International Journal of Computer (IJC - Global Society of Scientific Research and Researchers, GSSRR)
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An Evaluation of Project Managers\u27 Readiness for the Fourth Industrial Revolution in Tanzania
Full text linkThe Fourth Industrial Revolution (4IR) is changing how we work, study, and interact with one another and how we live. However, project managers and the general Tanzanian population are still not sufficiently aware of the 4IR, which results in a lack of readiness to conceptualize, carry out, and manage 4IR-related initiatives. This study aimed to evaluate Tanzanian project managers\u27 readiness for 4IR. The evaluation has four dimensions: social-economic effect, technological awareness, human capital development, and strategy and governance structure. The diffusion of innovations theory was the lens through which the research\u27s quantitative methodology was used. Data were gathered using an online survey to determine whether project managers were prepared and ready for the 4IR. Project managers completed the 50 valid samples from various industries, including manufacturing, consulting, construction, education & training, government, healthcare, and information technology. SPSS was used to analyze the data. The findings showed that despite a general lack of knowledge about 4IR, several project managers in Tanzania have varying knowledge about 4IR technologies like chatbots, drones, artificial intelligence, the Internet of Things (IoT), data analytics, blockchain, robotics, and cryptocurrency. The findings also showed that Tanzania\u27s project managers were not sufficiently prepared to begin, develop, and deploy 4IR goods and services due to inadequate 4IR-related governance structure, strategy, and human capital development skills. As a result, several suggestions for improvement are provided within the context of the four assessed readiness dimensions. The primary contribution of this research is to project managers\u27 level of 4IR preparedness and the recommendations that follow for policymakers, practitioners, academics, donors, the business sector, and young people interested in digital innovation. Additionally, the study advances our understanding of 4IR, project management, and digital transformation
Enhanced Covid-19 Contact-Tracing System
Full text linkCovid-19 is a global pandemic that has brought the world to a standstill. The virus originated from Wuhan China and has claimed the lives of over 5 million people according to World Health Organization. The Nigeria centre for Disease control is an agency that manages pandemics in Nigeria. They have created awareness son the management of Covic-19. Contact tracing of people that have come in contact with infected people poses a lot of problem. In this study, optimized system for con tact tracing of Covic-19 was carried out. Object Oriented Analysis Design Methodology (OOADM) was adopted and implementation was achieved with python programming language. The result obtained showed better and optimized performance in contact-tracing based on symptomatic (1) and asymptomatic (1+1) infection generation using fuzzy logic as an accurate decision making tool
Marrying Digital Performance Appraisal with Tutors’ Work Output: Evidence from Colleges of Education in Ghana
Full text linkIn this Digital Era teachers are required to develop professionalism in accordance with the development of time, science and technology, and the needs of society. Professional teachers should be able to understand the components of applicable educational concepts so as to appreciate the foundation and education policy, the development level of learners and learning approaches in accordance with the learning needs. The purpose of this study was to investigate the effects of digital performance appraisal on teachers’ performance in Colleges of Education in Ghana. The study employed descriptive research design where simple random sample was used to choose 5 principals and 139 tutors from Colleges of Education in Ahafo and Bono regions of Ghana. Data for this study was collected using close-ended questionnaire. Data analysis was done with Statistical Package for Social Sciences (SPSS version 25). The study results indicated that teachers’ appraisal processes are phenomenal in determining the performance of the teacher. The appraiser and appraisee need to be in collaboration in order to ensure the process is successfully undertaken. Communication is very essential on how the teachers’ appraisal is conducted and perceived. Training has the greatest influence on the appraisal of teachers which also affects significantly the performance of the teachers. Recommendations for further studies were made to address the study results found
Smart Technology Trends, Smart Supply Chain Management Implementation, and Smart Supply Chain Innovation Performance in Developing and Developed Economies
Full text linkMany companies and scholars predict that supply chain configuration will undergo a significant adjustment during this period of change in a digitally driven future. The supply chain and management industry view these risks as including the rising frequency of disruptions to the global trading system caused by both endogenous and exogenous risks, including extreme weather events, pandemics, cybersecurity threats, and financial crises, as well as the widespread use of technology created during the Fourth Industrial Revolution. Businesses that survive the upcoming changes in customer behavior will have invested in digitization and diversity. The researcher determines the association between smart supply chain management implementation and smart supply chain innovation performance, mediated by smart technology trends in developing and developed countries, using a descriptive quantitative approach. Results show that the performance of smart supply chain innovation and smart supply chain management are not correlated. Furthermore, smart supply chain management\u27s ability to mediate smart technological advances is notably lacking. As a result, the researcher produced an implementation strategy to improve the optimization of smart technological trends in the smart supply chain, in both developed and developing nations
Cross-Platform Android App Gateway Payment System using Xamarin
Full text linkMost mobile applications lacks cross platforms portability and capabilities. As such, developers tend to use specific code base that runs only on a native android application built using Java or a native iOS application built using Swift. In developing mobile application, same application is therefore required to be developed using the appropriate native app required software development. This leads to duplication of efforts, more cost, time consumption and maintenance. Although, the applications are the same, mobile application has to be developed separately because of platform independence. This paper proposes the use of Xamarin in developing mobile apps due to its cross-platform capabilities. Using Xamarin save cost, create a single code base for faster development and less maintenance while still maintaining native app performance and user experience. To substantiate Xamarin suitability, a gateway payment system was development and tested, the results showed actual cross platform functionalities in a seamless manner
Comparative Analysis of Skin Cancer Image with Classification and Clustering Algorithms
Full text linkSkin cancer is one of the most common and potentially life-threatening diseases worldwide. Early detection and accurate diagnosis are crucial for effective treatment and improved patient outcomes. In recent years, the integration of advanced technologies, such as artificial intelligence and image analysis, has revolutionized the field of dermatology. This article presents a comprehensive comparative analysis of algorithms for classifying and clustering skin cancer images. The goal is to improve the accuracy and efficiency of skin cancer diagnosis.
The study explores various machine learning algorithms used for skin cancer image classification, such as support vector machines (SVM), decision trees, and k-nearest neighbors (KNN). These algorithms are evaluated based on their capacity to distinguish between benign and malignant skin lesions, with a particular emphasis on sensitivity, specificity, and accuracy. Apart from classification, clustering algorithms are also examined to determine their potential in grouping similar skin lesions. This can assist dermatologists in identifying patterns and anomalies within extensive datasets. K-means, hierarchical clustering, and DBSCAN are among the algorithms assessed for their effectiveness in organizing images of skin cancer.
The comparative analysis in this article aims to provide insights into the strengths and weaknesses of various algorithms, their computational efficiency, and their performance on diverse datasets. Furthermore, it explores the potential of combining classification and clustering techniques to develop a skin cancer diagnosis system that is more robust and accurate
Information Communication Technology as an Effective Communication Tool in Rural Communities for the Post Covid 19 Era
Full text linkThis paper exposes the numerous sectors where benefits are imbued through the deployment of information and communication technology in conducting individual or communal task in rural communities despite the limitations presented due to the covid 19 pandemic. Using an expository style, the components of information and communication technology (ICT) are x-rayed as an independent concept, while its impact in ensuring effective communication in challenging conditions, in this case, rural community and the covid 19 pandemic. Furthermore, the numerous formats and constituent combinations of information and communication technology paraphernalia are highlighted. Particularly, areas of importance and relevance of information and communication technology in the post pandemic era are vigorously treated with insight into areas such as education, research, business, healthcare, governance and lifestyle. Finally, useful recommendations for all stakeholders are made for optimum productivity to be achieved
A Review on Detection of Diabetic Retinopathy using Deep Learning and Transfer Learning based Strategies
Full text linkDiabetic Retinopathy (DR) is considered to be one of the most widely observed and a complex variation of diabetes and stands as a leading cause of blindness globally. The occurrence of DR causes impairment in the retinal blood vessels and leads to unusual growth of blood arteries in the eye. Manual examinations and analysis suggests that the prevalence of DR has been enormously growing at an exponential rate and has already registered for more than 160 million cases worldwide. On the other hand, its diagnostic screening is not only challenging, but also computationally expensive at the same time. Due to the highlighting importance of its early diagnosis in terms of treatment, multiple concepts to DR detection have been used in the past few years. However, research in recent times has resulted in the fact that deep learning based CNN structures and Transfer Learning based MedNets have been popularly used in DR detection, due to its superior performance in the medical domain. As a result of such advancements in Deep Learning methodologies, this article proposes a review on automated approaches used to detect diabetic retinopathy using image processing and disease classification techniques. The review is further preceded with a comprehensive analysis on training a model with an already pre-trained network whose primary goal is to generate useful information and provide it to diabetic researchers, medical practitioners and patients
Natural Language Processing for Cyberbullying Detection
Full text linkWith the development of digital technologies and the popularity of social media, cyberbullying has become a serious public health concern that can lead to increased risk of mental and behavioral health issues or even suicide. Artificial intelligence like machine learning opens a lot of possibilities to combat cyberbullying, e.g. automatic cyberbullying detection. Most recent research focuses on improving performance by developing complex models that demand more resources and time to run. The research uses publicly available datasets without carefully evaluating their feasibility and limitations. This study uses natural language processing (NLP) to evaluate the model performance and examine the difference between fine-grained classification and binary classification as well as assess the feasibility and quality of the publicly available dataset. The results show that simple classifier can also achieve similar performance as that of more complex models if appropriate preprocessing is used, and the publicly available dataset may have limitations and quality issues that researchers should consider when using the data
Assessing Machine Learning\u27s Accuracy in Stock Price Prediction
Full text linkThis 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