检索结果-内蒙古大学图书馆

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

来源：评论

学校读者我要写书评

暂无评论

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...

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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...

引用

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

来源：评论

学校读者我要写书评

暂无评论

Developing An Efficient Vibrotactile Stimuli In computer Game 8

Developing An Efficient Vibrotactile Stimuli In Computer Gam...

引用

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

来源：评论

学校读者我要写书评

暂无评论

A Comparative Study of Automatic Hate Speech Detection Using Machine Learning 1

A Comparative Study of Automatic Hate Speech Detection Using...

引用

1st IEEE Karachi Section Humanitarian technology Conference, Khi-HTC 2024

作者： Siddiqui, Jawaid Ahmed Yuhaniz, Siti Sophiayati Memon, Zulfiqar Ali Razak Faculty of Technology and Informatics Kuala Lumpur Malaysia Computer Science Department Karachi Pakistan

ISBN: (纸本)9798350373042

There is no denying that social media's ubiquitous use and the knowledge it seamlessly disseminates have improved humanity. But despite its many benefits, this growth has also given rise to urgent worries, including the spread of hate speech. Modern research have embraced a variety of feature engineering techniques and machine learning algorithms as a remedy to this increasing difficulty inside the world of social media platforms. These initiatives aim to automatically identify hate speech across several datasets, offering a promising way to lessen this pervasive problem. As far as we are aware that no research has directly compared different feature engineering methods with different machine learning algorithms to determine which method produces the best results on a representative public dataset. This article's goal is to evaluate how well three different feature engineering approaches work with eight different machine learning algorithms using an open source free publicly available datasets that include 03 different classes. The results of the experiment revealed that the combination of bigram features and the (SVM) support vector machine algorithm performed the best overall, with an amazing accuracy rate of '79%'. Our research consequences touch on actual situations, making it a landmark study that potentially laid the foundation for future efforts aimed at automatically identifying hate speech. Further, the results of these comparisons will serve as state-of-the-art approaches against which further studies of automated text classification can be measured. © 2024 IEEE.

关键词： Social networking (online)

来源：评论

学校读者我要写书评

暂无评论

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

Reduction of Microarray Data Dimensions to Enhance Performan...

引用

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

来源：评论

学校读者我要写书评

暂无评论

Decoding Digital Hostility: Examining and Addressing Hate Speech on Social Media Platforms 1

Decoding Digital Hostility: Examining and Addressing Hate Sp...

引用

1st IEEE Karachi Section Humanitarian technology Conference, Khi-HTC 2024

作者： Siddiqui, Jawaid Ahmed Yuhaniz, Siti Sophiayati Memon, Zulfiqar Ali Razak Faculty of Technology and Informatics Kuala Lumpur Malaysia Computer Science Department Karachi Pakistan

ISBN: (纸本)9798350373042

There is no denying that the widespread usage of social media and the sharing of information have greatly benefited humanity. However, a number of issues have also emerged as a result of this increase in online engagement, most notably the spread of hate speech. Recent research has addressed this growing problem on social media platforms by automatically detecting hate speech in a variety of datasets using a variety of feature engineering approaches and machine/deep learning algorithms. It is interesting, though, that many of these studies - to the best of our knowledge - resort to identifying hate speech messages using traditional feature engineering techniques, which leads to less-than-ideal classification results. This is explained by the shortcomings of the feature engineering techniques now in use, specifically their vulnerability to the word order and word context problems. Therefore, more advanced strategies are desperately needed to address these issues and improve the precision of hate speech identification on social media platforms. To create fundamental lexical benchmarks is the goal. As distinguishing characteristics, our methodology makes use of the power of n-grams, which cover both character and word levels, and skip-grams, which cover both character and word levels. Notably, we successfully identify posts in all three defined categories with an impressive 78% accuracy. The results highlight how difficult it is to discriminate between vulgarities and hate speech. We also conduct a thorough exploration of potential directions for future research projects. © 2024 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

High-Dimensional Data Integration Model to Improve the Plants Quality

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：