检索结果-内蒙古大学图书馆

International Journal of Interactive Mobile Technologies 2022年第15期16卷 172-186页

作者： Ilias, Irma Syarlina Che Ramli, Suzaimah Wook, Muslihah Hasbullah, Nor Asiakin Department of Computer Science Faculty of Defense Science and Technology National Defence University of Malaysia Selangor Malaysia Computer Engineering Technology Section Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia

Images for social media are an essential component of any blog or social media post. As we now live in the age of the ‘camera in everyone’s pocket’, a new dynamic era of image creation and content has emerged. However, people use media due to specific motives or gratifications that lead to their satisfaction of social media use. This research study utilized uses and gratification theory (UGT), a sociology theory related to the motives of people using the media. The UGT factors include enjoyment, entertainment, social influence, social interaction, and information sharing. There were 441 data that were collected and analysed. IBM SPSS Statistics 20 and Smart PLS 3.3.3 were used for the data analysis. Result showed that information sharing was the most influencing factors on users’ satisfaction to use image in social media. Therefore, the factors identified in these studies could be used as a guideline and references in future study related to social media awareness and implications. © 2022. International Journal of Interactive Mobile Technologies. All Rights Reserved.

关键词： Social networking (online)

来源：评论

学校读者我要写书评

暂无评论

Intelligent Systems for Medical Diagnostics with the Detection of Diabetic Retinopathy at Reduced Entropy

Intelligent Systems for Medical Diagnostics with the Detecti...

引用

2023 IEEE International Conference on Network, Multimedia and information technology, NMITCON 2023

作者： Sindhu Madhuri, G. Somashekhara Reddy, D. Mahesh, T.R. Rajan, T. Vanitha, K. Shashikala, H.K. Manipal Institute of Technology Manipal Academy of Higher Education Department of Information Technology Karnataka Bengaluru India Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India School of Computer Science and Engineering Presidency University Bangalore India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350300826

Diabetes Mellitus (DM) is primarily defined by hyperglycemia, polyuria, and polyphagia and as a result of a complex interaction of hereditary and environmental variables, it has developed into a severechronic metabolic condition. Numerous diabetes problems might arise from uncontrolled high blood sugar. Long-term diabetes causes severe complications, some of which are fatal. Everywhere in the world, the prevalence of diabetes in patients is increasing at epidemic rates. Every year, diabetes and related disorders consume a sizable percentage of the national health budget. Several risk factors influence the etiopathogenesis of the disease and the emergence of the epidemic. The untreatable condition of diabetes may be managed by maintaining self-care in daily life, providing appropriate education about diabetes, and making significant advancements in knowledge, attitudes, skills, and management. Diabetes should be diagnosed as soon as possible because it can lead to several illnesses, including kidney failure, stroke, blindness, heart attacks, and lower limb amputation. This study aims to use relevant variables, create a prediction algorithm using machine learning, and choose the best classifier to produce results that are as close to clinical outcomes as possible with reduced entropy. The proposed approach is on selecting the characteristics that ail in the early detection of Diabetes Miletus utilizing Predictive analysis. The computational techniques K-Nearest Neighbour (KNN) and Decision Tree (DT) have been employed to detect DM at an early stage. The KNN outperforms with the best performance when evaluated against different performance metrics. © 2023 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Contextual defeasible reasoning framework for heterogeneous knowledge sources

Contextual defeasible reasoning framework for heterogeneous ...

引用

作者： Mahfooz ul Haque, Hafiz Muhammad Akhtar, Salwa Uddin, Ijaz Department of Software Engineering University of Lahore Lahore Pakistan Department of Computer Science University of Lahore Lahore Pakistan Department of Computer Science City University of Science and Information Technology Peshawar Pakistan University of Lahore Lahore Pakistan

Recent years have witnessed the rapid advances of smart computing paradigms in a ubiquitous environment. These paradigms make human life much easier, comfortable, secure and hassle free. In a smart computing environment, it is a fact that human users interact with the systems dynamically with or without human intervention using different modalities. The core emphasize is given on the intelligent systems that run in a highly decentralized environment with different communication mechanism. Literature highlighted numerous formalisms to bridge the communication modalities for different knowledge sources. Among others, Multi-context System (MCS) has been advocated as one of the most suitable formalism to interlink different contexts (domains) dynamically in the distributed environment. However, interaction of these knowledge sources sometime may produce inconsistent and conflicting results. In this work, we presents a contextual defeasible reasoning based multi-agent formalism to handle the inconsistency issues. This framework relies on the semantic knowledge sources which allow us to model context-aware non-monotonic reasoning agents to infer the desired goals using the extracted rules from the ontologies and handles inconsistencies using conflicting contextual information. We illustrate the validity and correctness of the proposed formalism using a simple case study of a smart healthcare system with the prototypal implementation of the system. © 2021 John Wiley & Sons Ltd.

关键词： Multi agent systems

来源：评论

学校读者我要写书评

暂无评论

Smart UML - Assignment Management Tool for UML Diagrams 4

Smart UML - Assignment Management Tool for UML Diagrams

引用

4th International Conference on Advancements in Computing, ICAC 2022

作者： Weerasinghe, D.N.H. Thiwanka, K.A.T. Jayasith, H.B.C. Onella Natalie, P.A.D. Samantha Rajapaksha, U.U. Karunasena, Anuradha Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

The great majority of manual processes have been automated in the current day. Nonetheless, there is no reliable approach for evaluating UML diagrams for plagiarism and correctness. Unified Modeling Language (UML) provides the basis for UML diagrams. UML Diagrams are typically used to graphically express a system's stakeholders, activities, objects, and classes to get a deep knowledge of a particular system. UML diagrams are widely used by industry experts as well as beginners. Class diagrams and use case diagrams are the most commonly used diagrams in UML. Though most computer science-related students get assessed in UML modelling, diagram plagiarism isn't considered in most exams since it's a tedious task to perform. The proposed system automates capturing details in UML diagram components and generating a model answer from the given question scenario to compare the diagrams and calculate the plagiarism percentage with the correction percentage. © 2022 IEEE.

关键词： Unified Modeling Language

来源：评论

学校读者我要写书评

暂无评论

CD-FL:Cataract Images Based Disease Detection Using Federated Learning

引用

computer Systems science & engineering 2023年第11期47卷 1733-1750页

作者： Arfat Ahmad Khan Shtwai Alsubai Chitapong Wechtaisong Ahmad Almadhor Natalia Kryvinska Abdullah Al Hejaili Uzma Ghulam Mohammad Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia School of Telecommunication Engineering Suranaree University of TechnologyNakhon Ratchasima30000Thailand Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka72388Saudi Arabia Information Systems Department Faculty of ManagementComenius University in BratislavaOdbojárovBratislava440Slovakia Faculty of Computers&Information Technology Computer Science DepartmentUniversity of TabukTabuk71491Saudi Arabia Department of Computer Science and Software Engineering International Islamic UniversityIslamabad44000Pakistan

A cataract is one of the most significant eye problems worldwide that does not immediately impair vision and progressively worsens over *** cataract prediction based on various imaging technologies has been addressed recently,such as smartphone apps used for remote health monitoring and eye *** recent years,advances in diagnosis,prediction,and clinical decision support using Artificial Intelligence(AI)in medicine and ophthalmology have been *** to privacy concerns,a lack of data makes applying artificial intelligence models in the medical field *** address this issue,a federated learning framework named CDFL based on a VGG16 deep neural network model is proposed in this *** study collects data from the Ocular Disease Intelligent Recognition(ODIR)database containing 5,000 patient *** significant features are extracted and normalized using the min-max normalization *** the federated learning-based technique,the VGG16 model is trained on the dataset individually after receiving model updates from two *** transferring the attributes to the global model,the suggested method trains the local *** global model subsequently improves the technique after integrating the new *** client analyses the results in three rounds to decrease the over-fitting *** experimental result shows the effectiveness of the federated learning-based technique on a Deep Neural Network(DNN),reaching a 95.28%accuracy while also providing privacy to the patient’s *** experiment demonstrated that the suggested federated learning model outperforms other traditional methods,achieving client 1 accuracy of 95.0%and client 2 accuracy of 96.0%.

关键词： Privacy-preserving cataract disease federated learning fundus images healthcare smartphone applications machine learning

来源：评论

学校读者我要写书评

暂无评论

Voice Enabled Intelligent Programming Assistant 4

Voice Enabled Intelligent Programming Assistant

引用

4th International Conference on Advancements in Computing, ICAC 2022

作者： Wataketiya, Ravindu Chandrasiri, Navinda Kithsiri, Ramesh Malwatta, Hirush Nadeeshani, Madhuka Siriwardana, Samanthi Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

In modern era where software development is of vital importance, software developers are challenged with conditions like Repetitive Strain Injury (RSI) which hinders their ability to work effectively. Furthermore, people with difficulties with using their hands also find it challenging to program in the traditional manner. As a solution, coding with one's voice has been experimented with, but current solutions lack interactivity and are harder to use and setup leaving much room for improvement in this domain. In this research work, by using input classifier models with accuracies over 90%, intent classifiers with accuracies over 70%, code parsing and various human computer interaction techniques, we developed a conversationally interactive, programming language agnostic, easy to setup and easy to use Voice Coding Assistant. This will potentially help a global audience of programmers to achieve their goals and improve productivity and lead a healthier life. We have named the system thus developed, "Venic". © 2022 IEEE.

关键词： Human computer interaction

来源：评论

学校读者我要写书评

暂无评论

Development of Low Resource Machine Learning Models for Child Cognitive Ability Assessments 4

Development of Low Resource Machine Learning Models for Chil...

引用

4th International Conference on Advancements in Computing, ICAC 2022

作者： Kahawanugoda, Adithya Gnanarathna, Kulanika Meegoda, Nilan Monarawila, Randika Samarasinghe, Pradeepa Lindamulage, Asiri Gawesha Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

Automated cognitive assessment tools are state-of-the-art in assessing cognition development. Due to the low availability of resources, building automated cognitive ability evaluation tools is challenging. This study focuses on developing machine learning models using a limited amount of data to assess Reasoning IQ, Knowledge IQ, Mental Chronometry and Attention-levels of Sinhala-speaking children between the age of 7 to 9 years. Our solution includes Sinhala speech recognition systems, image classification models, gaze estimation, blink count detection and facial expression recognition models to evaluate the above four cognitive ability measuring factors. Open domain speech recognition has been used to evaluate complex Sinhala child verbal responses and limited vocabulary responses were assessed using an end-to-end speech recognition system, respectively achieving 40.1% WER and 97.14% accuracy. Additionally, the image classification models for handwritten Sinhala letter recognition and two shape recognition models have gained 97%, 89% and 99% accuracy. The linear regression model for attention level evaluation that utilizes the inputs from a combination of eye-gaze estimation, facial expression recognition and blink rate detection models has gained 85% accuracy. © 2022 IEEE.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

Deep Forest-Based Fall Detection in Internet of Medical Things Environment

引用

computer Systems science & engineering 2023年第6期45卷 2377-2389页

作者： Mohamed Esmail Karar Omar Reyad Hazem Ibrahim Shehata College of Computing and Information Technology Shaqra UniversityP.O.Box 33Shaqra11961Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Faculty of Computers and Artificial IntelligenceSohag UniversitySohag82524Egypt Department of Computer and Systems Engineering Faculty of EngineeringZagazig UniversityZagazig44519Egypt

This article introduces a new medical internet of things(IoT)framework for intelligent fall detection system of senior people based on our proposed deep forest *** cascade multi-layer structure of deep forest classifier allows to generate new features at each level with minimal hyperparameters compared to deep neural ***,the optimal number of the deep forest layers is automatically estimated based on the early stopping criteria of validation accuracy value at each generated *** suggested forest classifier was successfully tested and evaluated using a public SmartFall dataset,which is acquired from three-axis accelerometer in a *** includes 92781 training samples and 91025 testing samples with two labeled classes,namely non-fall and *** results of our deep forest classifier demonstrated a superior performance with the best accuracy score of 98.0%compared to three machine learning models,i.e.,K-nearest neighbors,decision trees and traditional random forest,and two deep learning models,which are dense neural networks and convolutional neural *** considering security and privacy aspects in the future work,our proposed medical IoT framework for fall detection of old people is valid for real-time healthcare application deployment.

关键词： Elderly population fall detection wireless sensor networks internet of medical things deep forest

来源：评论

学校读者我要写书评

暂无评论

Secure Audio Transmission Over Wireless Uncorrelated Rayleigh Fading Channel

引用

computers, Materials & Continua 2022年第1期70卷 1603-1615页

作者： Osama S.Faragallah M.Farouk Hala S.El-sayed Mohsen A.M.El-bendary Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electrical Engineering Faculty of EngineeringMenoufia UniversityShebin El-Kom32511Egypt Department of Electronics Technology Faculty of Technology and EducationHelwan UniversityCairoEgypt

Audio communications and computer networking play essential roles in our daily lives,including many domains with different *** in these technologies are *** consequence,there is a dire need to secure these technologies up to *** paper presents an efficient model for secure audio signal transmission over the wireless noisy uncorrelated Rayleigh fading ***,the performance of the utilized multiple secret keys-based audio cryptosystem is analyzed in different transformation *** discrete cosine transform(DCT),the discrete sine transform(DST),and the discrete wavelet transform(DWT)are investigated in the utilizedmultiple secret key-based audio *** results show consistent results with the wireless noisy *** performance of the proposed multiple secret keys-based audio cryptosystem can be ranked concerning the employed domain as DWT,DCT,and DST transform *** simulation experiments proved that the presented multiple secret keysbased audio cryptosystemfor audio signals transmitted over the wireless noisy uncorrelatedRayleigh fading channel achieves reliable and secure wireless link utilizing combined multi security layers.

关键词： Audio communications encryption uncorrelated Rayleigh fading channel

来源：评论

学校读者我要写书评

暂无评论

Primary User-Awareness-Based Energy-Efficient Duty-Cycle Scheme in Cognitive Radio Networks

引用

computers, Materials & Continua 2022年第3期70卷 5991-6005页

作者： Zilong Jin Chengbo Zhang Kan Yao Dun Cao Seokhoon Kim Yuanfeng Jin School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)Nanjing University of Information Science and Technology Nanjing210044China Jiangsu Institute of Economic and Information Technology Nanjing210003China School of Computer and Communications Engineering Changsha University of Science and TechnologyChangsha410114China Department of Computer Software Engineering Soonchunhyang UniversityAsan31538Rep.of Korea Department of Mathematics Yanbian UniversityYanji133002China

Cognitive radio devices can utilize the licensed channels in an opportunistic manner to solve the spectrum scarcity issue occurring in the unlicensed ***,these cognitive radio devices(secondary users)are greatly affected by the original users(primary users)of licensed *** users have to adjust operation parameters frequently to adapt to the dynamic network environment,which causes extra energy *** consumption can be reduced by predicting the future activity of primary ***,the traditional prediction-based algorithms require large historical data to achieve a satisfying precision accuracy which will consume a lot of time and memory ***,many of these schemes lack methods to deal with the very busy network *** this paper,one semi-supervised learning algorithm,i.e.,tri-training,has been employed to investigate the prediction of primary *** on the prediction results of tri-training,a duty-cycle mechanism and an intermediate node selection approach are proposed to improve the energy *** results show the effectiveness of the proposed algorithm.

关键词： Cognitive radio tri-training duty-cycle intermediate node energy efficiency

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：