检索结果-内蒙古大学图书馆

Synchronized Optimization of Smart Home Appliance Scheduling and Cost-Effective Energy management Using Harris-Hawks Algorithm in Smart Grids

Synchronized Optimization of Smart Home Appliance Scheduling...

引用

2023 International Conference for technological engineering and its Applications in Sustainable Development, ICTEASD 2023

作者： Sundar Raj, A. Bansal, Saloni Al-Jawahr, Hassan m. Sheeba, T. Blesslin Sasikumar, m. Sapaev, J.B. Priya, Balappagari. Indu E.G.S. Pillay Engineering College Biomedical Engineering Tamil Nadu Nagapattinam India GLA University Computer Engineering and Applications Mathura India The Islamic University Najaf Iraq R.M.K. Engineering College Electronics and Communication Engineering Kavaraipettai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Computer Science and Engineering Tamil Nadu Chennai India National Research University Tashkent Institute of Irrigation and Agricultural Mechanization Engineers Tashkent Uzbekistan GRIET Telangana Hyderabad India

ISBN: (纸本)9798350336474

This work utilizes the Harris Hawks Optimization Algorithm (HHOA) to optimize Smart Home Appliance scheduling and construct an independent smart grid. The aim is to showcase that the HHOA is used to create smart grids that are both cost-effective and dependable. A smart grid has been constructed explicitly for the Ankara Gazi University technology Park structure. The structure's energy use and weather data have been observed and documented over a year. The suggested smart grid configuration incorporates Photovoltaics (PV), Wind Turbines (WT), Battery Energy Storing Systems (BESS), and Diesel Generators (DG), according to the available data. Determining these components' optimal capacity is essential to accomplish the desired goals of cost-effectiveness and dependability while maintaining full autonomous capabilities. Random evaluations without scheduling optimization lead to oversizing, increasing investment, functioning, and repair expenses. Inadequate capacity scheduling resulting from hasty judgments leads to difficulties in ensuring an uninterrupted energy supply. Hence, optimizing the schedule is crucial for the design of a smart grid to be efficient. After resolving the scheduling issue using the HHOA method, the outcomes are used to develop the suggested smart grid. The mATLAB modeling of the planned smart grid has shown a good performance, resulting in savings varying from 1.28% to 19.53% in the Total Net Present Cost (TNPC) value compared to alternative methods. © 2023 IEEE.

关键词： Smart power grids

来源：评论

学校读者我要写书评

暂无评论

Chaotic Particle Swarm Optimization Empowered Deep Convolutional Neural Network for Prostate Cancer Diagnosis 2

Chaotic Particle Swarm Optimization Empowered Deep Convoluti...

引用

2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Gokulnath, C. Kalaiarasi, N. Yeshaswini, R. Brinda, P. Jeyakarthika, K. Gopukumar, S.T. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Campus Kattankulathur Chengalpattu India Department of ECE R.M.K college of engineering and technology Puduvoyal Chennai India Department of computer science Rajarajeshwari college of engineering Kumbalgodu Bangalore560074 India Department of Computer science and Engineering Veltech High tech Dr rangarajan Dr sakunthala engineering college Avadi India Ramco Institute of Technology North Venganallur Village Virudhunagar District Tamil Nadu Rajapalayam626 117 India Department of Research Activities and Biostatistics Sarada Krishna Homoeopathic Medical College Kanniyakumari District Tamilnadu Kulasekharam629 161 India

ISBN: (纸本)9798350383867

Prostate cancer (PCa) Classification on magnetic resonance imaging (mRI) is an innovative use of medical imaging and machine learning (mL) models that objects to precisely recognize and classify PCa lesions in mRI scans. This advanced technique permits healthcare experts to analyze PCa more efficiently, define its level and severity, and improve modified treatment tactics. By analyzing subtle differences in tissue faces and recognizing cancerous areas, this methodology provides enhanced early recognition which is vital for patient results. It allows oncologists and radiologists to make knowledgeable verdicts, improving accuracy and efficacy of PCa diagnosis and successive treatment plans. This manuscript provides a proposal for Chaotic Particle Swarm Optimization Empowered Deep Convolutional Neural Network (CPSO-DCNN) technique for PCa Diagnosis. The developed CPSO-DCNN architecture incorporates a multi-faceted approach, starting with Wiener filtering (WF) for sound reduction, improving excellence and clarity of medical images. Feature extraction accomplished by employing SqueezeNet which is a great deep learning (DL) design expert at taking complex image patterns. This method further influences CPSO for hyperparameter tuning, enhancing its performance in order to support exact dataset characteristics. For critical task of PCa diagnosis, classification performed via bi-directional gated recurrent unit (BiGRU) networks and well-known for their capability to seize temporal needs within data. The simulation outcomes highlighted that CPSO-DCNN technique characterizes a substantial development in field of medical diagnostics. It provides a strong and exact solution for early recognition and classification of PCa. ©2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

machine Learning Classifiers to Decrease Diabetic Patients Probability of Hospital Readmission 4

Machine Learning Classifiers to Decrease Diabetic Patients P...

引用

4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Thenappan, S. Ramshankar, N. Hemavathy, N. Subashree, V. manjul, Ravi Ranjan Rajeswari, J. Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India R. M. D. Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Velammal Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India Ramchandra Chandarvansi Institute of Technology Department of Mechanical Engineering JharkhandBishrampur Palamu India Agni College of Technology Department of Electronics and Communication Engineering Tamilnadu India

ISBN: (纸本)9798350300093

Diabetes is a chronic condition that affects millions of individuals around the globe. Unfortunately, patients with diabetes have a significant risk of hospital readmission following discharge. Using machine learning classifiers, proposed a strategy to reduce the risk of readmission for 768 diabetes patients. This utilized a dataset containing patient demographic information, medical history, and laboratory results which are collected from Pima India Dataset. To predict the risk of readmission, and train several machine Learning models, including Logistic Regression, Random Forest, and Support Vector machines. The research indicates that the Random Forest model has the maximum degree of precision, at 97%. Age, glucose level, Hba1c levels, and comorbidities have been identified as the most significant factors contributing to the risk of readmission. On the basis of these findings, this recommends a personalized approach to diabetes management that takes individual patient characteristics and risk factors into account. This strategy could reduce the likelihood of readmission for diabetic patients and enhance their overall health. © 2023 IEEE.

关键词： Hospitals

来源：评论

学校读者我要写书评

暂无评论

A Novel Development of Artificial Intelligence Enabled Learning methodology for Human Depression Prediction Scheme

A Novel Development of Artificial Intelligence Enabled Learn...

引用

2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Ramkumar, G. Priyadharshini, R. Selvi, K. Kalaiselvi, K. Rathika, S. Kanagavalli, N. Saveetha University Department of Ece Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.K Engineering College Department of Information Technology Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Department of Data Analytics Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Tamil Nadu Chennai India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350375237

Human Depression Prediction is essential for several reasons, primarily centered around improving mental health outcomes and providing timely interventions. Firstly, early detection of depression allows for prompt and targeted interventions, enabling individuals to receive appropriate support and treatment before their condition worsens. The Human Depression Prediction Scheme (HDPS) introduces an innovative method for predicting depression by synergizing the capabilities of deep learning through the LeNet architecture with the optimization prowess of the Grey Wolf Optimization (GWO) algorithm. The proposed scheme achieves a commendable accuracy of 95%, attesting to its effectiveness in discerning depressive tendencies. Notably, the HDPS is implemented in Google Colab, emphasizing its accessibility and ease of use. This integration of advanced technologies and optimization techniques positions HDPS as a promising tool for early detection of depression, offering both high accuracy and practical implementation in a widely accessible computing environment. © 2024 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

The Role of Edge Computing in Enhancing Cloud Performance and Reducing Latency 7

The Role of Edge Computing in Enhancing Cloud Performance an...

引用

7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Sharma, Puneet Shyamala Devi, J. manikandan, K. Ani minisha, R. Harini, R. Karthik, S. Sr University School of Cs & Ai Telangana Warangal India Rajalakshmi Institute of Technology Chennai India Tamilnadu Vellore632014 India R.M. K. Engineering College Department of Artificial Intelligence and Data Science India Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Computer Science and Engineering India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Mathematics Avadi Tamilnadu Chennai600062 India

ISBN: (纸本)9798350350067

The flexibility of implementing edge computing over cloud infrastructures has become essential for responding to the need for reducing latency and improving data processing in a distributed environment. This research aims to determine the use of edge computing in enhancing the effectiveness of cloud utilization through conducting machine learning investigations utilizing Random Forest in the determination of the optimum contribution of resources. The work also set up and applied real-world datasets from Kaggle to illustrate that edge computing can alleviate the latency issue mainly by offloading computationally intensive tasks to edge nodes away from the central cloud. The results show that edge computing with the help of machine learning is the best solution for cutting-edge cloud environments as it is fast and does not aggravate the problems of excessive use of resources. Therefore, this research will be a great addition to the existing literature on the integration of cloud and edge computing and is hoped to offer useful information for future developments in the field of cloud computing. © 2024 IEEE.

关键词： Cloud platforms

来源：评论

学校读者我要写书评

暂无评论

HNCS: A Robust Development of Deep Learning Based Potato Plant Disease Identification Using Hybrid Neural Classification Strategy 4

HNCS: A Robust Development of Deep Learning Based Potato Pla...

引用

4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Priyadharshini, L. Kalaiselvi, P. Sujatha, S. Uma maheswari, S. Kandaswamy, Vijay Anand Suganthi, R. Saveetha Institute of Medical and Technical Science Department of Saveetha School of Law Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Sairam College of Engineering Department of Eee Karnataka Bangalore India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Biosciences Tamil Nadu Chennai India R.M.K. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of Electronics and Communication Engineering Poonamallee Tamil Nadu Chennai India

ISBN: (纸本)9798350354218

In recent decades, potato farming has exploded in popularity across India. On the other hand, diseases such as early stages of blight and later blight are making the production of potatoes more challenging and consequently more expensive. The goal here is to digitalize the system and automate the process of disease identification in potatoes so that production can grow. Our proposed algorithm, Hybrid Neural Classification Strategy (HNCS), will be cross-validated with an existing learning algorithm, Convolutional Neural Network (CNN), to assess the efficacy of the proposed scheme in diagnosing potato disease using leaf pictures. This article provides a picture of how automated systems based on processing and deep learning may identify and categorize illnesses affecting potato leaves. Image processing is the most effective method for identifying and studying these disorders. Over 4500 images of diseased potato leaves were used for this research;the images came from the plant town database, an open-source dataset on the Kaggle website, and a few pre-built models were used for recognition and characterization. The program's result demonstrates that deep learning outperforms all current tasks in potato disease detection, and it makes predictions with an accuracy of 98.73%. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Exploring Augmented Reality Triage Tools to Support mass Casualty Incidents

引用

Proceedings of the Human Factors and Ergonomics Society Annual meeting 2022年第1期66卷 1664 - 1666页

作者： Cassidy Rae Nelson Joseph Conwell Samuel Lally madelyn Cohn Kyle Tanous Jason moats Joseph L. Gabbard Department of Industrial and Systems Engineering Virginia Tech Department of Mathematics Virginia Tech Electrical and Computer Engineering Virginia TechSchool of Visual Arts Virginia Tech Department of Computer Science Virginia Tech Texas A&M Engineering Extension Service Texas A&M University

ARTTS is a head-worn Augmented Reality (AR) Triage Tool Suite containing an initial sorting tool, virtual assessment tool, and virtual triage tag to assist first responders in mass casualty incidents. The initial sorting tool prompts stunned responders through first-wave tasks to aid recalibration from shock to triage. The virtual assessment tool provides novice responders, potentially confused by the chaos, with a walkthrough of the SALT triage flowchart. Finally, current emergency medical triage processes leverage static paper tags susceptible to loss or illegible damage. ARTTS’ virtual triage tags are dynamic, can be updated automatically with vital sensors or manually through responder interaction, and employ user interface emergent features based on individual patient conditions. This paper describes the UX process used to develop ARTTS.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Automated semantic segmentation and Diagnosis of Coconut Tree Infection Using Drone Imagery and machine Learning approach for smart agriculture

Automated semantic segmentation and Diagnosis of Coconut Tre...

引用

Intelligent Systems and Advanced Applications (ICISAA), International Conference on

作者： m Vasumathy J. Jude moses Anto Devakanth mythili Thirugnanam R. RoselinKiruba R. Hamsaveni R. Nandhini Computer Science and Engineering Kingston Engineering College Vellore Tamilnadu India Department ofComputer Applications Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh India School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamilnadu India Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamilnadu India Computer Science and Engineering Sri Venkateswara College of Engineering & Technology (SVCET) Chittoor Andhra Pradesh India Computer Science and Applications D.K.M College for Women Vellore Tamilnadu India

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

The coconut industry of our Indian nation contributes an extensive range of economy to the national GDP. About 19,247 million coconuts, or 31.45% of the world’s production, were produced in India in the year 2021-2022 making it the top producer. However, the coconut farmers face major challenges from a plethora of pests and diseases of coconut trees, and yield loss, which affects the farmers economically, standing around at $\mathbf{1 0 - 1 5 \%}$. major pests, such as Rhinoceros beetles and red palm weevils, are highly destructible, in some scenarios leading even to the mortality of crops. In most of the cases, when trees that are affected need a cure, farmers have to climb the trees to put chemicals in crowns infested with the pests, exposing the farmers to harmful chemicals and risking severe injuries or permanent disabilities. As a solution, the proposed methodology involves the collection of extensive drone imagery from coconut plantations, followed by the annotation and preprocessing of the data. This work utilizes state-of-the-art deep learning models, including Unet, convolutional neural networks (CNNs) for semantic segmentation, to differentiate coconut trees from the background and other vegetation. Subsequently, machine learning classifiers are employed to diagnose diseases by analyzing visual symptoms present in the segmented trees. The proposed approach offers significant improvements in accuracy and efficiency compared with other traditional manual methods, providing real-time insights into tree health and disease management. This system has the potential to enhance crop monitoring, optimize resource allocation, and ultimately increase agricultural productivity.

关键词： Support vector machines Smart agriculture Accuracy Semantic segmentation Plantations Crops Convolutional neural networks Random forests Diseases Drones

来源：评论

学校读者我要写书评

暂无评论

An Innovative method to Detect Cyberbullying on Social media

An Innovative Method to Detect Cyberbullying on Social Media

引用

Electronics and Renewable Systems (ICEARS), International Conference on

作者： P. Pandiaraja K. muthumanickam m. Geetha P.C. Senthil mahesh Vedhashree S K C. Selvarathi Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Kongunadu College of Engineering and Technology Tholurpatti Trichy Tamil Nadu India Department of Computer Science and Engineering M.Kumarasamy College of Engineering Karur India Department of Computer Science and Engineering Excel Engineering College Komarapalayam Tamilnadu India Department of Electronics and Communication Engineering Karpagam Institute of Technology Coimbatore India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Today social media has fundamentally altered global communication and information exchange. However, as these platforms have become more widely used, so has cyber-hatred which is a significant problem that has caught academics' attention. This article has been addressed by a variety of methods from deep learning and machine learning, including recurrent neural networks, convolutional neural networks, logistic regression, and Naive Bayes. These techniques distinguish between various classes using mathematical concepts. However, sentiment-oriented data offers an accurate picture of how the public reads communications online, accurate classification necessitates a more "critical thinking" perspective. Two machine learning classifiers were employed in this study, one of which was built on a review of the literature to look at efficient classification techniques. Four online hatred datasets using logistic regression and multinomial naive Bayes. To better grasp the text in the datasets, the classifier results were enhanced by using XG-Boost algorithm.

关键词： Uniform resource locators Support vector machines Logistic regression Accuracy Cyberbullying Feature extraction Boosting Bayes methods Classification algorithms Random forests

来源：评论

学校读者我要写书评

暂无评论

Ensemble Learning: Importance, Applications, Challenges, and Future Work

Ensemble Learning: Importance, Applications, Challenges, and...

引用

Integration of Emerging technologies for the Digital World (ICIETDW), International Conference on

作者： N. manjunathan Shanmugapriya D P. Girija Vasudevan m murugesan S Siva Subramanian R Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Artificial Intelligence and Machine Learning IFET College of Engineering Villupuram Tamilnadu India Dept of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of CSE (ARTIFICIAL INTELLIGENCE) Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh Department of Computer Science and Engineering R.M.D Engineering College Kavaraipettai Department of Computer Science and Engineering RMK College of Engg and Tech

ISBN: (数字)9798331541170

ISBN: (纸本)9798331541187

Ensemble learning has become one of the most used approaches in machine learning that helps to increase predictive capabilities and model stability. Thus, this survey paper offers an extensive analysis of ensemble learning, stressing its significance, potential uses, inherent difficulties, and future research prospects. In the first step, we explain the basic ideas and the major ensemble approaches, including bagging, boosting, and stacking. We then discuss the flexibility of ensemble learning in classification, regression, and outlier detection problems and its implications in health informatics, finances, e-business, and self-driving systems. However, there are challenges associated with ensemble learning, for example, computational cost, the quality of data used, and the fact that the models themselves cannot be explained. These are the issues that can hinder the performance of ensemble models and the ideas to help to improve the efficiency of this method. In addition, we outline potential future research avenues such as novel ensemble learning methods, optimization of computational complexity, interpretability of models as well as new use cases in emerging fields like IoT, quantum computing, and edge computing. We also highlight the need to consider the ethical issues towards attaining fairness and accountability in ensemble learning. The results of this survey should contribute to raising the base of knowledge about ensemble learning and stimulate further research and development in this actively evolving area.

关键词： Surveys Quantum computing Computational modeling Stacking Predictive models Boosting Stability analysis Ensemble learning Bagging Research and development

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：