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A Hybrid WSVM-Levy Approach for Energy-Efficient Manufacturing Using Big Data and IoT

computers, Materials & Continua 2024年第12期81卷 4895-4914页

作者： Surbhi Bhatia Khan Mohammad Alojail Mahesh Thyluru Ramakrishna Hemant Sharma School of Science Engineering and EnvironmentUniversity of SalfordManchesterM68HDUK University Centre for Research and Development Chandigarh UniversityMohali140307India Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh11495Saudi Arabia Department of Computer Science and Engineering JAIN(Deemed-to-be University)Bangalore560030India Department of Computer Science and Engineering IES College of TechnologyBhopal462044India

In Intelligent Manufacturing,Big Data and industrial information enable enterprises to closely monitor and respond to precise changes in both internal processes and external environmental factors,ensuring more informed decision-making and adaptive system *** also promotes decision making and provides scientific analysis to enhance the efficiency of the operation,cost reduction,maximizing the process of production and so *** methods are employed to enhance productivity,yet achieving sustainable manufacturing remains a complex challenge that requires careful *** study aims to develop a methodology for effective manufacturing sustainability by proposing a novel Hybrid Weighted Support Vector-based Lévy flight(HWS-LF)*** objective of the HWS-LF method is to improve the environmental,economic,and social aspects of manufacturing *** this approach,Support Vector Machines(SVM)are used to classify data points by identifying the optimal hyperplane to separate different classes,thereby supporting predictive maintenance and quality control in *** Forest is applied to boost efficiency,resource allocation,and production optimization.A Weighted Average Ensemble technique is employed to combine predictions from multiple models,assigning different weights to ensure an accurate system for evaluating manufacturing ***,Lévy flight Optimization is incorporated to enhance the performance of the HWS-LF method *** method’s effectiveness is assessed using various evaluation metrics,including accuracy,precision,recall,F1-score,and *** show that the proposed HWS-LF method outperforms other state-of-the-art techniques,demonstrating superior productivity and system performance.

关键词： Sustainable manufacturing support vector machine random forest levy flight optimization weighted average ensemble

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Analysis on Improved Gaussian-Wiener filtering technique and GLCM based Feature Extraction for Breast Cancer Diagnosis 2

Analysis on Improved Gaussian-Wiener filtering technique and...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Ranjitha, K.V. Pushphavathi, T.P. Department of Computer Science and Engineering Ramaiah University of Applied Sciences Bangalore India Department of Computer Science and Engineering Faculty of Engineering and Technology Ramaiah University of Applied Sciences Bangalore India

Breast cancer is become the most prevailing and fastest growing disease. In medical imaging, the use of machine learning and deep learning algorithms is essential. Classification of the tumor to predict the chemotherapy response for survival is trivial. In this paper, an innovative Gaussian-Wiener filter combination is used for de-noising the MRI images. These pre-processed images with good image quality are selected for tumor detection. On the basis of these pre-processed outputs, important features are extracted to determine the spatial relationship between the image pixels which results in better texture analysis for the tumor images. Analysis is made on the ISPY-2 trial breast MRI database. Results are analyzed which gives better image quality performance for MRI images. The filters and feature extraction method analyzed is used further in the segmentation and optimization process for breast detection and diagnosis to get the best accuracy of nearly 100%. The results also show better texture analysis for extracting features using GLCM based method. Furthermore, the MRI images for these methods used are explained for better performance in the process of breast cancer detection and diagnosis. © 2024 Elsevier B.V.. All rights reserved.

关键词： Feature extraction

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Offshore Software Maintenance Outsourcing Process Model Validation:A Case Study Approach

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computers, Materials & Continua 2023年第3期74卷 5035-5048页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Adel Sulaiman Muhammad Akram Ahmad Salman Khan Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala TerengganuMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan

The successful execution and management of Offshore Software Maintenance Outsourcing(OSMO)can be very beneficial for OSMO vendors and the OSMO *** a lot of research on software outsourcing is going on,most of the existing literature on offshore outsourcing deals with the outsourcing of software development *** frameworks have been developed focusing on guiding software systemmanagers concerning offshore software ***,none of these studies delivered comprehensive guidelines for managing the whole process of *** is a considerable lack of research working on managing OSMO from a vendor’s ***,to find the best practices for managing an OSMO process,it is necessary to further investigate such complex and multifaceted phenomena from the vendor’s *** study validated the preliminary OSMO process model via a case study research *** results showed that the OSMO process model is applicable in an industrial setting with few *** industrial data collected during the case study enabled this paper to extend the preliminary OSMO process *** refined version of the OSMO processmodel has four major phases including(i)Project Assessment,(ii)SLA(iii)Execution,and(iv)Risk.

关键词： Offshore outsourcing process model model validation vendor challenges case study

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Image Classifications Methods Analysis with Differen Methods to for Identifying best Image Layout with High Resolution

Image Classifications Methods Analysis with Differen Methods...

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Mahesh, T.R. Vivek, V. Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350322309

This researcher investigated effective much eye opening and pattern detection algorithms. Finally, this article used two frameworks to argue that geospatial investigation systems for patterns are necessary. One of most popular method for analyzing photographs using image processing and analysis math is probably visual configuration methods. Imagery description is also necessary for the evaluation of remote sensing tools used for thorough land investigation. The three main image configuration techniques that are often used for far detection are opinion piece visual analysis, controlled groupings, and solos visual categorization. The approach used in these image layout techniques is combined in the present study. The present study also briefly summarizes each approach's use in exploring distant sensing info while also outlining the steps for each way using stream outlines and frameworks. Generally speaking, a quick overview of image layout techniques is included in the test. © 2023 IEEE.

关键词： Image classification

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iWBFCxr: improved weighted box fusion for analyzing chest x ray images for enhancing thoracic abnormalities

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International Journal of Information Technology (Singapore) 2025年第2期17卷 711-720页

作者： Verma, Sonia Devarajan, Ganesh Gopal Sharma, Pankaj Kumar Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Delhi - NCR Campus Delhi - Meerut Road Modinagar Uttar Pradesh Ghaziabad 201204 India Department of Computer Science ABES Engineering College UP Ghaziabad 201009 India

Chest x-ray studies can be automatically detected and their locations located using artificial intelligence (AI) in healthcare. To detect the location of findings, additional annotation in the form of bounding boxes is required, rather than image-level labeling. Accurate interpretation of chest radiographs is essential for diagnosing thoracic abnormalities. This paper introduces a novel method for abnormality detection in chest radiographs using the VinDr-CXR dataset. Labeled training sets are improved by incorporating data augmentation and advanced pre-processing techniques. To improve the accuracy of anomaly identification, the study makes use of ensemble object detection models, such as the modified weighted boxes fusion (iWBFCxr) technique. With an average precision (AP) of 0.2434 for class 14, experimental results on the VinDr-CXR dataset show an overall mean average precision (mAP) of 0.2791. The iWBFCxr approach, when combined with ensemble predictions, has the potential to improve abnormality localization. By integrating enhanced label merging with ensemble object detection models, our method improves abnormality localization in chest radiographs significantly, improving thoracic abnormality diagnosis accuracy and possibly leading to new medical interventions. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Automated classification Automated localization Chest radiographs computer vision Deep learning Machine learning Thoracic anomalies

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Analysis of Deep Learning and Machine Learning Methods for Breast Cancer Detection 1

Analysis of Deep Learning and Machine Learning Methods for B...

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1st International Conference on computer science and Emerging Technologies, CSET 2023

作者： Latha, D.U. Mahesh, T.R. Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350341737

Breast cancer is the disease with the greatest incidence rate and the fastest global spread. Compared to men, women are diagnosed with breast cancer far more frequently. If detected early enough, breast cancer can be successfully treated. One of the leading causes of death internationally, breast cancer accounted for 22.6% of all cancer-related deaths worldwide in 2013. This indicates that more than one crore cancer-related deaths were brought on by breast cancer. 14.7% of all cancer cases in India are caused by breast cancer. Early breast cancer recognition has been the subject of extensive investigation since it can help with treatment start-up and reduce overall mortality rates. Many instances can be managed and the overall death rate can be lowered with an early diagnosis. Breast cancer has been the subject of numerous research studies. Machine learning is the method being applied in research. Older machine learning-based research that excelled in their respective fields include those that used decision trees, KNN, SVM, and naive bays techniques. Better algorithms have been created as a result of these investigations. On the other hand, deep learning, a recently created technique, is currently being utilised to categorize breast cancer. Data science commonly makes use of convolutional neural networks, recurrent neural networks, and other deep learning-based techniques. Deep learning algorithms outperform machine learning algorithms in terms of performance. The most captivating portions of the photographs have been removed. Many academics utilize CNN to categorize the images. In a nutshell, CNN is the technique that is most frequently used to categorize images. This piece of work compares and contrasts the employment of machine learning and deep learning techniques in numerous journals. © 2023 IEEE.

关键词： Support vector machines

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Secure speaker identification in open and closed environments modeled with symmetric comb filters

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Multimedia Tools and Applications 2025年第18期84卷 19147-19189页

作者： Shafik, Amira Monir, Mohamad El-Shafai, Walid Khalaf, Ashraf A. M. Nassar, M.M. El-Fishawy, Adel S. El-Din, M. A. Zein Dessouky, Moawad I. El-Rabaie, El-Sayed M. Abd El-Samie, Fathi E. Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Laboratory Department of Computer Science Prince Sultan University Riyadh11586 Saudi Arabia Electrical Engineering Department Faculty of Engineering Minia University Minia61519 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Speech is a fundamental means of human interaction. Speaker Identification (SI) plays a crucial role in various applications, such as authentication systems, forensic investigation, and personal voice assistance. However, achieving robust and secure SI in both open and closed environments remains challenging. To address this issue, researchers have explored new techniques that enable computers to better understand and interact with humans. Smart systems leverage Artificial Neural Networks (ANNs) to mimic the human brain in identifying speakers. However, speech signals often suffer from interference, leading to signal degradation. The performance of a Speaker Identification system (SIS) is influenced by various environmental factors, such as noise and reverberation in open and closed environments, respectively. This research paper is concerned with the investigation of SI using Mel-Frequency Cepstral Coefficients (MFCCs) and polynomial coefficients, with an ANN serving as the classifier. To tackle the challenges posed by environmental interference, we propose a novel approach that depends on symmetric comb filters for modeling. In closed environments, we study the effect of reverberation on speech signals, as it occurs due to multiple reflections. To address this issue, we model the reverberation effect with comb filters. We explore different domains, including time, Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Discrete Sine Transform (DST) domains for feature extraction to determine the best combination for SI in case of reverberation environments. Simulation results reveal that DWT outperforms other transforms, leading to a recognition rate of 93.75% at a Signal-to-Noise Ratio (SNR) of 15 dB. Additionally, we investigate the concept of cancelable SI to ensure user privacy, while maintaining high recognition rates. Our simulation results show a recognition rate of 97.5% at 0 dB using features extracted from speech signals and their DCTs. Fo

关键词： Speech enhancement

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An Innovation Development of Neuro Controller for Condition Monitoring and Smart Industrial Instrumentation 2

An Innovation Development of Neuro Controller for Condition ...

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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Vanitha, K. Raja Praveen, K.N. Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

The Neuro Controller is an innovative piece of industrial instrumentation designed to monitor conditions in smart industrial settings. It is a powerful and versatile controller that can be used to monitor, control, and manage industrial processes. The Neuro Controller is equipped with advanced sensors and actuators, enabling it to accurately measure and control a variety of conditions. The Neuro Controller is able to monitor and control temperature, pressure, humidity, flow, and other parameters in an industrial setting. It is also capable of detecting changes in these parameters, allowing it to respond to changes quickly and accurately. The Neuro Controller uses advanced algorithms to analyze data and make decisions, allowing it to optimize and control processes efficiently. Additionally, the controller is able to integrate data from numerous sources, providing a comprehensive view of a process. In this paper, a smart Neuro Controller for Condition Monitoring and smart industrial instrumentation. The Neuro Controller is designed to provide enhanced safety and efficiency in industrial settings. It is capable of detecting potentially hazardous conditions and alerting personnel to take action. Additionally, the controller can be used to automate processes, reducing the risk of accidents and improving process efficiency. © 2023 IEEE.

关键词： Controllers

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Digital Twin-Based Automated Fault Diagnosis in Industrial IoT Applications

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computers, Materials & Continua 2023年第4期75卷 183-196页

作者： Samah Alshathri Ezz El-Din Hemdan Walid El-Shafai Amged Sayed Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Security Engineering LabPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electrical Energy Engineering College of Engineering&TechnologyArab Academy for Science Technology&Maritime TransportSmart Village CampusEgypt

In recent years, Digital Twin (DT) has gained significant interestfrom academia and industry due to the advanced in information technology,communication systems, Artificial Intelligence (AI), Cloud Computing (CC),and Industrial Internet of Things (IIoT). The main concept of the DT isto provide a comprehensive tangible, and operational explanation of anyelement, asset, or system. However, it is an extremely dynamic taxonomydeveloping in complexity during the life cycle that produces a massive amountof engendered data and information. Likewise, with the development of AI,digital twins can be redefined and could be a crucial approach to aid theInternet of Things (IoT)-based DT applications for transferring the data andvalue onto the Internet with better decision-making. Therefore, this paperintroduces an efficient DT-based fault diagnosis model based on machinelearning (ML) tools. In this framework, the DT model of the machine isconstructed by creating the simulation model. In the proposed framework,the Genetic algorithm (GA) is used for the optimization task to improvethe classification accuracy. Furthermore, we evaluate the proposed faultdiagnosis framework using performance metrics such as precision, accuracy,F-measure, and recall. The proposed framework is comprehensively examinedusing the triplex pump fault diagnosis. The experimental results demonstratedthat the hybrid GA-ML method gives outstanding results compared to MLmethods like LogisticRegression (LR), Na飗e Bayes (NB), and SupportVectorMachine (SVM). The suggested framework achieves the highest accuracyof 95% for the employed hybrid GA-SVM. The proposed framework willeffectively help industrial operators make an appropriate decision concerningthe fault analysis for IIoT applications in the context of Industry 4.0.

关键词： Automated fault diagnosis control system ML AI CC IIoT digital twins genetic algorithm GA-ML technique

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Designing a Heuristic Based Hybrid CNN with Attention Mechanism for the Effective Classification of Fish Species 5

Designing a Heuristic Based Hybrid CNN with Attention Mechan...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Bhanumathi, M. Arthi, B. Srm Institute of Science and Technology Department of Computer Science and Engineering India Department of Computer Science and Engineering India College of Engineering and Technology Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350321425

In recent days, the population of fish species is enormously increased. The measurement of the total population of the fish species is also a complex task. The population of fishes can be easily identified by its classification of variants. But, the categorization of fishes is an annoying task for marine ecologists and biologists. The fishery activities can be easily managed by calculating the total population and size. Several methods are developed for classifying the fish species, but they take more computational time and manpower. These limitations are overthrown by automatic methods. The automatic method monitors the activities of fish and their path. The major advantages of this automatic system are to reduce labor requirement and time. This research study suggests a new fish species categorization model with the help of a Hybrid Convolution Neural Network(CNN) with an Attention Mechanism (HCNN-AM). In the initial phase, the images are aggregated from the standard benchmark dataset. Then, the images are forwarded to the hybrid CNN with an attention mechanism for effectively classifying the fish species, in which the attributes are optimized with the aid of a hybrid Fitness-based Hybrid Rat Swarm with Billiards-inspired Optimization (FH-RSBO). Therefore, the planned method is an efficient substitute for strenuous and time-consuming methods of physical recognition by marine experts. Thus, it becomes the most advantageous task to monitor the fish biodiversity in their natural habitats. © 2023 IEEE.

关键词： Optimization

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