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2024 International Conference on information technology Research and Innovation, ICITRI 2024

作者： Nugroho, Yusuf Sulistyo Halim, Salsabil Aisyah Abdul Islam, Syful Kurniawan, Yogiek Indra Erlina, Tati Universitas Muhammadiyah Surakarta Informatics Engineering Faculty of Communication and Informatics Surakarta Indonesia BSMRSTU Computer Science and Engineering Faculty of Engineering Gopalganj Bangladesh Universitas Jenderal Soedirman Informatics Engineering Faculty of Engineering Purwokerto Indonesia Universitas Andalas Computer Engineering Faculty of Information Technology Padang Indonesia

ISBN: (纸本)9798350376210

As a high-level programming language, Python supports user-friendly coding and system integration. It is not only used for data analytics but is also applied in software development. Although it has several benefits, users have various issues while running Python code. When programmers face issues, they often use Stack Overflow (SO) to get assistance for their problems. However, many Pythonrelated questions on SO are not responded to and remain unanswered. Thus, in this paper, we analyzed these unanswered questions to identify potential weaknesses of user and discussion characteristics. We applied a mixed-methods approach, including quantitative and qualitative analysis of user reputation, question types, and discussion topics. Our analysis shows that unanswered questions are mostly from low-reputation users (72.52%), followed by mid-reputation users (17.93%), and high-reputation users (9.5 5%). The distribution of the unanswered question types shows that low-reputation users ask more 'how' type of question (38.90%) followed by mid and high-reputation users. In contrast, the 'what' and 'why' questions are mostly posted by high-reputation users, followed by mid and low-reputation users. Our word-cloud analysis suggests a correlation between user reputation and the question topics. We acknowledge that there are limitations in our study, include potential misclassifications due to the time of posting and biases in manual labeling, which may affect the accuracy of our findings. This study provides insights into unanswered questions posted by the community, open up potential areas for future work such as improving support mechanisms and community engagement. © 2024 IEEE.

关键词： Python

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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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Identification of Sexual Harassment in Social Media Comments Using IndoBERT and Support Vector Machine 8

Identification of Sexual Harassment in Social Media Comments...

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8th International Conference on Electrical, Telecommunication and computer Engineering, ELTICOM 2024

作者： Amanda, Christine Jaya, Ivan Arisandi, Dedy Universitas Sumatera Utara Faculty of Computer Science and Information Technology Department of Information Technology Medan Indonesia

ISBN: (纸本)9798331531249

The rise of social media platforms has facilitated user interaction but has also led to increased incidents of online harassment, particularly sexual harassment. This form of harassment often manifests through sexualised and demeaning comments, disproportionately targeting women. Identifying such comments is challenging due to the high volume of content and the subjective nature of harassment. Manual identification is time-consuming and inconsistent. Therefore, an automated system to identify sexually harassing comments is needed for more effective management of social media content. This research introduces a system combining the IndoBERT embedding method and the Support Vector Machine (SVM) algorithm to identify sexually harassing comments in Indonesian social media content. The dataset comprises 3500 comments from Instagram and YouTube. Evaluation using a Confusion Matrix reveals an accuracy rate of 91.1%, significantly outperforming traditional keyword-based approaches. The system demonstrated strong performance in identifying implicit sexual harassment, which is often overlooked by manual moderation systems. These findings suggest that integrating IndoBERT and SVM provides a practical and scalable solution for real-time content moderation, with potential applications in enhancing the safety of online spaces. © 2024 IEEE.

关键词： Support vector machines

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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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Exploring Extractive and Abstractive Approaches for Myanmar Language Text Summarization 5

Exploring Extractive and Abstractive Approaches for Myanmar ...

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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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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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Retrieval Augmented Generation for Document Query Automation using Open source LLMs 5

Retrieval Augmented Generation for Document Query Automation...

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5th International Conference on Advanced information Technologies, ICAIT 2024

作者： Ko, Khant Nyein, Thwet Yin Oo, Khine Khine Oo, Thant Zin Zin, Thet Thet University of Information Technology Yangon Myanmar University of Information Technology Faculty of Computer Science Yangon Myanmar

ISBN: (纸本)9798331510046

Ollama provides access to powerful open-source language models that can be integrated into various applications. It supports local hosting, controlling the model's usage and data privacy. LLMs are large language models also known as deep learning models which are pre-trained on a vast amount of data. Integrating with retrieval augmented generation (RAG) can improve the efficiency of the LLM applications by retrieving custom data. The specific website or input of a particular document file can be integrated into the system. The document queries are automatically added to the Excel file using UiPath Automation. Firstly, the proposed system prompts by directly passing through the two LLM models: the Phi3 model by Microsoft with 3 billion parameters and the Llama 3.1 model by Meta with 8 billion parameters for text input and output, which can be accessed from Ollama released by Meta. To achieve a desired output, the system can also prompt by passing through retrieval augmented generation (RAG). Finally, analyze the results of the two outputs by directly using LLM models and embedding them with RAG. According to the evaluation result, RAG integration with llama3.1 improves response quality and relevance for custom data. Phi3 is better in the latency evaluation result. © 2024 IEEE.

关键词： Deep learning

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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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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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Efficient Computation Offloading of IoT-Based Workflows Using Discrete Teaching Learning-Based Optimization

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computers, Materials & Continua 2022年第11期73卷 3685-3703页

作者： Mohamed K.Hussein Mohamed H.Mousa Department of Computer Science Faculty of Computers and InformaticsSuez Canal UniversityIsmailiaEgypt Department of Information Technology College of Computer Science at AlKamilUniversity of JeddahJeddahSaudi Arabia

As the Internet of Things(IoT)and mobile devices have rapidly proliferated,their computationally intensive applications have developed into complex,concurrent IoT-based workflows involving multiple interdependent *** exploiting its low latency and high bandwidth,mobile edge computing(MEC)has emerged to achieve the high-performance computation offloading of these applications to satisfy the quality-of-service requirements of workflows and *** this study,we propose an offloading strategy for IoT-based workflows in a high-performance MEC *** proposed task-based offloading strategy consists of an optimization problem that includes task dependency,communication costs,workflow constraints,device energy consumption,and the heterogeneous characteristics of the edge *** addition,the optimal placement of workflow tasks is optimized using a discrete teaching learning-based optimization(DTLBO)*** experimental evaluations demonstrate that the proposed offloading strategy is effective at minimizing the energy consumption of mobile devices and reducing the execution times of workflows compared to offloading strategies using different metaheuristics,including particle swarm optimization and ant colony optimization.

关键词： High-performance computing internet of things(IoT) mobile edge computing(MEC) workflows computation offloading teaching learning-based optimization

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