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The Rise of intelligence: A Comprehensive Review on AI and AI Automation 3

The Rise of Intelligence: A Comprehensive Review on AI and A...

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3rd international conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Bhardwaj, Cheshtaa Gla University Department of Computer Engineering and Applications Mathura India

ISBN: (纸本)9798331528201

This paper presents a comprehensive review of Artificial intelligence (AI), machine Learning (ML), and Deep Learning (DL) technologies, focusing on their impact in driving AI automation across diverse sectors with a special emphasis on robotics. As AI continues to evolve, its integration into automation systems has revolutionized industries, enhancing efficiency, precision, and decision-making. The role of AI automation in robotics is critically examined, covering key architectural frameworks that enable autonomous operation and intelligent decision-making. Furthermore, the security aspects associated with AI in robotics, identifying attack surfaces and potential vulnerabilities has been analysed. This review highlights the transformative potential of AI automation while addressing the challenges and security concerns that accompany its deployment in modern systems, particularly in robotics. © 2024 IEEE.

关键词： Adversarial machine learning

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3rd international conference on Artificial-Business Analytics, Quantum and machine Learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

3rd International Conference on Artificial-Business Analytic...

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3rd international conference on Artificial-Business Analytics, Quantum and machine Learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

ISBN: (纸本)9789819725076

The proceedings contain 60 papers. The special focus in this conference is on Artificial-Business Analytics, Quantum and machine Learning: Trends, Perspectives, and Prospects. The topics include: Particle Swarm Optimization for Efficient Data Dissemination in VANETs;a Reactive Approach for High-Accuracy and Data-Driven Customer Behaviour Analysis and Prediction;a Brief Survey on Fabric Defect Detection;CNN Based Real Time Detection of Words from Lip Movements and Automated into Text;application of Artificial intelligence for the Diagnosis of Dementia (Alzheimer): A Systematic Evaluation;shortest Job First with Gateway-Based Resource Management Strategy for Fog Enabled Cloud Computing;software Vulnerability Analysis Based on Statistical Characteristics;hate Speech Detection on Twitter: A Comparative Evaluation of Different machine Learning Techniques;detection of Heart Disease Using machine Learning;SVM-Based Framework for Breast Cancer Detection;hand Gesture recognition System Using machine Learning;deep Learning Method for Plant Disease recognition and Prediction;Lung Cancer Detection by Using CNN Architecture Models;deep Fake Analyser: A Review Based on Detecting the Deepfakes;predicting the Outcomes of La Liga Matches;Artifact Detection and Removal in EEG: A Review of Methods and Contemporary Usage;analyzing the Employee Attrition Rate: A Comparative Study of Various machine Learning Approaches to Foresee Employee Attrition;satellite Image Classification Using Deep Learning for Big Earth Data;tackling Misinformation Through Tweets: A Comparative Study of Various machine Learning Approaches;feature-Based Anomaly Detection in Static Social Networks;framework for Optimising Supply Chain Analysis Using machine Learning;evaluation of Load Forecasting in Intelligent Grid Systems Through machine Learning Techniques.

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A Critical Review of the Applications of Artificial intelligence in Recommender Systems 3

A Critical Review of the Applications of Artificial Intellig...

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3rd IEEE international conference on Computational intelligence and Knowledge Economy, ICCIKE 2023

作者： Pradeep, Aneesh Muytenbaeva, Zulfiya New Uzbekistan University Software Engineering Tashkent Uzbekistan New Uzbekistan University Industrial Management Tashkent Uzbekistan

ISBN: (纸本)9798350338263

Recommender systems are unsupervised machine learning algorithms that recommend goods, content, or services to people online based on their past actions and anticipated future preferences. With the help of advancing Artificial intelligence, primarily via computational intelligence and machine learning, recommender systems have improved user experience and satisfaction. This paper thoroughly discusses the applications of Artificial intelligence in recommender systems and the issues that arise with AI-based recommender systems. © 2023 IEEE.

关键词： Recommender systems

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Research on the Application of Artificial intelligence Technology in Microelectronics Testing and Fault Diagnosis 3

Research on the Application of Artificial Intelligence Techn...

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3rd international conference on Data Analytics, Computing and Artificial intelligence, ICDACAI 2024

作者： Zhang, Yiqun Liu, Hailin Xu, Feng Weifang Engineering Vocational College Shandong Qingzhou262500 China

ISBN: (纸本)9798331519032

This paper explores the integration of Artificial intelligence (AI) technologies in the field of microelectronics testing and troubleshooting. Traditional methods for testing and troubleshooting microelectronic devices often face challenges such as high complexity, time-consumption, and the need for expert *** technologies, with their advanced capabilities in machine learning, neural networks, and expert systems, offer promising solutions to these problems. This study provides an overview of the key AI approaches employed in microelectronics, discusses their implementation in various test and diagnostic scenarios, and evaluates their impact in terms of improved accuracy, efficiency, and automation. Through a detailed analysis of case studies and real-world applications, the paper demonstrates how AI-driven approaches are revolutionizing the microelectronics industry, paving the way for more reliable and sophisticated electronic devices. The study concludes with a discussion of the current limitations of AI technologies in the field and suggests potential directions for future research and development. © 2024 IEEE.

关键词： Expert systems

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Online Multilingual Spam Review Detection using Twin Support Vector machine and Pre-Trained Word Embedding 3

Online Multilingual Spam Review Detection using Twin Support...

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3rd international conference on Applied Artificial intelligence and Computing, ICAAIC 2024

作者： Giridharan, N. Arif, A. Karmeshwaran, P. Ranjan, M.N. K.S.Rangasamy College of Technology Department of Artificial Intelligence and Data Science Tamilnadu India

ISBN: (纸本)9798350375190

The main technique for binary classification in this suggested spam review detection system, which is tailored for the English language, is the Twin Support Vector machine (TSVM). By simultaneously taking into account both positive and negative classifications, the TSVM improves its ability to discriminate between reviews that are spam and those that are real. The proposed system uses a metaheuristic optimization strategy to maximize performance, using a proprietary method for optimizing feature weighting for the TSVM and fine-tuning hyper parameters. The system is intended to be both flexible and efficient;it can effectively identify and filter spam reviews from datasets that are in the English language. Thorough experimental assessments regularly reveal that the TSVM-based framework performs better than state-of-the-art algorithms, demonstrating its resilience and efficacy in tackling the problems associated with fraudulent online reviews written in English. © 2024 IEEE.

关键词： Support vector machines

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Development and Application of Online Learning System Based on Artificial intelligence 3

Development and Application of Online Learning System Based ...

引用

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

作者： Gao, Luyun Jinan Preschool Education College Jinan China National University Manila ManilaPH 1008 Philippines

ISBN: (纸本)9798350318609

With the continuous progress of subject knowledge such as Internet and computer technology, as well as the update and improvement of educational models and methods, people's cognition of the concept of artificial intelligence is getting deeper and deeper. Based on the current intelligent background, aiming at various types of problems involved in virtual reality, this paper uses machine automation algorithm to apply online learning system to real life, and studies the optimization of online learning system by artificial intelligence algorithm. After that, this paper also conducts related tests on the performance of the learning system, and the test results show that the resource utilization rate of the system is above 0.7, the data throughput is large, the stability is relatively good, and it is safe enough to run and meet the needs of users. © 2024 IEEE.

关键词： Learning systems

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Deep Learning Models for Discrimination of Parkinson's Disease and Multiple System Atrophy on Brain Magnetic Resonance Imaging 24

Deep Learning Models for Discrimination of Parkinson's Disea...

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3rd international conference on Frontiers of Artificial intelligence and machine Learning (FAIML)

作者： Du, Yuji Wu, Xuanyan Yang, Hao Liang, Dong Jin, Jianing Su, Dongning He, Chengzhang Ling, Yun Chen, Zhonglue Ren, Kang Feng, Tao Tan, Shan Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China Capital Med Univ Beijing Tiantan Hosp Ctr Movement Disorders Dept Neurol Beijing Peoples R China GYENNO SCI CO LTD Shenzhen 518000 Peoples R China

ISBN: (纸本)9798400709777

Parkinson's disease (PD) and Multiple System Atrophy (MSA) are both classic neurodegenerative diseases. However, the significant overlap of PD and MSA in clinical symptoms poses a substantial challenge for their diagnosis. To solve this problem, five deep learning-based 3D models are designed to classify PD and MSA using brain Magnetic Resonance Imaging (MRI) of patients. We conduct a comprehensive comparison and evaluation of these models with traditional clinical approaches. The network architecture with the best performance, i.e., ResNet34, is finally determined, which achieves an impressive accuracy of up to 91% and a sensitivity of 99%. In the experiment, we find that the performance of all five DNN methods is superior to that of both traditional clinical analysis methods in the experiment. Our study introduces an innovative method for computer-assisted diagnosis in the early detection of neurological diseases, which has important clinical significance.

关键词： Parkinson's disease AI-based clinical decision making Magnetic resonance imaging Deep learning

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Analysis of English Pronunciation Transformation Text Model based on Neural Network pattern recognition Algorithm 3

Analysis of English Pronunciation Transformation Text Model ...

引用

3rd international conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Gong, Yuanyuan Jiang Xi Teachers College NO.1 Binjiang Rd. Xin Jiang Xin district Jiangxi province Yintan City335000 China

ISBN: (纸本)9798350396157

This study proposes an English pronunciation transformation text model based on a neural network pattern recognition algorithm, which aims to address various challenges in speech processing through in-depth analysis and innovative approaches. The paper first introduces the basic principles of pattern recognition, emphasizing the transition from feature space to decision space. Subsequently, a literature review highlights the transformative impact of convolutional neural networks (CNNs) in diverse domains, including financial markets, neuroimaging, environmental monitoring, and more. Methodologically, the paper describes a novel pattern recognition algorithm based on spiking neural networks. The algorithm overcomes the limitations of multi-layer network learning through the Spike Propagation (Spike Prop) algorithm, which introduces error back-propagation for effective training. Ensemble methods are used to improve the generalization ability of the system, with a focus on neural network integration. For pronunciation transformation in text models, sound features are extensively studied, including features such as short-term energy, short-term average amplitude, and Mel-frequency cepstrum coefficients. The experimental part, conducted in a Windows 10 environment using Python, TensorFlow, and GTX1660 GPU, demonstrates positive results across 30 experimental groups. The system shows significant improvements in character recognition accuracy, a reduction in missing and multiple word occurrences, a reduction in typos, and an increase in word accuracy. These results provide a solid foundation for the future research and practical applications in natural language processing. © 2024 IEEE.

关键词： Character recognition

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Performance Analysis of LiDAR Data Processing on Multi-Core CPU and GPU Architectures 3

Performance Analysis of LiDAR Data Processing on Multi-Core ...

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3rd international conference on Computing and machine intelligence (ICMI)

作者： Alzyout, Mohammad S. Al Nounou, Abd Alrahman Tikkisetty, Yashwanth Naidu Alawneh, Shadi Oakland Univ Elect & Comp Engn Dept Rochester MI 48063 USA

ISBN: (纸本)9798350372977;9798350372984

The projection process of the LiDAR 3D Point Cloud data is one of the crucial steps in Computer vision applications. It involves several steps to achieve the finalized accurate results. Many current studies leverage the benefits of using a GPU in the computing capability. This paper presents a comparative study of the implementation and testing of this process on single-core, multi-core CPU and GPU architectures. The computational efficiency of each platform is evaluated through a series of benchmarks, including data extraction, segmentation, and transformation tasks. Our analysis reveals the inherent parallelization benefits of GPUs in handling large-scale point cloud data, while also considering the accessibility of multi-core CPUs. Also, a comparison between the NVIDIA RTX 3070 and NVIDIA RTX 4060 is provided. The RTX 3070 showed roughly a speed up of 8 times over the RTX 4060. In addition, the Multi-core implementation outperforms up to 10 times over the single-core. These results overall show the benefits of using the Multi-core and the GPU accelerating approaches to this application, with the availability for further improvements.

关键词： LiDAR 3D Point Cloud CPU GPU Multi-Core OpenACC CUDA ROI Extraction Segmentation Projection

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Graph-based Driving Style recognition from Electrophysiological Analysis during Car Following 3

Graph-based Driving Style Recognition from Electrophysiologi...

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3rd international conference on Digital Society and Intelligent Systems, DSInS 2023

作者： An, Yaning Guan, Wei Qi, Geqi Yu, Jixiang Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Ministry of Transport Beijing Jiaotong University Beijing100044 China School of Systems Science Beijing Jiaotong University Beijing100044 China Key Laboratory of Brain-Machine Intelligence for Information Behavior- Ministry of Education Shanghai International Studies University Shanghai200083 China

ISBN: (纸本)9798350331387

para>Driving styles can be used to characterize the behavioral characteristics of drivers, and provide a personalized foundation for the development of intelligent driving systems. The electrophysiological analysis is one of the emerging technologies for driving style recognition. However, most studies focused on classifying driving styles based on electroencephalogram signals have predominantly extracted the time-frequency features, which ignore the spatially coordinated activities of multiple brain regions. Therefore, this paper proposes a graph-based driving style classification method by implementing electrophysiological analysis on brain functional connectivity networks. By adopting graph convolution network on the time-frequency and functional connectivity features, the constructed model attains an accuracy of 88.3% in recognizing three car-following driving styles. This approach can help to promote the electrophysiological recognition of driving styles with a network perspective. © 2023 IEEE.

关键词： Brain

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