• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.137-142
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• 2021

Machine-learning systems have proven their worth in various industries, including healthcare and banking, by assisting in the extraction of valuable inferences. Information in these crucial sectors is traditionally stored in databases distributed across multiple environments, making accessing and extracting data from them a tough job. To this issue, we must add that these data sources contain sensitive information, implying that the data cannot be shared outside of the head. Using cryptographic techniques, Privacy-Preserving Machine Learning (PPML) helps solve this challenge, enabling information discovery while maintaining data privacy. In this paper, we talk about how to keep your data mining private. Because Data mining has a wide variety of uses, including business intelligence, medical diagnostic systems, image processing, web search, and scientific discoveries, and we discuss privacy-preserving in deep learning because deep learning (DL) exhibits exceptional exactitude in picture detection, Speech recognition, and natural language processing recognition as when compared to other fields of machine learning so that it detects the existence of any error that may occur to the data or access to systems and add data by unauthorized persons.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.21-26
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• 2022

This study touched on the effect of the different electronic course designs on the programming skills of university students. The researcher used the experimental research design of a quasi-experimental of two experimental groups to achieve the objectives of the study. The first group underwent an electronic course designed in the holistic pattern, and the second group was taught a course in a sequential pattern. This experimental design was intended to measure the impact of these two learning modes on the learners' cognitive and performance achievement of programming skills. An achievement test and observational form were the data collection tools. Data were analyzed statistically using Pearson correlation, Mann Whitney Test, and Alpha Cronbach. The findings revealed statistically- significant differences between the mean scores of the students of the first and second experimental groups in favor of the former concerning the observational form and the latter in the cognitive test. Based on the findings, some recommendations are suggested. Due to their effectiveness in the educational process, expanding using the e-courses at universities is vital. The university teachers are highly recommended to design e-courses and provide technical and material support to the e-courses user to fulfill their design purpose.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.245-251
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• 2021

This work aims to focus on the current features and characteristics of Human Element and Artificial intelligence (AI), ask some questions about future information security, and whether we can avoid human errors by improving machine learning and AI or invest in human knowledge more and work them both together in the best way possible? This work represents several related research results on human behavior towards information security, specified with elements and factors like knowledge and attitude, and how much are they invested for ISA (information security awareness), then presenting some of the latest studies on AI and their contributions to further improvements, making the field more securely advanced, we aim to open a new type of thinking in the cybersecurity field and we wish our suggestions of utilizing each point of strengths in both human attributions in software security and the existence of a well-built AI are going to make better future software security.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.254-266
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• 2021

The Internet of Medical Things (IoMTs) are to be considered an investment and an improvement to respond effectively and efficiently to patient needs, as it reduces healthcare costs, provides the timely attendance of medical responses, and increases the quality of medical treatment. However, IoMT devices face exposure from several security threats that defer in function and thus can pose a significant risk to how private and safe a patient's data is. This document works as a comprehensive review of modern approaches to achieving security within the Internet of Things. Most of the papers cited here are used been carefully selected based on how recently it has been published. The paper highlights some common attacks on IoMTs. Also, highlighting the process by which secure authentication mechanisms can be achieved on IoMTs, we present several means to detect different attacks in IoMTs

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.167-176
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• 2021

This is the world of computer science and innovations. In this modern era, every day new apps, webs and software are being introduced. As well as new apps and software are being introduced, similarly threats and vulnerable security matters are also increasing. Web apps are software that can be used by customers for numerous useful tasks, and because of the developer experience of good programming standards, web applications that can be used by an attacker also have multiple sides. Web applications Security is expected to protect the content of critical web and to ensure secure data transmission. Application safety must therefore be enforced across all infrastructure, including the web application itself, that supports the web applications. Many organizations currently have a type of web application protection scheme or attempt to build/develop, but the bulk of these schemes are incapable of generating value consistently and effectively, and therefore do not improve developers' attitude in building/designing stable Web applications. This article aims to analyze the attacks on the website and address security scanners of web applications to help us resolve web application security challenges.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.193-203
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• 2022

After the everyday use of systems and applications of artificial intelligence in our world. Consequently, machine learning technologies have become characterized by exceptional capabilities and unique and distinguished performance in many areas. However, these applications and systems are vulnerable to adversaries who can be a reason to confer the wrong classification by introducing distorted samples. Precisely, it has been perceived that adversarial examples designed throughout the training and test phases can include industrious Ruin the performance of the machine learning. This paper provides a comprehensive review of the recent research on adversarial machine learning. It's also worth noting that the paper only examines recent techniques that were released between 2018 and 2021. The diverse systems models have been investigated and discussed regarding the type of attacks, and some possible security suggestions for these attacks to highlight the risks of adversarial machine learning.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.149-153
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• 2022

This study examines the effect of using interactive electronic models to teach mathematical concepts on students' achievement in the linear algebra course at university. The field sample consisted of 200 students divided into two equal groups, an experimental group of 100 students and a control group of 100 students. The researcher used an achievement test in some mathematical concepts related to linear algebra. The results of the study showed that there were statistically significant differences (0.05) between the average achievement scores of the experimental and control groups in the post application of the achievement test, in favor of the experimental group. The size of the influence of the independent factor on the results of the study, which is "interactive electronic forms", on the dependent factor, which is the students' academic achievement in the prepared test, had a very large effect. Also, the results of the study showed that there were statistically significant differences (0.05) between the mean scores of the experimental group in the pre and post applications of the achievement test, in favor of the post application. The researcher recommended the use of interactive electronic models in teaching mathematical concepts at the university level and diversifying the strategies of teaching mathematics, using technology to attract learners and raise their academic achievement.

• Advances in nano research
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• v.14 no.5
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• pp.443-450
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• 2023

Carbon nanotubes (CNTs) have received increased interest in reinforcing research for polymer matrix composites due to their exceptional mechanical characteristics. Its high surface area/volume ratio and aspect ratio enable polymer-based composites to make the most of its features. This study focuses on the experimental tensile testing and fabrication of carbon nanotube reinforced composite (CNTRC) beams, exploring various micromechanical models. By examining the performance of these models alongside experimental results, the research aims to better understand and optimize the mechanical properties of CNTRC materials. Tensile properties of neat epoxy and 0.3%; 0.4% and 0.5% by CNT reinforced laminated single layer (0°/90°) carbon fiber composite beams were investigated. The composite plates were produced in accordance with ASTM D7264 standard. The tensile test was performed in order to see the mechanical properties of the composite beams. The results showed that the optimum amount of CNT was 0.3% based on the tensile capacity. The capacity was significantly reduced when 0.4% CNT was utilized. Moreover, the experimental results are compared with Finite Element Models using ABAQUS. Hashin Failure Criteria was utilized to predict the tensile capacity. Good conformance was observed between experimental and numerical models. More importantly is that Young' Moduli of the specimens is compared with the prediction Halpin-Tsai and Mixture-Rule. Although Halpin-Tsai can accurately predict the Young's Moduli of the specimens, the accuracy of Mixture-Rule was significantly low.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.9
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• pp.3034-3055
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• 2014

Wireless Sensor Networks have extensively been utilized for ambient data collection from simple linear structures to dense tiered deployments. Issues related to optimal resource allocation still persist for simplistic deployments including linear and hierarchical networks. In this work, we investigate the case of dimensioning parameters for linear and tiered wireless sensor network deployments with notion of providing extended lifetime and reliable data delivery over extensive infrastructures. We provide a single consolidated reference for selection of intrinsic sensor network parameters like number of required nodes for deployment over specified area, network operational lifetime, data aggregation requirements, energy dissipation concerns and communication channel related signal reliability. The dimensioning parameters have been analyzed in a pipeline monitoring scenario using ZigBee communication platform and subsequently referred with analytical models to ensure the dimensioning process is reflected in real world deployment with minimum resource consumption and best network connectivity. Concerns over data aggregation and routing delay minimization have been discussed with possible solutions. Finally, we propose a node placement strategy based on a dynamic programming model for achieving reliable received signals and consistent application in structural health monitoring with multi hop and long distance connectivity.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.8
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• pp.33-40
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• 2020

The main aim of the study is to test a house pricing model by combining hedonic and asset-based pricing models. An understanding of the relationship between house pricing and its return (the rental income) helps to establish houses as a significant asset class. The model tested the relationship between house pricing (dependent variable) and the house attributes (independent variables) derived from Freeman's framework of housing attributes. This study uses a large data-set of 1,899 sample of new, high-end houses purchased between 2016 and 2019 collected from the national capital region of India (Delhi-NCR). The algorithm was built in R-Script, and stepwise multiple linear regression was used to analyze the model. The analysis of the model proves that the three significant variables, namely, carpet area, pay-off, and annual maintenance charges explain the price function. Further, the model is statistically fit. The major contribution of the study is to understand the key factors and their influence on the house pricing. The model will be helpful in risk assessment in the housing investment and enhance the chances of investment. Policy-makers can use information about the underlying valuation drivers of the house prices to stabilize the market and also in framing the tax policies.