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Optimizing Multi-Class Classification in Educational data with Ensemble Learning and data Balancing Techniques 5

Optimizing Multi-Class Classification in Educational Data wi...

引用

5th International Conference on Intelligent data science Technologies and Applications, IDSTA 2024

作者： Al-Hammouri, Mohammad F. Hammouri, Ziad Akram Ali Almalkawi, Islam T. Lafee, Ansam The Hashemite University Dept. of Computer Engineering Zarqa Jordan Middle East University Dept. of Computer Science Amman Jordan

ISBN: (纸本)9798350354751

The field of education is increasingly embracing AI tools to improve student outcomes. This work aims to reduce academic failure in higher education by employing machine learning techniques to identify at-risk students early in their educational journey, enabling the implementation of supportive strategies to assist them. This study examines a dataset from a higher education institution and utilizes it to develop a classification model for predicting students' academic performance. The problem is formulated as a multi-class classification task with three categories: Graduate, Enrolled, and Dropout, with a significant imbalance skewed toward the Graduate. To improve prediction accuracy toward the minority class, the data balancing technique SMOTE with Edited Nearest Neighbor (SMOTE-ENN) is applied. Three popular classification models - Random Forest, XGBOOST and CatBoost - are employed. The findings show that SMOTE-ENN significantly improves classification results. Moreover, XGBOOST demonstrated the highest accuracy (94.6%) in correctly identifying all classes, as evidenced by the confusion matrix evaluation, achieving the highest results compared to previous work in the literature. Implementing these models allows for accurate predictions of students' performance and helps reduce dropout rates. © 2024 IEEE.

关键词： Contrastive Learning

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Modernization through ICT for Efficiency & Transparency: Equipment Revolving Fund 5

Modernization through ICT for Efficiency & Transparency: Equ...

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5th Technology Innovation Management and Engineering science International Conference, TIMES-iCON 2024

作者： Kamolvej, Pobsit Kovavisaruch, La-Or Dept. of Computer Science Faculty of Science Kasetsart University Bangkok Thailand National Electronics and Computer Center NSTDA Pathumthani Thailand

ISBN: (纸本)9798350362602

This paper examines the Equipment Revolving Fund in Thailand, a critical entity for maintaining and enhancing the nation's transportation infrastructure. While this organization undeniably ensures the quality of Thailand's transportation network, its effectiveness is hampered by manual processes and outdated information systems. These limitations can lead to operational inefficiencies, reduced transparency, and potential delays in crucial infrastructure projects. This study proposes a modernization initiative that leverages Information and Communication Technologies to improve the organization's operational efficiency significantly. We identify key challenges hindering the organization's effectiveness, including slow procurement procedures, limited data accessibility, and potential inefficiencies in construction material and equipment management. The paper proposes specific solutions to address these challenges including implementing cloud-based Enterprise Resource Planning. The successful implementation of these solutions is expected to expedite procurement processes, improve resource allocation for infrastructure projects, and enhance accountability within the Department of Highways. The paper acknowledges potential challenges associated with the modernization initiative, such as user adoption, data security, and system maintenance. However, it proposes thorough and effective mitigation strategies to ensure a successful implementation. The proposed solutions, while tailored to this organization, offer valuable insights for modernizing revolving fund management practices in other government agencies, particularly those collaborating with external entities like the Consolidated Financial Statement Program, Non-Budgetary Management System, Revolving Fund Evaluation System, and KrungThai Bank Corporate Online. ©2024 IEEE.

关键词： Efficiency

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Microwave Marvels: Design and Computational Analysis of an Ultra-thin Metamaterial Absorber for C, X and K Band 6

Microwave Marvels: Design and Computational Analysis of an U...

引用

6th International Conference on Electrical Engineering and Information and Communication Technology, ICEEICT 2024

作者： Tasnim, Nowshin Rattre, Naziya Hasan Shawon, Sarowar Morshed Alam, Mohammed Shamsul Alam, Touhidul Chittagong University of Engineering & Technology Dept. of Computer Science & Engineering Chittagong4349 Bangladesh International Islamic University Chittagong Dept. of Computer Science & Engineering Chittagong4318 Bangladesh University of Science & Technology Chittagong Dept. of Computer Science & Engineering Chattogram4202 Bangladesh Universiti Kebangsaan Malaysia Bangi43600 UKM Malaysia

ISBN: (纸本)9798350385779

Metamaterial absorbers are an advancement in material science as they provide more advantages in comparison to conventional materials. Achieving miniaturization with a multilayered structure is the primary challenge here. In this study, a novel metamaterial absorber design is proposed for microwave application. The proposed design is shaped as a squad of four segments where each segment contains an octagonal split ring and a central ring. The multi-layered design of the absorber helps to secure a high absorption rate. An extensive range of research is done to determine particular measurement units, and geometric shapes with the help of CST Studio Suit 2019. Here both ground and patch are designed using copper and the separator is designed with Roger. Moreover, the thickness of the absorber is only 3.4 mm which ensures its ultra-thin and lightweight nature. With an elaborated numerical analysis, it has been shown that the proposed absorber provides 90% to 99% absorption at the C, X, and K bands. Moreover, a concise computational analysis is accomplished with FR4 lossy, Rogers RT6010LM lossy, and Rogers RT5870LM lossy as dielectric constant. The key contribution of this research is the high absorption rate, coupled with minimal thickness and simple structure. © 2024 IEEE.

关键词： Metamaterials

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Analysis of Arabs Publications In the Field of computer Networks 14

Analysis of Arabs Publications In the Field of Computer Netw...

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14th International Conference on Information and Communication Systems, ICICS 2023

作者： El-Bashabsheh, Emran Younes, Mutaz Shatnawi, Mohammed Jordan University of Science and Technology Dept. of Computer Information Systems Irbid Jordan Jordan University of Science and Technology Dept. of Computer Science Irbid Jordan

ISBN: (纸本)9798350307870

Increasing technology led to increasing collaboration between the countries in general, especially the Arabic world. In recent years, many impacts affect the growth of research work in different Arabic countries. In this research, we show the growth of research work and its quality between Arabs and the rest of the world. We collected over 2500 publications and used it to create a new table that contains the intersection between each Arab country and each co-author's continent to show the relationship between each Arabic country with the rest of the world. We divided the data into two periods, before and after 2015, then we used Hue to apply our analysis on the data. We focused on the number of publications and the number of citation for each Arabic country. We noticed the rapid growth in the last period in some Arabic countries such as Jordan, Qatar, United Arab Emirates, and Saudi Arabia. In contrast, the rest of the countries had slow or decreasing growth. © 2023 IEEE.

关键词： Big data

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Unveiling the Arsenal of User data Protection Tools and Practices 1

Unveiling the Arsenal of User Data Protection Tools and Prac...

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Moiz, Syed Abro, Abdul Ahad Ebrahim, Mansoor Abro, Abdul Basit Iqra University Main Campus Dept. of Computer Science Karachi Pakistan Iqra University Main Campus Dept. of Software Engineering Karachi Pakistan Indus University Dept. of Computer Science Karachi Pakistan

ISBN: (纸本)9798350373042

In our rapidly evolving digital landscape, the imperative of safeguarding personal data has surged in significance. As lives increasingly intertwine with digital technologies, personal information has grown markedly, parallel to the escalating threats posed by cyberattacks and data breaches. This research paper provides a comprehensive exploration of data security and privacy, aiming to impart a profound understanding of the intrinsic value of data. It meticulously examines essential methodologies such as encryption, access control, multifactor authentication, and systems, all of which collectively form the bedrock of a resilient security framework. Additionally, this study delves into the intricate regulatory landscape that governs data protection, placing particular emphasis on well-established frameworks such as the General data Protection Regulation and the Health Insurance Portability and Accountability Act. It highlights emergent trends in data security, including the integration of artificial intelligence and machine learning for threat detection, while also addressing the distinctive challenges posed by the proliferation of Internet of Things (IoT) devices. In conclusion, this paper underscores the paramount importance of personal data protection in our digital age, offering invaluable insights and guidance for organizations and individuals endeavoring to bolster their defenses and uphold data integrity within our interconnected world. © 2024 IEEE.

关键词： data privacy

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Deep Learning-Driven Black Box Approach For Image Segmentation 1

Deep Learning-Driven Black Box Approach For Image Segmentati...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Sudha, Nuthalapati Kasetty, Sai Bhargav Madhuri, K. Sai Nikitha, Jajala Margret, Issac Neha Rajakumar, K. Mahindra University School of Engineering Telangana Hyderabad India V.I.T - University School of Computer Science and Engineering Tamilnadu Vellore India BVRIT Narsapur Dept. of Computer Science and Data Science Telangana Hyderabad India VNR VIET Dept. of CSE - AIML and IoT Telangana Hyderabad India

ISBN: (纸本)9798350356816

The diagnosis of a range of eye disorders needs to categorize the retinal vessels. computerized implementation of this process is becoming increasingly essential for automated screening systems for retinal diseases. To achieve a more accurate extraction of the retinal vessels, a new pre-processing step is proposed. These proposed pre-processes are also compared to other algorithms to assess their impact. The proposed pre-processing process consists of two phases. The first phase is the implementation and validation of the pre-processing modules, and the second phase is the implementation of these pre-processes onto the retinal vessels that were to be extracted. To achieve a significantly improved segmented vessel image, the proposed pre-process phase employs a common image-processing technique. In recent years, there has been a great deal of focus on retinal vessel identification studies, and the importance of assessing and confirming the findings of retinal vessel segmentation. © 2024 IEEE.

关键词： Image segmentation

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IRS Controlling Method With Machine Learning Based on Position Information 13

IRS Controlling Method With Machine Learning Based on Positi...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Tsuchiya, Yoshihiko Suga, Norisato Uruma, Kazunori Fujisawa, Masaya Tokyo University of Science Dept. of Information and Computer Technology Tokyo Japan Shibaura Institute of Technology Dept. of Information and Communication Engineering Tokyo Japan Kogakuin University Dept. of Computer Science Tokyo Japan

ISBN: (纸本)9798350355079

Intelligent Reflecting Surfaces (IRS) is attracting attention for wireless communications at high-frequency band. IRS can control radio propagation by reflecting radio waves and shifting their phase. As one of the methods to control IRS, location-based methods have been proposed. However, to achieve highly accurate IRS control by the methods, the assumed propagation model must match the actual propagation environment. This study proposes a method to control the IRS from location information adapted to the propagation environment by using machine learning. Numerical experiments confirm that the proposed method can appropriately control IRS regardless of the propagation environment. © 2024 IEEE.

关键词： Adversarial machine learning

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A BrainNet (BrN) based New Approach to Classify Brain Stroke from CT Scan Images

A BrainNet (BrN) based New Approach to Classify Brain Stroke...

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2023 International Conference on Advancement in Computation and computer Technologies, InCACCT 2023

作者： Tripura, Dhonita Haque, Imdadul Dutta, Mithun Dev, Shaikat Jahan, Tanjila Ghosh, Shomitro Kumar Islam, Md. Ashiqul Rangamati Science and Technology University Dept. of Computer Science and Engineering Rangamati Bangladesh Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350396485

Worldwide, brain stroke is known as the 2nd leading cause of death, and based on Indian history, three people have suffered every minute. There are mainly two different types of brain stroke: ischemic stroke and Hemorrhagic stroke used to train the proposed models. Ischemic stroke is the most common and it contributes mostly to 80% of the brain stroke and Hemorrhagic stroke contributes mostly to 20% of the brain stroke. In the proposed model, there has been used a hybrid model called BrainNet (BrN) as CNN(Convolutional Neural Network) and SVM(Support Vector Machine)to classify brain stroke disease. After applying the required proposed model, it has produced a smart score of 91.91% accuracy, and compared to the existing model it performs pretty well. The BrainNet (BrN) model is mainly designed based on a deep neural network with dataset collection, preprocessing, and feature extraction with the desired model and make the classification concerning SVM. With compare to the existing model, it is an acceptable performance that belongs to the collected dataset designed with Ischemic stroke and Hemorrhagic stroke disease within the total number of 2515 data. © 2023 IEEE.

关键词： Support vector machines

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Forecasting Nutrient Flows using Terrain Elevation-aware Spatial-Temporal Graph Neural Networks

Forecasting Nutrient Flows using Terrain Elevation-aware Spa...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Li, Jiaqi Wu, Yinghui Bradley, Alexandar Harding Akanbi, Olatunde Barcelos, Erika I. Bruckman, Laura S. French, Roger H. Case Western Reserve University Dept. Computer and Data Sciences Cleveland United States Case Western Reserve University Dept. Material Science and Engineering Cleveland United States

ISBN: (纸本)9798350362480

Spatiotemporal graph neural networks (STGNNs) have been adopted for predictive analysis in various scientific domains. Despite their promising performance, the dominance of big (geo)spatiotemporal data with large and heterogeneous dimensions raise computational challenges to effective adoption, generalization and fine-tuning of graph models. Moreover, continuous and high quality historical data may not always exist for such generalization. This paper proposes a framework that can co-evolve historical geospatial temporal datasets and an STGNN model by (1) incorporating elevation features optimized for water systems, and (2) integrating and interacting geospatial data discovery and graph learning with a "rehearsal"mechanism, that automatically generalize STGNNs to broader areas. The process divides spatiotemporal data into regional fragments with inferrable features, and iteratively (1) augment sparse training data in terms of feature similarity, (2) explore the augmented data by a trial "rehearsing"of the current model to decide a fraction of data to be adopted, over which a consistently good accuracy is observed, and (3) generalize STGNNs with promising regional data, ensured by rehearsal performance. This exploratory process hence learns to decide when and where to generalize STGNNs, for cost-effective generalization. Using real-world datasets, we experimentally verify the effectiveness and efficiency of our rehearsal framework. © 2024 IEEE.

关键词： Spatio-temporal data

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Analysis of Fraud Detection in Online Transactions Using Computational Models 5

Analysis of Fraud Detection in Online Transactions Using Com...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Khushi, N. Praveen, B. Rao, Trupthi Kodipalli, Ashwini Dept. of Artificial Intelligence and Data Science Global Academy of Technology Bangalore India Dept. of Artificial Intelligence and Data Global Academy of Technology Bangalore India

ISBN: (纸本)9798350361155

Financial fraud is becoming a more serious menace to the financial industry. One important tool for identifying credit card fraud in online payments is data mining. There are two primary reasons why credit card fraud, a data mining issue, becomes challenging to identify: First of all, the characteristics of typical conduct and fraud are ever-evolving. Second, credit card data sets are highly corrupted, to start with. Theft or misuse of your credit card information for personal use without your knowledge or agreement is known as credit card fraud. User behavior in previous transactions must be verified to detect these frauds. It can be classified as a fraud or a legitimate transaction when comparing its usage pattern with that of existing transactions. The variables chosen, the data set measurement strategy, and the detection techniques employed all have a major impact on the capacity to identify fraud in credit card purchases. It seems that information extraction has become the main task in detecting online payment fraud. For highly distorted credit card fraud data, this article uses K-Nearest Neighbor and Logistic Regression techniques. © 2024 IEEE.

关键词： data mining

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