检索结果-内蒙古大学图书馆

Comparative study of IoT- and AI-based computing disease detection approaches

Data science and Management 2025年第1期8卷 94-106页

作者： Rhmann, Wasiur Khan, Jalaluddin Khan, Ghufran Ahmad Ashraf, Zubair Pandey, Babita Khan, Mohammad Ahmar Ali, Ashraf Ishrat, Amaan Alghamdi, Abdulrahman Abdullah Ahamad, Bilal Shaik, Mohammad Khaja Department of Computer Application Lovely Professional University Punjab Phagwara144411 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur522502 India School of Computing Science and Engineering VIT Bhopal University Kothrikalan MP Sehore466114 India Babasaheb Bhimrao Ambedkar University Lucknow226025 India College of Commerce and Business Administration Dhofar University Salalah211 Oman Faculty of Computer Studies Arab Open University 18211 Bahrain Department of Computer Application Shri Ramswaroop Memorial University Lucknow225003 India College of Computing and Information Technology Shaqra University Shaqra11961 Saudi Arabia

The emergence of different computing methods such as cloud-, fog-, and edge-based Internet of Things (IoT) systems has provided the opportunity to develop intelligent systems for disease detection. Compared to other machine learning models, deep learning models have gained more attention from the research community, as they have shown better results with a large volume of data compared to shallow learning. However, no comprehensive survey has been conducted on integrated IoT- and computing-based systems that deploy deep learning for disease detection. This study evaluated different machine learning and deep learning algorithms and their hybrid and optimized algorithms for IoT-based disease detection, using the most recent papers on IoT-based disease detection systems that include computing approaches, such as cloud, edge, and fog. Their analysis focused on an IoT deep learning architecture suitable for disease detection. It also recognizes the different factors that require the attention of researchers to develop better IoT disease detection systems. This study can be helpful to researchers interested in developing better IoT-based disease detection and prediction systems based on deep learning using hybrid algorithms. © 2024 Xi'an Jiaotong University

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Sentiment Analysis with N-Gram Preprocessing for Online-Shopping Reviews in Indonesian Language 9

Sentiment Analysis with N-Gram Preprocessing for Online-Shop...

引用

9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Asian, Jelita Putra, Oktavianus Tegar N.K. Ayu, Media Anugerah Mantoro, Teddy Nusa Putra University Postgraduate Program - School of Computer Science Sukabumi Indonesia Sampoerna University Faculty of Engineering and Technology Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350370126

Currently, online Shopping platforms have grown significantly, especially during the COVID-19 pandemic. This condition motivates the need for analyzing how the users/customers' opinions on using such platform. Sentiment analysis, as a process of detecting, extracting, and classifying users' opinions and attitudes toward specific topics, is a good tool for the required analysis. This study aims to evaluate the performance of machine learning approach which combined with N-Gram technique in doing sentiment analysis. The dataset used in this study comes from scraping reviews in Bahasa Indonesia regarding the Shopee Apps. In this study, N=2 for the N-Gram was employed in the preprocessing process. Our main goal is to investigate whether the performance of machine learning in doing sentiment analysis can be improved by adding the N-Gram technique in its preprocessing. This work applied the Naive Bayes Classifier and k-Nearest Neighbor with K=11 as the machine learning algorithms. The best accuracy in this study was achieved by Naive Bayes Classifier after applying N-Gram Terms (N=2) with Split Validation (8:2), which is 97.26%. © 2023 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Enhancing Security Information and Event Management (SIEM) by Incorporating Machine Learning for Cyber Attack Detection 9

Enhancing Security Information and Event Management (SIEM) b...

引用

9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Ayu, Media Anugerah Erlangga, Dimas Mantoro, Teddy Handayani, Dini Sampoerna University Faculty of Engineering and Technology Department of Computer Science Jakarta Indonesia Nusa Putra University - Sukabumi Postgraduate Program - School of Computer Science Indonesia

ISBN: (纸本)9798350370126

Network security is a crucial component of Information technology, yet organizations continue to grapple with meeting established security benchmarks. Given the rise in cyber-attacks and the continuous emergence of new attack types, it's practically infeasible to persistently update attack patterns or signatures within security parameters. Key tools such as Intrusion Detection Systems (IDS) and Security Information and Event Management (SIEM) are instrumental in monitoring network traffic and identifying potential threats. However, these tools face limitations, such as the high volume of alerts produced by IDS and the use of rule-based method, also the inability of SIEM tools to analyze logs comprehensively to identify inappropriate activities. This research has conducted anomaly detection using machine learning process to classify cyber-attacks network flow collected from IDS that installed incident network infrastructure. The analysis of IDS using machine learning, integrated with SIEM. The algorithm used in this research was Random Forest Classifier using CSE-CID-IDS2018 dataset pre-processed with Principal Component Analysis (PCA). Results of the experiments show that Random Forest Classifier Model, when combined with Principal Component Analysis (PCA), yields the most commendable results when applied to a 70/30 training/testing data ratio with accuracy of 0.99953. © 2023 IEEE.

关键词： Network security

来源：评论

学校读者我要写书评

暂无评论

Features Reduction with PCA Technique for Malware Detection Using Machine Learning 9

Features Reduction with PCA Technique for Malware Detection ...

引用

9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Mantoro, Teddy Ginanjar, Agung Ayu, Media Anugerah Aditiawarman, Umar Sampoerna University Faculty of Engineering and Technology Department of Computer Science Jakarta Indonesia Nusa Putra University Postgraduate Program - School of Computer Science Sukabumi Indonesia

ISBN: (纸本)9798350370126

Malware had been a problem for quite some times since it spreads easily and can cause various problems. Currently, malware is also one of the big threats for internet users. With a huge number of internet users today, techniques that can automatically detect malware before it infects the system is required. This study aims to develop malware detection using machine learning approach with Principal Component Analysis (PCA) as feature reduction. PCA (Principal Component Analysis) is expected to be able to reduce the number of features which then could also reduce the learning time but do not reduce its accuracy significantly. There were four machine learning classifiers used in this study, i.e. K-Nearest Neighbor, Decision Tree, Naïve bayes, and Random Forest. The n-components used in this study were 20 and 34 and the ratio of test and train in the dataset was 35% for test and 65% for training. The results have shown that the best performance come from the detection using random forest with 34 n-component and 100 n-estimator with the average accuracy was 0.991688. © 2023 IEEE.

关键词： Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

HoloFarm: Enhancing agricultural learning through immersive technology

Software Impacts

引用

Software Impacts 2025年 24卷

作者： Ario, Muhamad Keenan Hasani, Muhammad Fikri Balqis, Khairatul Carment, Messya Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Extended reality in education has advanced, offering safe, immersive simulations. Agriculture, a key area, lacks urban exposure. HoloFarm, a VR-based farming simulation, addresses this gap using Unity and C#. It integrates physical movement, joystick navigation, and spatial audio for crop cultivation. Evaluated with 27 urban users via the Igroup Presence Questionnaire, it showed strong spatial (M=5.59) and general presence (M=5.81), though realism (M=4.10) and involvement (M=4.77). Future updates will enhance realism and enable collaborative learning, bridging theoretical and practical agricultural knowledge. © 2025 The Author(s)

关键词： Agricultural education Educational technology Farming simulation Immersive learning Virtual Reality

来源：评论

学校读者我要写书评

暂无评论

Mapping the Scientific Landscape of Integrated Scheduling Towards Industry 5.0 with A Bibliometric Analysis 6

Mapping the Scientific Landscape of Integrated Scheduling To...

引用

6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Putri Ardyanti, Anak Agung Ayu Xie, Zhiqiang Karnyoto, Andrea Stevens Pardamean, Bens Estiyanti, Ni Made School of Computer Science and Technology Harbin University of Science and Technology Harbin150000 China Faculty of Information Technology and Design Primakara University Bali 80226 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia School of Management and Business University of Debrecen Debrecen Hungary

The industry is rapidly transitioning from the 4.0 era to the 5.0 era, prompting renewed interest among scholars in scheduling problems. They allow operations to process and assemble various components simultaneously. This bibliometric analysis provides a comprehensive understanding of the diverse research perspectives on the potential challenges of integrated scheduling within the framework. We identified 357 Scopus articles and analysed co-occurrence keyword (CNK) by VOSViewer tools and Global Citation Score (GCS) by Scopus tools. The research data obtained from 1993 to 2023 mainly in the subject areas of Engineering, computer science, Maths, and others. CNK identified six cluster keywords, and nine most cited articles based on normalised GCS. These results contribute to our understanding of the development of scheduling algorithms, shifting from an emphasis on manufacturing transfers to contemporary demand-centric research directions. Furthermore, this research provides a valuable perspective for policymakers, plant personnel, and manufacturing managers to make informed decisions. © 2025 The Authors. Published by Elsevier B.V.

关键词： Job shop scheduling

来源：评论

学校读者我要写书评

暂无评论

Enhanced License Plate Detection using YOLOv8n: An Approach for Efficient Vehicle Identification 3

Enhanced License Plate Detection using YOLOv8n: An Approach ...

引用

3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Chandok, Vishesh Maurya, Sudhanshu Mirza, Kashish Kumar Yadav, Ajay Thomas Varghese, Sneha Garg, Rachit Symbiosis Institute of Technology Nagpur Campus Pune India Graphic Era Hill University Department of Computer Science & Engineering Uttarakhand Bhimtal Campus India United Institute of Management Department of Computer Application Prayagraj India Lovely Professional University School of Computer Science and Engineering Phagwara India

ISBN: (纸本)9798350384598

An essential feature of the intelligent transportation system is the automatic detection and recognition of license plate information. The e-payment systems for parking and toll collection in traffic control security could be utilized with this application. Numerous algorithms have been created for License Plate Recognition (LPR) and Identification, each with pros and cons depending on the circumstances. computer vision has increased in terms of techniques and developments since deep learning emerged and became popular in several artificial intelligence domains. It is one of the most efficient ways to monitor traffic signals, tax paid for parking, and police monitoring through Automatic LPR. Although it, as a technology, is widely used and developed, much more must be done to improve the system's accuracy. The current science community has excelled in efficiency and problem-solving in recent years. Along with the You Only Look Once (YOLO), targeted at the object detection area and applied to this study, our primary goal is to summarize and analyze several methods and advancements in LPR in the deep learning age. © 2024 IEEE.

关键词： Traffic signals

来源：评论

学校读者我要写书评

暂无评论

Lightweight Authenticated Key Agreement Protocol for Smart Power Grid Systems Using PUF

IEEE Open Journal of the Communications Society

引用

IEEE Open Journal of the Communications Society 2024年 5卷 3568-3580页

作者： Zahoor, Amina Mahmood, Khalid Saleem, Muhammad Asad Badar, Hafiz Muhammad Sanaullah Le, Tuan-Vinh Das, Ashok Kumar Department of Computer Science Sahiwal57000 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Emerson University Multan Department of Emerging Computing Technologies Multan61000 Pakistan Fu Jen Catholic University Bachelor's Program of Artificial Intelligence and Information Security New Taipei City24206 Taiwan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India

The Smart Power Grid (SPG) is pivotal in orchestrating and managing demand response in contemporary smart cities, leveraging the prowess of Information and Communication Technologies (ICTs). Within the immersive SPG environment, the ubiquitous deployment of smart meters stands as a testament to their paramount importance in the realm of vigilant monitoring and oversight. These smart meters are installed on high-tension electricity lines and transmit information about electricity outages and other issues to the utility center. To access services from utility centers, smart meters need to communicate securely over a public channel, even though the network itself is insecure. However, potential attacks from adversaries ( Ad) can exploit this communication. Therefore, protecting this communication is of utmost importance. Several privacy-preserving authentication protocols designed for SPG have been introduced in the literature. Nevertheless, a significant number of these protocols exhibit vulnerabilities to diverse security attacks. This article introduces a lightweight and anonymous authentication protocol specifically designed to address these concerns in the SPG environment. Our protocol ensures both security and efficiency, surpassing other comparable protocols in terms of its lightweight nature. By employing both formal and informal analysis, we showcase the robustness of our protocol against significant attacks while remaining lightweight. The proposed protocol provides added security features and incurs 23.8879% lower computation cost than related protocols. Consequently, our protocol is highly suitable for implementation in the SPG system. © 2020 IEEE.

关键词： Hash functions

来源：评论

学校读者我要写书评

暂无评论

Density Based Spatial Clustering of applications with Noise and Sentence Bert Embedding for Indonesian Utterance Clustering

Density Based Spatial Clustering of Applications with Noise ...

引用

2023 International Conference on computer science, Information technology and Engineering, ICCoSITE 2023

作者： Hasani, Muhammad Fikri Heryadi, Yaya Arifin, Yulyani Lukas Suparta, Wayan Bina Nusantara University BINUS Graduate Program Computer Science School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350320954

Task oriented chatbots are a sub-topic related to chatbots, where chatbots will perform certain tasks with specific goals. One part of creating a task-oriented chatbot is doing intent classification. Intent classification is a task of text classification. As in general text classification, the required dataset requires a label to carry out the classification process. To speed up and help the utterance analysis process, there is already a method, namely clustering, and Density-based clustering is a part of clustering that can determine cluster patterns based on arbitrary data, with DBScan as one of its algorithms. This research used 10000 client utterance data of awhatsapp based e-commerce conversation. SentenceBert also used as a state of art sentence embedding. This research yield silhouette score of 0.327 as the best result from eps of 0.1 and MinPts of 95. However, based on the cluster result, sentences labelled as noise can be further clustered. Text Preprocessing, text augmentation and sentence embedding techniques can be explored to increase the cluster performance. © 2023 IEEE.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

A Survey of Current Progress in Depression Detection Using Deep Learning and Machine Learning

引用

Biomedical Materials and Devices 2025年 1-25页

作者： Kumari, Meena Singh, Gurpreet Pande, Sagar Dhanraj Computer Application Lovely Professional University Jalandhar - Delhi Grand Trunk Rd Punjab Phagwara 144411 India Department of Computer Science and Engineering Lovely Professional University Jalandhar - Delhi Grand Trunk Rd Punjab Phagwara 144411 India Computer Science and Engineering School of Engineering and Technology Pimpri Chinchwad University Maharashtra Pune 412106 India

Generally, depression is the most prevalent mental disorder, which has affected hundreds of people around the world and causes a high risk of committing suicide. Automatic diagnosis of depression detection plays a vital role in examining people’s mental illness through various Artificial Intelligence (AI) techniques and has a high probability of reducing patient’s burden by early treatments. In this research, the crucial area of depression detection is examined, with an emphasis on the crucial function of hybridized algorithms. More studies have been developed worldwide to provide a review of depression detection techniques. Still, obtaining significant information about the input data and the different mechanisms utilized in each research for depression detection is challenging. Consequently, this review presents a systematic review of various Deep Learning (DL) and Machine Learning (MI) techniques based on the input data utilized in the models for detecting depression, heralding a seismic shift in our understanding of depression and the diagnostic and support systems connected to it. More specifically, this review examines the literature from 50 recent research articles based on depression detection topics incorporated with its significant morphology that helps findings in the creation of novel models in the future. Further, this review discusses the research gaps, challenges, dataset collection, interdisciplinary cooperation, other ideal strategies, and future scope for progressing and catalyzing the research in this domain. In addition, this review presents an overview to identify the objectives and restrictions of different literature works presented in the domain of depression detection. Moreover, the developed review assists in advancing the knowledge about the technologies adopted for depression detection and serves as a suitable reference for upcoming researchers working in this domain. © The Author(s), under exclusive licence to Springer science+Business M

关键词： Deep learning Depression detection Machine learning Multimodal data Natural language processing

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：