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SSRN 2023年

作者： Ismail, Ahmad Muhaimin Hamid, Siti Hafizah Ab Sani, Asmiza Abdul Daud, Nur Nasuha Mohd Department of Software Engineering Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia Faculty of Ocean Engineering Technology and Informatics University Malaysia Terengganu Kuala Terengganu Terengganu21030 Malaysia

ContextThe performance of the defect prediction model by using balanced and imbalanced datasets makes a big impact on the discovery of future defects. Current resampling techniques only address the imbalanced datasets without taking into consideration redundancy and noise inherent to the imbalanced *** address the imbalance issue, we propose Kernel Crossover Oversampling (KCO), an oversampling technique based on kernel analysis and cross-over interpolation. Specifically, the proposed technique aims to generate balanced datasets by increasing data diversity in order to reduce redundancy and *** first represents multidimensional features into two-dimensional features by employing Kernel Principal Component Analysis (KPCA). KCO then divides the plotted data distribution by deploying spectral clustering to select the best region for interpolation. Lastly, KCO generates the new defect data by cross-interpolating different data templates within the selected data *** to the prediction evaluation conducted, KCO consistently produced F-scores ranging from 21% to 63% across six datasets, on average. According to the experimental results presented in this study, KCO provides more effective prediction performance than other baseline *** experimental results show that KCO within project and cross project predictions especially consistently achieve higher performance of F-score results. © 2023, The Authors. All rights reserved.

关键词： Interpolation

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A parallel proposal with message passing for the implementation of a Pipeline in the development of the video game SIMON 35

A parallel proposal with message passing for the implementat...

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35th European Modeling and Simulation Symposium, EMSS 2023

作者： Rossainz-López, Mario Cabrera-Hernández, Liosbel Sánchez-Rinza, Bárbara Capel-Tuñon, Manuel Faculty of Computer Science Autonomous University of Puebla Av. San Claudio and 14 Sur Street San Manuel PueblaC.P. 72570 Mexico Software Engineering Department College of Informatics and Telecommunications ETSIIT University of Granada Daniel Saucedo Aranda s/n Granada18071 Spain

ISBN: (纸本)9788885741881

The design and development of the inter-process communication pattern called Pipeline is presented as a proposal of Parallel Object Composition to solve simple way problems that can be solved with this same parallel control structure. A particular class library called JPMI (Java Passing Message Interface) is used for parallel programming with message passing and to implement an original and particular version of the well-known video game called SIMON with the objective, on the one hand, to show the usefulness of this design within Structured Parallel Programming and, on the other hand, that this proposal serves to guarantee good performance in the execution of real time applications. An example of this type of applications is precisely video games. The parallel algorithm implemented as a Composition of Parallel Objects is based on the development and use of a methodology where the algorithmic design represents the parallel control structure common to a given algorithmic technique that can use the pipeline communication pattern, generating a generic and abstract parallel program from which programs that solve specific problems using the same communication pattern can be derived. The implementation of this proposal within structured parallel programming tries to facilitate to the novice programmer in parallelism the reusability, genericity, and uniformity of code abstract enough to be suitable for any problem that can be solved with a pipeline offered implemented with a parallel message passing structure. This particularized proposal for the implementation of the SIMON video game is compared with another using a thread library called boost and ZeroC Ice for remote invocation of distributed objects. The execution times and speedups of both proposals are compared to identify how similar or different they are in their respective performances with training tests using AI modules with sequences of 500000 colors in a cluster of 2 Intel Xeon CPUs of 8 cores each and 2 nodes,

关键词： Parallel programming

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Essential proteins identification based on weak consensus model and neighborhood aggregation centrality

Essential proteins identification based on weak consensus mo...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Hu, Zhipeng Li, Gaoshi Wu, Jingli Luo, Xinlong Liu, Jiafei Peng, Wei Zhu, Xiaoshu Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guilin541004 China Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining & Security Guilin541004 China Guangxi Normal University College of Computer Science and Engineering Guilin541004 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Guilin University of Electronic Science and Technology School of Computer and Information Security & School of Software Engineering Guilin China

ISBN: (纸本)9798350337488

Essential proteins play an essential role in cell survival and replication. Currently, more and more computational methods are developed to identify essential proteins, which overcome the time-consuming, costly and inefficient shortcomings with biological experimental methods. In order to improve the recognition rate, some new methods by fusing multiple features are developed, but they seldom consider the connection among features. After analyzing a large number of methods based on multi-feature fusion, a weak consensus model to fuse features is proposed in this paper. Then, this paper uses the weak consensus model to fuse protein-protein interaction network, gene expression data, and orthologous data, thus proposing a new method, WOL. Then experiments are performed on one *** dataset. The results show that compared with WDC, PeC, ION, JDC, NCCO and E_POC, WOL has a higher recognition rate. © 2023 IEEE.

关键词： Essential proteins orthologous data protein-protein interaction weak consensus model

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Histopathology of Malignant Tissues Using High-Resolution Microscopy @ 630nm

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Lasers in engineering 2025年第4-6期59卷 295-308页

作者： Amjad, M. Ullah, H. Ullah, S. Batool, Z. Nazir, A. Gilanie, G. Habib, S. Abdullah-Al-Wadud, M. Dad, M. Umar Biophotonics Imaging Techniques Laboratory Department of Physics The Islamia University of Bahawalpur Baghdad-ul Jadeed Campus Bahawalpur Pakistan Department of Histopathology Nishtar Medical University Multan Pakistan Department of Artificial Intelligence Faculty of Computing The Islamia University of Bahawalpur Bahawalpur Pakistan Institute of Chemistry The Islamia University of Bahawalpur 63100 Pakistan Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia School of Physics and Electronic Science East China Normal University Shanghai200062 China

Tissue samples (10 samples of basal cell carcinoma (BCC) of nose, 10 specimens of adenocarcinoma prostate, 12 of ovary, 12 of renal cell carcinoma (RCC) of kidney, and 14 samples of breast cancer) were received in paraffin-embedded blocks, stained with H&E and investigated using a high-resolution microscope. The BCC of the skin showed tumor nests, necrosis, cytological atypia, and abnormal cell to nuclei ratio, and the nodular type and Breslow thickness is 3.0 cm. For prostate cancer, Gleason’s grade is 4+4 and the obtained score is 8/10. Its grade group is 4. In ovary cancer, the squamous discrepancy is very usual. Some alternates are present like mucin rich and secretory, small cells with scanty cytoplasm settled in fragments, violent tumors, and cystic and solid areas in addition to hemorrhage and necrosis. In RCC, tumor cells have sharply outlined cell boundaries and the cytoplasm ranges from optically clear to deeply granular. Tubular, papillary, and cystic formation and abundant glycogen, abnormal mitotic activity with debris all over the tissue sample is seen. The breast tumor showed a grey-white solid area, with 10 lymph nodes identified in the axilla. The specimen is of the type right mastectomy with axillary clearance and the tumor types are invasive ductal cell carcinoma having grade 2 and lobular carcinoma. Resected margins are free of tumor. Axillary lymph node 3/10 shows metastatic carcinoma. ©2025 Old City Publishing, Inc.

关键词： Tumors

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Exploring the role of project status information in effective code smell detection

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Cluster Computing 2025年第1期28卷 1-18页

作者： Alkharabsheh, Khalid Alawadi, Sadi Crespo, Yania Taboada, José A. Software Engineering Department Prince Abdullah bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Al-Salt19117 Jordan School of Computer Science Blekinge Tekniska Högskola Blekinge Tekniska Högskola Blekinge Karlskrona37179 Sweden Departamento de Informática Escuela de Ingeniería Informática Universidad de Valladolid Paseo de Belén 15 Valladolid47011 Spain Department of Electronics and Computing Universidad de Santiago de Compostela Santiago15782 Spain

Repairing code smells detected in the code or design of the system is one of the activities contributing to increasing the software quality. In this study, we investigate the impact of non-numerical information of software, such as project status information combined with machine learning techniques, on improving code smell detection. For this purpose, we constructed a dataset consisting of 22 systems with various project statuses, 12,040 classes, and 18 features that included 1935 large classes. A set of experiments was conducted with ten different machine learning techniques by dividing the dataset into training, validation, and testing sets to detect the large class code smell. Feature selection and data balancing techniques have been applied. The classifier’s performance was evaluated using six indicators: precision, recall, F-measure, MCC, ROC area, and Kappa tests. The preliminary experimental results reveal that feature selection and data balancing have poor influence on the accuracy of machine learning classifiers. Moreover, they vary their behavior when utilized in sets with different values for the selected project status information of their classes. The average value of classifiers performance when fed with status information is better than without. The Random Forest achieved the best behavior according to all performance indicators (100%) with status information, while AdaBoostM1 and SMO achieved the worst in most of them (> 86%). According to the findings of this study, providing machine learning techniques with project status information about the classes to be analyzed can improve the results of large class detection. © The Author(s) 2024.

关键词： software quality

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Fidelity-Preserved Reversible Data Hiding in JPEG Images Based on Adaptive Position Selection 8th

Fidelity-Preserved Reversible Data Hiding in JPEG Images Bas...

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8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： Yue, Zhen Ren, Hua Li, Ming Liang, Cun-liang Hai, Ben-zhai Li, Rui-ping Faculty of Education Henan Normal University Henan Xinxiang453007 China Beijing Key Lab of Intelligent Telecommunication Software and Multimedia School of Computer Beijing University of Posts and Telecommunications Beijing100876 China College of Computer and Information Engineering Henan Normal University Xinxiang453007 China

ISBN: (纸本)9783031067662

Reversible data hiding (RDH) in JPEG images has gained considerable attention from scholars in recent years. However, it is not easy for JPEG images to exploit the pixel redundancy for RDH design like uncompressed images. In this paper, we propose a fidelity-preserved RDH in JPEG images. The proposed scheme embeds the secret data using the histogram shifting method after considering different distortion costs of non-zero AC coefficients at different positions. Accord-ing to the block entropy obtained by summing the squares of all pixels of each 8 × 8 block, the quantified DCT blocks are divided into the smoothed blocks and texture blocks, and the obtained smoothed blocks are sorted in ascending order. The AC coefficients with values 1 and/or −1 in the smoother blocks are used for the embedding, the non-zero AC coefficients are shifted toward the right or the left at most by one, and the others remain invariant. Experiment results have verified the effectiveness of the proposed method. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Pixels

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Brain Tumor Auto-Segmentation on Multimodal Imaging Modalities Using Deep Neural Network

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computers, Materials & Continua 2022年第9期72卷 4509-4523页

作者： Elias Hossain Md.Shazzad Hossain Md.Selim Hossain Sabila Al Jannat Moontahina Huda Sameer Alsharif Osama S.Faragallah Mahmoud M.A.Eid Ahmed Nabih Zaki Rashed Department of Software Engineering Daffodil International UniversityDhaka 1207Bangladesh Department of Computing and Information System Daffodil International UniversityDhaka 1207Bangladesh Department of Computer Science&Engineering BRAC UniversityDhaka 1212Bangladesh Department of Information and Communication Engineering Bangladesh University of Professionals(BUP)Dhaka 1216Bangladesh Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Electrical Engineering College of EngineeringTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Electronics and Electrical Communications Engineering Department Faculty of Electronic EngineeringMenouf 32951Egypt

Due to the difficulties of brain tumor segmentation, this paper proposes a strategy for extracting brain tumors from three-dimensional MagneticResonance Image (MRI) and Computed Tomography (CT) scans utilizing3D U-Net Design and ResNet50, taken after by conventional classificationstrategies. In this inquire, the ResNet50 picked up accuracy with 98.96%, andthe 3D U-Net scored 97.99% among the different methods of deep *** is to be mentioned that traditional Convolutional Neural Network (CNN)gives 97.90% accuracy on top of the 3D MRI. In expansion, the imagefusion approach combines the multimodal images and makes a fused image toextricate more highlights from the medical images. Other than that, we haveidentified the loss function by utilizing several dice measurements approachand received Dice Result on top of a specific test case. The average mean scoreof dice coefficient and soft dice loss for three test cases was 0.0980. At thesame time, for two test cases, the sensitivity and specification were recordedto be 0.0211 and 0.5867 using patch level predictions. On the other hand,a software integration pipeline was integrated to deploy the concentratedmodel into the webserver for accessing it from the software system using theRepresentational state transfer (REST) API. Eventually, the suggested modelswere validated through the Area Under the Curve–Receiver CharacteristicOperator (AUC–ROC) curve and Confusion Matrix and compared with theexisting research articles to understand the underlying problem. ThroughComparative Analysis, we have extracted meaningful insights regarding braintumour segmentation and figured out potential gaps. Nevertheless, the proposed model can be adjustable in daily life and the healthcare domain to identify the infected regions and cancer of the brain through various imagingmodalities.

关键词： Brain cancer segmentation 3D U-Net ResNet50 Dice measurement ROC-AUC

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Through the Sci-Fi Lens: Exploring Social Media's Influence on Body Image and Bullying Among Adolescents in Boarding Schools 21

Through the Sci-Fi Lens: Exploring Social Media's Influence ...

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21st International Learning and Technology Conference, L and T 2024

作者： Mustapha, Jawahir Che Ghazali, Muhammad Azmin Mohamed Abdullah, Munaisyah Sulaiman, Nor Intan Saniah Rahman, Hezlin Aryani Abd Universiti Kuala Lumpur Malaysian Institute of Information Technology Informatics & Analytics Section Kuala Lumpur Malaysia Universiti Kuala Lumpur Malaysian Institute of Information Technology Computer Engineering Tech. Section Kuala Lumpur Malaysia Universiti Kuala Lumpur Malaysian Institute of Information Technology Software Engineering Section Kuala Lumpur Malaysia Universiti Utara Malaysia College of Arts and Science School of Quantitative Sciences Kedah Malaysia Universiti Teknologi Mara Centre of Statistical and Decision Science Studies Faculty of Computer and Mathematical Sciences Selangor Malaysia

ISBN: (纸本)9798350393569

This research investigates the complex relationships between social media, body image, and bullying among adolescent boarding school students, with an emphasis on the Malaysian context. Utilizing science fiction as a conceptual lens, the study presents hypothetical future scenarios to investigate how technological advancements could impact these interconnected issues. The paper seeks to provide nuanced insights into the ethical and policy implications of social media use in boarding schools by drawing on key theories such as Social Comparison Theory, Social-Ecological Model and Uses and Gratifications Theory. Through comparative analysis of hypothetical scenarios, this study aims to provide educators, policymakers, and technologists with a comprehensive understanding of the challenges and opportunities at this intersection. The ultimate objective is to contribute to the ongoing discourse concerning adolescent mental health and to propose actionable strategies for mitigating negative outcomes in the unique context of Malaysian boarding schools. © 2024 IEEE.

关键词： Social networking (online)

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A novel three-phase primary-side control topology for high-power IPT system

A novel three-phase primary-side control topology for high-p...

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Wireless Power Week (WPW)

作者： Zhihao He Duleepa Thrimawithana Martin Neuburger Grant Covic Department of Electrical Computer and Software Engineering University of Auckland Auckland New Zealand Faculty of Mobility and Technology Esslingen University of Applied Sciences Goppingen Germany

There is a recognised need for developing multiphase inductive power transmission (IPT) systems to shorten the charging time in the wireless charging field. Researchers have not treated the converter needed to drive the multi-phase coils in much detail. This paper proposes a three-phase integrated boost multilevel converter (TP-IBMC) based on a multi-phase IPT system. There are advantages of multi-level converters and the boost function in TP-IBMC. Using a digitized modulation scheme, it can output a square wave voltage with variable amplitude to regulate the power in the primary. The TP-IBMC includes three bridge arms (a, b, c), each of which contains a DC inductor to achieve energy storage and multiple sub-modules to realize variable levels. The number of sub-modules can be increased or decreased. This paper establishes a three-phase IPT model considering the mutual inductance between phases and discusses the capacitor voltage balancing strategy. A primary-side control IPT simulation model compliant with the SAE J2954 WPT 3 standard is built to verify that TP-IBMC can regulate output power through a digitised modulation scheme, under the fluctuations of coupling factor (i.e., 0.14 - 0.31) and battery voltage (i.e., 280 V - 420 V).

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Enhancing Dental Caries Identification with Deep Learning: A Study of Convolutional Neural Networks and Transfer Learning Approaches

SSRN

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SSRN 2024年

作者： Mannan, Fayqa Adeel, Muhammad Arif, Erssa Li, Jianqiang Ullah, Amin Nawazish, Samra Mahmood, Tariq Rehman, Amjad Shoukat, Ijaz Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Computer Science Government College University Faisalabad38000 Pakistan Department of Computer Science School of Science National Textile University Faisalabad37610 Pakistan Department of Computer Science Faculty of Computing Riphah International University Faisalabad38000 Pakistan Beijing Engineering Research Center for IoT Software and Systems 100124 China Department of Computer Science Faculty of IT and Computer Science University of Central Punjab Lahore Pakistan School of Computer Science and Engineering Southeast University Nanjing China Software Engineering Department University of Engineering and Technology Taxila Pakistan Artificial Intelligence & Data Analytics Lab CCIS Prince Sultan University Riyadh11586 Saudi Arabia Faculty of information Sciences University of Education Vehari Campus Vehari61100 Pakistan

Various deep learning techniques have been employed to diagnose dental caries using X-ray images. In this study, we utilized deep learning models, including Convolutional Neural Networks (CNNs) and transfer learning models such as Visual Geometry Group (VGG16), VGG19, ResNet50, and Inception V3, to identify dental caries in periapical radiographs. The traditional CNN model comprises three sets of 2D convolutional layers, followed by an activation layer, a max-pooling layer, a flatten layer, one dense layer, another activation layer, dropout, another dense layer, and a final activation layer. For the VGG models, ResNet50, and Inception V3, the top convolutional layers were frozen to prevent retraining of the model, with only the last layers trained to produce the required output. The accuracy achieved by CNN, VGG16, VGG19, ResNet50, and Inception V3 was 90%, 96%, 73%, 70%, and 73%, respectively. VGG16 exhibited the highest accuracy among the models. Hyperparameters were carefully selected using a grid search approach, and the results were validated using the Shuffle-Split-Cross (SSC) validation method. These findings underscore the effectiveness of transfer learning in analyzing medical images, particularly in the diagnosis of dental caries. © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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