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5th International Conference on Advanced information Technologies, ICAIT 2024

作者： Theint, Khin Nyo Nyo Minn, Tun Ye Myat, Aung Khant Wai, Thinn Thinn Tar, Hmway Hmway Thant, Win Win Zin, Thet Thet University of Information Technology Yangon Myanmar University of Information Technology Faculty of Computer Science Yangon Myanmar

ISBN: (纸本)9798331510046

Text summarization is one of the most difficult natural language processing (NLP) tasks. It is the process of creating concise, intelligent summaries from a variety of sources, including books, news articles, and social media posts. This method saves readers time in today's busy environment while giving them fast access to important information. This paper presents extractive and abstractive summarization approaches for Myanmar text. While abstractive summarizing creates the complete summary from scratch using the input text, extractive summarization chooses a subset of sentences from the original text for the summary. Abstractive summarizing is more sophisticated than extractive summarization since it involves human-like elements to create the summary from the ground up. The TextRank algorithm and cosine similarity were used for extractive summarization. The mT-5 model, a transformer-based seq2seq model, was employed to carry out abstractive summarization on the XL-Sum dataset. As a result, the extractive summarization of the proposed system yields 43.14 of the ROUGE-1 score, while the abstractive summarization contributes 36.67 of the ROUGE-1 score. © 2024 IEEE.

关键词： Abstractive Summarization Cosine Similarity Extractive Summarization mT5 Natural Language Processing Text Summarization

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Customer Segmentation Based on Recency, Frequency, Monetary, Variety and Duration (RFMVD) 2

Customer Segmentation Based on Recency, Frequency, Monetary,...

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2nd International Conference on technology Innovation and Its Applications, ICTIIA 2024

作者： Sundari, Agus Lydia, Maya Silvi Muchtar, Muhammad Anggia Universitas Sumatera Utara Faculty of Computer Science and Information Technology Department of Data Science and Artificial Intelligence Medan Indonesia Universitas Sumatera Utara Faculty of Computer Science and Information Technology Department of Computer Science Medan Indonesia

ISBN: (纸本)9798350351613

In banking, maintaining customer retention and customer satisfaction are important. Effective customer segmentation can be a strategic tool to improve customer loyalty and business performance. This research can assist banks in developing marketing strategies to retain customers and improve services based on bill payment transactions. The data that will be used in this study is bill payment transaction data at regional banks in 2023, as for the types of bill payments made by customers, namely electricity, water, telephone payments, and internet bills. The amount of data used is 702,174 rows. This research uses K-Means clustering to group customers based on recency (the last time the customer made a transaction), frequency (the number of customers making transactions), monetary (the nominal amount of money spent by the customer), variety (the number of types of bills paid) and duration (the average duration of time the customer makes the previous transaction until the next transaction) (RFMVD). The results of customer segmentation based on recency, frequency, monetary, variety and duration using K-Means clustering produce 3 customer groups, namely passive customers, loyal customers, and VIP customers. The silhouette score value on customer segmentation using K-Means clustering is 0.7303, which indicates that the grouping is quite good and the number of clusters is correct. © 2024 IEEE.

关键词： bill payments Customer segmentation K-Means clustering RFMVD Model silhouette score

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Optimizing Electric Vehicle Charging Infrastructure through Machine Learning: A Study of Charging Patterns and Energy Consumption

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第21期18卷 149-170页

作者： Alsarhan, Ayoub Alnatsheh, Athari Aljaidi, Mohammad Al Makkawi, Tuqa Aljamal, Mahmoud Alsarhan, Tamam Department of Information Technology Faculty of Prince Al-Hussien bin Abdullah for IT The Hashemite University Zarqa Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan King Abdullah II School of Information Technology The University of Jordan Amman Jordan

The rapid adoption of electric vehicles (EVs) has created a pressing need for efficient charging infrastructure. but challenges such as inconsistent demand and poor placement remain. An effective distribution of sufficient EV charging stations (CSs) is one of the major obstacles preventing the market penetration of EVs and the realization of a sustainable transportation system in urban areas. In this paper, a new machine learning technique is proposed in order to optimize the placement of EV charging stations (EVCSs) in metropolitan areas based on an energy consumption prediction model. A dataset from 148,136 charging transactions in Boulder, Colorado, is used with the proposed model. Key algorithms such as KNeighborsRegressor and RandomForestRegressor were incorporated to solve the placement problem. The analysis revealed significant demand fluctuations during peak commute hours, with the KNeighborsRegressor model demonstrating superior prediction accuracy. These insights can guide more effective infrastructure planning and resource allocation, ultimately enhancing the efficiency and user experience of EV charging networks and promoting sustainable urban transportation. © 2024 by the authors of this article.

关键词： Charging stations

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Enhancing Acoustic-based Speech Emotion Recognition Using CNN 5

Enhancing Acoustic-based Speech Emotion Recognition Using CN...

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5th International Conference on Advanced information Technologies, ICAIT 2024

作者： Hlaing, Hnin Oo Thet Zin, Thet Wai, Thinn Thinn Nway Oo, Aung University of Information Technology Yangon Myanmar University of Information Technology Faculty of Computer Science Yangon Myanmar

ISBN: (纸本)9798331510046

Speech Emotion Recognition (SER) is a crucial field aiming to enhance machine understanding of human emotions. Among all the variety of data sources, we choose the acoustic features of the speech to explore both positive and negative aspects in feature extraction, aggressive data augmentation, and complex model architecture, highlighting its potential to inflate false confidence (overfitting) and hallucinations if not wielded with caution. Furthermore, our investigation explores the complex link between accuracy and feature set size, highlighting the need to extract rich emotional characteristics from speech. An intriguing finding of our analysis is: downscaling the filter size and learning rate of the neural network works better with small training dataset and the model can differentiate diverse emotions more accurately. This finding paves the way for improving the classification model, potentially leading to more robust emotion recognition systems. This multifaceted analysis goes beyond the conventional focus on accuracy, challenging assumptions and pinpointing areas for future exploration in the ever-evolving field of speech-based emotion recognition. © 2024 IEEE.

关键词： Convolutional Neural Feature Extraction Network (CNN)Data Augmentation Speech Emotion Recognition (SER)

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Sentiment Analysis on Twitter and Instagram Data towards Nuclear Waste

Sentiment Analysis on Twitter and Instagram Data towards Nuc...

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2024 International Conference on Electronics and Devices, Computational science, ICEDCS 2024

作者： Bing, Chao Universiti Putra Malaysia Faculty of Computer Science and Information Technology Department of Computer Science Selangor Malaysia

ISBN: (纸本)9798331527624

A magnitude 9.0 earthquake on March 11, 2011, caused core meltdowns at Fukushima Daiichi Nuclear Power Plant, leading Japan to begin discharging treated wastewater into the Pacific Ocean on August 24, 2023. This decision sparked global discussions on social media platforms like Twitter and Instagram. 9,794 tweets from Twitter and 3,042 posts from Instagram were collected to conducts sentiment analysis for assessing public sentiment and informing policymaking related to nuclear waste management. Using Latent Dirichlet Allocation (LDA) for topic modeling and Bidirectional Encoder Representations from Transformer (BERT) for sentiment analysis, the research finds a predominantly negative public sentiment towards the discharge of treated wastewater. © 2024 IEEE.

关键词： Tweets

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Custom Object Detection for Fresh and Rotten Fruit Classification 5

Custom Object Detection for Fresh and Rotten Fruit Classific...

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5th International Conference on Advanced information Technologies, ICAIT 2024

作者： Thant, Khine Zin Khant Paing, Minn Thant, Win Win Hmway Tar, Hmway Oo, Aung Nway University of Information Technology Yangon Myanmar University of Information Technology Faculty of Computer Science Yangon Myanmar

ISBN: (纸本)9798331510046

Automated grading and processing of fruits is a complex task which requires the creation of effective fruit vision detection systems. This paper presents an introduction of Fruit detection and recognition system for grading apples, bananas and oranges though incorporation of deep learning methodologies. For this study, two separate databases of fruits including the Kaggle and Google databases were carefully prepared. The images were annotated with precision and augmented using the Roboflow web-based platform, categorizing each image into one of six classes: fresh apple, rotten apple, fresh orange, rotten orange, fresh banana, rotten banana. After data collection, the next step involved was data partitioning into training and validation datasets through a data splitting technique. Regarding the model training process, the YOLOv8 and YOLOv5 model was used. The evaluation that was conducted on the trained model was impressive, as the system returned a high value for accuracy, precision, recall, F1-score, and mAP. The proposed system not only can enhance fruit grading efficiency but also has a wide range of applications in other agricultural fields. Moreover, this study helps toward some significant concerns in agricultural industry, particularly in the area of fruit recognition. It aims to improve the automation the automation of post-harvest fruit quality selection, ensuring more efficient and sorting and grading processes. This extensive review is quite beneficial as it provides insights into the future direction on how to better improve automatic fruit picking technology for better efficiency and optimization. © 2024 IEEE.

关键词： Citrus fruits

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EduGym Post-Mortem: Open Educational Resource for Educational Game

EduGym Post-Mortem: Open Educational Resource for Educationa...

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2025 International Conference on Advancement in Data science, E-learning and information System, ICADEIS 2025

作者： Tarigan, Jos Timanta Zamzami, Elviawaty Muisa Zendrato, Niskarto Nababan, Anandhini Medianty Universitas Sumatera Utara Faculty of Computer Science and Information Technology Medan Indonesia

ISBN: (纸本)9798331513320

This paper aims to delve into the development of EduGym, an educational microlearning game specifically designed for mobile platforms. The discussion highlights the implementation and integration of Open Educational Resources (OER) and examines their significant impact on the development process. The project strategically used OER as the primary content source to ensure seamless future content expansion without requiring changes to the core application. This approach also empowers other developers to repurpose and adapt the content for various educational applications. Furthermore, the paper addresses key challenges encountered during the development process from a software engineering standpoint, including maintaining flexibility, ensuring scalability, and managing resource allocation. The study outlines the overall architecture of EduGym, detailing the development workflow and the ways in which OER integration influenced both development and resource management. The aim is to offer valuable insights and practical guidance for developers and educators who are exploring similar methodologies, informed by the lessons learned and experiences gained through the creation of EduGym. © 2025 IEEE.

关键词： Game design

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A survey on graph-based methods for Software Vulnerability Detection 1

A survey on graph-based methods for Software Vulnerability D...

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Abu-Huliqah, Ebtsam N.A. Al Gaphari, Ghaleb H. Ghurab, Mossa Department of Computer Science Faculty of Computer and Information Technology Sana’a University Sana’a Yemen

ISBN: (纸本)9798331533557

In computer security, finding software vulnerabilities is essential since they provide serious security hazards to both individuals and companies. Known vulnerability variations or recently undiscovered vulnerabilities are frequently difficult to find using traditional vulnerability detection techniques. Graph representations are useful and successful methods for finding software system vulnerabilities. Conventional techniques for detecting vulnerabilities frequently depend on pattern matching and structural analysis, which may not be sufficient to find intricate and dynamic vulnerabilities. Graph-based methods, on the other hand, offer a more thorough and adaptable representation that catches the complex interrelationships and dependencies between code pieces. This research aims to make progress in the field of vulnerability detection by exploring the potential of graph-based techniques to address existing challenges and limitations: handling large-scale graphs can be computationally expensive, and detecting zero-day vulnerabilities remains a challenging task. By investigating how graph representations can effectively manage code diversity, capture intricate relationships, and improve vulnerability identification, we contribute to developing more robust and adaptive security mechanisms. Our comprehensive survey provides a thorough overview of graph-based vulnerability detection methods, offering insights into their potential, limitations, and effectiveness. This research serves as a valuable resource for researchers and practitioners, guiding them in selecting appropriate methods and motivating further developments in this domain. © 2024 IEEE.

关键词： Graph neural networks

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A novel medical steganography technique based on Adversarial Neural Cryptography and digital signature using least significant bit replacement

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International Journal of Cognitive Computing in Engineering 2024年第1期5卷 379-397页

作者： Hameed, Mohamed Abdel Hassaballah, M. Abdelazim, Riem Sahu, Aditya Kumar Department of Computer Science Faculty of Computers and Information Luxor University Luxor85951 Egypt Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University AlKharj16278 Saudi Arabia Department of Computer Science Faculty of Computers and Information South Valley University Qena Egypt Department of Information Systems College of Information Technology Misr University for Science and Technology Giza Egypt Amrita School of Computing Amrita Vishwa Vidyapeetham Andhra Pradesh Amaravati522503 India

With recent advances in technology protecting sensitive healthcare data is challenging. Particularly, one of the most serious issues with medical information security is protecting of medical content, such as the privacy of patients. As medical information becomes more widely available, security measures must be established to protect confidentiality, integrity, and availability. Image steganography was recently proposed as an extra data protection mechanism for medical records. This paper describes a data-hiding approach for DICOM medical pictures. To ensure secrecy, we use Adversarial Neural Cryptography with SHA-256 (ANC-SHA-256) to encrypt and conceal the RGB patient picture within the medical image's Region of Non-Interest (RONI). To ensure anonymity, we use ANC-SHA-256 to encrypt the RGB patient image before embedding. We employ a secure hash method with 256bit (SHA-256) to produce a digital signature from the information linked to the DICOM file to validate the authenticity and integrity of medical pictures. Many tests were conducted to assess visual quality using diverse medical datasets, including MRI, CT, X-ray, and ultrasound cover pictures. The LFW dataset was chosen as a patient hidden picture. The proposed method performs well in visual quality measures including the PSNR average of 67.55, the NCC average of 0.9959, the SSIM average of 0.9887, the UQI average of 0.9859, and the APE average of 3.83. It outperforms the most current techniques in these visual quality measures (PSNR, MSE, and SSIM) across six medical assessment categories. Furthermore, the proposed method offers great visual quality while being resilient to physical adjustments, histogram analysis, and other geometrical threats such as cropping, rotation, and scaling. Finally, it is particularly efficient in telemedicine applications with high achieving security with a ratio of 99% during remote transmission of Electronic Patient Records (EPR) over the Internet, which safeguards the patien

关键词： Steganography

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Predicting Student Behavior Using a Neutrosophic Deep Learning Model

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Neutrosophic Sets and Systems 2025年 76卷 288-310页

作者： Shitaya, Ahmed Mohamed Wahed, Mohamed El Syed Abd El Khalek, Saied Helemy Ismail, Amr Shams, Mahmoud Y. Salama, A.A. Department of Mathematics and Computer Science Faculty of Science Port Said University Egypt Department of Computer Science Faculty of computer and information sciences Suez Canal University Egypt Department. of Communication and Computer Engineering Air Defense University Egypt Department of Cybersecurity College of Engineering and Information Technology Buraydah Private Colleges Buraydah51418 Saudi Arabia Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt

We developed an information system using an object-oriented programming language and a distributed database (DDB) consisting of multiple interconnected databases across a computer network, managed by a distributed database management system (DDBMS) for easy access. An intelligent system was designed to assess the difficulty level of preliminary exams and select top-performing advanced students using a Neutrosophic Deep Learning Model. The dataset was randomly split into training (80%) and testing (20%) sets, and the model, trained with the Adam optimizer at a 0.001 learning rate over 50 epochs, incorporated early stopping based on validation loss. This system, implemented at a traditional Egyptian university, achieved a 95% accuracy in predicting student dropout. Student behavior, influenced by personal, environmental, and academic factors, is often evaluated subjectively, leading to inconsistent results. Traditional machine learning approaches struggle with the inherent uncertainty in behavioral data. To address this, we combined neutrosophic theory—a mathematical framework that accounts for truth, falsity, and indeterminacy—with deep learning, which excels at learning complex data relationships, to predict student outcomes such as dropout rates. Evaluating the model on student data, including attendance and grades, showed superior accuracy, achieving a determination coefficient of 0.95, demonstrating the approach's potential for identifying at-risk students and enabling targeted interventions. © 2025, University of New Mexico. All rights reserved.

关键词： Contrastive Learning

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