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International Conference on Parallel Distributed and Grid Computing (PDGC)

作者： D. Saraswathi A. Murugavel M. Malathi Irina Pavlenko Shanmuga Priya S Gourav Kalra Department of Computer Science PSG College of Arts & Science Coimbatore Tamilnadu India Department of Computer Science and Engineering V. S. B. Engineering College (An Autonomous Institution) Karur Tamilnadu India Department of Mathematics Bannari Amman Institute of Technology Erode Tamilnadu India Department of Enterprise Economics Regional and Personnel Management Kuban State University Krasnodar Russia Department of Artificial Intelligence and Data Science Saveetha Engineering College Chennai Tamilnadu India Department of Mechanical Engineering Maharishi Markandeshwar (Deemed to be University) Mullana Haryana India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

Job role recommendation systems are essential for effectively connecting job searchers with possible employment prospects, particularly in aligning candidates' talents with job needs. Conventional methods, such Support Vector Machines (SVM) and Logistic Regression, demonstrate limitations in addressing non-linear connections and extensive, unbalanced datasets. This study presents a skill-oriented job position recommendation system employing the XGBoost model, a sophisticated gradient-boosted decision tree technique. The suggested system effectively manages extensive datasets, executes feature extraction from skill vectors, and enhances matching precision using a non-linear methodology. Experimental findings demonstrate that the suggested model attains an accuracy of 92.3%, markedly surpassing current methodologies, such as SVM (89.5%) and Logistic Regression (86.1%). The suggested model achieves an F1 score of 0.91 and a recall of 0.90, demonstrating its effectiveness in reducing false positives and false negatives. This study presents an innovative resolution to the issue of skill-based job matching, with applicable implications in recruiting, talent acquisition, and job recommendation systems. Future endeavours may concentrate on refining feature extraction techniques and integrating more layers in neural networks to further enhance scalability and accuracy.

关键词： Support vector machines Training Logistic regression Accuracy Computational modeling Scalability Feature extraction Vectors Recommender systems Standards

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Strategic Decision Making in the AI Age: Examining Neural Network Models in Business Management

Strategic Decision Making in the AI Age: Examining Neural Ne...

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Communication, Computer sciences and engineering (IC3SE), International Conference on

作者： Bhavana Jamalpur Prithu Sarkar M Hari Krishna Melanie Lourens K. Shantha Lakshmi Navdeep Singh School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Grade II Amity School of Communication Amity University Kolkata Institute of Aeronautical Engineering Dundigal Hyderabad Deputy Dean Faculty of Management Sciences Durban University of Technology South Africa Department of Mathematics M. Kumarasamy College of Engineering Karur Lovely Professional University Phagwara

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

Over the last 10 years, developments in machine learning techniques and artificial intelligence (AI) tools have garnered interest from corporations and contemporary literature. However, even if AI technology has a lot of promise to resolve problems, there are still issues with its practical use and a dearth of knowledge on how to use it strategically to generate commercial value. Businesses can quickly gather and analyse large quantities of data with the help of AI-powered systems, which facilitates speedier and better-informed decision-making. All things considered, incorporating AI into business decision-making has the power to propel organisational performance and influence how business is conducted in the future.

关键词： Ethics Navigation Decision making Neural networks Force Machine learning Propulsion

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CodeEnhance: A Codebook-Driven Approach for Low-Light Image Enhancement

arXiv

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arXiv 2024年

作者： Wu, Xu Hou, XianXu Lai, Zhihui Zhou, Jie Zhang, Ya-Nan Pedrycz, Witold Shen, Linlin The Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China The Department of Electrical & Computer Engineering University of Alberta University of Alberta Canada

Low-light image enhancement (LLIE) aims to improve low-illumination images. However, existing methods face two challenges: (1) uncertainty in restoration from diverse brightness degradations;(2) loss of texture and color information caused by noise suppression and light enhancement. In this paper, we propose a novel enhancement approach, CodeEnhance, by leveraging quantized priors and image refinement to address these challenges. In particular, we reframe LLIE as learning an image-to-code mapping from low-light images to discrete codebook, which has been learned from high-quality images. To enhance this process, a Semantic Embedding Module (SEM) is introduced to integrate semantic information with low-level features, and a Codebook Shift (CS) mechanism, designed to adapt the pre-learned codebook to better suit the distinct characteristics of our low-light dataset. Additionally, we present an Interactive Feature Transformation (IFT) module to refine texture and color information during image reconstruction, allowing for interactive enhancement based on user preferences. Extensive experiments on both real-world and synthetic benchmarks demonstrate that the incorporation of prior knowledge and controllable information transfer significantly enhances LLIE performance in terms of quality and fidelity. The proposed CodeEnhance exhibits superior robustness to various degradations, including uneven illumination, noise, and color distortion. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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The -model expansion approach for solving the (2+1)-dimensional NLSE with Kerr law nonlinearity and STD with multiplicative white noise in birefringent fibers

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Optical and Quantum Electronics 2024年第3期56卷 1-25页

作者： AlQahtani, Salman A. Shohib, Reham M. A. Alngar, Mohamed E. M. Alawwad, Abdulaziz M. New Emerging Technologies and 5G Network and Beyond Research Chair Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Basic Science Department Higher Institute of Foreign Trade & Management Sciences New Cairo Academy Cario Egypt Basic Science Department Faculty of Computers and Artificial Intelligence Modern University for Technology & Information Cairo Egypt Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

With incorporating of multiplicative white noise, we assess the Itô-coupled NLSE and STD in two and one dimensions with Kerr law nonlinearity and birefringent fibers. Light dynamics in birefringent fibers, including the propagation of soliton waves, the instability of modulation, and the occurrence of stochastic events, all have a connection to this issue. The study aims to facilitate in the growth of fiber lasers, benefiting fields such as in the fields of material processing, medicine, and communications. To achieve this, the $$\Phi ^{6}$$ -model approach to expansion is adopted. In addition to Jacobi-elliptic solutions, this work’s analysis also yields dark solitons, bright solitons, singular solitons, and periodic solutions. Because of such results, fiber laser systems in industries like manufacturing, medicine, and communications may be improved upon. The Jacobi-elliptic solution, as well as bright and dark solitons, may be seen in both one- and two-dimensional representations.

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A Novel Cohesive Swarm intelligence Bat Optimization (CSIBO) Algorithm for Handling an Economic Load Dispatch Problems in Energy Systems

A Novel Cohesive Swarm Intelligence Bat Optimization (CSIBO)...

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Disruptive Technologies (ICDT), International Conference on

作者： Pitchala Vijaya Kumar Shilaja C Ashok C R Priyanka Pramila G. Anitha HARIHARAN N Department of Electrical and Electronics Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Electronics and Communication Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Tamilnadu India Department of Artificial Intelligence and Data Science R.M.K. Engineering College ECE R.M.D. Engineering College S&H(Electrical And Electronics Engineering) R.M.K.College of Engineering and Technology

The paper develops a novel Cohesive Swarm intelligence Bat Optimization (CSIBO) technique for handling an Economic Load Dispatch (ELD) problem in power systems. This article examines and analyses a variety of power generators, including fuel cells, wind turbines, and solar photovoltaic (PV). This work's original contribution is an efficient optimization of the power generators' cost function with a reduced time required for processing, total number of iterations, and speed of convergence. Here, the objective function is computed based on cost for all RES power generation system with the use of CSIBO algorithm. In addition, an equality and in equality constraints are considered in this work for minimizing the total dispatch cost. The performance and simulation findings of the proposed CSIBO-based ELD model are assessed and contrasted using a range of factors. Lastly, the aggregate findings show that the suggested CSIBO performs better than alternative methods, yielding better outcomes for every RES.

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Autonomic and adaptive cyber-defense algorithms for healthcare applications

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Healthcare Analytics 2024年 5卷 100345-100345页

作者： Shabaz, Mohammad Farouk, Ahmed Ahmad, Salman Nazir, Shah Mehbodniya, Abolfazl Model Institute of Engineering and Technology J&K Jammu India Department of Computer Science Faculty of Computers and Artificial Intelligence South Valley University Hurghada Egypt University of the West of Scotland Paisley United Kingdom Department of Computer Science University of Swabi Swabi Pakistan Department of Electronics and Communication Engineering Kuwait College of Science and Technology (KCST) 7th Ring Road Doha Kuwait

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An Automatic CNN-based Face Mask Detection Algorithm Tested During the COVID-19 Pandemics

An Automatic CNN-based Face Mask Detection Algorithm Tested ...

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2022 International Conference of Yearly Reports on Informatics, Mathematics, and engineering, ICYRIME 2022

作者： De Magistris, Giorgio Iacobelli, Emanuele Brociek, Rafal Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Department of Mathematics Applications and Methods for Artificial Intelligence Faculty of Applied Mathematics Silesian University of Technology Gliwice44-100 Poland Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy

The ongoing COVID-19 pandemic has highlighted the importance of wearing face masks as a preventive measure to reduce the spread of the virus. In medical settings, such as hospitals and clinics, healthcare professionals and patients are required to wear surgical masks for infection control. However, the use of masks can hinder facial recognition technology, which is commonly used for identity verification and security purposes. In this paper, we propose a convolutional neural network (CNN) based approach to detect faces covered by surgical masks in medical settings. We evaluated the proposed CNN model on a test set comprising of masked and unmasked faces. The results showed that our model achieved an accuracy of over 96% in detecting masked faces. Furthermore, our model demonstrated robustness to different mask types and fit variations commonly encountered in medical settings. Our approaches reaches state of the art results in terms of accuracy and generalization. © 2022 Copyright for this paper by its authors.

关键词： COVID-19

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Graph Contrastive Learning with Decoupled Augmentation

Graph Contrastive Learning with Decoupled Augmentation

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shihao Gao Caoshuo Li Cunli Mao Xulong Zhang Xiaoyang Qu Taisong Jin Jianzong Wang Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Xiamen University China Ping An Technology Co. Ltd. Shenzhen China Faculty of Information Engineering and Automation Kunming University of Science and Technology China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology China Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education of China Anyang Normal University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Graph contrastive learning based on augmentation strategies has recently demonstrated remarkable performance. Existing methods typically jointly leverage attribute and structural augmentations to generate graph views, learning data invariance information through contrasting sample pairs. However, this joint approach may deviate from the expectation of semantically similar before and after augmentation. The propagation of attribute information in graphs usually occurs through their structure, meaning that structural and attribute augmentations can interfere with each other and potentially distort the graph’s semantics. To address this, we propose a decoupled augmentation framework for graph contrastive learning, which eliminates the mutual interference between the two levels of augmentation while fully exploring graph information. Specifically, our framework employs separate encoders to learn data invariance under different augmentation levels, and it considers the positive gains generated between these levels. Experimental results on five public datasets show that the proposed method is more competitive than state-of-the-art approaches.

关键词： Acoustic distortion Semantics Contrastive learning Interference Signal processing Acoustics Speech processing

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A Heterogeneous Network-based Contrastive Learning Approach for Predicting Drug-Target Interaction

arXiv

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arXiv 2024年

作者： Hu, Junwei Bewong, Michael Kwashie, Selasi Zhang, Wen Nofong, Vincent M. Wu, Guangsheng Feng, Zaiwen College of Informatics Huazhong Agricultural University Hubei Wuhan China School of Computing Mathematics & Engineering Charles Sturt University Wagga WaggaNSW Australia Artificial Intelligence & Cyber Futures Institute Charles Sturt University BathurstNSW Australia Faculty of Engineering University of Mines and Technology Tarkwa Ghana School of Mathematics and Computer Science Xinyu University China

Drug-target interaction (DTI) prediction is crucial for drug development and repositioning. Methods using heterogeneous graph neural networks (HGNNs) for DTI prediction have become a promising approach, with attention-based models often achieving excellent performance. However, these methods typically overlook edge features when dealing with heterogeneous biomedical networks. We propose a heterogeneous network-based contrastive learning method called HNCL-DTI, which designs a heterogeneous graph attention network to predict potential/novel DTIs. Specifically, our HNCL-DTI utilizes contrastive learning to collaboratively learn node representations from the perspective of both node-based and edge-based attention within the heterogeneous structure of biomedical networks. Experimental results show that HNCL-DTI outperforms existing advanced baseline methods on benchmark datasets, demonstrating strong predictive ability and practical effectiveness. The data and source code are available at https://***/Zaiwen/HNCL-DTI. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Unsupervised Social Bot Detection via Structural Information Theory

arXiv

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arXiv 2024年

作者： Peng, Hao Zhang, Jingyun Huang, Xiang Hao, Zhifeng Li, Angsheng Yu, Zhengtao Yu, Philip S. School of Cyber Science and Technology Beihang University No. 37 Xue Yuan Road Haidian District Beijing100191 China State Key Laboratory of Public Big Data Guizhou University No. 2708 South Section of Huaxi Avenue Huaxi District Guizhou Province Guiyang City550025 China Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin541004 China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Kunming650500 China College of Science University of Shantou No. 243 University Road Shantou515063 China School of Computer Science and Engineering Beihang University No. 37 Xue Yuan Road Haidian District Beijing100191 China Faculty of Information Engineering and Automation Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Kunming650500 China Department of Computer Science University of Illinois at Chicago ChicagoIL60607 United States

Research on social bot detection plays a crucial role in maintaining the order and reliability of information dissemination while increasing trust in social interactions. The current mainstream social bot detection models rely on black-box neural network technology, e.g., Graph Neural Network, Transformer, etc., which lacks interpretability. In this work, we present UnDBot, a novel unsupervised, interpretable, yet effective and practical framework for detecting social bots. This framework is built upon structural information theory. We begin by designing three social relationship metrics that capture various aspects of social bot behaviors: Posting Type Distribution, Posting Influence, and Follow-to-follower Ratio. Three new relationships are utilized to construct a new, unified, and weighted social multi-relational graph, aiming to model the relevance of social user behaviors and discover long-distance correlations between users. Second, we introduce a novel method for optimizing heterogeneous structural entropy. This method involves the personalized aggregation of edge information from the social multi-relational graph to generate a two-dimensional encoding tree. The heterogeneous structural entropy facilitates decoding of the substantial structure of the social bots network and enables hierarchical clustering of social bots. Thirdly, a new community labeling method is presented to distinguish social bot communities by computing the user's stationary distribution, measuring user contributions to network structure, and counting the intensity of user aggregation within the community. Compared with ten representative social bot detection approaches, comprehensive experiments demonstrate the advantages of effectiveness and interpretability of UnDBot on four real social network datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Information theory

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