International Journal of Machine Learning and Networked Collaborative Engineering最新文献_第2页

Cloudbin: Internet of Things Based Waste Monitoring System Cloudbin:基于物联网的垃圾监控系统

International Journal of Machine Learning and Networked Collaborative Engineering Pub Date : 2019-07-08 DOI: 10.30991/IJMLNCE.2019V03I02.001

Vikram Puri, Bhuvan Puri, Sandeep Singh Jagdev, Tri Tran Huu Minh

引用次数: 1

Deep Neural Network with Stacked Denoise Auto Encoder for Phishing Detection 基于堆叠噪声自动编码器的深度神经网络网络钓鱼检测

International Journal of Machine Learning and Networked Collaborative Engineering Pub Date : 2019-07-08 DOI: 10.30991/ijmlnce.2019v03i02.005

Sumathi Kothandan, V. Sujatha

{"title":"Deep Neural Network with Stacked Denoise Auto Encoder for Phishing Detection","authors":"Sumathi Kothandan, V. Sujatha","doi":"10.30991/ijmlnce.2019v03i02.005","DOIUrl":"https://doi.org/10.30991/ijmlnce.2019v03i02.005","url":null,"abstract":"Sensitive information such as credit card information, username, password and social security number etc, can be stolen using a fake page that imitates trusted website is called phishing. The attacker designs a similar webpage either by copying or making small manipulation to the legitimate page so that the online user cannot distinguish the legitimate and fake websites. A Deep Neural Network (DNN) was introduced to detect the phishing Uniform Resource Locator (URL). Initially, a 30-dimension feature vector was constructed based on URL-based features, Hypertext Markup Language (HTML)-based features and domain-based features. These features were processed in DNN to detect the phishing URL. However, the irrelevant, redundant and noisy features in the dataset increase the complexity of DNN classifier. So the feature selection is required for efficient phishing attack detection. But feature selection is a time-consuming process since it is an independent process. So in this paper, a feature vector is generated by DNN itself using Stacked Denoise Auto Encoder (SDAE). Moreover, the noisy data such as missing features affect the efficiency of phishing detection so the SDAE is trained to reconstruct a clean input feature vector. The initial input feature vector is corrupted by setting some feature vectors as zero. Then the corrupted feature vector is then mapped with basic auto encoder, to a hidden representation from which the input feature vector is reconstructed. The reconstructed features are given as input to DNN which selects the most relevant features and predicts the phishing URL. Hence the sparse feature representation of SDAE increases the classification accuracy of DNN. The experiments are conducted in Ham, Phishing Corpus and Phishload datasets in terms of accuracy, precision, recall and F-measure to prove the effectiveness of DNN-SDAE.","PeriodicalId":338210,"journal":{"name":"International Journal of Machine Learning and Networked Collaborative Engineering","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128119057","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

Information Processing in GLIF Neuron Model with Noisy Conductance 带噪声电导的GLIF神经元模型的信息处理

International Journal of Machine Learning and Networked Collaborative Engineering Pub Date : 2019-07-08 DOI: 10.30991/ijmlnce.2019v03i02.004

V.D.S. Baghela, S. K. Bharti, S. Choudhary

{"title":"Information Processing in GLIF Neuron Model with Noisy Conductance","authors":"V.D.S. Baghela, S. K. Bharti, S. Choudhary","doi":"10.30991/ijmlnce.2019v03i02.004","DOIUrl":"https://doi.org/10.30991/ijmlnce.2019v03i02.004","url":null,"abstract":"In this article, we investigate the generalized leaky integrate-and-fire (GLIF) neuron model with stochastic synaptic conductance. A neuron remains connected with other neuron via dendrites and axons at synapse, which can be treated as an electrical capacitor. Dendrites carry electro-chemical signals from input neuron to synapse whereas axons are responsible for their transmission form synapse to other neurons. Concentration of these electro-chemicals in synapse varies during entire time period. We investigate the effect of varying concentration of electro-chemicals at synapse in a single neuron model. Concentration variation of electro-chemicals at synapse is incorporated as noise in GLIF model. Excitatory and inhibitory synaptic conductance of neuron in GLIF is assumed as stochastic entities driven by Gaussian White noise. Stationary state membrane potential distribution for the proposed model is computed with reflecting boundary conditions, which is noticed as geometrically distributed. In order to investigate spiking activity and information encoding mechanism, an extensive simulation based study has been carried out. Temporal encoding technique is used to analyze the encoding mechanism. It is noticed that ISI distribution has higher variance with respect to excitatory input than inhibitory input.","PeriodicalId":338210,"journal":{"name":"International Journal of Machine Learning and Networked Collaborative Engineering","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121642755","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

Machine Learning Prediction of Wikipedia Time Series Data using: R Programming 使用R编程的维基百科时间序列数据的机器学习预测

International Journal of Machine Learning and Networked Collaborative Engineering Pub Date : 2019-06-10 DOI: 10.30991/ijmlnce.2019v03i02.002

Yagyanath Rimal

引用次数: 0

Deterministic Machine Learning Cluster Analysis of Research Data: using R Programming 研究数据的确定性机器学习聚类分析:使用R编程

International Journal of Machine Learning and Networked Collaborative Engineering Pub Date : 2019-03-31 DOI: 10.30991/IJMLNCE.2019V03I01.004

Yagyanath Rimal

引用次数: 0

A Robust Decomposition Based Algorithm For Removal Of Pattern Noise From Images 一种基于鲁棒分解的图像去噪算法

International Journal of Machine Learning and Networked Collaborative Engineering Pub Date : 2019-03-31 DOI: 10.30991/IJMLNCE.2019V03I01.005

Vinit Kumar Gunjan, F. Shaik

引用次数: 0

Smart Building: A Low Cost Indoor Positioning and Intelligent Path Finding 智能建筑:低成本的室内定位和智能寻径

International Journal of Machine Learning and Networked Collaborative Engineering Pub Date : 2019-03-31 DOI: 10.30991/IJMLNCE.2019V03I01.002

Farzad Kiani, Alex Gunagwera

{"title":"Smart Building: A Low Cost Indoor Positioning and Intelligent Path Finding","authors":"Farzad Kiani, Alex Gunagwera","doi":"10.30991/IJMLNCE.2019V03I01.002","DOIUrl":"https://doi.org/10.30991/IJMLNCE.2019V03I01.002","url":null,"abstract":"Despite the rapid improvement in mobile devices, overall gradual growth in the ubiquitous computing field, the wide applicability, more usefulness of location based services in general and indoor navigation. The Global Positioning System (GPS) has undergone tremendous improvement since the 1900s and it, indeed is considered one of the most successful navigation systems known to date. However, it is still inefficient for sufficiently accurate positioning in both indoor environments and environments with many tall buildings such as skyscrapers since such buildings block or interfere with its signal transmissions. In particular, building a sufficiently accurate, efficient and relatively cheap indoor navigation system in a GPS-free environment is still a challenging task with a lot of tradeoffs and constraints to put into consideration. In this paper, a simple yet robust, low-cost, context-aware user-interactive, user-friendly hybrid of fingerprinting and dead reckoning indoor navigation system suitable for both the visually and the physically disabled as well that takes advantage of the results yielded by sensor fusion is proposed. The presented system is also designed to allow for efficient evacuation of users in cases of emergences. The prototype is made majorly of the following parts; user tracking, optimal, context-aware and dynamic route calculation and planning and dynamic route representation with an upper bound of 2m and an average of 0.8-1.3m accuracy. All that is required from the user is a smart phone without installation of extra hardware.","PeriodicalId":338210,"journal":{"name":"International Journal of Machine Learning and Networked Collaborative Engineering","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127680686","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

IJMLNCE Editorial Note Volume No 03, Issue No 01 IJMLNCE编辑说明第03卷，第01期

International Journal of Machine Learning and Networked Collaborative Engineering Pub Date : 2019-03-31 DOI: 10.30991/ijmlnce.2019v03i01

V. K. Solanki, V. G. Díaz

引用次数: 0

A Study on Internet of Things in Women and Children Healthcare 物联网在妇幼保健中的应用研究

International Journal of Machine Learning and Networked Collaborative Engineering Pub Date : 2019-03-31 DOI: 10.30991/IJMLNCE.2019V03I01.001

Nishargo Nigar

{"title":"A Study on Internet of Things in Women and Children Healthcare","authors":"Nishargo Nigar","doi":"10.30991/IJMLNCE.2019V03I01.001","DOIUrl":"https://doi.org/10.30991/IJMLNCE.2019V03I01.001","url":null,"abstract":"Individual entities are being connected every day with the advancement of Internet of Things (IoT). IoT contains various application domains and healthcare is one of them indeed. It is receiving a lot of attention recently because of its seamless integration with electronic health (eHealth) and telemedicine. IoT has the capability of collecting patient data incessantly which surely helps in preventive care. Doctors can diagnose their patients early to avoid complications and they can suggest further modifications if needed. As the whole process is automated, risk of errors is reduced. Administrative paperwork and data entry tasks will be automated due to tracking and connectivity. As a result, healthcare providers can engage themselves more in patient care. In traditional healthcare services, an individual used to have access to minimal insights into his own health. Hence, they were less conscious about themselves and depended wholly on the healthcare facilities for unfortunate events. But they can track their vitals, activities and fitness with the aid of connected devices now. Furthermore, they can suggest their preferred user interfaces. This paper describes several methods, practices and prototypes regarding IoT in the field of healthcare for women and children.","PeriodicalId":338210,"journal":{"name":"International Journal of Machine Learning and Networked Collaborative Engineering","volume":"18 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132287113","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

Artificial Intelligence to Prevent Road Accidents 防止交通事故的人工智能

International Journal of Machine Learning and Networked Collaborative Engineering Pub Date : 2019-03-31 DOI: 10.30991/IJMLNCE.2019V03I01.003

B. Jaidev, Sonakshi Garg, Sandhya Makkar

引用次数: 1