Communications on Applied Electronics最新文献_第2页

Classification of Imbalanced Data of Medical Diagnosis using Sampling Techniques 基于抽样技术的医学诊断不平衡数据分类

Communications on Applied Electronics Pub Date : 2021-05-31 DOI: 10.5120/CAE2021652883

Varsha Babar

引用次数: 1

On the Effect of Typing Hand on Mobile-based Keystroke Dynamics 基于移动设备的击键动力学中打字手的影响

Communications on Applied Electronics Pub Date : 2021-05-27 DOI: 10.5120/CAE2021652878

O. Akinsowon, N. Poh

引用次数: 0

Deep Learning Approach for the Detection of Plant Diseases 植物病害检测的深度学习方法

Communications on Applied Electronics Pub Date : 2021-02-28 DOI: 10.5120/CAE2021652877

C Padmavathi

{"title":"Deep Learning Approach for the Detection of Plant Diseases","authors":"C Padmavathi","doi":"10.5120/CAE2021652877","DOIUrl":"https://doi.org/10.5120/CAE2021652877","url":null,"abstract":"Plant disease detection is one of the most active topics in the modern agriculture. The disease in plants are season-based which depends on the presence of the pathogen, crops, environmental conditions and varieties grown. The monitoring of leaf area is important in studying physiological capabilities associated with plant. This work makes use of image processing technique for the detection of disease and the use of Support Vector Machine for the classification of plant leaf disease. Plant Leaf disease detection and classification is performed, depending on various extracted features from plant leaves utilizing different image processing and deep learning techniques. Detection of plant leaf disease involves steps like data collection, image processing techniques like contrast enhancement, RGB to HSI, K-means clustering, feature extraction, segmentation and SVM based classification. This approach is useful when image dimensions are large and a reduced feature representation is required to efficiently complete tasks such as image matching and retrieval. The proposed work mainly concentrates on four major diseases that affect the plant leaf namely Alternaria alternata, Anthracnose, Bacterial blight and Cercospora leaf spot. The dataset considered for each disease is 22, 23, 20 and 20 respectively. The results of a test case for each of the four diseases are quantified and the percentage of disease affected area was observed to be 15.0013% in Alternaria alternata, 15.0015% in Anthracnose, 15.0142% in Bacterial blight and 23.0225% in Cercospora leaf spot.","PeriodicalId":174733,"journal":{"name":"Communications on Applied Electronics","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127721499","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

Customer Analysis Model for Trusted Online Merchant 可信网络商家客户分析模型

Communications on Applied Electronics Pub Date : 2020-10-28 DOI: 10.5120/cae2020652872

O. Emam, Arafa Taher, Hanan Fahmy

{"title":"Customer Analysis Model for Trusted Online Merchant","authors":"O. Emam, Arafa Taher, Hanan Fahmy","doi":"10.5120/cae2020652872","DOIUrl":"https://doi.org/10.5120/cae2020652872","url":null,"abstract":"Trust is one of the most important factors which influence the behavior of online shopping. Previous researchers indicated several factors that affect online brand shopping. Security, privacy, and trust are among these factors. This paper tries to propose a new model is called Merchant Segmentation Trust (MST) based on the huge amount of data comes from online shopping not on the last-mentioned factors using bigdata techniques. The propose model (MST) consist of four phases, the first phase is processing the data (cleaned, prepared, transformed) to extract the useful transactions attributes like order date, ship date, number of complaints, duration of complaints, net profit, etc. And aggregate all merchant with its transactions in form that can be used by the clustering algorithm. The second phase is to using the bigdata techniques as the huge amount of data comes from the rapid growth of online shopping by using K-means clustering algorithm. The third phase using traditional Recency, Frequency, Monetary (RFM) model to rank the resulting clusters to define the most trustable merchant, finally evaluate the clustering accuracy by using sum square error (SSE) and extract the most trustable merchant by the propose MST model compared against the traditional factors which result from the review or from the survey to ensure that merchant’s behavior can rate the trust among other merchants.","PeriodicalId":174733,"journal":{"name":"Communications on Applied Electronics","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116888982","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

Realization of Fractance Device using Fifth Order Approximation 用五阶逼近实现分数器件

Communications on Applied Electronics Pub Date : 2020-09-28 DOI: 10.5120/CAE2020652869

B. T. Krishna

引用次数: 7

AI-enabled Digital Transformation of Insurance Industry 基于人工智能的保险业数字化转型

Communications on Applied Electronics Pub Date : 2020-09-28 DOI: 10.5120/CAE2020652870

Aruna Chaubey, Sachchidanand Singh

{"title":"AI-enabled Digital Transformation of Insurance Industry","authors":"Aruna Chaubey, Sachchidanand Singh","doi":"10.5120/CAE2020652870","DOIUrl":"https://doi.org/10.5120/CAE2020652870","url":null,"abstract":"Insurance is a financial instrument used by insured, individuals and businesses, as a risk mitigation tool. It's a promise by the insurer that when an event occurs, a certain loss amount would be paid to the insured. Artificial Intelligence (AI) technologies like Machine Learning (ML) and Natural Language Processing (NLP) can handle processing of the large data volume of the insurance domain daily chores like automated claims, underwriting, chatbots more accurately and faster compared to the humans etc. Artificial intelligence (AI) is enhancing every step in the insurance value chain like instantly customizable life insurance and on-demand property coverage. In this paper, we'd try to draw conclusions & recommendations to perform successful digital transformations for an insurer based on our industry experiences of helping some of the world's largest insurers to go through that transformation journey successfully. We'd also explain the various avenues and touch points where latest technologies such as Artificial Intelligence (AI) can be used to create a market differentiator and value creator to the insurers. General Terms Artificial Intelligence (AI), Digital Technology, Machine Learning (ML), Natural Language Processing (NLP)","PeriodicalId":174733,"journal":{"name":"Communications on Applied Electronics","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124170118","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

Securing IoT Systems using Blockchain Algorithms 使用区块链算法保护物联网系统

Communications on Applied Electronics Pub Date : 2020-09-28 DOI: 10.5120/CAE2020652871

O. Emam, Hanan Fahmy, Menna Mamdouh

引用次数: 0

An Enhanced-Received Signal Strength Technique for Estimating Mobile Station Position in Wireless Sensor Networks 一种无线传感器网络中移动站位置估计的增强接收信号强度技术

Communications on Applied Electronics Pub Date : 2020-07-29 DOI: 10.5120/cae2020652861

A. Adeyelu, Onaji J. Onah, Iwuese J. Orban

{"title":"An Enhanced-Received Signal Strength Technique for Estimating Mobile Station Position in Wireless Sensor Networks","authors":"A. Adeyelu, Onaji J. Onah, Iwuese J. Orban","doi":"10.5120/cae2020652861","DOIUrl":"https://doi.org/10.5120/cae2020652861","url":null,"abstract":"Techniques to track an object from a remote location given certain information have been required for several hundred years. Existing outdoor techniques to locate a Mobile Station (MS) within a cellular network require optimization both in terms of accuracy and latency. In this paper, an enhanced Mobile Station Positioning (MSP) model for Wireless Sensor Networks was developed and its performance was appraised using accuracy and latency metrics in line with Received Signal Strength (RSS) procedure. This model used the strength of the signal received at four Base Stations (BS) positioned within the neighborhood of the MS to locate the MS. The mathematical model was formulated using circles equation and Taylor’s series expansion. The estimated position of the MS was calculated using Linear Least Square (LLS) solution iteratively. The result showed that the model located the MS within error distances of 199m for 67% and 339m for 95% of the time it was deployed. This result outperformed the RSS technique using three BS which located the MS within 256m at 67% deployment and 368m at 95% of the time the model was used. This gave approximately 15% improvement in accuracy. Simulation results also revealed that the latency experienced when the BSs were increased from three to four increased by 13.65% (0.024 seconds). It can be concluded that increasing the number of BSs from three to four gave a significant better accuracy in locating a MS within the BSs. General Terms Mobile Station Positioning techniques,","PeriodicalId":174733,"journal":{"name":"Communications on Applied Electronics","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115710787","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

Cloud Application Security using Hybrid Encryption 使用混合加密的云应用程序安全性

Communications on Applied Electronics Pub Date : 2020-05-29 DOI: 10.5120/cae2020652866

Alabi Orobosade, Thompson Aderonke, Alese Boniface, Arome J.

引用次数: 20

A Secure Alternative for Electronic Toll Collection using Barcodes 使用条形码的电子收费的安全替代方案

Communications on Applied Electronics Pub Date : 2020-04-28 DOI: 10.5120/cae2020652860

Alese Boniface, Thompson Aderonke, Ayeni Olaniyi, Aliu Folasade

引用次数: 1