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Communications and Information Technologies (GCCIT), Global Conference on

作者： V. Saravanan A. Adaikkammai G. Sasikala Iyswariya A A. Babisha M. Latha Department of Nano Electronics Materials and Sensors Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India Department of Computer Science and Business Systems R.M.D. Engineering College Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Department of ECE R.M.K. Engineering College Chennai Department of Artificial Intelligence and Data Science Panimalar Engineering College Department of IT SRM Institute of Science and Technology

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

The rapid advancement of artificial intelligence (AI) has brought about a significant revolution in image processing. This revolutionary technology has enabled the analysis, recognition, and interpretation of images in ways that were previously inconceivable. An investigation into the most recent developments in artificial intelligence techniques that have improved the efficiency and precision of image processing tasks is presented in this study. We survey the emergence of transformer architectures and the integration of deep learning models, namely Convolutional Neural Networks (CNNs), to explore how these technologies have transformed the understanding and application of images in several industries. An overview of existing image processing methods and the limits of those approaches is presented at the beginning of our research. This highlights the necessity of more advanced AI-driven solutions. After that, we examine the advancements that have been made in artificial intelligence, such as the incorporation of attention processes, the development of more effective CNN architectures, and the utilisation of generative adversarial networks (GANs) for the purpose of picture synthesis and augmentation. The purpose of this research is to investigate the impact that artificial intelligence has on particular applications of image processing, such as autonomous car navigation systems, facial recognition, medical imaging, and satellite images analysis. Specifically, we focus on the enhancements in diagnostic accuracy, environmental monitoring, security, and safety that arose as a result of the adoption of artificial intelligence in these sectors through the use of case studies. Furthermore, we discuss the issues that are associated with artificial intelligence in image processing. These challenges include the necessity for explainable AI models, the requirement for computational resources, and the protection of data privacy. A number of potential solutions to these problems

关键词： Industries Image recognition Image analysis Transformers Satellite navigation systems Generative adversarial networks Satellite images Convolutional neural networks Security artificial intelligence

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A BERT-Based Pre-Training Model for Solving Math Application Problems

A BERT-Based Pre-Training Model for Solving Math Application...

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2022 IEEE International Conference on Intelligent Education and Intelligent Research, IEIR 2022

作者： Jia, Yuhao Wang, Pingheng Zhang, Zhen Cheng, Chi Li, Zhifei Yu, Xinguo Central China Normal University Faculty of Artificial Intelligence in Education Wuhan China Wuhan Sports University School of Sports Engineering and Information Technology Wuhan China Wuhan University School of Information Management Wuhan China Hubei University School of Computer Science and Information Engineering Wuhan China Central China Normal University National Engineering Research Center for E-Learning Wuhan China

ISBN: (纸本)9798350345629

Solving the math application problem is hot research in intelligence education. An increasing number of research scholars are using pre-trained models to tackle machine solution problems. Noteworthily, the semantic relationships required in the machine solution task are for describing math problems, while those of the BERT model with pre-training weights are of general significance, which will cause a mismatched word vector representation. To solve this problem, we proposed a self-supervised pre-training method based on loss priority. We use the input data from the downstream task datasets to fine-tune the existing BERT model so that the dynamic word vector it obtained can better match the downstream tasks. And the size of the loss value of each data batch in each round of training will be recorded to decide which data should be trained in the next round, so that the model has a faster convergence speed. Furthermore, considering that in large-scale mathematics application problems, some problems have almost the same forms of solution. We proposed a machine solution model training algorithm based on the analogy of the same problem type. Extensive experiments on two well-known datasets show the superiority of our proposed algorithms compared to other state-of-the-art algorithms. © 2022 IEEE.

关键词： Semantics

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A Unified Framework for Exploratory Learning-Aided Community Detection Under Topological Uncertainty

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IEEE Transactions on Network science and engineering 2025年第4期12卷 3159-3176页

作者： Hou, Yu Tran, Cong Li, Ming Shin, Won-Yong Yonsei University School of Mathematics and Computing Computational Science and Engineering Seoul03722 Korea Republic of Posts and Telecommunications Institute of Technology Faculty of Information Technology Hanoi100000 Viet Nam Zhejiang Institute of Optoelectronics Jinhua321004 China Zhejiang Normal University Zhejiang Key Laboratory of Intelligent Education Technology and Application Jinhua321004 China Graduate School of Artificial Intelligence Pohang37673 Korea Republic of

In social networks, the discovery of community structures has received considerable attention as a fundamental problem in various network analysis tasks. However, due to privacy concerns or access restrictions, the network structure is often uncertain, thereby rendering established community detection approaches ineffective without costly network topology acquisition. To tackle this challenge, we present META-CODE, a unified framework for detecting overlapping communities via exploratory learning aided by easy-to-collect node metadata when networks are topologically unknown (or only partially known). Specifically, META-CODE consists of three iterative steps in addition to the initial network inference step: 1) node-level community-affiliation embeddings based on graph neural networks (GNNs) trained by our new reconstruction loss, 2) network exploration via community-affiliation-based node queries, and 3) network inference using an edge connectivity-based Siamese neural network model from the explored network. Through extensive experiments on three real-world datasets including two large networks, we demonstrate: (a) the superiority of META-CODE over benchmark community detection methods, achieving remarkable gains up to 65.55% on the Facebook dataset over the best competitor among our selected competitive methods in terms of normalized mutual information (NMI), (b) the impact of each module in META-CODE, (c) the effectiveness of node queries in META-CODE based on empirical evaluations and theoretical findings, and (d) the convergence of the inferred network. © 2013 IEEE.

关键词： Structured Query Language

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Contrastive Learning for Improved Abstractive Sentence Summarization

Contrastive Learning for Improved Abstractive Sentence Summa...

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Advanced Computing Technologies (ICoACT), International Conference on

作者： Ulligaddala Srinivasarao Karthikeyan R Mohamed Sithik M Faizal Mukthar Hussain S Ohammed Uveise S A Hariharan Shanmugasundaram Department of CSE GITAM (Deemed to be) University Hyderabad India Department of CSE(AI&ML) Vardhaman College of Engineering Hyderabad India Department of CSE School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of CSE Mohamed Sathak Engineering College Ramanathapuram India Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

Text summarizing aims to overview the material while maintaining essential details concisely. Lately, summary tasks have been included in the contrastive learning process in addition to visual representation. To optimize their similarities, contrastive learning summarizing algorithms currently concentrate on modelling and semantic similarity in all documents, targets, and data summaries. They do not, however, consider the impact that sentence semantics have on the text. In this study, we propose an RNN technique to develop the model for detecting the salient information. The sentence salience is based on the semantic similarity between the summary sentences of the original content, taking into account the distance of the loss of contrast in the form of soft weights functions. Consequently, our proposed model minimizes the semantic similarity given the textual summaries and possible noise and maximizes the semantic words between summaries and relevant information. Three popular datasets—multi news, CNN/Daily Mail, and Booksum datasets achieved the highest accuracy of 0.69% in the news datasets. The experiment outcomes demonstrate that the suggested strategy can considerably raise baseline performance and outperform other contrastive learning techniques.

关键词： Computers Visualization Semantics Noise reduction Noise Contrastive learning Encoding data models data mining Postal services

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Driving Behavior Safety Assessment in Edge Computing Using Multitask Discriminative TS Fuzzy Model

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IEEE Transactions on Consumer Electronics 2024年

作者： Gu, Yi Chen, Zhiyu Wang, Lu Shen, Jinsong Bashir, Ali Kashif Wang, Wei Jiangnan University School of Artificial Intelligence and Computer Science Wuxi214122 China Jiangnan University Engineering Research Center of Intelligent Technology for Healthcare Ministry of Education Wuxi214122 China Chongqing University School of Big Data and Software Engineering Chongqing400044 China Jiangsu Keli Internet Information Technology Co. Ltd Jingjiang214500 China Manchester Metropolitan University Department of Computing and Mathematics United Kingdom Woxsen University Woxsen School of Business Hyderabad502 345 India Lebanese American University Department of Computer Science and Mathematics Beirut Lebanon Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China

Driving behavior safety analysis in edge computing involves the real-time collection and processing of vehicle driving data. Fuzzy models have advantages in analyzing driving behavior due to their excellent ability to handle uncertain information. This study develops a multitask discriminative Takagi-Sugeno fuzzy model (MD-TS-FM) for driving behavior safety assessment. To comprehensively analyze the correlation and differences in multitask driving behaviors, the designed consequent part consists of two parts: task-shared part reflects the shared intrinsic structure information that are consistent across different driving tasks, and task-specific part reflects distinct characteristics and variations specific to each task, allowing the model to address the unique aspects of different driving behaviors. Accordingly, the task-shared consequent part is characterized by low-rank property, which mines global structural information within the fuzzy space;whereas the task-specific consequent part exhibits sparsity, which removes non-discriminative and irrelevant information. This sparsity ensures that the model focuses on the most critical features for each specific task. Furthermore, a discriminative diversity term is introduced to enhance the diversity between tasks, which explores the consistent information of task-shared consequents while reducing the overlap of task-specific consequents. Experimental results indicate that the MD-TS-FM model can be effectively applied to driving behavior safety assessment. © 1975-2011 IEEE.

关键词： driving behavior safety assessment edge computing fuzzy model multitask learning

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Low-Profile/Dual-Band Planar Phased Array Patch Antenna for THz Communications

Low-Profile/Dual-Band Planar Phased Array Patch Antenna for ...

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Future Telecommunications and artificial intelligence (IC-FTAI), International Conference on

作者： H. J. Basherlou N. O. Parchin C. H. See A. S. I. Amar Y. M. Madany M. Shen School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh UK Communications and Electronics Department Alexandria University Alexandria Egypt Faculty of Computers and Artificial Intelligence Al Ryada University for Science and Technology Egypt Department of Electronic Systems Aalborg University Aalborg Denmark

ISBN: (数字)9798331534080

ISBN: (纸本)9798331534097

This research presents a new 4x4 planar phased array antenna designed specifically to meet the evolving requirements of future 6G communications in the Terahertz (THz) frequency range. Each antenna element is uniquely configured, utilizing a low-profile coaxial-fed patch antenna that incorporates a rotated π-shaped slot. This innovative design enables dual-band functionality, allowing the antenna to operate effectively at both 1.5 THz and 3 THz frequencies. Furthermore, the array's fundamental characteristics demonstrate excellent radiation patterns, gain, and beam-steering capabilities, which are essential for supporting the demanding specifications of 6G technology.

关键词： Phased arrays 6G mobile communication Wireless communication Slot antennas Patch antennas Dual band Terahertz radiation Telecommunications Terahertz communications Antenna radiation patterns

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Competitive Facility Location under Random Utilities and Routing Constraints

arXiv

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arXiv 2024年

作者： Pham, Hoang Giang Dam, Tien Thanh Duong, Ngan Ha Mai, Tien Hà, Minh Hoàng ORLab Faculty of Computer Science Phenikaa University Ha Dong Hanoi Viet Nam ORLab-SLSCM Faculty of Data Science and Artificial Intelligence National Economics University Hai Ba Trung Hanoi Viet Nam School of Computing and Information Systems Singapore Management University 80 Stamford Rd 178902 Singapore Department of Engineering Computer Science and Mathematics University of L'Aquila AQ L'Aquila67100 Italy

In this paper, we study a facility location problem within a competitive market context, where customer demand is predicted by a random utility choice model. Unlike prior research, which primarily focuses on simple constraints such as a cardinality constraint on the number of selected locations, we introduce routing constraints that necessitate the selection of locations in a manner that guarantees the existence of a tour visiting all chosen locations while adhering to a specified tour length upper bound. Such routing constraints find crucial applications in various real-world scenarios. The problem at hand features a non-linear objective function, resulting from the utilization of random utilities, together with complex routing constraints, making it computationally challenging. To tackle this problem, we explore three types of valid cuts, namely, outer-approximation and submodular cuts to handle the nonlinear objective function, as well as sub-tour elimination cuts to address the complex routing constraints. These lead to the development of two exact solution methods: a nested cutting plane and nested branch-and-cut algorithms, where these valid cuts are iteratively added to a master problem through two nested loops. We also prove that our nested cutting plane method always converges to optimality after a finite number of iterations. Furthermore, we develop a local search-based metaheuristic tailored for solving large-scale instances and show its pros and cons compared to exact methods. Extensive experiments are conducted on problem instances of varying sizes, demonstrating that our approach excels in terms of solution quality and computation time when compared to other baseline approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Location

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The Sentiment Analysis of Human Behavior on Products and Organizations using K-Means Clustering and SVM Classifier 3

The Sentiment Analysis of Human Behavior on Products and Org...

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3rd International Conference on Intelligent engineering and Management, ICIEM 2022

作者： Chaudhury, Sushovan Achyutha Prasad, N Chakrabarti, Sudakshina Kumar, Chevella Anil Elashiri, Mohamed A. University of Engineering and Management West Bengal Kolkata India East West Institute of Technology Department of Computer Science and Engineering Karnataka Bangalore India Chettinad Academy of Research and Education Anatomy Chettinad Hospital and Research Institute Tamilnadu Chengalpattu India Jawaharlal Nehru Technological University Kukatpally Hyderabad India Beni-Suef University Faculty of Computers & Artificial Intelligence Computer Science Department Institute Beni-Suef Egypt

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

Sentiment analysis Sentiment analysis is the process of extracting information from the text and it is considered as opinion mining. Machine learning experiments have been shown in the study that involves review, blog, for some texts that are written in different languages including Dutch, English and French. Set of example sentence have been set that are manually labeled, neutral, positive or negative. The study covers the curiosity of the consumer regarding the specific consumption products. Categorization models have been developing that has been used in the study. Number of issues that includes noisy nature of the text has been discussed in the study. With an accuracy of roughly 83 percent for English texts, we can determine positive, negative, and neutral sentiments toward the subject under investigation using unigram features augmented with linguistic information. The role of active learning approaches in minimizing the number of instances that need to be manually annotated is discussed in this article. Our studies also give data on the transferability of learned models between domains and languages. Here, Sentiment analysis of a particular person has studied using a K-Means Clustering and SVM classifier to classify the sentiment of a person from the text © 2022 IEEE.

关键词： K-means clustering

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Detection of Abnormalities in Chest X-Rays Using Convolutional Neural Networks

Detection of Abnormalities in Chest X-Rays Using Convolution...

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Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario (ICPSITIAGS), IEEE International Conference on

作者： Harshith Babu Rahul Mishra P. Karthigaikumar Parikshit N. Mahalle Prasanta Kumar Parid Sachin Pund Computer Science and Engineering Hindusthan College of Engineering & Tech Coimbatore India Centre for Interdisciplinary Research in Business and Technology Institute of Engineering and Technology Chitkara University Punjab India Department of Electronics And Communication Engineering Karpagam College of Engineering Coimbatore Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Department of Rural Managementn KIIT University Bhubaneswar Department of Industrial Engineering Shri Ramdeobaba College of Engineering and Management Nagpur Maharashtra India

ISBN: (数字)9798350329773

ISBN: (纸本)9798350329780

The purpose of this look is to increase an automated gadget for detecting chest X-ray imaging abnormalities through a Convolutional Neural community (CNN). The usage of a CNN permits real-time detection of abnormalities that could otherwise be missed via radiologists, as well as increases the efficiency of clinical experts by using being able to hit upon ability abnormalities without the need for extra tests. In this observation, 285 chest scans had been accrued from a local health facility and analyzed using a custom-built CNN. Through the use of various convolutional layers, various functions have been extracted and applied to stumble on abnormalities. Adjustments to the model have been made by including additional convolutional layers and adjusting the filters. The ensuing model carried out a ninety-seven—2 % accuracy fee in identifying abnormalities in chest X-rays. The outcomes of this look display the hit implementation of a CNN in detecting abnormalities in chest X-rays. In addition, further studies are had to refine the model and boom accuracy. Moreover, similar research is recommended to examine its use in scientific decision-making for sufferers. The results of this have a look at can be seen as a step forward in the improvement of automated clinical analysis structures and their usage in clinical decision-making.

关键词： Filters Decision making Lung cancer Real-time systems Convolutional neural networks Clinical diagnosis Information technology

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Sentiment Analysis of Movie Review using Hybrid Optimization with Convolutional Neural Network in English Language

Sentiment Analysis of Movie Review using Hybrid Optimization...

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artificial intelligence and Smart Systems (ICAIS), International Conference on

作者： Vishal M. Tidake Nilanjan Mazumdar A. Suresh Kumar B. Nageswara Rao Gulnaz Fatma I. Infant Raj B. Kiran Bala Sanjivani College of Engineering Savitribai Phule Pune University Department of Business Administration University of of Science and Technology Meghalaya Computer Science and Engineering Coimbatore Department of Science and Humanities Lendi Institute of Engineering and Technology Jonnada Vizinagaram Dept of English Language Center Jazan University Jazan Saudi Arabia Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Trichy Tamil Nadu India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Trichy Tamil Nadu India

There seems to be a growing amount of user-generated material online as more people become familiar with the Internet. Understanding hidden thoughts, emotions, and attitudes in tweets, emails, comments, and reviews is difficult yet essential for market analysis, brand tracking, social media tracking, and customer support. Sentiment Analysis (SA) identifies the emotional undertone of a string of words and also might basically be employed to comprehend a user’s attitude, thoughts, and emotions. The Harris Hawks Optimization - Sparrow S earch Algorithm with Convolutional Neural Network i.e., (HH-SSA-CNN) proposed in this study is an innovative SA algorithm. Pre-processing, sentiment categorization, and feature extraction make up the procedure. The preprocessing phase removes the unwanted info from input text evaluations using NLP algorithms. A hybrid technique that combines review-related features and aspect-related features has been presented for efficiently retrieving the features. This method creates unique composite features for every review. The created HH- SSA-CNN is used to accomplish sentiment categorization. This approach has been used in the IMDb dataset. To assess the model’s efficacy, the outcomes of the HH-SSA-CNN model are contrasted with those of alternative methodologies. The result indicates that the developed model accurately classifies the sentiments while compared to other existing methods.

关键词： Sentiment analysis Analytical models databases Social networking (online) Motion pictures Feature extraction Internet

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