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3rd IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Vasker, Nishat Nafisa, Anika Tabassum Rahman, Md Arifur Hasan, Mahamudul East West University Department of Computer Science and Engineering Dhaka Bangladesh Trine University Department of Information Studies 1 University avenue AngolaIN United States University of Minnesota Department of Computer Science and Engineering Twin Cities United States

ISBN: (纸本)9798331534400

Heart disease is a major worldwide health concern, necessitating advances in precise and cost-effective diagnostics. While machine learning (ML) models have shown promise in this field, their opaque nature frequently impedes clinical use due to a lack of interpretability. This work investigates the effectiveness of Explainable AI (XAI) approaches, specifically Kernel SHAP, to increase the transparency and trustworthiness of machine learning models for heart disease categorization. We evaluated the interpretability and performance of a logistic regression model using a well-known heart disease dataset. Our logistic regression model has an accuracy of 85.2%, precision of 82.5%, recall of 84.7%, and F1-score of 83.6%. Kernel SHAP analysis gave useful insights, demonstrating that characteristics like 'ca' (number of main arteries) and 'thal' (thalassemia) have the high influence on predictions. These findings suggest that adding XAI approaches might significantly increase the confidence and acceptance of ML models in healthcare contexts, eventually leading to better patient outcomes. © 2024 IEEE.

关键词： Logistic regression

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Investigating the Impact of Developer Proficiency on Bug-Fixing Efficiency and Accuracy 8

Investigating the Impact of Developer Proficiency on Bug-Fix...

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8th International Conference on Information Technology, InCIT 2024

作者： Sananda, Fatema-Tuz-Zohora Ahmed, Shohel Ollyo 1 Quantum Drive Patira Dhaka1229 Bangladesh Department of Computer Science and Engineering Gazipur Bangladesh

ISBN: (纸本)9798350366303

The efficiency and accuracy of bug-fixing are vital for timely issue resolution, software quality, and user satisfaction. Assigning a high-proficiency developer to fix bugs can significantly impact resolution times, bug accuracy, code complexity, team productivity, and user experience. This paper evaluates developer proficiency by analyzing bug repository data and its effects on bug-fixing efficiency and accuracy. We introduced a method to measure developer proficiency (DP) based on data from bug repositories, identifying six influencing factors validated by industry practitioners. Then we empirically studied three open-source bug repositories comprising 85,386 issues to investigate the impact of developer proficiency on bug-fixing efficiency and accuracy. Using multiple non-parametric tests, we found that proficient developers are generally more efficient and accurate in bug-fixing. The outcomes of our study can significantly help project managers and team leads to understand the importance of assigning proficient developers to enhance bug-fixing efficiency and accuracy during the software maintenance phase. © 2024 IEEE.

关键词： Software quality

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A Stochastic Biofilm Disruption Model Based on Quorum Sensing Mimickers

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IEEE Transactions on Molecular, Biological, and Multi-Scale Communications 2023年第3期9卷 346-350页

作者： Gulec, Fatih Eckford, Andrew W. Department of Electrical Engineering and Computer Science York University TorontoONM3J 1P3 Canada

Quorum sensing (QS) mimickers can be used as an effective tool to disrupt biofilms which consist of communicating bacteria and extracellular polymeric substances (EPS). In this paper, a stochastic biofilm disruption model based on the usage of QS mimickers is proposed. A chemical reaction network (CRN) involving four different states is employed to model the biological processes during the biofilm formation and its disruption via QS mimickers. In addition, a state-based stochastic simulation algorithm is proposed to simulate this CRN. The proposed model is validated by the in vitro experimental results of Pseudomonas aeruginosa biofilm and its disruption by rosmarinic acid as the QS mimicker. Our results show that there is an uncertainty in state transitions due to the effect of the randomness in the CRN. In addition to the QS activation threshold, the presented work demonstrates that there are underlying two more thresholds for the disruption of EPS and bacteria, which provides a realistic modeling for biofilm disruption with QS mimickers. © 2023 IEEE.

关键词： Random processes

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Deep Learning for MANET Routing

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2023年 1卷 412-424页

作者： Danilchenko, Kiril Azoulay, Rina Reches, Shulamit Haddad, Yoram University of Waterloo Department of Electrical and Computer Engineering WaterlooN2L 3G1 Canada Jerusalem College of Technology Department of Computer Science Jerusalem91160 Israel

Power control and scheduling are among the most well-known resource allocation challenges in wireless networks, and are often solved as optimization problems with constraints. However, solving these optimization challenges by using optimal algorithms often incurs a significant time complexity, which creates considerable discrepancies between the theoretical results and real-time processing required. In this study, we propose a novel machine learning-based perspective to address this issue. We propose a scheduling and power control deep neural network SPCDNet method and its modification SPCDNetR. SPCDNet solves the scheduling problem for point-to-point transmission requests while SPCDNetR solves the more complex problem, where the input transmission list is composed of ordered routes which should be satisfied. Both SPCDNet and SPCDNetR are trained in a supervised manner and show near-optimal performance on the test set. Our results demonstrate that SPCDNet and SPCDNetR can serve as a computationally inexpensive solution (regarding time complexity), compared with state-of-the-art schemes, while showing to be near-optimal approximation solutions to the time scheduling and power control challenges. Moreover, we found that both SPCDNet and SPCDNetR reach efficient solutions for large problem instances, even though they were trained on small problems. © 2023 CCBY.

关键词： Time division multiple access

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Near-Field Communications: A Comprehensive Survey

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IEEE Communications Surveys and Tutorials 2024年第3期27卷 1687-1728页

作者： Liu, Yuanwei Ouyang, Chongjun Wang, Zhaolin Xu, Jiaqi Mu, Xidong Swindlehurst, A. Lee School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of University College Dublin School of Electrical and Electronic Engineering College of Engineering and Architecture DublinD04 V1W8 Ireland Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom University of California Department of Electrical Engineering and Computer Science IrvineCA92697 United States BelfastBT3 9DT United Kingdom

Multiple-antenna technologies are evolving towards larger aperture sizes, extremely high frequencies, and innovative antenna types. This evolution is fostering the emergence of near-field communications (NFC) in future wireless systems. Considerable attention has been directed towards this cutting-edge technology due to its potential to enhance the capacity of wireless networks by introducing increased spatial degrees of freedom (DoFs) in the range domain. Within this context, a comprehensive review of the state of the art on NFC is presented, with a specific focus on its 1) fundamental operating principles, 2) channel modeling, 3) performance analysis, 4) signal processing techniques, and 5) integration with other emerging applications. Specifically, 1) the basic principles of NFC are characterized from both physics and communications perspectives, unveiling its unique properties in contrast to far-field communications. 2) Building on these principles, deterministic and stochastic near-field channel models are explored for spatially-discrete (SPD) and continuous-aperture (CAP) arrays. 3) Based on these models, existing contributions to near-field performance analysis are reviewed in terms of DoFs/effective DoFs (EDoFs), the power scaling law, and transmission rate. 4) Existing signal processing techniques for NFC are systematically surveyed, which include channel estimation, beamforming design, and low-complexity beam training. 5) Major issues and research opportunities in incorporating near-field models into other promising technologies are identified to advance NFC's deployment in next-generation networks. Throughout this paper, promising directions are highlighted to inspire future research endeavors in the realm of NFC, underscoring its significance in the advancement of wireless communication technologies. © 1998-2012 IEEE.

关键词： Near field communication

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Segmentation of CAD models using hybrid representation

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虚拟现实与智能硬件(中英文) 2025年第2期7卷 188-202页

作者： Claude UWIMANA Shengdi ZHOU Limei YANG Zhuqing LI Norbelt MUTAGISHA Edouard NIYONGABO Bin ZHOU State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Peace-Bay Academy of Virtual Reality Shenyang 110004China China Aerospace Systems Engineering Corporation Beijing 100070China Department of Signal and Information Processing Beihang UniversityBeijing 100191China Department of Computer Science University of SherbrookeSherbrooke J1C0A1Canada

In this paper,we introduce an innovative method for computer-aided design(CAD)segmentation by concatenating meshes and CAD *** previous CAD segmentation methods have achieved impressive performance using single representations,such as meshes,CAD,and point ***,existing methods cannot effectively combine different three-dimensional model types for the direct conversion,alignment,and integrity maintenance of geometric and topological ***,we propose an integration approach that combines the geometric accuracy of CAD data with the flexibility of mesh representations,as well as introduce a unique hybrid representation that combines CAD and mesh models to enhance segmentation *** combine these two model types,our hybrid system utilizes advanced-neural-network techniques to convert CAD models into mesh *** complex CAD models,model segmentation is crucial for model retrieval and *** partial retrieval,it aims to segment a complex CAD model into several simple *** first component of our hybrid system involves advanced mesh-labeling algorithms that harness the digitization of CAD properties to mesh *** second component integrates labelled face features for CAD segmentation by leveraging the abundant multisemantic information embedded in CAD *** combination of mesh and CAD not only refines the accuracy of boundary delineation but also provides a comprehensive understanding of the underlying object *** study uses the Fusion 360 Gallery *** results indicate that our hybrid method can segment these models with higher accuracy than other methods that use single representations.

关键词： B-RepNet hybrid segmentation CAD models classification MeshCNN MeshCAD-Net

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Deep learning–based prediction model of occurrences of major adverse cardiac events during 1-year follow-up after hospital discharge in patients with AMI using knowledge mining

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Personal and Ubiquitous Computing 2022年第2期26卷 259-267页

作者： Kim, Young Joong Saqlian, Muhammad Lee, Jong Yun Department of Computer Science School of Computer and Electrical Engineering Chungbuk National University 1 Chungdae-ro Chungbuk Cheongju28644 Korea Republic of

Traditional regression-based approaches do not provide good results in diagnosis and prediction of occurrences of cardiovascular diseases (CVD). Therefore, the goal of this paper is to propose a deep learning–based prediction model of occurrence of major adverse cardiac events (MACE) during the 1, 6, 12 month follow-up after hospital admission in acute myocardial infarction (AMI) patients using knowledge mining. We used the Korea Acute Myocardial Infarction Registry (KAMIR) dataset, a cardiovascular disease database registered in 52 hospitals in Korea between 1 January, 2005, and 31 December, 2008. Among 14,885 AMI patients, 10,813 subjects in age from 20 to 100 years with the 1-year follow-up traceability without coding errors were finally selected. For our experiment, the training/validation/test dataset split is 60/20/20 by random sampling without replacement. The preliminary deep learning model was first built by applying training and validation datasets and then a new preliminary deep learning model was generated using the best hyperparameters obtained from random hyperparameter grid search. Lastly, the preliminary prediction model of MACE occurrences in AMI patients is evaluated by test dataset. Compared with conventional regression-based models, the performances of machine/deep learning–based prediction models of the MACE occurrence in patients with AMI, including deep neural network (DNN), gradient boosting machine (GBM), and generalized linear model (GLM), are also evaluated through a matrix with sensitivity, specificity, overall accuracy, and the area under the ROC curve (AUC). The prediction results of the MACE occurrence during the 1, 6, and 12-month follow-up in AMI patients were the AUC of DNN (1 M 0.97, 6 M 0.94, 12 M 0.96), GBM (0.96, 0.95, 0.96), and GLM (0.76, 0.67, 0.72) in machine learning–based models as well as GRACE (0.75, 0.72, 0.76) in regression model. Compared with previous models, our deep learning–based prediction models significantly ha

关键词： Decision support systems

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A Utility-frequency Skyline Itemsets Pattern Mining Algorithm with Threshold Restrictions

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第4期8卷 1183-1202页

作者： Liu, Yadong Tayeb, Shahab Wu, Jimmy Ming-Tai College of Computer Science and Engineering Shandong University of Science and Technology Qianwangang Road 579 Qingdao China Department of Electrical and Computer Engineering California State University Engineering East 274 Fresno United States Department of Information Management National Kaohsiung University of Science and Technology No1 University Rd. Yanchao Dist Kaohsiung City824 Taiwan

Skyline frequent utility patterns have been extensively studied. However, the existing skyline pattern mining algorithms are inefficiency and time consuming, and can be inadequate in practical applications, as sometimes traditional skyline pattern may obtain points that are relatively extreme in one dimension. These one-sided extreme points are not the points that the user would like to see in many applications, and the user prefer to see the skyline itemsets with relatively balanced two dimensions. Therefore, this paper proposes a new pattern of skyline mining with threshold settings. In the process of new skyline pattern mining, multiple thresholds are used to exclude extreme points that do not satisfy the user’s needs in certain dimensions, while also greatly reducing the number of search size and improving the speed of searching the target itemsets. In addition, the intelligent recommendation system of an online shopping website is taken as an example to demonstrate the application potential of the new pattern in a service AI system. In this paper, the UFmax array structure is applied so that it can be used not only to get the itemsets with the highest utility at the same frequency, but also to exclude the set of items whose utility and frequency both do not satisfy the threshold. The SFUTPMiner algorithm is introduced to make skyline pattern mining more accurate and efficient. Experiments were conducted on six databases respectively, and the proposed algorithm was compared with the current popular mining algorithm. The results show that the designed new pattern of skyline mining with threshold settings not only provides a streamlined and optimised set of result items, but also surpasses the previous traditional pattern in the aspect of runtime, search space and memory consumption. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Data mining

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Event-Triggered Fixed-Time Consensus of Second-Order Nonlinear Multi-Agent Systems with Delay and Switching Topologies

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Journal of Shanghai Jiaotong university(science) 2024年第4期29卷 625-639页

作者： XING Youjing GAO Jinfeng LIU Xiaoping WU Ping School of Information Science and Engineering Zhejiang Sci-Tech UniversityHangzhou310018China Department of Systems and Computer Engineering Carleton UniversityOttawaK1S 5B6Canada

To address fixed-time consensus problems of a class of leader-follower second-order nonlinear multi-agent systems with uncertain external disturbances,the event-triggered fixed-time consensus protocol is ***,the virtual velocity is designed based on the backstepping control method to achieve the system consensus and the bound on convergence time only depending on the system ***,an event-triggered mechanism is presented to solve the problem of frequent communication between agents,and triggered condition based on state information is given for each *** is available to save communication resources,and the Zeno behaviors are ***,the delay and switching topologies of the system are also ***,the system stabilization is analyzed by Lyapunov stability ***,simulation results demonstrate the validity of the presented method.

关键词： event-triggered mechanism fixed-time consensus multi-agent systems switching topologies

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Advancing horizons in remote sensing: a comprehensive survey of deep learning models and applications in image classification and beyond

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Neural Computing and Applications 2024年第27期36卷 16727-16767页

作者： Paheding, Sidike Saleem, Ashraf Siddiqui, Mohammad Faridul Haque Rawashdeh, Nathir Essa, Almabrok Reyes, Abel A. Department of Computer Science and Engineering Fairfield University 1073 N Benson Rd FairfieldCT06824 United States Department of Applied Computing Michigan Technological University 1400 Townsend Dr HoughtonMI49931 United States Department of Computer Science West Texas AM University 2501 4th Ave CanyonTX79016 United States Department of Mathematics Computer Science and Data Science John Carroll University 1 John Carroll Blvd University HeightsOH44118 United States

In recent years, deep learning has significantly reshaped numerous fields and applications, fundamentally altering how we tackle a variety of challenges. Areas such as natural language processing (NLP), computer vision, healthcare, network security, wide-area surveillance, and precision agriculture have leveraged the merits of the deep learning era. Particularly, deep learning has significantly improved the analysis of remote sensing images, with a continuous increase in the number of researchers and contributions to the field. The high impact of deep learning development is complemented by rapid advancements and the availability of data from a variety of sensors, including high-resolution RGB, thermal, LiDAR, and multi-/hyperspectral cameras, as well as emerging sensing platforms such as satellites and aerial vehicles that can be captured by multi-temporal, multi-sensor, and sensing devices with a wider view. This study aims to present an extensive survey that encapsulates widely used deep learning strategies for tackling image classification challenges in remote sensing. It encompasses an exploration of remote sensing imaging platforms, sensor varieties, practical applications, and prospective developments in the field. © The Author(s) 2024.

关键词： Remote sensing

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