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Support vector machine with discriminative low-rank embedding

CAAI Transactions on Intelligence Technology 2024年第5期9卷 1249-1262页

作者： Guangfei Liang Zhihui Lai Heng Kong Computer Vision Institute College of Computer Science and Software EngineeringShenzhen UniversityShenzhenChina Shenzhen Institute of Artificial Intelligence and Robotics for Society ShenzhenChina Department of Breast and Thyroid Surgery BaoAn Central Hospital of ShenzhenShenzhenChina

Support vector machine(SVM)is a binary classifier widely used in machine ***,neglecting the latent data structure in previous SVM can limit the performance of SVM and its *** address this issue,the authors propose a novel SVM with discriminative low-rank embedding(LRSVM)that finds a discriminative latent low-rank subspace more suitable for SVM *** extension models of LRSVM are introduced by imposing different orthogonality constraints to prevent computational inaccuracies.A detailed derivation of the authors’iterative algorithms are given that is essentially for solving the SVM on the low-rank ***,some theorems and properties of the proposed models are presented by the *** is worth mentioning that the subproblems of the proposed algorithms are equivalent to the standard or the weighted linear discriminant analysis(LDA)*** indicates that the projection subspaces obtained by the authors’algorithms are more suitable for SVM classification compared to those from the LDA *** convergence analysis for the authors proposed algorithms are also ***,the authors conduct experiments on various machine learning data sets to evaluate the *** experiment results show that the authors’algorithms perform significantly better than other algorithms,which indicates their superior abilities on classification tasks.

关键词： iterative methods machine leaning support vector machunes

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CNN-Based Pneumonia Detection and Severity Analysis in Chest X-Ray Images for Enhanced Diagnostic Support

CNN-Based Pneumonia Detection and Severity Analysis in Chest...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Muthukumar, D. Surendiran Chowdary, Bommineni Sreekar Kumar, D. Senthil Jagadeesan, P. Mythili, B. Swapna, B. Kalasalingam Academy of Research and Education Department of computer science and Engineering Tamilnadu Srivilliputhur India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Virudhunagar India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Vel tech Multi tech Dr.Rangarajan Dr. Sakunthala Engineering college Department of Computer Science and Engineering Tamil Nadu Chennai India Dr.M.G.R Educational and Research Institute Department of Electronics and communication engineering Chennai India

ISBN: (纸本)9798331509828

Pneumonia continues to pose a substantial global health challenge, necessitating prompt and precise diagnosis to enhance patient outcomes. In order to detect pneumonia and classify its severity into mild, moderate, and severe phases, the web-based program uses deep learning algorithms to analyse chest X-ray pictures. A Convolutional Neural Network (CNN) model that has been trained on publicly accessible datasets and achieved high accuracy is integrated into the application. Furthermore, it provides personalized health recommendation, downloadable PDF reports, and a local hospital database to facilitate users in locating nearby treatment facilities. The system utilizes Fast API for backend processing and user-friendly interfaces to enhance accessibility and efficiency. This solution underscores the potential of artificial intelligence in supporting early diagnosis and effective pneumonia management. © 2025 IEEE.

关键词： Convolutional neural networks

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Cloud-Based Oxygen Management System with Seamless HTTP Protocol Integration 4

Cloud-Based Oxygen Management System with Seamless HTTP Prot...

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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Hemalatha, S. Ramasamy, Jayabharathi Selvi, K. Anandan, P. Meenakshi, R. Maniraj, S.P. Tagore Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India S. A. Engineering College Faculty of Management Studies Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350307757

In this study, an efficient system to improve the oxygen supply in hospitals and other healthcare institutions is proposed. It uses Internet of things (IoT) devices and cloud based data transmission and processing system. The controller unit collects the oxygen sensor data for the analysis and uses HTTP protocols to send and receive data with cloud computing in HTTP website server. The cloud is data storage, processes and real-time monitoring, analysis and control system. It has a user interface with viewed administrators and medical professionals. The interface provides real-time data regarding oxygen supply, consumption patterns, and possible concerns. Authorized workers can access and manage the system via the display, allowing for timely adjustments to oxygen supply settings, optimizations to refill schedules, and responses to emergency circumstances. It's adaptable, flexible, and resilient, which are benefits. There will be less need to worry about oxygen shortages and interruptions, and healthcare institutions of all sizes can benefit from this. The cloud's remote access and control capabilities provide continuous functioning and rapid reaction times. It's an effective solution for hospitals and other healthcare facilities that need to control their oxygen supplies. With its cloud-based design, the solution increases productivity, improves patient safety, and simplifies oxygen management. © 2024 IEEE.

关键词： HTTP

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Effective Vehicle Detection Using Improved Faster Recursive Convolutional Neural Network Model

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SN computer science 2023年第2期4卷 105页

作者： Mahendra, G. Roopashree, H.R. Department of Computer Science & Engineering GSSS Institute of Engineering & Technology for Women Affiliated to VTU Karnataka Belagavi India

In recent decades, vehicle recognition plays an essential and effective role in the intelligent transportation system and traffic safety. Currently, the deep learning approaches made an effective impact in the fast vehicle detection application. In the real-time traffic monitoring video sequences, it is difficult to recognize the smaller vehicle targets and multi-scale vehicle targets in the complex scenes. A new fully automated vehicle detection model is implemented in this manuscript to address the above-mentioned issue. After obtaining the videos from KITTI dataset, the mask is created for specific classes like car, pedestrian, and cyclist. Additionally, the data augmentation is accomplished using the techniques like zoom-out, zoom-in, shift, shear, flipping, and rotation. The data augmentation enhances the performance of the deep learning models by creating different and new examples for training the dataset. The deep learning models perform accurately, if the dataset is rich and sufficient. After data augmentation, an improved faster Recursive Convolutional Neural Network (R-CNN) model is developed for vehicle detection. The improved faster R-CNN model first extracts discriminative feature values from the images utilizing U-Net and Visual Geometry Group (VGG) 19 pre-trained methods. Then, it creates the region proposal to improve the detection performance and narrow the search space. On the KITTI dataset, the improved faster R-CNN model achieved 90.59% of average precision and 0.45 s of processing time, which are better compared to the existing models. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Autonomous driving computer vision Data augmentation Deep learning Faster R-CNN model Surveillance video Vehicle detection

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Heterogeneous Image Knowledge Driven Visual Perception

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 255-257页

作者： Lan Yan Wenbo Zheng Fei-Yue Wang IEEE the College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China the School of Computer Science and Artificial Intelligence Wuhan University of TechnologyWuhan 430070China the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Dear Editor,This letter is concerned with visual perception closely related to heterogeneous *** the huge challenge brought by different image modalities,we propose a visual perception framework based on heterogeneous image knowledge,i.e.,the domain knowledge associated with specific vision tasks,to better address the corresponding visual perception problems.

关键词： Visual visual knowledge

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Crop Quality Quantification: A Federated Learning CNN's Approach to Broccoli Leaf Disease Detection

Crop Quality Quantification: A Federated Learning CNN's Appr...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Suryavanshi, Ankita Mehta, Shiva Gopal, Lisa Thapliyal, Siddhant Jain, Vishal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India Sharda University Computer Science and Engineering Sharda School of Engineering and Technology U.P. Greater Noida India

ISBN: (纸本)9798350361537

This article proposes a thorough research study between federated learning and CNNs, where both methods are utilized for the detection and severity categorization of broccoli leaf diseases. The algorithm uses the data, the separation of four clients' enthused, to point out the severity of diseases among four categories that start from mild to critical illness. A federated learning system has used client data instead of a global model to train a learning model where privacy issues have not arisen. The study of the results revealed that the FedAvg algorithm-based federated learning method, which is precisely featured with that property, kept each individual's observation data as an origin point to create a highly effective global model. The research depended on macro-economic, micro-economic, and weighted averages to reflect the model's accomplishments at both local and international levels. For all the intensely local ratings whatsoever, the customers SB-1, SB-2, and SB-3 showed a macro average score of 92.17 and 96.49, respectively, at a swell of 96.64 and 97.1. These scores reflect that the model's accuracy and recall are improving, which is constantly witnessed among clients. Scores were calculated by burden weighting. Since the first computation of the score was 92.26, sb-1. In the long term, scores for sb-2, sb-3, and sb-4 trended upward, with sb-4 finally peaking at 97.08. After the weighted averages, the micro averages are statistically one in line;the outcome is 92.24 in sb-1 and 97.08 in sb-4. This invariably drives the model to the correct result, implying data consistency. All the local areas had a macro average score of 97.65, a micro score of 97.63, and a weighted average score of 97.63. © 2024 IEEE.

关键词： Federated learning

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ISO 24617-8 Applied: Insights from Multilingual Discourse Relations Annotation in English, Polish, and Portuguese 20

ISO 24617-8 Applied: Insights from Multilingual Discourse Re...

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20th Joint ACL - ISO Workshop on Interoperable Semantic Annotation, ISA 2024

作者： Tomaszewska, Aleksandra Silvano, Purificação Leal, António Amorim, Evelin Institute of Computer Science Polish Academy of Sciences Poland University of Porto Centre for Linguistics of the University of Porto Portugal Institute for Systems and Computer Engineering Technology and Science Portugal

ISBN: (纸本)9782493814326

The main objective of this study is to contribute to multilingual discourse research by employing ISO-24617 Part 8 (Semantic Relations in Discourse, Core Annotation Schema – DR-core) for annotating discourse relations. Centering around a parallel discourse relations corpus that includes English, Polish, and European Portuguese, we initiate one of the few ISO-based comparative analyses through a multilingual corpus that aligns discourse relations across these languages. In this paper, we discuss the project’s contributions, including the annotated corpus, research findings, and statistics related to the use of discourse relations. The paper further discusses the challenges encountered in complying with the ISO standard, such as defining the scope of arguments and annotating specific relation types like Expansion. Our findings highlight the necessity for clearer definitions of certain discourse relations and more precise guidelines for argument spans, especially concerning the inclusion of connectives. Additionally, the study underscores the importance of ongoing collaborative efforts to broaden the inclusion of languages and more comprehensive datasets, with the objective of widening the reach of ISO-guided multilingual discourse research. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： ISO Standards

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Automated Insect Detection and Classification Using Pelican Optimization Algorithm with Deep Learning on Internet of Enabled Agricultural Sector

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SN computer science 2024年第5期5卷 1-13页

作者： Assiri, Mohammed Elhameed, Elmouez Samir Abd Kumar, Arun Singla, Chinu Department of Computer Science College of Sciences and Humanities- Aflaj Prince Sattam bin Abdulaziz University Aflaj 16273 Saudi Arabia Department of Computer Science College of Post-Graduated Studies Sudan University of Science and Technology Khartoum Sudan Department of Electronics and Communication Engineering New Horizon College of engineering Bengaluru India Computer Engineering Thapar Institute of Engineering and Technology Punjab Patiala India

Recently, the combination of Deep Learning (DL) methods within the Internet of Things (IoTs) has developed in the agricultural field, especially in the domain of pest management. This study considers the implementation and development of an innovative method for Insect Detection and Classification using DL within the environment of the IoTs in agriculture. The developed system leverages advanced DL approaches for analyzing images captured by IoT-enabled devices, enabling real-time identification and categorization of insect pests. By continuously incorporating these technologies, this research goals to increase the efficiency and precision of pest monitoring, finally providing to sustainable agricultural technologies and increased crop yield. This study presents an Automated Insect Detection and Classification using Pelican Optimization Algorithm with Deep Learning (AIDC-POADL) technique on Internet of Enabled Agricultural Sector. The main aim of the AIDC-POADL system is to detect and categorize various kinds of insects exist in the agricultural field. In the primary stage, the AIDC-POADL technique involves DenseNet-121 model to learn complex features in the input images. Besides, the hyperparameter selection of the DenseNet-121 model developed by the POA. At last, multilayer perceptron (MLP) model can be applied to discriminate the insects into various classes. To validate the improved performance of the AIDC-POADL algorithm, a series of simulations were involved. The experimental outcomes stated that the AIDC-POADL technique offers enhanced recognition results over other approaches. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Agricultural region Deep learning Insect detection Internet of things Pelican optimization algorithm

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Gender Classification from Fingerprint Using Hybrid CNN-SVM

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Journal of Artificial Intelligence and Technology 2024年第1期4卷 82-87页

作者： J.Serin Keren T.Vidhya I.S.Mary Ivy Deepa V.Ebenezer A.Jenefa Department of Information Technology Women’s Christian CollegeChennaiIndia Department of Computer Science and Engineering Women’s Christian CollegeChennaiIndia Department of Computer Science and Engineering Karunya Institute of Technology and SciencesCoimbatoreIndia

Gender classification is used in numerous applications such as biometrics,criminology,surveillance,HCI,and business *** biometric factors like gait,face,hand shape,and iris have been used to classify people into genders,the majority of research has focused on facial traits due to their more recognizable *** research employs fingerprints to classify gender,with the intention of being relevant for future *** methods for gender classification utilizing fingerprints have been presented in the literature,including ANN,KNN,Naive Bayes,the Gaussian mixture model,and deep learning-based *** these classifiers have shown good classification accuracy,gender classification remains an unexplored field of study that necessitates the development of new approaches to enhance recognition accuracy,computation,and running *** this paper,a CNN-SVM hybrid framework for gender classification from fingerprints is proposed,where preprocessing,feature extraction,and classification are the three main *** main goal of this study is to use CNN to extract fingerprint *** features are then sent to an SVM classifier to determine *** hybrid model’s performance measures are examined and compared to those of the conventional CNN *** a CNN-SVM hybrid model,the accuracy of gender classification based on fingerprints was 99.25%.

关键词： digital image processing fingerprint gender classification hybrid CNN-SVM hybrid model pattern recognition

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A Mango Disease Prediction for Smart Agiculture using Machine Learning Algorithms

A Mango Disease Prediction for Smart Agiculture using Machin...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Lavanya K A. Packialatha Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies Chennai Tamilnadu India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

The country's economy relies heavily on agriculture, and the health of the crops is essential to its success. Crop diseases that go undetected can cost the agriculture industry. Thus early detection and identification are crucial. It is possible to avoid crop diseases from destroying the harvest if they are accurately diagnosed and detected. Because healthy and diseased plants appear identical in their early stages, farmers cannot distinguish between the two by watching the crop leaf. India exports vast mangoes, making it an economically and environmentally significant fruit. About 1500 mango species are grown in India, with over 1000 commercial types. There are a lot of diseases that harm mangoes, affecting their look, taste, and economy. The "Prediction of Disease in Mango Fruit Crops" A complex warning system that uses machine learning and IoT. One of the main objectives is to develop a system that can forecast disease outbreaks on mango fruit harvests using historical weather information and crop yield Mango trees in India are plagued by a fungus called Anthracnose, which is the most frequent disease of its kind. Anthracnose, a highly contagious fungus, requires a quick and accurate method of diagnosis. As a result, an in-depth examination of the plants is essential before initiating any control measures. This study examines machine learning(ML) and deep learning (DL) strategies for detecting and classifying mango plant diseases. The performance of ML and DL-based classification models for mango crops and their datasets and feature extraction approachesare examined in this work. Finally, a variety of issues involved in plant disease identification are explored.

关键词： Support vector machines Deep learning Plant diseases Accuracy Costs Biological system modeling Crops Data models Agriculture Internet of Things

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