Salem Journal of Science, Information & Communication Technology

This study aims at evaluating work-life balance in modern organization by examining the role of the internet in Niger Delta Development Commission. To achieve this, the study raised four objectives which were to; find out the extent to which internet used for work in Niger Delta Development Commission; determine how often members of staff of NDDC use the Internet to reach their work-life balance; ascertain the influence of work-life balance on job satisfaction of employees in NDDC. This study was anchored on three theories; Technological Determinism theory and Border, Boundary theory and Ecological System Theory. The study adopted the survey research design with questionnaire and interview guide as data gathering instruments. The population of the study comprised all the staffs of NDDC headquarter. Therefore, the total population of the study of this study is 879, and Taro Yamane sample size determination was used to arrive at the sample size of 275. Data obtained from the instrument were analyzed using Weighted Mean Score (four point Likert scale). The finding of this study showed that the internet is often used for work in the NDDC. The findings of this study showed that the staff of NDDC constantly use the interment to reach work-life balance. Based on the strength of the finding, the study concluded that the internet is often used for work in the NDC. The study also concluded that the staff of NDDC constantly use the interment to reach work-life balance. The study recommended that organizations should ensure that they equip their offices with modern equipment especially internet facilities so as to enable their workers achieve work-life balance. The study also recommended that organizations and the NDDC in particular should always provide free Wifi for their workers to ease the burden of work on then.

Infertility affects millions of couples globally, with in vitro fertilization (IVF) emerging as a common assisted reproductive technology (ART). Despite its success, predicting IVF outcomes remains complex due to the multifactorial nature of fertility. This study presents a deep learning-based approach to predict IVF success in Nigeria by analyzing and comparing the predictive power of male and female fertility factors. A comprehensive dataset comprising clinical and laboratory data from both partners was collected and preprocessed. Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs) were employed to develop models trained on male-only, female-only, and combined datasets. Evaluation metrics such as accuracy, precision, recall, F1-score, and AUC-ROC were used to assess performance. The results reveal that models trained on combined male and female factors significantly outperformed those trained on individual datasets, with an overall accuracy of 87.3% and an AUC of 0.91. Female age, oocyte quality, and endometrial thickness were identified as strong predictors, while sperm morphology and motility also showed substantial influence. These findings highlight the importance of integrated data analysis for improving IVF prognostication. This research underscores the potential of AI-driven decision support systems in enhancing clinical strategies and personalized treatment planning for infertile couples.