Journal of Cybersecurity and Information Management

Journal DOI

https://doi.org/10.54216/JCIM

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2690-6775ISSN (Online) 2769-7851ISSN (Print)

Energy efficient Laser based embedded system for blind turn traffic control

Deepak Prashar, Gouri Shankar Chakraborty, Sudan Jha*

The usage of embedded system in the traffic control unit is not new. Many advanced intelligent systems are being used to reduce the human effort, and wasting time. As a result, road congestion and accident rate are being decreased. But in some critical spots like blind turn in the hill areas where the turn is too much sharp that it’s impossible to identify the existence of coming vehicles and the result may be very dangerous which need to be improved more with advance traffic controlling system. In this paper, we are going to propose a laser based embedded system which can efficiently control the traffic in the blind turn areas through giving proper signals in any situation. A driver can easily identify both the types and numbers of the vehicles coming from the other side by getting signals from the signal terminal. The proposed system is flexible and cost effective so that it can play a vital role by being widely used in the near future.

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Doi: https://doi.org/10.54216/JCIM.020201

Vol. 2 Issue. 2 PP. 35-43, (2020)

A novel approach for Spam Email Filtering Using Machine Learning

Subhalaxmi Sahoo , Sudan Jha , Deepak Prashar

Spam emails also known as unsolicited emails (maybe commercial or maybe not) i.e. those mails which are sent without our request or concern. Email spam is the practice of sending unwanted emails, mostly contains commercial messages to randomly generated persons. In the internet email spam is widespread because of such low cost of sending emails other than any other means of communication. It is important to filter spam emails because most of the malicious activities performed in the internet done through email spamming. Though there are many spam filters are available we still get huge amount of spam emails. This is not because the filters are not accurate & effective; the reason is the generation of quick and effective counters of the algorithm used in the filters. In our project we used mainly three supervised learning algorithms namely Linear SVC, Multinomial NB, and k-NN to implement the filter. We used these algorithms to train the system about spam email by using the feature called word count vector which is generated by processing a dataset filled with existing emails containing both spam and legitimate emails. The full process of the project and the result of the execution by implementing the three models/algorithms are discussed.

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Doi: https://doi.org/10.54216/JCIM.020202

Vol. 2 Issue. 2 PP. 44-57, (2020)

An Efficient Machine Learning based Cervical Cancer Detection and Classification

Ahmed N. Al Masri , Hamam Mokayed

Cervical cancer (CC) is the fourth commonly occurring cancer among females over the globe. It accounts for 7.9% of woman cancer as identified by world health organization (WHO). The most important reason for increased mortality due to cervical cancer is the deficiency of effective initial treatment. The asymptomatic nature is a main problem faced in the analysis of CC from initial stage. Recently, computer aided diagnosis (CAD) model has gained significant attention in the disease diagnostic process. At the same time, machine learning (ML) finds its use in several medical applications and is utilized as classifier for the initial detection of cancerous cells occurs from cervix area of uterus. With this motivation, this study introduces an intelligent ML based CAD (IML-CAD) technique to classify cervix cancer. The IML-CAD technique involves different stages of operations to detect and classify the cancerous cervix cells. In addition, the IML-CAD technique involves histogram based segmentation to determine the affected regions. Moreover, local binary patterns (LBP) based feature extractor and least squares support vector machine (LS-SVM) based classifier is designed for CC classification. To showcase the better performance of the IML-CAD technique, a series of simulations is performed and the experimental results highlighted the superior performance of the IML-CAD technique over the other techniques.

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Doi: https://doi.org/10.54216/JCIM.020203

Vol. 2 Issue. 2 PP. 58-67, (2020)

Modeling of Optimal Adaptive Weighted Clustering Protocol for Vehicular Ad hoc Networks

M. Elhoseny , X. Yuan

Vehicular ad hoc network (VANET) is a mobile adhoc network widely used in intelligent transportation systems (ITS). Owing to the unique features of VANET like self-organized, recurrent link interruptions, and quick topology modifications, the design of an effective clustering protocol is a challenging problem. The clustering process is considered an optimization problem and can be solved using metaheuristic algorithms. Therefore, this paper presents an adaptive weighted clustering protocol with artificial fish swarm optimization (AWCP-AFSO) algorithm for VANET. The proposed AWCP-AFSO technique aims to select the CHs effectively and thereby accomplishes energy efficiency. To construct clusters, the AWCP-AFSO algorithm derives an objective function from electing an optimal set of CHs. A wide range of simulations are performed, and the results are investigated in terms of several performance measures. The experimental values showcased the betterment of the AWCP-AFSO technique over the recent techniques. 

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Doi: https://doi.org/10.54216/JCIM.020204

Vol. 2 Issue. 2 PP. 68-76, (2020)