检索结果-内蒙古大学图书馆

8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Hossain, Mohammad Alamgir Metwally, Abdelnasser Abdelwakil Khan, Asif Ali Amin, Sherif Tawfik Hamdan, Alfadil Ahmed Limkar, Suresh Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department ofComputer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department ofComputer and Network Engineering College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of Artificial Intelligence & Data Science AISSMS Institute of Information Technology Maharashtra Pune India

ISBN: (纸本)9789819765805

CVD continues to pose a substantial global health burden, demanding the need for the creation of precise predictive models that can aid in the timely identification and evaluation of risks. This research paper aims to provide a thorough examination of the prediction of CVD by utilizing CNN in aggregation with hyperparameter tuning techniques. This paper examines the effectiveness of three widely recognized CNN architectures, namely ResNet, VGGNet, and Xception CVD. The Tree-structured Parzen Estimator (TPE) is utilized as a method for tuning hyperparameters in order to optimize the CNN models. The study commences with a comprehensive examination of the dataset, encompassing a wide array of cardiovascular CT image data. The chosen CNN architectures serve as the fundamental framework for our predictive models. The selection of these architectures is based on their established track record in effectively addressing image analysis tasks and their capacity to accurately capture complex patterns and features present in medical images. The models undergo training using a dataset in order to acquire knowledge about distinctive features that are linked to various cardiovascular conditions. The experimental findings presented in the study indicate that the utilization of the Xception architecture, in aggregation with TPE for hyperparameter optimization, yields the most favorable predictive accuracy. Specifically, the achieved accuracy rate of 98.26% is notably remarkable. This result highlights the significance of not only choosing advanced CNN architectures but also optimizing their hyperparameters to fully exploit their potential in predicting cardiovascular disease. This establishes it as a promising tool for the early diagnosis and risk assessment of CVD. This study establishes a fundamental basis for future investigations in the advancement of precise and dependable predictive models applicable to various medical imaging contexts. © The Author(s), under exclusive license

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Effective Prediction of Autism Disease using Data Mining Techniques 6

Effective Prediction of Autism Disease using Data Mining Tec...

引用

6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Aishwarya, D. Charulatha, R.P. Niranjan, M. Malathi, K. Department ofComputer Science and Engineering Karpagam Academy of Higher Education Coimbatore India Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore India

ISBN: (纸本)9798350398496

The medical costs associated with autism spectrum disorder (ASD), a neurodevelopmental condition, can be significantly reduced with early identification. However, lengthy wait times and high costs come along with an ASD diagnosis. The cost of autism and the increase in ASD cases globally highlight the urgent need for the development of straightforward screening methods. A short and useful ASD test will soon be available to help healthcare professionals and allow people determine whether a formal clinical diagnosis is required or not. ASD cases are rising globally, necessitating the need for datasets pertaining to behavioral features. Due to the dearth of such datasets, it is difficult to perform in-depth research to improve the efficiency, sensitivity, specificity, and predictive accuracy of the ASD screening technique. There are currently only few diagnostic or screening trials, and the greater part of databases on autism are genetic in origin. A brand-new dataset with twenty characteristics is proposed for adult autism screening. Upcoming study may benefit from this information, particularly when identifying the fundamental components of autism and improving the classification of individuals with ASD. The ten behavioural variables (AQ-10Adult) and ten personality traits includedin this dataset can be used to identify ASD cases from controls, according to behavioural study. © 2023 IEEE.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Bidirectional LSTM-Based Energy Consumption Forecasting:Advancing AI-Driven Cloud Integration for Cognitive City Energy Management

引用

Computers, Materials & Continua 2025年第5期83卷 2907-2926页

作者： Sheik Mohideen Shah Meganathan Selvamani Mahesh Thyluru Ramakrishna Surbhi Bhatia Khan Shakila Basheer Wajdan Al Malwi Mohammad Tabrez Quasim Department of Computer Science and Engineering Srinivasa Ramanujan CentreSASTRA Deemed UniversityKumbakonam612001India Department of Computer Science and Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bangalore562112India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK University Centre for Research and Development Chandigarh UniversityMohali140413PunjabIndia Centre for Research Impact and Outcome Chitkara University Institute of Engineering and TechnologyChitkara UniversityRajpura140401PunjabIndia Department of Information Systems College ofComputer and Information SciencePrincessNourah bintAbdulrahmanUniversityP.O.Box 84428Riyadh11671Saudi Arabia College of Computer Science Informatics and Computer Systems Department King Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy *** this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly variable and *** study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory(LSTM)*** a dataset containing over twomillionmultivariate,time-series observations collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear *** bidirectional LSTM architecture processes data in both forward and backward directions,capturing past and future contexts at each time step,whereas existing unidirectional LSTMs consider only a single temporal *** design,combined with dropout regularization,leads to a 20.6%reduction in RMSE and an 18.8%improvement in MAE over conventional unidirectional LSTMs,demonstrating a substantial enhancement in prediction accuracy and *** to existing models—including SVM,Random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMSE of 0.2213 during testing,significantly outperforming these *** results highlight the model’s superior ability to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive *** integrating advanced machine learning techniqueswith IoT and cloud infrastructure,this research contributes to the development of intelligent,sustainable urban environments.

关键词： Deep learning bidirectional LSTM energy consumption forecasting time-series analysis predictive modeling machine learning in energy management

来源：评论

学校读者我要写书评

暂无评论

Advancements in Chatbot Technology for Enhanced Customer Support in Online Retail 9

Advancements in Chatbot Technology for Enhanced Customer Sup...

引用

9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Keerthi Kumar, N. Maheswari, K. Abinaya, A. Ramya, J. Ande, Pavan Kumar Ramaian, Christu Paul Bms Institute of Technology And Management Department of Mechanical Engineering Karnataka Bengaluru560064 India Cmr Technical Campus Department of Computer Science And Engineering Telangana Hyderabad501401 India Panimalar Engineering College Department of Computer Science And Engineering Tamil Nadu Chennai600123 India KoneruLakshmaiah Education Foundation Department OfComputer Science And Engineering Andhra Pradesh Guntur522502 India Hindustan Institute of Technology And Science Department of Automobile Engineering Tamil Nadu Chennai603103 India

ISBN: (纸本)9798350365092

In order to improve customer service and boost sales, this paper describes the creation of an ecommerce sales chatbot. In order to grasp natural language, the system employs machine learning. It is based on a chatbot that is modular. Framework. There are several parts to the system. A web-based framework for natural language teaching comes first. Chat bots are employed in a variety of industries, including e-commerce, healthcare, and education. The objective of this venture is to make Hebron, an online chatbot for the Contract College People group Shopping center. Python and Respond are both utilized in the chatbot's creation. Conventional customer service chatbots frequently copy human discussion, yet they have serious protection and data adaptability issues. Here, we present SuperAgent, a customer support chatbot that utilizes broad and promptly open internet business data. © 2024 IEEE.

关键词： Sales

来源：评论

学校读者我要写书评

暂无评论

Enhancement of coercivity,permeability,and dielectric properties of Co_(0.2)Zn_(0.3)Ni_(0.5)Eu_(x)Fe_(2-x)O_(4) ferrites for memory and high frequency devices

引用

Journal of Rare Earths 2024年第6期42卷 1128-1135,I0005页

作者： I.Sardar M.D.Hossain M.S.Sikder M.N.I.Khan M.R.Rahman Physics Discipline Khulna UniversityKhulna9208Bangladesh Department of Physics Khulna University of Engineering&TechnologyKhulna9203Bangladesh Department ofComputer Science and Engineering Northern University of Business&Technology KhulnaKhulna9100Bangladesh Material Science Division Atomic Energy CentreDhaka1000Bangladesh

Spinel cubic ferrites have huge applications in me mory and high frequency *** the improvement of these modern devices,the magnetic coercivity,permeability,and dielectric properties of a ferrite are the important *** article focuses on improving the magnetic coercivity,magnetic permeability,and dielectric properties of Co_(0.2)Zn_(0.3)Ni_(0.5)Eu_(x)Fe_(2-x)O_(4) ferrites,where x=0.00,0.06,and *** X-ray diffraction(XRD),Fourier transform infrared spectroscopy(FTIR),field emission scanning electron microscopy(FESEM),energy dispersive X-ray(EDX),vibrating sample magnetometer(VSM),and an impedance analyzer were used to characterize the structural,magnetic,and dielectric properties of the *** XRD patterns indicate the formation of spinel cubic structure of the samples with a secondary peak(EuFeO_(3))for Eu doped *** densities and porosities of the samples follow an inverse trend,where the doped samples’lattice parameters are increased with the increment of rare earth Eu *** FTIR analysis also proves the spinel cubic phase of the *** average grain size of the ferrites is obtained via FESEM images,and it is increased from 121 to 198 *** analysis confirms that doping of the Eu content also changes other hysteresis loop properties of Co_(0.2)Zn_(0.3)Ni_(0.5)Eu_(x)Fe_(2-x)O_(4) ***,the coercivity of the Eu doped samples is greater than that of the mother alloy(x=0.00).The EDX study shows that there is no impurity contamination in the *** permeability and dielectric measurements show an improved quality factor of the Eu-doped samples with low magnetic and dielectric *** dependent resistivity and impedance analysis also show the improved *** the observed properties of the samples,all the investigated ferrites might be strong candidates for potential applications in memory devices,magnetic sensors,and high frequency applications.

关键词： Ferrites Raree arths Coercivity Permeability EDX Resistivity

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

2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Vasumathy, M. Devakanth, J. Jude Moses Anto Thirugnanam, Mythili Roselinkiruba, R. Hamsaveni, R. Nandhini, R. Kingston Engineering College Computer Science and Engineering Tamilnadu Vellore India Madanapalle Institute of Technology & Science Department ofComputer Applications Andhra Pradesh Madanapalle India Vellore Institute of Technology School of Computer Science and Engineering Tamilnadu Vellore India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Computer Science and Engineering Tamilnadu Chennai India Computer Science and Engineering Andhra Pradesh Chittoor India D.K.M College for Women Computer Science and Applications Tamilnadu Vellore632001 India

ISBN: (纸本)9798331539948

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 10-15%. 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. © 2024 IEEE.

关键词： Drones

来源：评论

学校读者我要写书评

暂无评论

GASTon: A Graph-Exploration System for Indexing, Annotating and Visualizing PubMed Articles to Enhance the Analysis of Social deTerminants of Health

GASTon: A Graph-Exploration System for Indexing, Annotating ...

引用

16th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2023

作者： Bottoni, Simone Trombetta, Alberto Bertini, Flavio Montesi, Danilo Bonin, Francesca Pascale, Alessandra Gleize, Martin Tommasi, Pierpaolo Department of Theoretical and Applied Sciences University of Insubria Varese Italy Department of Mathematical Physical and Computer Sciences University of Parma Parma Italy Department ofComputer Science and Engineering University of Bologna Bologna Italy IBM Research Europe Dublin Ireland

Many works have shown associations between social determinants of health (SDoH)-the social circumstances in which people live-and health-related outcomes. However, the lack of SDoH data increases the challenges in measuring and understanding their effect on people's health and health systems. In this paper, we present GASTon, a system for the indexing, annotation, and graph-based rendering of PubMed information to enable the search and retrieval of SDoHs in scientific literature. Our work provides a way to associate specific concepts with peer-reviewed articles to simplify the search for social factors. It builds a knowledge graph based on PubMed publications and associates them with concepts extracted from the Unified Medical Language System (UMLS) Metathesaurus, GASTon allows a full-text search and graph-based navigation and supports an overview of the concepts and related publications. Moreover, the architecture allows scale-up thanks to its containerized nature and parallelization capabilities. The system is open-source under the Apache V2 license. © 2023 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Advancements in Chatbot Technology for Enhanced Customer Support in Online Retail

Advancements in Chatbot Technology for Enhanced Customer Sup...

引用

International Conference on science Technology engineering and Management (ICONSTEM)

作者： N Keerthi Kumar K. Maheswari A Abinaya J Ramya Pavan Kumar Ande Christu Paul Ramaian Department of Mechanical Engineering BMS Institute of Technology and Management Bengaluru Karnataka Department of Computer Science and Engineering CMR Technical Campus Hyderabad Telangana India Department of Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India Department ofComputer Science and Engineering KoneruLakshmaiah Education Foundation Guntur Andhra Pradesh India Department of Automobile Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

关键词： Customer services Natural languages Education Medical services Machine learning Chatbots Mathematics

来源：评论

学校读者我要写书评

暂无评论

Attribute Assailability and Sensitive Attribute Frequency based Data Generalization Algorithm for Privacy Preservation

Attribute Assailability and Sensitive Attribute Frequency ba...

引用

2021 International Conference on Forensics, Analytics, Big Data, Security, FABS 2021

作者： Basapur, Satish B. Shylaja, B.S. Venkatesh Dr. Ambedkar Institute of Technology Department of Information Science and Engineering Bengaluru560056 India Dr. Ambedkar Institute of Technology Department OfComputer Science and Engineering Bengaluru560056 India University Visvesvaraya College of Engineering Bangalore University Department of Computer Science and Engineering Bengaluru560 001 India

ISBN: (纸本)9781665420051

The data publishing and analysis expose users' and users group's sensitive data and their identities. As a result, the adversary initiate inference attack using exposed users' identifies and associated sensitive information. Some of the users' attribute and users' group attributes values are highly vulnerable and their contiguity increase privacy breach. The traditional k-anonymity based privacy models prevent identity disclosure but fails to prevent sensitive attribute values disclosure and ignore repercussion of assailable attributes on users' group privacy. This research propose a novel data generalization algorithm that enhances users' group privacy and data utility. The proposed method calculate the susceptibility of quasi-identifier attributes, frequency of sensitive attributes and their association. These values are used for form equivalence class that satisfies k-anonymity and β-likeness property. The proposed generalization algorithm considers both susceptibility, frequency of sensitive attributes and their association while anonymizing data. The extensive experiments are carried out on open source Apache Spark environment. The simulation results demonstrate that efficiency and effectiveness of proposed algorithm. This research work outperform other state-art data privacy model in terms of thwarting inference attack, information loss and accuracy of model. © 2021 IEEE.

关键词： Sensitive data

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

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：