Journal of Applied Research in Technology & Engineering

IoT, machine learning and photogrammetry in small hydropower towards energy and digital transition: potential energy and viability analyses

Helena M. Ramos — 2023-05-31

This research aims to evaluate and put into practise the design of a small hydropower plant on a stream at São Vicente, in Madeira Island, supported by internet of things (IoT). The photogrammetry technique is also used with a comprehensive digital transformation, in which new concepts, methods and models, such as machine learning (ML), and big data analytics play an important role due to the huge availability time series that have to be exploited in hydropower design studies. Nowadays, digitalization and massive data availability are imposing new ways to address many of the current challenges associated with the energy and digital transition. This research is based on a simple small hydropower design, to present an integrated methodology using new methods assigned by an internet protocol system, which includes the development of different steps and components supported by GIS, photogrammetry and the use of advanced tools, with the support of a drone survey with internet communication (IoT) that allow the generation of experimentally-based estimates in situ characterization, the volumetric flow, the hydrological data treatment, the hydraulic calculations and economic estimations for a real hydro project. Therefore, hydrological variables, hydraulic analysis and topographical survey are carried out in the IoT application platform supported by new tools and methods to optimise the size of hydraulic structures, estimate the performance and potential of the hydropower plant towards the best solution for energy and digital transition. Firstly, the data-base for the all study and posterior sizing of the case study of hydropower plant are defined and then the corresponding analyses and results are presented. Then, the cost estimation for the construction, maintenance and operation of the selected elements that compose the hydropower topology are determined, as well as the respective economic balance, considering the annual energy production. In addition, both economic and environmental return on investment is discussed. Finally, an analysis to equate the cost estimates and the respective benefits of hydropower generation using this new approach applicability is stablished, taking into account some economic indicators to determine the profitability of the project.

Assessment of lean practices in small and medium garment manufacturing companies in South-Western Nigeria

Olufemi Sylvester Bamisaye — 2023-05-31

The Nigerian garment industry has significantly contributed to the Gross Domestic Product of the country. However, this multibillion naira, small and medium scale enterprises have a higher potential to generate huge revenue, provided lean manufacturing concepts are adopted. In this study, the awareness level and the adoption of lean concepts were assessed using a survey technique. In the survey, 40 complete responses were received by administering 60 copies of a structured questionnaire based on five-point Likert scale. By analyzing the responses using descriptive statistics and mean item score, it was deduced that teamwork is the most predominant lean concept that is currently been practiced in the South-Western Nigeria garment industry with the highest mean score of 3.63. Just-in-time supply, workforce commitment, daily schedule and product design have witnessed the influence of the implementation of lean practices. In the garment industry, the practice of the few lean concepts resulted in a significant improvement in the quality of the garments produced. However, a lack of understanding on how to implement the lean concepts has been a major barrier in this industry. Hence, comprehensive trainings, seminars and workshops for the key players in the industry are proposed to increase the knowledge and adoption of lean concepts.

Performance evolution for sentiment classification using machine learning algorithm

Faisal Hassan — 2023-05-31

Machine Learning (ML) is an Artificial Intelligence (AI) approach that allows systems to adapt to their environment based on past experiences. Machine Learning (ML) and Natural Language Processing (NLP) techniques are commonly used in sentiment analysis and Information Retrieval Techniques (IRT). This study supports the use of ML approaches, such as K-Means, to produce accurate outcomes in clustering and classification approaches. The main objective of this research is to explore the methods for sentiment classification and Information Retrieval Techniques (IRT). So, a combination of different machine learning algorithms is used with a dataset from amazon unlocked mobile reviews and telecom tweets to achieve better accuracy as it is crucial to consider the previous predictions related to sentiment classification and IRT. The datasets consist of user reviews ratings and algorithms utilized consist of K-Means Clustering algorithm, Logistic Regression (LR), Random Forest (RF), and Decision Tree (DT) algorithms. The amalgamation of each algorithm with the K-Means resulted in high levels of accuracy. Specifically, the K-Means combined with Logistic Regression (LR) yielded an accuracy rate of 99.98%. Similarly, the K-Means integrated with Random Forest (RF) resulted in an accuracy of 99.906%. Lastly, when the K-Means was merged with the Decision Tree (DT) Algorithm, the accuracy obtained was 99.83%.We exhibited that we could foresee efficient, effective, and accurate outcomes.

Automation of production plan generating workbook at leather footwear company of Lahore Pakistan by using VBA in Microsoft Excel

Muhammad Ahmed Kalwar — 2023-06-08

In Small and Medium Enterprises (SMEs), all the reporting tasks are carried out in Microsoft Excel. The employees spend all of their time working on the reports and in the case of an error in the report; a tremendous amount of their time is incurred on the detection of that error. At one of the leather footwear companies in Lahore, Pakistan, report automation was carried out using visual basic for Application (VBA) in Microsoft Excel. The purpose of automation was to increase the reporting efficiency and minimize the chance of error. The authors automated the generation of production plan papers, which used to take 3.11 minutes to be made per plan paper. 3.11 minutes were required just for a single order of only one footwear article). This research provides the framework for the automation of manual reporting in Microsoft Excel. This automation was conducted by using VBA in Microsoft Excel. In the VBA code, the loops and conditional statements were used to program the manual activities to be performed in the report. Initially, the manual method was demonstrated in detail then way of report automation was the focus of discussion. The comparison of both methods was conducted in terms of time utilization. The manual method encompassed a series of activities whereas; the automated template included the buttons with few clicks. A time study of report-making by manual and automated method was conducted which indicated that the automated method was 1.36 minutes faster than the manual method. This research contributes to the provision of a detailed framework, with the help of which any manual work in Microsoft Excel can be automated. It was also indicated by this research that SMEs who cannot afford the implementation of Enterprise Resource Planning (ERP) software, have the option of VBA in Microsoft Excel by which they can enhance their reporting efficiency and office employees` productivity.