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2022 International Conference on computer, Artificial Intelligence, and Control engineering, CAICE 2022

作者： Guo, Nan Li, Jin-Hui Gao, Shen Northeastern University School of Computer Science and Engineering Shenyang China Northeastern University School of Software College Shenyang China

ISBN: (纸本)9781510655942

News recommendation is one of the important tasks in recommendation tasks. The existing recommendation process is essentially a system loop feedback process. The content fed back by the recommendation system will be greatly affected by the user's click behavior, while the news recommendation model usually learns from the user's click behavior, which will lead to the system recommending news that users have clicked/liked to watch with high probability and pushing other types of news with low probability, resulting in the problem of insufficient diversity. To effectively address this type of problem, we first analyzed the news data statistically and found the bias of "viewing frequency", and then proposed a multi-perceptual news recommendation method based on dynamic multi-convolution kernel CNN and attention mechanism. We use CNN with convolution kernels of different sizes to extract headline features in the news feature extractor. And then combine them in an attentive way. Form title features with more representations. The user feature extractor is divided into biased user feature extractor and unbiased user feature extractor. One is to learn biased user attributes and perceive user characteristics biased and the other is to learn unbiased user attributes and perceive user characteristics without prejudice. Then we use the cosine similarity loss, that is, to increase the difference between the two user characteristics through cosine similarity. The most important point is that ensure that the accuracy of the model is not reduced as much as possible while debiasing, we combine the two features in an attentive manner, and finally propose a ranking method to improve the diversity of recommendations. Extensive experiments show that our method can not only remove biases, but also achieve a high accuracy of the model. © 2022 SPIE.

关键词： Dynamics

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Smart Classroom: A Deep Learning Approach towards Attention Assessment through Class Behavior Detection

Smart Classroom: A Deep Learning Approach towards Attention ...

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2022 Advances in science and engineering Technology International Conferences, ASET 2022

作者： Parambil, Medha Mohan Ambali Ali, Luqman Alnajjar, Fady Gochoo, Munkhjargal United Arab Emirates University College of IT Department of Computer Science and Software Engineering Al Ain United Arab Emirates

ISBN: (纸本)9781665418010

The role of education in shaping future generations is crucial. The rapid advancement in Artificial Intelligence (AI) has prompted a considerable turn of events in the educational field. Advanced AI technologies have the potential to replace traditional classroom practices, giving more importance to students' preferences. Assessing the attention in students is the building block to improve both instructing and learning. Most of the time, it becomes puzzling for an instructor to track every student and know their interest in learning, especially in a vast scenario. In such cases, instructors are often unaware of the most coherent approach while teaching students with different learning capabilities. Therefore, this paper proposed a system that incorporates AI to develop a real-time vision-based smart classroom that autonomously monitors students' attention, as well as, emotions and gives live graphical feedback to the instructor. Our system was able to monitor the presence of a student in the classroom and analyze the students' attention and emotion. The proposed approach was able to assign numerical scores to the student for the concentration level in the class. The stated system can help the instructors evaluate the student's attention instantaneously and more accurately, thus supporting them in conducting the session suitably that may lead to better academic performance. © 2022 IEEE.

关键词： Students

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ENCL: Empirical Evaluation of Consumer Behaviour Analysis Model Using Internet of Things (IoT) Based Enhanced Neural Classification Logic

ENCL: Empirical Evaluation of Consumer Behaviour Analysis Mo...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Merlin, S. Sudha Malathy, K. Ravi, S. Arun, M. Raut, S.P. Fawareh, H. Dmi College of Engineering Department of Electrical and Electronics Engineering Palanchur Chennai India S.A. Engineering College Department of Mca Chennai India Department of Vlsi Microelectronics Chennai India Kings College of Engineering Dept of Computer Science and Engineering Punalkulam Thanjavur India Yeshwantrao Chavan College of Engineering Department of Civil Engineering Nagpur India Zarqa University Faculty of Information Technology Software Engineering Dept Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

The proliferation of consumer data can be attributed to 'the Internet of Things (IoT),' which makes it easier to access and more detailed than before. In this research our purpose is to evaluate the effectiveness of a consumer behavior analysis model using data from IoT. The quality of the model is evaluated on multiple parameters - accuracy, predictability and business sense for marketing. It uses the analytics of huge amounts for data from IoT devices to predict patterns, customer preferences and trends. The proposed method called as Enhanced Neural Classification Logic (ENCL) is cross-validated with the already existing method, which is termed as Consumer behavior Forecasting Strategy (CBFS), and helps users to share gather or process information in a fast and cost-effective way through the proposed method. The results are useful to grasp the promise of IoT in more effective consumer behavior analysis. The research portrays the strengths, weaknesses and possible areas of improvement in this model which will be beneficial for both academic and industry practitioners. As such, the objective of this study is to show how an IoT-based consumer behavior analysis can increase business growth and customer satisfaction. The experimental evaluations demonstrate significant improvements in predictive performance over traditional methods, providing actionable insights for targeted marketing and strategic decision-making. The results highlight ENCL's potential to transform consumer behavior analysis by delivering more precise and nuanced understandings of market dynamics. © 2024 IEEE.

关键词： CBFS Consumer Behavior Data Analysis ENCL Internet of Things IoT Marketing Strategy Model Evaluation

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Stratifying large software files to improve prediction performance in software defect prediction 9

Stratifying large software files to improve prediction perfo...

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9th ACIS International Virtual Conference on Applied Computing and Information Technology, ACIT 2022

作者： Alshehri, Yasser Ali Alnazzawi, Noha Hijazi, Haneen Alharbi, Rawan Computer Science and Engineering Dept Yanbu Industrial College Saudi Arabia Dept of Software Engrg Hashemite University Jordan

ISBN: (纸本)9781450397605

Size is one of the significant factors associated with bugs, and it has been used to predict software faults. We believe that stratifying software files based on size can play an essential role in improving prediction performance. This study explored the effect of size by stratifying our sample based on each unit's size and distributing software units in multiple stratified groups based on an equal distribution approach. We stratified the Eclipse Europa project files, and we reported the performance of each stratified group and compared them. We used two popular classifiers, decision tree J48, and random forest, to implement this experiment. These classifiers presented similar results on the same group of files. The results indicated that predicting faults with large files is better than predicting those in small files. In addition, the results showed higher median values of all performance measures and less variation in each measure. © 2022 ACM.

关键词： Decision trees

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F-IKOS: An Abstract Interpretation-based Static Analyzer for Fortran Programs 12

F-IKOS: An Abstract Interpretation-based Static Analyzer for...

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12th International Workshop on Quantitative Approaches to software Quality, QuASoQ 2024

作者： Zou, Sheng Chen, Liqian Fan, Guangsheng Huang, Renjie Yin, Banghu College of Computer Science and Technology National University of Defense Technology Changsha410073 China State Key Laboratory of Complex & Critical Software Environment Changsha410073 China College of Systems Engineering National University of Defense Technology Changsha410073 China

The Fortran programming language is widely utilized in numerical computation and scientific computing. Fortran programs are prone to potential runtime errors related to numerical properties due to the large number of numerical operations. In this paper, we present F-IKOS, an abstract interpretation-based static analyzer for Fortran programs on top of IKOS, which soundly handles floating-point types in Fortran programs. Firstly, we translate Fortran programs to LLVM IR using compiler front-end Flang. After that, we extend IKOS to support sound floating-point analysis and then employ it to analyze the translated LLVM IR. Particularly, when analyzing floating-point types in programs, we first abstract floating-point expressions into real-number expressions with interval coefficients, and then linearize these expressions into real-number expressions with scalar coefficients. These linear expressions are subsequently handled by abstract domains originally designed for real-number types to produce sound analysis results. We have conducted experiments on representative Fortran programs to show the efficiency and effectiveness of F-IKOS. The experimental results are encouraging: F-IKOS soundly analyzes runtime errors in complex programs, outperforming other analyzers. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： FORTRAN (programming language)

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Hybrid Machine Learning Model for Face Recognition Using SVM

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computers, Materials & Continua 2022年第8期72卷 2697-2712页

作者： Anil Kumar Yadav R.K.Pateriya Nirmal Kumar Gupta Punit Gupta Dinesh Kumar Saini Mohammad Alahmadi IES College of Technology Bhopal462044India Maulana Azad National Institute of Technology Bhopal462003India Department of Information Technology Manipal University JaipurJaipur303007India Department of Computer and Communication Engineering Manipal University JaipurJaipur303007India Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah23218Saudi Arabia

Face recognition systems have enhanced human-computer interactions in the last ten ***,the literature reveals that current techniques used for identifying or verifying faces are not immune to *** Component Analysis-Support Vector Machine(PCA-SVM)and Principal Component Analysis-Artificial Neural Network(PCA-ANN)are among the relatively recent and powerful face analysis *** to PCA-ANN,PCA-SVM has demonstrated generalization capabilities in many tasks,including the ability to recognize objects with small or large data *** from requiring a minimal number of parameters in face detection,PCA-SVM minimizes generalization errors and avoids overfitting problems better than ***-SVM,however,is ineffective and inefficient in detecting human faces in cases in which there is poor lighting,long hair,or items covering the subject’s *** study proposes a novel PCASVM-based model to overcome the recognition problem of PCA-ANN and enhance face *** experimental results indicate that the proposed model provides a better face recognition outcome than PCA-SVM.

关键词： Face recognition system(FRS) face identification SVM discrete cosine transform(DCT) artificial neural network(ANN) machine learning

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An Intelligent Defect Detection Algorithm for PCB based on Deep Learning 6

An Intelligent Defect Detection Algorithm for PCB based on D...

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6th International Conference on Advances in Image Processing, ICAIP 2022

作者： Zhu, Xiangyuan Xiao, Xiuchun Lan, Zhiming Hong, Qiuhong Hou, Miao School of Computer Science and Software Zhaoqing University China College of Electronics and Information Engineering Guangdong Ocean University China

ISBN: (纸本)9781450397155

As an essential component of modern machines, printed circuit board (PCB) is widely used in various electronic products. Its quality significantly affects the quality of products. However, the production process of PCB is often accompanied with defects. In this paper, a defect detection algorithm is proposed. Data augmentation such as flipping, shifting, brightness adjustment, rotation, and Guass noise are carried out to diversify the dataset. You only look once (YOLO) v5s is then introduced to detect the PCB defects. Through parameter tuning and optimization, a trained detection model is achieved. F1-score and mean average precision (mAP) are used to assess the performance of the model. The experiment results show that the mAP and F1-score are 99.3% and 99.0%, respectively. The model developed based on YOLO-v5s algorithm can achieve superior performance, which is competent to detect the defects of PCBs. © 2022 ACM.

关键词： Printed circuit boards

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In-Range Farthest Point Queries and Related Problem in High Dimensions 49

In-Range Farthest Point Queries and Related Problem in High ...

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49th EATCS International Conference on Automata, Languages, and Programming, ICALP 2022

作者： Huang, Ziyun Xu, Jinhui Department of Computer Science and Software Engineering Penn State Erie The Behrend College United States Department of Computer Science and Engineering State University of New York BuffaloNY United States

ISBN: (纸本)9783959772358

Range-aggregate query is an important type of queries with numerous applications. It aims to obtain some structural information (defined by an aggregate function F(·)) of the points (from a point set P) inside a given query range B. In this paper, we study the range-aggregate query problem in high dimensional space for two aggregate functions: (1) F(P ∩ B) is the farthest point in P ∩ B to a query point q in d and (2) F(P ∩ B) is the minimum enclosing ball (MEB) of P ∩ B. For problem (1), called In-Range Farthest Point (IFP) Query, we develop a bi-criteria approximation scheme: For any ϵ > 0 that specifies the approximation ratio of the farthest distance and any γ > 0 that measures the "fuzziness" of the query range, we show that it is possible to pre-process P into a data structure of size Õϵ,γ(dn1+ρ) in Õϵ,γ(dn1+ρ) time such that given any Rd query ball B and query point q, it outputs in Õϵ,γ(dnρ) time a point p that is a (1 − ϵ)-approximation of the farthest point to q among all points lying in a (1 + γ)-expansion B(1 + γ) of B, where 0 © Ziyun Huang and Jinhui Xu;licensed under Creative Commons License CC-BY 4.0

关键词： Aggregates

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Client Segmentation and Customization in E-Commerce: Applications of Machine Learning from a Management Perspective

Client Segmentation and Customization in E-Commerce: Applica...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Sriprasadh, K. Palit, Shamik Pravallika, B. Manjunatha Lenka, Reena Singla, Atul Srm Institute Of Science And Technology Faculty Of Science And Humanities Department Of Computer Science And Applications Chennai Vadapalani India University Of Stirling Rak Campus Department Of Computing Science And Software Engineering United Arab Emirates Institute Of Aeronautical Engineering Dundigal Hyderabad India New Horizon College Of Engineering Department Of Mechanical Engineering Bangalore India University Pune India Lovely Professional University Phagwara India

ISBN: (纸本)9798350366846

A machine learning-based e-commerce personalised recommendation system helps address the issue of information overload that inevitably arises when consumers have more and more options in e-commerce, leading to an increasingly complex structure. Customer segmentation is a crucial component of contemporary marketing strategy since it enables companies to effectively adjust their advertising campaigns and customise their communications with customers. Through the analysis of enormous quantities of consumer behaviour data and the discovery of trends that can be utilised for dividing customers into segments, ML algorithms provide a potent tool for automating the procedure of customer segmentation. We report our research and results for identifying a customer's segment. In order to perform machine learning, we integrate the Python Tensorflow library with Pandas for manipulating data frames. To address any discrepancies or gaps in the collected client data, it is refined. Key features are found, and to find hidden patterns and relationships, an exploratory analysis of the data is done. After that, we decide on a suitable machine learning technique to divide the clientele into various groups. We also experiment with several clustering models, including conventional machine learning techniques. © 2024 IEEE.

关键词： Python

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Beam Splitting Sensing for Terahertz Near-Field Channel Estimation 3

Beam Splitting Sensing for Terahertz Near-Field Channel Esti...

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3rd International Conference on Intelligent Communications and Computing, ICC 2023

作者： Tang, Shaochen Dong, Zhicheng Zhou, Weixi Cai, Donghong Zhou, Enzhi Lan, Ping College of Information Science Technology Tibet University Lhasa850000 China School of Computer Science Sichuan Normal University Chengdu610066 China College of Cyber Security Jinan University Guangzhou510632 China School of Computer and Software Engineering Xihua University Chengdu610039 China

ISBN: (纸本)9798350308327

Terahertz technology is considered as one of the key techniques for future 6G due to its ultra-wide bandwidth and ultra-high communication rate. However, terahertz communication channel estimation faces several significant challenges, one is that extremely-large-scale MIMO communication typically occurs in the near field, which will result in traditional far-field channel estimation schemes no longer being suitable. The other is the beam splitting effect caused by terahertz wideband communication, which leads to a large impact on the steering vector caused by different subcarrier frequencies. In order to solve these problems, this paper first studied the model of THz near-field beam splitting;secondly discussed the construction method of the THz near-field polar dictionary matrix;and then proposed a two-stage beam splitting sensing (Two-stage-BSS) algorithm to improve the accuracy of near-field channel estimation and reduce the impact of beam splitting. The experimental results validate the effectiveness of the proposed algorithm. © 2023 IEEE.

关键词： Channel estimation

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