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Transfer learning-Based Evolutionary Multi-task Optimization 18th

Transfer Learning-Based Evolutionary Multi-task Optimization

引用

18th international conference on Bio-Inspired computing: Theories and Applications (BIC-TA)

作者： Li, Shuai Zhu, Xiaobing Li, Xi Hebei GEO Univ Sch Informat Engn Shijiazhuang 050031 Hebei Peoples R China

ISBN: (纸本)9789819722716;9789819722723

Multi-task optimization (MTO) is an emerging research topic to optimize multiple related tasks simultaneously. It aims to enhance task interrelationships by leveraging shared information and features, thereby improving model performance. Evolutionary transfer optimization (ETO), applied to address multitask problems using evolutionary algorithms, incorporates the principles of transfer learning. It utilizes knowledge and experience from source tasks to expedite the optimization process of target tasks. We introduce a transfer learning-based strategy where valuable information from one task is transferred as comprehensively as possible to another task. This article proposes an idea that is based on joint distribution adaptation (JDA) and employs population individual replacement methods as knowledge transfer, differential evolution as the underlying optimizer, called transfer learning-based evolutionary multi-task optimization algorithm (TLEMTO). To validate the effectiveness of the proposed algorithm, the experiment is conducted on CEC17 multi-task optimization problem benchmarks, the results show that TLEMTO is superior to the compared state-of-the-art algorithms.

关键词： Evolutionary transfer optimization Multi-task optimization Differential evolution Transfer learning Joint distribution adaptation

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Enhancing the Accuracy of Manufacturing Process Error Detection Through SMOTE-Based Oversampling Using Machine learning and Deep learning Techniques

Enhancing the Accuracy of Manufacturing Process Error Detect...

引用

2024 international conference on Integrated Circuits, Communication, and computing Systems, ICIC3S 2024

作者： Boyapati, Sai Venkat Rakshitha, Guduru Bhanu Sri Reddy, Maryada Rishitha Saleti, Sumalatha SRM University Department of Computer Science Engineering AP Guntur India

ISBN: (纸本)9798350364088

A production competency study leads to a rise in the manufacturing sectors' strategic emphasis. Developing semiconductor materials is a highly complex approach that necessitates numerous evaluations. It is impossible to emphasize the significance of the quality of the product. We put up a number of methods for automatically creating a prognostic model that is effective at identifying equipment flaws throughout the semiconductor materials' wafer fabrication process. The SECOM dataset is representative of semiconductor production procedures that go through numerous tests performed. There are imbalanced statistics in the dataset, so our proposed methodology incorporates SMOTE (Synthetic Minority Over-sampling Technique) functionality that is introduced to mitigate the imbalance of the training dataset by leveling off any unbalanced attributes. Detecting faults in the manufacturing process improves semiconductor quality and testing efficiency, and is used to validate both approaches to Machine learning and Deep learning algorithms. This is accomplished by collecting performance metrics during the development process. Another aspect of our effort to cut down on the training time for testing is highlighted in our research report. © 2024 IEEE.

关键词： learning algorithms

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A Comparative Study of Deep Image Retrieval Models Leveraging Deep Features

A Comparative Study of Deep Image Retrieval Models Leveragin...

引用

2024 international conference on Electrical, Electronics and computing Technologies, ICEECT 2024

作者： Jain, Ashish Varshney, Sudeep SET Sharda University Dept. of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350378092

This review investigates how deep learning methods can be utilized for efficient image retrieval based on content. Obtaining accurate images from vast digital collections poses significant challenges, motivating research in CBIR. The effectiveness of these methods varies depending on the dataset's type and size, with certain algorithms excelling with specific dataset characteristics. An extensive and well-structured review of successful image retrieval techniques is given in this paper. Our primary objective is to evaluate various deep learning models applied by researchers and compare their performance based on the outcome of evaluation matrices. These models encompass CNNs, DBNs, and other deep architectures tailored for image retrieval tasks. By synthesizing insights from this review, researchers can make informed decisions regarding model selection and potentially enhance retrieval performance by leveraging advanced deep learning features. The importance of deep features in image retrieval is the ability to capture complex visual patterns and semantic information that cannot be easily extracted by traditional handcrafted features. With the increasing volume of online images, Image retrieval using deep learning has become crucial for applications like object recognition, image retrieval, and image search engines. © 2024 IEEE.

关键词： Contrastive learning

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AI-Driven Diagnostics: Comparative Analysis of Machine learning and Deep learning Approaches in Multiple Sclerosis Diagnostics 11

AI-Driven Diagnostics: Comparative Analysis of Machine Learn...

引用

11th IEEE international conference on Advances in computing and Communications, ICACC 2024

作者： Anitha, L. Narmadha, D. Naveen Sundar, G. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Coimbatore India

ISBN: (纸本)9798350379136

Multiple Sclerosis (MS) is a chronic autoimmune disorder that targets the central nervous system, resulting in considerable physical and cognitive disabilities. Timely and precise diagnosis is essential for the effective management and treatment of Multiple Sclerosis (MS). Predicting MS involves using various types of data to assess the risk and diagnose the disease. The data commonly used are clinical, imaging, speech and gene data. This work presents a review of machine learning and deep learning methods, encompassing a broad spectrum of algorithms such as Support Vector Machines (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Convolutional Neural Networks (CNNs), and Artificial Neural Networks (ANNs). Furthermore, the survey gives a comparative analysis of the dataset used, algorithms employed and the performance metrics. The survey also compares the implemented results with DL using the gene data. It also identifies current challenges in MS prediction, such as heterogeneity of MS, integration of multimodal data, validation and clinical implementation. Our goal is to offer a valuable resource for researchers and practitioners. © 2024 IEEE.

关键词： Convolutional neural networks

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Safeguarding Audio Integrity: Ensemble Architectures for Real-World Fake Audio Identification 15

Safeguarding Audio Integrity: Ensemble Architectures for Rea...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Kasturi, Soundarya Lahari Nivetha, S. Selva Rani, B. Vairamuthu, S. Tiwari, Sourabh Yadav, Devendra Shankarappa, Rashmi T. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India Voice Intelligence Group Samsung R&d Institute Bengaluru India

ISBN: (纸本)9798350370249

Through this research, we introduce a unique Audio Spoof Detection System for distinguishing between actual, fraudulent, and generated audio recordings. Strong detection algorithms are required since the spread of audio modification techniques poses a major threat to the integrity of audio-based systems. Combination of 7 key voice features from cepstral coefficients and spectrograms were used to cover all possible spoofing. We are proposing ensemble methods to enhance the accuracy of conventional machine learning models such as Random Forest, State Vector Classifier (SVC), K-Nearest Neighbors (KNN) etc. and deep learning models such as Recurrent Neural Networks (RNN), Time-Delay Neural Networks (TDNN), SqueezeNet etc. The proposed model is evaluated using the ASVspoof 2021 logical access (LA) and Deep Voice dataset. Experimental results show that our proposed model significantly elevates performance compared to the baseline and state-of-the-art models. © 2024 IEEE.

关键词： Audio Spoof Detection CNN Deep learning Ensemble learning Machine learning Random Forest TDNN

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Efficient Deep Post-Decision State learning for Privacy-Conscious Offloading in MEC-Enabled 6G Networks 1

Efficient Deep Post-Decision State Learning for Privacy-Cons...

引用

1st international conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Saeed, Mamoon M. Mokhtar, Rania A. Elbasheir, Mohammed S. Ali, Elmustafa Sayed Ahmed, Zeinab E. Ahmed, Mayada A. Yemen Faculty of Engineering School of Elecs Eng. Sudan University of Science and Technology Khartoum Sudan School of Electronic Engineering Sudan University of Science and Technology Khartoum Sudan Department of Electrical & Electronic Engineering Faculty of Engineering Red Sea University Sudan Department of Electrical and Computer International Islamic University Malaysia

ISBN: (纸本)9798331533557

With the growing demand for data-intensive applications, offloading tasks to multi-access edge computing (MEC) servers has become essential. However, privacy concerns arise as sensitive data is transmitted to remote servers. To address this, the proposed method uses deep learning techniques to intelligently analyze and classify data packets, ensuring that only non-sensitive information is offloaded. This paper proposes a novel approach called a deep post-decision state (PDS) learning privacy-concerned offloading in MEC-enabled 6G Environments. A Post-Decision State (PDS) approach that selectively selects data packets while maintaining privacy is used to train the deep learning model. The suggested method successfully balances the trade-off between increasing system performance and safeguarding user privacy to achieve privacy-aware offloading in MEC-enabled 6G networks, as shown by the experimental findings. Also, it performs noticeably better than the traditional DQN, according to simulation data. © 2024 IEEE.

关键词： Adversarial machine learning

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ThomaSIGHTS: Evaluating Nursery Students’ learning Styles through Body Language and Engagement

ThomaSIGHTS: Evaluating Nursery Students’ Learning Styles t...

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2024 IEEE international conference on computing, ICOCO 2024

作者： Carranza, Genevieve V. Akmad, Shellamie B. Danque, Paulo B. Centeno, Criselle J. Mangrobang, Marilou B. Mercado, Mark Anthony S. Information Technology Department Pamantasan ng Lungsod ng Maynila Manila Philippines

ISBN: (纸本)9798331530303

Identifying diverse learning styles in early childhood education is a significant challenge that can hinder academic progress. To address this, this study presents ThomaSIGHTS, a system developed to analyze nursery students' body language and engagement for effective learning style prediction. The system utilizes YOLO and DeepSORT algorithms for body language detection, the Random Forest algorithm to predict the percentages of visual, auditory, and kinesthetic learning styles, and Hybrid Filtering to suggest tailored teaching approaches. Evaluations conducted through a survey involving IT professionals and nursery teachers, adhering to ISO/IEC 25010 standards, resulted in high scores for Functional Suitability (4.17), Usability (4.35), Reliability (4.33), and Performance Efficiency (4.36). Additionally, the system achieved an mean precision of 82.08% an average recall of 59.42%, an average F1 score of 68.25%, an overall accuracy of 62.17%, and an average precision (AP) of 79.5%. The confusion matrix results indicated the identification of 4,305 true positives and 3,195 true negatives, demonstrating strong classification capabilities, although 2,272 false positives and 917 false negatives revealed areas for improvement. These findings demonstrate that ThomaSIGHTS is a reliable tool for detecting students’ body language, predicting learning styles percentages, and suggesting tailored teaching approaches, thereby enhancing classroom engagement and addressing the educational needs of nursery students. ©2024 IEEE.

关键词： Contrastive learning

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WiPSCE 2024 - Proceedings of the 19th WiPSCE conference in Primary and Secondary computing Education Research

WiPSCE 2024 - Proceedings of the 19th WiPSCE Conference in P...

引用

19th WiPSCE conference in Primary and Secondary computing Education Research, WiPSCE 2024

ISBN: (纸本)9798400710056

The proceedings contain 42 papers. The topics discussed include: unpacking approaches to learning and teaching machine learning in K-12 education: transparency, ethics, and design activities;enhancing understanding of data traces and profiling among K-9 students through an interactive classroom game;data-related practices for creating artificial intelligence systems in K-12;investigating the impact of programming activities on computational thinking and AI literacy in Spanish schools;coaching teachers to teach computer science with equity-focused teacher standards;automated assessment: does it align with teachers’ views?;improvement of computational thinking skills through unplugged activities in upper secondary education;observing students’ behavior during problem solving: determinants of success;and extrinsic barriers to integrating computer science in elementary school subject areas in the United States.

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Research on blended teaching mode based on online learning and intelligent assessment system

Research on blended teaching mode based on online learning a...

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2023 international conference on Information Education and Artificial Intelligence, ICIEAI 2023

作者： Fan, Xuefei Wang, Yuan BinHai School of Foreign Affairs of Tianjin Foreign Studies University Tianjin300270 China DaLian Shipping College Liaoning DaLian116052 China

ISBN: (纸本)9798400716157

In view of the unsatisfactory teaching effect caused by students' weak foundation, poor learning initiative and lack of learning interest in higher vocational colleges, the traditional teaching method was reformed, and the hybrid teaching mode based on online learning and intelligent assessment system was explored by taking the course "Marine engineering Fundamentals"as an example. This study first investigated the mixed teaching orientation of higher vocational teaching reform and the situation of teachers' application of online course resources, deeply analyzed the problems existing in teachers' application of online course resources, and then proposed the application strategy of online course resources for higher vocational teachers to improve the application efficiency of online course resources. It includes online learning and intelligent evaluation, which makes it organically integrated with classroom teaching, and realizes the maximization of the application benefit of online course resources. Finally, this paper adopts the form of development and design of online learning and intelligent evaluation system to realize the blended teaching mode of online teaching. Through this system, it realizes pre-class turnover, breaks through difficulties and grasps key points. Finally, it adopts intelligent evaluation system to form process evaluation, and finally completes the mixed teaching mode of online teaching and intelligent evaluation system. © 2023 ACM.

关键词： teaching

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Modeling and Performance Analysis of PEM Fuel Cells Using Machine learning Techniques 3

Modeling and Performance Analysis of PEM Fuel Cells Using Ma...

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3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Gaddam, Shamitha Reddy, P. Srihith Joshi, Shruti V. Bnm Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Bnm Institute of Technology Department of Electrical Engineering Bengaluru India

ISBN: (纸本)9798331515911

Proton Exchange Membrane Fuel Cells (PEMFCs) play a vital role in the transition to clean energy technologies, requiring accurate and efficient models for their control, optimization, and real-time monitoring. This research focuses on creating machine learning models as Support Vector Regression (SVR), Gradient Boosting (GBR), XGBoost, and Random Forest (RFR) and MLPRegressor to predict PEMFC key performance attributes. Using a dataset from Hamidi et al [1]., which focuses on Nafion 112 membranes and MEA activation tests. Input features include current density, voltage, power density, gas pressure, relative humidity, and impedance components (real and imaginary). The primary output is cell voltage, a key performance indicator. Among the models, Random Forest Regressor achieved the best results with an accuracy of 94.715, R2 score of 0.8049, MSE of 0.001036, and RMSE of 0.03219. Feature importance analysis revealed relative humidity, current density, and voltage as the most influential factors. This research illustrates the use of machine learning techniques in modeling the functioning of attributes of PEMFCs, providing insights for optimizing fuel cell performance and accelerating the inculcation of clean energy technologies. © 2025 IEEE.

关键词： Support vector regression

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