检索结果-内蒙古大学图书馆

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Mahajan, Prerna Dahiya, Priya Upadhyay, Rimpi Department of Computer Science and Information Technology Bangalore India Noida Institute of Engineering and Technology Department of Artificial Intelligence Uttar Pradesh Greater Noida India Tula's Institute Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350383522

Every aspect of daily life in buildings (whether commercial or residential) relies on a constant supply of energy. Energy is used whenever a building's lights are turned on, the temperature is adjusted (using space heaters or air conditioners), or a device is used (such as a computer or printer in an office building or a microwave, water heater, or washing machine in a home or hotel). The HVAC (Heating, Ventilating, and Air Conditioning) system of a commercial building is responsible for a significant percentage of the structure's overall energy use. In order to keep all these devices running properly in present and in the coming years, it is crucial that fuel is enhanced or used as effectively as possible. This study provides a comprehensive review of the literature on the use of Machine Learning techniques to the problem of energy optimization in buildings. © 2024 IEEE.

关键词： Commercial buildings Energy optimization HVAC system Machine Learning

来源：评论

学校读者我要写书评

暂无评论

Al-Biruni Earth Radius(BER)Metaheuristic Search Optimization Algorithm

引用

computer systems science & Engineering 2023年第5期45卷 1917-1934页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Seyedali Mirjalili Nima Khodadad Mona A.Al duailij Amel Ali Alhussan Doaa Sami Khafaga Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Centre for Artificial Intelligence Research and Optimization Torrens University AustraliaFortitude ValleyQLD 4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722Korea Department of Civil and Environmental Engineering Florida International UniversityMiamiUSA Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Metaheuristic optimization algorithms present an effective method for solving several optimization problems from various types of applications and *** metaheuristics and evolutionary optimization algorithms have been emerged recently in the literature and gained widespread attention,such as particle swarm optimization(PSO),whale optimization algorithm(WOA),grey wolf optimization algorithm(GWO),genetic algorithm(GA),and gravitational search algorithm(GSA).According to the literature,no one metaheuristic optimization algorithm can handle all present optimization *** novel optimization methodologies are still *** Al-Biruni earth radius(BER)search optimization algorithm is proposed in this *** proposed algorithm was motivated by the behavior of swarm members in achieving their global *** search space around local solutions to be explored is determined by Al-Biruni earth radius calculation method.A comparative analysis with existing state-of-the-art optimization algorithms corroborated the findings of BER’s validation and testing against seven mathematical optimization *** results show that BER can both explore and avoid local *** has also been tested on an engineering design optimization *** results reveal that,in terms of performance and capability,BER outperforms the performance of state-of-the-art metaheuristic optimization algorithms.

关键词： Metaheuristics evolutionary optimization exploration exploitation mutation Al-biruni earth radius

来源：评论

学校读者我要写书评

暂无评论

Enhancing Student's Performance Classification Using Ensemble Modeling

Iraqi Journal for Computer Science and Mathematics

引用

Iraqi Journal for computer science and Mathematics 2023年第4期4卷 204-214页

作者： Nafea, Ahmed Adil Mishlish, Muthanna Shaban, Ali Muwafaq Shaban AL-Ani, Mohammed M. Ali Alheeti, Khattab M. Mohammed, Hussam J. Department of Artificial Intelligence College of Computer Science and IT University of Anbar Ramadi Iraq Department of Mathematics Faculty of Education for pure science University of Anbar Ramadi Anbar Iraq Department of Information System University of Anbar Ramadi Iraq Selangor Bangi Malaysia Department of Computer Science University of Anbar Ramadi Iraq

A precise prediction of student performance is an important aspect within educational institutions to improve results and provide personalized support of students. However, the predication accuracy of student performance considers an open issue within education field. Therefore, this paper proposes a developed approach to identify performance of students using a group modeling. This approach combines the strengths of multiple algorithms including random forest (RF), decision tree (DT), AdaBoosts, and support vector machine (SVM). Afterward, the last ensemble estimates as one of the bets logistic regression methods was utilized to create a robust and reliable predictive model because it considers The experiments were evaluated using the Open University Learning Analytics Dataset (OULAD) benchmark dataset. The OULAD dataset considers a comprehensive dataset containing various characteristics related to the student’s activities thereby five cases based on the utilized dataset were investigated. The experiment results showed that the proposed ensemble model presented its ability with accurate results to classify student performance by achieving 95% of accuracy rate. As a result, the proposed model exceeded the accuracy of individual basic models by using the strengths of various algorithms to improve the generalization by reducing the potential weaknesses of individual models. Consequently, the education institutes can easily identify students who may need additional support and interventions to improve their academic performance. © The Author(s), 2023.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Segmentation Of Intracerebral Haemorrhage CT-Scan Images Using N-Net Architecture 3

Segmentation Of Intracerebral Haemorrhage CT-Scan Images Usi...

引用

3rd International Conference on Electrical, Electronics, information and Communication Technologies, ICEEICT 2024

作者： Santhi, P. Maheswari, S. Deepa, K. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai Avadi India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Tiruchirapalli Avadi India

ISBN: (纸本)9798350369083

In recent years, image segmentation has emerged as a critical task in computer vision and various applications such as medical image processing, autonomous driving, and satellite imagery analysis. Deep learning techniques, particularly convolutional neural networks (CNNs), have played a pivotal role in revolutionizing image segmentation by offering unprecedented accuracy and efficiency. Many algorithms have used for the segmentation such as U-Net, V-Mesh, U-Net++ and etc. But, U-Net having the challenges in parameter transfer, model size and in speed. To overcome these issues, this paper explores the evolution of image segmentation techniques into ICH (Intracerebral Haemorrhage) for better diagnosis. This model is tested using ICH dataset available in Kaggle. In which, N-Net have produced the better performance in terms of precision, Dice, Recall and F1 Score. © 2024 IEEE.

关键词： Mesh generation

来源：评论

学校读者我要写书评

暂无评论

Improving Big Data Recommendation system Performance Using NLP Techniques With Multi-attributes

引用

Informatica (Slovenia) 2024年第5期48卷 63-70页

作者： Omar, Hoger K. Frikha, Mondher Jumaa, Alaa Khalil ENETCOM ATISP Research Lab University of Sfax Sfax Tunisia Department of Computer Science College of Computer Science and Information Technology University of Kirkuk Kirkuk Iraq Department of Electronics National School of Electronics and Telecommunications of Sfax University of Sfax Tunisia Database Technology Department Technical College of Informatics Sulaimani Polytechnic University Kurdistan Region Sulaimani46001 Iraq

Due to the wide availability of big data, institutions and companies are currently concentrating on developing highly effective recommender systems for their users. Traditional recommender systems use standard information such as user, item, and ratings. However, this data may not be sufficient for precise results. To enhance accuracy, it is recommended to include additional information such as textual data in the recommendation system. When dealing with large textual data, employing Natural Language Processing (NLP) techniques is essential for effective data analysis. Therefore, this work proposed a novel big data recommender system that enhances collaborative filtering (CF) results by leveraging NLP techniques and dealing with multiple attributes. The study constructs two big data recommendation system models using a machine learning algorithm. In both models, the Alternating Least Squares (ALS) algorithm within the Apache Spark big data tool has been used. The first model did not incorporate NLP techniques, while the second model considered the novel NLP techniques by taking into account the user's review comments. A dataset of more than 3 million ratings and reviews was gathered from the Amazon website with a size of 3.1 GB. The results showed significant improvement after incorporating the suggested NLP-based techniques with multiple attributes. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Hand-Foot-Mouth Disease Classification using Features from Fibre Grating Biosensor Spectral Data 4

Hand-Foot-Mouth Disease Classification using Features from F...

引用

4th IEEE International Conference on Artificial Intelligence in Engineering and technology, IICAIET 2022

作者： Mahmood, Atif Azzuhri, Saaidal Razalli Qureshi, Adnan N. Jaan, Palwasha Sadia, Iqra University of Central Punjab Faculty of Information Technology and Computer Science Lahore Pakistan University of Malaya Department of Computer System & Technology Kuala Lumpur Malaysia Virtual University of Pakistan Faculty of Science & Technology Lahore Pakistan

ISBN: (数字)9781665468374

ISBN: (纸本)9781665468374

Hand, Foot and Mouth disease (HFMD) is a common viral childhood disease affected by the family of enterovirus and Coxsackie. Current laboratory identification is based on the RT-PCR test, which is expensive, time-consuming, and unsuitable for the pandemic. The SPR- TFBG was biofunctionalized with monoclonal antibody (Mab). Mab is a bioreceptor with an affinity for the virus for detecting EV-A71. A dataset of reflectance spectra of 660 samples of different virus impurities measured with SPR-TFBG biosensor to detect EV-71 virus was developed. The extracted signal has around 4000 different features based on wavelength information. The first subset was selected based on the region of interest analysis, and the dimension has reduced from 4000 to 1496 features. The dimensionality of the large feature set is reduced based on the statistical feature engineering procedure using 10 features including mean, variance, skewness, RMS, kurtosis, standard deviation, range, crest factor, impulse factor and shape factor. Subsequently, classification of the virus (signal) data is achieved through SVM and it is evaluated with different types of kernels. For the evaluation of classifiers, we used accuracy, sensitivity, precision and F1 score performance metrics. The obtained results of accuracy are 87.88 for linear SVM, 86.06 for radial basis, 75.76 for sigmoid SVM, and 75.15 for polynomial SVM, respectively. The results show that for our experiments, Linear SVM performs better than radial, polynomial and sigmoid kernels. This is because projecting the data onto higher dimensions is not required as data exhibits linear properties confirmed by White Neural Network (WNN) test for nonlinearity. © 2022 IEEE.

关键词： Viruses

来源：评论

学校读者我要写书评

暂无评论

Towards Seamless Glucose Monitoring: A Smart Non-Invasive system for Diabetes Patients 2

Towards Seamless Glucose Monitoring: A Smart Non-Invasive Sy...

引用

2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Pawar, Adesh Kelzarkar, Sanket G. Choudhary, Vishal Kumar Daburkar, Ashwini Kumar, Praveen Hore, Rutuja Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha India Faculty of Engineering and Technology Department of Biomedical Engineering Maharashtra Wardha India

ISBN: (纸本)9798331528713

Blood Glucose Monitoring levels are essential to the treatment of diabetes and must be done continuously. Patients often don't comply with conventional glucose monitoring techniques since they cause discomfort and are invasive. An Arduino microcontroller and infrared (I.R.) sensor are used in this work to provide a robust, non-invasive glucose monitoring system. I.R. light is transmitted through the skin to measure the quantity of light reflected, and this relationship exists between the amount of light and the amount of glucose in the interstitial fluid. In real-time, the Arduino analyses blood glucose levels by analyzing IR Spectroscopy and using machine learning techniques. The device features wireless connectivity, enabling it to send glucose readings to a mobile application for continuous monitoring, historical data analysis, and customized alerts. Initial trials indicate that this non-invasive system provides accurate glucose measurements comparable to traditional methods, significantly improving user comfort and compliance. This innovative approach promises a pain-free, user-friendly, and cost-effective solution for diabetes management, paving the way for more widespread adoption of continuous glucose monitoring. © 2024 IEEE.

关键词： Glucose sensors

来源：评论

学校读者我要写书评

暂无评论

LayOut Loud: An AI-Powered Augmented Reality and Mobile Application for Room Interior Design and Layout Optimization 4

LayOut Loud: An AI-Powered Augmented Reality and Mobile Appl...

引用

4th International Conference on Intelligent Cybernetics technology and Applications, ICICyTA 2024

作者： Cuevas, John Raphael G. Rili, John Kenneth R. Santillan, Anny Neolicia G. Agustin, Vivien A. Gonzales, Mary Grace N. Centeno, Criselle J. Regala, Richard C. College of Information Systems and Technology Management Pamantasan ng Lungsod ng Maynila Department of Information Technology Manila Philippines College of Information Systems and Technology Management Pamantasan ng Lungsod ng Maynila Department of Computer Science Manila Philippines Pamantasan ng Lungsod ng Maynila Faculty of College of Engineering and Technology Department of Information Technology Manila Philippines College of Information Systems and Technology Management Pamantasan ng Lungsod ng Maynila Department of Information Technology Manila Philippines Pamantasan ng Lungsod ng Maynila College of Engineering and Technology Department of Computer Science Manila Philippines

ISBN: (纸本)9798331506490

LayOut Loud is an AI-powered augmented reality (AR) and mobile application designed to revolutionize room interior design by offering tailored, real-time solutions for layout optimization. The primary objective of this tool is to design AI-powered features that analyze user-provided room dimensions, furniture types, and style preferences. By leveraging advanced algorithms, LayOut Loud generates personalized design suggestions that cater to the individual needs and aesthetic tastes of users, ensuring that each interior design experience is customized and unique. A key goal of the application is to enhance user experience and decision-making through a clustering algorithm. This algorithm categorizes furniture pieces based on their suitability and appearance in living spaces, streamlining the selection process for users. By enabling the visualization of these items in a 3D model, users can make informed decisions about their interior design choices, ensuring that each piece contributes to the overall aesthetic and functional harmony of the space. Additionally, LayOut Loud focuses on improving its AR capabilities by allowing users to capture their room layouts and view virtual furniture arrangements in real time. This feature provides practical, interactive design solutions, empowering users to experiment with different configurations and instantly see how potential changes would appear in their actual living environments. Ultimately, LayOut Loud offers a user-friendly, immersive platform that transforms the way individuals approach interior design. © 2024 IEEE.

关键词： Architectural design

来源：评论

学校读者我要写书评

暂无评论

Detection and Classification of Malignant Melanoma Using Deep Features of NASNet

引用

SN computer science 2023年第1期4卷 1-13页

作者： Abbas, Qaiser Gul, Anza Department of Computer Science University of Engineering and Technology (UET) Lahore Pakistan Department of Information Technology Lahore Leads University Lahore Pakistan

Skin cancer is one of the leading causes of death worldwide. Malignant melanoma is the most lethal form of skin cancer which can spread to other areas of the body. Although it is rare, still 75% of skin cancer deaths are caused due to malignant melanoma. Early detection can help to combat melanoma. However, early detection is very challenging due to various visual similarities between melanoma and non-melanoma. In this paper, we present an automated system for early melanoma detection. Our technique is based on deep transfer learning, in which we utilized a pre-trained neural network model named as NASNet. The features from the pre-trained model are transferred to the new dataset to detect melanoma. We modified the original architecture and added global average pooling and our classification layers. To overcome issues of a smaller dataset, we use carefully selected label and feature-preserving geometric transformations to increase images. The proposed model is trained on dermoscopic images from the International Skin Imaging Collaboration (ISIC 2020) dataset. As compared to the prior methods, our proposed model is efficient and shows state-of-the-art performance with an accuracy of more than 97% on the test dataset. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Deep learning Deep transfer learning Dermoscopic images Melanoma detection NASNet Skin cancer

来源：评论

学校读者我要写书评

暂无评论

Enhancing Security and Privacy in Health Care Using Cyber-physical systems Through Machine Learning 8th

Enhancing Security and Privacy in Health Care Using Cyber-ph...

引用

8th International Conference on information and Communication technology for Intelligent systems, ICTIS 2024

作者： Takale, Dattatray G. Gawali, Piyush P. Deshmukh, Gopal B. Mahalle, Parikshit N. Mehta, Pradnya S. Kashid, Shraddha S. Sule, Bipin Derle, Deepak R. Department of Computer Engineering Vishwakarma Institute of Information Technology SPPU Pune India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology SPPU Pune India Department of Computer Engineering MIT ADT University Pune India and Humanities Vishwakarma Institute of Technology Pune India Department of Computer Science and Application KTHM College Nashik India

ISBN: (纸本)9789819766772

The importance of securing cyber-physical systems (CPS) is paramount to safeguarding the interconnected networks and devices, especially in sectors such as blockchain, health care, and the "Internet of Things" (IoT). The importance of authentication for users and devices in CPS security is highlighted in this paper. As a result of the intricate interconnectivity of networks, the integration of cyber-physical systems (CPS) in health care has revolutionized the delivery of medical services in an era of rapidly advancing technology. There are, however, serious concerns surrounding the privacy and security of sensitive healthcare data as a result of this integration. In this paper, a revolutionary approach to address these concerns is presented by utilizing machine learning techniques to address these problems. To enhance security measures within healthcare CPSs and protect privacy, Random Forest, a powerful ensemble learning algorithm, is employed as an integral part of the security measures. The proposed system utilizes a combination of anomaly detection, adaptive learning, and multi-attribute feature selection in order to identify potential security breaches while simultaneously ensuring patient confidentiality. As an additional security precaution, encryption protocols are implemented to ensure that the transmission of data is secure, preventing unauthorized access to the data as well. "As a comprehensive evaluation of the system's performance, a number of evaluation metrics are used, including Accuracy, Precision, Recall, F1 Score, and AUC-ROC". Based on these results, it is evident that this approach is effective in providing robust security and privacy protections in healthcare CPS, thereby establishing a new paradigm for delivering safe and confidential health care in the digital age. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cybersecurity

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：