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Iraqi Journal for computer science and Mathematics 2024年第3期5卷 552-582页

作者： Ali, Ali Mahmoud Mohammed, Mazin Abed Informatics Institute for Postgraduate Studies Iraqi Commission for Computers & Informatics Baghdad Iraq Department of Artificial Intelligence College of Computer Science and Information Technology University of Anbar Anbar31001 Iraq

Integrating multiple omics data can significantly improve the accuracy of cancer subclassification, a challenging task due to the high dimensionality and limited sample sizes. The integration of these data sets can enhance model performance. This study addresses these challenges by employing Quantum Cat Swarm Optimization (QCSO) for feature selection, along with K-means clustering and Support Vector Machine (SVM) for classification. Using QCSO, the most significant features were identified, resulting in an increase in accuracy from 81% to 100%. Performance was evaluated using accuracy, F1-score, precision, recall, ROC, and the silhouette metric, all of which confirmed the effectiveness of the feature selection approach. Additionally, this method enhances the classification of samples while making the models more interpretable, providing better insights into the molecular mechanisms of cancer. This work contributes to advancing knowledge in the field of cancer research and biology in general. © 2024 College of Education, Al-Iraqia University. All rights reserved.

关键词： Support vector machines

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A Fault-prone Module Prediction Combining Past and Ongoing Test Results

Computer Software

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computer Software 2024年第4期41卷 67-73页

作者： Hirose, Ryoichi Monden, Akito Nishiura, Kinari Tsunoda, Masateru Graduate School of Environmental Life Natural Science and Technology Okayama University Japan Faculty of Information and Human Sciences Kyoto Institute of Technology Japan Dept. of Informatics Faculty of Engineering and Science Kinadai University Japan

The aim of this study is to improve the efficiency of software testing and/or review by predicting modules that are likely to contain a bug. In cross-version bug prediction, which uses bug data from a previous version to predict bugs in software currently under development, it is difficult to achieve high prediction accuracy due to differences in bug factors between versions. In this research, three methods (test method, mix method, select method) are proposed to address this challenge by incorporating ongoing test results, in addition to bug data from past versions. An evaluation experiment targeted 13 open-source software projects, comparing conventional cross-version prediction with the three proposed methods using seven evaluation metrics. The result indicates that each proposed method achieves higher accuracy in bug prediction than the conventional cross-version prediction when utilizing test results for approximately 10% of the modules in the developing software. Particularly, the select method demonstrated superior performance in comparison. © 2024 Japan Society for Software science and technology. All rights reserved.

关键词： Software testing

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Cybernet Model:A New Deep Learning Model for Cyber DDoS Attacks Detection and Recognition

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computers, Materials & Continua 2024年第1期78卷 1275-1295页

作者： Azar Abid Salih Maiwan Bahjat Abdulrazaq Department of Information Technology Technical College of DuhokDuhok Polytechnic UniversityDuhokIraq Department of Computer Science Faculty of ScienceUniversity of ZakhoDuhokIraq

Cyberspace is extremely dynamic,with new attacks arising *** cybersecurity controls is vital for network *** Learning(DL)models find widespread use across various fields,with cybersecurity being one of the most crucial due to their rapid cyberattack detection capabilities on networks and *** capabilities of DL in feature learning and analyzing extensive data volumes lead to the recognition of network traffic *** study presents novel lightweight DL models,known as Cybernet models,for the detection and recognition of various cyber Distributed Denial of Service(DDoS)*** models were constructed to have a reasonable number of learnable parameters,i.e.,less than 225,000,hence the name“lightweight.”This not only helps reduce the number of computations required but also results in faster training and inference ***,these models were designed to extract features in parallel from 1D Convolutional Neural Networks(CNN)and Long Short-Term Memory(LSTM),which makes them unique compared to earlier existing architectures and results in better performance *** validate their robustness and effectiveness,they were tested on the CIC-DDoS2019 dataset,which is an imbalanced and large dataset that contains different types of DDoS *** results revealed that bothmodels yielded promising results,with 99.99% for the detectionmodel and 99.76% for the recognition model in terms of accuracy,precision,recall,and F1 ***,they outperformed the existing state-of-the-art models proposed for the same ***,the proposed models can be used in cyber security research domains to successfully identify different types of attacks with a high detection and recognition rate.

关键词： Deep learning CNN LSTM Cybernet model DDoS recognition

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Modified CNN Model for Classifying Gender of Thermal Images Using Cloud Computing

Informatica (Slovenia)

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Informatica (Slovenia) 2023年第10期47卷 123-128页

作者： Jalil, Alyaa J. El-Seidy, Essam Daoud, Sameh S. Reda, Naglaa M. Department of Computer Science Faculty of Computer Science and Information Technology Basrah University Basrah Iraq Department of Mathematics Faculty of Science Ain Shams University Cairo Egypt

The utilization of thermal images has become widespread in various applications, particularly for thermal examination and night surveillance systems. Although many details associated with thermal imaging are virtually obscured or unclear, thermal imaging offers numerous benefits, including the ability to determine an individual's gender even in cases where human vision is impaired. Based on thermal faces, a convolutional neural network (CNN) has been developed to distinguish between people's genders. Since cloud computing offers a suitable setting and gained attraction, its concept has been adopted. Thermal databases were used for the experiments. The suggested model provides an overall accuracy of 99% and a gender categorization precision of 97.75%. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Cloud computing

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Text Line Segmentation on Myanmar Handwritten Documents Using Directional Gaussian Filter 21

Text Line Segmentation on Myanmar Handwritten Documents Usin...

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21st IEEE International Conference on computer Applications, ICCA 2024

作者： Wai, Nilar Phyo War, Nu University of Computer Studies Faculty of Computer Science Mandalay Myanmar Myanmar Institute of Information Technology Faculty of Information Technology Mandalay Myanmar

ISBN: (纸本)9789998601673

Text line segmentation is a crucial step in document processing. Although text line segmentation techniques for handwritten documents in other languages (English, Chinese, Arabic) have developed, there is still room for improving text line segmentation in Myanmar handwritten documents. It is a challenging task to segment text lines in handwritten documents due to variations in handwriting styles, irregular spacing between lines and characters, and touching characters between lines. A method for Myanmar handwritten documents proposes to overcome these problems. The proposed system extracts bounding boxes of characters from text images by applying an anisotropic Gaussian method and connected component analysis (CCA). To extract correcting text lines by touching characters between lines, text lines are extracted by using proposed algorithm based on adaptive blob scale. To evaluate the proposed method, Myanmar handwritten documents dataset is collected from undergraduate students and high school students. According to the experimental results, the proposed technique achieves 97.5% accuracy rate with correct lines. © 2024 IEEE.

关键词： Gaussian distribution

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Code switching: exploring perplexity and coherence metrics for optimizing topic models of historical documents

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2024年第4期8卷 103-118页

作者： Yusof, Muhammad Abdullah Saee, Suhaila Faculty of Computer Science and Information Technology University Malaysia Sarawak Sarawak Malaysia

The Latent Dirichlet Allocation (LDA) model has two important hyperparameters that control the document-topic distribution known as alpha (α), and topic-word distribution known as beta (β). It is important to find the suitable values for both hyperparameters to achieve an accurate topic cluster. Using a single evaluation method to determine the optimal hyperparameters values is insufficient due to the size and complexity of the dataset. Thus, an experiment was conducted to study the relationship between the hyperparameters with perplexity, coherence scores and to establish a baseline for further topic modelling studies. It is the first study that focuses on multiple languages in Sarawak Gazette data for topic modelling. The study was conducted on LDA using Gensim package. The result shows that while perplexity scores were good indicator of the model’s ability to predict new or hidden data, the word cluster within topic does not always reflect the similarity or relationships between words which compromised topic interpretation. The lowest perplexity score was observed when α was set to 5 and β to 0.4. The coherence evaluation indicated the optimal number of topics for each set of hyperparameter values although the relationship with hidden words remains unclear. The coherence score is highest when the number of topics was 5 and 4. In conclusion, the perplexity scores are effective indicators of word prediction accuracy for each hyperparameter setting. While coherence captures the optimal number of topics needed to produce high-coherence word cluster within a topic. Combining both evaluation methods ensures optimal results, producing topics that are both accurate and interpretable. © (2024), (Society of Sytematic Innovation). All rights reserved.

关键词： Hyperparameter Latent Dirichlet Allocation perplexity topic coherence topic modelling

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Lung Cancer Detection Using Modified AlexNet Architecture and Support Vector Machine

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computers, Materials & Continua 2023年第1期74卷 2039-2054页

作者： Iftikhar Naseer Tehreem Masood Sheeraz Akram Arfan Jaffar Muhammad Rashid Muhammad Amjad Iqbal Faculty of Computer Science and Information Technology The Superior UniversityLahore54000Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Faculty of IT University of Central PunjabLahore54100Pakistan

Lung cancer is the most dangerous and death-causing disease indicated by the presence of pulmonary nodules in the *** is mostly caused by the instinctive growth of cells in the *** nodule detection has a significant role in detecting and screening lung cancer in Computed tomography(CT)scan *** detection plays an important role in the survival rate and treatment of lung cancer ***,pulmonary nodule classification techniques based on the convolutional neural network can be used for the accurate and efficient detection of lung *** work proposed an automatic nodule detection method in CT images based on modified AlexNet architecture and Support vector machine(SVM)algorithm namely *** proposed model consists of seven convolutional layers,three pooling layers,and two fully connected layers used to extract *** vector machine classifier is applied for the binary classification of nodules into benign *** experimental analysis is performed by using the publicly available benchmark dataset Lung nodule analysis 2016(LUNA16).The proposed model has achieved 97.64%of accuracy,96.37%of sensitivity,and 99.08%of specificity.A comparative analysis has been carried out between the proposed LungNet-SVM model and existing stateof-the-art approaches for the classification of lung *** experimental results indicate that the proposed LungNet-SVM model achieved remarkable performance on a LUNA16 dataset in terms of accuracy.

关键词： Lung cancer alexnet luna16 computed tomography support vector machine

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Robust and resourceful automobile insurance fraud detection with multi-stacked LSTM network and adaptive synthetic oversampling

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International Journal of Applied Decision sciences 2024年第2期17卷 230-249页

作者： Nti, Isaac Kofi Adu, Kwabena Nimbe, Peter Nyarko-Boateng, Owusu Adekoya, Adebayo Felix Appiahene, Peter Department of Computer Science and Informatics University of Energy and Natural Resources Sunyani Ghana School of Information Technology University of Cincinnati OH United States

Insurance companies worldwide are concerned about financial losses due to false claims. Automobile insurance fraud (AIF) has become more sophisticated, causing the yearly loss of trillions of dollars. AIF is tough to establish, and acquiring a thorough knowledge of the problem is complex. Also, AIF investigators have relied on manual claims inspection, proving costly, inefficient, and time-consuming. This paper proposed a robust and resourceful approach to AIF detection with multi-stacked LSTM (MSLSTM) reinforced with the adaptive synthetic (ADASYN) sampling algorithm for imbalanced learning. We experiment with the proposed model with a publicly available AIF dataset from Kaggle. Using accuracy, recall, precision, F1-score, and AUC, we compared the performance of our proposed MSLSTM model with well-known machine learning algorithms and previous AIF detection works. Our results showed a fair performance (accuracy = 95%, precision = 94%, AUC = 97% and F1-score = 92%) of the MSLSTM model than other algorithms and works. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Losses

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Drone-Based Public Surveillance Using 3D Point Clouds and Neuro-Fuzzy Classifier

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computers, Materials & Continua 2025年第3期82卷 4759-4776页

作者： Yawar Abbas Aisha Ahmed Alarfaj Ebtisam Abdullah Alabdulqader Asaad Algarni Ahmad Jalal Hui Liu Faculty of Computing and AI Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Technology College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Science and Engineering College of InformaticsKorea UniversitySeoul02841Republic of Korea Cognitive Systems Lab University of BremenBremen28359Germany

Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot ***,recognizing actions from such videos poses the following challenges:variations of human motion,the complexity of backdrops,motion blurs,occlusions,and restricted camera *** research presents a human activity recognition system to address these challenges by working with drones’red-green-blue(RGB)*** first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while reducing background interference before converting from RGB to grayscale *** YOLO(You Only Look Once)algorithm detects and extracts humans from each frame,obtaining their skeletons for further *** joint angles,displacement and velocity,histogram of oriented gradients(HOG),3D points,and geodesic Distance are *** features are optimized using Quadratic Discriminant Analysis(QDA)and utilized in a Neuro-Fuzzy Classifier(NFC)for activity ***-world evaluations on the Drone-Action,Unmanned Aerial Vehicle(UAV)-Gesture,and Okutama-Action datasets substantiate the proposed system’s superiority in accuracy rates over existing *** particular,the system obtains recognition rates of 93%for drone action,97%for UAV gestures,and 81%for Okutama-action,demonstrating the system’s reliability and ability to learn human activity from drone videos.

关键词： Activity recognition geodesic distance pattern recognition neuro fuzzy classifier

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A Framework for Democratizing Open-Source Decision-Making using Decentralized Autonomous Organization 6

A Framework for Democratizing Open-Source Decision-Making us...

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6th International Conference on Computing and informatics, ICCI 2024

作者： Afifi, Khalifa H. Fouad, Khaled M. Asian, Heba K. Faculty of Information Technology and Computer Science Nile University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Egypt Faculty of Computers and Engineering New Mansoura University Egypt Cairo Egypt Center of Informatics Science Faculty of Information Technology and Computer Science Nile University Cairo Egypt

ISBN: (纸本)9798350373875

The open Source Software (OSS) became the backbone of the most heavily used technologies, including operating systems, cloud computing, AI, Blockchain, Bigdata Systems, IoT, and many more. Although the OSS individual contributors are the primary power for developing the OSS projects, they do not contribute to the OSS project's decisionmaking as much as their contributions in the OSS Projects development. This paper proposes a framework to democratize the OSS Project's decision-making using a blockchain-related technology called Decentralized Autonomous Organization (DAO). Using DAO, contributors get incentive tokens as a reward in return for contributions. The earned tokens can be used in the process of decision-making governance. This governance model tends to allow OSS individual contributors to have a public voice in the OSS project's development roadmap in particular, and the OSS decision making governance in general. © 2024 IEEE.

关键词： Open source software

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