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21st IEEE Student Conference on Research and Development, SCOReD 2023

作者： Alqushaibi, Alawi Ragab, Mohammed Gamal Sumiea, Ebrahim Hamid Al-Selwi, Safwan Mahmood Hasan, Mohd Hilmi Abdulkadir, Said Jadid Alhussian, Hitham Al-Asbahi, Mahmoud Mustafa Universiti Teknologi PETRONAS Computer and Information Sciences Perak Malaysia Universiti Teknologi PETRONAS Centre for Research in Data Science Perak Malaysia Universiti Malaysia Terengganu Faculty of Ocean Engineering Technology and Informatics Terengganu Malaysia

ISBN: (纸本)9798350318821

Detection of heart disease is critical, as it is a common and serious health condition. Predicting its occurrence is vital in the healthcare domain, and machine learning techniques are increasingly being utilized to accurately identify patients at risk. To evaluate how well machine learning models can effectively predict heart disease, we used a pre-existing dataset from the UCI repository's Cleveland database. Though the dataset originally contained 303 records and 76 attributes, we followed the literature by narrowing the focus to 14 relevant attributes in order to improve the precision of various algorithmic approaches. Our study employed five machine learning classifiers, namely KNearest Neighbors (KNN), CatBoost Classifier (CBC), Decision Tree Classifier (DT), XGBClassifier (XGBC), and Support Vector Classifier (SVM). We evaluated their performance using different metrics like accuracy, precision, AUC, and F1 score on the heart disease dataset. We found that the CatBoost Classifier had the best performance, achieving high accuracy and AUC with 0.88% and 0.94% values, respectively. This suggests that machine learning algorithms could aid in identifying heart disease at an early stage and have significant implications for improving patient outcomes in healthcare. © 2023 IEEE.

关键词： Forecasting

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An Efficient Pairing-Free Certificateless Signcryption Scheme Under the Standard Model for VANET

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IEEE Internet of Things Journal 2025年第12期12卷 22142-22154页

作者： Liu, Xianxiang Zhou, Yanwei Yang, Bo Zhu, Tianqing Zhang, Mingwu Shaanxi Normal University School of Computer Science Xi'an710119 China Sichuan University Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China City University of Macau Faculty of Data Science Macau999078 China Hubei University of Technology School of Computer Wuhan430068 China

With the rapid advancement of the Internet of Things (IoT), its applications are becoming increasingly essential in actual application. Specifically, the recent surge in electric vehicles has spurred significant advancements in vehicle ad hoc networks (VANET). Therefore, efficient data transmission is a critical challenge in IoT, especially VANET. The certificateless signcryption (CLS) scheme has high authentication efficiency, which has become increasingly important in IoT. However, most existing schemes rely on bilinear pairing, resulting in high calculation costs or failure to achieve their claimed security. Furthermore, the security of some CLS schemes is proved in the random oracle, which limits the usefulness of the CLS scheme. To further address these issues, we propose an efficient and secure CLS scheme in the standard model for VANET, which enhances its practical applicability in VANET. Our construction employs formal security proofs and cost simulations to ensure the scheme's security and low calculation costs, making it suitable for VANET. Finally, the security and efficiency analyses prove that our scheme offers enhanced security features and superior performance compared to existing schemes. © 2014 IEEE.

关键词： Cryptography

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Evaluating Pretrained Deep Learning Models for COVID-19 Detection in Medical Imaging

Evaluating Pretrained Deep Learning Models for COVID-19 Dete...

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Renewable Energy Technologies and Modern Communications Systems: Future and Challenges, Conference on

作者： A. AL-Shehri Bushra Sahar F. Sabbeh Mohamed M. Dessouky Department of Computer Science and Artificial Intelligence College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Department of Data Science College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Information Systems Department Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt

ISBN: (数字)9798331519445

ISBN: (纸本)9798331519452

COVID-19 is a respiratory disease for which reverse transcription-polymerase chain reaction (RT-PCR) is the standard detection method. This study introduces a hybrid deep learning approach to support the diagnosis of respiratory abnormalities, using COVID-19 as a case study. Multiple datasets were utilized, including the COVID-19 Radiography dataset (Kaggle), the SARS-CoV-2 CTScan dataset, and proprietary data from the King Faisal Medical Complex. Seven pre-trained deep learning models—VGG16, VGG19, MobileNet, DenseNet201, ResNet50, InceptionResNetV2, and InceptionV3—were evaluated for their performance. InceptionResNetV2 and InceptionV3 achieved perfect accuracy (100%) on the COVID-19 Radiography dataset and 99% accuracy on the SARS-CoV-2 CT-Scan dataset. Additionally, augmentation techniques were explored to improve model robustness. This research contributes significantly to medical imaging, offering practical insights for advancing diagnostic and monitoring practices for COVID-19 and other respiratory conditions.

关键词： COVID-19 Deep learning Solid modeling Accuracy data augmentation Robustness Diagnostic radiography Medical diagnostic imaging X-ray imaging Standards

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Speech Emotion Recognition Using Pre-trained and Fine-Tuned Transfer Learning Approaches 7th

Speech Emotion Recognition Using Pre-trained and Fine-Tuned...

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7th International Conference on Smart City Applications, SCA 2022

作者： Chakhtouna, Adil Sekkate, Sara Adib, Abdellah Team Computer Science Artificial Intelligence and Big Data MCSA Laboratory Faculty of Sciences and Technologies of Mohammedia Hassan II University of Casablanca Mohammedia Morocco Higher National School of Arts and Crafts of Casablanca Casablanca Morocco

ISBN: (纸本)9783031268519

Speech Emotion Recognition (SER) seems to be an exciting task due to its promising uses in many areas. A large number of studies have extracted acoustic descriptors from input speech and used them as features to train and test emotion recognition models. On the other hand, dimensional representations of speech signal such as spectrograms have been explored to appraise deep neural networks and more specifically the Convolutional Neural Network (CNN). To address this last issue, the current study aims to take advantage of the implementation of transfer learning architectures by using Mel spectrograms feature extraction mechanism to transform speech cues into images and then forward them to the pre-trained models. As well as make a comparative analysis of the performance using four different pre-trained architectures (VGG-16, VGG-19, EfficientNet B0 and EfficientNetV2 B0) both including and excluding the fine-tuning process. The experimental results were performed with two different configurations, pre-trained and fine-tuned models, the VGG-19 has obtained the highest accuracy of 79.83 % on the held-out test. An evaluation carried out on the Italian database of Elicited Mood in Speech (DEMoS) proves the effectiveness of the suggested model, which outperforms the state-of-the-art in SER. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Emotion Recognition

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Enhancing Educational Strategy Through K-Means Clustering: A Study on Academic Departments

Enhancing Educational Strategy Through K-Means Clustering: A...

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International Joint Conference on computer science and Software Engineering (JCSSE)

作者： Aomsap Inkongngarm Jakramate Bootkrajang Samerkae Somhom Areerat Trongratsameethong Prasert Luekhong Data Science Consortium Faculty of Engineering Chiang Mai University Chiang Mai Thailand Department of Computer Science Faculty of Science Chiang Mai University Chiang Mai Thailand College of Integrated Science and Technology Rajamangala University of Technology Chiang Mai Thailand

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

This study explores the use of k-means clustering for strategic analysis of academic departments within universities, focusing on enrollment and performance metrics. This research aimed at enhancing university administration through data-driven decision making. The research highlights the potential of k-means clustering for resource allocation, curriculum planning, and improving student success. The study categorizes departments into three distinct groups through data collection and cleansing, followed by the application of the Elbow method for optimal cluster determination. These clusters offer insights into departmental dynamics, guiding strategic interventions in areas ranging from high-performance departments to those facing enrollment challenges. The findings underscore the significance of aligning academic offerings with performance excellence, revealing the strategic benefits of a data-informed approach in higher education. This research not only fills a gap in applying quantitative analysis at the departmental level but also opens pathways for further exploration of machine learning in educational strategy, underscoring the transformative potential of analytics in academic management.

关键词： Machine learning algorithms Statistical analysis Education Decision making Refining Focusing Machine learning

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AllSAT for Combinational Circuits 26

AllSAT for Combinational Circuits

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26th International Conference on Theory and Applications of Satisfiability Testing, SAT 2023

作者： Fried, Dror Nadel, Alexander Shalmon, Yogev Department of Mathematics and Computer Science The Open University of Israel Ra'anana Israel Intel Corporation Haifa Israel Faculty of Data and Decision Sciences Technion Haifa Israel The Open University of Israel Ra'anana Israel

ISBN: (纸本)9783959772860

Motivated by the need to improve the scalability of Intel's in-house Static Timing Analysis (STA) tool, we consider the problem of enumerating all the solutions of a single-output combinational Boolean circuit, called AllSAT-CT. While AllSAT-CT is immediately reducible to enumerating the solutions of a Boolean formula in Conjunctive Normal Form (AllSAT-CNF), our experiments had shown that such a reduction, followed by applying state-of-The-Art AllSAT-CNF tools, does not scale well on neither our industrial AllSAT-CT instances nor generic circuits, both when the user requires the solutions to be disjoint or when they can be non-disjoint. We focused on understanding the reasons for this phenomenon for the well-known iterative blocking family of AllSAT-CNF algorithms. We realized that existing blocking AllSAT-CNF algorithms fail to generalize efficiently for AllSAT-CT, since they are restricted to Boolean logic. Consequently, we introduce three dedicated AllSAT-CT algorithms that are ternary-logic-Aware: A ternary simulation-based algorithm TALE, a dual-rail&MaxSAT-based algorithm MARS, and their combination. Specifically, we introduce in MARS two novel blocking clause generation approaches for the disjoint and non-disjoint cases. We implemented our algorithms in our new tool HALL. We show that HALL scales substantially better than any reduction to existing AllSAT-CNF tools on our industrial STA instances as well as on publicly available families of combinational circuits for both the disjoint and the non-disjoint cases. © 2023 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. All rights reserved.

关键词： Timing circuits

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Sentiment Analysis: Indonesia Netflix User's Comment Using Multiple Lexicon-Based Dictionaries

Sentiment Analysis: Indonesia Netflix User's Comment Using M...

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Intelligent Cybernetics Technology & Applications (ICICyTA), International Conference on

作者： Sebastianus Adi Santoso Mola Tiwuk Widiastuti Rosa V. K. Isabela Ohe Roma Andrea Stevens Karnyoto Bens Pardamean Faculty of Science and Engineering University of Nusa Cendana Kupang Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

A subscription movie streaming service from the United States, Netflix, has been present in Indonesia since 2016 and provides a wide variety of films without showing any single advertisements that can be viewed from anywhere, as long as there is access to the internet. Despite these advantages, many users have complained through the Google Play Store's comments column. Some of the common complaints include frequent buffering and connectivity issues, dissatisfaction with the limited selection of Indonesian movies, lack of subtitles for specific languages, or pricing concerns have also been raised. In this study developed two combined scenario methods using the InSet and SentiStrength_id dictionary to obtain better performance and compare them against independent InSet and SentiStrength_id. This study collected users' comments for the Netflix app in the Google Play Store as a dataset using web scraping techniques through the Google Collaboration tool. The dataset contains 3250 rows spanning the period from January 22, 2024, through June 6, 2024. To ensure proper processing of the text, data cleaning, lowercasing, normalization, tokenization, stemming, and stopwords removal are conducted. The results show that most user opinions are negative. The InSet dictionary has an accuracy of 92%, SentiStrength_id 78%, Combined scenario 1 is 87%, and scenario 2 reaches the highest among others which is 92.52%.

关键词： Sentiment analysis Dictionaries Accuracy Films Collaboration Pricing Motion pictures Tokenization Cleaning Internet

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A Taxonomy of Schema Changes for NoSQL databases

arXiv

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arXiv 2022年

作者： Chillón, Alberto Hernández Klettke, Meike Ruiz, Diego Sevilla Molina, Jesús García Faculty of Computer Science University of Murcia Murcia Spain Faculty of Computer Science and Data Science University of Regensburg Regensburg Germany

Schema evolution is a crucial aspect in database management. The proposed taxonomies of schema changes have neglected the set of operations that involves relationships between entity types: aggregation and references, as well as the possible existence of structural variations for schema types, as most of NoSQL systems are schemaless. The distinction between entity types and relationship types, which is typical of graph schemas, is also not taken into account in the published works. Moreover, NoSQL schema evolution poses the challenge of having different data models, and no standard specification exists for them. In this paper, a generic approach for evolving NoSQL and relational schemas is presented, which is based on the U-Schema unified data model that includes aggregation and reference relationships, and structural variations. For this data model, we introduce a taxonomy of schema changes for all the U-Schema elements, which is implemented by creating the Orion database-independent language. We will show how Orion can be used to automatically generate evolution scripts for a set of NoSQL databases, and the feasibility of each schema operation will be analyzed through the performance results obtained. The taxonomy has been formally validated by means of Alloy, and two case studies show the application of Orion. © 2022, CC BY-NC-SA.

关键词： Taxonomies

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Hybrid Machine Learning Approach of "KNN – LSTM" for detection of Brain Tumour in MR images 15

Hybrid Machine Learning Approach of "KNN – LSTM" for detect...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Uganya, G. Gunale, Anuja Govindrao Kulkarni, Shruthi V. Rajalakshmi, D. Valsaraj, Vani Srividhya, B. Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Engineering UPL University of Sustainable Technology Block no 402 Ankleshwar-Valia Road Gujarat Vataria India Department of AIML HKBK College of Engineering opposite manyata tech park Nagawara Karnataka Bengaluru India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Application Faculty of Humanities and Sciences SRM Institute Of Science and Technology Tamil Nadu Chengalpattu India

ISBN: (纸本)9798350370249

Now a days, more existing techniques are available to found tumours on brain from MR images. But still the researchers are concentrated on to detect the brain tumour accurately with less time. So, this work proposed the hybrid machine learning algorithm of "K-Nearest Neighbour – Long Short-Term Memory" ("KNN-LSTM") with increased ‘accuracy’, ‘sensitivity’, ‘specificity’, with less ‘time consumption’. In this work, the brain tumour is identified by predicting the MR images from huge dataset with usage of machine learning approach of "KNN-LSTM". Brain tumour detection is difficult when to compare the abnormal and typical tissues of large capacity. In order to enable a speedy recovery following therapy, brain tumours should be identified as soon as feasible. If the brain tumour is discovered early on, patients can benefit from improved care. Due to the irregular spreading of the abrasions, brain tumours are still difficult to identify even with intensive study. Sometimes the tiny tumours look like normal, but it is challenging to find it. So, the techniques are need to increase the classification accuracy, and selecting the useful features. This paper proposes a hybrid machine learning model "KNN-LSTM" for categorizing the tumours with predicting through MR images. MRI brain imaging collection is used for our experimentation. The "KNN" algorithm usage is to extract the important characteristics from pictures once the data has been pre-processed effectively. The proposed work predicts the brain tumour with 99.15% of ‘accuracy’, 98% of ‘sensitivity’, 97.12% of ‘specificity’ and testing time consumption of 0.5 minutes. © 2024 IEEE.

关键词： Image segmentation

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Achieving fairness in team-based FPS games:A skill-based matchmaking solution

Achieving fairness in team-based FPS games:A skill-based mat...

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作者： Ruotian Wu Xiangcheng Meng Haoshen Chen Zixuan Zhu Bo Wang Faculty of Mathematics University of Waterloo School of Data Science The Chinese University of Hong Kong (Shenzhen) Future technology college South China University of Technology School of Computing and Data Science Xiamen University Malaysia School of Computer Science and Technology Harbin University of Science and Technology

Matchmaking is a critical part of online games which is often related to player *** pursue a fair user experience,matchmaking mechanisms typically try to put players with similar skill levels into the same *** traditional process relies heavily on the outcome of the game instead of the in-game performance of *** paper proposes a new rating system to represent the skill level of both players and teams,along with a new definition to measure the degree of fairness of a matchmaking *** clustering methods(K-means,AGG and BKPP) are investigated to perform the matchmaking and the results are evaluated based on the newly-proposed *** matchmaking results generated by the AGG method appear to reach the best degree of *** source codes related to the project are available at https://***/WrtTZ/Achieving-Fairness-in-Team-Based-FPS-Games-A-Skill-Based-Matchmaking-Solution.

关键词： Matchmaking Player Rating System Game Fairness Balanced Clustering First Person Shooter

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