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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Alamsyah, Nur Budiman Danestiara, Venia Restreva Akbar, Imannudin Sinaga, Arnold Ropen Nursyanti, Reni Faculty of Technology and Informatics UNIBI Bandung Indonesia

ISBN: (纸本)9798331539603

Fluctuations in weather conditions present a significant challenge for accurate prediction, particularly when considering multiple interdependent variables such as temperature and precipitation. To address this issue, this study proposes a multivariate regression approach driven by advanced feature engineering techniques. Specifically, we employed Random Forest and XGBoost models, leveraging temporal, interaction, and lag features to enhance the predictive accuracy. The proposed method was evaluated using a real-world weather dataset, where both models demonstrated substantial improvements in prediction per-formance. The Random Forest model achieved a Mean Squared Error (MSE) of 0.059 for temperature and 0.019 for precipitation, with an R-squared (R2) of 0.995 and 0.999, respectively. Similarly, the XGBoost model yielded an MSE of 0.043 for temperature and 1.826 for precipitation, with an R2 of 0.996 and 0.983. These results underscore the effectiveness of the feature engineering process combined with robust multivariate regression models, offering a promising approach for accurate weather prediction. © 2024 IEEE.

关键词： Prediction models

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Code Mixing Malay Online Text Sentiment Analysis: Proposed Feature Extraction and Feature Selection Scheme 5

Code Mixing Malay Online Text Sentiment Analysis: Proposed F...

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5th International Conference on Artificial Intelligence and Data Sciences, AiDAS 2024

作者： Selvaraju, Surendran Sjarif, Nilam Nur Amir Anuar, Mohd Syahid Mohd University Technology Malaysia Razak Faculty of Technology & Informatics Malaysia

ISBN: (纸本)9798331528553

Significant gaps in existing sentiment analysis models conventionally cater to monolingual dataset. These conventional models often fail to capture the nuanced expressions found in code mixing text. The syntax, grammar and semantic rules of Malay are varying than English. Besides, code mixing online text also includes slang, abbreviation and non-standard language variations. This adds to the complexities in understanding the context and meaning of the code mixing text. The purpose of this research is to design a proposed feature extraction and feature selection scheme for sentiment analysis in code mixing Malay online text. The novel approach incorporates contextual word embeddings from mBERT model alongside other feature extraction and feature selection techniques which tailored specifically for code mixing Malay online text. Code mixing bilingual words lexicon is created and compiled from various online sources for sentiment word identification. This proposed scheme aims to contribute to sentiment analysis on code mixing Malay online text by providing a scalable and effective tool for sentiment analysis in multilingual contexts. © 2024 IEEE.

关键词： Semantics

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Comparative Analysis of SVM, Naïve Bayes, and Logistic Regression in Detecting IoT Botnet Attacks

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International Journal of Advanced Computer Science and Applications 2025年第4期16卷 338-343页

作者： Siswanto, Apri Aji, Luhur Bayu Efendi, Akmar Alfaruqi, Dhafin Azriansyah, M. Rafli Raihan, Yefrianda Informatics Department Faculty of Engineering Universitas Islam Riau Pekanbaru Indonesia Faculty Data Science and Information Technology INTI International University Malaysia

The rapid proliferation of Internet of Things (IoT) devices has significantly increased the risk of cyberattacks, particularly botnet intrusions, which pose serious security threats to IoT networks. Machine learning-based Intrusion Detection Systems (IDS) have emerged as effective solutions for detecting such attacks. This study presents a comparative analysis of three widely used machine learning classifiers—Support Vector Machine (SVM), Naïve Bayes (NB), and Logistic Regression (LR)—to assess their performance in detecting IoT botnet attacks. The experiment uses the BoTNeTIoT-L01 dataset, applying preprocessing techniques such as data cleaning, normalization, and feature selection to enhance model accuracy. The models are trained and evaluated based on standard performance metrics, including accuracy, precision, recall, F1-score, and AUC-ROC. The results indicate that SVM outperforms the other classifiers in terms of detection accuracy and robustness, particularly in detecting malware based on PE files. These findings offer valuable insights into selecting suitable machine learning models for securing IoT environments. Future work will further explore integrating advanced feature selection techniques and deep learning models to improve detection performance. © (2025), (Science and Information Organization). All rights reserved.

关键词： Logistic regression

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Sentiment Analysis in Code Mixing Malay Text: A Review 5

Sentiment Analysis in Code Mixing Malay Text: A Review

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5th International Conference on Artificial Intelligence and Data Sciences, AiDAS 2024

作者： Selvaraju, Surendran Sjarif, Nilam Nur Amir Anuar, Mohd Syahid Mohd University Technology Malaysia Razak Faculty of Technology & Informatics Malaysia

ISBN: (纸本)9798331528553

Sentiment analysis in code mixing received a significant amount of attention in recent years. Code mixing involved two or more languages in a text. Significant gaps in existing sentiment analysis models conventionally cater to monolingual only. These conventional models often fail to capture the nuanced expressions found in code mixing text. Sentiment analysis of Malay text has drawn a lot of attention from scholar especially in online platforms. The syntax, grammar and semantic rules of Malay are varying than English. Additionally, code mixing text also includes slang, abbreviation and non-standard language variations. This adds to the complexities in understanding the context and meaning of the code mixing text. Therefore, this review paper investigated and presents sentiment analysis in code mixing briefly. Different approaches are also discussed in this paper from previous works, ranging from lexicon based approach to advanced machine learning based approach. Lastly, this review highlights the unique challenges and significant opportunities in sentiment analysis for code mixing Malay text. © 2024 IEEE.

关键词： Semantics

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A Recommendation System for Double Track Program Skill Selection Using the Analytic Hierarchy Process (AHP) 10

A Recommendation System for Double Track Program Skill Selec...

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10th IEEE Information technology International Seminar, ITIS 2024

作者： Bughowi, Mohamad Kholid Sunaryono, Dwi Baskoro, Fajar Faculty of Intelligent Electrical and Informatics Technology Department of Informatics Engineering Surabaya Indonesia

ISBN: (纸本)9798331521295

The Double Track Program prepares high school students for careers or entrepreneurship through skills training, aiming to reduce post-graduation unemployment in East Java. It offers seven skill areas: multimedia, culinary arts, fashion design, beauty treatment, electrical engineering, electrical technology, and light vehicle engineering. Students choose one skill area for a year. However, mismatched expectations can lead to wasted time, poor outcomes, and frustration. To address this, a web-based recommendation system was developed to suggest skill areas based on criteria such as job availability, salary, interests, facilities, and entrepreneurial opportunities. The system employs the Analytic Hierarchy Process (AHP) method to support decision-making. It is implemented as a website using TypeScript, Go, the *** framework, and PostgreSQL for data storage. Testing with 77 eleventh-grade students from Dharma Wanita Surabaya High School showed that the top three recommendations achieved an average accuracy of 77.3036%, while the top five reached 77.5510%. Overall, the system demonstrated an average accuracy of 78.4521%, proving effective in guiding students toward skill areas that align with their goals. © 2024 IEEE.

关键词： Decision making

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A Self-adapting Pixel Antenna - Substrate Lens System for Infrared Frequencies

Periodica Polytechnica Electrical Engineering and Computer S...

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Periodica Polytechnica Electrical Engineering and Computer Science 2025年第1期69卷 14-25页

作者： Shubbar, Mustafa Rakos, Balázs Department of Automation and Applied Informatics Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Műegyetem rkp. 3. BudapestH-1111 Hungary

In this work, we propose a concentrator lens - antenna arrangement, potentially suitable for infrared sensing and energy harvesting rectenna systems. The structure consists of a pixel antenna, self-adapting for the direction of the incident infrared radiation, and a concentrator lens, both optimized for the mid-infrared spectrum. The silicon substrate lens is situated above the pixel antenna, enabling the concentration of the incident light on the antenna;silicon was chosen due to its transparency in the IR spectrum. We examine how various parameters of the lens, in conjunction with the different states of the pixel antenna, affects the performance of the system. The simulations show that the gain of the arrangement increases considerably in correlation with the radius of the lens. The results suggest that the energy conversion efficiency of infrared rectenna systems can be enhanced by several orders of magnitude with the utilization of the proposed arrangement. © 2025 Budapest University of technology and Economics. All rights reserved.

关键词： Rectennas

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A new CNN-based watermarking method for color medical images in a fusion domain

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Neural Computing and Applications 2025年 1-19页

作者： Hosny, Khalid M. Abdel-Aziz, Mostafa M. Lashin, Nabil A. Hamza, Hanaa M. Department of Information Technology Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Department of Information Technology Faculty of Information Technology and Computer Science Sinai University Arish Campus Al-Arish45518 Egypt

The transmission of medical images via medical agencies raises security concerns, necessitating increased security measures to ensure integrity and security. However, many watermarking algorithms overlook equipoise;the relation between robustness, invisibility, and payload capacity results in a less satisfactory performance. To bridge this gap, we propose a new blind watermarking method for securing medical images based on a convolutional neural network in a fusion domain. First, we transmute the host color image using a phase-only transform (PHOT) to detect the surface pattern. We feed the detected surface pattern into a pre-trained VGG19 model to impeccably extract the stable feature vector. We encrypt the extracted image features using a hybrid Chirikov map to enhance their robustness and reliability. The synergy between the pre-trained VGG19 model and PHOT in an integrated domain effectively captures additional native and localized image features. A hybrid Fibonacci Q-matrix method scrambles the binary watermark to enhance security. Integrating double encryption into the proposed method markedly enhances its resistance against diverse countermeasures. Experimental outcomes indicate that the proposed scheme performs efficiently in robustness and invisibility. The obtained PSNR value was up to 60.15 dB, which is optimal for human perception. The embedded watermark can be retrieved without any warping. The retrieved watermark seems to be authentic, exhibiting ideal BER and NC values. In almost all attack circumstances, the BER values approached zero while the NC values got closer. The proposed method demonstrates notable enhancements in robustness and invisibility compared to prior methods. © The Author(s) 2025.

关键词： Watermarking

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Fog Computing Service Placement Orchestrator 11

Fog Computing Service Placement Orchestrator

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11th International Conference on Electrical and Electronics Engineering, ICEEE 2024

作者： Šatkauskas, Nerijus Venčkauskas, Algimantas Morkevičius, Nerijus Kaunas University of Technology Faculty of Informatics Kaunas Lithuania

ISBN: (纸本)9798350362541

Fog computing being an extension of cloud computing is meant to provide services at the edge of the network. It is expected to deal with the QoS problems which cloud computing cannot solve alone. One of the challenges in this heterogeneous device environment with a high mobility is an optimized service placement. Highly mobile end-devices and constantly changing fog node resources require distributed orchestration methods for optimized performance and resilience. We propose a service placement orchestrator model which is distributed across all the fog nodes for better resilience. It supports end-device mobility since end-devices can dynamically connect to fog nodes. And the service placement is optimized based on IMOPSO and AHP methods. As it is visible from the test results, currently a placement problem solving takes at least 50% of time in a powerful node such as PC, but it can slow down even more in a less powerful hardware. However, it is a fully functional solution in real-life conditions with up to 10 nodes and 30 services delivering a placement in almost 0.5 second. © 2024 IEEE.

关键词： Fog computing

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Basketball Board Detection Using YOLO Algorithms 29

Basketball Board Detection Using YOLO Algorithms

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29th International Conference Information Society and University Studies, IVUS 2024

作者： Širmenis, Juozas Lukoševičius, Mantas Faculty of Informatics Kaunas University of Technology Kaunas Lithuania

This research aims to detect basketball board, rim, net, and shooting box using different versions of the YOLO algorithm: YOLOv5, YOLOv7, and YOLOv9. The study found that the YOLOv9-C model provided the best performance with a precision of 0.996, recall of 1, mAP_0.5 of 0.995, and mAP_0.5-0.95 of 0.899. YOLOv7 demonstrated the fastest inference time for the detection task and the smallest model size. Meanwhile, YOLOv5 exhibited architectural flexibility and the potential to be the optimal model in terms of detection speed, model size, and precision. © 2024 CEUR-WS. All rights reserved.

关键词： Basketball

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Addressing Scalability Issues of Blockchains with Hypergraph Payment Networks

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2427-2440页

作者： Kotzer, Arad Ladoczki, Bence Tapolcai, Janos Rottenstreich, Ori Technion -Israel Institute of Technology Haifa Israel Budapest University of Technology and Economics Department of Telecommunications and Artificial Intelligence Faculty of Electrical Engineering and Informatics Hungary

Payment channels are auspicious candidates in layer-2 solutions to reduce the number of on-chain transactions on traditional blockchains and increase transaction throughput. To construct payment channels, peers lock funds on 2-of-2 multisig addresses and open channels between one another to transact via instant peer-to-peer transactions. Transactions between peers without a direct channel are made possible by routing the payment over a series of adjacent channels. In certain cases, this can lead to relatively low transaction success rates and high transaction fees. In this work, we introduce pliability to constructing payment channels and graft edges with more than two endpoints into the payment graph. We refer to these constructions as hyperedges. We present hyperedge-based topologies to form hypergraphs and compare them to Bitcoin's Lightning network and other state-of-the-art solutions. The results demonstrate that hyperedge-based implementations can both increase transaction success rate, in addition to decreasing the network cost by more than 50% compared to that of the Lightning Network. © 2004-2012 IEEE.

关键词： Blockchain

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