Asian Journal of Research in Computer Science
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Designing and Implementation of Academic Based Network Software
No full textAims: Design of an Academic Network Based Software (ANBS) for teaching, learning and more collaboration between the teachers and learners.
Study Design: An agile software was used to assure subsystem effective before moving to another.
Place and Duration of Study: Federal Polytechnic Kaura Namoda Zamfara State Nigeria 2021.
Methodology: The designing of the online learning module of this kind using Agile software development method which is necessary in certain situations, especially where research and development is being conducted. It involves the development process of incremental and iterative approach from different schools in the institution which includes School of Business Management Studies, School of Engineering Technology, School of Science Technology, School of Environmental Science, School of General Studies respectively. The system was structured in module which includes Administrator, Instructor and Learner modules respectively.
Conclusion: It is expected that the new system will effectively and efficiently handle most of the problems of students learning experience and teacher mode of delivery of lectures in Federal Polytechnic Kaura Namoda
Unified Modeling Language for Requirements Engineering, Strategies and Best Practices for FinTech and Beyond
No full textEffective requirements management is crucial in the rapidly evolving landscape of Financial Technology (FinTech) to deliver innovative and customer-centric solutions. Unified Modeling Language (UML) has emerged as a powerful tool in the FinTech industry for requirements management, providing a standardized notation and comprehensive set of diagrams. By leveraging UML, FinTech companies can overcome challenges such as miscommunication and inconsistency. UML fosters a common understanding among stakeholders, aligning perspectives and enabling informed decisions. This article explores strategies and best practices in leveraging UML for FinTech requirements management, addressing complexities, utilizing key UML diagram types, and overcoming challenges. Clear communication, collaboration, documentation standards, traceability, tool support, requirement validation, iterative approach, and model-driven development are highlighted as key practices. By adopting these strategies, organizations can streamline requirements engineering, ensure compliance, and deliver high-quality FinTech solutions. These strategies are not limited to the FinTech industry but can be applied in any industry and domain within the realm of software engineering and requirements management
Enhancing Campus Communication and Collaboration: Design and Development of a Social Community Media Website for Universitas Multimedia Nusantara, Indonesia
No full textProblem: The Multimedia Nusantara University (UMN) is one of Tangerang\u27s universities with hundreds to thousands of students and academics. Currently, the UMN campus uses bulletin boards, student e-mails, social media accounts of a campus organization, and various other media to disseminate information to a large number of students and the UMN academic community; however, these media have deficiencies, such as a lack of exposure to this information or information that is not relevant to the recipient\u27s specialization or study program.
Aim: The primary aim of this research was to create and implement a social community media website system at Universitas Multimedia Nusantara (UMN) using the Rapid Application Development technique. Furthermore, the objective of the study was to evaluate and determine the level of satisfaction about the user interface of the social community media website system implemented by UMN.
Study Design: This study was designed with Rapid Application Development to build a social media UMN community website.
Place and Duration of Study: Department of Informatic Universitas Multimedia Nusantara, between September 2022 to July 2023.
Methodology: Designing and developing a social media community website system using the Rapid Application Development methodology, which consists of three stages: requirement planning, design workshop, and implementation. Rapid Application Development is a method that allows designing and developing a system quickly and effectively, with good results.
Results: The social community media website system has been effectively developed and constructed, and the results of User Acceptance Testing calculations have been obtained. Specifically, the Perceive Usefulness question yielded a score of 88.08%, while the Perceive Satisfaction question yielded a score of 88.67%.
Conclusion: Based on the findings derived from the user satisfaction questionnaire, it can be inferred that the users exhibit a high level of agreement with the functionality and efficacy of the Sircle social media platform
Analyzing Election Sentiments in Tweets with Gated Recurrent Units (GRU)
No full textSentiment analysis, a key task in natural language processing, is important for detecting the emotional tone portrayed in text. In this study, we focus on implementing a Gated Recurrent Unit (GRU) model to analyze attitudes within the 2020 Donald Trump Election tweets dataset. By setting the GRU model with carefully selected parameters, the aim of the study is to unveil the inherent sentiment patterns in the dataset. To develop the sentiment analysis model, the study devised a three phase methodology which that include data preprocessing, feature selection using correlation matrix, and lastly the implementation of GRU. Futhermore, we provided the outcomes of our experiment, evaluating the model\u27s performance through important measures such as accuracy, precision, and recall. Notably, our data exhibit an exceptional accuracy rate of 93%, verifying the model\u27s power to appropriately categorize attitudes. Additionally, both recall and precision receive outstanding ratings of 94% and 96%, indicating the model\u27s skill in distinguishing both positive and negative attitudes. This inquiry emphases the effective usage of the GRU model in sentiment analysis, shedding light on the emotional nuances within the 2020 Donald Trump dataset and enriching our understanding of sentiments during the election period
Systematic Study of Computer Virus using Virus Definition and Scanning Techniques
No full textBackground: Computer virus pose increased risk to computer data integrity, they cause loss of important information and data and cost a huge enormous amount in wasted effort in restoration or duplication of lost and damaged data. Everyday new viruses are discovered, and as the problem increases, there is the need to employ modern tools to identify them and to eliminate them from the devices.
Objective: This study will examine computer viruses and their impact to computer system.
Methodology: The study adopted the mixed research method of quantitative and qualitative techniques. In this study data was collected using both interview and observation methods, as well as comprehensive search of the literature review of various scholars from relevant databases such as Research Gate, Google Scholar, and dimension.
Results: This study identified how devices are vulnerable to computer virus attack and how modern technologies can actually increase the propagation of computer viruses. It also describes the technologies that are available to identify and eliminate the computer viruses, hence, propound probable good uses of virus technology
Computer Vision for Healthy Driving Detection Using Convolution Neural Network
No full textDriving involves a rigorous act where the driver controls the operation of a motor vehicle. There have been few deployments of healthy driving applications, while some of these applications are machine learning applications some are program-driven applications. Nigeria as a developing country has little or no trained datasets for healthy driving, therefore this research will be charged with collecting local data for driving events to be trained. The datasets were collected as images. These images were extracted for driving events braking, safe driving, and speeding. The images were locally collected for Nigeria driving settings and computer vision techniques were applied to the data. The machine learning algorithm used to evaluate the model is Convolution Neural Network, the editors used for image labelling and coding the system are Jupyter notebook and VS Code. Python programming language and its libraries were also used. The classification results for model loss, accuracy, validate loss and validate accuracy and the performance of the model is 0.99 or 99%, based on this the last epoch was recorded and the loss was 0.03 or 3%.This classification result proved that the data collected from Nigeria is trainable. The trained data can be used by researchers all over the world working on safe and healthy systems in Nigeria for driving. The result also presented a convolution neural network as an algorithm suitable for healthy driving detection using computer vision. The predicted values for the three driving events were all positive. The three driving events were all detected perfectly while running the parallel testing without being perverse
Application Research on Semantic Analysis Using Latent Dirichlet Allocation and Collapsed Gibbs Sampling for Topic Discovery
No full textTopic discovery is a process of identifying the main topics present in a collection of documents. It is a crucial step in text mining, digital humanities, and information retrieval, as it allows one to extract meaningful information from large volumes of unstructured text data. The most widely used algorithm for topic discovery is Latent Dirichlet Allocation (LDA). LDA assumes that the words in each document are generated by a small number of underlying topics, and the algorithm learns the topics from the text data automatically. One of the main problems of LDA is that the topics extracted are of poor quality if the document does not coherently belong to a single topic. However, Gibbs sampling operates on a word-by-word basis, which allows it to be used on documents with a variety of topics and modifies the topic assignment of a single word. The paper presents application research on Latent Dirichlet Allocation and Collapsed Gibbs Sampling Semantic Analysis for topic discovery
Future Trails for Integer Programming and Relations to Artificial Intelligence
No full textAims: A review of methods and approaches for solving linear integer problems is presented in this work. These problems are classified as NP-hard optimization algorithms in artificial intelligence.
Study Design: we have used the Google scholar to collect the data resources from past 5 years to analysis the techniques and methods used in different algorithms in artificial intelligence.
Methodology: Exact optimum solution for this class of challenges also need use of substantial computer resources. The current direction in which several researcher focuses their efforts to effectively address numerous difficult practical issues is the creation of efficient hybrid techniques that combine in an appropriate way the finest elements of multiple methods (precise or estimated). The approximation algorithms\u27 core heuristic techniques might be classified as constructive algorithms and local-improvement algorithms.
Results: We examined three artificial intelligence algorithms utilizing the linear integer programming approach. Algorithm based on population It has also been demonstrated that a population of a critical size is necessary for a population-based optimization method to be effective. The genetic algorithm is shown next. The goal value associated with this solution may be utilized to effectively reduce the search tree in bound and branch type integer programming methods. Finally, we analyze the particle swarm optimization (PSO) approach, which demonstrates that In most cases, PSO outperforms the Branch and Bound method in solving such issues quickly.
Conclusion: In actuality, integer optimization issues describe a wide spectrum of real-world difficulties. Their population and size are constantly growing. Although while accurate methods for integer issues have substantially improved in recent years, their long runtimes and memory needs make them unsuitable for actual medium and large-scale applications
Artificial Intelligence in Health Sector: Current Status and Future Perspectives
No full textThe developing fields of artificial intelligence (AI)/ machine learning (ML) offer a significant potential to improve healthcare services. Many areas of clinical practice, scientific research, and healthcare management have included AI/ML techniques. Screening and daily fitness monitoring, diagnostic services in gastroenterology, pathology, and radiology, as well as support for clinical decision-making and palliative care, are the main categories involved. However, there are significant obstacles to the widespread use of AI/ML in healthcare, including higher installation and maintenance costs, potentially harmful medical mistakes, a lack of ethical frameworks for AI, unemployment, and reduced capacity building within the human workforce. Many business initiatives have now been created in the field of healthcare AI/ML innovation. They offer everything from advanced diagnostics to vitals monitoring in their products and services. In short, AI/ML may be extremely important in addressing the difficulties with complexity and the explosion of data in the healthcare system. AI/ML is a component of contemporary healthcare, and its further adoption is contingent on thoroughly addressing pertinent issues
Procedural Terrain Generation by Sampling a 2D Monochrom Perlin Noise Map in Unity
No full textThis article presents a study on procedural terrain generation using Perlin noise in the Unity game engine, building upon prior research and established techniques [1,2]. The objective of the this study is to create realistic and visually appealing landscapes by leveraging algorithmic generation [3]. By utilizing a two dimensional monochrome Perlin noise map, the algorithm generates terrains with organic features such as hills, valleys, and mountains [4]. The research employs the Unity game engine, which offers a wide range of tools and technologies for implementing procedural generation algorithms [5].
The performance analysis conducted during this study yielded important insights. The execution time test revealed that the algorithm\u27s execution time increased significantly with larger grid sizes, emphasizing the need to consider the scale and resolution of the terrains [6]. This finding highlights the trade-off between generating detailed terrains and the time required for the algorithm to complete its computations. Additionally, the memory usage test demonstrated a quadratic relationship between memory usage and grid size, indicating the necessity of efficient memory management [7] for generating higher-resolution terrains. Optimizing memory usage is crucial to avoid excessive consumption and ensure efficient resource utilization.
These findings from the performance analysis provide valuable guidance for optimizing and refining the algorithm discussed in this article. Techniques such as algorithmic refinements, parallelization [8,6], and memory optimization strategies can be employed to reduce execution times and minimize memory overhead.
By understanding the algorithm\u27s performance characteristics and leveraging the insights gained from the analysis, developers can make informed decisions to generate visually compelling and diverse landscapes within the Unity game engine