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Data and Ensemble Machine Learning Fusion Based Intelligent software Defect Prediction System

computers, Materials & Continua 2023年第6期75卷 6083-6100页

作者： Sagheer Abbas Shabib Aftab Muhammad Adnan Khan Taher MGhazal Hussam Al Hamadi Chan Yeob Yeun School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Software Faculty of Artificial Intelligence and SoftwareGachon UniversitySeongnam13120Korea Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan School of Information Technology Skyline University CollegeUniversity City SharjahSharjahUAE Center for Cyber Security Faculty of Information Science and TechnologyUKMBangiSelangor43600Malaysia College of Engineering and IT University of Dubai14143UAE EECS Department Center for Cyber Physical SystemsKhalifa UniversityAbu Dhabi127788UAE

The software engineering field has long focused on creating high-quality software despite limited *** defects before the testing stage of software development can enable quality assurance engineers to con-centrate on problematic modules rather than all the *** approach can enhance the quality of the final product while lowering development *** defective modules early on can allow for early corrections and ensure the timely delivery of a high-quality product that satisfies customers and instills greater confidence in the development *** process is known as software defect prediction,and it can improve end-product quality while reducing the cost of testing and *** study proposes a software defect prediction system that utilizes data fusion,feature selection,and ensemble machine learning fusion techniques.A novel filter-based metric selection technique is proposed in the framework to select the optimum features.A three-step nested approach is presented for predicting defective modules to achieve high *** the first step,three supervised machine learning techniques,including Decision Tree,Support Vector Machines,and Naïve Bayes,are used to detect faulty *** second step involves integrating the predictive accuracy of these classification techniques through three ensemble machine-learning methods:Bagging,Voting,and ***,in the third step,a fuzzy logic technique is employed to integrate the predictive accuracy of the ensemble machine learning *** experiments are performed on a fused software defect dataset to ensure that the developed fused ensemble model can perform effectively on diverse *** NASA datasets are integrated to create the fused dataset:MW1,PC1,PC3,PC4,and *** to the results,the proposed system exhibited superior performance to other advanced techniques for predicting software defects,achieving a remarkable accuracy rate of 92.08%.

关键词： Ensemble machine learning fusion software defect prediction fuzzy logic

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Tumor type classification and candidate cancer-specific biomarkers discovery via semi-supervised learning

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Biophysics Reports 2023年第2期9卷 57-66页

作者： Peng Chen Zhenlei Li Zhaolin Hong Haoran Zheng Rong Zeng School of Computer Science and Technology University of Science and Technology of China Anhui Key Laboratory of Software Engineering in Computing and Communication University of Science and Technology of China Department of Systems Biology University of Science and Technology of China CAS Key Laboratory of Systems Biology Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences School of Life Science and Technology ShanghaiTech University

Identifying cancer-related differentially expressed genes provides significant information for diagnosing tumors, predicting prognoses, and effective treatments. Recently, deep learning methods have been used to perform gene differential expression analysis using microarray-based high-throughput gene profiling and have achieved good results. In this study, we proposed a new robust multiple-datasetsbased semi-supervised learning model, MSSL, to perform tumor type classification and candidate cancer-specific biomarkers discovery across multiple tumor types and multiple datasets, which addressed the following long-lasting obstacles:(1) the data volume of the existing single dataset is not enough to fully exert the advantages of deep learning;(2) a large number of datasets from different research institutions cannot be effectively used due to inconsistent internal variances and low quality;(3) relatively uncommon cancers have limited effects on deep learning methods. In our article, we applied MSSL to The Cancer Genome Atlas(TCGA) and the Gene Expression Comprehensive Database(GEO) pan-cancer normalized-level3 RNA-seq data and got 97.6% final classification accuracy, which had a significant performance leap compared with previous approaches. Finally, we got the ranking of the importance of the corresponding genes for each cancer type based on classification results and validated that the top genes selected in this way were biologically meaningful for corresponding tumors and some of them had been used as biomarkers, which showed the efficacy of our method.

关键词： Deep learning Tumor type classification Cancer-specific biomarkers MSSL

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AI-Driven Virtual Mock Interview Development

AI-Driven Virtual Mock Interview Development

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International Symposium on Soft Computing and Intelligent systems (SCIS)

作者： Prabhat Kumar Mishra Arun Kumar Arulappan In-Ho Ra Thanga Mariappan L Gina Rose G Young-Seok Lee School of Computer Science Engineering and Information Systems VIT University Vellore India School of Computer & Software Kunsan National University Gunsan Republic of Korea Department of Computer Technology Anna University Chennai India

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

The integration of Artificial Intelligence (AI) into educational technologies marks a significant shift in learning methodologies and operational dynamics within educational institutions. At the forefront is an AI-driven virtual mock interview platform designed to address the high Customer Acquisition Costs (CAC) in the edtech sector, especially for interview preparation services. This initiative harnesses a blend of AI technologies, including ADA 2 for creating context-aware embeddings and Machine Learning (ML), to transform the traditional mock interview process into a dynamic, cost-effective system. Central to the platform is its use of advanced Natural Language Processing (NLP) techniques and GPT-4 Large Language Model (LLM), automating the process of mock interviews and providing personalized feedback, ensuring a preparation journey that meets specific candidate needs and mirrors real interview scenarios. A key evaluation among 100 students from a cohort of 1800 demonstrated a 90% cost reduction for three mock interviews, reducing expenses from ₹3000 to just ₹300 per candidate. This cost efficiency significantly enhances access to quality interview preparation, improving student satisfaction and accessibility. Moreover, the platform provides valuable insights into student performance, setting a new standard in educational technology by offering an effective, personalized interview preparation experience. This project reflects a holistic approach to student development and the critical role of technology in addressing the evolving needs of learners

关键词： Costs Large language models Learning (artificial intelligence) Educational technology Transforms Machine learning Natural language processing Mirrors Interviews Standards

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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Enhancing Intrusion Detection systems Using RNN, LSTM, and Hybrid RNN-LSTM Models

Enhancing Intrusion Detection Systems Using RNN, LSTM, and H...

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Machine Learning and Autonomous systems (ICMLAS), International Conference on

作者： Sahar Ebadinezhad Nooshin Nooraei Nia Nasratullah Shirzad Nwabueze Kenneth Osemeha Department of Computer Information Systems Computer Information Systems Research and Technology Center (CISRTC) Near East University Nicosia Turkey Artificial Intelligence Near East University Nicosia North Cyprus Software Engineering Department Near East University Nicosia North Cyprus

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Due to The lack of comparison studies and practical applications of RNN, LSTM, and hybrid RNN-LSTM models for intrusion detection systems, especially when managing class imbalances in complex network datasets, represents a gap in the literature. This study investigates the application of deep learning techniques to improve the detection capabilities of IDS. For this study, we applied and assessed Recurrent Neural Network, Long Short-Term Memory, and Hybrid RNN-LSTM models on a UNSW-NB15 dataset, and to redress class imbalance we trained and validated our models using synthetic minority over-sampling. The RNN-LSTM model was shown to perform the best with a 9 4. 0 0 % accuracy in comparison to other models. These experimental results signify the possibility of enhancing IDS performance and a dependable method of detecting various network intrusions with the use of hybrid models comprised of RNN & LSTM.

关键词： Deep learning Recurrent neural networks Accuracy Computational modeling Intrusion detection computer architecture Network security Hyperparameter optimization Network intrusion Long short term memory

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Scope-Bounded Reachability in Valence systems 32

Scope-Bounded Reachability in Valence Systems

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32nd International Conference on Concurrency Theory, CONCUR 2021

作者： Shetty, Aneesh K. Krishna, S. Zetzsche, Georg Department of Computer Science and Engineering Iit Bombay India Max Planck Institute for Software Systems Kaiserslautern Germany

ISBN: (纸本)9783959772037

Multi-pushdown systems are a standard model for concurrent recursive programs, but they have an undecidable reachability problem. Therefore, there have been several proposals to underapproximate their sets of runs so that reachability in this underapproximation becomes decidable. One such underapproximation that covers a relatively high portion of runs is scope boundedness. In such a run, after each push to stack i, the corresponding pop operation must come within a bounded number of visits to stack i. In this work, we generalize this approach to a large class of infinite-state systems. For this, we consider the model of valence systems, which consist of a finite-state control and an infinitestate storage mechanism that is specified by a finite undirected graph. This framework captures pushdowns, vector addition systems, integer vector addition systems, and combinations thereof. For this framework, we propose a notion of scope boundedness that coincides with the classical notion when the storage mechanism happens to be a multi-pushdown. We show that with this notion, reachability can be decided in PSPACE for every storage mechanism in the framework. Moreover, we describe the full complexity landscape of this problem across all storage mechanisms, both in the case of (i) the scope bound being given as input and (ii) for fixed scope bounds. Finally, we provide an almost complete description of the complexity landscape if even a description of the storage mechanism is part of the input. © Aneesh K. Shetty, S. Krishna, and Georg Zetzsche;.

关键词： Vectors

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COVID-19 Detection System: A Comparative Analysis of System Performance Based on Acoustic Features of Cough Audio Signals

COVID-19 Detection System: A Comparative Analysis of System ...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Asmaa Shati Ghulam Mubashar Hassan Amitava Datta Department of Computer Science and Software Engineering University of Western Australia Australia Department of Information Systems King Khalid University Saudi Arabia

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

A wide range of respiratory diseases, such as cold and flu, asthma, and COVID-19, affect people’s daily lives worldwide. In medical practice, respiratory sounds are widely used in medical services to diagnose various respiratory illnesses and lung disorders. The traditional diagnosis of such sounds requires specialized knowledge, which can be costly and reliant on human expertise. Recently, cough audio recordings have been used to automate the process of detecting respiratory conditions. This research aims to examine various acoustic features that enhance the performance of machine learning (ML) models in detecting COVID-19 from cough signals. This study investigates the efficacy of three feature extraction techniques, including Mel Frequency Cepstral Coefficients (MFCC), Chroma, and Spectral Contrast features, on two ML algorithms, Support Vector Machine (SVM) and Multilayer Perceptron (MLP), and thus proposes an efficient COVID-19 detection system. The proposed system produces a practical solution and demonstrates higher state-of-the-art classification performance with an AUC of 0.843 on the COUGHVID dataset and 0.953 on the Virufy dataset for COVID-19 detection.

关键词： COVID-19 Support vector machines Training Computational modeling System performance Machine learning Feature extraction

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Multi-Dataset Multi-Task Learning for COVID-19 Prognosis

arXiv

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arXiv 2024年

作者： Ruffini, Filippo Tronchin, Lorenzo Wu, Zhuoru Chen, Wenting Soda, Paolo Shen, Linlin Guarrasi, Valerio Unit of Computer Systems & Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Rome Italy College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical Engineering City University of Hong Kong Hong Kong

In the fight against the COVID-19 pandemic, leveraging artificial intelligence to predict disease outcomes from chest radiographic images represents a significant scientific aim. The challenge, however, lies in the scarcity of large, labeled datasets with compatible tasks for training deep learning models without leading to overfitting. Addressing this issue, we introduce a novel multi-dataset multi-task training framework that predicts COVID-19 prognostic outcomes from chest X-rays (CXR) by integrating correlated datasets from disparate sources, distant from conventional multi-task learning approaches, which rely on datasets with multiple and correlated labeling schemes. Our framework hypothesizes that assessing severity scores enhances the model's ability to classify prognostic severity groups, thereby improving its robustness and predictive power. The proposed architecture comprises a deep convolutional network that receives inputs from two publicly available CXR datasets, AIforCOVID for severity prognostic prediction and BRIXIA for severity score assessment, and branches into task-specific fully connected output networks. Moreover, we propose a multi-task loss function, incorporating an indicator function, to exploit multi-dataset integration. The effectiveness and robustness of the proposed approach are demonstrated through significant performance improvements in prognosis classification tasks across 18 different convolutional neural network backbones in different evaluation strategies. This improvement is evident over single-task baselines and standard transfer learning strategies, supported by extensive statistical analysis, showing great application potential. © 2024, CC BY-NC-SA.

关键词： COVID-19

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Multimodal Deep Neural Networks for Digitized Document Classification

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computer systems Science & engineering 2024年第3期48卷 793-811页

作者： Aigerim Baimakhanova Ainur Zhumadillayeva Bigul Mukhametzhanova Natalya Glazyrina Rozamgul Niyazova Nurseit Zhunissov Aizhan Sambetbayeva Department of Computer Engineering Khoja Akhmet Yassawi International Kazakh-Turkish UniversityTurkistanKazakhstan Department of Computer and Software Engineering L.N.Gumilyov Eurasian National UniversityAstanaKazakhstan Department of Information and Computing Systems Non-Profit Joint Stock Company Abylkas Saginov Karaganda Technical UniversityKaragandaKazakhstan Department of Information Systems Al-Farabi Kazakh National UniversityAlmatyKazakhstan

As digital technologies have advanced more rapidly,the number of paper documents recently converted into a digital format has exponentially *** respond to the urgent need to categorize the growing number of digitized documents,the classification of digitized documents in real time has been identified as the primary goal of our study.A paper classification is the first stage in automating document control and efficient knowledge discovery with no or little human *** intelligence methods such as Deep Learning are now combined with segmentation to study and interpret those traits,which were not conceivable ten years *** learning aids in comprehending input patterns so that object classes may be *** segmentation process divides the input image into separate segments for a more thorough image *** study proposes a deep learning-enabled framework for automated document classification,which can be implemented in higher *** further this goal,a dataset was developed that includes seven categories:Diplomas,Personal documents,Journal of Accounting of higher education diplomas,Service letters,Orders,Production orders,and Student ***,a deep learning model based on Conv2D layers is proposed for the document classification *** the final part of this research,the proposed model is evaluated and compared with other machine-learning *** results demonstrate that the proposed deep learning model shows high results in document categorization overtaking the other machine learning models by reaching 94.84%,94.79%,94.62%,94.43%,94.07%in accuracy,precision,recall,F-score,and AUC-ROC,*** achieved results prove that the proposed deep model is acceptable to use in practice as an assistant to an office worker.

关键词： Document categorization deep learning machine learning classification digitization

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Energy Optimised Security against Wormhole Attack in IoT-Based Wireless Sensor Networks

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computers, Materials & Continua 2021年第8期68卷 1967-1981页

作者： Hafsa Shahid Humaira Ashraf Hafsa Javed Mamoona Humayun Nz Jhanjhi Mohammed A.AlZain Department of Computer Science and Software Engineering International Islamic University IslamabadIslamabadPakistan Department of Information Systems College of Computer and Information ScienceJouf UniversityAl-JoufSaudi Arabia School of Computer Science and Engineering(SCE) Taylor’s UniversitySelangorMalaysia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

An IoT-based wireless sensor network(WSN)comprises many small sensors to collect the data and share it with the central *** sensors are battery-driven and resource-restrained devices that consume most of the energy in sensing or collecting the data and transmitting *** data sharing,security is an important concern in such networks as they are prone to many threats,of which the deadliest is the wormhole *** attacks are launched without acquiring the vital information of the network and they highly compromise the communication,security,and performance of the *** the IoT-based network environment,its mitigation becomes more challenging because of the low resource availability in the sensing *** have performed an extensive literature study of the existing techniques against the wormhole attack and categorised them according to their *** analysis of literature has motivated our *** this paper,we developed the ESWI technique for detecting the wormhole attack while improving the performance and *** algorithm has been designed to be simple and less complicated to avoid the overheads and the drainage of energy in its *** simulation results of our technique show competitive results for the detection rate and packet delivery *** also gives an increased throughput,a decreased end-to-end delay,and a much-reduced consumption of energy.

关键词： IoT Internet of Things energy wormhole WSN wireless sensor networks

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