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Software Reliability Assessment Using Hybrid Neuro-Fuzzy Model

computer Systems science & Engineering 2022年第6期41卷 891-902页

作者： Parul Gandhi Mohammad Zubair Khan Ravi Kumar Sharma Omar H.Alhazmi Surbhi Bhatia Chinmay Chakraborty Department of Computer Applications Manav Rachna International Institute of Research and StudiesFaridabad121006India Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinahSaudi Arabia Department of Computer Applications Chandigarh Group of CollegesLandran140307India College of Computer Sciences and Information Technology King Faisal UniversitySaudi Arabia Department of Electronics&Communication Engineering Birla Institute of TechnologyMesra JharkhandIndia

Software reliability is the primary concern of software developmentorganizations, and the exponentially increasing demand for reliable softwarerequires modeling techniques to be developed in the present era. Small unnoticeable drifts in the software can culminate into a disaster. Early removal of theseerrors helps the organization improve and enhance the software’s reliability andsave money, time, and effort. Many soft computing techniques are available toget solutions for critical problems but selecting the appropriate technique is abig challenge. This paper proposed an efficient algorithm that can be used forthe prediction of software reliability. The proposed algorithm is implementedusing a hybrid approach named Neuro-Fuzzy Inference System and has also beenapplied to test data. In this work, a comparison among different techniques of softcomputing has been performed. After testing and training the real time data withthe reliability prediction in terms of mean relative error and mean absolute relativeerror as 0.0060 and 0.0121, respectively, the claim has been verified. The resultsclaim that the proposed algorithm predicts attractive outcomes in terms of meanabsolute relative error plus mean relative error compared to the other existingmodels that justify the reliability prediction of the proposed model. Thus, thisnovel technique intends to make this model as simple as possible to improvethe software reliability.

关键词： Software quality reliability neural networks fuzzy logic neuro-fuzzy inference system

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Predicting Future Automobile Ownership Trends in Uzbekistan Using Linear Regression 2nd

Predicting Future Automobile Ownership Trends in Uzbekistan ...

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2nd international Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Ather, Danish Singh, Malikhan Arora, Kavita Pathak, Sonal Qurashi, Suhail Javed Singh, Anupam Amity University Tashkent Uzbekistan Computer Science and Engineering College of Engineering & Technology Mathura India School of Computer Applications Manav Rachna International Institute of Research and Studies Haryana Faridabad India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India

ISBN: (纸本)9789819735938

The rapid urbanization and economic growth of Uzbekistan over the past decades have engendered significant changes in automobile ownership trends. Understanding these trends is crucial for infrastructure planning, sustainable transportation strategies, and environmental considerations. This Paper seeks to employ Machine Learning (ML) techniques to forecast the number of cars per 100 households in Uzbekistan by 2025, based on historical data from 2010, 2015, and 2020. The dataset, derived from official statistics provided by the State Committee of the Republic of Uzbekistan on Statistics, shows a remarkable increase from 21 cars per 100 households in 2010 to 48 by 2020. Leveraging regression models, we aim to predict the expected growth for 2025. Preliminary analyses hint at a continuing upward trend, impacted potentially by factors such as Population growth, GDP growth, urban migration, and increasing global connectivity. The findings from this research will not only serve as a prediction tool but will also shed light on the socio-economic factors influencing car ownership, providing valuable insights to policymakers and urban planners. While the limited data points present a challenge for complex model utilization, the study underscores the potential of ML in forecasting and the need for more comprehensive data collection in future studies. The predictive insights drawn from this research can significantly aid in shaping transportation policies, mitigating potential environmental impacts, and planning urban infrastructures in Uzbekistan. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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Leveraging AI for Enhancing Robustness and Optimization in Constraint-Based Job-Shop Scheduling with Operators

Leveraging AI for Enhancing Robustness and Optimization in C...

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2024 international Conference on Decision Aid sciences and applications, DASA 2024

作者： Mahfuri, Mahmoud Ghwanmeh, Sameh Khishe, Mohammad Saeed, Ali International University for Science and Technology Computer and Information Technology Department Syria College of Computer Information Technology American University in the Emirates Computer Science Dept United Arab Emirates Innovation Center for Artificial Intelligence Applications Yuan Ze University Taiwan Jadara Research Center Jadara University Jordan Computer Center Northern Technical University Ninevah Iraq

ISBN: (纸本)9798350369106

in complex scheduling scenarios, such as the Job Scheduling Problem (JSP) faced by human operators, the challenge is to optimize the total time mandatory to complete all jobs and the reliability of the schedule. This paper presents a unified approach that combines constraint satisfaction (CSP) techniques with heuristic algorithms to resolve JSP using operators called JSO(n, p), where n is the number of tasks and p is the number of data. The main aims are to minimize the total time required to complete all tasks and to maximize system consistency under uncertainties such as equipment failure or operator unavailability. We analysis branch-and-bound (BB) methods, neural networks, and hybrid algorithms to achieve optimal software solutions. This paper also assesses the impact of bus constraints on traditional scheduling models and suggests solutions for reliability. The evaluation of various algorithmic strategies and extensive experiments on many sampling problems demonstrate the balance between robustness and optimality. © 2024 IEEE.

关键词： Scheduling algorithms

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Artificial Intelligence in Account Management: Innovation, Challenges, and Strategic Outlook

Artificial Intelligence in Account Management: Innovation, C...

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2024 international Conference on Decision Aid sciences and applications, DASA 2024

作者： Mahfuri, Mahmoud Ghwanmeh, Sameh Saeed, Ali Q Khishe, Mohammad International University for Science and Technology Computer and Information Technology Department Syria College of Computer Information Technology American University in the Emirates Computer Science Dept United Arab Emirates Computer Center Northern Technical University Ninevah Iraq Innovation Center for Artificial Intelligence Applications Yuan Ze University Taiwan Jadara Research Center Jadara University Jordan Jordan

ISBN: (纸本)9798350369106

This article presents the growing role of artificial intelligence (AI) in account management, through highlighting its increasing significance in computing and financial administration. AI is rapidly becoming a transformative tool across various sectors, and its application in tax administration presents both opportunities and challenges. The article explores AI-driven business opportunities, growth trends, and strategic planning within the field. in addition to address the critical technical, ethical, and legal considerations, including intellectual property rights, that must be navigated to ensure responsible and effective implementation. © 2024 IEEE.

关键词： Account Management Artificial Intelligence (AI) Machine Learning (ML) Predictive Analytics

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Evasion Techniques in Cybersecurity: An In-Depth Analysis

Evasion Techniques in Cybersecurity: An In-Depth Analysis

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2024 international Conference on Artificial Intelligence and Quantum Computation-Based Sensor applications, ICAIQSA 2024

作者： Turukmane, Anil V Khekare, Ganesh Shelke, Nilesh Sakarkar, Gopal Buchade, Siddhant School of Computer Science & Engineering Vellore Institute of Technology-AP Amaravati India Symbiosis Institute of Technology Nagpur Campus Symbiosis International Deemed University Pune India Dr. Vishwanath Karad MIT World Peace University Department of Computer Science and Applications Pune India

ISBN: (纸本)9798331517953

Because of the rising sophistication of cybersecurity threats, it is imperative to have a thorough grasp of the evasion strategies used by malevolent actors. This study offers a detailed analysis of evasion strategies in several cybersecurity sectors, such as intrusion detection systems, network security, and endpoint protection. This paper employs a methodical approach to examine the development of these methods, showcases actual case studies, and suggests approaches for detection and mitigation. The findings emphasize how crucial it is to conduct ongoing research and development to combat new cyber threats. Key discoveries are reviewed, and directions for further research are indicated. © 2024 IEEE.

关键词： Network intrusion

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The Efficient Traffic Sign Detection and Recognition for Taiwan Road Using YOLO Model with Hybrid Dataset 9

The Efficient Traffic Sign Detection and Recognition for Tai...

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9th international Conference on Applied System Innovation, ICASI 2023

作者： Suwattanapunkul, Taweelap Wang, Lung-Jen National Pingtung University International Master Program in Information Technology and Applications Taiwan National Pingtung University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350398380

According to the trend of worldwide car sales have grown up, this cause may increase accidents on the road due to human error. The self-driverless car has been developed to solve this problem. One task of the self-driverless car is traffic sign detection and recognition (TSDR), which will help drivers notify the traffic sign installed on the road in advance. Taiwan roads have specific traffic signs, and no Taiwan traffic sign public dataset is available. In this paper, our proposed object detection method was experimentally performed using YOLOv5s6 and YOLOv8s models on three different datasets, as Tsinghua-Tencent 100K (TT100k), the self-created Taiwan traffic sign (TWTS), and the hybrid dataset, which combine the traffic scenes between TT100k and TWTS dataset. The output results from each dataset and each model, which is trained on the same parameter, will be compared to validate the proposed method. The experiment results' comparison of the hybrid dataset between YOLOv5s6 and YOLOv8s models display the results of the mAP@.5 is about 65% and 76.2%, respectively, which means the performance of the YOLOv8s is higher than the YOLOv5s6 when using hybrid dataset. © 2023 IEEE.

关键词： Object detection

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Sentiment Analysis on the Elevated Expressway in Bangladesh: A Machine Learning and Deep Learning Approach 2

Sentiment Analysis on the Elevated Expressway in Bangladesh:...

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2nd international Conference on Power, Electrical, Electronics and Industrial applications, PEEIACON 2024

作者： Jeba, Samiha Maisha Khan, Rajib Ahmed, Md Shoaib Aurpa, Tanjim Taharat Dash, Satya Ranjan International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Bangladesh Boise State University Department of Computer Science Idaho Boise United States Bangabandhu Sheikh Mujibur Rahman Digital University Dept. of Educational Technology Bangladesh School of Computer Applications Kiit University Odisha India

ISBN: (纸本)9798331517984

User comments on social media or any video streaming platforms show how people feel about something, including different opinions, feelings, and problems of people interacting with it. It can assist people and organizations in making wellinformed choices by offering insightful information on how people feel about a topic or issue. The Elevated Expressway is a crucial infrastructure in Bangladesh and a controversial project that has sparked public debate and criticism. The 46.73 km road project connecting Hazrat Shahjalal international Airport to Kutubkhali on the Dhaka-Chittagong Highway has eased the commute time from the Airport to Farmgate from over an hour to 10 minutes. Regarding this project, People's comments on YouTube as a digital platform for voicing their opinions and contributing to a shared narrative that goes beyond traditional media. In this work, we examine users' sentiments about Elevated Expressway from YouTube comments, employing various Machine Learning (ML) algorithms and Deep Learning (ML) architecture. Based on these analyses, the BERT architecture arises as the most satisfying model, with an accuracy of 84.34% and a macro f1 score of 80.14% for classifying public sentiment as positive, negative, or neutral. © 2024 IEEE.

关键词： Video streaming

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Multi-Criteria Decision-Making Model to Achieve Sustainable Developmental Goals in Industry 4.0 for Smart City Infrastructure

HighTech and Innovation Journal

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HighTech and Innovation Journal 2024年第4期5卷 1135-1153页

作者： Akila, D. Pal, Souvik Sarkar, Bikramjit Jayalaksshmi, S. Muthaiyah, Saravanan Anbananthen, Kalaiarasi Sonai Muthu Department of Computer Applications Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences India Department of Management Information Systems Saveethas College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences India Department of Computer Science and Engineering JIS College of Engineering Kalyani India Department of Information Technology Vels Institute of Science Technology and Advanced Studies India School of Business and Technology International Medical University Kuala Lumpur57000 Malaysia Faculty of Information Science and Technology Multimedia University Melaka75450 Malaysia

Due to a shortage of funding and other market challenges, Small and Medium-sized Enterprises (SMEs) face difficulties in adopting new technologies. Numerous technological obstacles negatively impact the long-term commercial achievement of SMEs. The deployment of Industry 4.0hopes to resolve these technological challenges. A sustainable city is a complex structure where economic, societal, and ecological components interact and compete. There is a scarcity of l methodologies for measuring interactions in this complex structure. Industry 4.0 aims to obtain higher performance effectiveness, profitability, and automation. The main goal is to develop a reliable method of evaluating small and medium-sized enterprises (SMEs) adopting Industry 4.0 technologies, particularly concerning smart city applications. This paper aims to determine the influence of Industry 4.0 in fostering economic efficiency and sustainability amongst these SMEs. The study introduces a multi-criteria decision-making (SC-MCDM) system designed to test an SME’s achievement of their targeted sustainable developmental goals. A technique for computing the interaction between various standards, i.e., (static interactions and dynamical pattern resemblance), as well as the weightage of variables of every indicator generated by the connection, is included within SC-MCDM. Furthermore, applying the suggested technique is validated by assessing the sustainable development goals of twelve Chinese cities within the Triple Bottom Line (TBL) paradigm. From a geographic-temporal viewpoint, spatial variations in city sustainability reveal regional sustainable inequalities. Indicator scores suggest that the most significant factors for most communities are the lack of research spending, falling financing in stationary assets, shortage of financial development, and inadequate shared transit. Furthermore, the growth of tertiary industries, improvement of energy performance, expansion of green areas, and reduction of poll

关键词： Sustainable city

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Recognition of Data Breach Method from Story Using Advanced Deep Learning 3

Recognition of Data Breach Method from Story Using Advanced ...

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3rd international Conference for Innovation in technology, INOCON 2024

作者： Jeba, Samiha Maisha Aurpa, Tanjim Taharat Alom, Md Samiul Joy, Md. Wadud Mahmud Ahamed, Asm Shakil Dash, Satya Ranjan Ahmed, Md Shoaib International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Bangladesh Bangabandhu Sheikh Mujibur Rahman Digital University Department of Data Science Bangladesh Kiit University School of Computer Applications Odisha India Boise State University Department of Computer Science BoiseID United States

ISBN: (纸本)9798350381931

We often open our eyes in the morning with the appalling news of data breaches of different popular companies. This is a significant threat not only to giant companies but also to the general people's privacy. Various methods are maneuvered for data breaches, and identifying these methods in a short can help understand the loss and take proper steps against these incidents. Researchers nowadays focus on the data breaches issue to reduce its threats to the tech industry. In this research, we have used advanced deep learning methods(e.g., BERT, XLNet, and Electra) to predict data breach methods using the data breach story. The proposed classifier has provided an accuracy of 92.86%. © 2024 IEEE.

关键词： Deep learning

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Systematic Literature Review on Sentiment Analysis in Airline Industry

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SN computer science 2025年第1期6卷 1-26页

作者： Hasib, Khan Md Naseem, Usman Keya, Ashfia Jannat Maitra, Shithi Mithu, Kamruzzaman Alam, Md. Golam Rabiul Department of Computer Science and Engineering Brac University Mohakhali Dhaka 1212 Bangladesh Department of Computer Science and Engineering Bangladesh University of Business and Technology Mirpur Dhaka 1216 Bangladesh School of Computing Macquarie University Sydney 2109 NSW Australia Unilever BD Ltd. Shanta Forum Dhaka 1208 Bangladesh Department of Computer Science and Engineering United International University United City Dhaka 1212 Bangladesh

The significance of sentiment analysis lies in its ability to identify key areas of customer satisfaction and dissatisfaction, revealing patterns that can guide enhancements in airline services and strategic decision-making. Additionally, the identification of topics within airline passengers’ opinions across various flight classes is crucial, achieved through topic modeling. By analyzing these opinions, we can discern passengers’ priorities and expectations, which are essential for delivering superior service. This survey converges the sparse implications and provides a dense, comprehensive understanding of the state of the art through a Systematic Literature Review (SLR). Our SLR breaks down the research process into three distinct phases: Planning (defining research questions and protocol), Conducting (identifying and assessing relevant studies), and Document Review (synthesizing findings). We analyze 60 papers on sentiment analysis and topic modeling to examine the application and effectiveness of machine learning and deep learning methods in the airline industry. Our study highlights critical tasks in airline sentiment analysis, including multilingual sentiment analysis, and provides an overview of popular datasets, their fundamental properties, and the machine learning and deep learning models applied to them. The existing literature is critically examined to uncover both strengths and weaknesses, yielding additional insights into the prevailing research paradigms. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Airline industries Customer feedback analysis Machine learning Sentiment analysis Systematic literature review Topic modeling

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