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6th International Conference on Recent Innovations in Computing, ICRIC 2023

作者： Mahbub, Saifullah Easin Arafat, Md. Rahman, Chowdhury Rafeed Ferdows, Zannatul Hasan, Masum Department of Computer Science and Engineering United International University United City Madani Avenue Dhaka1212 Bangladesh Data Science and Engineering Department Faculty of Informatics Eötvös Loránd University Pázmány Péter Str. 1/A Budapest1117 Hungary Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka1000 Bangladesh School of Computing National University of Singapore Singapore Singapore Department of Computer Science University of Rochester RochesterNY United States

ISBN: (纸本)9789819734412

Code review is considered a key process in the software industry for minimizing bugs and improving code quality. Inspection of review process effectiveness and continuous improvement can boost development productivity. Such inspection is a time-consuming and human-bias-prone task. We propose a semi-supervised learning-based system ReviewRanker which is aimed at assigning each code review a confidence score which is expected to resonate with the quality of the review. Our proposed method is trained based on simple and well-defined labels provided by developers. The labeling task requires little to no effort from the developers and has an indirect relation to the end goal (assignment of review confidence score). ReviewRanker is expected to improve industry-wide code review quality inspection by reducing human bias and the effort required for such a task. The system has the potential of minimizing the back-and-forth cycle existing in the development and review process. Usable code and dataset for this research can be found at: https://***/saifarnab/code_review. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Self-supervised learning

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Construction of Scholar Portraits and Intelligent Analysis Method for Academic Information Based on Pre-Trained Models

Construction of Scholar Portraits and Intelligent Analysis M...

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Cross Strait Quad-Regional Radio science and Wireless Technology Conference (CSRWTC)

作者： Fu Mo Wenjian Liu Hao Zhong Faculty of Data Science City University of Macau Macau China School of Computer Science Guangdong University of Science and Technology Dongguan China School of Computer Science South China Normal University Guangzhou China

ISBN: (数字)9798331507794

ISBN: (纸本)9798331507800

The aim of this paper is to propose an intelligent analysis method based on pretrained models for academic information, which can be used to construct scholar profiles, explore research hotspots, and mine scholar collaboration networks. By integrating academic papers, patents, scientific research projects and activity data of social media, the Pretrained(such as ChatGLM) model is used to construct a multidimensional scholar portrait, including the natural attributes of scholars, academic achievements, cooperation networks and research interests. Based on the generated scholar profiles, we analyze the evolution of research interests of scholars and construct a scholar collaboration network with the aim of dynamically tracking changes in research directions and revealing academic collaboration relationships. The experimental results show that the performance evaluation demonstrates good performance on key metrics such as precision, recall, and F1-score, proving the potential of the proposed method in academic management and research resource allocation. This paper provides intelligent technical support for smart education and research management and offers a data-driven solution for predicting future research trends.

关键词： Wireless communication Performance evaluation Analytical models Patents Social networking (online) Collaboration Educational technology Market research data models Resource management

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Optimal Deep Canonically Correlated Autoencoder-Enabled Prediction Model for Customer Churn Prediction

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computers, Materials & Continua 2022年第11期73卷 3757-3769页

作者： Olfat M.Mirza GJose Moses R.Rajender E.Laxmi Lydia Seifedine Kadry Cheadchai Me-Ead Orawit Thinnukool Department of Computer Science College of Computers and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science Engineering Malla Reddy Engineering College500100India Department of Computer Science Engineering Lendi Institute of Engineering&TechnologyDenkada535005India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeNorway Program in Statistics and Information Management Faculty of ScienceMaejo UniversityChiang Mai50290Thailand Department of Modern Management and Information Technology College of Arts Media and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Presently,customer retention is essential for reducing customer churn in telecommunication *** churn prediction(CCP)is important to predict the possibility of customer retention in the quality of *** risks of customer churn also get essential,the rise of machine learning(ML)models can be employed to investigate the characteristics of customer ***,deep learning(DL)models help in prediction of the customer behavior based characteristic *** the DL models necessitate hyperparameter modelling and effort,the process is difficult for research communities and business *** this view,this study designs an optimal deep canonically correlated autoencoder based prediction(ODCCAEP)model for competitive customer dependent application *** addition,the O-DCCAEP method purposes for determining the churning nature of the *** O-DCCAEP technique encompasses preprocessing,classification,and hyperparameter ***,the DCCAE model is employed to classify the churners or ***,the hyperparameter optimization of the DCCAE technique occurs utilizing the deer hunting optimization algorithm(DHOA).The experimental evaluation of the O-DCCAEP technique is carried out against an own dataset and the outcomes highlighted the betterment of the presented O-DCCAEP approach on existing approaches.

关键词： Churn prediction customer retention deep learning machine learning archimedes optimization algorithm

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Ciao AI: the Italian adaptation and validation of the Chatbot Usability Scale

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Personal and Ubiquitous Computing 2023年第6期27卷 2161-2170页

作者： Borsci, Simone Prati, Elisa Malizia, Alessio Schmettow, Martin Chamberlain, Alan Federici, Stefano Faculty of Behavioural Management and Social Sciences Department of Learning Data Analysis and Technology Group of Cognition Data and Education University of Twente Enschede Netherlands Department of Surgery and Cancer Faculty of Medicine NIHR London IVD Imperial College of London London United Kingdom Department of Engineering "Enzo Ferrari" University of Modena and Reggio Emilia Modena Italy Computer Science Department University of Pisa Pisa Italy Molde University College Molde Norway School of Computer Science University of Nottingham Nottingham United Kingdom Department of Philosophy Social and Human Sciences and Education University of Perugia Perugia Italy

Chatbot-based tools are becoming pervasive in multiple domains from commercial websites to rehabilitation applications. Only recently, an eleven-item satisfaction inventory was developed (the ChatBot Usability Scale, BUS-11) to help designers in the assessment process of their systems. The BUS-11 has been validated in multiple contexts and languages, i.e., English, German, Dutch, and Spanish. This scale forms a solid platform enabling designers to rapidly assess chatbots both during and after the design process. The present work aims to adapt and validate the BUS-11 inventory in Italian. A total of 1360 questionnaires were collected which related to a total of 10 Italian chatbot-based systems using the BUS-11 inventory and also using the lite version of the Usability Metrics for User eXperience for convergent validity purposes. The Italian version of the BUS-11 was adapted in terms of the wording of one item, and a Multi-Group Confirmatory Factorial Analysis was performed to establish the factorial structure of the scale and compare the effects of the wording adaptation. Results indicate that the adapted Italian version of the scale matches the expected factorial structure of the original scale. The Italian BUS-11 is highly reliable (Cronbach alpha: 0.921), and it correlates to other measures of satisfaction (e.g., UMUX-Lite, τb = 0.67;p © 2023, The Author(s).

关键词： Buses

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Proposing R&D Bridge Manager Competency Development Framework

Proposing R&D Bridge Manager Competency Development Framewor...

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2022 IEEE Technology and Engineering Management Society Conference - Asia Pacific, TEMSCON-ASPAC 2022

作者： Chalarak, Nawarerk Uchihira, Naoshi Faculty of Science and Technology Thammasat University Thammasat Research Unit in Data Innovation and Artificial Intelligence Department of Computer Science Pathum Thani Thailand School of Knowledge Science Japan Advanced Institute of Science and Technology Ishikawa Japan

ISBN: (数字)9781665427692

ISBN: (纸本)9781665427692

With the increasing demand for global R&D collaboration to utilize knowledge resources around the world, a competency development framework (CDF) for managers who facilitate research collaboration between R&D teams in different countries is necessary for the managers to perform their jobs effectively and to improve the efficiency of utilizing global knowledge resources. This paper proposes a CDF for R&D bridge managers (BMs) who bridge R&D organizations in different countries. A CDF assists with designing professional development programs. The proposed framework is based on relevant literature and empirical studies. It defines competencies that would most likely help BMs enhance research collaboration in global R&D projects. This is similar to how the project manager competency development framework impacts the performance of a project manager. A workshop method is proposed as a competency development method for BMs. This paper presents the first R&D bridge manager competency development framework (BMCDF) for BMs who facilitate research collaboration in global R&D projects. The managers can use the BMCDF for their professional development, especially in the context of global R&D projects. organizations may use this framework as a guide for developing competency-based training programs for their R&D personnel. © 2022 IEEE.

关键词： Managers

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Message Verification Protocol Based on Bilinear Pairings and Elliptic Curves for Enhanced Security in Vehicular Ad Hoc Networks

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computers, Materials & Continua 2024年第10期81卷 1029-1057页

作者： Vincent Omollo Nyangaresi Arkan A.Ghaib Hend Muslim Jasim Zaid Ameen Abduljabbar Junchao Ma Mustafa A.Al Sibahee Abdulla J.Y.Aldarwish Ali Hasan Ali Husam A.Neamah Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and TechnologyBondo40601Kenya Department of Applied Electronics Saveetha School of EngineeringSIMATSChennaiTamilnadu602105India Information Technology Department Management Technical CollegeSouthern Technical UniversityBasrah61004Iraq Department of Computer Science College of Education for Pure SciencesUniversity of BasrahBasrah61004Iraq College of Big Data and Internet Shenzhen Technology UniversityShenzhen518118China Huazhong University of Science and Technology Shenzhen InstituteShenzhen518000China National Engineering Laboratory for Big Data System Computing Technology Shenzhen UniversityShenzhen518060China Computer Technology Engineering Department Iraq University CollegeBasrah61004Iraq Department of Mathematics College of Education for Pure SciencesUniversity of BasrahBasrah61004Iraq Institute of Mathematics University of DebrecenDebrecenH-4002Hungary Department of Electrical Engineering and Mechatronics Faculty of EngineeringUniversity of DebrecenDebrecen4028Hungary

Vehicular ad hoc networks(VANETs)provide intelligent navigation and efficient route management,resulting in time savings and cost reductions in the transportation ***,the exchange of beacons and messages over public channels among vehicles and roadside units renders these networks vulnerable to numerous attacks and privacy *** address these challenges,several privacy and security preservation protocols based on blockchain and public key cryptography have been proposed ***,most of these schemes are limited by a long execution time and massive communication costs,which make them inefficient for on-board units(OBUs).Additionally,some of them are still susceptible to many *** such,this study presents a novel protocol based on the fusion of elliptic curve cryptography(ECC)and bilinear pairing(BP)*** formal security analysis is accomplished using the Burrows–Abadi–Needham(BAN)logic,demonstrating that our scheme is verifiably *** proposed scheme’s informal security assessment also shows that it provides salient security features,such as non-repudiation,anonymity,and ***,the scheme is shown to be resilient against attacks,such as packet replays,forgeries,message falsifications,and *** the performance perspective,this protocol yields a 37.88%reduction in communication overheads and a 44.44%improvement in the supported security ***,the proposed scheme can be deployed in VANETs to provide robust security at low overheads.

关键词： Attacks bilinear elliptic curve cryptography(ECC) privacy security vehicular ad hoc network(VANET)

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Comparison of Random Search and Grid Search for Optimizing SVM in Stroke Disease Classification

Comparison of Random Search and Grid Search for Optimizing S...

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computer sciences, Engineering, and Technology Innovation (ICoCSETI), International Conference on

作者： Dyan Reformasi Audyna Sukma Negara Majid Rahardi Afrig Aminuddin Ferian Fauzi Abdulloh Sunil Raj Y Bima Pramudya Asaddulloh Faculty of Computer Science Universitas Amikom Yogyakarta Sleman Indonesia Department of Data Science St Xavier’s College (Autonomous) Palayamkottai India

ISBN: (数字)9798331508616

ISBN: (纸本)9798331508623

This study examines the application of machine learning for stroke disease classification, emphasizing the need for precise model optimization to enhance predictive accuracy. The research focuses on the performance of the Support Vector Machine (SVM) algorithm, comparing two hyperparameter tuning approaches: Random Search and Grid Search. The methodology includes stages of Exploratory data Analysis (EDA), data Preprocessing, Classification Modeling, Performance Evaluation, and Hyperparameter Tuning using a stroke classification dataset. Results reveal that SVM optimized with Random Search achieved an accuracy of 90.44%, outperforming the Grid Search method. These findings highlight the significance of effective data preprocessing and model optimization in developing reliable predictions and advancing strategies for better stroke prevention and management. The study demonstrates the pivotal role of machine learning in early stroke detection and risk assessment, providing insights into practical applications for healthcare. The results establish that random search is preferred for improving SVM performance in critical diagnostic tasks.

关键词： Support vector machines Technological innovation Accuracy data preprocessing Machine learning Predictive models data models Tuning Optimization Diseases

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Evaluation of Cloud Recovery System for Financial Applications Based on Hadoop 7

Evaluation of Cloud Recovery System for Financial Applicatio...

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7th IEEE International Symposium on Telecommunication Technologies, ISTT 2024

作者： Lu, Li Lu, Tong Sali, Aduwati School of Microelectronics and Data Science Anhui University of Technology Maanshan243002 China Department of Computer and Communication Systems Engineering Faculty of Engineering Selangor Serdang43400 Malaysia Universiti Putra Malaysia Selangor Serdang43400 Malaysia

ISBN: (纸本)9798350392074

The rise of cloud-based disaster recovery systems has reshaped disaster recovery practices. Integrated into the Hadoop framework with core components like Hadoop Distributed File System (HDFS), MapReduce, and Yet Another Resource Negotiator (YARN), these systems ensure data integrity and business continuity during crises while enhancing data reliability and operational efficiency, surpassing traditional recovery systems. This study presents a comprehensive comparative analysis of traditional and Hadoop-integrated cloud recovery systems, focusing on hardware architecture and performance. Architecturally, it highlights the stark differences between conventional models and Hadoop-based innovations. The results show that the Hadoop-backed recovery system exhibits remarkable improvement, reducing Recovery Time Objective (RTO) from over 18 seconds to under 7 seconds while preserving data integrity as per Recovery Point Objective (RPO) standards. This finding underscores the academic importance of evaluating cloud-based disaster recovery within the Hadoop context, offering insights into its transformative potential for disaster recovery strategies. © 2024 IEEE.

关键词： data integrity

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Channel Estimation for 29GHz mm Wave MIMO Systems in Tropical Region 7

Channel Estimation for 29GHz mm Wave MIMO Systems in Tropica...

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7th IEEE International Symposium on Telecommunication Technologies, ISTT 2024

作者： Lu, Li Sali, Aduwati School of Microelectronics and Data Science Anhui University of Technology Maanshan243002 China Faculty of Engineering Department of Computer and Communication Systems Engineering Selangor Serdang43400 Malaysia Universiti Putra Malaysia Selangor Serdang43400 Malaysia

ISBN: (纸本)9798350392074

This paper focuses on the 29GHz mmWave MIMO communication system in tropical environments, considering the impact of rain attenuation and providing a detailed analysis of channel estimation and system performance. Experimental results indicate that as the signal-to-noise ratio (SNR) increases, the mean square error (MSE) of channel estimation decreases. Under the same SNR conditions, MIMO systems with different antenna configurations exhibit distinct channel estimation error characteristics. Notably, in the 128x128 antenna configuration, the channel estimation error is most pronounced. Furthermore, increasing the number of training signals significantly reduces channel estimation errors, especially in the 128x128 antenna configuration. This study offers a performance evaluation of the 29GHz mmWave MIMO communication system in tropical regions, providing valuable insights for further research and optimization in this field. © 2024 IEEE.

关键词： Signal to noise ratio

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Deep Reinforcement Learning for Resource Management in Blockchain-Enabled Federated Learning Network

IEEE Networking Letters

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IEEE Networking Letters 2022年第3期4卷 137-141页

作者： Hieu, Nguyen Quang Tran, The Anh Nguyen, Cong Luong Niyato, Dusit Kim, Dong In Elmroth, Erik University of Technology Sydney School of Electrical and Data Engineering SydneyNSW2007 Australia Nanyang Technological University School of Computer Science and Engineering Jurong West Singapore Phenikaa University Faculty of Computer Science Hanoi10000 Viet Nam Sungkyunkwan University Department of Electrical and Computer Engineering Seoul16419 Korea Republic of Umeå University Department of Computing Science Umeå901 87 Sweden

Blockchain-enabled Federated Learning (BFL) enables model updates to be stored in blockchain in a reliable manner. However, one problem is the increase of the training latency due to the mining process. Moreover, mobile devices have energy and CPU constraints. Therefore, the machine learning model owner (MLMO) needs to decide the data and energy that the mobile devices use for the training and determine the block generation rate to minimize the system latency and mining cost while achieving the target accuracy. Under the uncertainty of BFL, we propose to use deep reinforcement learning to find the optimal decisions for the MLMO. © 2019 IEEE.

关键词： Neural networks

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