2019 15th International Conference on Electronics, Computer and Computation (ICECCO)最新文献_第3页

Visible Light Communication: A potential 5G and beyond Communication Technology 可见光通信:一种潜在的5G及以后的通信技术

2019 15th International Conference on Electronics, Computer and Computation (ICECCO) Pub Date : 2019-12-01 DOI: 10.1109/ICECCO48375.2019.9043201

S. Idris, Usman Mohammed, Jaafaru Sanusi, Sadiq Thomas

引用次数: 13

Using Solar Photovoltaic Systems to Significantly Reduce Power Production Problems in Nigeria and Create a Greener Environment 利用太阳能光伏系统显著减少尼日利亚的电力生产问题，创造更绿色的环境

2019 15th International Conference on Electronics, Computer and Computation (ICECCO) Pub Date : 2019-12-01 DOI: 10.1109/ICECCO48375.2019.9043257

Onyedikachi Vincent Okereke, Fatima Aliyu, Jonathan Dangwaran, Sadiq Thomas, Biliyok Akawu Shekari, Hussein U. Suleiman

引用次数: 1

Deep Learning Methods for Filter Extraction in Tomato fruits 番茄果实过滤提取的深度学习方法

2019 15th International Conference on Electronics, Computer and Computation (ICECCO) Pub Date : 2019-12-01 DOI: 10.1109/ICECCO48375.2019.9043283

F. I. Lawan, L. Ismaila, Steve A. Adeshina, H. I. Muhammed, L. Csató

引用次数: 0

Rain Induced Attenuation Prediction in the Ku Band of Nigerian Communication Satellite over Abuja Earth Station 阿布贾地面站上空尼日利亚通信卫星Ku波段雨致衰减预报

2019 15th International Conference on Electronics, Computer and Computation (ICECCO) Pub Date : 2019-12-01 DOI: 10.1109/ICECCO48375.2019.9043291

Abubakar Umar Turaki, Gokhan Koyunlu, Nyangwarimam Obadiah Ali, Abubakar Idrissa, G. Sani, Omotayo Oshiga

引用次数: 0

DyRED: An Enhanced Random Early Detection Based on a new Adaptive Congestion Control 基于一种新的自适应拥塞控制的增强随机早期检测

2019 15th International Conference on Electronics, Computer and Computation (ICECCO) Pub Date : 2019-12-01 DOI: 10.1109/ICECCO48375.2019.9043276

Sunday Barde Danladi, Faruku Umar Ambursa

{"title":"DyRED: An Enhanced Random Early Detection Based on a new Adaptive Congestion Control","authors":"Sunday Barde Danladi, Faruku Umar Ambursa","doi":"10.1109/ICECCO48375.2019.9043276","DOIUrl":"https://doi.org/10.1109/ICECCO48375.2019.9043276","url":null,"abstract":"Over the years congestion has been a major issue affecting the internet leading to an increase in packet loss and delay. Researchers have proposed different algorithms to address the issue of congestion from Drop Tail, Early Random Drop to Active Queue Management (AQM). Random Early Detection (RED) is the first Active Queue Management (AQM) technique that was developed to support transport-layer congestion and decrease the impacts of network congestion on the router buffer. The idea behind RED is to sense and detect incipient congestion early and notify connections of congestion either by dropping packets arriving or by reducing its sending rate. Although various other AQM techniques have been proposed by researchers, RED is still the most commonly used algorithm for congestion avoidance and researches is still ongoing to enhance the performance of RED. In this paper, we have developed an extension to RED to address the limitation of RED and the algorithm is then compared with RED under various network scenarios. The results of the evaluation shows that the new method has outperformed RED.","PeriodicalId":166322,"journal":{"name":"2019 15th International Conference on Electronics, Computer and Computation (ICECCO)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117302565","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: 9

Machine Learning Classification Algorithms for Adware in Android Devices: A Comparative Evaluation and Analysis Android设备中广告软件的机器学习分类算法:比较评价与分析

2019 15th International Conference on Electronics, Computer and Computation (ICECCO) Pub Date : 2019-12-01 DOI: 10.1109/ICECCO48375.2019.9043288

Joseph Yisa Ndagi, J. Alhassan

{"title":"Machine Learning Classification Algorithms for Adware in Android Devices: A Comparative Evaluation and Analysis","authors":"Joseph Yisa Ndagi, J. Alhassan","doi":"10.1109/ICECCO48375.2019.9043288","DOIUrl":"https://doi.org/10.1109/ICECCO48375.2019.9043288","url":null,"abstract":"Exponential growth experienced in Internet usage has paved the way to exploit users of the Internet, a phishing attack is one of the means that can be used to obtained victim confidential details unwittingly across the Internet. A high false-positive rate and low accuracy have been a setback in phishing detection. In this research 17 different supervised learning techniques such as RandomForest, Systematically Developed Forest (SysFor), Spectral Areas and Ratios Classifier (SPAARC), Reduces Error Pruning Tree (RepTree), RandomTree, Logic Model Tree (LMT), Forest by Penalizing Attributes (ForestPA), JRip, PART, Nearest Neighbor with Generalization (NNge), One Rule (OneR), AdaBoostM1, RotationForest, LogitBoost, RseslibKnn, Library for Support Vector Machine (LibSVM), and BayesNet were employed to achieve the comparative analysis of machine classifier. The performance of the classifier algorithms was rated using Accuracy, Precision, Recall, F-Measure, Root Mean Squared Error, Receiver Operation Characteristics Area, Root Relative Squared Error False Positive Rate and True Positive Rate using WEKA data mining tool. The research revealed that quite several classifiers also exist which if properly explored will yield more accurate results for phishing detection. RandomForest was found to be an excellent classifier that gives the best accuracy of 0.9838 and a false positive rate of 0.017. The comparative analysis result indicates the achievement of low false-positive rate for phishing classification which suggests that anti-phishing application developer can implement the machine learning classification algorithm that was discovered to be the best in this study to enhance the feature of phishing attack detection and classification.","PeriodicalId":166322,"journal":{"name":"2019 15th International Conference on Electronics, Computer and Computation (ICECCO)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116316654","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: 1

Big Data Analytics in Healthcare: A Review 医疗保健中的大数据分析:综述

2019 15th International Conference on Electronics, Computer and Computation (ICECCO) Pub Date : 2019-12-01 DOI: 10.1109/ICECCO48375.2019.9043183

N. C. Onyemachi, O. Nonyelum

引用次数: 5

An Enhanced Face Recognition Method for Lighting Problem 一种针对光照问题的增强人脸识别方法

2019 15th International Conference on Electronics, Computer and Computation (ICECCO) Pub Date : 2019-12-01 DOI: 10.1109/ICECCO48375.2019.9043199

Cemil Turan, A. Aitimov, B. Kynabay, Aimoldir Aldabergen

引用次数: 0

Improved Handwritten Digit Recognition method using Deep Learning Algorithm 改进的基于深度学习算法的手写数字识别方法

2019 15th International Conference on Electronics, Computer and Computation (ICECCO) Pub Date : 2019-12-01 DOI: 10.1109/ICECCO48375.2019.9043235

R. Jantayev, Y. Amirgaliyev

引用次数: 3

Performance Evaluation of Machine Learning Algorithms for Hypertext Transfer Protocol Distributed Denial of Service Intrusion Detection 机器学习算法在超文本传输协议分布式拒绝服务入侵检测中的性能评估

2019 15th International Conference on Electronics, Computer and Computation (ICECCO) Pub Date : 2019-12-01 DOI: 10.1109/ICECCO48375.2019.9043262

Rukayya Umar, M. Olalere, I. Idris, Raji Abdullahi Egigogo, G. Bolarin

引用次数: 1