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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Umanesan, R. Harshavardhana, C.N. Somashekar, P. Siva Kota Reddy, P. Dhiliphan Kumar, M. Singh, Ramanjeet Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India Government First Grade College for Women Department of Mathematics Holenarasipura India Department of Mathematics Mysuru India Jss Science and Technology University Department of Mathematics Mysuru India Department of Management Studies Madurai India Chandigarh University Gharuan Mohali India

ISBN: (纸本)9798350352931

Software for enterprise resource planning (ERP) has grown in popularity as a means to streamline corporate processes that are critical for survival in the modern, globalized marketplace. In order to cater to the specific needs of their global customer base in terms of both price and availability, suppliers in the textile industry are under increasing pressure to enhance their manufacturing management practices. This task is becoming more dependent on the careful selection and usage of information technology. Preprocessing, feature extraction, and training are the three phases that make up this method. Time difference relevance and K-L distance are components of preprocessing. The feature selection approach employs Kernel Principal Component Analysis for the factor extraction phase. An GNN was used for the model's training. The proposed technique outperforms CNN and SVM with an average accuracy of 9 2. 1 6percent. © 2024 IEEE.

关键词： Textile industry

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Selective and Adaptive Incremental Transfer Learning with Multiple Datasets for Machine Fault Diagnosis

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computers, Materials & Continua 2024年第1期78卷 1363-1379页

作者： Kwok Tai Chui Brij B.Gupta Varsha Arya Miguel Torres-Ruiz School of Science and Technology Hong Kong Metropolitan UniversityHong Kong999077China Department of Computer Science and Information Engineering Asia UniversityTaichung41354Taiwan Center for Advanced Information Technology Kyung Hee UniversitySeoul02447Korea Symbiosis Centre for Information Technology Symbiosis International UniversityPuneMaharashtra411042India Department of Electrical and Computer Engineering Lebanese American UniversityBeirut1102Lebanon School of Computing Skyline University CollegeSharjah1797United Arab Emirates Department of Business Administration Asia UniversityTaichung41354Taiwan Center for Interdisciplinary Research University of Petroleum and Energy StudiesDehradun248007India Department of Computer Science&Engineering Chandigarh UniversityChandigarh160036India Centro de Investigación en Computación Instituto Politécnico NacionalUPALM-ZacatencoMexico City07320Mexico

The visions of Industry 4.0 and 5.0 have reinforced the industrial *** have also made artificial intelligence incorporated as a major *** machine faults has become a solid foundation for automatically recognizing machine failure,and thus timely maintenance can ensure safe *** learning is a promising solution that can enhance the machine fault diagnosis model by borrowing pre-trained knowledge from the source model and applying it to the target model,which typically involves two *** response to the availability of multiple datasets,this paper proposes using selective and adaptive incremental transfer learning(SA-ITL),which fuses three algorithms,namely,the hybrid selective algorithm,the transferability enhancement algorithm,and the incremental transfer learning *** is a selective algorithm that enables selecting and ordering appropriate datasets for transfer learning and selecting useful knowledge to avoid negative *** algorithm also adaptively adjusts the portion of training data to balance the learning rate and training *** proposed algorithm is evaluated and analyzed using ten benchmark *** with other algorithms from existing works,SA-ITL improves the accuracy of all *** studies present the accuracy enhancements of the SA-ITL,including the hybrid selective algorithm(1.22%-3.82%),transferability enhancement algorithm(1.91%-4.15%),and incremental transfer learning algorithm(0.605%-2.68%).These also show the benefits of enhancing the target model with heterogeneous image datasets that widen the range of domain selection between source and target domains.

关键词： Deep learning incremental learning machine fault diagnosis negative transfer transfer learning

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Adaptive energy-preserving algorithms for guiding center system

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Plasma science and Technology 2023年第4期25卷 11-22页

作者：朱贝贝刘健张嘉炜祝爱卿唐贻发 Department of Applied Mathematics School of Mathematics and PhysicsUniversity of Science and Technology BeijingBeijing 100083Peopleʼs Republic of China School of Nuclear Science and Technology University of Science and Technology of ChinaHefei 230026Peopleʼs Republic of China Advanced algorithm Joint Lab Shandong Computer Science CenterQilu University of TechnologyJinan 250014Peopleʼs Republic of China The State Key Laboratory of Scientific and Engineering Computing The Institute of Computational Mathematics and Scientific/Engineering ComputingAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190Peopleʼs Republic of China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049Peopleʼs Republic of China

We develop two types of adaptive energy preserving algorithms based on the averaged vector field for the guiding center dynamics,which plays a key role in magnetized *** adaptive scheme is applied to the Gauss Legendre’s quadrature rules and time stepsize respectively to overcome the energy drift problem in traditional energy-preserving *** new adaptive algorithms are second order,and their algebraic order is carefully *** results show that the global energy errors are bounded to the machine precision over long time using these adaptive algorithms without massive extra computation cost.

关键词： guiding center system energy-preserving methods adaptive energy conservation

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Liver Tumour Parameter Prediction Using Feature Extraction and Supervised Function 1

Liver Tumour Parameter Prediction Using Feature Extraction a...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Kumar, Ankit Singh, Kamred Udham Singh, Teekam Banu, Nargis Rathore, Yogesh Kumar Pandey, Saroj Kumar Gla University Department of Computer Engineering & Apllications UP Mathura India School of Computing Graphic Era Hill University Dehradun India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Geetanjali Institute of Technical Studies Dabok Udaipur India India

ISBN: (纸本)9798350330915

The process of extracting features is essential to simplifying and condensing a wide variety of data dimensions while at the same time combining essential factors into a single influential parameter. In the field of medical research, predicting liver tumours represents a daunting task, which calls for the development of a more advanced detection mechanism that is focused towards achieving higher levels of identification precision. Even though experienced medical professionals can make preliminary predictions regarding liver tumours based on their clinical experience and diagnostic tools such as imaging and scans, there is still a compelling need for systematic assistance in making a final decision. This is especially true when evaluating patient symptoms. For the sake of our application, the approach of feature extraction that we have choose to use is called linear discriminant analysis (LDA). LDA displays its usefulness by being able to accommodate classification techniques with more than two classes and by exceeding logistic regression in terms of accuracy of classification. We make use of the power of supervised functions, in particular decision trees and the Naive Bayes algorithm, in order to make an accurate prediction about the existence of liver illness. These algorithms produce predictions regarding the existence of liver disease with a high degree of accuracy by using the factors discovered during LDA analysis. For the purposes of our simulation work, we make use of the flexible scikit-learn library in Python. This library offers a dependable infrastructure for the performance of machine learning activities. The part of this research under 'Methodology and Implementation' digs into the specifics of our approach and outlines the actions that are required to evaluate the effectiveness of our prediction model. © 2023 IEEE.

关键词： classification algorithm feature extraction LDA Linear Discriminant Analysis Liver tumor Supervised function Supervised function

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Optimized Phishing Detection with Recurrent Neural Network and Whale Optimizer Algorithm

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computers, Materials & Continua 2024年第9期80卷 4895-4916页

作者： Brij Bhooshan Gupta Akshat Gaurav Razaz Waheeb Attar Varsha Arya Ahmed Alhomoud Kwok Tai Chui Department of Computer Science and Information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPuneMaharashtra411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India Ronin Institute MontclairNJ 07043USA Management Department College of Business AdministrationPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Business Administration Asia UniversityTaichung413Taiwan Department of Electrical and Computer Engineering Lebanese American UniversityBeirut1102Lebanon Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Electronic Engineering and Computer Science Hong Kong Metropolitan University(HKMU)HomantinHong KongChina

Phishing attacks present a persistent and evolving threat in the cybersecurity land-scape,necessitating the development of more sophisticated detection *** machine learning approaches to phishing detection have relied heavily on feature engineering and have often fallen short in adapting to the dynamically changing patterns of phishingUniformResource Locator(URLs).Addressing these challenge,we introduce a framework that integrates the sequential data processing strengths of a Recurrent Neural Network(RNN)with the hyperparameter optimization prowess of theWhale Optimization Algorithm(WOA).Ourmodel capitalizes on an extensive Kaggle dataset,featuring over 11,000 URLs,each delineated by 30 *** WOA’s hyperparameter optimization enhances the RNN’s performance,evidenced by a meticulous validation *** results,encapsulated in precision,recall,and F1-score metrics,surpass baseline models,achieving an overall accuracy of 92%.This study not only demonstrates the RNN’s proficiency in learning complex patterns but also underscores the WOA’s effectiveness in refining machine learning models for the critical task of phishing detection.

关键词： Phishing detection Recurrent Neural Network(RNN) Whale Optimization Algorithm(WOA) cybersecurity machine learning optimization

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Artificial Intelligence driven Deep Learning for Competitive Intelligence to enhance Market Analysis and Strategic Positioning 4

Artificial Intelligence driven Deep Learning for Competitive...

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4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Shukla, Sunil Singh, Jagendra Nassa, Vinay Kumar Saba, Masarath Bhatia, Jitesh Elangovan, Muniyandy Graphic Era Hill University School of Computing Dehradun India Bennett University School of Computer Science Engineering & Technology Greater Noida India Ggsi University Tecnia Institute of Advanced Studies Dept. of Information Communication Technology Delhi India Hyderabad India Epsilon Department of Client Engagement MA United States Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Biosciences Chennai India

ISBN: (纸本)9798350354218

This research provides insight into the successful application of modern deep learning techniques such as Transfer Learning, BERT, and Autoencoders to develop a powerful tool to sustain market analysis and strategic positioning in competitive intelligence. The conducted analysis proved that BERT was the most effective technique, as it produced the highest results in regards of accuracy, precision, recall, and F1 score values in all ten trials. The results of the experiment showed that with accuracy ranging from 90.5% to 92.3%, precision from 91.2% to 93.0%, recall from 89.7% to 91.7%, and F1 score from 90.4% to 92.2%, BERT proved to be the best technique for extracting insights from various textual data sources. In and of itself, Transfer Learning also yielded promising results, and its accuracy ranged from 85.2% to 88.6%, precision from 86.5% to 89.9%, recall from 83.7% to 87.3%, and F1 score from 85.0% to 88.5%. Additionally, given its ability to adapt to particular tasks with quite limited labeled data, Transfer Learning is a valuable tool for market analysis. In this experiment for Autoencoders, it has been shown that they provided a slightly lower performance than BERT and Transfer Learning. The reconstruction error of the deep learning method used in this experiment ranged from 0.018 to 0.025, while accuracy varied from 87.9% to 90.3%. Nevertheless, further optimization can make autoencoders a promising approach for market analysis. Thus, this research indicates the vital importance of leveraging advanced deep learning techniques for gaining a competitive advantage in the present-day business environment. © 2024 IEEE.

关键词： Contrastive Learning

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Optimization of Resource Allocation in the Cloud Environment using Regression Models

Optimization of Resource Allocation in the Cloud Environment...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： R. Nathiya S. K. Piramu Preethika Department of Information Technology School of Computing Sciences VEL’S Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Cloud computing is a developing technology that separates computations from infrastructures making it far more effective to traditional self-hosted computing methods. It saves operating costs and has a positive environmental impact by limiting resource waste, excessive provisioning, and consumption of energy. It also helps to improve the quality of service and user experience by decreasing delays, boosting throughput, and preventing bottlenecks. This study investigates the effectiveness of using regression models to direct cloud resource allocation. The cloud environment is well-equipped to both minimize cloud costs more efficiently and respond to changing demands by making decisions based on real-time data.

关键词： Cloud computing Costs Limiting Linear regression Quality of service Throughput User experience Real-time systems Resource management Optimization

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TPU-Accelerated Deep Learning for Accurate Satellite Land Classification

TPU-Accelerated Deep Learning for Accurate Satellite Land Cl...

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3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024

作者： Babu, Tina Nair, Rekha R. Kishore, S. Department of Computer Science and Engineering School of Advanced Computing Alliance University Bengaluru India Nitte Institute of Professional Education Karnataka Mangalore India

Tensor Processing Units (TPUs) offer significant computational advantages for deep learning tasks, particularly in processing high-dimensional satellite imagery. This study presents a systematic approach to land classification using Convolutional Neural Networks (ConvNets) accelerated by TPUs. The implemented ConvNet architecture achieved 94.5% classification accuracy across ten land cover categories, outperforming traditional machine learning methods by 6-9% and standard CNN implementations by 4%. The model demonstrated high precision (93.8%), recall (94.2%), and F1-score (94.0%) across diverse land cover types, with particularly strong performance in identifying water bodies (95.1% F1-score) and forest regions (94.9% F1-score). Through ex- tensive hyperparameter optimization enabled by TPU acceleration, training time was reduced by 60% compared to GPU-based implementations. The study utilized a dataset of 27,000 labeled satellite images across 10 land cover categories, with robust validation through 5-fold cross-validation. The improved performance and computational efficiency demonstrate the viability of TPU-accelerated deep learning for large-scale geospatial analysis applications. © 2025 The Author(s).

关键词： Convolutional neural networks

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Adaptive Random Evolution Whale Optimization Algorithm (AREWOA) Based Workflow Scheduling in a Virtualized Cloud Environment

Adaptive Random Evolution Whale Optimization Algorithm (AREW...

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International Conference on computing and Networking Technology (ICCNT)

作者： R. Nathiya S.K. Piramu Preethika Department of Information Technology School of Computing Sciences VEL’S Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Cloud workflow task scheduling refers to assigning workflow tasks submitted by users to appropriate computer resources for execution. The workflow scheduler assigns the user request to the available Virtual Machine (VM) hosted on cloud servers during scheduling. However, while limiting energy consumption, the workflow application processing schedule length (makespan) and associated costs must stay within the users budget and deadline. The tasks are chosen using the optimal scheduling technique to reduce Energy Consumption (EC) and maximize Resource Utilization (RU) without compromising system performance. In this paper, Adaptive Random Evolution Whale Optimization Algorithm (AREWOA) based scheduling is proposed to reduce EC, maximize RU, and workflow execution time. AREWOA is performed based on the dual-operation strategy partnership. Initially, the Gaussian distribution is used in the randomization procedure to raise the variety of the population. Secondly, a new skip step factor is introduced to enhance the optimizer capability to solve local optimum, and specific reinforcement is applied to the hunter process to enhance the whale’s exclusive exploration or exploitation skills. An adaptive weight factor is applied to maintain algorithm stability and a balance among exploration and exploitation. Dynamic provisioning of infinite heterogeneous resources, AREWOA is thought to demonstrate cloud-based Infrastructure-as-a-Service (IaaS). This approach is simulated in Cloud Simulator and the results shows that the efficiency of the methods.

关键词： Cloud computing Energy consumption Schedules Processor scheduling Heuristic algorithms System performance Optimal scheduling Whale optimization algorithms Stability analysis Servers

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Cloud-Enabled Predictive Vehicle Health Monitoring and Maintenance with LSTM Networks for Time-Series Analysis 1

Cloud-Enabled Predictive Vehicle Health Monitoring and Maint...

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1st International Conference on Innovations in Communications, Electrical and computer Engineering, ICICEC 2024

作者： Vinya, Viyyapu Lokeshwari Manivannan, K.K. Manivannan, S.K. Sathiyamoorthy, C. Jagadeeswaran, M. Meenakshi, B. SRM Institute of Science and Technology Department of Computing Technologies School of Computing Kattankulathur Tamil Nadu Chennai India Easwari Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Vit Bhopal University Department of Physics School of Advanced Sciences and Languages Madhya Pradesh Bhopal India J.J College of Engineering and Technology Department of Information Technology Tamil Nadu Tiruchirappalli India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha University Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha School of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376517

Maximizing operational efficiency and customer satisfaction in the automobile sector depends on maintaining vehicle health and avoiding maintenance downtime. Vehicle Internet of Things (IoT) devices capture massive volumes of sensor data, enabling predictive maintenance. This research offers cloud-based predictive vehicle health monitoring and maintenance systems using Long Short-Term Memory (LSTM) networks for time-series analysis. System architecture uses IoT sensors in automobiles to gather real-time data on engine temperature, oil pressure, and vibration. This data is stored, analyzed, and predicted on clouds. To assess time-series sensor data and forecast failures or maintenance requirements, LSTM networks, a form of recurrent neural network, are used. The proposed system uses data preprocessing to manage missing values and outliers, feature extraction to find significant sensor data patterns, and LSTM-based predictive modeling to predict vehicle health conditions. Technology may predict future faults by continually monitoring and evaluating the vehicle's state, allowing proactive maintenance to avoid expensive breakdowns and downtime. The cloud-based architecture is scalable and flexible, providing smooth connection with fleet management systems and remote vehicle health monitoring across scattered sites. The suggested method optimizes maintenance schedules and resource allocation using predictive insights from real-time sensor data to improve operational efficiency and save costs. It may improve predictive vehicle health monitoring and maintenance in the automobile sector, improving dependability, safety, and cost. © 2024 IEEE.

关键词： Customer satisfaction

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