Asian Journal of Research in Computer Science
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Prevalence and Causes of Software Piracy among Tertiary Students in Ghana
No full textIn the era of the Knowledge Economy, Intellectual Property Rights (IPRs) have become a crucial aspect of the twenty-first-century environment. Software piracy, characterized by the unauthorized copying, downloading, sharing, selling, or installation of copyrighted software, remains a serious problem worldwide, and Ghana is no exception. Various forms of software piracy, such as software lifting, hard disk loading, counterfeiting, and unauthorized renting, are prevalent in the country, leading to negative economic consequences. These consequences include distorted competition, loss of tax revenue and jobs due to the absence of a legitimate market, and increased costs for recovery. The impact of software piracy affects the social well-being of the Ghanaian citizenry. This research aims to explore the causes and effects of software piracy in Ghana, especially among tertiary students, and proposes potential solutions. A quantitative survey design was used via an online questionnaire to collect data from a sample of 47 students in the tertiary institutions especially public universities in Ghana with backgrounds in IT and are familiar with evolving trends in IT. The findings revealed that software piracy is prevalent in the country as a result of a lack of awareness, poor economic conditions, and weak legal framework and/or enforcement. The study recommends increased public education and awareness, strict enforcement of laws related to software piracy, and the promotion of domestic software development as measures to address the menace
Customer Preference for Electronic Payment Systems for Goods: A Case Study of Some Selected Shopping Malls, Bolgatanga
No full textThe transformation of payment systems in the digital age has reshaped the way customers interact with businesses. This study, conducted at Melcom Mall in Bolgatanga, Upper East Region, Ghana, delves into ”Customer Preference for Electronic Payment Systems for Goods.” Through extensive surveys and analysis, this paper reveals critical insights into the electronic payment behaviors and preferences of customers. Key findings underscore the dominance of monthly electronic payment usage, with mobile wallets emerging as the preferred method. Customers exhibit high levels of satisfaction, emphasizing the importance of user-friendly and secure payment solutions. Convenience and security play pivotal roles in shaping customer choices, while rewards and promotional campaigns also significantly influence their preferences. Despite the success of electronic payments, customers express concerns about hidden fees, technical issues, and the responsiveness of customer support. In response, we propose recommendations to enhance transparency, user education, technical infrastructure, payment options, security measures, tailored promotions, and responsive customer support. This paper serves as a valuable guide for merchants and policymakers in optimizing electronic payment systems, fostering customer satisfaction, and adapting to the evolving landscape of digital payment methods
Industry-Scale Application of Full-Width Digital Camera Inspection during Effective Printing on Plastic Foil at the Company "Flexograf"
No full textThe company printing plastic sheets with eight-color machines has sought help from the Faculty of Computer Science to reduce wastage of raw materials. During the study and research in this matter, it was found that the losses in raw materials are caused by the impossibility of controlling the printing through the current adequate technology that the "Flexograf" company had at its disposal. The printing on the plastic film was supervised with a digital camera with the possibility of control at a dimension of 15 [cm]. On the other hand, the width of the printed plastic film is consistently 130[cm]. Therefore, the digital control camera for control is moved along the x-axis nine times to control the printing width of 130[cm] of the plastic film. After each displacement, the digital camera was stopped for a duration of three seconds. Wherever there was a printing defect in the part that was not monitored by the digital camera and given the printing speed of the machine for a length of 400 meters of plastic sheet per minute, it caused a significant loss of raw material if there were printing defects or lack of color. This problem has been solved by replacing the 15[cm] wide inspection digital camera with a 130[cm] wide full inspection digital camera. The effect of changing the full inspections digital camera achieved the goal and reduced the raw material losses to the minimum very close to zero
Content-based Filtering and Web Scraping in Website for Recommended Anime
No full textAim: This research aim is to determine the level of user satisfaction using the Delone and Mclean models obtained from the implementation of the content-based filtering method in the anime recommendation system.
Study Design: This study was designed with Delone and Mclean and with a Content-based Filtering method and web-scrapping to build an anime recommendation system.
Place and Duration of Study: Department of Informatic Universitas Multimedia Nusantara, between July 2022 and December 2022.
Methodology: The initial step in this research was collecting data using web scrapping and questionnaires, then followed by a literature study, and after that continued with system design and application development. After the application is made the next step is to get the level of user satisfaction with Delone and Mclean, and the final step is writing a report from this research.
Results: The design and development of a system by implementing a content-based filtering method to the website-based have been successfully created, and the results of calculating the level of user satisfaction calculated from 43 respondents using the Delone and Mclean methods show, an anime recommendation system with content-based filtering methods has good result with a user satisfaction percentage of 74.23%.
Conclusion: The anime system recommendation application has been successfully made and the results of user satisfaction are 74.23%
Classification and Segmentation of Brain Tumor Using EfficientNet-B7 and U-Net
No full textTumors are caused by uncontrolled growth of abnormal cells. Magnetic Resonance Imaging (MRI) is modality that is widely used to produce highly detailed brain images. In addition, a surgical biopsy of the suspected tissue (tumor) is required to obtain more information about the type of tumor. Biopsy takes 10 to 15 days for laboratory testing. Based on a study conducted by Brady in 2016, errors in radiology practice are common, with an estimated daily error rate of 3-5%. Therefore, using the application of artificial intelligence, is expected to simplify and improve the accuracy of doctor\u27s diagnose
Development of a COVID-19 Patients’ Fatality Prediction System Using Swarm Intelligent Convolution Neural Network
No full textAims: This work aims to develop a system that can be used to accurately and timely predict the fatality of a positively tested COVID-19 patient through the use of a deep learning technique – a swarm intelligent convolutional neural network.
Methodology: The dataset used in this study was acquired from the Kaggle repository database. The dataset contains the Lung Chest X-Ray images of COVID-19 patients. The images were pre- processed to obtain the desired image quality for further processing. This was followed by segmenting the pre-processed images. An Enhanced Firefly Algorithm (EFA) was formulated by applying the roulette wheel selection procedure to model the movement process of the firefly as a deterministic process to assist the standard Firefly Algorithm (FA) and application of Chaotic Sinusoidal Map Function to model the attractive process of the firefly which establishes a balance between exploration and exploitation in FA. The EFA was applied to optimize Convolution Neural Network (CNN) hyper-parameters (number of layers, number of filters per layer, filter size and batch size). The segmented result was subsequently presented to EFA-CNN feature extraction and prediction of COVID-19 patient fatality cases. The formulated deep learning models (EFA-CNN and CNN) were implemented using Matrix Laboratory 2020a software. The implemented models were evaluated using specificity, sensitivity, false positive rate, accuracy, and recognition time/rate to determine the performance of the developed models.
Results: The findings revealed that the EFA-CNN model performs better in the prediction of COVID-19 patients’ fatality compared to the CNN model. It was also discovered that the formulated EFA applied to select optimal values of the hyper-parameters for the CNN architecture accounted for improved recognition accuracy and reduced recognition time of the developed COVID-19 Patients’ Fatality Prediction System.
Conclusion: The system developed will assist both the government and healthcare workers in providing the needed computational capability for the prediction of the fatality level of a positively tested COVID-19 patient
Packing Problem and Random Coverage in Continuous Domain
No full textThe packing problem is a well known-problem. There are several versions of this problem. In this paper we consider packing or covering of a disc of a given radius r by a number of discs of unit radius. We introduced two types of packing, hexagonal packing and square packing. We show that hexagonal packing is better in the sense that it needs less discs to cover a disc of higher radius. Coverage problem is similar to the packing problem in continuous domain. Coverage is essential in wireless sensor networks. In this paper we also discuss the coverage problem in random deployment scenario
Development of a Genetic Algorithm Based Geodesic Active Contour for Iris Based Ethnicity Prediction System
No full textThe developments in science and technology have made it possible to use biometrics in applications where it is required to establish or confirm the identity of individuals. Among all possible biometric characteristics, the use of iris texture for recognition of individuals has been proven to be highly reliable. However, existing iris prediction systems have suffered from inability to handle more constrained acquisition (processing non-ideal iris images), high processing time and inappropriate parameter settings which usually results in inaccurate segmentation and poor classification results. This research therefore developed an improved segmentation and classification algorithms for iris-based ethnicity prediction system featuring the three major tribes in Nigeria. Six hundred (600) iris images from three major tribes in Nigeria (Yoruba, Hausa and Ibo) were locally captured for the database. Genetic Algorithm based Geodesic Active Contour (GAGAC) and standard Geodesic Active Control (GAC) were used for iris segmentation while Standard Support Vector Machine (SVM) and Galactic Swarm Optimisation SVM (GSOSVM) was used for iris classification. GAGAC and GSOSVM were used in the designing of the iris-based ethnicity prediction system at segmentation and classification stage. The developed iris-based ethnicity prediction system gave an improved predictive performance over the conventional one. The developed system can be used in different areas where higher security authentication is required
Analysis of Long-Range Wireless Area Network Enabled Smart Agriculture for Sustainable Food Production
No full textTechnology advancements and increased connection are bolstering the movement toward digital transformation in agricultural and food systems, which might lead to the proliferation of robots, fully automated farms and tractors, and spraying drones. The next-generation (4G) wireless network connection, known as 5G, will be necessary provide the massive data transmission volumes and low latency that may have many positive effects on IoT and blockchain applications in the agriculture and food industries. This study makes use of LoRaWAN (Long Range Wireless Area Network) for its energy-harvesting capabilities in remote agriculture monitoring. According to the findings of the experiments, LoRaWAN is the best technology to utilize in a system for monitoring agriculture when network lifespan and power consumption are important factors. Following application criteria, the experimental findings may be utilized to pick wireless technology for agricultural monitoring
Measuring the Voltage, Current and Resistance of the LDR Sensor through the Arduino UNO
No full textThe main purpose of this paper is to build an intelligent electronic system based on LDR (Light Dependent Resistor) sensor. A very interesting but also very useful part of this scientific research is the measurement of electrical quantities with high precision as intelligent machines know and can achieve, and one of them in this case is the Arduino Uno microcontroller. These measurements of electrical quantities such as voltage, resistance, and current intensity are used in programmable logic in the microcontroller to decide when to light a light or more and when not to light. Of course, this problem has been solved by using the LDR sensor in the electronic system. Such an electronic and programmable system works perfectly. The work itself contains solutions of electrical circuits in an analytical way, connection of electronic circuits with the Arduino Uno module, communication with the input and output devices of the ATMEGA 328P microcontroller interface and the fusion of the source code - Sketch. This paper will serve as a good basis for further research in determining the wavelength of light which represents an obstacle for people with vision problems. All the measurements of electrical quantities through the Arduino Uno microcontroller have also been confirmed with the measurements made through the universal voltmeter instrument. The results of each measurement are very approximate, of course, many times faster and more accurate than those with a microcontroller