• International Journal of Computer Science & Network Security
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• 제21권4호
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• pp.277-283
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• 2021

The objective of this project is to conduct an underwater survey. The primary goal is to develop a device that can achieve the desired output under test conditions. For this reason, certain practical considerations must be taken into account, and the implementation is then developed to be carried out to obtain stable performance with the available hardware based on that experiment. The experiment was performed via BlueROV2 (Remotely Operated Vehicle) using RaspberryPi and softwares such as QGC (QGroundControl) and ArduPilot. This paper explains the work, the results with the collected data and how we implemented the work is presented in the end. The intention of this experiment is to connect two PCs using RaspberryPi with MAVLink communication using a Commercial-Off-The-Shelf device.

• International Journal of Computer Science & Network Security
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• 제22권4호
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• pp.229-236
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• 2022

This study investigates the impact of flipped classroom strategy in developing students' achievement and acquisition of life skills. The study employed a quasi-experimental design where students were divided into two groups: an experimental (N=22) and a control (N=22). The randomly selected and assigned sample consisted of sixth-year elementary school students studying English as a basic course. The findings revealed statistically significant differences between the two group's means in both achievement and life skills tests in favor of the experimental group. Students of the experimental group who studied using the flipped classroom strategy outperformed the control group who studied in the standard way in achieving the English language and in the life situations test, where the effect size of the use of the strategy was large in both dependent variables. The study is concluded with some recommendations to facilitate the use of flipped classroom strategy for EFL teachers. This can be achieved by training teachers on using the strategy and providing technological resources at schools to implement the strategy efficiently.

• International Journal of Computer Science & Network Security
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• 제22권7호
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• pp.308-314
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• 2022

As a result of the global effect of infectious diseases like COVID-19, remote patient monitoring has become a vital need. Surgical ICU monitors are attached around the clock for patients in critical care. Most ICU monitor systems, on the other hand, lack an output port for transferring data to an auxiliary device for post-processing. Similarly, strapping a slew of wearables to a patient for remote monitoring creates a great deal of discomfort and limits the patient's mobility. Hence, an unique remote monitoring technique for the ICU monitor's physiologically vital readings has been presented, recognizing this need as a research gap. This mechanism has been put to the test in a variety of modes, yielding an overall accuracy of close to 90%.

• International Journal of Computer Science & Network Security
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• 제22권8호
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• pp.343-351
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• 2022

A brain tumor forms when some tissue becomes old or damaged but does not die when it must, preventing new tissue from being born. Manually finding such masses in the brain by analyzing MRI images is challenging and time-consuming for experts. In this study, our main objective is to detect the brain's tumorous part, allowing rapid diagnosis to treat the primary disease instantly. With image processing techniques and deep learning prediction algorithms, our research makes a system capable of finding a tumor in MRI images of a brain automatically and accurately. Our tumor segmentation adopts the U-Net deep learning segmentation on the standard MICCAI BRATS 2018 dataset, which has MRI images with different modalities. The proposed approach was evaluated and achieved Dice Coefficients of 0.9795, 0.9855, 0.9793, and 0.9950 across several test datasets. These results show that the proposed system achieves excellent segmentation of tumors in MRIs using deep learning techniques such as the U-Net algorithm.

• International Journal of Computer Science & Network Security
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• 제21권11호
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• pp.23-30
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• 2021

Sentiment Analysis has become very important field of research because posting of reviews is becoming a trend. Supervised, unsupervised and semi supervised machine learning methods done lot of work to mine this data. Feature engineering is complex and technical part of machine learning. Deep learning is a new trend, where this laborious work can be done automatically. Many researchers have done many works on Deep learning Convolutional Neural Network (CNN) and Long Shor Term Memory (LSTM) Neural Network. These requires high processing speed and memory. Here author suggested two models simple & bidirectional deep leaning, which can work on text data with normal processing speed. At end both models are compared and found bidirectional model is best, because simple model achieve 50% accuracy and bidirectional deep learning model achieve 99% accuracy on trained data while 78% accuracy on test data. But this is based on 10-epochs and 40-batch size. This accuracy can also be increased by making different attempts on epochs and batch size.

• International Journal of Computer Science & Network Security
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• 제21권11호
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• pp.11-16
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• 2021

The article proposes that empathy differs in severity and types among students of different specialties. And to test this hypothesis, an experimental study of students from 4 different faculties was carried out. As a result of the study, it was found that empathy is more pronounced among students of "humanitarian" specialties, and students of "exact" specialties have the least pronounced empathic abilities.

• International Journal of Computer Science & Network Security
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• 제22권12호
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• pp.239-244
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• 2022

The study aimed to build a program in activities for early childhood students to develop some of the basic skills necessary for the age of artificial intelligence, to achieve the objectives of the study , the researcher used the experimental design, and the research sample consisted of 37 early childhood students. The study used the following tools: Experimental treatment subject: the proposed program in the activities, Measurement and evaluation tool: testing the basic skills needed for the age of artificial intelligence. The study concluded several results: There is a statistically significant difference (α≤0.05) between the average grades of the early childhood students in the research group in the tribal and remote measurements to test the basic skills necessary for the age of artificial intelligence in favor of the students grades in the dimensional measurements. Practical application of the study through benefiting from the proposed program of activities prepared in the current study in planning and implementing activities to develop the basic skills necessary for the age of artificial intelligence among early childhood students.

• International Journal of Computer Science & Network Security
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• 제23권9호
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• pp.91-94
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• 2023

This study aims to evaluate the Higher Diploma in English for the Primary Stage from the diploma students' perspectives. A questionnaire was designed consisting of 25 items distributed in two areas: cognitive/academic preparation and professional/skill preparation. The following statistical analyses were used: means, standard deviations, t-test, and one-way analysis of variance (ANOVA). The study results showed that the level of evaluation of the two domains in the program was low. The study also showed no statistically significant differences between the means of educational diploma students when evaluating the Higher Diploma in English for the Primary Stage due to their academic specialization (Arabic language, social sciences, and Islamic studies). In conclusion, the researcher suggested a developmental mechanism derived from the study results to improve the higher Diploma in English for the Primary Stage.

• International Journal of Computer Science & Network Security
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• 제23권6호
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• pp.176-180
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• 2023

Aim of this study to examine the mediating role of network service between perceived quality, retailer service and customer satisfaction during COVID-19. Primary data gathered through adopted questionnaire from previous studies and 200 university students were asked to fill online questionnaire during COVID-19 situation in country. Structural Equation Modelling technique applied in order to test the proposed hypothesis generated from existing literature review. Findings revealed full mediation effect of network service for both perceived quality and retailer service on customer satisfaction during COVID-19. New insights of this study are key role of network services is identified and university students' satisfaction is measured for online classes in developing country, Pakistan. In future serial mediation is suggested for validity of existing results in developed and developing countries.

• International Journal of Computer Science & Network Security
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• 제24권1호
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• pp.107-118
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• 2024

With the seamless growth of the technology, network usage requirements are expanding day by day. The majority of electronic devices are capable of communication, which strongly requires a secure and reliable network. Network-based intrusion detection systems (NIDS) is a new method for preventing and alerting computers and networks from attacks. Machine Learning is an emerging field that provides a variety of ways to implement effective network intrusion detection systems (NIDS). Bagging and Boosting are two ensemble ML techniques, renowned for better performance in the learning and classification process. In this paper, the study provides a detailed literature review of the past work done and proposed a novel ensemble approach to develop a NIDS system based on the voting method using bagging and boosting ensemble techniques. The test results demonstrate that the ensemble of bagging and boosting through voting exhibits the highest classification accuracy of 99.98% and a minimum false positive rate (FPR) on both datasets. Although the model building time is average which can be a tradeoff by processor speed.