Journal of Intelligent Systems and Internet of Things最新文献_第6页

Fuzzy-Based Clustering for Larger-Scale Deep Learning in Autonomous Systems Based on Fusion Data 基于融合数据的自治系统大规模深度学习的模糊聚类

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.090105

Ibrahim Najem, Tabarak Ali Abdulhussein, M. Ali, Asaad Shakir Hameed, Inas Ridha Ali, M. Altaee

{"title":"Fuzzy-Based Clustering for Larger-Scale Deep Learning in Autonomous Systems Based on Fusion Data","authors":"Ibrahim Najem, Tabarak Ali Abdulhussein, M. Ali, Asaad Shakir Hameed, Inas Ridha Ali, M. Altaee","doi":"10.54216/jisiot.090105","DOIUrl":"https://doi.org/10.54216/jisiot.090105","url":null,"abstract":"Problems in autonomous systems may be tackled with the help of the AS-FC-DL approach, which integrates autonomous fuzzy clustering and deep learning methods. The system can anticipate human behavior on crowded roadways by employing these techniques to recognize patterns and extract features from complicated unsupervised data. Each image point's membership value is associated with the cluster's epicenter using the fuzzy clustering methodology in the AS-FC-DL approach. Using least-squares methods, this approach finds the optimal position for each data point within a probability space, which may be anywhere among multiple clusters. Data points from an unlabeled dataset may be organized into distinct groups using a deep learning technique called cluster analysis. Data fusion from many sources, including sensor data and video data, can improve the AS-FC-DL method's precision and performance. The algorithm is able to deliver an all-encompassing and precise evaluation of human behavior on crowded roadways by fusing data from many sources. The AS-FC-DL approach may also be employed in autonomous vehicles to help them learn from their experiences and improve their performance. Using reinforcement learning, a model for autonomous vehicle driving may be constructed. The AS-FC-DL approach helps the self-driving car traverse the area with increased precision and efficiency by allowing it to recognize structures and extract features from complicated unsupervised data.","PeriodicalId":122556,"journal":{"name":"Journal of Intelligent Systems and Internet of Things","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126622226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI-Driven Smart Homes: Challenges and Opportunities 人工智能驱动的智能家居:挑战与机遇

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.080205

Waleed Abd Elkhalik

引用次数: 0

Smart Sensor Networks for Industrial IoT Applications 工业物联网应用的智能传感器网络

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.080204

Nihal N. Mostafa, Esmeralda Kazia

{"title":"Smart Sensor Networks for Industrial IoT Applications","authors":"Nihal N. Mostafa, Esmeralda Kazia","doi":"10.54216/jisiot.080204","DOIUrl":"https://doi.org/10.54216/jisiot.080204","url":null,"abstract":"Smart Sensor Networks (SSNs) are an indispensable part of the Industrial Internet of Things (IIoT), which seeks to improve efficiency, productivity, and safety in different industrial applications. SSNs consist of a large number of sensors, regularly deployed in a wireless ad-hoc network, which communicates with each other and with other devices, such as gateways and servers. Nevertheless, the building of SSNs in IIoT environments encounters many challenges, such as trust management, data reliability, privacy, and security. These challenges necessitate proposing novel solutions and protocols, to provide a reliable, secure, and efficient SSN. To this end, this study presents a novel DL system that can effectively discriminate between normal traffics and malicious traffic in SSNs. A convolutional feature extractor is developed to learn important discriminative features necessary for the early detection of security threats in SSNs. Then, an improved LSTM (ILSTM) is presented to model the temporal dynamics of the SSNs flows, which helps model long interdependency between traffic samples. A focal loss function is applied to deal with the imbalance between class samples. Experimental analysis is performed on an open-source SSN security dataset, named WSN-DS, the findings demonstrated the competitive advantages of our system over the prevailing solutions.","PeriodicalId":122556,"journal":{"name":"Journal of Intelligent Systems and Internet of Things","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132637864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effective Drive an Autonomous Vehicle, The Environment Characteristics Are Extracted Via Intelligent Image Processing 有效驾驶自动驾驶汽车，通过智能图像处理提取环境特征

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.070104

M. Sumithra, G. Sundar, B. Buvaneswari, K. Sridharan, V. Kumar

{"title":"Effective Drive an Autonomous Vehicle, The Environment Characteristics Are Extracted Via Intelligent Image Processing","authors":"M. Sumithra, G. Sundar, B. Buvaneswari, K. Sridharan, V. Kumar","doi":"10.54216/jisiot.070104","DOIUrl":"https://doi.org/10.54216/jisiot.070104","url":null,"abstract":"With the development of image handling technology, computerized technology, and the theory of image preparation, it has become clear that image processing is a crucial area of computer application. It is frequently used in many logical and designing applications, such as remote detection, medicine, meteorology, exchanges, and so on. However, with the swift development of picture preparation technology, it is becoming more and more important to precisely and successfully evaluate the quality of a picture. Recently, image quality evaluation has grown in importance as a study area in the field of developing picture data, which has attracted a lot of attention from academics. The importance of picture quality primarily takes into account two aspects: picture loyalty and picture coherence. picture quality directly depends on depending on the optical characteristics of the imaging equipment, image contrast, instrument clamor, and other factors. It may provide checking intentions to depict gaining, handling, and various connections through quality assessment. The evaluation of image quality assessment has become one of the essential breakthroughs of picture data designing to create a meaningful assessment of all components of picture preparation. People have needed to learn picture loyalty and the understandability of the quantitative estimation strategy using the picture a lot framework plan as the assessment premise for a very long time, but one of the people on the human visual characteristics is still not fully understood, in particular the description methods of psychological characteristics in human vision is also difficult to learn the quantitative evaluation of image quality, so, extensive investigation is required.","PeriodicalId":122556,"journal":{"name":"Journal of Intelligent Systems and Internet of Things","volume":"309 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132331374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Survey on Meta-heuristic Algorithms for Global Optimization Problems 全局优化问题的元启发式算法综述

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.010104

A. H. Zaied, Mahmoud Ismail, S. El-Sayed

引用次数: 12

Classification of Diabetic Foot Thermal Images Using Deep Convolutional Neural Network 基于深度卷积神经网络的糖尿病足热图像分类

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.080102

R. Yousef, M. Eid, M. A. Mohamed

引用次数: 0

Intelligent Multi-Level Feature Fusion Using Remote Sensing and CNN Image Classification Algorithm 基于遥感和CNN图像分类算法的智能多层次特征融合

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.090103

M. Altaee, Talib. A., M. Jalil, Ali. J., T. A. Alalwani

引用次数: 0

Handling within-word and cross-word pronunciation variation for Arabic speech recognition (knowledge-based approach) 处理阿拉伯语语音识别中的词内和词间发音变化(基于知识的方法)

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.010202

Ibrahim El-Henawy, Marwa Abo Abo-Elazm

引用次数: 3

A survey on gel images analysis software tools 凝胶图像分析软件工具综述

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.010103

Mahmoud H.Alnamoly, A. Alzohairy, I. El-Henawy

{"title":"A survey on gel images analysis software tools","authors":"Mahmoud H.Alnamoly, A. Alzohairy, I. El-Henawy","doi":"10.54216/jisiot.010103","DOIUrl":"https://doi.org/10.54216/jisiot.010103","url":null,"abstract":"One of the most severe sources of information for a molecular biologist is the gel image generated by using gel electrophoresis during the experiment of issr-pcr, sds-pages, and rapd-pcr. DNA and protein gel images are obtained through the gel electrophoresis separations techniques of DNA and protein fragments. The separation of the polymorphic bands is based on the sizes of the negatively charged DNA fragments running from the negative cathode toward the positive anode. Each gel image has some vertical lanes; each lane corresponds to one sample and has several horizontal bands. The resulting images produced by Gel electrophoresis are sometimes difficult to interpret so that it was important to develop software tools to analyze the gel images to help biologists in the process of analyzing gel image as they draw their conclusions according to the results that generated from gel image analyzer software. In this article, we present a survey of some commercial and non-commercial software tools that are used for analyzing gel images. We develop a novel software for processing and analyzing the gel electrophoresis images, computing the molecular weights, saving them as excel sheet, clustering the bands based on their molecular weights using k-means algorithm, Applying band matching using a tolerance value entered by the user, determine the similarities between samples, drawing the corresponding phylogenetic tree, saving a report of the experiment as a pdf, and printing this report. The novel software will provide the biologist with the ability of manual processing, automatic processing, and semi-automatic processing.","PeriodicalId":122556,"journal":{"name":"Journal of Intelligent Systems and Internet of Things","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128328659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

A Machine Learning Approach for Energy-Efficient IoT Systems 节能物联网系统的机器学习方法

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.010105

Mahmoud M. Ismail

引用次数: 0