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2023 IEEE International Conference on computing, ICOCO 2023

作者： Harizan, Ahmad Hafiy Halim, Shamimi A Shamsuddin, Mohd Razif Ahmad, Rashidi Ahmad, Azlin School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi Mara Selangor Shah Alam Malaysia Graduate School of Medicine Kpj Healthcare University Negeri Sembilan Nilai Malaysia School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi Mara Selangor Shah Alam Malaysia

ISBN: (纸本)9798350302684

Cardiovascular Diseases (CVD) rank as the primary factor responsible for non-traumatic incidents of Out-of-Hospital Cardiac Arrest (OHCA) among adults and stands out as the predominant contributor to both mortality and morbidity rates in Malaysia. Consequently, it is crucial to promptly identify individuals afflicted by CVD or those at an elevated risk of experiencing cardiac arrest. This proactive identification is essential for effectively addressing and mitigating this issue. Given the elevated mortality rate, identifying variables potentially leading to cardiac arrest presents a significant challenge, underscoring the necessity for primary preventive measures and comprehensive risk assessment strategies. In this study, machine learning models;Support Vector Machines (SVM), Logistic Regression (LR) and Random Forest (RF) are used to build a prognostic model that can predict the probability of having cardiac arrest as an outcome. The data used to train and test the models merge five heart disease datasets collected from UCI Machine Learning Repository with 11 selected common attributes. The data was pre-processed, analysed and fit to the models to determine the patient attributes that are important for the models. The study suggested that older patients and patients with higher ST depression levels are more likely to get cardiac arrest. The slope of the peak exercise ST segment is found to be the most important attribute influencing the prediction of the models. After evaluation by cross-validation, RF model achieved the highest accuracy and performed best with n_estimators parameter tuned to 40. The model was then deployed on a web application to enable live prediction by user input of patient attributes, hoping to contribute to the early detection of OHCA. © 2023 IEEE.

关键词： Hospitals

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Performance Evaluation of Deep Dense Layer Neural Network for Diabetes Prediction

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Computers, Materials & Continua 2023年第7期76卷 347-366页

作者： Niharika Gupta Baijnath Kaushik Mohammad Khalid Imam Rahmani Saima Anwar Lashari Department of Computer Science and Engineering Shri Mata Vaishno Devi UniversityKatraJ&KIndia College of Computing and Informatics Saudi Electronic UniversityRiyadhSaudi Arabia

Diabetes is one of the fastest-growing human diseases worldwide and poses a significant threat to the population’s longer *** prediction of diabetes is crucial to taking precautionary steps to avoid or delay its *** this study,we proposed a Deep Dense Layer Neural Network(DDLNN)for diabetes prediction using a dataset with 768 instances and nine *** also applied a combination of classical machine learning(ML)algorithms and ensemble learning algorithms for the effective prediction of the *** classical ML algorithms used were Support Vector Machine(SVM),Logistic Regression(LR),Decision Tree(DT),K-Nearest Neighbor(KNN),and Naïve Bayes(NB).We also constructed ensemble models such as bagging(Random Forest)and boosting like AdaBoost and Extreme Gradient Boosting(XGBoost)to evaluate the performance of prediction *** proposed DDLNN model and ensemble learning models were trained and tested using hyperparameter tuning and K-Fold cross-validation to determine the best parameters for predicting the *** combined ML models used majority voting to select the best outcomes among the *** efficacy of the proposed and other models was evaluated for effective diabetes *** investigation concluded that the proposed model,after hyperparameter tuning,outperformed other learning models with an accuracy of 84.42%,a precision of 85.12%,a recall rate of 65.40%,and a specificity of 94.11%.

关键词： Diabetes prediction hyperparameter tuning k-fold validation machine learning neural network

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Random Forest-Based Approach to Android Malware Detection in Machine Learning 5

Random Forest-Based Approach to Android Malware Detection in...

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5th International Conference on Artificial Intelligence and Data Sciences, AiDAS 2024

作者： Saufan, Muhammad Hafiz Mohd Ahmad, Samsiah Zulkifli, Zalikha Nasarudin, Nur Hasni Ariff, Mohamed Imran Mohamed Sarkam, Nor Aslily Jrc Technology Sdn Bhd Taman Century Penang Gelugor11700 Malaysia Computing Sciences Studies College of Computing Informatics and Mathematics Universiti Teknologi Mara Perak Branch Tapah Campus Tapah Malaysia Mathemathical Science Studies College of Computing Informatics and Mathematics Universiti Teknologi Mara Perak Branch Tapah Campus Tapah Malaysia

ISBN: (纸本)9798331528553

The proliferation of Android devices has led to an increase in the number of applications available, but it has also made them a prime target for malware attacks. Ensuring the security of these devices is crucial, as malware can lead to data breaches, financial losses, and privacy violations. Traditional methods of malware detection often struggle to keep up with the rapidly evolving nature of these threats. This paper introduces a Random Forest-based method for detecting malware on Android devices. It leverages machine learning techniques to assess the effectiveness of Random Forest algorithms in identifying malware. The experimental findings highlight the method's impressive performance, achieving an accuracy of 99.91%, precision of 99.88%, recall of 99.93%, Fl-score of 99.91 %, and specificity of 99.89%. These results suggest that this approach is a highly promising solution for malware detection on Android platforms. Future work will focus on further optimizing the algorithm, exploring its application to other platforms, and enhancing its ability to detect newly emerging malware threats. © 2024 IEEE.

关键词： Android malware

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Cross Feature Fusion of Fundus Image and Generated Lesion Map for Referable Diabetic Retinopathy Classification 17th

Cross Feature Fusion of Fundus Image and Generated Lesion ...

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17th Asian Conference on Computer Vision, ACCV 2024

作者： Mok, Dahyun Bum, Junghyun Tai, Le Duc Choo, Hyunseung Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9789819609000

Diabetic Retinopathy (DR) is a primary cause of blindness, necessitating early detection and diagnosis. This paper focuses on referable DR classification to enhance the applicability of the proposed method in clinical practice. We develop an advanced cross-learning DR classification method leveraging transfer learning and cross-attention mechanisms. The proposed method employs the Swin U-Net architecture to segment lesion maps from DR fundus images. The Swin U-Net segmentation model, enriched with DR lesion insights, is transferred to generate a lesion map. Both the fundus image and its segmented lesion map are used as complementary inputs for the classification model. A cross-attention mechanism is deployed to improve the model’s ability to capture fine-grained details from the input pairs. Our experiments, utilizing two public datasets, FGADR and EyePACS, demonstrate a superior accuracy of 94.6%, surpassing current state-of-the-art methods by 4.4%. To this end, we aim for the proposed method to be seamlessly integrated into clinical workflows, enhancing accuracy and efficiency in identifying referable DR. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

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NDAS: Noise-Decomposed Abnormal Segmentation for Robust Medical Image Retrieval 19

NDAS: Noise-Decomposed Abnormal Segmentation for Robust Medi...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Kim, Ju-Chan Van Nguyen, Pham Le, Duc-Tai Choo, Hyunseung College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798331507817

This paper presents NDAS (Noise-Decomposed Abnormal Segmentation), an innovative framework for robust medical image retrieval and segmentation. By explicitly decomposing noise and abnormal features, NDAS enhances retrieval accuracy and segmentation precision. Experimental results on the BraTS 2019 dataset validate NDAS’s superiority over CBMIR and SBMIR methods, achieving the highest Top-5 accuracy and Dice coefficient. This demonstrates its effectiveness in handling noise, isolating pathological regions, and retrieving diagnostically relevant medical images. NDAS offers a significant advancement in medical imaging analysis, emphasizing its potential for clinical applications requiring precision and reliability. © 2025 IEEE.

关键词： Image segmentation

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i-NVMe: Isolated NVMe over TCP for a Containerized Environment 42

i-NVMe: Isolated NVMe over TCP for a Containerized Environme...

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42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Lee, Seongho Yeom, Ikjun Kim, Younghoon College of Computing and Informatics Sungkyunkwan University Department of Computer Science and Engineering Suwon Korea Republic of

ISBN: (纸本)9798350334142

Non-Volatile Memory Express (NVMe) over TCP is an efficient technology for accessing remote Solid State Drives (SSDs);however, it may cause a serious interference issue when used in a containerized environment. In this study, we propose a CPU isolation scheme for NVMe over TCP in such an environment. The proposed scheme measures the CPU usage of the NVMe over TCP worker, charges it to containers in proportion to their NVMe traffic, and schedules containers to ensure isolated sharing of the CPU. However, because the worker runs with a higher priority than normal containers, it may not be possible to achieve CPU isolation with container scheduling alone. To solve this problem, we also control the CPU usage of the worker by throttling NVMe over TCP traffic. The proposed scheme is implemented on a real testbed for evaluation. We perform extensive experiments with various workloads and demonstrate that the scheme can provide CPU isolation even in the presence of excessive NVMe traffic. © 2023 IEEE.

关键词： Containers

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Selecting Machine Learning Models for Student Performance Prediction Aligned with Pedagogical Objectives

Selecting Machine Learning Models for Student Performance Pr...

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2023 IEEE International Conference on computing, ICOCO 2023

作者： Khan, Ijaz Zabil, Mohammed Hazli Mohamed Ahmad, Abdul Rahim Jabeur, Nafaa College of Computing and Informatics Univsiti Tenaga Nasional Department of Computing Kajang Malaysia Buraimi University College Information Technology Dept. Al-Buraimi Oman Deparment of Computing and Informatics' Kajang Malaysia German University of Technology Computer Science Dept. Muscat Oman

ISBN: (纸本)9798350302684

Machine learning classifiers emerge as productive tools to develop prediction models which forecast the final outcome of the students, in a course, and provide an opportunity to the instructor to take appropriate measures. A single prediction model may not be enough to achieve all the objectives of an instructor. The selection of appropriate prediction models is yet a challenging task. This paper proposes a novel framework that applies a set of machine learning classifiers over the training dataset of a course. Along with the training dataset, the instructor clarifies the prime of objective of the binary model whether it must focus over the identification of fail, pass, or both students. The framework recommends a convenient model to achieve the specific objectives of the instructor. This research concludes that models inclined to reduce misclassification of minority class are suitable if the primary objective of the institution is the correct identification of students who are struggling to achieve the minimum course requirements. Further, a model specialized solely in amplifying the correct classification of majority class instance is appropriate if the aim is to focus on the correct identification of excellent students. The empirical analysis in this research leads towards the fact that accuracy alone is not an adequate metric to assess the performance of a model and that systematic selection of evaluation metrics is required to develop constructive models. © 2023 IEEE.

关键词： Students

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Stony Coral Species Recognition System Using Deep Learning 4

Stony Coral Species Recognition System Using Deep Learning

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4th International Conference on Artificial Intelligence and Data Sciences, AiDAS 2023

作者： Rusli, Nur Nadhirah Mohtar, Itaza Afiani College of Computing Informatics and Mathematics Universiti Teknologi Mara Computer Science Studies Perak Malaysia

ISBN: (纸本)9798350318432

Existing applications for identifying coral species are difficult to find as compared to other artificial intelligence applications, for example, plant recognition applications. People usually recognize coral species by rough observation and identify the information by themselves. It is difficult to recognize the coral species because each species has a wide range of colors, textures, shapes and are very similar to each other, making it rather hard to differentiate unless by trained eyesight. Therefore, a web application system utilizing Convolutional Neural Network (CNN), to identify the name, family name and other related information of a stony coral's species is proposed. 10 Stony coral species were selected for this project. A total of 3831 images were split to train (80%), test (10%) and validate (10%) the model. The model was able to achieve training accuracy of 99%, validation accuracy of 97% and testing accuracy of 91.9% after 50 epochs. The precision, recall and F1 score achieved were 0.88, 0.96, and 0.92 respectively. As a conclusion, the model can classify the stony coral species. This project can be integrated with other platforms in the future. such as mobile applications. © 2023 IEEE.

关键词： Image classification

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Performance Evaluation of Contextualized Arabic Embeddings: The Arabic Sentiment Analysis Task

Performance Evaluation of Contextualized Arabic Embeddings: ...

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International Conference on Data Analytics and Management, ICDAM 2022

作者： Dakalbab, Fatima Elnagar, Ashraf Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9789811976148

Sentiment analysis is a growing topic of study that straddles several disciplines, consisting of machine learning, natural language processing, and data mining. Its goal is to automate the extraction of conveyed concepts from a text. Much research has been undertaken in this field, particularly on English texts, because of their wide uses, whereas other languages, like Arabic, have gotten far less attention. The objective is to implement a multi-classification sentiment analyzer for Arabic text while using four recently published contextualized embedding models. The proposed work is an intrinsic evaluation metric of the four models. The results of the experiments revealed that MARBERT and ARABERT models are found to be the best. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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Towards unified management of software capstone projects in Saudi universities: a survey-based study

Arab Gulf Journal of Scientific Research

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Arab Gulf Journal of Scientific Research 2022年第2期40卷 118-138页

作者： Ahmad, Ehsan Alammary, Ali College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

Purpose: Saudi universities have incorporated capstone projects in the final year of an undergraduate study. Although universities are following recommendations of the National Commission for National Commission for Academic Accreditation and Assessment (NCAAA) and Accreditation Board for Engineering and Technology (ABET), no detailed guidelines for management and assessment of capstone projects are provided by these accreditation bodies. Variation in the management and assessment practices of capstone project courses and analysis of the students' capabilities to align with industry demands, to realize Vision 2030, is challenging. This study investigates the current practices for structure definition, management and assessment criteria used for capstone project courses at undergraduate level for information technology (IT) programs at Saudi universities. Design/methodology/approach: A web-based questionnaire is administered using a web service commonly used for questionnaires and polls to investigate the structure, management and assessment of capstone projects at the undergraduate level offering software engineering, computer science and information technology (SECSIT) programs. In total, 42 faculty members (with range of experience of managing/advising capstone projects from 1 to more than 10 years) from 22 Saudi universities (out of more than 30 universities offering SECSIT undergraduate programs) participated in the study. Findings: The authors have identified that Saudi universities are facing challenges in the utilized process model, the distribution of work and marks, the knowledge sharing approach and the assessment scheme. To cope with these challenges, the authors recommend the use of an incremental development process, the utilization of a project-driven approach, the development of a national level digital archive and the implementation of homogeneous assessment scheme. Social implications: To contribute to the national growth and to fulfill the market d

关键词： Assessment Capstone projects Management NCAAA Survey Vision 2030

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