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A Fuzzy C-Means With Support Vector Regression for Calibration and Fault Detection in Modern Instruments

A Fuzzy C-Means With Support Vector Regression for Calibrati...

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Mobile networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Mohammed I. Habelalmateen Satish Reddy Kasarla Jajimoggala Sravanthi P. Kavitha S Meenakshi Sundaram Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electronics and Communication Engineering Hyderabad Institute of Technology And Management Hyderabad India Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Department of Computer Science and Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Artificial Intelligence and Data Science Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

From the past few decades, Calibration and Fault Detection in modern instruments are crucial for ensuring accuracy, reliability, and safety by configuring instruments to provide accurate measurements and identifying anomalies or malfunctions. These processes prevent accidents, reduce downtime, and increase productivity, but also have drawbacks, including being time-consuming and labor-intensive, requiring specialized expertise and equipment, and being expensive, while also being susceptible to environmental factors and requiring frequent recalibration. To overcome these problems, a Fuzzy C-Means with Support Vector Regression (FCM-SVR) is proposed for detecting calibration and fault in modern instruments using Predictive Electrical Fault Analysis (PEFA) dataset. The collected data is undergone preprocessing using Label Encoder (LE) to normalize data without reducing the dimension. Then, Empirical Mode Decomposition and Sample Entropy are considered for feature extraction for robust fault detection and to reduce false alarm. At last, FCM-SVR is employed as classifier for enhancing computational efficiency, flexibility, and overall performance. From the results, it clearly shows that the proposed FCM-SVR obtained higher values in the context of precision, recall, F1 score and accuracy when compared to existing method K-Nearest Neighbor (KNN).

关键词： Support vector machines Wireless communication Accuracy Fault detection Instruments Nearest neighbor methods Feature extraction Entropy Calibration Computational efficiency

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Automatic Fake News Detection on Social networks using Multimodal Approach of BERT and ResNet110

Automatic Fake News Detection on Social Networks using Multi...

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： R. Udayakumar Nagendar Yamsani Sri Lavanya Sajja Yaragani Ashok Kumar Lathakumari K R CS & IT Kalinga University Chhattisgarh India School of Computer Science and Artificial Intelligence SR University Warangal India Malla Reddy Engineering College for Women (Autonomous) Hyderabad India Department of Computer Science and Engineering (Data Science) CMR Technical Campus Hyderabad India Nitte Meenakshi Institute of Technology Bengaluru India

With the rapid development of online content-sharing and social media platforms has exploded for spreading fake news which create people trusting in unfavorable stories. This scenario allows to determine public opinion and produce chaos and panic through population. Therefore, detecting fake news has one of most significant topics which aims to identify a certain news is real or fake. The solution of fake news detection is split into three various classes such as content, social context and knowledge-based techniques. In this paper, a multimodal approach is proposed for detecting fake news for text and image, as well as classifying the news is real or fake. The Bidirectional Encoder Representations from Transformers (BERT) is used to extract text features and ResNet110 is used for image feature extraction. This model is estimated on Buzzfeed and ISOT dataset and attains better results using accuracy, precision, recall, specificity, and F1-score. The obtained result shows that the BERT+ResNet110 model achieves better accuracy of 98.51% on Buzzfeed dataset and 99.68% on ISOT dataset which ensures accurate detection compared to other existing methods like Random Forest (RF), Multi Support Vector Machine (MSVM) and Long Short-term Memory-Levy Flight (LSTM-LF).

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COVID19 detection in chest x-ray using vision-transformer with different patch dimensions 2

COVID19 detection in chest x-ray using vision-transformer wi...

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2nd International Conference on Machine Learning and data Engineering, ICMLDE 2023

作者： Kadry, Seifedine Abualigah, Laith González Crespo, Rubén Verdú, Elena Damasevicius, Robertas Singh, Vijendra Rajinikanth, Venkatesan Department of Applied Data Science Noroff University College Kristiansand Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Computer Science Department Al Al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Higher School of Engineering and Technology Universidad Internacional de la Rioja Logroño26006 Spain Department of Applied Informatics Vytautas Magnus University Kaunas53361 Lithuania School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India

computerized disease detection systems (CDDs) have proven effective for automatic screening in recent years. Among the standard procedures in hospitals for faster and more accurate diagnosis is medical imaging-based disease screening. We aim to develop a CDD that detects COVID-19 using chest X-rays pre-trained vision transformers (PVTs). This scheme includes the following steps: (1) collecting images and resizing them, (2) implementing PVT for feature extraction, and (3) binary classifying the results and validating the proposed schemes. To prove the merit of the developed scheme, 4800 images (2400 normal and 2400 COVID-19) are analyzed. MLP classifiers verify the PVT performance using patch sizes of 6, 12, and 24. A patch size 24 results in 97.5% accuracy for the proposed CDD system. When patch sizes are increased to 12, accuracy increases to over 98%. For this specific task, smaller patch sizes are more effective. These high-accuracy results demonstrate the effectiveness of the developed scheme for detecting COVID-19 in chest X-rays. © 2024 Elsevier B.V.. All rights reserved.

关键词： Diagnosis

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An Algorithm for Detecting Surface Defects in Industrial Strip Steel based on Receptive Field and Feature Information Supplementation

An Algorithm for Detecting Surface Defects in Industrial Str...

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International Conference on computer Supported Cooperative Work in Design

作者： Qingyu Xu Jiguo Yu Anming Dong Zihao Shang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China School of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Big Data Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Data Intelligence Information Technology Co. Ltd. Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In the context of Industry 4.0 and the rise of intelligent manufacturing, the quality of industrial products is becoming more and more important. Strip steel surface defect detection, as a key link in industrial production, is crucial to ensure the quality of industrial products. However, due to the irregularity of the defect scale and the inconspicuous defect features in the surface defect image of strip steel, it is difficult for the existing detection algorithms to realize the effective detection of defects. In order to better extract the features of defects and improve the network’s ability to detect defects, this paper proposes an algorithm for detecting surface defects on industrial strip steel based on receptive field and feature information supplementation. First, we design a receptive field (RF) module to replace the residual structure in the C3 module, which we name C3RF. This module can effectively increase the network’s receptive field, allowing the network to fully capture irregular defect features without increasing the cost. Second, for the characteristics that defective features are not obvious and tend to lose detail information as the network deepens, an extra information supplemental branching feature fusion pyramid (EFPN) is proposed on the basis of the original PAFPN architecture to compensate for the detail information that is lost by the fragile features in deeper layers. Finally, convolutional block attention module (CBAM) is introduced to replace the spatial pooling pyramid (SPPF) in the baseline network, which enhances the contrast between defects and backgrounds, and improves the classification and localization ability of the network. Our network achieves an accuracy of 82.2% on the publicly available strip steel defect detection dataset, which is a 4.0% improvement over the baseline. The results show that the network constructed in this paper can realize effective defect detection.

关键词： Radio frequency Location awareness Strips Production Feature extraction Real-time systems Hardware

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CNN segmentation of skin melanoma in pre-processed dermoscopy images 2

CNN segmentation of skin melanoma in pre-processed dermoscop...

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2nd International Conference on Machine Learning and data Engineering, ICMLDE 2023

作者： Kadry, Seifedine Verdú, Elena Damasevicius, Robertas Abualigah, Laith Singh, Vijendra Rajinikanth, Venkatesan Department of Applied Data Science Noroff University College Kristiansand Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Higher School of Engineering and Technology Universidad Internacional de la Rioja Logroño26006 Spain Department of Applied Informatics Vytautas Magnus University Kaunas53361 Lithuania Computer Science Department Al Al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India

Using medical data to improve diagnosis accuracy has recently become common practice in hospitals. A modern computing environment has enabled real-time diagnosis of medical data using Convolutional Neural networks (CNNs). To extract and evaluate skin melanoma recorded with digital dermatoscopy images (DDI), we developed a CNN segmentation framework. In this proposal, four phases are proposed: (i) DDI collection and resizing, (ii) DDI enhancement using pre-processing techniques, (iii) CNN segmentation for lesion extraction, (v) Comparing the extracted sections to the ground truth images, and (v) Verifying whether the framework is valid. Using DDI pre-processed with (i) Traditional procedures, (ii) Otsu's thresholding, (iii) Kapur's thresholding, and (iv) Fuzzy-Tsallis thresholding, this proposal examines the different CNN segmentation schemes presented in the literature. For mining skin lesions, the Moth-Flame Algorithm (MFA) combined with tri-level thresholding achieves an optimal threshold for the DDI. With Fuzzy-Tsallis thresholding images, the VGG-UNet performs better than the alternatives. This framework helps to achieve better values of Jaccard (88.47±2.13%), Dice (93.08±1.17%), and Accuracy (98.64±0.71%) on the chosen DDI database. © 2024 Elsevier B.V.. All rights reserved.

关键词： Image enhancement

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Face Emotion Based Music Recommendation System Using Modified Convolution Neural Network

Face Emotion Based Music Recommendation System Using Modifie...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE), International Conference on

作者： A Jaya Mabel Rani M.Nivetha S N M Jothi Swaroopan K Hari Kumar Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of EEE R.M.K. Engineering College Chennai Department of Computer Science St Jude’s College Tamil Nadu India

In this paper, proposed a new vision to personify music recommendation by face emotion recognition technology. Emotions play a crucial role in the way humans perceive and respond to music. This system aims to enhance the user experience by recommending music based on their real-time facial expressions, allowing for a more immersive and emotionally resonant listening experience. So here employed deep learning techniques to detect facial emotions accurately and use them as input to our music recommendation algorithm. The proposed method modified convolution neural network ensures user privacy and data security by processing emotions locally on the user's device without uploading any sensitive information to external servers. Through experimental evaluation, here demonstrate the effectiveness and usability of the emotion-based music recommendation system and finally shown this method is very effective with the help of accuracy and F1 score.

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Automatic liver-vessel examination from CT slice using Kapur's thresholding and watershed algorithm 2

Automatic liver-vessel examination from CT slice using Kapur...

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2nd International Conference on Machine Learning and data Engineering, ICMLDE 2023

computerized medical image examination (CMIE) plays a significant role in modern hospitals to achieve the necessary tasks, like segmentation and classification. By segmenting an image, we can extract a particular section for examination. A two-dimensional computed tomography (CT) slice was used for liver-vessel examination (LiVE). A simple automatic technique for supporting LiVE is being developed in this research. A CT slice is collected, a 3D to 2D conversion is done, (ii) Kapur's tri-level thresholding and Hummingbird-Optimizer is used to enhance the CT slice, (iii) the watershed algorithm (WA) is used to extract the vessel, and (iv) the WA is compared and verified against the segmentation methods chosen. WA provides better segmentation results than other methods because it is an automatic approach. Using the chosen image database, the proposed technique achieves an overall segmentation accuracy of >97%. Other segmentation problems can be used in the future to verify the merit of this scheme. © 2024 Elsevier B.V.. All rights reserved.

关键词： Medical imaging

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A Dual Machine Learning Approach for Differentiating Spindle Cell and Giant Cell Carcinomas and Assessing Severity Levels

A Dual Machine Learning Approach for Differentiating Spindle...

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International Conference on Electronics, Materials Engineering and Nano-Technology (IEMENTech)

作者： R. Rajan V.Naresh Kumar Reddy U. Veeresh Surendran R K S Balamurugan Department of Artificial Intelligence and Data Science Karpaga Vinayaga College of Engineering and Technology Chengalpattu Tamil Nadu India Department of Computer Science and Engineering Marri Laxman Institute of Technology and Management Dundigal Hyderabad India Department of CSEAIML VNR Vignan Jyoti Institute of Engineering & Technology Hyderabad India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering Karpaga Vinayaga College of Engineering and Technology Chengalpattu Tamil Nadu India

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

Lung cancer is a major health challenge throughout the world, there is an urgent need for innovative method for detection and diagnosis. This paper introduced a dual machine learning framework to improve the diagnosis and treatment guidelines. Bidirectional Encoder Representations from Transformers is utilized to analysis of patient history, obtained accuracy of 92.5%, showing deep context understanding of Medicals chronicle. And also Convolutional Neural Network (CNN) was utilized to differentiate between Gait cell carcinoma and spindle cell carcinoma and achieved the accuracy of 94.2% and 93.7% respectively. High performance metrics like AUC, ROC ranging from 97% for both carcinoma types. These findings demonstrated a more precise method for lung cancer diagnosis and suitable treatment planning.

关键词： Analytical models Accuracy Lung cancer Bidirectional control Transformers Feature extraction Encoding Planning Convolutional neural networks History

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A Dual Machine Learning Approach for Differentiating Spindle Cell and Giant Cell Carcinomas and Assessing Severity Levels 8

A Dual Machine Learning Approach for Differentiating Spindle...

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8th International Conference on Electronics, Materials Engineering and Nano-Technology, IEMENTech 2025

作者： Rajan, R. Reddy, V.Naresh Kumar Veeresh, U. Surendran, R. Balamurugan, K.S. Karpaga Vinayaga College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chengalpattu India Marri Laxman Institute of Technology and Management Department of Computer Science and Engineering Dundigal Hyderabad India VNR Vignan Jyoti Institute of Engineering & Technology Department of CSEAIML Hyderabad India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Karpaga Vinayaga College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chengalpattu India

ISBN: (纸本)9798331520762

关键词： Diseases

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Cardio-Vascular Disease Prediction using Machine Learning Techniques

Cardio-Vascular Disease Prediction using Machine Learning Te...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Srinivas Konda Naresh Kumar Kar Padmaja Pulicherla Gandla Shivakanth Ramachandra A C Department of Data Science CMR Technical Campus Hyderabad India Department of CSE GITAM Deemed to be University Hyderabad India Department of Computer Science and Engineering Hyderabad Institute of Technology and Management Telangana India Department of Computer Science & Engineering K L University Vadeeshwaram Vijayawada India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

The main goal of this study is to use data Mining Method and Artificial Neural Network to develop a system that can automatically and rapidly predict the risk of coronary heart disease (ANN). The IRT Perundurai Medical College and Hospital's master health checkup data on occupational drivers were used to test this idea (PMCH). Analysis for risk identification is performed in the first stage of the hybrid approach suggested in this study, and level prediction is performed in the second. The sensitivity, specificity, precision, receiver operating curve, area under curve, 10-fold cross validation technique, and the F-measure are used for this investigation. The initial step of the study involves thinking about the most common and changeable dangers. Systolic blood pressure, diastolic blood pressure, and body mass index (BMI) are three biophysical variables, whereas fasting blood sugar, postprandial blood sugar, and triglyceride levels are three blood chemical factors (TG). All of these characteristics have a predetermined margin value that is based on WHO guidelines. Support Vector Machine (SVM), Naive Bayes (NB), and the C4.5 algorithm in Decision Tree are the three approaches used to categorize these variables and forecast the risk (DT). The C4.5 algorithm fared best in forecasting CHD risk when the three approaches were compared using the performance metrics, as discovered by the investigation. The decision tree C4.5 method outperformed the other two classifiers with an improved 99.5% accuracy and 99.67% sensitivity. The increased percentage demonstrates that the Decision tree method delivered consistent results that were better to those produced by the Naive Bayes and SVM models.

关键词： Support vector machines Heart Sensitivity Prediction algorithms Blood pressure Classification algorithms Decision trees

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