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19th IEEE International Conference on e-Business engineering, ICEBE 2023

作者： Li, Chih-En Liu, Wei-Lun Lai, Kun-Yi Chiang, Yu-Hsiang Yan, Xiaowei Chen, Hao-Xiang Li, Hsu-Ching Cheng, Wan-Shu National Kaohsiung University of Science and Technology Department of Electrical Engineering Kaohsiung Taiwan Institute for Systems Biology Seattle Washington United States Providence University Dep. of Computer Science and Information Management Taichung Taiwan

ISBN: (纸本)9798350325553

Immunotherapy utilizes the potential of the patient's immune system to fight disease. T-lymphocytes have become the central focus of the immune system in the fight against cancer. One of the fundamental studies in immunotherapy is how to accurately identify various types of immune cells. Therefore, the study of immune-related cells will be an important reference for the treatment of COVID19 or other related diseases. In this paper, a series of experiments were conducted with COVID-19 patients as the starting point to design a strategy to identify the important subtypes of CD$4^{+}$T cells. In this paper, an intelligent machine learning algorithm is proposed according to the results of different experiments of linear and nonlinear dimension reduction algorithms, integrating the important characteristics of these two algorithms and improving the performance. Our experimental results show that the algorithm designed based on the front-end experiments can effectively distinguish the important lymphocytes. The proposed algorithm not only improves the performance, but also the dimension reduction procedure keeps the important features of both global and regional data. The experimental results of this paper will be beneficial for the subsequent analysis of the clustering in identifying important subtypes of CD$4^{+}$T cells. Most importantly, this wm significantly improve the procedure of manually integrating and labeling the recorded data after several tedious and monotonous analyses with external software. © 2023 IEEE.

关键词： COVID-19

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An Effective Detection and Classification of Schizophrenia Patients from Eeg Signals Using Machine Learning Algorithms

SSRN

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SSRN 2023年

作者： Tamilarasi, K. Pavithra, Sekar Kumar, Anuj S. School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India

A brain condition called schizophrenia has a significant impact on people’s thinking, behaviors, and lives on a global state. The prevalence of the condition rises along with the population, and those who suffer from schizophrenia lose their capacity for thought and behavior with time. All these issues arise because it is challenging to identify schizophrenia patients. As a result, proper care and quality patient treatment require an accurate and quick diagnosis procedure. This article aims to improve the accuracy of . detection by using EEG signals and Machine Learning algorithms It is tested using the Resting state EEG dataset, which demonstrates a high level of capability in distinguishing between schizophrenia patients and healthy controls. The EEG data is converted to different forms, where the given machine learning techniques extract the EEG signals to identify patients with schizophrenia and healthy control. We are utilizing the 28-file public dataset in this study. In the file, there are 14 healthy individuals as well as 14 SCZ sufferers. In accordance with the findings, (1) the decision tree successfully distinguished SCZ patients from healthy individuals using EEG signals with an accuracy of 89.12%, and (2) logistic regression was also used to successfully identify the affected patient using EEG signals with an accuracy of 69.78%. (3) The SCZ patient was correctly identified by SVM with an accuracy of 65.51%. Our research demonstrates the potential for machine learning to play a significant role in aiding in the diagnosis of schizophrenia © 2023, The Authors. All rights reserved.

关键词： Decision trees

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Dr_ OrangeApp: Revolutionizing Real-Time Pest Detection in Citrus Orchards 24

Dr_ OrangeApp: Revolutionizing Real-Time Pest Detection in C...

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4th Asia-Pacific Artificial Intelligence and Big Data Forum, AIBDF 2024

作者： Moshayedi, Ata Jahangir Jawadi, Mohammad Jalil Roy, Atanu Shuvam Rostami, Elahe Wang, Zhonghua Yang, Liqin Xu, Qun School of Information Engineering Jiangxi University of Science and Technology Jiangxi Ganzhou China Department of Computer Science and Engineering Indian Institute of Technology Uttar Pradesh Kanpur India Institute of Quality Standards & Testing Technology for Agro-products Fujian Provincial Key Laboratory of Quality and Safety of Agricultural Products Fujian Academy of Agricultural Sciences Fujian Fuzhou China Jiangxi Environmental Engineering Vocational College Jiangxi Ganzhou China

ISBN: (纸本)9798400710865

Orange pests pose a significant threat to citrus crops, leading to reduced yield and quality, making effective control essential for healthy orchards and agricultural productivity. In this paper the novel idea of The development of the Dr_ Orange application which is facilitates real-time monitoring of pest infestations, enabling timely farmer interventions that improve crop health, reduce pesticide usage, and enhance productivity. To make the Application the Personal image classifier along the interface used to make the model. the have some features like Chart, report and QR generating which can help the farmers to detect the pests faster and also follow up the process of curiness. To test the app two type of test investigated and the The app has been rigorously tested on 313 images, over the five repeatable pests yielding strong overall performance with an average accuracy and recall of 0.75, perfect precision of 1.00, and an F1 score of 0.856. As the final test the application was evaluated in an orange orchard, successfully identifying and reporting the occurrence of Citrus Leafminer pests in 10 trees. Although the app demonstrates high precision, challenges such as variations in lighting conditions, the angle of the photographs, and data quality can affect result accuracy. Repeatedly taking pictures can help mitigate these effects and improve the interface’s impact on detection accuracy. based on obtained results, the proposed App enhances pest management efficiency for orange crops while also promoting improved agricultural productivity. © 2024 Copyright held by the owner/author(s).

关键词： Citrus fruits

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通过深度神经网络与皮层网络的多级整合增强自然感知的神经编码模型

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science Bulletin 2024年第11期69卷 1738-1747页

作者：李远宁杨虎铮顾实 School of Biomedical Engineering&State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech UniversityShanghai 201210China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Department of Computer and Information Science University of PennsylvaniaPhiladelphiaPA 19104USA Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhen 518110China

Cognitive neuroscience aims to develop computational models that can accurately predict and explain neural responses to sensory inputs in the cortex. Recent studies attempt to leverage the representation power of deep neural networks(DNNs) to predict the brain response and suggest a correspondence between artificial and biological neural networks in their feature representations. However, typical voxel-wise encoding models tend to rely on specific networks designed for computer vision tasks, leading to suboptimal brain-wide correspondence during cognitive tasks. To address this challenge, this work proposes a novel approach that upgrades voxel-wise encoding models through multi-level integration of features from DNNs and information from brain networks. Our approach combines DNN feature-level ensemble learning and brain atlas-level model integration, resulting in significant improvements in predicting whole-brain neural activity during naturalistic video perception. Furthermore, this multi-level integration framework enables a deeper understanding of the brain's neural representation mechanism, accurately predicting the neural response to complex visual concepts. We demonstrate that neural encoding models can be optimized by leveraging a framework that integrates both data-driven approaches and theoretical insights into the functional structure of the cortical networks.

关键词： Neural encoding Visual perception Artificial neural networks Functional neuroimaging Naturalistic stimuli

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STA+: Spatiotemporal Adaptation with Adaptive Model Selection for Video Action Recognition 4

STA+: Spatiotemporal Adaptation with Adaptive Model Selectio...

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4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Li, Minghui Zhang, Chunjie Zheng, Xiaolong Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology School of Computer Science and Technology Beijing China Chinese Academy of Sciences State Key Laboratory of Multimodal Artifical Intelligence Systems Institute of Automation Beijing China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing China

ISBN: (纸本)9798350349252

Recent breakthroughs in video models have achieved remarkable success by integrating vision transformers into the video domain through adaptation. However, prevalent approaches in existing literature often entail a single branch, incorporating additional spatiotemporal learning modules. This approach cannot fully satisfy the unique and targeted learning requirements of temporal and spatial representations. It lacks a well-balanced spatiotemporal understanding of videos, particularly in recognizing human actions, which requires a comprehensive understanding of space and time. In this study, we introduce a novel two-stream architecture named SpatioTemporal Adaptation with Adaptive Model Selection (STA+) to achieve synchronized spatiotemporal understanding of video with only RGB inputs. We propose an adaptive model selection method based on the intrinsic categorization judgment of the samples to enhance overall performance. We validate the effectiveness of our proposed method through rigorous experiments conducted on public benchmarks such as Diving-48 and Something-Something- V2. Our method consistently achieves competitive performance on these datasets. © 2024 IEEE.

关键词： adaptation spatiotemporal Video action recognition

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YOLOv9 in Action: A Futuristic Approach to Real-Time License Plate Recognition and Analysis

YOLOv9 in Action: A Futuristic Approach to Real-Time License...

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Intelligent Signal Processing and Effective Communication Technologies (INSPECT), IEEE International Conference on

作者： M Ranjith Kumar G Jeevan Sendur S Praveen Kumar R Annamalai School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

The automated recognition and identification of license plates is an essential element of intelligent transportation systems that enable effective traffic management, security measures, and the development of efficient e-payment algorithms in toll booths and parking facilities. Even though license plate recognition (LPR) technology is fairly advanced, significant efforts are being made to enhance its accuracy and resilience The growing adoption of deep learning technologies across multiple industry domains, particularly computer vision, presents a novel idea implemented in this paper. To develop an approach to improve detection accuracy using YOLOv9 and transfer learning. Through combining the power of YOLOv9 with transfer learning, it is feasible to tackle the traditional LPR system's inherent flaws. Character recognition, picture pre-processing, license plate extraction, and character segmentation are the four main parts of the proposed system. Each step involves unique methodologies, primarily focusing on character recognition methodologies using YOLOv9 which has not been adequately tackled in existing research. By thorough testing and analysis, this study seeks to further the current developments in LPR systems, ensuring discernibly better-established intelligent transportation solutions. We have achieved a mAP50 Score of 99.5% and a mAP50-95 score of 71 %

关键词： computer vision Accuracy Transfer learning Transportation Signal processing Real-time systems Character recognition Security License plate recognition Testing

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End-to-End Semi-Supervised 3D Instance Segmentation with PCTeacher

End-to-End Semi-Supervised 3D Instance Segmentation with PCT...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Linfeng Li Na Zhao School of Computer Science and Engineering Nanyang Technological University Information Systems Technology and Design Singapore University of Technology and Design

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

3D instance segmentation is a fundamental and critical task for enabling robots to operate effectively in unstructured 3D environments. In order to address the challenges posed by the high demand for large-scale annotated data and the limited availability of such data in the context of 3D instance segmentation, we study semi-supervised 3D instance segmentation problem and propose a novel end-to-end framework based on the mean teacher paradigm, named PCTeacher. Our PCTeacher generates both point-level and cluster-level pseudo labels to harness knowledge from unlabeled data. It notably enhances the training stability through end-to-end training and improves pseudo-label quality. Specifically, for point-level pseudo labels, PCTeacher employs a multi-view fusion strategy to achieve higher precision and recall. Regarding cluster-level pseudo labels, it introduces a hybrid grouping strategy to generate more potential proposals and utilizes a point-cluster agreement-based thresholding (PCAT) mechanism to fully exploit cluster-level pseudo labels. By combining and strengthening both point-level and cluster-level pseudo labels, our PCTeacher achieves state-of-the-art performance on two benchmark datasets across multiple labeled data ratios with a more compact network compared to the existing method.

关键词： Instance segmentation Training Three-dimensional displays Accuracy Benchmark testing Hybrid power systems Reliability

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Prediction of Traffic Accidents Using Deep Learning Ensemble Model

Prediction of Traffic Accidents Using Deep Learning Ensemble...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2023

作者： Sekhar, G Soma Mahadik, Shubhangi Ramana, Attili Venkata Chandra Shaker Reddy, Pundru Pughazendi, N. Asif, S. Computer Science and Engineering Vardhaman College of Engineering Telangana Hyderabad India Bharati Vidyapeeth's Institute of Management & Information Technology Master of Computer Applications Navi Mumbai India Hyderabad India SR University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India Computer Science and Engineering Panimalar Engineering College Tamil Nadu Chennai India Marri Laxman Reddy Institute of Technology and Management CSE-Data Science Telangana Hyderabad India

ISBN: (纸本)9798350319200

Because of the widespread negative effects that traffic accidents can have on people's lives, the study of how to assess and anticipate them using cutting-edge technology is an important area of study. The research of traffic accidents has traditionally relied on data made public by traffic-entities and road-police forces. However, with the advent of social-media, researchers now have access to detailed, real-time data on road accidents occurring across a region, allowing for more comprehensive analyses that take into account previously unreported incidents. This paper uses an ensemble-Deep-Learning(EDL) strategy made up of Gated-Recurrent Units(GRUs) and Convolutional-Neural-Networks(CNNs) to offer a model to forecast traffic accidents utilizing data mined from social-media and the open web. The acquired results are compared to those of published baseline methods and other researchers. The outcomes are encouraging, showing that the proposed EDL approach outperforms the conventional models and other DL approaches published in the literature when applied to the problem setting. Authorities in charge of preventing accidents on the roads can use the data generated by the program. © 2023 IEEE.

关键词： DL ML risk prediction traffic accident traffic accidents

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Enabling Communication-Efficient Federated Learning via Distributed Compressed Sensing 42

Enabling Communication-Efficient Federated Learning via Dist...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Guan, Yixuan Liu, Xuefeng Ren, Tao Niu, Jianwei Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing China Zhongguancun Laboratory Beijing China Chinese Academy of Sciences Laboratory for Internet Software Technologies Institute of Software Beijing China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China

ISBN: (纸本)9798350334142

Federated learning (FL) trains a shared global model by periodically aggregating gradients from local devices. Communication overhead becomes a principal bottleneck in FL since participating devices usually suffer from limited bandwidth and unreliable connections in uplink transmission. To address this problem, the gradient compression methods based on compressed sensing (CS) theory have been put forward recently. However, most existing CS-based works compress gradients independently, ignoring the gradient correlations between participants or adjacent communication rounds, which constrains the achievement of higher compression rates. In view of the above observation, we propose a novel gradient compression scheme named FedDCS, guided by distributed compressed sensing (DCS) theory. Following the design philosophy of separate encoding and joint decoding in DCS, FedDCS compresses gradients for participants in each round separately while reconstructing them at the central server jointly via fully exploiting correlated gradients from the previous round, which are known as side information (SI). Benefiting from this design, reconstruction performance is significantly improved with fewer decoding errors also iterations under the identical compression rate, and the total uploading bits to achieve model convergence are considerably reduced. Theoretical analysis and extensive experiments conducted on MNIST and Fashion-MNIST both verify the effectiveness of our approach. © 2023 IEEE.

关键词： Compressed sensing

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Surrogate-Assisted Multi-Class Collaborative Teaching and Learning Optimizer for High-Dimensional Industrial Optimization Problems

Surrogate-Assisted Multi-Class Collaborative Teaching and Le...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Bi, Jing Wang, Ziqi Yuan, Haitao Yang, Jinhong Zhang, Jia College of Computer Beijing University of Technology Beijing100124 China Beijing University of Technology Beijing Laboratory of Smart Environmental Protection Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Cssc Systems Engineering Research Institute Beijing China Lyle School of Engineering Southern Methodist University Dept. of Computer Science DallasTX United States

ISBN: (纸本)9781665410205

Swarm intelligence and evolutionary algorithms are widely applied in industrial scheduling, mobile edge computing, etc due to their strong robustness and fast optimization speed. However, some real-world industrial optimization problems involve numerous decision variables, known as high-dimensional problems. Current algorithms often require considerable computational resources to evaluate objective function values because of high-dimensional decision spaces. Moreover, they are also prone to be trapped in local optima. To solve the above problems, this work proposes an improved algorithm named Surrogate-assisted Multi-class Collaborative Teaching and learning optimizer (SMCT). A multi-class collaborative teaching and learning optimizer is proposed as a base optimizer to improve exploration and exploitation abilities. Furthermore, an autoencoder-assisted radial basis function is proposed as the surrogate model to replace true function evaluations, thereby saving computational resources and balancing the complexity and accuracy in fitting true models. Finally, experimental results demonstrate that SMCT surpasses its existing peers in both search accuracy and convergence speed across eight high-dimensional benchmark functions. © 2024 IEEE.

关键词： Scheduling algorithms

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