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Handling class imbalance problem in software maintainability prediction:an empirical investigation

Frontiers of computer science 2022年第4期16卷 5-18页

作者： Ruchika Malhotra Kusum Lata Discipline of Software Engineering Department of Computer Science&EngineeringDelhi Technological UniversityDelhi110042India University School of Management and Entrepreneurship East Delhi CampusDelhi Technological UniversityDelhi110095India

As the complexity of software systems is increasing;software maintenance is becoming a challenge for software *** prediction of classes that require high maintainability effort is of utmost necessity to develop cost-effective and high-quality *** research of software engineering predictive modeling,various software maintainability prediction(SMP)models are evolved to forecast *** develop a maintainability prediction model,software practitioners may come across situations in which classes or modules requiring high maintainability effort are far less than those requiring low maintainability *** condition gives rise to a class imbalance problem(CIP).In this situation,the minority classes’prediction,i.e.,the classes demanding high maintainability effort,is a ***,in this direction,this study investigates three techniques for handling the CIP on ten open-source software to predict software *** empirical investigation supports the use of resampling with replacement technique(RR)for treating CIP and develop useful models for SMP.

关键词： sofiware maintenance software maintainability imbalanced learning

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A Machine Learning Based Predictive Model to Diagnose Heart Failure Patients using Imbalanced Classification Problem 4

A Machine Learning Based Predictive Model to Diagnose Heart ...

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4th International Conference on Advancements in Computational sciences, ICACS 2023

作者： Mudassar, Muhammad Afzal, Mehtab Muhammad, Tufail Univeristy of Lahore Department of Software Engineering Lahore Pakistan Air Univeristy Islamabad Aerospace and Aviation Campus Department of Computer Science Kamra Pakistan

ISBN: (纸本)9781665463775

Heart failure (HF) is now one of the most common diseases, causing approximately seventeen million death cases every year all over the world. HF occurs due to less pumping ratio of blood by the heart that a normal human being needs to survive. In this regard, research studies have been proposed to predict the causes behind the heart failure of a patient using the 'Heart failure clinical records (HFCR's) dataset. Although, much research has been performed on this dataset, however, there is a lack of construction of a more reliable predictive model that helps to predict HF patients with better prediction results. We aimed to apply imbalance learning to handle the imbalance dataset as a very few researchers applied it. We trained the models using six ensemble and three non-ensemble classifiers with the help of multiple experiments. In the end, we performed an evaluation measure to compare our prediction results with the previous research work. Our proposed model gives a significant increase in accuracy value as well as in precision, recall, and f1-score. © 2023 IEEE.

关键词： Forecasting

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Project Assessment in Offshore software Maintenance Outsourcing Using Deep Extreme Learning Machines

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computers, Materials & Continua 2023年第1期74卷 1871-1886页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Saqib Raza Ahmad Salman Khan Yasir Mahmood Nazri Kama Azri Azmi Assad Alzayed Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala NerusMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi Malaysia54100Kuala LumpurMalaysia Computer Science and Information Systems Department College of Business Studies Public Authority for Applied Education and Training(PAAET)AdailiyahKuwait Department of Computer Science Sharif College of Engineering&TechnologyLahore54000Pakistan

software maintenance is the process of fixing,modifying,and improving software deliverables after they are delivered to the *** can benefit from offshore software maintenance outsourcing(OSMO)in different ways,including time savings,cost savings,and improving the software quality and *** of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients’*** goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO *** projects belong to OSMO vendors,having offices in developing countries while providing services to developed *** the current study,Extreme Learning Machine’s(ELM’s)variant called Deep Extreme Learning Machines(DELMs)is used.A novel dataset consisting of 195 projects data is proposed to train the model and to evaluate the overall efficiency of the proposed *** proposed DELM’s based model evaluations achieved 90.017%training accuracy having a value with 1.412×10^(-3) Root Mean Square Error(RMSE)and 85.772%testing accuracy with 1.569×10^(-3) RMSE with five DELMs hidden *** results express that the suggested model has gained a notable recognition rate in comparison to any previous *** current study also concludes DELMs as the most applicable and useful technique for OSMO client’s project assessment.

关键词： software outsourcing deep extreme learning machine(DELM) machine learning(ML) extreme learning machine assessment

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Towards Intelligent Detection and Classification of Rice Plant Diseases Based on Leaf Image Dataset

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computer Systems science & engineering 2023年第11期47卷 1385-1413页

作者： Fawad Ali Shah Habib Akbar Abid Ali Parveen Amna Maha Aljohani Eman A.Aldhahri Harun Jamil Department of IT The University of HaripurHaripur22620Pakistan Department of Computer Science University of Engineering and TechnologyTaxila47080Pakistan Department of Computer Science GANK(S)DC KTS HaripurHaripur22620Pakistan College of Pharmacy Gachon University Medical CampusNo.191HambakmoeroYeonsu-guIncheon21936Korea Software Engineering Department College of Computer Science and EngineeringUniversity of JeddahJeddah23218Saudi Arabia College of Computer Sciences and Engineering Department of Computer Science and Artificial IntelligenceUniversity of JeddahJeddahSaudi Arabia Department of Computer Engineering Jeju National UniversityJejusi63243Korea

The detection of rice leaf disease is significant because,as an agricultural and rice exporter country,Pakistan needs to advance in production and lower the risk of *** this rapid globalization era,information technology has increased.A sensing system is mandatory to detect rice diseases using Artificial Intelligence(AI).It is being adopted in all medical and plant sciences fields to access and measure the accuracy of results and detection while lowering the risk of *** Neural Network(DNN)is a novel technique that will help detect disease present on a rice leave because DNN is also considered a state-of-the-art solution in image detection using sensing *** in this paper,the adoption of the mixed-method approach Deep Convolutional Neural Network(Deep CNN)has assisted the research in increasing the effectiveness of the proposed *** CNN is used for image recognition and is a class of deep-learning neural *** is popular and mostly used in the field of image recognition.A dataset of images with three main leaf diseases is selected for training and testing the proposed *** the image acquisition and preprocessing process,the Deep CNN model was trained to detect and classify three rice diseases(Brown spot,bacterial blight,and blast disease).The proposed model achieved 98.3%accuracy in comparison with similar state-of-the-art techniques.

关键词： Rice plant disease detection convolution neural network image classification biological classification

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CIXG: A Comprehensive Approach to Driver Gene Identification and Causal Interpretation

CIXG: A Comprehensive Approach to Driver Gene Identification...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Yawen Chen, Yiwen Nie, Shanling Yang, Hai East China University of Science and Technology Department of Computer Science and Engineering Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China National University of Singapore Center for Continuing and Lifelong Education Singapore University of Sydney Faculty of Engineering Australia

ISBN: (纸本)9798350386226

With the ongoing advancements in science and technology and the increasing research focus on cancer-related issues, there has been a proliferation of omics-related resources for in-depth analysis and exploration. This burgeoning volume and complexity of biological data have fostered the integration of machine-learning techniques into biology. As a result, numerous machine-learning strategies have been established to identify driver mutations. Yet, many of these strategies produce complex models, complicating comprehension and thereby clouding the impact of input features on the resulting predictions. Our analysis presented the CIXG framework, which integrates a driver gene prediction module using XGBoost with a causality interpretation module anchored on CXPlain. This architecture enables quantifying each input feature's contribution to the prediction outcome and ensures precise predictions of driver genes. When benchmarked against the state-of-the-art (SOTA) method, CIXG demonstrated superior accuracy in pinpointing driver genes across pan-cancer studies and within the 32 specific cancer types. Importantly, our results underscored that mutation features chiefly influence CIXG's predictive prowess, with additional support from other omics features. © 2024 IEEE.

关键词： Adversarial machine learning

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A High Availability Microservices Architecture Implementation using Saga and Backup Mechanism 10

A High Availability Microservices Architecture Implementatio...

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10th International Conference on Dependable Systems and Their Applications, DSA 2023

作者： Lee, Wen-Tin Song, Ping-Yi Tsai, Ming-Kai Wu, Meng-Hsien Ma, Shang-Pin National Kaohsiung Normal University Department of Software Engineering and Management Kaohsiung Taiwan National Taiwan Ocean University Department of Computer Science and Information Engineering Keelung Taiwan

ISBN: (纸本)9798350304770

In recent years, the proliferation of complex applications has led to the emergence of microservices architecture as the preferred approach for developing largescale applications. Consequently, numerous design patterns for microservice communication have evolved, with service orchestration gaining widespread acceptance as a standard solution in recent *** study delves into design patterns for microservice data consistency and develops the service orchestration library, Anser-Saga. This library enables the creation of backups and restart points of distributed transaction states, ensuring eventual data consistency between microservice endpoints. Furthermore, it guarantees that our orchestrator can store, compensate for, and restart the orchestration process in case of an unexpected failure, ensuring transaction integrity. We conducted experiments involving actual microservice invocations to evaluate the availability and performance of microservices. The findings revealed that the Anser-Saga library demonstrated superior performance compared to the DTM solution. © 2023 IEEE.

关键词： Architecture

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Semantic Overlap Summarization among Multiple Alternative Narratives: an Exploratory Study 29

Semantic Overlap Summarization among Multiple Alternative Na...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Bansal, Naman Akter, Mousumi Karmaker, Shubhra Kanti Lab Department of Computer Science and Software Engineering College of Engineering Auburn University United States

In this paper, we introduce an important yet relatively unexplored NLP task called Semantic Overlap Summarization (SOS), which entails generating a single summary from multiple alternative narratives which can convey the common information provided by those narratives. As no benchmark dataset is readily available for this task, we created one by collecting 2, 925 alternative narrative pairs from the web and then, went through the tedious process of manually creating 411 different reference summaries by engaging human annotators. As a way to evaluate this novel task, we first conducted a systematic study by borrowing the popular ROUGE metric from text-summarization literature and discovered that ROUGE is not suitable for our task. Subsequently, we conducted further human annotations to create 200 document-level and 1, 518 sentence-level ground-truth overlap labels. Our experiments show that the sentence-wise annotation technique with three overlap labels, i.e., {Absent (A), Partially-Present (PP), and Present (P)}, yields a higher correlation with human judgment and higher inter-rater agreement compared to the ROUGE metric. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Semantics

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Investigating the Effect of Sliding Window Length for EEG-based Anxiety Classification

Investigating the Effect of Sliding Window Length for EEG-ba...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Shikha Sethia, Divyashikha Indu, S. Delhi Technological University Department of Computer Science and Engineering New Delhi India Delhi Technological University Department of Software Engineering New Delhi India Delhi Technological University Department of Electronics and Communication New Delhi India

ISBN: (纸本)9798350361537

Numerous studies have been conducted in various domains, such as emotion, seizure, and Alzheimer's, to investigate the impact of different sliding window lengths on EEG signal-based feature extraction. However, the impact of sliding window length on anxiety recognition remains underexplored. Hence, this paper aims to investigate the influence of different sliding window lengths on anxiety disorder classification using the EEG-based DASPS dataset. The study examines both time and frequency domain features to evaluate the effectiveness of varying window lengths. Additionally, a genetic feature selection algorithm is applied to balance the anxiety classification performance and model complexity. The comparative analysis of sliding window lengths reveals that shorter window sizes can enhance anxiety classification performance. Moreover, utilizing the feature selection algorithm improves classification performance by 2.16% and 2.54%, respectively, for the best-performing Gradient Boosting and XGBoost classifiers with a 1-second window size. These findings offer valuable insights for determining window lengths in EEG signal analysis for anxiety detection systems. © 2024 IEEE.

关键词： Frequency domain analysis

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New Algorithms for Distributed Fair k-Center Clustering: Almost Accurate as Sequential Algorithms 23

New Algorithms for Distributed Fair k-Center Clustering: Alm...

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23rd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2024

作者： Wu, Xiaoliang Feng, Qilong Huang, Ziyun Xu, Jinhui Wang, Jianxin School of Computer Science and Engineering Central South University Xiangjiang Laboratory Changsha China Department of Computer Science and Software Engineering Penn State Erie The Behrend College Erie United States Department of Computer Science and Engineering State University of New York at Buffalo Buffalo United States Hunan Provincial Key Lab on Bioinformatics Central South University Xiangjiang Laboratory Changsha China

Fair clustering problems have been paid lots of attention recently. In this paper, we study the k-Center problem under the group fairness and data summarization fairness constraints, denoted as Group Fair k-Center (GFkC) and Data Summarization Fair k-Center (DSFkC), respectively, in the massively parallel computational (MPC) distributed model. The previous best results for the above two problems in the MPC model are a 9-approximation with violation 7 (WWW 2022) and a (17 + ∊)-approximation without fairness violation (ICML 2020), respectively. In this paper, we obtain a (3 + ∊)- approximation with violation 1 for the GFkC problem in the MPC model, which is almost as accurate as the best known approximation ratio 3 with violation 1 for the sequential algorithm of the GFkC problem. Moreover, for the DSFkC problem in the MPC model, we obtain a (4 + ∊)-approximation without fairness violation, which is very close to the best known approximation ratio 3 for the sequential algorithm of the DSFkC problem. Empirical experiments show that our distributed algorithms perform better than existing state-of-the-art distributed methods for the above two problems. © 2024 International Foundation for Autonomous Agents and Multiagent Systems.

关键词： Machine learning

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Cloud Security Service for Identifying Unauthorized User Behaviour

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computers, Materials & Continua 2022年第2期70卷 2581-2600页

作者： D.Stalin David Mamoona Anam Chandraprabha Kaliappan S.Arun Mozhi Selvi Dilip Kumar Sharma Pankaj Dadheech Sudhakar Sengan Department of Computer Science and Engineering IFET College of EngineeringVillupuram605108Tamil NaduIndia Department of Computer Sciences and Software Engineering Faculty of Basic and Applied SciencesInternational Islamic UniversityIslamabadPakistan Department of Information Technology Bannari Amman Institute of TechnologySathyamangalam638401Tamil NaduIndia Department of Computer Science Engineering DMI St.John the Baptist UniversityMangochiLilongweMalawi Department of Mathematics Jaypee University of Engineering and TechnologyGuna473226Madhya PradeshIndia Department of Computer Science and Engineering Swami Keshvanand Institute of TechnologyManagement and Gramothan(SKIT)Jaipur302017RajasthanIndia Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveli627152Tamil NaduIndia

Recently,an innovative trend like cloud computing has progressed quickly in *** a background of distributed networks,the extensive sprawl of internet resources on the Web and the increasing number of service providers helped cloud computing technologies grow into a substantial scaled Information Technology service *** cloud computing environment extracts the execution details of services and systems from end-users and ***,through the system’s virtualization accomplished using resource pooling,cloud computing resources become more *** attempt to design and develop a solution that assures reliable and protected authentication and authorization service in such cloud environments is described in this *** the help of multi-agents,we attempt to represent Open-Identity(ID)design to find a solution that would offer trustworthy and secured authentication and authorization services to software services based on the *** research aims to determine how authentication and authorization services were provided in an agreeable and preventive *** on attack-oriented threat model security,the evaluation *** considering security for both authentication and authorization systems,possible security threats are analyzed by the proposed security systems.

关键词： Cloud computing user behaviour access control security model

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