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5th IEEE International Conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Kumar Silivery, Arun Mohan Rao, Kovvuru Ram Solleti, Ramana Osmania University Computer Science and Engineering Hyderabad India Vasavi College of Engineering Department of Information Technology Hyderabad India Bhavan's Vivekananda College Department of Computer Science Hyderabad India

ISBN: (纸本)9781665493604

There has been rapid advancement in networks and technology over the past few years, and Internet service providers are now available anywhere in the world. The number of thefts has increased, and many current systems have been breached. As a result, the need for creating information security technology to spot new attacks has grown significantly. An Intrusion Detection System (IDS) combines machine learning and deep learning algorithms to discover network abnormalities, is an essential information security technology. The central concept of this study is to find the elusive assault bundle by using a high-level intrusion detection system with high network performance. Likewise, in this approach, the attack detection is completed. The proposed system has shown that empowering brings incredible precision. © 2023 IEEE.

关键词： Support vector machines

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Deep Learning based Advanced Image Recognition in Autonomous Vehicles in Industry 4.0 3

Deep Learning based Advanced Image Recognition in Autonomous...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Aruna, R. Manjula Devi, P. Suresh, D. Arul Pandiyan, M. Sathya, S. Geetha, K. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vtu Affiliated Amc Engineering College Department of Information Science and Engineering Banglore India Unnamalai College of Arts and Science Department of Computer Science Kovilpatti India Bannari Amman Institute of Technolgy Department of Information Science and Engineering Erode India Bannari Kongu Engineering College Department of Mathematics Erode India Dean of Academics and Research Jct College of Engineering and Technology Coimbatore India

ISBN: (纸本)9798350375190

The automation with integration of numerous devices are important factors in the manufacturing and production sectors in Industry 4.0. This involves the use of autonomous vehicles for diverse applications. They are based on advanced image recognition systems. The efficient functioning of the system is achieved using deep learning techniques. These networks have the ability to automatically learn from complex data. These deep learning techniques are used for the detection of obstacles, lane following and object recognition. The various constraints in obtaining the optimum performance are achieved using ant colony optimization techniques. This helps in obtaining the overall efficiency of autonomous operations. They helps in evaluating optimum solutions through iterative updation of the artificial pheromones. They are used for the optimization of hyperparameters and training parameters. They helps in fine tuning of the model for various requirements of autonomous vehicles in Industry 4.0. They results in reduced computational costs with improved convergence. The integration of deep learning with ACO helps in overcoming the challenges in the deployment of advanced image recognition systems in autonomous vehicles. They helps in improving the perception capabilities of the vehicles. These autonomous vehicles provide adaptive solutions for various processes in the manufacturing and production sectors. © 2024 IEEE.

关键词： Image recognition

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A Concise Overview of Cloud-based Platform for Intelligent Converters

A Concise Overview of Cloud-based Platform for Intelligent C...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Narmadha, T. Shree, Ritu Alam, Intekhab Computer Science Engineering Department Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Maharishi University of Information Technology Engineering Department Uttar Pradesh India

ISBN: (纸本)9798350357769

This research presents the mechanical implementation and management of freely available modules for the construction of smart conversions systems. Converts that are scattered or unsophisticated are not clever converts. To transmit the maximum power, they have stabilizers that have comparable impedance online. In current energy environments, intelligent adaptations provide fascinating solutions;in fact, their uses have become more and more varied. The suggested method was developed and tested in a laboratory environment using both real photovoltaic units and a solar cell simulator. Innovative conversions are shown in research, and enhanced intelligent capabilities allow for on-the-fly configuration adjustments, real-time diagnostics, and the management of the system's programs. Furthermore, the project's capabilities allows for a wide range of combinations and groups of various energy sources, generators, and modulation techniques. To the best of our knowledge, this project remains the sole platform for smart conversions that is available for free and is currently being utilized by research, industry, and government. © 2023 IEEE.

关键词： Cloud-Connected Smart Converters DC/AC Transformer Electrical Architectural Experimental Results FPGA technology Maximum Power Point Tracking MPPT Techniques Power Shield Design and Efficiency Analysis Renewable Energy Techniques Smart Conversion Devices Wi-Fi Networking Module

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Fuzzy control design for time-delay systems under adaptive event-triggered mechanism

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Neural Computing and Applications 2024年第9期36卷 4727-4739页

作者： Du, Zhenbin Qu, Zifang Kao, Yonggui Wu, Zhaojing School of Computer and Control Engineering Yantai University Shandong Yantai264005 China School of Mathematics and Information Science Shandong Technology and Business University Shandong Yantai264005 China Department of Mathematics Harbin Institute of Technology Weihai264209 China School of Mathematics and Information Sciences Yantai University Shandong Yantai264005 China

This manuscript studies the fuzzy H∞ control design problem for nonlinear time-varying delay systems in fuzzy form via adaptive event-triggered mechanism. The fuzzy model and an adaptive event-triggered controller are connected in closed-loop form. A looped-functional is established to conduct stability analysis, and the main feature of this functional can fully reflect the sampling feature of control systems. Thus, the achieved results are less conservative. The Wringter inequality is also used to further relax stability analysis. Finally, sufficient conditions are achieved for the existence of event-triggered fuzzy controller, and the resulting closed-loop system reaches an H∞ control performance. Two examples verify the efficiency of the presented control strategy. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023.

关键词： Time delay

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Generating A New Shilling Attack for Recommendation Systems

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computers, Materials & Continua 2022年第5期71卷 2827-2846页

作者： Pradeep Kumar Singh Pijush Kanti Dutta Pramanik Madhumita Sardar Anand Nayyar Mehedi Masud Prasenjit Choudhury Department of Computer Science&Engineering National Institute of TechnologyDurgapurIndia Graduate School Duy Tan UniversityDa NangVietnam Faculty of Information Technology Duy Tan UniversityDa NangVietnam Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

A collaborative filtering-based recommendation system has been an integral part of e-commerce and *** keep the recommendation systems reliable,authentic,and superior,the security of these systems is very *** the existing shilling attack detection methods in collaborative filtering are able to detect the standard attacks,in this paper,we prove that they fail to detect a new or unknown *** develop a new attack model,named Obscure attack,with unknown features and observed that it has been successful in biasing the overall top-N list of the target users as *** Obscure attack is able to push target items to the top-N list as well as remove the actual rated items from the *** proposed attack is more effective at a smaller number of k in top-k similar user as compared to other existing *** effectivity of the proposed attack model is tested on the MovieLens dataset,where various classifiers like SVM,J48,random forest,and naïve Bayes are utilized.

关键词： Shilling attack recommendation system collaborative filtering top-N recommendation biasing shuffling hit ratio

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Enhanced Learning Mechanisms for Extracting Relevant information with Advanced Neural Networks 2

Enhanced Learning Mechanisms for Extracting Relevant Informa...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Santhosh Kumar, C. Verma, Devika A. Choudhary, Sanjay Savita Kamalraj, R. Kumar, V. Mathan Prince Shri Venkateshwara Padmavathy Engineering College Department of Civil Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Mechanical Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Information Technology Karnataka Bangalore India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India

ISBN: (纸本)9798350308846

Recent advances in synthetic intelligence (AI) technologies, including deep learning, have generated a hobby in growing advanced neural networks for extracting relevant facts from big unstructured datasets. In this painting, we recommend numerous enhanced modern mechanisms to improve the accuracy and speed of the latest information extraction from textual content analytics programs. In particular, we explore the usage of transfer today's semi supervised latest and reinforcement learning tactics to allow the powerful use of brand new categorized and unlabeled education statistics. We propose architectures for combining multiple neural networks and evaluate their performance on various responsibilities. Experimental results on a selection of contemporary benchmark datasets show that our cutting-edge techniques outperform strategies on duties such as text category, sentiment evaluation, and natural language understanding. The proposed more advantageous modern mechanisms may have several potential applications in finance, healthcare, and criminal investigation. Our proposed strategies can be relevant to various AI obligations and represent a critical step in cutting-edge green and correct information extraction from unstructured statistics. © 2023 IEEE.

关键词： Consisting Correct Extraction Represent Variety

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Progressive edge-sensing dynamic scene deblurring

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Computational Visual Media 2022年第3期8卷 495-508页

作者： Tianlin Zhang Jinjiang Li Hui Fan School of Information and Electronic Engineering Shandong Technology and Business UniversityYantai 264005China School of Computer Science and Technology Shandong Technology and Business UniversityYantai 264005China Institute of ZhongKe Network Technology Yantai 264005China

Deblurring images of dynamic scenes is a challenging task because blurring occurs due to a combination of many *** recent years,the use of multi-scale pyramid methods to recover high-resolution sharp images has been extensively *** have made improvements to the lack of detail recovery in the cascade structure through a network using progressive integration of data *** new multi-scale structure and edge feature perception design deals with changes in blurring at different spatial scales and enhances the sensitivity of the network to blurred *** coarse-to-fine architecture restores the image structure,first performing global adjustments,and then performing local *** this way,not only is global correlation considered,but also residual information is used to significantly improve image restoration and enhance texture *** results show quantitative and qualitative improvements over existing methods.

关键词： image deblurring dynamic scenes multiscale edge features

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An End-to-End Mandarin Audio-Visual Speech Recognition Model with a Feature Enhancement Module

An End-to-End Mandarin Audio-Visual Speech Recognition Model...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Wang, Jinxin Yang, Chao Guo, Zhongwen Li, Xiaomei Wang, Weigang Ocean University of China Faculty of Information Science and Engineering Qingdao China School of Computer Science University of Technology Sydney Sydney Australia

ISBN: (纸本)9798350337020

Compared to relying only on audio information, incorporating visual information improves speech recognition accuracy in noisy environments. Existing works are prone to design specific architecture for feature extraction, neglecting feature enhancement. In this paper, we propose an end-to-end Mandarin audio-visual speech recognition model with a Feature Enhancement Module. Specifically, we design a Feature Enhancement Module (FEM) that uses deconvolution and up-sampling to obtain the twin enhanced data for generating high-resolution feature representation. We further develop the Visual Feature Enhancement Module (Visual FEM) and Audio Feature Enhancement Module (Audio FEM) to enhance feature extraction from both visual data and audio data. We incorporate the proposed modules into the blocks of the Residual Network for accurate audio-visual speech recognition. We conducted experiments on the CAS-VSR-W1k and Chinese Mandarin Lip Reading (CMLR) datasets. The experimental results show that the proposed method outperforms the selected competitive baselines and the state-of-the-art, indicating the superiority of our proposed modules. © 2023 IEEE.

关键词： Audio-visual recognition deep learning feature enhancement extraction multi-modality feature extraction

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Smartphone-based Road Condition Sensing using Machine Learning techniques 21

Smartphone-based Road Condition Sensing using Machine Learni...

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Pipanmekaporn, Luepol Kamonsantiroj, Suwatchai Sutthaweekul, Ruslee Lee, Wilaiporn Srisomboon, Kanabadee King Mongkut University of Technology North Bangkok Department of Computer and Information Science Bangkok10800 Thailand King Mongkut University of Technology North Bangkok Department of Electrical and Computer Engineering Bangkok10800 Thailand

ISBN: (纸本)9798350377088

To ensure the safety and facilitation of transportation, the road condition should be evaluated continually for determining the maintenance plan and reducing the risk of accidents. In this paper, we propose a low-cost and automated road condition sensing method that uses smart phone as the sensing device and exploits machine learning as the evaluator. To evaluate the road quality, the vibration, vehicle location and vehicle speed are gathered via an accelerometer of the smartphone. Then, this data is preprocessed and fed to the machine learning model for prediction the road condition. Moreover, we compare the road condition prediction performance of three machine learning models including MaxEnt, XGBoost and Support Vector machine (SVM) where the XGBoost shows the best prediction performance among others. Therefore, we implement the XGBoost for our automated road condition sensing system. Then, our proposed device can be used to evaluate the road condition with short evaluation time, high accuracy and low-cost. © 2024 IEEE.

关键词： Smartphones

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Examining learners’ cognitive processes and learning emotions in digital game-based vocabulary learning: evidence from eye-tracking technology and facial emotion recognition

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Interactive technology and Smart Education 2024年第2期22卷 304-331页

作者： Wang, Chun-Chia Chen, Hsuan-Chu Hung, Jason C. School of Information and Design Chang Jung Christian University Tainan Taiwan Department of Digital Media Design Chang Jung Christian University Tainan Taiwan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan

Purpose: This research explored the intersection of cognitive processes, emotions and their impacts on digital game-based vocabulary learning (DGVL) among university students. Recognizing the scant research in this area, especially with integrating innovative technologies, this study aims to understand the influence of these elements using advanced monitoring tools. Design/methodology/approach: This inquiry was carried out as an observational study involving 44 university students segmented into three English language proficiency levels: high, intermediate and low based on their English course scores. The methodological tools included a portable eye tracker to observe visual behaviors and deep learning technology to identify and analyze the participants’ emotional responses and engagement with the DGVL during the learning process. Findings: The results showed that distinct fixation sequences and variations in visual attention during DGVL were correlated with different levels of competency, suggesting a direct correlation between visual engagement and language competence. In addition, emotional transitions, predominantly from engagement (“flow”) to challenge (“frustration”), were common among participants, reflecting the emotional dynamics of learning. Furthermore, all participants consistently focused on the English vocabulary definitions, indicative of their targeted approach to understanding and test preparation. These findings highlighted the intricate dynamics between emotions and cognitive processes in learning environments. Originality/value: Contribution of this study shows the interplay of cognitive engagement and emotional experiences in the context of DGVL. It underscored the complex nature of these factors and their collective influence on learners’ visual and emotional engagement, offering valuable implications for educational strategies and technological applications in language learning. © 2024, Emerald Publishing Limited.

关键词： Affective computing Convolutional neural networks Digital game-based learning Eye-tracking technology Transfer learning

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