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International Conference on Information and Communication Systems (ICICS)

作者： Eman Alnagi Mohammad Azzeh Computer Science Department Princess Sumaya University for Technology Amman Jordan Data Science Department Princess Sumaya University for Technology Amman Jordan

ISBN: (数字)9798331540760

ISBN: (纸本)9798331540777

Just-In-Time Software Defect Prediction (JIT-SDP) is a technique that allows teams to predict defects in software on the commit level instead of the release level. Using JIT-SDP will spare a lot of time and effort for teams, companies, and even clients. The need of predicting software defects in early phases urged researchers to study and propose many models in this direction. This paper aims to survey research in the last decade and inspect the evolution of JIT-SDP since the year 2012 until 2023. 30 research papers have been collected, classified, and discussed. It has been found that Machine Learning and Deep Learning are the most used techniques in this scope. Effort-Aware models and Cross-Project models are also two vital scenarios for JIT-SDP. Three research questions have been proposed in this research regarding the most frequent techniques used for prediction, the most frequent project (datasets), and the most frequent evaluation metrics adopted in the research. It has been intended to draw a timeline of the evolution of JIT-SDP models that can lead to more novel models to be proposed in the future.

关键词： Surveys Measurement Deep learning Logistic regression Communication systems Companies Predictive models

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Leveraging Digital Twins and AI for Enhanced Clinical Decision Support in Endometrial Cancer Treatment 5

Leveraging Digital Twins and AI for Enhanced Clinical Decisi...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Palarimath, Suresh Maran, Pyingkodi Wilfred Blessing, N.R. Cibi, A. Kavitha, S.J. Steffi, S. College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman Kongu Engineering College Department of Computer Application Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Virtusa Consulting Service Private Limited Data & Analytics Visualizaation - Delivery Chennai India Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9798331518097

Digital twins are a relatively new concept that have lately garnered interest in the industrial sector. Digital twins are virtual models that mirror actual products. In this research, we suggest using the technology of digital twins to the field of healthcare in order to enhance clinical decision support and to provide more individualized treatment for patients. When artificial intelligence (AI) is combined with digital twins, healthcare professionals are able to more effectively analyze large volumes of varied data and increase their ability to make diagnostic and therapeutic decisions. In this research, we provide a conceptual framework for leveraging digital twins and AI to solve existing limits in cancer care, notably endometrial cancer therapy. This research study mainly focuses on cancer care, and more specifically on endometrial cancer treatment. In addition, we analyze the possible challenges and opportunities that may arise throughout the process of integrating this technology in healthcare settings. Our overarching objective is to improve the standard of treatment as well as the clinical results for people who have cancer. © 2024 IEEE.

关键词： Lung cancer

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The Computational Curse of Big data for Bayesian Additive Regression Trees: A Hitting Time Analysis

arXiv

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arXiv 2024年

作者： Tan, Yan Shuo Ronen, Omer Saarinen, Theo Yu, Bin Department of Statistics and Data Science National University of Singapore Singapore Department of Statistics UC Berkeley United States Department of Electrical Engineering and Computer Sciences UC Berkeley United States Center for Computational Biology UC Berkeley United States

Bayesian Additive Regression Trees (BART) is a popular Bayesian non-parametric regression model that is commonly used in causal inference and beyond. Its strong predictive performance is supported by theoretical guarantees that its posterior distribution concentrates around the true regression function at optimal rates under various data generative settings and for appropriate prior choices. In this paper, we show that the BART sampler often converges slowly, confirming empirical observations by other researchers. Assuming discrete covariates, we show that, while the BART posterior concentrates on a set comprising all optimal tree structures (smallest bias and complexity), the Markov chain's hitting time for this set increases with n (training sample size), under several common data generative settings. As n increases, the approximate BART posterior thus becomes increasingly different from the exact posterior (for the same number of MCMC samples), contrasting with earlier concentration results on the exact posterior. This contrast is highlighted by our simulations showing worsening frequentist undercoverage for approximate posterior intervals and a growing ratio between the MSE of the approximate posterior and that obtainable by artificially improving convergence via averaging multiple sampler chains. Finally, based on our theoretical insights, possibilities are discussed to improve the BART sampler convergence *** Codes 62G08, 65C40 © 2024, CC BY.

关键词： Big data

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Calligraphy Alphabet Perception Using Artificial Intelligence

Calligraphy Alphabet Perception Using Artificial Intelligenc...

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International Conference on science Technology Engineering and Management (ICONSTEM)

作者： Venkatesh S D. Jeevitha J. Anitha Gnanaselvi Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Department of Computer Science and Engineering Jeppiaar Engineering College Chennai

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

The human brain has a simple time analyzing and processing images. The brain is able to rapidly deconstruct and distinguish an image's various components when the eye perceives it. With the Convolutional Neural Network (CNN) as its foundation, this research suggests deep learning conceptual models. When the algorithms are compared, it becomes clear that CNN-based classification of handwritten alphabets performs better than other algorithms in terms of accuracy. The Manual Net, Alex Net, and LeNet Architectures are among the CNN algorithms employed in this research. The convulational layer, max pooling, flattening, feature assortment, rectifier lined unit, and completely linked softmaxx layers are each components of the aforementioned designs. The proposed network is tested using an image dataset comprising 530 training photos and 2756 testing images. The top precision and cost-efficient model will be used in the Django context to build a handler line for supplying the appeal to be recognized and obtaining the productivity outcome of recognized appeal.

关键词： Training Productivity Accuracy Rectifiers User interfaces Mathematical models Classification algorithms

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Dissipative flow features of Carreau nanofluid with thermal radiation inside plane wall channel:Jeffery-Hamel analysis

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Propulsion and Power Research 2023年第2期12卷 253-272页

作者： Sohail Rehman Hashim Fuad A.MAl-Yarimi Sultan Alqahtani Mohammed Awade Department Mechanical Engineering School of Material Sciences and EngineeringGeorgia Institute of TechnologyAtlantaGAUSA Department of Mathematics and Statistics University of HaripurHaripur 22620Pakistan Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia College of Engineering Mechanical Engineering DepartmentKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems King Khalid UniversityMuhayel AseerSaudi Arabia

The current article communicates a numerical investigation on laminar flow of dissipative generalized Newtonian Carreau nanofluid flowing through vertical conduit with converging and diverging plane *** and concentration characteristics due to enthalpy change,activation energy,and non-linear thermal radiation have been examined in the presence of buoyancy *** channel walls for both temperature and volumetric fraction are assumed to be *** instability mechanism of nanofluids is reported using a two-phase nanofluid model,which works reasonably well for nanoparticle concentrations below a certain threshold.A Jeffery-Hamel(J-H)flow model is developed by assuming an incompressible purely radial flow of Carreau nanofluids with heat and mass *** the suitable non-dimensional variables,the resulting nonlinear partial differential equations are turned into a system of ordinary differential *** modified governing equations are then numerically solved using the built-in boundary value problem solver bvp4c,on the template form of commercial software *** impacts of material,geometrical and thermophysical parameters governing the J-H problem are discussed and *** indicate that higher buoyance forces incline the velocity profiles in converging enclosure,while a slight reduction is perceived in opposing forces.A significant decrease of wall heat transmission is reflected for larger values of activation energy and radiation *** endorsing this communication,a comparison analysis is established with existing research and noticed a remarkable ***,the flow inside converging and diverging channels are deployed in nuclear reactors that use plate-type nuclear energies,high heat-flux condensed heat exchangers,high-performance micro-electronic cooling systems,jets,rockets nozzles,and jet propulsion inlet.

关键词： Nanofluids Buoyancy forces Converging/diverging channels Thermal radiation Activation energy Carreau fluid

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Optimizing Supply Chain Efficiency: Integrating Deep Learning and Blockchain Technologies 1

Optimizing Supply Chain Efficiency: Integrating Deep Learnin...

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1st International Conference on Intelligent Technologies for Sustainable Electric and Communications Systems, iTech SECOM 2023

作者： Shanmugapriyaa, K.R. Swetha, S. Janani, N. Bavithra, K. Padmavathi, K. Coimbatore Institute of Technology Department of Artificial Intelligence and Data Science Tamilnadu India Kathir College of Engineering Department of Computer Engineering Tamilnadu India Psg Institute of Technology and Applied Research Department of Electrical and Electronics Engineering Tamilnadu India Hindusthan Polytechnic College Department of Computer Engineering Tamilnadu India

ISBN: (纸本)9798350371185

The research introduces an innovative supply chain management system tailored for agriculture, aiming to enhance operational efficiency and transparency through cutting-edge technologies such as deep learning and blockchain. The system comprises four modules, each leveraging machine learning algorithms to optimize various agricultural processes. The first module utilizes LSTM models to predict crop yields and diseases, empowering farmers with valuable insights into crop health and potential yields. The second module focuses on inventory management optimization using DQN algorithms, enabling farmers to efficiently utilize resources and minimize waste. The third module employs GNN technology to optimize routes for accessing industry-specific points of interest, simplifying farmers' access to relevant information and resources. Lastly, the fourth module addresses significant challenges in agriculture, including crop demand, rotation planning, market trends, pricing strategies, and market identification, employing a DBN model. This study showcases the transformative power of machine learning algorithms in revolutionizing productivity and profitability within the agriculture sector, offering actionable insights and optimization tools to farmers. © 2023 IEEE.

关键词： Blockchain

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MAReraser: Metal Artifact Reduction with Image Prior Using CNN and Transformer Together

MAReraser: Metal Artifact Reduction with Image Prior Using C...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zheng, Songwei Zhang, Dong Yu, Chunyan Jia, Linghui Zhu, Longlong Huang, Zhanchao Zhu, Danhong Yu, Hao Fuzhou University College of Computer and Data Science Fuzhou China Fujian Medical University School and Hospital of Stomatology Department of Prosthodontics Fuzhou China Fuzhou China Fuzhou University Zhicheng College Fuzhou China

ISBN: (纸本)9798350386226

This paper presents a new dual domain network with image prior based on Convolutional Neural Network (CNN) and Transformer simultaneously for CT Metal Artifact Reduction (MAR). Challenges in MAR derive from the following aspects: firstly, the different morphologies of metal artifacts complexify resolving the issue just in a single domain;secondly, albeit many methods excel in quantitative metrics, yet the restored anatomical structures are over-smooth blurring reconstructed CT images;thirdly, MAR demands better performance as a clinical application, but the approaches relying on CNN or Transformer struggle due to CNN's restricted spatial scope and Transformer's ignorance to the local details, respectively, that is, CNN focuses on the local information while Transformer emphasizes the global information with higher computational complexity. To address these problems, we put forward MAReraser, a novel dual domain network, to deal with metal artifacts. MAReraser removes metal artifacts in both the projection and image domains, effectively reducing heteromorphic metal artifacts. Moreover, MAReraser introduces the image prior generated by an image prior subnet to refine the quality of reconstructed CT images. The prior subnet is pretrained in an expanded dataset which incorporates CT images corrected by diverse traditional MAR methods, providing extra potential prior knowledge from different perspectives. Further, the network backbone of MAReraser integrates CNN and Transformer, enabling complementary local and global feature extraction and balancing computational complexity. Extensive experiment results demonstrate that our method outperforms several other approaches whether in quantitative metrics or in qualitative visualization results. © 2024 IEEE.

关键词： computerized tomography

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Differential evolution using multi-strategy for the improvement of optimization performance

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Neural Computing and Applications 2024年 1-28页

作者： Liu, Nengxian Luo, Jianbin Chang, Jie Pan, Jeng-Shyang College of Computer and Data Science Fuzhou University No. 2 Wulongjiang North Avenue Fuzhou University Town Fujian Fuzhou350108 China College of Civil Engineering Fuzhou University No. 2 Wulongjiang North Avenue Fuzhou University Town Fujian Fuzhou350108 China Department of Medical Information Wannan Medical College Higher Education Park No. 22 Wenchang West Road Anhui Wuhu240001 China College of Computer Science and Engineering Shandong University of Science and Technology No. 579 Qianwangang Road Huangdao District Shandong Qingdao266590 China

Differential evolution (DE) is an effective population-based optimization approach that has been widely used to deal with scientific and engineering problems. However, the performance of DE method is largely dependent on its trial vector produce strategy, namely, mutation strategy, crossover operation and its corresponding control parameters. As claimed by the ‘No free Lunch theorem’, each mutation or crossover strategy has its fatal flaws;hence, the DE method having a single operation strategy cannot solve all types of optimization problems. Therefore, we propose a novel multi-strategy DE (MS-DE) in this study. First, the proposed algorithm uses combined mutation strategies including two powerful mutation strategies and selects them in a probabilistic way. Second, an improved crossover operation is introduced to tackle the stagnation problem. When a stagnation occurs, DE employs the top p-best vector to conduct crossover operation. Third, the control parameters are tuned in novel adaptation schemes. Finally, a local search is utilized in the proposed method to accelerate the convergence. The proposed MS-DE method is examined on CEC2017 test suite, and experiment results confirm its outperformance over several state-of-the-art DE methods. Furthermore, the proposed MS-DE is applied to two constrained engineering problems. The comparison results on these two problems also demonstrate the efficiency of our proposed MS-DE. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Optimization algorithms

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Digital Twin System for Smart Campuses

Digital Twin System for Smart Campuses

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2024 IEEE International Conference on Control science and Systems Engineering, ICCSSE 2024

作者： Wang, Huijie Cai, Yuanyuan Yue, Fabu Luo, Hao Abdipoor, Sina Goh, Say Leng School of Big Data BaoShan University Yunnan BaoShan678000 China Universiti Putra Malaysia Department of Computer Science 43400 Malaysia Universiti Malaysia Sabah Faculty of Computing and Informatics Labuan87000 Malaysia

ISBN: (纸本)9798331517199

In an era where educational institutions are increasingly embracing technology to improve efficiency and enhance learning environments, the concept of smart campuses has emerged as a crucial development. The traditional approach of monitoring campus environments through video footage and manual inspections is becoming outdated as technology advances. To empower smarter campus management and enhance the educational experience, this study presents a systematic approach for smart campuses built on the foundation of digital twins. This technology creates a virtual replica of the physical campus, enabling real-time monitoring of various aspects like security, teaching, maintenance, environment, assets, and energy usage. We simulate real-world campus behavior by constructing this digital counterpart. The resulting digital twin image can be visualized through various user-friendly interfaces, including virtual reality and 3D models, empowering managers to make informed decisions for a more efficient and secure campus environment. Implementing a digital twin system in a smart campus setting demonstrates significant improvements in operational efficiency, security management, and environmental sustainability. By leveraging the power of digital twins, educational institutions can transition towards smarter, more responsive, and sustainable campus environments, ultimately enriching the educational experience for students and staff. © 2024 IEEE.

关键词： 3d modeling campus management digital twin real-time monitoring

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Cross-domain NER with Generated Task-Oriented Knowledge: An Empirical Study from Information Density Perspective

Cross-domain NER with Generated Task-Oriented Knowledge: An ...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Zhang, Zhihao Lee, Sophia Yat Mei Wu, Junshuang Zhang, Dong Li, Shoushan Cambria, Erik Zhou, Guodong School of Computer Science & Technology NLP Lab Soochow University China Department of Chinese and Bilingual Studies The Hong Kong Polytechnic University Hong Kong Beijing Jinghang Research Institute of Computing and Communication China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798891761643

Cross-domain Named Entity Recognition (CDNER) is crucial for Knowledge Graph (KG) construction and natural language processing (NLP), enabling learning from source to target domains with limited data. Previous studies often rely on manually collected entity-relevant sentences from the web or attempt to bridge the gap between tokens and entity labels across domains. These approaches are time-consuming and inefficient, as these data are often weakly correlated with the target task and require extensive pre-training. To address these issues, we propose automatically generating task-oriented knowledge (GTOK) using large language models (LLMs), focusing on the reasoning process of entity extraction. Then, we employ task-oriented pre-training (TOPT) to facilitate domain adaptation. Additionally, current cross-domain NER methods often lack explicit explanations for their effectiveness. Therefore, we introduce the concept of information density to better evaluate the model's effectiveness before performing entity recognition. We conduct systematic experiments and analyses to demonstrate the effectiveness of our proposed approach and the validity of using information density for model evaluation. © 2024 Association for Computational Linguistics.

关键词： Domain Knowledge

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