International Journal of Information Technology, Research and Applications

The Role of Information and Communication Technology on the Service Quality of Commercial Banks in Tanzania: the Case of NBC Bank Plc

2025-05-05T04:03:15+00:00

ICT plays a huge role on the improvement of business processes in businesses, particularly in the banking sector. However, the extent to which ICT is employed is still questionable. This research examines the impact of ICT on the service quality of commercial banks in Tanzania. Thematic Content Analysis (TCA) was used for data analysis. The study revealed that ICT has a positive impact on the service quality of commercial banks. Three dimensions of services, namely Internet Banking, Automated Teller Machines, and Mobile Banking, were used for benchmarking. The findings of this study will provide valuable insights for NBC Ltd and other commercial banks in Tanzania to improve their service quality through the effective use of ICT. Policymakers may use the study findings to develop strategies that can make service delivery more effective and less costly.

Predictive Modeling of the Impact of Smartphone Addiction on Students’ Academic Performance Using Machine Learning

2025-05-05T03:58:36+00:00

ABSTRACT

Objectives: The goal of this study was to use machine learning techniques to create and validate predictive models for detecting smartphone addiction. The study sought to find significant aspects linked to smartphone addiction and assess the models’ capacity to correctly recognize those at risk by examining a mix of data such as behavioral, psychological, and demographic. Methods: Five hundred participants between the ages of 16 and 45 made up the dataset. Data such as self-reported smartphone usage habits, Smartphone Addiction Scale (SAS) scores, and demographics were all included in this study. Recursive Feature Elimination (RFE) and other feature selection approaches were used to determine the important predictors of smartphone addiction. Predictive models were then built using machine learning techniques like Random Forest, Gradient Boosting, and Logistic Regression. The dataset was split into subsets for training (70%) and testing (30%), for developing and assessing the model. Key metrics like accuracy, precision, recall, and the F1-score were used to evaluate the model's performance. Findings: With an accurate record of 91.2%, precision of 88.7%, recall of 90.5%, and F1-score of 89.6%, the Gradient Boosting machine learning model outperformed the other techniques. Daily screen time, app usage frequency, sleep disturbance from smartphone use, and psychological traits like impulsivity and anxiety were among the major indicators found. Novelty: By combining behavioral data with sophisticated and intricate machine learning models, this study presents a novel and notable method for accurately predicting smartphone addiction. In contrast to other research, this study focuses on using explainable AI methods to derive useful insights, which could enhance the interpretability of predictive models for more future purposes.

APPLYING STRUCTURATION THEORY TO ELECTRONIC GOVERNMENT FOR IMPROVED GOVERNANCE IN KENYA'S COUNTY GOVERNMENTS

2025-07-01T13:07:38+00:00

The Kenyan government in this modern age has made strides and invested to embrace electronic government (e-government) in almost all ministries to enable speedy service delivery and enhance transparency and accountability by eliminating inefficient processes and bureaucracies as much as possible. However, some professionals lack Information Communication Technology (ICT) skills to manage various projects. The general objective of this study sought to conceptualize structuration theory into ICT training as a requirement for e-government in Trans Nzoia and Kisumu county governments. This paper assesses the ICT training needs among staff working in Trans Nzoia and Kisumu County governments. Interviews and Document reviews from both case counties and national governments are the data collection methods. The study contextualizes Structuration theory into ICT being a theory from the field of sociology and a constructivist paradigm approach is employed. Purposive sampling technique is used targeting participants in the two county governments. Thematic Analysis’s procedures and processes are adopted for data analysis. The findings contribute to knowledge in that structuration theory is conceptualized into ICT; on Structures, Signification was operationalized into ICT implicit and explicit knowledge; on Interaction, Meaning was operationalized into ICT information and Communication dimension into Information dimension. I recommend a comprehensive and coherent approach to ICT training and capacity building at all educational tiers, country wide roll out of ICT infr

Towards Intelligent Adaptive Cruise Control: Integrating AI, Edge Computing, and V2V Communication for Urban Environments

2025-06-15T03:42:55+00:00

Traditional Adaptive Cruise Control (ACC) systems offer significant safety and comfort benefits in highway scenarios but remain inadequate in the dynamic and unpredictable context of urban driving. This paper introduces an enhanced framework, termed Intelligent Adaptive Cruise Control (iACC), which integrates Artificial Intelligence (AI), Edge Computing, and Vehicle-to-Vehicle (V2V) communication to address urban mobility challenges. The iACC system utilizes reinforcement learning and predictive modeling for proactive decision-making, while edge computing ensures low-latency responses to environmental stimuli. V2V communication supports collaborative traffic behavior, facilitating smoother acceleration, safer navigation through pedestrian zones, and better adaptation to urban complexity. Simulation scenarios demonstrate the proposed system’s ability to outperform traditional ACC in response time, safety, and driver comfort. This work contributes to the advancement of urban autonomous mobility and presents a scalable foundation for future smart city transportation systems.

Leaf Disease Predictions Using Deep Learning Techniques - Potato

2025-04-10T16:16:21+00:00

The Study of leaf diseases is important to obtain healthy crop yields and confirm food security. Detection of potato leaf diseases at an early stage is of great significance to the agricultural industry. Detecting this disease early helps farmers protect their plants. But soil and climate pollution are highly unfavorable for potato growth and it leads to disease such as scab, black scurf, blackleg, dry rot, and pink rot.
Though, identifying diseases in potato leaves is challenging because of the composite symptoms and variability in leaf presences. This involves the advance of an operative and efficient method that can overcome these contests and improve disease detection accuracy. Predicting potato leaf disease at early stage is crucial and this research paper proposes a deep machine learning approach utilizing Convolutional Neural Network Especially Residual Network50 Version 2(ResNet50V2) model that can rapidly and accurately identify plant disease. The comparative study of the leaf disease works on three models CNN, EfficientB0, ResNet50V2 model. Comparing these models the study reached the expected testing and train accuracy. The study highlights the importance of feature fusion and predicting early leaf disease in enhancing disease diagnoses.