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共 265 条记录，以下是71-80 订阅

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Solving Multi-class Imbalance Problems Using Improved Tabular GANs 23rd

Solving Multi-class Imbalance Problems Using Improved Tabula...

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23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Farou, Zakarya Kopeikina, Liudmila Horvath, Tomas Eotvos Lorand Univ Inst Ind Acad Innovat Dept Data Sci & Engn Pazmany Peter Setany 1-C H-1117 Budapest Hungary Pavol Jozef Safarik Univ Inst Comp Sci Jesenna 6 Kosice 04001 Slovakia

ISBN: (纸本)9783031217524;9783031217531

Multi-class imbalance problems are non-standard derivative data science problems. These problems are associated with the skewness in the data underlying distribution, which, in turn, raises numerous issues for conventional machine learning techniques. To address the lack of data in imbalance problems, we can either collect new data or oversample the underrepresented classes by synthesizing artificial data from original instances. This paper focuses on the latter and introduces two novel tabular GAN variants to handle multi-class imbalance problems. Empirical results on three datasets from the UCI repository demonstrated that the suggested approaches that use our proposed filtering algorithm based on neighboring rules improved the ability of the decision tree classification model to recognize underrepresented class instances, decreased the bias toward the majority class, and enhanced its generalization ability.

关键词： Imbalanced learning Generative adversarial networks data augmentation data filtering Multi-class classification

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Network-Based Control for Nonlinear System 3

Network-Based Control for Nonlinear System

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3rd international conference on intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Latheef, Abdul Benny, Soniya Devadhas, Glan Chemperi Apj Abdul Kalam Technological University Vimal Jyothi Engineering College Kerala Thiruvananthapuram India Vimal Jyothi Engineering College Computer Science and Engineering Chemperi Kannur India Vimal Jyothi Engineering College Electronics and Instrumentation Chemperi Kannur India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

The most popular technique for classifying images now is the convolutional neural network. During its training, it is crucial to adjust two factors: the learning rate and gradient. The pre-processing phases involve the fuzzy clustering approach. To change the learning rate, two Event-Based control loops called E (Exponential) and PD (Proportional Derivative)-Control are used. An exponential control loop is employed to prevent an abrupt decline in learning rate as the model gets closer to its ideal state. The learning rate is used by the proportional derivative control loop to determine when to move on to the subsequent data batch. The weight is updated using the back propagation approach based on the loss value that was determined. The proposed method improves the performance with an Accuracy of 96.8%. © 2022 IEEE.

关键词： Backpropagation

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3rd international conference on Machine learning, Image Processing, Network Security and data Sciences, MIND 2021

3rd International Conference on Machine Learning, Image Proc...

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3rd international conference on Machine learning, Image Processing, Network Security and data Sciences, MIND 2021

ISBN: (纸本)9789811958670

The proceedings contain 68 papers. The special focus in this conference is on Machine learning, Image Processing, Network Security and data Sciences. The topics include: Analyzing Wearable data for Diagnosing COVID-19 Using Machine learning Model;comparative Analysis of Classification Methods to Predict Diabetes Mellitus on Noisy data;a Robust Secure Access Entrance Method Based on Multi Model Biometric Credentials Iris and Finger Print;region Classification for Air Quality Estimation Using Deep learning and Machine learning Approach;neuroevolution-Based Earthquake Intensity Classification for Onsite Earthquake Early Warning;detection of Credit Card Fraud by Applying Genetic Algorithm and Particle Swarm Optimization;traditional Indian Textile Designs Classification Using Transfer learning;classification of Electrocardiogram Signal Using Hybrid Deep learning Techniques;automated Detection of Type 2 Diabetes with Imbalanced and Machine learning Methods;fault Diagnosis in Wind Turbine Blades Using Machine learning Techniques;real-Time Detection of Vehicles on South Asian Roads;stock Market Prediction Using Ensemble learning and Sentimental Analysis;Multiple Feature-Based Tomato Plant Leaf Disease Classification Using SVM Classifier;a Methodological Review of Time Series Forecasting with Deep learning Model: A Case Study on Electricity Load and Price Prediction;unexpected Alliance of Cardiovascular Diseases and Artificial Intelligence in Health Care;a Novel Smartphone-Based Human Activity Recognition Using Deep learning in Health care;An Enhanced Deep learning Approach for Smartphone-Based Human Activity Recognition in IoHT;classification of Indoor–Outdoor Scene Using Deep learning Techniques;prediction of the Reference Evapotranspiration data from Raipur Weather Station in Chhattisgarh using Decision Tree-Based Machine learning Techniques;deep Transfer learning and intelligent Item Packing in Retail Management;preface.

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Detection of False Information in Spanish Using Machine learning Techniques 23rd

Detection of False Information in Spanish Using Machine Lear...

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23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Tretiakov, Arsenii Martin, Alejandro Camacho, David Univ Politecn Madrid Dept Comp Syst Engn Calle Alan Turing Madrid 28031 Spain

ISBN: (纸本)9783031217524;9783031217531

This research focuses on the detection of false claims in Spanish through the use of machine learning techniques. Most of the current work related to automated, or semi-automated, fake news detections are carried out for the English language, however, there is still a large room for improvement in other languages such as Spanish. The detection of fake news content and its dissemination (spread) in online platforms is an open and hard problem, this work is focused on the detection of false and misleading information spreading during the election campaign in the Spanish Parliament and Catalonia crisis in 2019, migration crisis, COVID-19 pandemic, and hate speech against minorities. We propose the use of a machine learning model adapted for dealing with human language understanding tasks, called BERT, which has been trained and experimentally tested. We have collected a corpus of different types of false information and claims such as articles, posts on Facebook, WhatsApp's messages, tweets, and others. The results evidence how usage of machine learning techniques can help in the identification of false statements with more than 88% accuracy, and in collecting samples of false information. The experiments, with a comparison between different machine learning methods, have also been carried out using previous datasets, providing a comparison between different approaches.

关键词： Fake news False information Mass media Social media Machine learning Media literacy

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Empowering Leather Quality Assurance: Leveraging Convolutional Neural Networks for Precise Defect Detection and Classification 3

Empowering Leather Quality Assurance: Leveraging Convolution...

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3rd international conference on intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Logeswaran, K. Suresh, P. Savitha, S. Sah, Swati Kr, Prasanna Kumar Rajadevi, R. Vasugi, M. Sujit, B. Akilesh, T. Kongu Engineering College Center of Excellence in Real-Time Analytic Department of Artificial Intelligence Tamilnaud Erode India Vellore Institute of Technology Department of Database Systems Tamil Nadu Vellore India K.S.R College of Engineering Department of Computer Science and Engineering Tamil Nadu Tiruchengode India Sharda University School of Engineering and Technology Uttar Pradesh Greater Noida India Kongu Engineering College Department of Information Technology Tamilnaud Erode India Kongu Engineering College Department of Artificial Intelligence Tamilnaud Erode India

ISBN: (纸本)9798331523893

Ensuring the development of high-quality leather products in the industrial sector requires a strong focus on leather quality assurance. In this study, a unique method for enhancing leather quality assurance using Convolutional Neural Networks (CNNs) is presented. Our suggested solution is able to detect and classify leather defects with amazing accuracy and efficiency by utilizing deep learning. CNN integration for leather fault categorization and detection offers a more reliable and affordable solution, ensuring better product quality and satisfied customers. Existing methods for inspecting leather products often suffer from inconsistent quality control, subjective manual inspection processes, and the complexity of accurately identifying and classifying various types of leather defects. By addressing these challenges, the proposed system aims to empower leather manufacturers with a reliable and efficient solution for ensuring consistent product quality and reducing production costs. Convolutional Neural Networks (CNNs) are integrated to provide a highly automated and effective solution that overcomes these scaling challenges. The proposed CNN-based method uses deep learning algorithms to decode images, which allows the system to swiftly and precisely analyse enormous volumes of visual data. To make sure the CNN can differentiate between various defect classes and keep a low rate of false positives and false negatives, its accuracy, precision, recall, and F1 score should be carefully assessed. © 2025 IEEE.

关键词： Fault tolerance

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Automating Language Proficiency Evaluation in English Language Teaching: An NLP Framework 3

Automating Language Proficiency Evaluation in English Langua...

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3rd international conference on Communication, Control, and intelligent Systems, CCIS 2024

作者： Pathak, Punit Pearlin, E. Punithaasree, K.S. Madhura, K. Rajeswari, A. Ruban Alphonse, Fredrick Auro University School of Liberal Arts and Human Sciences Surat India Panimalar Engineering College Department of English Chennai India Department of English Erode India Manipal Academy of Higher Education Department of Information Technology Manipal Institute of Technology Bengaluru Manipal India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Avadi Department of English Chennai India Institute of Finance and International Management Department of Languages Bengaluru India

ISBN: (纸本)9798331528201

Language proficiency assessment is a critical aspect of English Language Teaching (ELT), serving as a foundation for gauging learners' language skills and guiding instructional strategies. However, traditional assessment methods often face challenges such as subjectivity, inefficiency, and potential biases, hindering accurate and objective evaluation. In response to these limitations, this study introduces a pioneering Natural Language Processing (NLP) framework aimed at transforming language proficiency assessment in ELT. The primary objective of this research is to develop an automated, objective, and scalable approach to language proficiency evaluation that aligns with established proficiency frameworks such as the Common European Framework of Reference (CEFR) or the ACTFL Proficiency Guidelines. The novelty of the proposed framework lies in its innovative integration of advanced NLP techniques, particularly leveraging Generative Pre-trained Transformer (GPT) models, with Support Vector Machine (SVM) classification. The framework's architecture is designed to analyze linguistic data comprehensively, extracting relevant features and predicting proficiency levels accurately. Evaluation results showcase the framework's exceptional performance, achieving an accuracy of 98%, precision of 88%, recall of 82%, and F1-Score of 85%. These results underscore the framework's effectiveness in objectively assessing language proficiency levels across diverse educational settings. The contributions of this study extend beyond methodological advancements;they offer significant implications for practitioners and researchers in the field of ELT. The suggested framework has the potential to completely transform teaching and learning outcomes in ELT by offering a strong instrument for impartial and trustworthy language proficiency evaluation, promoting improved language acquisition and communication skills among students. This work represents a significant breakthrough in the field o

关键词： Support vector machines

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Deep learning Detection of Autism Spectrum Disorder in Children and Adults 3rd

Deep Learning Detection of Autism Spectrum Disorder in Child...

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3rd international conference on intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Sucharitha, M. Jyothi, B. Madupu, Arunkumar Vanitha, Padala VIT-AP University Andhra Pradesh Amaravati India Malla Reddy College of Engineering and Technology Hyderabad India

ISBN: (纸本)9789819783540

Autism spectrum disorder (ASD) is a medical condition originating as a growth-oriented illness that can affect children's social and cognitive skills, resulting in patterns of conduct, limited interests, difficulty communicating, and social engagement difficulties. Early identification of ASD can reduce the frequency and severity of the signs and symptoms. Deep learning (DL) is one of the most modern techniques that can be used for early ASD diagnosis or prevention of long-term consequences. As a result, automated methods for collecting objective measures of autism have been developed, and characteristics of the voice were in addition to being stated as distinct by healthcare providers, nevertheless have also shown promising efficacy in multiple research studies making use of deep learning techniques that are based on computerized exclusion comparing children with ASD to children with typical development (TD). To address these concerns, it suggests a feature recovery auto-encoder algorithm and an aggregate optimization procedure that may attain reliability for a large amount of unpolished data by utilizing a deep-learning-based technique that employs many different models to identify autism. Because of deep learning, the resulting results of these machine learning models were sent to a centralized database where a meta-classifier was developed to determine the approach that is more accurate in detecting autism. For feature extraction, four independent ASD patient datasets including over 600 records of affected children and adults were gathered from various repositories. The suggested approach accurately detects ASD in both children and adults at 99.45 and 99.84, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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automated Techniques on Indian Legal documents: A Review 3

Automated Techniques on Indian Legal documents: A Review

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3rd international conference on intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Nineesha, P. Deepalakshmi, P. Sree Buddha College of Engineering Department of Computer Science and Engineering Kerala Ayathil Elavumthitta Pathanamthitta689625 India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamilnadu Krishnankoil626126 India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

The usage of legal documents has increased tremendously due to its easy availability in electronic format. This has given a wide range of exposure to the research areas. An automated mechanism that processes legal data can lead the law to a new dimension. Different deep learning and artificial intelligence techniques have been applied to the Indian legal document. In this paper, we review about the different techniques like Recurrent Network Networks (RNN), Bidirectional Encoder Representations from Transformers (BERT), Robustly Optimized BERT Pre-training Approach(RoBERTa), Best Matching (BM25) algorithm, Support Vector Machine (SVM), Long Short-Term Memory(LSTM) that have been applied on the Indian legal document for summarising, prediction, text classification and multi-class text classification. This review will give a conclusion that the deep learning techniques will give a better performance than the others. © 2022 IEEE.

关键词： Classification (of information)

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A Novel End-to-End Ensemble UNet Network Based on Edge Computing for Tool Wear Detection 24

A Novel End-to-End Ensemble UNet Network Based on Edge Compu...

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3rd Asia conference on Algorithms, Computing and Machine learning, CACML 2024

作者： Chen, Jianwen Tsai, Meng-Shiun Lin, Wan-Ju Hung, Che-Lun Department of Computer Science National Tsing Hua University Hsinchu300044 Taiwan Department of Mechanical Engineering National Taiwan University Taipei106319 Taiwan Department of Mold and Die Engineering National Kaohsiung University of Science and Technology Kaohsung807618 Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei11221 Taiwan

ISBN: (纸本)9798400716416

automated tool wear condition monitoring is essential for ensuring product quality. However, achieving both high detection precision and utilizing edge computing are critical for advancing intelligent manufacturing. Consequently, this article develops a tool wear detection system utilizing edge computing techniques. Firstly, a novel end-to-end ensemble UNet-based model is proposed. Through end-to-end feature fusion training, it effectively addresses the robustness and accuracy issues inherent in single models. Secondly, multiple neural computing sticks are involved to reduce the need for cloud transmission and preserve data privacy, thereby enabling scalability and flexibility in deployment. Experiments demonstrate that mean intersection over union (mIoU) and mean dice coefficient (mDice) in tool wear detection can be improved by over 3% and 2%, respectively. Furthermore, the execution time can be accelerated by a factor of 4. © 2024 ACM.

关键词： Smart manufacturing

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A Simple and Efficient Deep learning-Based Framework for Vegetable Recognition 3

A Simple and Efficient Deep Learning-Based Framework for Veg...

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3rd international conference on Image Processing and intelligent Control, IPIC 2023

作者： Gong, Xian The University of Adelaide Computer Science Adelaide Australia

ISBN: (纸本)9781510668140

Since the 21st century, artificial intelligence has been continuously evolving in various fields, particularly in agriculture. Vegetables, as a critical component of agriculture and human diets, have always been a focal point in terms of cultivation, production, and sales. Compared to traditional vegetable classification that requires professional knowledge and experience, AI technology utilizes computer vision to achieve automated classification. This study presents a deep learning-based vegetable recognition system aimed at automating the identification and classification of vegetables. The system utilizes a convolutional neural network (CNN) as its fundamental algorithm, integrating the traditional CNN architecture, which comprises convolutional layers, pooling layers, and fully connected layers. In comparison to other vegetable recognition systems on the market, this system utilizes a simpler architecture for processing and classifying vegetable images, significantly improving the accuracy and compatibility of identification. The research steps comprise data collection, image preprocessing, model training, and model testing. Experimental results demonstrate that the system can rapidly and accurately identify and classify various vegetables, with an average accuracy rate exceeding 95% on the test dataset, showcasing high practical value. © 2023 SPIE.

关键词： Vegetables

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