检索结果-内蒙古大学图书馆

13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Srivastava, Jyoti Routray, Sidheswar School of Engineering Indrashil University Department of Computer Science and Engineering Gujarat Rajpur Mehsana India Ganpat University Faculty of Engineering and Technology Department of Computer Engineering Gujarat Kherwa Mehsana India School of Technology Pandit Deendayal Energy University Department of Computer Science and Engineering Gandhinagar382007 India

ISBN: (纸本)9798350380583

Cyber Physical System (CPS) enhances the functionality of various cyber and physical equipment of Smart Healthcare System (SHS) and provides automation in the healthcare sector using Artificial Intelligence (AI) techniques. SHS collects medical data with the help of sensors or actuators equipped with smart medical devices that act as the physical component of CPS, then processes and analyzes these medical data with the help of cyber components of CPS to provide automation in health monitoring, doctor consultancy, medical prescriptions, medicine delivery and various other perspectives of the healthcare sector. These advancements in the healthcare sector result in great assistance for healthcare individuals like doctors, patients, nursing staff, etc, but at the same time deal with very sensitive parameters like time, efficiency, reliability, security, etc, that need to be managed with utmost accuracy for preserving each life in this global smart healthcare environment. There can be various efficiency parameters in CPS enabled SHS namely the number of hops, average energy consumption, resource utilization, etc, This work elaborately defines various characteristics of CPS in SHS, efficiency parameters, and major challenges for the successful implementation of CPS enabled SHS. © 2024 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Neutrosophic Price Indices with an Applied Study on Pesticide Prices

引用

Neutrosophic Sets and systems 2023年 58卷 603-610页

作者： Alhabib, Rafif Alhabib, Amani Salama, A.A. Department of Mathematics Faculty of Science Hama University Hama Syria Faculty of Agricultural Engineering Albaath University Homs Syria Department of Mathematics and Computer Science Faculty of Science Port Said University Port Said Egypt

An index is a tool for comparing a phenomenon or several phenomena dating back to different periods to know the amount of change in the phenomena or the difference between them. For example, we compare the price of a commodity in a particular year with its price in a given year, compare the prices of a group of commodities in one year with their prices in another year, or compare the price of a commodity in two different places. For example, we compare the price of a commodity in one city with its price in another city. This paper presents a study of these Indices from a neutrosophic point of view. Which allows us to study Indices with uncertain or non-welldefined price data. In addition, access to results that determine the extent of the change in prices as accurately as possible, and thus the optimal planning for the next stage and the development of appropriate solutions. An applied example was presented to study the change in the prices of pesticides used to combat the "vine stem borer" insect using the neutrosophic Indices. © (2023). All Rights Reserved.

关键词： Pesticides

来源：评论

学校读者我要写书评

暂无评论

Improvement of myopia using brain vision training with variable focal length VR

Improvement of myopia using brain vision training with varia...

引用

2024 International Conference on Advanced Mechatronic systems, ICAMechS 2024

作者： Shindou, Kazuma Taguchi, Akira Bao, Yue Tokyo City University Faculty of Information Engineering Department of Computer Science Tokyo Japan

ISBN: (纸本)9798350366204

There is growing interest in vision improvement methods because poor vision impairs quality of life and causes a variety of problems in daily living and cognitive function. However, many existing vision improvement methods are invasive, expensive, and require large training spaces and long training *** this study, we focused on the positive effects of HMD use on visual acuity and devised a noninvasive visual acuity training method using an HMD with variable focal distance and Gabor stimuli to enhance its effectiveness. In this training method, the image formation distance during training is adjusted according to the subject's visual acuity, providing training at the optimal difficulty level for each subject. It also aims to efficiently enhance visual function through perceptual learning using Gabor *** results of this study showed that this training method improved visual acuity by an average of 35% in 1/8th the training time of the conventional method. This indicates that the proposed method is an effective way to improve visual acuity in a short period of time without the need for expensive equipment or a large training space. © 2024 IEEE.

关键词： Vision aids

来源：评论

学校读者我要写书评

暂无评论

Single-Loop Stochastic Algorithms for Difference of Max-Structured Weakly Convex Functions 38

Single-Loop Stochastic Algorithms for Difference of Max-Stru...

引用

38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Hu, Quanqi Qi, Qi Lu, Zhaosong Yang, Tianbao Department of Computer Science & Engineering Texas A&M University United States Department of Computer Science The University of Iowa United States Department of Industrial and Systems Engineering University of Minnesota United States

In this paper, we study a class of non-smooth non-convex problems in the form of minx[maxy∈Y (x, y) − maxz∈Z ψ(x, z)], where both Φ(x) = maxy∈Y (x, y) and Ψ(x) = maxz∈Z ψ(x, z) are weakly convex functions, and (x, y), ψ(x, z) are strongly concave functions in terms of y and z, respectively. It covers two families of problems that have been studied but are missing single-loop stochastic algorithms, i.e., difference of weakly convex functions and weakly convex strongly-concave min-max problems. We propose a stochastic Moreau envelope approximate gradient method dubbed SMAG, the first single-loop algorithm for solving these problems, and provide a state-of-the-art non-asymptotic convergence rate. The key idea of the design is to compute an approximate gradient of the Moreau envelopes of Φ, Ψ using only one step of stochastic gradient update of the primal and dual variables. Empirically, we conduct experiments on positive-unlabeled (PU) learning and partial area under ROC curve (pAUC) optimization with an adversarial fairness regularizer to validate the effectiveness of our proposed algorithms. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Hybrid Reptile-Snake Optimizer Based Channel Selection for Enhancing Alzheimer’s Disease Detection

引用

Journal of Bionic engineering 2025年第2期22卷 884-900页

作者： Digambar Puri Pramod Kachare Smith Khare Ibrahim Al-Shourbaji Abdoh Jabbari Abdalla Alameen Department of Electronics and Telecommunication Ramrao Adik Institute of TechnologyD.Y.Patil CampusNavi-MumbaiMaharashtra400706India Maersk Mc-Kinney Moller Institute Faculty of EngineeringUniversity of Southern DenmarkOdenseDenmark Department of Electrical and Electronics Engineering Jazan UniversityJazan45142Saudi Arabia Department of Computer Science University of HertfordshireHatfieldUK Department of Computer Engineering and Information College of Engineering in Wadi AlddawasirPrince Sattam Bin Abdulaziz UniversityWadi Alddawasir11991Saudi Arabia

The global incidence of Alzheimer's Disease(AD)is on a swift *** Electroencephalogram(EEG)signals is an effective tool for the identification of AD and its initial Mild Cognitive Impairment(MCI)stage using machine learning *** of AD using EEG involves multi-channel ***,the use of multiple channels may impact the classification performance due to data redundancy and *** this work,a hybrid EEG channel selection is proposed using a combination of Reptile Search Algorithm and Snake Optimizer(RSO)for AD and MCI detection based on decomposition *** Mode Decomposition(EMD),Low-Complexity Orthogonal Wavelet Filter Banks(LCOWFB),Variational Mode Decomposition,and discrete-wavelet transform decomposition techniques have been employed for subbands-based EEG *** extracted thirty-four features from each subband of EEG ***,a hybrid RSO optimizer is compared with five individual metaheuristic algorithms for effective channel *** effectiveness of this model is assessed by two publicly accessible AD EEG *** accuracy of 99.22% was achieved for binary classification from RSO with EMD using 4(out of 16)EEG ***,the RSO with LCOWFBs obtained 89.68%the average accuracy for three-class classification using 7(out of 19)*** performance reveals that RSO performs better than individual Metaheuristic algorithms with 60%fewer channels and improved accuracy of 4%than existing AD detection techniques.

关键词： Alzheimer's Disease Brain disorder Electroencephalogram Reptile Search Algorithm Snake Optimizer Optimization

来源：评论

学校读者我要写书评

暂无评论

Performance Analysis of Energy and Secure Efficient Routing for Wireless Sensor Networks

Performance Analysis of Energy and Secure Efficient Routing ...

引用

2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Hussein, Zainab Mohammed Najim, Ali Hamza Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

Wireless Sensor Networks (WSNs) are essential for various applications, but their architecture makes them vulnerable to attacks. While traditional security methods like authentication and encryption offer some protection, they often fall short against specific node attacks. This paper introduces the Secure Energy Aware Routing (SEAR) strategy, which uses trust-based assessments to exclude malicious nodes while optimizing energy consumption. SEAR factors in node trust, hop time, and residual energy to ensure reliable data transmission through trustworthy nodes. MATLAB simulations on networks of varying sizes (100-500) compare SEAR with existing methods (EAD and EASER), demonstrating SEAR's superior performance. Results show significant improvements in packet delivery ratio (13-16%), network throughput (19-24 %), energy consumption (32-36%), packet loss (16-190/0), and end-to-end latency (21-26%), proving SEAR's effectiveness in enhancing WSN security, energy efficiency, and performance. © 2024 IEEE.

关键词： MATLAB

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-Based Web Application for Real-Time Apple Leaf Disease Detection and Classification 2

Deep Learning-Based Web Application for Real-Time Apple Leaf...

引用

2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Kumar, Satish Kumar, Rakesh Gupta, Meenu Obaid, Ahmed J. Chandigarh University Department of Computer Science and Engineering Punjab India University of Kufa Faculty of Computer Science and Mathematics Najaf Iraq Technical Engineering College Al-Ayen University Department of Computer Technical Engineering Thi-Qar Iraq

ISBN: (纸本)9798350370188

Agriculture is the most significant industry in the economy of India. Various kinds of diseases affect the leaves of plants and influence the productivity of crops. Apple farmers are also constantly facing challenges in boosting their yield and protecting apple trees from diseases. The prevalence of diseases and pests significantly hampers apple production, leading to substantial financial losses for the industry each year. Farmers may not have expertise in leaf disease prediction. Detecting Apple Leaf Diseases (ALD) swiftly and accurately is crucial for effectively handling and curbing these issues within orchards. Specifically, advancements in computer vision methods utilizing Deep Learning (DL) have opened up avenues for identifying and understanding these diseases at an early stage directly on the leaves. Web application based on the DL model is proposed to address this issue, which can predict Healthy and Alternaria, Leaf Spot, Marssonina Blotch, and Powdery mildew disease of the affected leaf. © 2024 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Exploring the Use of Machine Learning and the Internet of Things in Intelligent Agriculture: Short Survey 10

Exploring the Use of Machine Learning and the Internet of Th...

引用

10th International Conference on Optimization and Applications, ICOA 2024

作者： El Majdouby, Karima El Mohadab, Mohamed Daoud, Rachid Ait Abouelmehdi, Karim University of Chouaib Doukkali Elites laboratory Faculty of Sciences Computer Science department Eljadida Morocco Sultan Moulay Slimane University Systems Engineering Laboratory Computer Science department Beni Mellal Morocco

ISBN: (纸本)9798350387353

Agriculture is evolving towards more sustainable practices thanks to the integration of the machine learning and Internet of Things, which addresses many of the issues related to agricultural production and leads to increased yields with reduced input and labor costs. Smart farming has developed solutions to many conventional agricultural problems to automatically manage and monitor farmland. These technologies reduce the risk of field management in the event of bad weather, and also provide benefits to farmers in the event of labor shortages. The Internet of Things ensures the connection of physical objects that are equipped with sensors to the internet, enabling them to collect and exchange data and communicate with each other and with computer systems to gather information, perform analysis and take actions in real time that require rapid data analysis, and this task can be accomplished through machine learning algorithms with greater efficiency and high-quality decision-making. In addition, we carry out an in-depth state-of-the-art survey to identify very recent work that has implemented the Internet of Things and machine learning in smart agriculture, analyzing the development of practical solutions and discussing recent research trends and future prospects. © 2024 IEEE.

关键词： Internet of things Machine learning Smart Agriculture

来源：评论

学校读者我要写书评

暂无评论

Teamwork Optimization with Deep Learning Based Fall Detection for IoT-Enabled Smart Healthcare System

引用

computers, Materials & Continua 2023年第4期75卷 1353-1369页

作者： Sarah B.Basahel Saleh Bajaba Mohammad Yamin Sachi Nandan Mohanty E.Laxmi Lydia Department of Management Information Systems Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddahSaudi Arabia Department of business Administration Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Management Information Systems Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddah21589Saudi Arabia School of Computer Science&Engineering(SCOPE) VIT-AP UniversityAmaravatiAndhra PradeshIndia Department of Computer Science and Engineering GMR Institute of TechnologyAndhra PradeshRajam 532127India

The current advancement in cloud computing,Artificial Intelligence(AI),and the Internet of Things(IoT)transformed the traditional healthcare system into smart *** services could be enhanced by incorporating key techniques like AI and *** convergence of AI and IoT provides distinct opportunities in the medical *** is regarded as a primary cause of death or post-traumatic complication for the ageing ***,earlier detection of older person falls in smart homes is required to improve the survival rate of an individual or provide the necessary ***,the emergence of IoT,AI,smartphones,wearables,and so on making it possible to design fall detection(FD)systems for smart home *** article introduces a new Teamwork Optimization with Deep Learning based Fall Detection for IoT Enabled Smart Healthcare systems(TWODLFDSHS).The TWODL-FDSHS technique’s goal is to detect fall events for a smart healthcare ***,the presented TWODL-FDSHS technique exploits IoT devices for the data collection ***,the TWODLFDSHS technique applies the TWO with Capsule Network(CapsNet)model for feature *** last,a deep random vector functional link network(DRVFLN)with an Adam optimizer is exploited for fall event detection.A wide range of simulations took place to exhibit the enhanced performance of the presentedTWODL-FDSHS *** experimental outcomes stated the enhancements of the TWODL-FDSHS method over other models with increased accuracy of 98.30%on the URFD dataset.

关键词： Internet of things smart healthcare deep learning team work optimizer capsnet fall detection

来源：评论

学校读者我要写书评

暂无评论

Machine Learning-Based Prediction of COVID-19: A Robust Approach for Early Diagnosis and Treatment 5th

Machine Learning-Based Prediction of COVID-19: A Robust Appr...

引用

5th International Conference on Trends in Computational and Cognitive engineering, TCCE 2023

作者： Johora, Fatema Tuj Mahfuja, Israt Binte Masuqur Rahman, A.N.M. Rahman, Md Mosfikur Rahman, Md Sadekur Department of Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh Department of Computer Science and Engineering Daffodil International University Daffodil Bangladesh Department of Information Systems Kobe Institute of Computing Kobe Japan

ISBN: (纸本)9789819719228

The worldwide health catastrophe sparked by the COVID-19 epidemic continues, emphasizing the need for novel solutions in prediction, early diagnosis, and treatment. While vaccine development has progressed, the virus’s eradication remains questionable. This research presents a strong machine learning-driven approach to addressing this complicated problem. Rapid viral transmission and the lack of a single cure hamper illness diagnosis and treatment, particularly in highly populated developing countries like Bangladesh. Against this context, machine learning and artificial intelligence have emerged as critical instruments for the improvement of health care and medical research. This research investigates several machine learning algorithms and approaches that show potential for COVID-19 prediction. Among these is an algorithm that assesses individual vulnerability and environmental variables, allowing for self-diagnosis and early intervention, reducing the strain on healthcare institutions and government resources. This study demonstrates how machine learning may improve forecast accuracy and provide proactive treatment in pandemic management. Advanced techniques, such as K-nearest neighbor, Decision Tree, Random Forest, AdaBoost, XGBoost, Stochastic Gradient Descent, Linear SVC, Perceptron, Naive Bayes, Support Vector Machines, Logistic Regression, and Discriminant Analysis, are essential for achieving the best results. Finally, this research emphasizes the need for novel responses to global health challenges. Healthcare systems may improve their forecasting capacities and early intervention techniques by leveraging machine learning. This paper highlights technology’s revolutionary impact in redefining healthcare paradigms and encouraging resilience in the face of enormous difficulties. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19

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：