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Cloud-Enabled Bio-Inspired optimization: A Scalable Solution for Complex Real-World Problems 3

Cloud-Enabled Bio-Inspired Optimization: A Scalable Solution...

引用

3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Shreya Sakthi, U.R. Sruthi, S. Shruti, S.G. Rajganesh, N. Department of Computer Science and Engineering Sri Venkateswara College of Engineering Sriperumbudur India

ISBN: (纸本)9798331527549

Bio-inspired optimization algorithms, encompassing swarm intelligence, neural networks, fuzzy logic, and evolutionary algorithms, are powerful tools for addressing complex real-world optimization problems. However, their effectiveness is often constrained by the computational demands of training and execution, which surpass the capabilities of traditional computing platforms—particularly when dealing with massive datasets or intricate optimization challenges. These limitations restrict the practical applicability of such algorithms. This work introduces a framework that leverages cloud computing as a scalable and efficient platform to overcome these computational barriers. By integrating bio-inspired optimization techniques with the elastic and high-performance resources of cloud environments, this research demonstrates significant improvements in both the scope and efficiency of these algorithms. Experimental results validate the proposed approach, showcasing enhanced performance, reduced execution time, and the ability to solve larger and more complex real-world problems, thereby unlocking the full potential of bio-inspired optimization in practical applications. © 2025 IEEE.

关键词： Cloud platforms

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Gear Misalignment optimization for Electric Drive Unit Using Machine learning Based Prescriptive Analytics

Gear Misalignment Optimization for Electric Drive Unit Using...

引用

2025 International conference on Advances in Design, Materials, Manufacturing and Surface Engineering for Mobility, ADMMS 2025

作者： Penumatsa, Venkata Ramana Raju Thomas, Benson Black, Derrick Jain, Sachin Tata Consultancy Services Ltd. India General Motors LLC United States

In Electric vehicle Drive Unit Gears, high mesh misalignments result in shift in load distribution of a gear pair that can increase contact and bending stresses. It can move the peak bending and contact stresses to the edge of the face width and increase gear noise as well. Lower misalignment value is often required to reduce the peak bending and contact stresses and have a balanced load distribution along the gear flank, which in turn helps in reducing noise and improving durability of drive unit. This paper delineates Prescriptive Analytics method that combines virtual simulations, Machine learning (ML) and optimization techniques to minimize different gear misalignments for the electric vehicle drive units. Generally, the manual optimization process is carried out by sequential modifications of stiffness of individual components. However, this process is time consuming and does not account for interactions between the components. In this study, firstly, Machine learning models are developed based on design of experiments (DOE) simulations. These ML models are used as surrogates for actual simulations in generic algorithms (Differential Evolution) based optimization techniques. It finally prescribes changes in stiffness of different components to get optimum misalignment value. © 2025 SAE International and SAEINDIA;General Motors.

关键词： Electric load distribution

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A Hybrid Strategy for the Solution of Economic Load Dispatch Problem 3

A Hybrid Strategy for the Solution of Economic Load Dispatch...

引用

3rd IEEE International conference on Power Electronics, intelligent Control and Energy Systems, ICPEICES 2024

作者： Sahay, Shuvam Kumar, Niranjan Nit Jamshedpur Dept. Of Electrical Engineering Jamshedpur India

ISBN: (纸本)9798350387193

The primary responsibility of load dispatch centers is to maximize economic efficiency while maximizing operational efficiency, which is achieved by minimizing the cost of real power generation at various generating units for any given load demand. This study introduces a hybrid approach combining Levy flights, Particle Swarm optimization (PSO), and Sine Cosine approach (SCA) to address the Economic Load Dispatch (ELD) problem. The LFPSO is utilized to locate the global optimum, while the SCA is employed to explore the local optimum. The suggested method is evaluated on the standard IEEE 30 bus system and the findings are compared with other techniques. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Gearbox Cross-speed Fault Diagnosis Based on Transfer learning of Stator Current 3

Gearbox Cross-speed Fault Diagnosis Based on Transfer Learni...

引用

3rd International conference on Robotics, Artificial Intelligence and intelligent Control (RAIIC)

作者： Luo, Beice Chen, Chunjun Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Peoples R China Southwest Jiaotong Univ Technol & Equipment Rail Transit Operat & Mainten Chengdu 610031 Peoples R China

ISBN: (纸本)9798350352634;9798350352627

During the gearbox fault diagnosis, aiming at the problems of diversified forms of faults and loading conditions, the lack of fault labels in the actual sample data, and the insufficient generalisation ability of the traditional deep learning model, a deep transfer learning method is proposed for the stator currents of gearbox drive motor. We build a gearbox fault experimental bench, set up a cross-speed loading condition, and collect current signals at different speeds as training set for transfer learning. A convolutional neural network is used as a feature extractor to extract similar data features of samples under different speed. We use the Multiple Kernel Maximum Mean Discrepancy (MK-MMD) as a feature distribution loss to measure the difference in feature distribution across the feature under different speed, and finally achieve the joint transfer learning of the fault features from the source domain to the target domain. A deep transfer model is trained to achieve the fault diagnosis task. The analysis of experimental data shows that the method can achieve cross-speed fault diagnosis of gearboxes to a certain extent, with an average diagnosis rate of 74% above.

关键词： Gearbox fault diagnosis Transfer learning Multiple Kernel Maximum Mean Discrepancy Motor stator current

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Relevant Musical Schema-based Human Emotion Controller using Deep learning Techniques 3

Relevant Musical Schema-based Human Emotion Controller using...

引用

3rd IEEE International conference on intelligent Techniques in Control, optimization and Signal Processing, INCOS 2024

作者： Nagaraj, P. Sudar, K. Muthamil Krishna, Gollamandala Vamsi Yeswanth, Grandhi Srinivas Charan, Gurrala Sai Charan, Nidiganti Kalasalingam Academy of Research and Education Krishnankoil Department of Computer Science and Engineering Virudhunagar India Mepco Schlenk Engineering College Department of Computer Science and Engineering Virudhnagar India

ISBN: (纸本)9798350361186

Musical Schema negotiation describes how a user's facial expressions may convey their emotional state or mood. You may see these expressions in the live video from the system camera. Many efforts are being made to teach machines to recognize various human emotions or moods in the field of computer vision and machine learning (ML). There are several techniques available in machine learning to identify human emotions. We propose a system that detects mood in real time and generates a mood-based music player that recommends songs depending on the observed mood. This is now a function that comes pre-installed on the traditional music player applications on our phones. One major benefit of mood detection integration is customer satisfaction. Among this system's objectives are to analyze the user's look, forecast their expression, and suggest music based on how they seem. © 2024 IEEE.

关键词： Deep learning

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Fault Diagnosis of Rotating Equipment Bearings Based on VMD-MSGWO-SVM 3

Fault Diagnosis of Rotating Equipment Bearings Based on VMD-...

引用

3rd conference on Fully Actuated System Theory and Applications (FASTA)

作者： Deng, Jibao Liu, Yimin Li, Zixuan Wuhan Maintenance & Installat Petrochem Engn Co L Wuhan 430082 Peoples R China Wuhan Univ Sci & Technol Acad Informat Sci & Engn Wuhan 430081 Peoples R China

ISBN: (纸本)9798350373707;9798350373691

In this paper, the bearing fault problem of rotating equipment is studied by using support vector machine (SVM) model classification method. In order to solve the problem of low accuracy of using SVM to detect the fault for bearing, the method of variational mode decomposition (VMD) is introduced to extract features from original vibration data and the intelligent optimization algorithm of MSGWO is introduced to optimize parameters of SVM, which can improve fault diagnosis performance of classification system. A variety of comparative experiments are carried out by using FEMTO-ST bearing data set which is open source on the internet. The results show that the proposed algorithm has high prediction accuracy and is superior to the present methods in classification.

关键词： SVM Rotating Equipment Bearings Fault VMD Improved Grey Wolf optimization Algorithm

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Innovative Approach for Early Heart Disease Prediction: Gaussian Mixture Model and K-Nearest Neighbour 3

Innovative Approach for Early Heart Disease Prediction: Gaus...

引用

3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Surendran, R. Anandaraj, B. Deepa, P. Mageshwari, M. Balamurugan, K.S. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Madnapalle Institute of Technology & Science Andhra Pradesh Madanapalle India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer science and engineering Karpaga Vinayaga College of Engineering and Technology Tamil Nadu Chengalpattu India Department of Electronics and communication engineering Karpaga Vinayaga College of Engineering and Technology Tamil Nadu Chengalpattu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

An Electrocardiogram (ECG) contributes significantly to early diagnosis and classification of heart diseases, arrhythmia which means irregular heart rate. Regrettably, the process became difficult due to asymmetric and minimal amplitude from recordings of ECG and cannot do the extraction from multiple signals. By using machine learning technology, it can classify and extract multiple samples, but cannot extract the data features from complex data structures. To overcome the limitations, previously introduced metaheuristic optimization (MHO) to improve the classification and extraction. Proposed a method which supports and K-Nearest Neighbour (KNN) Gaussian Mixture Model (GMM) algorithms for determine the abnormalities, predicting cardiac events, and improve diagnostic accuracy. As a result, this can help us to get results more quickly and accurately with sensitivity 99.7% and accuracy 99.2%, for helping healthcare professionals in diagnosing arrythmias, myocardial infarction and any other heart conditions. © 2025 IEEE.

关键词： Adversarial machine learning

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Design and Development of Music intelligent Education System Based on Artificial Intelligence 3

Design and Development of Music Intelligent Education System...

引用

3rd IEEE International conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Xu, Bo Wuhan Business University Hubei Wuhan China

ISBN: (纸本)9798350318609

In traditional teaching, students may find it difficult to receive timely and personalized feedback, and some students may lose interest in music learning due to the monotony of traditional learning methods. This article aimed to design a music intelligent education system based on artificial intelligence (AI), providing rich learning content and personalized guidance to enhance students' interest in learning. The artificial intelligence based music intelligent education system designed in this article was divided into basic information management module, student music task management module, personalized recommendation module, interactive community module, and artificial intelligence algorithm module. It can monitor student performance, provide personalized feedback and guidance, help students improve, and provide a richer and more interesting learning experience. The experimental results of this article showed that under traditional music education, only 21 students were very interested in music, while under the music intelligent education system, there were 120 students who were very interested in music. The music intelligent education system based on artificial intelligence can not only compensate for the shortcomings of traditional music education, but also provide students with more personalized, efficient, and interesting learning experiences. © 2024 IEEE.

关键词： Collaborative filtering

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intelligent Tutoring System for English Language Acquisition Based on Computer Algorithms 3

Intelligent Tutoring System for English Language Acquisition...

引用

3rd International conference on Data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Li, Xing Hunan University of Medicine NO.492 Jinxi South Road Hecheng District Hunan Huaihua China

ISBN: (纸本)9798331519032

With the continuous deepening of computer application, computer-assisted instruction (CAI) has made remarkable progress in foreign language learning. Foreign language learning mainly relies on memory-based learning methods, and CAI has a unique advantage in assisting memory by providing information in the form of visuals, images, sounds and animations, thus constructing a multidimensional knowledge layer in the brain and enhancing learning efficiency. At present, CAI English learning software mainly focuses on memorizing words and quizzes, but tutoring software specifically for college English level exams is scarce. In this paper, based on computer algorithms, combining collaborative filtering algorithm and convolutional neural network algorithm, an intelligent tutoring system for English language acquisition is designed and realized. The experimental data show that students using the intelligent tutoring system show more significant improvement in English proficiency improvement than traditional learning methods, and the average improvement score of the experimental group is about 6 points higher than that of the control group, further verifying the effectiveness of the system in improving learning efficiency. © 2024 IEEE.

关键词： Adversarial machine learning

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A Survey of 3D Gaussian Splatting: optimization Techniques, Applications, and AI-Driven Advancements 3

A Survey of 3D Gaussian Splatting: Optimization Techniques, ...

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3rd International conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Santosh Reddy, P. Abhiram, H. Archish, K.S. B.N.M. Institute of Technology Department of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798331515911

3D Gaussian Splatting (3DGS) has emerged as a transformative technique for real-time, high-quality 3D rendering. This paper surveys the advancements in 3DGS, focusing on optimization strategies, applications across diverse domains, and AI-driven innovations that enhance its utility. optimization techniques, such as Mini-Splatting and sparsification frameworks, address challenges like computational overhead and memory inefficiency while maintaining visual fidelity. AI has further expanded the capabilities of 3DGS through frameworks like SceneTeller, ART3D and GaussianEditor, enabling text-to-3D generation, object-level manipulation, and artistic scene creation with multi-view consistency. Applications of 3DGS range from autonomous driving and robotics to virtual and augmented reality, highlighting its adaptability and efficiency. This survey underscores the importance of continued research to refine 3DGS's scalability and applicability, solidifying its role at the intersection of computer graphics and artificial intelligence. © 2025 IEEE.

关键词： Augmented reality

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