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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Yamamoto, Takumi Kanoga, Suguru Sugiura, Yuta Tokyo Japan Keio University Faculty of Science and Technology Department of Information and Computer Science Kanagawa Japan

ISBN: (纸本)9798331531614

Blood pressure (BP) is a vital parameter in medical treatment and diagnosis, and as a non-invasive method to measure BP, some deep learning models have been proposed to estimate BP from photoplethysmograms (PPGs). However, the datasets and the method of dividing them into training and testing subsets are not uniform, making it difficult to compare them fairly. In this study, we compared the performance of nine deep learning models for estimating systolic and diastolic BP using PPGs. We used PulseDB, which has "calibration-based subset"and "calibration-free subset"as test subsets. The calibration-based subset has the same subject's data in the training subset, and the calibration-free subset does not have the same subject's data in the training subset. The results showed that ST-ResNet performed the best, and it is important to evaluate the models using both calibration-based and calibration-free subsets, and to prevent the overfitting by using the weight decay. © 2025 IEEE.

关键词： Blood pressure

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Robustified principal component analysis for feature selection in EEG signal classification

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2023年第5期7卷 46-54页

作者： Martin, R. Faculty of Computer Science & Information Technology Jazan University Saudi Arabia

Feature engineering is an important step in data analysis, especially for machine learning applications. A wide range of feature selection methods are being used in Electroencephalography (EEG) signal processing applications. Principal Component Analysis (PCA) is considered an ideal method for feature selection whenever high dimensional data is obtained, especially in signal processing applications. Following an examination of various EEG signal processing frameworks, PCA emerged as the winner in the battle to reduce dimensionality. Despite its widespread use, it has been found to be ineffective for EEG signal processing problems like epileptic seizure detection due to the nonlinear nature of the signal properties. Traditional methods for solving PCA are insufficient in this case, so suggest a novel technique. In this paper, PCA is explored with an EEG classification model. The proposed work demonstrates how PCA is robustified for an EEG signal processing scenario by applying kernel functions. Statistical features are extracted from EEG data after preprocessing by the Desecrate Wavelet Transform (DWT). Initially, the classical PCA algorithm is applied for feature selection by reducing the dimensionality. Later, the algorithm is robustified by applying a Gaussian kernel in a nonlinear, high-dimensional feature space. In an EEG classification of epileptic seizure detection, the adoption of robustified PCA outperforms conventional PCA in terms of accuracy © 2023, International Journal of Systematic Innovation. All rights reserved

关键词： Dimensionality Reduction Electroencephalography (EEG) Feature Engineering PCA Signal Processing

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A Conceptual Model e-Collaboration for Rural Tourism - Combining Soft System Methodology and UML 7

A Conceptual Model e-Collaboration for Rural Tourism - Combi...

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7th information Systems International Conference, ISICO 2023

作者： Purwaningsih, Mardiana Purwandari, Betty Hidayanto, Achmad Nizar Faculty of Computer Science Universitas Indonesia Indonesia Faculty of Information Technology Perbanas Institute Indonesia

Through the IT utilization, stakeholders in rural tourism can engage in e-collaboration to develop tourism products and services together. While existing literature has not extensively addressed the e-collaboration model specifically tailored for rural tourism, this research sheds light on the emerging role of IT and its value within this context. To effectively model the intricate and less-structured environment of e-collaboration in rural tourism, the SSM is deemed the most suitable approach. Nonetheless, one limitation of SSM is the absence of explicit references during the implementation stage, requiring an additional methodological approach to realize the e-collaboration conceptual model within a system. To overcome this limitation, the present study applies both SSM and UML an e-collaboration system for rural tourism. The findings of this research can serve as a valuable reference for the development of an e-collaboration tool. © 2023 The Authors. Published by Elsevier B.V.

关键词： information services

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An AI-based Framework for Olive Disease Identification Using SqueezeNet, Artificial Neural Networks, and Support Vector Machines 30

An AI-based Framework for Olive Disease Identification Using...

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30th IEEE International Conference on Telecommunications, ICT 2024

作者： Al-Khateeb, Shahed N Yarmouk University Faculty of Information Technology Computer Science Dept Irbid Jordan

ISBN: (纸本)9798350356694

Plant diseases pose a hazard to world food security. According to data from the Jordanian Department of Statistics, the olive crop was Jordan's greatest agricultural crop in terms of planted area and quantity produced in 2022. This study intends to use deep learning and machine learning approaches to identify olive crop illnesses. Image data was acquired from several sources and integrated into a single dataset. The data was split among 70% training and 30% testing. Deep learning SqueezeNet methods were used to extract features. Data training was conducted using both Artificial Neural Networks and Support Vector Machines models. In terms of accuracy, the Artificial Neural Network provided 92.9%, surpassing the Support Vector Machines model score of 85.5%. © 2024 IEEE.

关键词： Crops

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Sentiment Analysis of Libyan Middle Region Using Machine Learning with TF-IDF and N-grams 2nd

Sentiment Analysis of Libyan Middle Region Using Machine Lea...

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2nd International Libyan Conference on information and Communication Technologies, ILCICT 2023

作者： Habberrih, Abdullah Abuzaraida, Mustafa Ali Computer Science Department Faculty of Information Technology Misurata University Misurata Libya

ISBN: (纸本)9783031626234

Arabic dialects are commonly used on social media platforms by Arabic speakers to express their opinions and connect with each other. However, due to the lack of standardized rules or grammars, analyzing Arabic dialects with NLP tools can be more challenging than standard Arabic. Moreover, the poems domain within Arabic dialects is considered to be more challenging than other domains due to structural differences between poems and regular expressions. This study investigates the use of TF-IDF and N-grams with Lemmatization techniques to develop machine learning classifiers for sentiment analysis in the domain of poems within the Libyan dialect, specifically the Libyan Middle Region dialect. Three experiments were conducted using ML classifiers, namely, SVM, NB, and LR. The first experiment explored classifiers’ performance using .TF-IDF with Unigrams, whereas the second experiment investigated the use of TF-IDF with Trigrams, and the third experiment examined the impact of combining Unigrams and Trigrams on the classifiers’ performance. The experimental results indicate that utilizing Unigrams with TF-IDF can enhance classifier performance, whereas Trigrams with TF-IDF can have a negative effect on the classifiers’ performance. Notably, SVM achieved the highest accuracy of 69.04% in the first experiment, while LR achieved the highest accuracy of 58.60% in the second experiment, and SVM achieved an accuracy of 68.92% in the third experiment. Furthermore, in terms of precision, LR in the first experiment outperformed the other classifiers across all experiments with 70.49%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Sentiment analysis

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Developing An Efficient Vibrotactile Stimuli In computer Game 8

Developing An Efficient Vibrotactile Stimuli In Computer Gam...

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

作者： Tarigan, Jos Timanta Sikoko, Arivan Satya Selvida, Desilia Universitas Sumatera Utara Faculty of Computer Science and Information Technology Medan Indonesia

ISBN: (纸本)9798331531249

This research investigates the impact of haptic feedback on gaming experience using controllers. Haptic feedback is common in modern console gaming in the type of vibrotactile. Modern gamepads come with motor that can be triggered to simulate vibrotactile. Our objective is to study the use of vibrotactile in game development. In this research, we developed a third person action RPG game and implement several vibrotactile feature in the game. Our objective is to find the best way to implement vibrotactile based on the in-game event. To measure the effectiveness of the implementation, we invited 15 respondents to test the proposed solution. Based on the questionnaire filled in by the respondents, most of the test shows positive feedback. Our respondents agree that the implemented haptic feedback is a good addition to our game. Furthermore, most respondents agree that the vibration feature add a unique experience during the gameplay. © 2024 IEEE.

关键词： Game Development Haptic Feedback Human-computer Interaction Vibrotactile

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High-Dimensional Data Integration Model to Improve the Plants Quality

High-Dimensional Data Integration Model to Improve the Plant...

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2024 International Conference on Control and Automation, Electronics, Robotics, Internet of Things, and Artificial Intelligence, CERIA 2024

作者： Hasibuan, Nelly Astuti Sihombing, Poltak Nababan, Erna Budhiarti Amalia Universitas Sumatera Utara Faculty of Computer Science and Information Technology Medan Indonesia

ISBN: (纸本)9798331511166

High Dimensional Data is data that has many dimensions or features with the aim of providing more complete and detailed information about an object or phenomenon. Plant images are objects that have data with many dimensions or features. Plant images can be analyzed to determine plant quality. For example, coffee plant images can be analyzed to determine the quality of coffee plants. Determination of coffee plant quality in previous studies only used one object such as leaves or stems and or fruit. In this study, the quality of coffee plants is measured using leaves, stems, fruit, and hypotheses from coffee plant experts. Analysis of coffee plant images can produce information in the form of size, color and health of plants. This information can be found by extracting image features. However, the large number of features is an obstacle in grouping. Clustering techniques are used to group the large amount of data in the dataset. Expert hypotheses will be used to validate the dataset, determine centroid values and determine weight values to determine plant quality. The obstacle in analyzing multidimensional data is the difficulty of visualizing the number of variables because the available methods generally only process low-dimensional data. The purpose of this study is to produce an integration model for analyzing plant quality datasets by optimizing machine learning to group high-dimensional data. © 2024 IEEE.

关键词： Data quality

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Reduction of Microarray Data Dimensions to Enhance Performance of Naïve Bayes in Classification 4

Reduction of Microarray Data Dimensions to Enhance Performan...

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4th International Conference of science and information technology in Smart Administration, ICSINTESA 2024

作者： Ramadhani, Putri Tsatsabila Nababan, Erna Budhiarti Efendi, Syahril Universitas Sumatera Utara Faculty of Computer Science and Information Technology Medan Indonesia

ISBN: (纸本)9798350376111

According to data Global Burden of Cancer Survey (Globocan) by World Health Organization (WHO) in 2018 there were 18.1 million new cases with a death rate of 9.6 million deaths, where 1 in 8 men and 1 in 11 women died of cancer. The death rate could be prevented with a comprehensive management system that starts with early detection, prevention, and treatment [2]. In the study of microarray data for cancer early detection, the properties of high-dimensional data, small sample sizes, and noise, must be treated seriously because they can affect the classifier's performance. This is critical for dealing with the curse of dimensionality and the inability to identify important information hidden in microarray data. Therefore, we propose PCA method for dimensionality reduction of microarray data to support Naive Bayes algorithm to complish classification tasks. Based on the studies we conducted, we discovered an increases of performance classification where the accuracy achieved is 9 2. 1 1%, precision 9 0. 2 0% and the f 1-score is 9 3. 8 8% for ovarian cancer and prostate cancer had the accuracy is 93.55%, precision and f 1 - score reached 9 2. 8 6%. © 2024 IEEE.

关键词： classification data science machine learning reduction dimension

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Deep Learning Approach for Automatic Cardiovascular Disease Prediction Employing ECG Signals

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computer Modeling in Engineering & sciences 2023年第11期137卷 1677-1694页

作者： Muhammad Tayyeb Muhammad Umer Khaled Alnowaiser Saima Sadiq Ala’Abdulmajid Eshmawi Rizwan Majeed Abdullah Mohamed Houbing Song Imran Ashraf Department of Computer Science&Information Technology The Islamia University of BahawalpurBahawalpur63100Pakistan Department of Computer Engineering College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Science Khwaja Fareed University of Engineering and Information TechnologyRahim Yar KhanPakistan Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Faculty of Computer Science and Information Technology Universiti Tun Husein OnnMalaysia(UTHM)Bahru80536Malaysia Research Centre Future University in EgyptNew Cairo11745Egypt Department of Information Systems University of MarylandBaltimore CountyBaltimoreMD 21250USA Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-si38541Korea

Cardiovascular problems have become the predominant cause of death worldwide and a rise in the number of patients has been observed ***,electrocardiogram(ECG)data is analyzed by medical experts to determine the cardiac abnormality,which is *** addition,the diagnosis requires experienced medical experts and is ***,automated identification of cardiovascular disease using ECGs is a challenging problem and state-of-the-art performance has been attained by complex deep learning *** study proposes a simple multilayer perceptron(MLP)model for heart disease prediction to reduce computational *** dataset containing averaged signals with window size 10 is used as an *** competing deep learning and machine learning models are used for comparison.K-fold cross-validation is used to validate the *** outcomes reveal that the MLP-based architecture can produce better outcomes than existing approaches with a 94.40%accuracy *** findings of this study show that the proposed system achieves high performance indicating that it has the potential for deployment in a real-world,practical medical environment.

关键词： Cardiovascular disease prediction electrocardiograms deep learning multilayer perceptron

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Crafting the Chance: Linear Congruential Generator in Randomization System in RPGs

Crafting the Chance: Linear Congruential Generator in Random...

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2024 International Conference on information technology Systems and Innovation, ICITSI 2024

作者： Tarigan, Jos Timanta Salim, Avin Chaili Nababan, Anandhini Medianty Wijaya, Brian Universitas Sumatera Utara Faculty of Computer Science and Information Technology Medan Indonesia

ISBN: (纸本)9798331511470

In role-playing games (RPGs), randomization plays an important role in designing the loot system. The unpredictability of rewards enhances player engagement and replayability. Thus, it is crucial for developers to build an algorithm to simulate an engaging randomization method. This research proposed the use of Linear Congruential Generator (LCG) based pseudo-random numbers generator in an RPG. To test the performance of the proposed method, we developed our own game titled "Rudantara", a third person roguelike game. The game employed LCG-based random generators to randomized loot drops, statistics, and upgrades. The objective of this research is to measure the performance of the proposed randomization method in our game and analyze its impact on gameplay, fairness and player satisfaction. © 2024 IEEE.

关键词： Game design

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