Determination of sales targets made by palm shell export companies is often not appropriate and effect the amount of inventory of palm shells sold based on weight which will be reduced if stored too long. Implementation of Least Square method for forecasting the sale of palm shells on web platforms aims to help the company to determine sales targets more accurately. By using this application, companies can forecast for the sale of palm shells for the next month in one year starting from one month after the actual sales period that has been entered. Data testing using Mean Absolute Error (MAPE) shows the error generated is 5.935%, Black Box testing results reach 100%, and User Acceptance Testing shows users agree the application in accordance with the requirements and forecasting results is clearly displayed.

For the betterment of teaching methodology student’s evaluation is an integral part of any educational organization. To achieve this process the authority needs to know how the teachers are teaching and therefore the interaction between the learners and therefore educators. This paper develops an android based automated tool for performance evaluation of a course teacher (CTE) which is able to create an educator’s performance report from the student’s evaluation based on some predefined questionnaire by using an android mobile device with internet connectivity from anywhere and anytime. The performance report is auto generated together with a graph and it also creates a file to send the teacher if the authority wants to inform the educator. With the assistance of this technique, course teachers can easily understand their current situation of their corresponding courses where they should focus on.

This paper examines the security issues on electronic commerce websites in Ghana using technical and nontechnical procedures. The study assessed e-commerce websites for the security tools employed to protect user data and other related privacy issues on the websites. It also analyzed e-commerce websites for encryption security tools that protect customer data and test e-commerce websites for the presence of security vulnerabilities that could threaten the security of the sites and their users using w3af. The study used a combination of three methods; web content analysis, information security audit and testing of the websites using w3af, a vulnerability assessment tool. Web application attack and audit framework (w3af) was used to test and identify possible vulnerabilities on the e-commerce websites that could be used by malicious users to steal customer data for fraudulent intent. The research focused to reveal the security vulnerabilities present on e-commerce websites that could affect the trust of clients, the satisfaction of clients, and patronage of e-commerce services by customers. The study found credit card number disclosures, full path disclosures vulnerabilities, cross-site request forgery vulnerabilities and social security number exposures of clients on the e-commerce websites. These security weaknesses in these e-commerce websites have been highlighted as findings in the study that would inform policy direction on electronic data collection, protection and use in the e-commerce industry in Ghana. The findings will also inform industry players in the e-commerce sector on the need to strengthen security on their websites and caution customers to be security conscious on all e-commerce websites. The major significance of the study is the fact that majority of the electronic commerce websites have a lot of vulnerabilities making them unsecure for customers to trust their private data into their care. This study as such informs the customer society and the electronic commerce industry of these security weaknesses and the urgent need to get them fixed. Some solutions have been suggested in the paper to assist in fixing these security vulnerabilities. These solutions have provided the best results. A diligent application of these methods in addressing the vulnerabilities would provide a more secure and less vulnerable e-commerce websites for users. The precautions suggested could assist protect customers and reduce cyber threats during online shopping.

Collecting feedback from a few students after the exams has been the norm in educational institutions. Forms are given to students to assess the course the lecturer has taught. The main purpose of developing student opinion mining system is to create a faster and easier method of collecting feedback from student, and also give lecturers and school administrators an easier way of analysing the feedback collected from students. The significance of this application is that it is less expensive and present a more confidential way of getting students opinion. The major tools used in developing this application are Python, Scikit learn, Textblob, Pandas and SQLite.. Django provides an in-built server that allows the application to run on the localhost.. In this project dataset gotten from online feedback form distributed to students was used for the sentiment analysi ,Chi-square was used for feature selection and the support vector machine algorithm was used for sentiment classification. The application will help the university administrators and lecturers to identify the strengths and weaknesses of the lecturer based on the textual evaluation made by the students.

In the present study, the parameter responsible to find out pressure drops in a pipeline network system has been modeled by Gene Expression Programming Based on the experimental data. The different factors like Pipe diameter, Particle diameter, liquid density, Solid density liquid Viscosity, Volume fraction, Velocity, Solid concentration are taken into consideration as the input parameter. GEP model was developed to predict the pressure drop within the pipeline system. GEP model predicts the pressure drop with an accuracy of mean R-Square 0.999153373.As the input parameter is responsible for the selection of soft computing method and both ANN and GEP model is considered in order to validate the output parameters. The result of GEP has been compared with an ANN model, to observe the level of accuracy of the predicted pressure drop with a correlation to predict pressure drop shown by equation 6. The obtained results of both GEP and ANN models are being compared and GEP predicted results are found to be better in predicting the output parameter. The mean absolute error is found to be 15.566 % by the ANN model wherein the GEP model predicts with an accuracy of 8.993 %.The results indicate that the GEP is better tool to predict pressure drop with more accuracy.