International Journal of Computer (IJC)

Design and Development of Bomb Defusing Robot Controlled by Gesture

Scholastica U. Nnebe, Vincent. I.Nwankwo, Michael .E Chukwuma — Mon, 10 Oct 2022 00:00:00 +0000

Neutralizing explosives is one of precision and accuracy tasks handled by law enforcement personnel; it can be error-prone and life threatening. This paper seeks to demonstrate an improved use of remote controlled robots for Explosive Ordinance Disposal (EOD) using gesture manipulation. There exist a variety of EOD robots capable of handling different types of Explosives. This work is concerned with a class of EOD that uses the principle of wireless transmission of data packets within the ISM 2.4GHz spectrum, electrical H-Bridge circuits, servo-mechanisms and potential division in its underlying architecture and operation. The class is a fully integrated and non-autonomous system used by Law Enforcement Agents as First Responders. The developed EOD robot possesses two arms for gripping and manipulation. The robot arms are controlled by inputting commands on a handheld remote control or by human arm gesticulation. For gesture control, flexing the index finger closes the arm’s gripper, flexing the thumb moves the wheels forward and backward, tilting the back of the controlling hand tilts the robot’s elbow and rotating the wrist controls the waist.

Survey Analyses of The Specific Impacting Factors in Devising a Machine Learning Prediction model for The General Election Process in Kosovo

Lavdim Beqiri, Majlinda Fetaji, Zoran Zdravev, Bekim Fetaji — Sun, 04 Dec 2022 00:00:00 +0000

The focus of the research study was analyses of impacting factors and later to incorporate those insights into variables to be measured for devising a machine learning predictive model for prognosis and prediction of the general election turnout in Kosovo. We have developed a novel method for recognizing the main impacting factors in elections. Our method shows that finding out whether different ways of collecting different data of election voters can lead to much better prediction and understanding of the election process. In order to do that we needed to analyze the specific impacting factors in the election process in Kosovo are investigated during the study. The data has derived from an originally collected survey dataset that contains the impacting factors previously identified and assessed regarding the general parliamentary elections in Kosovo has been realized. Insights and recommendation has been discussed and argumented.

An Efficient Method to Enhance Health Care Big Data Security in Cloud Computing Using the Combination of Euclidean Neural Network And K-Medoids Based Twin Fish Cipher Cryptographic Algorithm

Arnav Goyal — Mon, 10 Oct 2022 00:00:00 +0000

Big data is a phrase that refers to the large volumes of digital data that are being generated as a consequence of technology improvements in the health care industry, e-commerce, and research, among other fields. It is impossible to analyze Big Data using typical analytic tools since traditional data storage systems do not have the capacity to deal with such a large volume of data. Cloud computing has made it more easier for people to store and process data remotely in recent years. By distributing large data sets over a network of cloudlets, cloud computing can address the challenges of managing, storing, and analyzing this new breed of data It's possible for private data to be leaked when it is kept in the cloud, as users have no control over it. This paper proposes a framework for a secure data storage by using the K-medoids-based twin fish cipher cryptographic algorithm. We first normalize the data using the Filter splash Z normalization and then apply the Euclidean neural network to compute similarity, which ensures data correctness and reduces computational cost. As a result, the suggested encryption strategy is used to encrypt and decode the outsourced data, thereby protecting private information from being exposed. The whole experiment was conducted using health data from a large metropolis from the Kaggle database. Using the recommended encryption method, users will be able to maintain their privacy while saving time and money by storing their large amounts of data on the cloud.

Design and Implementation of Decentralized Voting System on the Ethereum Blockchain

S.U. Nnebe, C.S. Okafor, T.I. Onyeyili, G. Nathaniel — Thu, 20 Oct 2022 00:00:00 +0000

This work involves the design and implementation of a decentralized voting system on the Ethereum blockchain, which is a peer-to-peer network. The system is helpful in carrying out free and fair elections as information stored on the blockchain is immutable. This voting application uses solidity as the backend language and the web3 library for reading and interacting with the blockchain. JavaScript, Hyper Text Markup Language (HTML), and Cascading Style Sheets (CSS) are used to design the front end and the control logic for the website. The voting system works with the locally installed Ethereum node. The user visits the website and registers his details which are then uploaded to the blockchain in the cryptographically hashed pattern. After registering, the user is directed to the voting page, which reads the intelligent contract data and allows the user to cast his vote and at the same time update the blockchain. This system can be deployed in schools, organizations, countries, anywhere there is a need for governance and democratic voting. The prototype built was tested and found to be working perfectly.

Relative Influence of Social Media Socio-Technical Information Security Factors on Medical Information Breaches in selected Medical Institutions in Uganda

Mon, 28 Nov 2022 00:00:00 +0000

This manuscript presents a study based on research conducted to assess the relative impact of social media (SM) socio-technical information security factors on medical information breaches in selected medical institutions in Uganda. The study was motivated by reported cases of medical data breaches through the use of SM. Procedurally, the study used an online survey method using Google Forms and a literature search technique. Data were solicited from 566 medical students from Mbarara University of Science and Technology (MUST), and Kampala International University (KIU), accordingly. The key datasets collected included respondent’s demographic profile, SM usage characteristics, and medical information breaches. Through literature search, key SM socio-technical information security factors were identified. Afterwards, Spearman’s rank correlational analysis was performed to determine the type of relationships existing between SM socio-technical information security factors and medical information breaches. According to the percentage distribution summary of medical information breaches, the respondent’s level of agreement ranges from 39% to 43%. Spearman’s rank correlational coefficients (r-value) indicate significant levels (p ? 0.05) of correlational relationships for the key factors identified. However, 6 of the factors presented negative and stronger relationships, while 3 factors yielded weaker correlational relationships. Relatively, the results showed stronger relationships between the social dimensional factors, compared to the technical dimension. The negative relationships could imply that an increase in end-user compliance levels of SM socio-technical information security factors would minimize the occurrence of medical information breaches on SM. While the stronger relationship factors indicate the key SM usage factors associated with medical information breaches. Overall, the study outcome would provide empirical basis for medical institutions, SM researchers, and practitioners to rationalize and leverage SM usage in operations.

Mobile Network Access Points using Self Organising Drone Constellations

Isaack Adidas Kamanga, Johanson Miserigodisi Lyimo — Thu, 20 Oct 2022 00:00:00 +0000

Nowadays with artificial intelligence and automation requires much remote sensing. Sensors can be fixed or mobile. Mobile sensor networks are easy to deploy in a new location however, one of the challenges is figuring out how to interconnect these mobile sensors and link them to a core network. This paper proposes a technique of setting a mobile network that miniature base stations or access points be carried by drones in an automatically structured constellation to enable network connectivity between sensors. The paper presents a swing and adjusting technique to determine the ideal deployment of mobile base stations carried by drones, one base station per drone to connect as many sensors as possible without having prior information on sensor distribution. Swing and adjusting, coverage control, collision avoidance, and self-organizing drone constellation are all part of the algorithm. The suggested approach shows promising results according to simulations.

Sentiment Analysis of Nigerian Students’ Tweets on Education: A Data Mining Approach

Mayowa S. Alade, Joshua M. Nwankpa — Sun, 02 Oct 2022 00:00:00 +0000

The paper is aimed at investigating data mining technologies by acquiring tweets from Nigerian University students on Twitter on how they feel about the current state of the Nigerian university system. The study for this paper was conducted in a way that the tweet data collected using the Twitter Application was pre-processed before being translated from text to vector representation using a feature extraction technique such Bag-of-Words. In the paper, the proposed sentiment analysis architecture was designed using UML and the Naïve Bayes classifier (NBC) approach, which is a simple but effective classifier to determine the polarity of the education dataset, was applied to compute the probabilities of the classes. Furthermore, Naïve Bayes classifier polarized the tweets' wording as negative or positive for polarity. Based on our investigation, the experiment revealed after data cleaning that 4016 of the total data obtained were utilized. Also, Positive attitudes accounted for 40.56%, while negative sentiments accounted for 59.44% of the total data having divided the dataset into 70:30 training and testing ratio, with the Naïve Bayes classifier being taught on the training set and its performance being evaluated on the test set. Because the models were trained on unbalanced data, we employed more relevant evaluation metrics such as precision, recall, F1-score, and balanced accuracy for model evaluation. The classifier's prediction accuracy, misclassification error rate, recall, precision, and f1-score were 63 %, 37%, 63%, 62%, and 62% respectively. All of the analyses were completed using the Python programming language and the Natural Language Tool Kit packages. Finally, the outcome of this prediction is the highest likelihood class. These forecasts can be used by Nigerian Government to improve the educational system and assist students to receive a better education.