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Enhancing Software Cost Estimation Using Feature Selection and Machine Learning Techniques

Computers, Materials & Continua 2024年第12期81卷 4603-4624页

作者： Fizza Mansoor Muhammad Affan Alim Muhammad Taha Jilani Muhammad Monsoor Alam Mazliham Mohd Su’ud Department of Software Engineering FAST-National University of Computer&Emerging SciencesKarachi75030Pakistan Faculty of Engineering Science and Technology IQRA UniversityKarachi72500Pakistan Department of Computer Science Bahria UnivesityKarachi74800Pakistan Faculty of Computing Riphah International UniversityIslamabad44600Pakistan Faculty of Computing and Informatics Multimedia University(MMU)Cyberjaya63100SelangorMalaysia

Software cost estimation is a crucial aspect of software project management,significantly impacting productivity and *** research investigates the impact of various feature selection techniques on software cost estimation accuracy using the CoCoMo NASA dataset,which comprises data from 93 unique software projects with 24 *** applying multiple machine learning algorithms alongside three feature selection methods,this study aims to reduce data redundancy and enhance model *** findings reveal that the principal component analysis(PCA)-based feature selection technique achieved the highest performance,underscoring the importance of optimal feature selection in improving software cost estimation *** is demonstrated that our proposed method outperforms the existing method while achieving the highest precision,accuracy,and recall rates.

关键词： Machine learning software cost estimation PCA hyper parameter feature selection

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Classifying Stunting Status in Toddlers Using K-Nearest Neighbor and Logistic Regression Analysis 7

Classifying Stunting Status in Toddlers Using K-Nearest Neig...

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7th International Conference on Data Science and Its Applications, ICoDSA 2024

作者： Armando Sibuea, Alvin Tolopan Harry Gunawan, Putu Indwiarti Telkom University Faculty of Informatics Bandung Indonesia School of Computing Telkom University CoE Humic Bandung Indonesia

ISBN: (纸本)9798350365351

Stunting is a critical child growth disorder, characterized by a height below the norm for one's age group. Despite a notable decrease in stunting prevalence in Indonesia from 37% in 2014 to 21.6% in 2022, achieving the targeted reduction to 14% by 2024 remains imperative. This study contributes to this national health goal by developing a robust predictive model for stunting in toddlers using machine learning. The research employs two models, K-Nearest Neighbors (KNN) and Logistic Regression. The dataset used for this research shows a big gap of data imbalance, for the majority of its data is significantly higher than the minority. Both of the chosen method is focusing on mitigating data imbalance through oversampling and undersampling techniques. The KNN model is particularly suited for this study due to its effectiveness in handling the complex, non-linear patterns often found in multifaceted health data like stunting indicators. It consistently demonstrated high accuracy, averaging 0.980, and reaching 0.987 for F1-Score. Logistic Regression, chosen for its ability to provide clear interpretability, especially useful in understanding the impact of various health indicators, also performed well with an average accuracy of 0.877 and an F1-Score of 0.894. This study highlights the significance of machine learning in addressing child stunting, providing effective tools for prediction. The combination of KNN's ability to handle complex data and Logistic Regression's interpretability, along with data balancing, contributes to the goal of reducing stunting prevalence. In summary, this research tackles child stunting, a pressing issue in Indonesia. By leveraging machine learning techniques, it develops predictive models to aid in stunting prevention. KNN excels in capturing complex patterns, while Logistic Regression offers interpretability. These models offer promise in reaching the vital goal of reducing stunting to 14% by 2024. © 2024 IEEE.

关键词： Logistic regression

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Revisit the COVID-19 Pandemic as a Catalyst Towards Digital Transformation in Malaysia Higher Education Institutions 8

Revisit the COVID-19 Pandemic as a Catalyst Towards Digital ...

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8th IEEE International Conference on Recent Advances and Innovations in Engineering, ICRAIE 2023

作者： Abbas, Wan Faezah Naz'Ri Mahrin, Mohd Maarop, Nurazean College of Computing Informatics and Mathematics Selangor Shah Alam Malaysia Razak Faculty of Technology and Informatics Kuala Lumpur Malaysia

ISBN: (纸本)9798350315516

The emergence of the COVID-19 pandemic resulted in an unprecedented disruption to global higher education systems. This led to a sudden shift towards online learning, which heavily relies on digital technologies. There existed a pressing necessity to adapt, automate and digitalize the organizational process. The objective of this study is to clarify the impact and opportunities that resulted from the COVID-19 pandemic towards Digital Transformation (DT) in Malaysia Higher Education Institutions (HEIs). This study adopted a qualitative methodology, and the data acquired was analyzed using thematic analysis. This paper presents the outcomes of semi-structured interviews with IT personnel from Malaysian HEIs during the pandemic to learn about their challenges and how the pandemic affected DT efforts. DT post-pandemic impact on HEIs were clarified by research studies. This study found four primary impact themes: cultural change, IT infrastructure development, policy refinement, and digital skill resulted from the COVID-19 pandemic in expediting DT process in Malaysia HEIs. © 2023 IEEE.

关键词： COVID-19

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Reinforcement Learning with Deep Features: A Dynamic Approach for Intrusion Detection in IOT Networks

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IEEE Access 2025年 13卷 92319-92337页

作者： Khayat, Mohamad Barka, Ezedin Serhani, Mohamed Adel Sallabi, Farag Shuaib, Khaled Khater, Heba M. United Arab Emirates University College of Information Technology Al Ain United Arab Emirates Zagazig University Faculty of Computers and Informatics Zagazig44519 Egypt University of Sharjah College of Computing and Informatics Sharjah United Arab Emirates

Intrusion detection in Internet of Things (IoT) networks is essential to identify and mitigate security breaches and unauthorized access to connected devices. As IoT devices continue to advance, securing interconnected systems against malicious attacks is essential to ensure data privacy, system integrity, and user safety. However, traditional intrusion detection systems (IDSs) often struggle to adapt to novel and evolving threats. Developing a system that can autonomously learn and enhance intrusion detection capabilities to effectively identify and mitigate emerging threats is challenging. To address this issue, we propose a reinforcement learning (RL)-based approach for enhancing cybersecurity in IoT networks. First, various preprocessing techniques such as handling missing values, outliers, and min-max scaling normalization were applied. Then, domain-specific features related to network traffic patterns, including packet size distribution, packet count, and packet rate, were extracted. Statistical measures, including mean, variance, and entropy, were calculated from these features to determine temporal and spatial variations in the network data. Moreover, deep learning models, including long short-term memory networks and convolutional neural networks (CNNs), were used to automatically extract high-level features from raw data such as network logs or sensor readings. The extracted feature sets were concatenated within a feature fusion layer that enabled efficient dimensionality reduction via principal component analysis. A hybrid optimization approach was also introduced for feature selection using green anaconda optimization and the chaotic learning osprey optimization algorithm. Furthermore, an RL-based IDS (RL-IDS) that incorporates RNNs and autoencoders with a deep Q-network was proposed to enhance threat detection. Experimental outcomes emphasize the superiority of the method, obtaining 99.45% accuracy with 70% training data and 99.80% with 80%. Precision a

关键词： Network intrusion

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Infodeslib: Python Library for Dynamic Ensemble Learning using Late Fusion of Multimodal Data 4

Infodeslib: Python Library for Dynamic Ensemble Learning usi...

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4th International Workshop on Knowledge-Infused Learning, KiL 2024

作者： Juraev, Firuz El-Sappagh, Shaker Abuhmed, Tamer College of Computing and Informatics Sungkyunkwan University Korea Republic of Faculty of Computer Science and Engineering Galala University Egypt

There has been a notable increase in research focusing on dynamic selection (DS) techniques within the field of ensemble learning. This leads to the development of various techniques for ensembling multiple classifiers for a specific instance or set of instances during the prediction phase. Despite this progress, the design and development of DS approaches with late fusion settings and their explainability remain unexplored. This work proposes an open-source Python library, Infodeslib, to address this gap. The library provides an implementation of several DS techniques, including four dynamic classifier selections and seven dynamic ensemble selection techniques, all of which are integrated with late data fusion settings and novel explainability features. Infodeslib offers flexibility and customization options, making it a versatile tool for various complex applications that require the fusion of multimodal data and various explainability features. Multimodal data, which integrates information from diverse sources or sensor modalities, is a common and essential setting for real-world problems, enhancing the robustness and depth of data analysis. These data can be fused in two main ways: early fusion, where different modalities are combined at the feature level before model training, and late fusion, where each modality is processed separately and the results are combined at the decision level. The library is fully documented following the Read the Docs standards. The documentation, code, and examples are available anonymously on GitHub at https://***/InfoLab-SKKU/infodeslib. © 2024 Copyright for this paper by its authors.

关键词： Python

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Efficient Power Control for UAV Based on Trajectory and Game Theory

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Computers, Materials & Continua 2023年第3期74卷 5589-5606页

作者： Fadhil Mukhlif Ashraf Osman Ibrahim Norafida Ithnin Roobaea Alroobaea Majed Alsafyani Information Assurance and Security Research Group(IASRG) School of ComputingFaculty of EngineeringUniversiti Teknologi MalaysiaJohorMalaysia Faculty of Computing and Informatics University Malaysia SabahKota KinabaluMalaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia

Due to the fact that network space is becoming more limited,the implementation of ultra-dense networks(UDNs)has the potential to enhance not only network coverage but also network *** Aerial Vehicle(UAV)communications have recently garnered a lot of attention due to the fact that they are extremely versatile and may be applied to a wide variety of contexts and purposes.A cognitive UAV is proposed as a solution for the Internet of Things ground terminal’s wireless nodes in this *** the IoT system,the UAV is utilised not only to determine how the resources should be distributed but also to provide power to the wireless *** quality of service(QoS)offered by the cognitive node was interpreted as a price-based utility function,which was demonstrated in the form of a non-cooperative game theory in order to maximise customers’net utility *** energyefficient non-cooperative game theory power allocation with pricing strategy abbreviated as(EE-NGPAP)is implemented in this study with two trajectories Spiral and Sigmoidal in order to facilitate effective power management in Internet of Things(IoT)wireless *** has also been demonstrated,theoretically and by the use of simulations,that the Nash equilibrium does exist and that it is one of a *** proposed energy harvesting approach was shown,through simulations,to significantly reduce the typical amount of power thatwas *** is taken into consideration to agree with the objective of 5G *** order to converge to Nash Equilibrium(NE),the method that is advised only needs roughly 4 iterations,which makes it easier to utilise in the real world,where things aren’t always the same.

关键词： UAV spiral&sigmoid trajectory drones IoT game theory energy efficiency 6G

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SYSTEMATIC REVIEW: OPPORTUNITIES AND CHALLENGES OF MACHINE LEARNING TECHNIQUES FOR CARDIOVASCULAR DISEASE PREDICTION

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Xinan Jiaotong Daxue Xuebao/Journal of Southwest Jiaotong University 2024年第2期59卷 97-127页

作者： Qtaishat, Ahmed Wan Awang, Wan Suryani General Foundation Program Department of Information Technology Sohar University Sohar Oman Faculty of Informatics and Computing University Sultan Zainal Abdlidin Besut Campus Besut Terengganu 22200 Malaysia

Access to healthcare is a fundamental pillar of human well-being, yet cardiovascular diseases (CVD) persist as leading contributors to global mortality. This study explores the transformative potential of Machine Learning (ML) and Deep Learning (DL) algorithms in predicting a spectrum of CVDs, encompassing Heart Failure (HF), Arrhythmia, and coronary artery disease (CAD). With a focus on HF identification and associated risk factors, our primary aim is to rigorously evaluate the efficacy of diverse ML and DL models for CVD diagnosis. Through meticulous analysis of varied datasets, we examine different ML and DL methodologies to determine their predictive capabilities in the realm of CVD. Our findings illuminate promising advancements in CVD prediction accuracy, particularly in heart failure identification. Moreover, we elucidated the intricate interplay between cardiac and pulmonary functions in the context of heart disease, shedding light on disease mechanisms and novel diagnostic avenues. The novelty of our research lies in its comprehensive evaluation of ML and DL algorithms across heterogeneous datasets, fostering the refinement of CVD prediction strategies. By elucidating the effectiveness of various approaches, our study offers invaluable insights for healthcare practitioners and researchers striving to optimize CVD diagnosis and management. Ultimately, the integration of advanced computational techniques holds immense promise for bolstering cardiovascular healthcare outcomes and mitigating the global burden of CVD. © 2024 by the authors.

关键词： Forecasting

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Enhancing intrusion detection in IIoT: optimized CNN model with multi-class SMOTE balancing

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Neural computing and Applications 2024年第24期36卷 14643-14659页

作者： Eid, Abdulrahman Mahmoud Soudan, Bassel Nassif, Ali Bou Injadat, MohammadNoor Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Data Science and AI Faculty of Information Technology Zarqa University Zarqa Jordan

This work introduces an intrusion detection system (IDS) tailored for industrial internet of things (IIoT) environments based on an optimized convolutional neural network (CNN) model. The model is trained on a dataset that was balanced using a novel multi-class implementation of synthetic minority over-sampling technique (SMOTE) that ensures equal representation of all classes. Additionally, systematic optimization will be used to fine tune the hyperparameters of the CNN model and mitigate the effects of the increased size of the training dataset. Evaluation results will demonstrate substantial improvement in performance when the optimized CNN model is trained on the balanced dataset. The proposed IDS will be evaluated using the IIoT-specific WUSTL-IIOT-2021 dataset, and then its generalization capability will be verified using the non-domain specific UNSW_NB15 dataset. The model’s performance will be evaluated using accuracy, precision, recall, and F1-score metrics. The results will demonstrate that the proposed IDS is highly effective with performance exceeding 99.9% on all performance metrics. The IDS is also highly effective in detecting intrusion for generic IT networks achieving improvements in excess of 30% compared to the default baseline model. The results emphasize the versatility and effectiveness of the proposed IDS model, making it a reliable and adaptable solution for enhancing network security across diverse network environments. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Neural network models

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M3-Net: A Multi-Scale Nuclei Segmentation Model for Breast Cancer Histopathology Using Contextual Patches and Attention Mechanism 22

M3-Net: A Multi-Scale Nuclei Segmentation Model for Breast C...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Sufyan, Arbab Fauzi, Mohammad Faizal Ahmad Kuan, Wong Lai Multimedia University Faculty of Engineering Cyberjaya Malaysia Multimedia University Faculty of Computing & Informatics Cyberjaya Malaysia

ISBN: (纸本)9798331520526

Histopathological analysis, particularly of nuclear morphology, is critical for identifying malignancies. Accurate nuclei segmentation plays a pivotal role in this process, as it enables detailed assessment of nuclear size, shape, and distribution patterns. Traditional segmentation methods, however, often fail to capture fine details, lack broader context, and struggle with overlapping nuclei of varying sizes and shapes, especially when relying on single-scale approaches. To address these challenges, we propose a multi-scale context-based encoder-decoder model named M3-Net (Multi-Scale, Multi-Level, Multi-Stream Network) that integrates both global and local tissue features. Evaluations demonstrate that M3-Net effectively segments overlapping nuclei and diverse structures, providing a robust solution for automated nuclei segmentation in breast cancer pathology. © 2025 IEEE.

关键词： Image segmentation

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Data Gathered with Automatic Tools from European Parliamentary Chambers 17

Data Gathered with Automatic Tools from European Parliamenta...

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17th Workshop on Recent Advances in Slavonic Natural Language Processing, RASLAN 2023

作者： Mikušek, Ota Faculty of Informatics Masaryk University Czech Republic Lexical Computing Brno Czech Republic

ISBN: (纸本)9788026317937

This paper reflects on the set of tools developed in my bachelor’s thesis, titled"Continuous Automatic Development of European Parliamentary Corpora." Despite the existence of numerous corpora offering speeches from the parliaments of the European Union, the developed toolset is designed to gather and build such corpora with minimal human intervention. With nine months of practical application, this paper presents insights into the faced challenges and their respective solutions, providing an overview since the initial release of the toolset. © Tribun EU 2023.

关键词： automatic maintenance of tools automatic tools continuous downloading corpus development corpus processing parliamentary protocols

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