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4th International Conference on Electrical Engineering and Informatics, ICon EEI 2024

作者： Yanti, Novi Defit, Sarjon Okfalisa Universitas Islam Negeri Sultan Syarif Kasim Riau Faculty of Science and Technology Department of Informatics Engineering Pekanbaru Indonesia Universitas Putra Indonesia Yptk Padang Faculty of Computer Science Department of Information Technology Padang Indonesia

ISBN: (纸本)9798331540791

Research in the area of knowledge management for improving academic performance has been on the rise in recent years. The effectiveness of knowledge management in improving the quality of decision-making in higher education is a new challenge in the drive for university performance improvement. Therefore, this paper tries to propose one conceptual framework used to measure academic performance in universities. By applying knowledge management process integrated with Business Intelligence system with data-driven analysis concept. This can provide new insights into the theme of academic knowledge management processes for future research opportunities. The model developed is based on comprehensive research developments on research trends and mapping of knowledge management articles and academic performance as well as a systematic literature review. This literature review examines the current state, clusters, and main topics of research related to knowledge management based on authors, sources, cited articles, countries of article authors, and journals that are productive in producing articles. Herein, 429 articles are utilized as metadata taken from the Scopus database published in 2018-2024. Hence, the data analysis of scientific articles is then reviewed using a bibliometric approach and article mapping with using tool VOSviewers to get a network visualization image. The findings of bibliometric analysis point to a rise in publications and a strong level of interest among academics in the field of knowledge management for performance evaluation in higher education institutions. © 2024 IEEE.

关键词： Research and development management

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Implementation of N-Gram Method For Automating News Ticker Generation Based on Indonesian Language 1

Implementation of N-Gram Method For Automating News Ticker G...

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1st IEEE International Conference on Cryptography, Informatics, and Cybersecurity, ICoCICs 2023

作者： Felipe Aulia, Indra Nababan, Erna Budhiarti Universitas Sumatera Utara Faculty of Computer Science and Information Technology Department of Information Technology Medan Indonesia Telkom University School of Computing Jakarta Indonesia

ISBN: (纸本)9798350339437

A news ticker is brief news displayed at the bottom of the television screen during specific news programs. The news ticker has a time-concerned nature, where the presentation of news heavily relies on time. The faster news is delivered through the news ticker mechanism, the better it is for the information recipients. Currently, news ticker writing still depends on human resources, which has various limitations such as limited availability for 24 hours and obstacles caused by human factors themselves. Therefore, researchers propose the implementation of Natural Language Generation using the N-Gram method to automate news ticker writing and help overcome the limitations of the existing news ticker writing mechanism The results of this study show that the N-Gram method, particularly Bigram, successfully generates sports news automatically. Furthermore, the evaluation, which involved individuals aged between 20 until 35 and language experts, found that the news ticker generated by the system was rated highly in terms of naturalness. The overall assessment ranged from very high to perfect. © 2023 IEEE.

关键词： Sports

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Advancing Software Defect Detection and Prevention: Bridging Gaps in Early-Stage and Evolving Software systems 5th

Advancing Software Defect Detection and Prevention: Bridging...

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5th International Symposium on Signal and Image Processing, ISSIP 2024

作者： Poonia, Ramesh Chandra Upreti, Kamal Alam, Mohammad Shabbir Department of Computer Science CHRIST University Delhi India College of Computer Science and Information Technology Jazan University Jazan Saudi Arabia

ISBN: (纸本)9789819795147

Software defect prediction (SDP) is a critical method in modern software development, saving costs while ensuring the delivery of high-quality software systems. This study investigates the vital importance of SDP, focusing on its function in detecting and correcting software faults that might lead to system failures. SDP forecasts defect-proneness and optimizes software-testing processes by using software metrics such as lines of code and change information. The chapter examines the progress of SDP research since the turn of the century, emphasizing the academic emphasis on refining static characteristics and establishing efficient learning methods for building high-performance defect predictors. Recognizing the economic consequences of software flaws, particularly in major engineering projects, this chapter emphasizes the importance of SDP in limiting project failures and economic losses in the twenty-first century. Several defect prediction methods are investigated in the context of software quality, with an emphasis on ongoing attempts to prevent and discover errors early in the software development lifecycle. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Software quality

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A comprehensive examination of load balancing algorithms in cloud environments: a systematic literature review, comparative analysis, taxonomy, open challenges, and future trends

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Iran Journal of computer science 2024年第3期7卷 663-698页

作者： Prity, Farida Siddiqi Hossain, Md. Maruf Department of Information and Communication Engineering Noakhali Science and Technology University Noakhali 3814 Bangladesh Department of Information and Communication Engineering Pabna University of Science and Technology Pabna 6600 Bangladesh Department of Computer Science and Engineering Shanto-Mariam University of Creative Technology Dhaka 1230 Bangladesh

Cloud computing is a robust paradigm that empowers users and organizations to procure services tailored to their needs. This model encompasses many offerings, including storage solutions, platforms for seamless deployment, and convenient access to web services. Load balancing, a fundamental pillar in cloud computing, is crucial in distributing requests across multiple servers to optimize resource utilization and reduce response times. However, load balancing presents a common challenge in the cloud environment, as it hampers the ability to maintain optimal application performance while adhering to the stringent requirements of Quality of Service (QoS) measurements and Service Level Agreement (SLA) compliance mandated by cloud providers to enterprises. The equitable workload distribution across servers poses a significant challenge for cloud providers. Hence, an efficient load-balancing technique should optimize resource utilization in Virtual Machines (VMs) to ensure maximum user satisfaction and overall system efficiency. However, existing review papers on load balancing in cloud environments often exhibit limitations, lacking in-depth analyses, graphical representations, and comprehensive evaluations of performance metrics. This review paper aims to fill these gaps by providing a novel taxonomy of load balancing algorithms divided into four categories (types of algorithms, nature of problem, metrics, and simulation tools) and thoroughly examining their objectives, parameters, and operational flows. It evaluates the strengths and weaknesses of these algorithms, considering their nature and type, and employs qualitative QoS parameter-based criteria for effectiveness evaluation. The paper also includes a comparative analysis of simulation tools, visual representations, and experimental results. By offering valuable insights, open issues, recommendations, and future directions, this review paper equips researchers, practitioners, and cloud service providers with the k

关键词： Algorithms Cloud computing Load balancing Taxonomy

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Modeling and Verification of the Causal Broadcast Algorithm Using Colored Petri Nets 3

Modeling and Verification of the Causal Broadcast Algorithm ...

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3rd International Conference on Distributed Computing and High Performance Computing, DCHPC 2024

作者： Namvaritazehkand, Leila Pashazadeh, Saied University of Tabriz Faculty of Electrical and Computer Engineering Department of Computer Engineering East Azerbaijan Tabriz Iran University of Tabriz Faculty of Electrical and Computer Engineering Department of Information Technology East Azerbaijan Tabriz Iran

ISBN: (纸本)9798350381580

The Causal Broadcast (CB) algorithms are presented for message passing between processes of distributed systems (DSs). These algorithms play an essential role in distributed computing. Therefore, the correctness of their performance is a high priority. Recently, researchers used the theorem-proving technique to verify causal consistency algorithms. To our knowledge, no CB algorithm presented for message-passing has been modelled and verified by a mechanical proof method. For the first time, this paper models and verifies one of the most basic CB algorithms using one of the formal methods called colored Petri nets because this algorithm has been used as the base algorithm in developing the most recent algorithms. We first model this algorithm hierarchically at the application and protocol layers using the CPN tool. Then, by analyzing the state space, we verify the properties of the CB, namely integrity, causal delivery, validity, and termination. The novelty of this paper lies in its pioneering use of colored Petri nets to model and verify a CB algorithm for the first time. This modeling method can potentially serve as a guiding method for future endeavors in modeling distributed algorithms. © 2024 IEEE.

关键词： Message passing

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A Comparative Analysis of Joomla, Drupal, Wordpress and ***: Exploring Features, Performance, and Suitability

A Comparative Analysis of Joomla, Drupal, Wordpress and ***:...

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2023 IEEE Asia-Pacific Conference on computer science and Data Engineering, CSDE 2023

作者： Prasad, Ashwin Ashika Sharma, Neeraj Anand Kumar, Anal School of Science and Technology The University of Fiji Department of Computer Science and Mathematics Fiji College of Engineering Science and Technology Fiji National University Department of Computing Sciences and Information System Fiji

ISBN: (纸本)9798350341072

In the dynamic landscape of web development, the judicious choice of platform and structure plays a pivotal role. This comprehensive study conducts a thorough comparison of four prominent web development technologies: ***, Joomla, Drupal, and WordPress. With a primary focus on scrutinizing features, performance, and overall suitability, the research aims to provide valuable insights for developers, businesses, and decision-makers. By objectively evaluating the merits and limitations of each technology, this study seeks to empower stakeholders with essential knowledge, enabling them to make informed decisions at the onset of web development projects. © 2023 IEEE.

关键词： Decision making

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A Blockchain-Based Protocol for Malicious Price Discrimination

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Journal of computer science & technology 2022年第1期37卷 266-276页

作者： Li-De Xue Ya-Jun Liu Wei Yang Wei-Lin Chen Liu-Sheng Huang School of Computer Science and Technology University of Science and Technology of ChinaHefei 230000China Information Department the First Affiliated Hospital of Soochow UniversitySuzhou 215000China

Serious price discrimination emerges with the development of big data and mobile networks,which harms the interests of *** solve this problem,we propose a blockchain-based price consensus protocol to solve the malicious price discrimination faced by *** give a mathematical definition of price discrimination,which requires the system to satisfy consistency and *** distributed blockchain can make the different pricing of merchants transparent to consumers,thus satisfying the *** aging window mechanism of our protocol ensures that there is no disagreement between any node on the consensus on price or price discrimination within a fixed period,which meets the ***,we evaluate its performance through a prototype implementation and experiments with up to 100 user *** results show that our protocol achieves all the expected goals like price transparency,consistency,and timeliness,and it additionally guarantees the consensus of the optimal price with a high probability.

关键词： blockchain digital economy distributed application price discrimination price transparency

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Robust Tweets Classification Using Arithmetic Optimization with Deep Learning for Sustainable Urban Living

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SN computer science 2024年第5期5卷 549页

作者： Hamza, Manar Ahmed Hashim, Aisha Hassan Abdalla Motwakel, Abdelwahed Elhameed, Elmouez Samir Abd Osman, Mohammed Kumar, Arun Singla, Chinu Munjal, Muskaan Department of Electrical and Computer Engineering International Islamic University Malaysia Kuala Lumpur 53100 Malaysia Department of Computer and Self Development Preparatory Year Deanship Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia Department of Management Information Systems College of Business Administration in Hawtat bani Tamim Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia Department of Computer Science College of Post-Graduated Studies University of Science and Technology Omdurman Sudan Department of Information System Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan Department of Electronics and Communication Engineering New Horizon College of engineering Bengaluru India Computer Engineering Thapar Institute of Engineering and Technology Punjab Patiala India

Natural Language Processing (NLP) with Deep Learning (DL) for Tweets Classification includes use of advanced neural network designs to analyse and classify Twitter messages. DL techniques like recurrent neural network (RNN) or transformer-based frameworks like BERT are used to mechanically learn difficult linguistic patterns and contextual info from tweet data. These techniques able to capture subtleties of language with sarcasm, sentiment, and context-specific meanings and making them suitable for tasks like sentiment analysis or topic classification in realm of social media. Leveraging deep symbols learned from great amounts of textual data, these NLP techniques permit precise and nuanced classification of tweets, donating to enhanced information retrieval, sentiment tracking, and trend analysis in dynamic and fast-paced world of social media communication. In this view, this research develops an arithmetic optimization algorithm with deep learning based tweets classification (AOADL-TC) approach for sustainable living. The goal of the AOADL-TC technique is to identify and discriminate different kinds of sentiments that exist in the tweet data. At the initial stage, the AOADL-TC model pre-processes tweet data to convert uniform data into a useful format. For sentiment detection, the AOADL-TC technique applies a parallel bidirectional gated recurrent unit (BiGRU) model. At last, tuning of parameters related to parallel BiGRU model performed by AOA. An wide set of tests carried out to illustrate better performance of AOADL-TC model. The experimental outcomes portrayed that AOADL-TC technique demonstrates the supremacy of the AOADL-TC technique in terms of different evaluation metrics. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning Machine learning Natural language processing Sentiment analysis Social media

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A Comprehensive Evaluation of State-of-the-Art Deep Learning Models for Road Surface Type Classification

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Intelligent Automation & Soft Computing 2023年第8期37卷 1275-1291页

作者： Narit Hnoohom Sakorn Mekruksavanich Anuchit Jitpattanakul Image Information and Intelligence Laboratory Department of Computer EngineeringFaculty of EngineeringMahidol UniversityNakhon Pathom73170Thailand Department of Computer Engineering School of Information and Communication TechnologyUniversity of PhayaoPhayao56000Thailand Department of Mathematics Faculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok10800Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research InstituteKing Mongkut’s University of Technology North BangkokBangkok10800Thailand

In recent years,as intelligent transportation systems(ITS)such as autonomous driving and advanced driver-assistance systems have become more popular,there has been a rise in the need for different sources of traffic situation *** classification of the road surface type,also known as the RST,is among the most essential of these situational data and can be utilized across the entirety of the ITS ***,the benefits of deep learning(DL)approaches for sensor-based RST classification have been demonstrated by automatic feature extraction without manual *** ability to extract important features is vital in making RST classification more *** work investigates the most recent advances in DL algorithms for sensor-based RST classification and explores appropriate feature extraction *** used different convolutional neural networks to understand the functional architecture better;we constructed an enhanced DL model called SE-ResNet,which uses residual connections and squeeze-and-excitation mod-ules to improve the classification *** experiments with a publicly available benchmark dataset,the passive vehicular sensors dataset,have shown that SE-ResNet outperforms other state-of-the-art *** proposed model achieved the highest accuracy of 98.41%and the highest F1-score of 98.19%when classifying surfaces into segments of dirt,cobblestone,or asphalt ***,the proposed model significantly outperforms DL networks(CNN,LSTM,and CNN-LSTM).The proposed RE-ResNet achieved the classification accuracies of asphalt roads at 98.98,cobblestone roads at 97.02,and dirt roads at 99.56%,respectively.

关键词： Road surface type classification deep learning inertial sensor deep pyramidal residual network squeeze-and-excitation module

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Autonomous Navigation Development of Solar Panel Cleaning Vehicle 28th

Autonomous Navigation Development of Solar Panel Cleaning Ve...

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28th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2023

作者： Cheng, Wen-Chang Hsiao, Hung-Chou Department of Computer Science and Information Engineering Chaoyang University of Technology Taichung City Taiwan Department of Information Management Chaoyang University of Technology Taichung City Taiwan

ISBN: (纸本)9789819717132

This study presents an automated navigation system for solar panel cleaning vehicles. Since self-positioning and navigation cannot be achieved on solar panels through self-built mapping, we utilize the vanishing points formed by the perspective transformation of many coplanar parallel lines on the solar panel to accomplish navigation. This is complemented by fuzzy control to align the vehicle with the parallel lines and proceed along the straight lines of the solar panel. When the solar panel cleaning vehicle reaches the boundary, it will turn to the next row's direction to continue cleaning along the straight lines of the solar panel until the cleaning is complete or autonomous navigation is deactivated. Preliminary experimental results show that the average standard deviation of the x-coordinate during straight-line movement is 1.70 pixels, and the average error between the maximum and minimum x-coordinates is approximately 6.33 pixels. After adjusting the speeds of the two-wheel servo motors, calibrating the camera’s centre point, and repeating the experiments, the results demonstrate an average standard deviation of 0.43 pixels, and the maximum and minimum errors in the x-coordinate of straight lines have decreased to 2 pixels, achieving practical usability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Hough transforms

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