International Journal of Computer (IJC)

Influence of Internet Usage on Academic Performance of College of Education Students: Rhetoric or Reality?

2023-06-15T11:42:24+00:00

The study examined the influence of internet usage on the academic performance of College of Education students in Ghana as being rhetoric or reality. The study adopted descriptive survey design. All year groups (levels 100-400) of St. Joseph’s College of Education were considered as the main population for the study while stratified random sampling technique was used to select 132 respondents. Researchers’ designed questionnaire was used for data collection where Statistical Package for Social Sciences (SPSS version 25) was used for data analysis. The findings of the study revealed that Internet’s influence on the academic performance of the respondents used for the study is a clear-cut reality other than lip service. Internet provides opportunity to acquire special skills; improves their performance during examination; enhances students to study ahead of their teachers; improves students reading competence; promotes their computer skills towards academic activities among others. Nevertheless, few of the respondents reported that Internet usage distracts their attention and prevents them from attending lectures regularly. Based on that, it was recommended that school counselors with the support of the administrators organise enlightenment programmes for students on how to use the internet to improve academic performance. Students in the understudy institution should be encouraged to use the Internet in searching for information that will enhance and improve their academic performance. It is important also to expose the school counselors on training to computer appreciation so that they can give right counselling direction on Internet usage by students regarding their academic activities.

Delineating International Cooperation in the Fight against Cybercrime in Cameroon

2023-09-05T02:18:24+00:00

The advent of new technologies and the increase in their use have ushered in a new chapter in how things are being done in contemporary society. Though plausible, it has also paved the way for crimes (cybercrimes) to be committed through electronic means on a global scale. This has greatly undermined the territorial integrity of nations, and it poses a significant problem to the global community in general. Currently, the effect of cybercrime is something the global economy cannot afford to ignore. It has increased security risks of critical infrastructures, brought about massive privacy invasion and attacks on businesses, and state security. It is difficult to stop crimes of this nature since technology is always evolving and the world is becoming more connected. It therefore requires a well-coordinated and concerted effort from governments around the world to contain crimes of this nature. It is in this line of reasoning that the Cameroon government has made significant strides through the 2010 law on cyber security and cyber criminality (Hereafter referred to as the Cyber Law) to foster cooperation with other nations in a bit to curb the spread of cybercrime in Cameroon. Despite so, the efforts are not sufficient and the prevalent nature of these offenses today still largely smashed government efforts to the ground. This paper sets out to examine the efficiency of the measures taken by the Cameroon Government to forge international cooperation with the aim to combat cybercrime.

Assessing Machine Learning's Accuracy in Stock Price Prediction

2023-08-05T09:10:32+00:00

This research examines how well machine learning models can predict the closing price of traded stocks. The financial industry has seen an increase, in the use of these models due to the availability of datasets and technological advancements. The study compares machine learning models such as Linear Regression, Random Forest and K Nearest Neighbor (KNN) to determine which ones are the accurate predictors and what factors contribute to their effectiveness. To gain insights into model performance a diverse dataset consisting of five stocks from sectors is used. Data analysis and modeling are conducted using Python programming language with libraries, like Pandas, NumPy, Matplotlib and Scikit learn. The performance evaluation metric utilized is Mean Squared Error (MSE). The research findings have the potential to assist investors and traders in making decisions while also contributing to the growth of the financial industry.

Student Attrition Prediction Using Machine Learning Techniques

2023-08-23T19:34:27+00:00

In educational systems, students’ course enrollment is fundamental performance metrics to academic and financial sustainability. In many higher institutions today, students’ attrition rates are caused by a variety of circumstances, including demographic and personal factors such as age, gender, academic background, financial abilities, and academic degree of choice. In this study, machine learning approaches was used to develop prediction models that predicted students’ attrition rate in pursuing computer science degree, as well as students who have a high risk of dropping out before graduation. This can help higher education institutes to develop proper intervention plans to reduce attrition rates and increase the probability of student academic success. Student’s data were collected from the Federal University Lokoja (FUL), Nigeria. The data were preprocessed using existing weka machine learning libraries where the data was converted into attribute related file form (arff) and resampling techniques was used to partition the data into training set and testing set. The correlation-based feature selection was extracted and used to develop the students’ attrition model and to identify the students’ risk of dropping out. Random forest and random tree machine learning algorithms were used to predict students' attrition. The results showed that the random forest had an accuracy of 79.45%, while the random tree's accuracy was 78.09%. This is an improvement over previous results where 66.14% and 57.48% accuracy was recorded for random forest and random tree respectively. This improvement was as a result of the techniques used. It is therefore recommended that applying techniques to the classification model can improve the performance of the model.