Journal of Engineering Applied Science and Humanities

Detection of “Cocoa Swollen Shoot Disease” in Ghanaian Cocoa Trees Based on Convolutional Neural Network (CNN) and Deep Learning Technique

2023-06-26T01:37:24+00:00

The application of Convolutional Neural Networks and Deep Learning Techniques in the detection of "Cocoa Swollen Shoot" disease in Ghanaian cocoa trees have demonstrated its effectiveness and reliability. This approach provides a valuable tool for cocoa farmers and agricultural authorities to promptly identify and manage the disease, contributing to the sustainable production of cocoa and the preservation of Ghana's cocoa industry. Recent advances in diagnostics have made image analysis one of the main areas of research and development. Selecting and calculating these characteristics of a disease is a difficult task. Among deep learning techniques, deep convolutional neural networks are actively used for image analysis. This includes areas of application such as segmentation, anomaly detection, disease classification, and computer-aided diagnosis. The objective, which we aim for in this article, is to extract information in an effective way for a better diagnosis of the plants attending the disease of “swollen shoot”.

Factors Influencing Stigmatization among Staff Survivors of Covid-19

2022-04-27T13:27:29+00:00

The novel coronavirus (2019-nCoV, or COVID-19) epidemic first broke out in Wuhan and has been spreading in whole China and the world. The number of new infections and deaths is increasing, which became public health concern. The main objective of the study is to assess factors influencing stigmatization among staff survivors of Covid-19 at the Korle-Bu Teaching Hospital. The study was conducted at Korle-Bu Teaching Hospital. A descriptive study was employed as the study type and a cross-sectional study as the design. The population for the study consisted of staff survivors of Covid-19 at Korli-Bu Teaching Hospital. A convenient sampling technique was used to select 335 participants who arrived using Yamane’s formula. Structured questionnaires were used to collect the data. Data were analyzed using STATA version 16 software. Frequencies, percentages, and regression analysis were used at a 0.05 significance level. The study found an adequate knowledge of health workers and stigmatization. Participants scored an average percentage concerning the items used to measure the survivors and stigma. The logistic regression analysis showed that there was an established factor that determined stigma among health workers at Korli Bu Teaching Hospital. Survivors of Covid-19 are a global challenge among health workers and are mostly stigmatized due to their poor knowledge of Covid-19.

Factors Affecting HIV Clients’ Exposures and Adherence to Toxoplasmosis Preventive Measures at the Individual, Interpersonal, Community and Institutional Levels: A Literature Review

2022-05-16T18:40:57+00:00

This study deals with the review of various literature relating to the topic under study, and has been sub-sectioned in accordance as follow key concepts of cerebral toxoplasmosis in HIV, risk factors of cerebral toxoplasmosis, preventive measures against cerebral toxoplasmosis, challenges of HIV patients with adherence to preventive measures, pitfalls in health care affecting the implementation of preventive measures, and theoretical framework. However, Factors affecting HIV clients' exposures and adherence to toxoplasmosis preventive measures can be categorized into individual, interpersonal, community, and institutional levels. These factors influence the extent to which HIV clients are exposed to toxoplasmosis and their willingness and ability to follow preventive measures. Based on research works on resistance patterns done in various parts of the West African sub-region, there has been an increasing worry over how effective co-trimoxazole is as a prophylactic medication against bacterial and parasitic opportunistic infections in HIV patients. CNS manifestations are often the primary feature of HIV/AIDS that leads to its diagnosis in an estimated 1 in 10 patients presenting with neurologic disorders. In late-stage HIV disease, CNS disorders are often associated with poorer outcomes.