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2024 International Conference on computer Engineering, Network and Intelligent Multimedia, CENIM 2024

作者： Sung, Feliks Vanedick, Varick Purnomo, Fredy Jabar, Bakti Amirul School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350368802

Cloud gaming technology runs the game on a cloud server, creating a gaming experience without the need for powerful devices. However, this technology still endures setbacks such as network problems which lowers the quality of experience for players. This study aims to evaluate the impact of factors on the accessibility and quality of player experience using a Systematic Literature Review (SLR) and PRISMA Flow. We found 219 journals and processed 12 to answer questions relating to research topics. Results found factors (Network connection, Cost, Resolution) that influence the quality of player experience and in turn player accessibility, also explained some developments in mobile cloud gaming and cloud gaming in general. We concluded that Cloud Gaming has a large impact on the quality of player experience and accessibility for players, but for the priority of improvement, the quality of player experience is more prominent. © 2024 IEEE.

关键词： Cloud platforms

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MEC-EnergySaver: Unleashing Efficiency Through D2D and Data Compression 16

MEC-EnergySaver: Unleashing Efficiency Through D2D and Data ...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Ghosh, Anindita Mukherjee, Poulomi De, Tanmay National Institute of Technology Department of Computer Science and Engineering Durgapur India

ISBN: (纸本)9798350383119

The shift from basic cell phones to smartphones has ushered in intelligent devices, alongside a surge in mobile apps that reshape daily life. Simultaneously, technologies like data analytics, AI and IoT have enabled smart homes and advanced transport systems, demanding substantial computational power from mobile devices. To tackle this challenge, mobile-edge computing (MEC) has emerged as a solution. Unlike remote cloud computing, MEC offers computing services at the network's edge, allowing mobile users to transfer their computing tasks to MEC servers located nearby. However, MEC systems face challenges in efficient computation offloading and distributing resources in a way that reduces energy use and processing times. The evolution of mobile devices and the rise of data-intensive applications have led to the need for efficient computing solutions like MEC, with the potential to integrate data compression for energy savings. This paper introduced a two-phase approach for request servicing in MEC systems. The first phase prioritizes Device-to-Device (D2D) connections based on device proximity, efficiently pairing devices for data exchange. Unpaired devices are directed to the nearest MEC node. In the second phase, requests not fulfilled via D2D connections are handled by strategically positioned MEC nodes, ensuring comprehensive coverage and minimizing network energy consumption. © 2024 IEEE.

关键词： D2D communication data compression energy efficiency MEC

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Sentimental Analysis-Based Recommended System for Products Using Machine Learning 8th

Sentimental Analysis-Based Recommended System for Products U...

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8th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2023

作者： Mamatha Bai, B.G. Likhith, S.R. Itagi, Salma Department of Computer Science Engineering Nitte Meenakshi Institute of Technology Karnataka Bengaluru India Department of School of Computer Science and Engineering and Information Science Bio-Intelligence Lab Presidency University Karnataka Bengaluru India Department of Computer Science Engineering Sai Vidya Institute of Technology Karnataka Bengaluru India

ISBN: (纸本)9789819976218

Sentiment Analysis is widely used in the process of mining the data, to predict emotion of a sentence through Natural Language Processing (NLP). The main aim is to find the accurate polarity of a sentence. Therefore, to find the polarity or sentiment of a user or customer for a product there is a need for automated data analysis techniques. In this paper, a detailed analysis of classification techniques is used in Sentimental Analysis of Amazon Product Reviews with recommendation for a best buy product on web. Multinomial Naïve Bayes, Random Forest, Logistic Regression, Decision Tree, and SVM classifiers are tested and compared. Random Forest gives the best accuracy of 94.94%. Web scraping extracts five Amazon products on *** and recommends the best buy product on the basis of polarity score of each product and here Samsung Galaxy M01 is recommended as the best buy product. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification Machine learning Polarity Sentimental analysis Web scraping

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Catboost in Fetal Health Classification 11

Catboost in Fetal Health Classification

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Yulistiani, Risma Kurniadi, Felix Indra School of Computer Science Bina Nusantara University Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350389654

The evaluation of fetal health is vital in safeguarding the overall welfare of pregnant women and their unborn children. Using the Catboost algorithm, we address the difficulty of precisely determining the health status of the fetus in this study. A variety of machine learning models were contrasted, such as AdaBoost, LightGBM, and XGBoost. The outcomes of experiments employing both train-test split and K-Fold cross-validation demonstrate that CatBoost consistently attains commendable levels of accuracy, precision, and recall, with values varying between 0.94 and 0.97 across distinct folds. © 2024 IEEE.

关键词： Electronic health record

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Comparison of a Combined Model (K-Nearest Neighbor Algorithm and Support Vector Machine Algorithm), K-Nearest Neighbor Algorithm, and Support Vector Machine Algorithm to Detect Hate Speech on Social Media 9

Comparison of a Combined Model (K-Nearest Neighbor Algorithm...

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9th International Conference on Information Management and technology, ICIMTech 2024

作者： Mackenzie, Gabriel Meyliana Yunanda, Rezki Suryaningrum, Kristien Margi School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

In this study we delve into the use of a combined machine learning model to counter the growing problems of hate speech on social media. Hate speech can significantly impact individuals, communities, and society. To counteract this, we have proposed a combined K-Nearest Neighbor (KNN) algorithm with a Support Vector Machine (SVM) algorithm called the KNN-SVM. We used a publicly available dataset from Kaggle [18]. The dataset contains labels such as hate speech, offensive language, and normal text. Our aim is to improve the classification metrics for accuracy, precision, recall, and F1-score on detecting hate speech. Then we compared our model's performance to the individual KNN and SVM model. While all models faced significant challenges due to the imbalanced data with fewer hate speech examples, KNN-SVM achieved a promising result against the other models. It demonstrated the highest recall and F1-score for hate speech detection, indicating its effectiveness in identifying these crucial instances. However, limitations exist. The model's overall accuracy was lower than the individual SVM model. Further research is needed to enhance the model's effectiveness through data refinement techniques like balanced labeling and exploring advanced algorithms like deep learning. © 2024 IEEE.

关键词： Adversarial machine learning

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Fixed-time command filtered output feedback control for twin-roll inclined casting system with prescribed performance

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Mathematical Biosciences and Engineering 2024年第2期21卷 2282-2301页

作者： Gao, Dongxiang Zhang, Yujun Wu, Libing Liu, Sihan School of Computer and Software Engineering University of Science and Technology Liaoning Liaoning CO Anshan114051 China School of Science University of Science and Technology Liaoning Liaoning CO Anshan114051 China

The article investigates the issue of fixed-time control with adaptive output feedback for a twin-roll inclined casting system (TRICS) with disturbance. First, by using the mean value theorem, the nonaffine functions are decoupled to simplify the system. Second, radial basis function neural networks (RBFNNs) are introduced to approximate an unknown term, and a nonlinear neural state observer is created to handle the effects of unmeasured states. Then, the backstepping design framework is combined with prescribed performance and command filtering techniques to demonstrate that the scheme proposed in this article guarantees system performance within a fixed-time. The control design parameters determine the upper bound of settling time, regardless of the initial state of the system. Meanwhile, it ensures that all signals in the closed-loop system (CLS) remain bounded, and it can also maintain the tracking error within a predefined range within a fixed time. Finally, simulation results assert the effectiveness of the method. © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License.

关键词： Closed loop systems

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UI/UX Design Impact on E-Commerce Attracting Users 9th

UI/UX Design Impact on E-Commerce Attracting Users

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Hasan, Tsabbita Isnina Silalahi, Christine Irene Rumagit, Reinert Yosua Pratama, Galih Dea Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

In this era of digitalization, e-commerce platforms serve as vital transactional bridges between users and sellers, playing quite an important role on serving our interests and needs. With the efficiency and effectiveness offered, it's no wonder e-commerce platforms has become quite popular. This causes the competitive landscape in online marketing quite fierce, with e-commerce platforms striving to retain existing users while attracting new ones and expand their growth even more. Playing quite a huge role to this is the User Interface and User Experience (UI/UX) design, serving as one of the fundamentals in shaping positive user perceptions, leading to user engagement and dynamics with the platform. This research will discuss aspects that can attract users in terms of UI/UX design perspective. By employing a quantitative approach along with quantitative analytics, we aim to uncover the underlying factors that contribute to user attraction and retention. Through a comprehensive analysis of UI/UX design elements such as color theory, navigational ease, importance of homepage design, design priority, and user satisfaction, this study seeks to elucidate the key drivers of UI/UX design, enhancing user engagement and satisfaction. Through this research, our analysis on four well-known e-commerce platforms in Indonesia (Shopee, Tokopedia, Blibli, and Lazada) reveals that Tokopedia ranks highest in user satisfaction with a Mean Opinion Score (MOS) of 3.93 out of 5, followed by Shopee with 3.92, Blibli with 3.83 and lastly Lazada with 3.58. The result aligned with how Tokopedia has been applying the right design on aspects we deemed important leading to satisfactory user satisfaction. Thus, highlighting the importance of homepage design, navigational ease, and color theory in attracting and retaining users. The findings of this research validate the significance of these UI/UX elements and provide practical guidance for improving e-commerce design. © 2024 The Authors.

关键词： Marketplaces

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Sign Language Detection to Enhance Online Communication for Deaf and Mute Individuals through Deep Learning Models

Sign Language Detection to Enhance Online Communication for ...

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2024 International Conference on computer Engineering, Network and Intelligent Multimedia, CENIM 2024

作者： Ruslim, Gladys Jessica Salim, Nicholas Matthew Edbert, Ivan Sebastian Suhartono, Derwin Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350368802

Communicating online presents significant hurdles for deaf and mute people, who frequently confront challenges due to speech and hearing limitations. These limitations can make their digital interactions difficult and irritating, as many online platforms and communication tools are not designed with their special needs in mind. This study intends to improve these people's online communication experiences by creating an object detection and classification model that detects Sign Language using computer vision. Three deep learning models, VGG16, ResNet50, and MobileNet, are evaluated using the SIBI Sign Language Dataset. The comparison focused on determining the most effective approach for accurate sign language detection. The results show that the VGG16 model outperforms the other models by 0.9680 accuracy and 0.9694 F1 score. By incorporating such technologies, a critical gap in digital accessibility can be closed, allowing these individuals to participate more completely in the digital world. © 2024 IEEE.

关键词： Convolutional neural networks

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T5-LDSum: Leveraging T5 Transformer in Hybrid Abstractive-Extractive Long Document Summarization 6

T5-LDSum: Leveraging T5 Transformer in Hybrid Abstractive-Ex...

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6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： Magdy, Mostafa Abdelbaky, Abdelrahman M. Mohamed, Ensaf Hussein Helwan University Master of Computer Science Faculty of Computers and Artificial Intelligence Egypt Helwan University Bachelor of Computer Science Faculty of Computers and Artificial Intelligence Egypt Nile University Computer Science School of Information Technology and Computer Science Giza Egypt Helwan University Faculty of Computers and Artificial Intelligence Computer Science Department Egypt

ISBN: (纸本)9798350378511

In the era of digital information overload, the ability to summarize books efficiently emerges as an invaluable skill. Book summarization condenses extensive texts into digestible, concise summaries, enabling readers to grasp the essence of a book without committing to reading it in its entirety. In scholarly research, various techniques for text summarization are employed, including extractive summarization, abstractive summarization, and hybrid approaches. This paper presents a novel hybrid method that leverages the strengths of both extractive and abstractive techniques, with a particular focus on the T5 text-to-text transformer model. Our methodology begins with a BERT-based extractive model paired with the LexRank algorithm to generate preliminary summaries. These summaries are subsequently refined using the T5 transformer model, known for its powerful text generation capabilities. We conducted two empirical studies using Oscar Wilde's 'The Picture of Dorian Gray,' specifically focusing on Chapter 13. Our experiments analyzed the efficacy of summaries derived from the top 20% and top 60% of sentences ranked by the extractive model. The findings indicate a consistent pattern: summaries based on the 60% sentence extraction significantly outperform those from the 20% extraction across all assessed metrics-Rouge, precision, recall, F1 scores, and human evaluations. The integration of the T5 transformer model in the refinement process is highlighted as a key component in achieving high-quality, coherent summaries. © 2024 IEEE.

关键词： abstractive summarization Book summarization evaluation metrics extractive summarization hybrid summarization

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Color-Based Auto-Rickshaw Detection Using Convolutional Neural Networks for Intelligent Transportation Systems 2

Color-Based Auto-Rickshaw Detection Using Convolutional Neur...

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2nd International Conference on Information and Communication technology, ICICT 2024

作者： Hasan, Md. Mehedi Aman, Nakib Jahan, Md Wadud Developer Experience Hub Rajshahi Bangladesh Department of Computer Science and Engineering Pabna University of Science and Technology Pabna Bangladesh Department of Computer Science and Engineering Varendra University Rajshahi Bangladesh

ISBN: (纸本)9798331508227

The investigation in this paper is centered on a new methodology for improving the effectiveness of intelligent transportation systems by identifying and distinguishing autorickshaws using Convolutional Neural Networks (CNN). The strategy proposed involves training a CNN model to automatically recognize and differentiate the colors of auto-rickshaws, which in turn significantly enhances the detection capabilities. Using hierarchical convolution feature models, the CNN algorithm independently extracts relevant features from images, thereby aiding in precise object detection. An assessment of the model's performance is conducted using a carefully selected dataset containing 1000 auto-rickshaw images, illustrating a high level of accuracy with minimal margin of error. This research makes a valuable contribution to the advancement of automated transport detection within the realm of traffic management. It presents a more streamlined and impactful methodology aimed at improving the overall efficiency and reliability of the system. ©2024 IEEE.

关键词： Convolutional neural networks

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