorphic Darwin chips. The proposed integrated system architecture includes a neuromorphic Darwin chip, digital signal processing unit, microcontroller unit, field programmable gate arrays, and coding and decoding modules to encode and reconstruct analog spiking signals. The study evaluates the encoding module and the heterogeneous integration structure and conducts signal integrity analysis. To achieve optimal signal integrity performance, the article proposes a novel binocular eye diagram analysis technique. This innovative approach guides the encoding algorithm modification and improves the overall system performance. This research is the first to combine joint field-circuit simulation, heterogeneous integration modeling, and signal integrity analysis of the Darwin neuromorphic chip, and it is expected to serve as a valuable reference for future studies on similar systems. © 2022 IEEE.

关键词： Neural networks

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Pruning Networks With Cross-Layer Ranking & k-Reciprocal Nearest Filters

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IEEE Transactions on Neural Networks and Learning Systems 2023年第11期34卷 9139-9148页

作者： Lin, Mingbao Cao, Liujuan Zhang, Yuxin Shao, Ling Lin, Chia-Wen Ji, Rongrong School of Informatics Xiamen University Media Analytics and Computing Laboratory Department of Artificial Intelligence Xiamen361005 China Tencent Youtu Laboratory Shanghai200233 China School of Informatics Xiamen University Fujian Key Laboratory of Sensing and Computing for Smart City Computer Science Department Xiamen361005 China Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates National Tsing Hua University Department of Electrical Engineering and the Institute of Communications Engineering Hsinchu30013 Taiwan Institute of Artificial Intelligence Xiamen University Xiamen361005 China

This article focuses on filter-level network pruning. A novel pruning method, termed CLR-RNF, is proposed. We first reveal a 'long-tail' pruning problem in magnitude-based weight pruning methods and then propose a computation-aware measurement for individual weight importance, followed by a cross-layer ranking (CLR) of weights to identify and remove the bottom-ranked weights. Consequently, the per-layer sparsity makes up the pruned network structure in our filter pruning. Then, we introduce a recommendation-based filter selection scheme where each filter recommends a group of its closest filters. To pick the preserved filters from these recommended groups, we further devise a ${k}$ -reciprocal nearest filter (RNF) selection scheme where the selected filters fall into the intersection of these recommended groups. Both our pruned network structure and the filter selection are nonlearning processes, which, thus, significantly reduces the pruning complexity and differentiates our method from existing works. We conduct image classification on CIFAR-10 and ImageNet to demonstrate the superiority of our CLR-RNF over the state-of-the-arts. For example, on CIFAR-10, CLR-RNF removes 74.1% FLOPs and 95.0% parameters from VGGNet-16 with even 0.3% accuracy improvements. On ImageNet, it removes 70.2% FLOPs and 64.8% parameters from ResNet-50 with only 1.7% top-five accuracy drops. Our project is available at https://***/lmbxμCLR-RNF. © 2012 IEEE.

关键词： computer architecture

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An Efficient medical Image Deep Fusion Model Based on Convolutional Neural Networks

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computers, Materials & Continua 2023年第2期74卷 2905-2925页

作者： Walid El-Shafai Noha A.El-Hag Ahmed Sedik Ghada Elbanby Fathi E.Abd El-Samie Naglaa F.Soliman Hussah Nasser AlEisa Mohammed E.Abdel Samea Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Higher Institute of Commercial Science Management Information SystemsEl-Mahala El-KobraEgypt Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr El-SheikhEgypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Medical Imaging and Interventional Radiology National Liver InstituteMenoufia UniversityEgypt

medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and *** learning provides a high performance for several medical image analysis *** paper proposes a deep learning model for the medical image fusion *** model depends on Convolutional Neural Network(CNN).The basic idea of the proposed model is to extract features from both CT and MR ***,an additional process is executed on the extracted *** that,the fused feature map is reconstructed to obtain the resulting fused ***,the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching(HM),Histogram Equalization(HE),fuzzy technique,fuzzy type,and Contrast Limited Histogram Equalization(CLAHE).The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement *** realistic datasets of different modalities and diseases are tested and ***,real datasets are tested in the simulation analysis.

关键词： Image fusion CNN deep learning feature extraction evaluation metrics medical diagnosis

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Machine learning-based classification of rock discontinuity trace:SMOTE oversampling integrated with GBT ensemble learning

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International Journal of Mining science and Technology 2022年第2期32卷 309-322页

作者： Jiayao Chen Hongwei Huang Anthony G.Cohn Dongming Zhang Mingliang Zhou Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education Department of Geotechnical EngineeringTongji UniversityShanghai 200092China School of Computing University of LeedsLS29JT LeedsUnited Kingdom Department of Computer Science and Technology Tongji UniversityShanghai 211985China School of Civil Engineering Shandong UniversityJinan 250061China Luzhong Institute of Safety Environmental Protection Engineering and MaterialsQingdao University of Science and TechnologyZibo 255000China School of Mechanical and Electrical Engineering Qingdao University of Science and TechnologyQingdao 260061China

This paper presents a hybrid ensemble classifier combined synthetic minority oversampling technique(SMOTE),random search(RS)hyper-parameters optimization algorithm and gradient boosting tree(GBT)to achieve efficient and accurate rock trace identification.A thirteen-dimensional database consisting of basic,vector,and discontinuity features is established from image *** data points are classified as either‘‘trace”or‘‘non-trace”to divide the ultimate results into candidate trace *** is found that the SMOTE technology can effectively improve classification performance by recommending an optimized imbalance ratio of 1:5 to 1:***,sixteen classifiers generated from four basic machine learning(ML)models are applied for performance *** results reveal that the proposed RS-SMOTE-GBT classifier outperforms the other fifteen hybrid ML algorithms for both trace and nontrace ***,discussions on feature importance,generalization ability and classification error are conducted for the proposed *** experimental results indicate that more critical features affecting the trace classification are primarily from the discontinuity ***,cleaning up the sedimentary pumice and reducing the area of fractured rock contribute to improving the overall classification *** proposed method provides a new alternative approach for the identification of 3D rock trace.

关键词： Tunnel face Rock discontinuity trace Machine learning Gradient boosting tree Generalization ability

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Explainable AI-Based Humerus Fracture Detection and Classification from X-Ray Images 26

Explainable AI-Based Humerus Fracture Detection and Classifi...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Barua, Koushick Mahmud, Tanjim Barua, Anik Sharmen, Nahed Basnin, Nanziba Islam, Dilshad Hossain, Mohammad Shahadat Andersson, Karl Hossain, Sazzad Rangamati Science and Technology University Department of Computer Science and Engineering Rangamati4500 Bangladesh Chattogram Maa-O-Shishu Hospital Medical College Dept. of Obstetrics and Gynecology Chittagong4100 Bangladesh Leeds Beckett University Department of Computer Science and Engineering United Kingdom Chattogram Veterinary and Animal Sciences University Dept. of Physical and Mathematical Sciences Chittagong4202 Bangladesh University of Chittagong Department of Computer Science and Engineering Chittagong4331 Bangladesh Luleå University of Technology Pervasive and Mobile Computing Laboratory Luleå971 87 Sweden Department of Computer Science and Engineering Dhaka1207 Bangladesh

ISBN: (纸本)9798350359015

The human skeletal framework relies heavily on bones, and one such crucial component is the 'Humerus.' Positioned in the upper arm, extending from the shoulder to the elbow junction, the Humerus provides essential structural support for muscles and facilitates upper-body movement, particularly in the arms and hands. Consequently, Humerus fractures significantly impact daily life, causing disruptions and limitations. This paper presents a thorough exploration of an Explainable AI-based Humerus Fracture Detection and Classification system, employing various deep learning models. Leveraging a dataset of 1266 X-ray images, encompassing fractured and non-fractured humerus bones from the publicly available 'MURA' dataset, our research evaluates the effectiveness of Convolutional Neural Networks (CNN), VGG16, VGG19, DenseNet121, and DenseNet169 in detecting fractures. After 30 epochs of training, we assessed their performance using critical metrics: accuracy, precision, recall, and F1 score. Notably, DenseNet121 and DenseNet169 exhibited superior accuracy, precision, and recall, laying a robust foundation for automated humerus fracture diagnosis. We also introduced two ensemble models, "Ensemble-1 (VGG16 and VGG19)"and "Ensemble-2 (DenseNet121 and DenseNet169),"which delivered substantial improvements in accuracy, precision, recall, and F1 score, showcasing the potential of ensemble techniques in clinical settings. Furthermore, we enhanced model interpretability and transparency by incorporating Saliency Maps and GRAD-CAM (Gradient-weighted Class Activation Mapping) for Explainable AI (XAI). This visualization allowed us to identify regions of interest in X-ray images contributing to the model's predictions, providing valuable insights for medical practitioners. © 2023 IEEE.

关键词： Convolutional neural network Explainable AI (XAI) Humerus X-ray diagnosis

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A Distributed Framework for Large-scale Protein-protein Interaction Data Analysis and Prediction Using MapReduce

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 160-172页

作者： Lun Hu Shicheng Yang Xin Luo Huaqiang Yuan Khaled Sedraoui MengChu Zhou School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China School of Computer Science and Technology Wuhan University of TechnologyWuhan 430070China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of SciencesUrumqi 830000China Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

Protein-protein interactions are of great significance for human to understand the functional mechanisms of *** the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them *** address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using *** do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and *** solutions are then devised to overcome these *** particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of *** that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and *** results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

关键词： Distributed computing large-scale prediction machine learning MapReduce protein-protein interaction(PPI)

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