The integration of artificial intelligence (AI) and the internet of things (IoT), known as artificial intelligence of things (AIoT), is driving significant advancements in the aquaculture industry, offering solutions to longstanding challenges related to operational efficiency, sustainability, and productivity. This review explores the latest research studies in AIoT within the aquaculture industry, focusing on real-time environmental monitoring, data-driven decision-making, and automation. IoT sensors deployed across aquaculture systems continuously track critical parameters such as temperature, pH, dissolved oxygen, salinity, and fish behavior. AI algorithms process these data streams to provide predictive insights into water quality management, disease detection, species identification, biomass estimation, and optimized feeding strategies, among others. Much as AIoT adoption in aquaculture is advantageous on various fronts, there are still numerous challenges, including high implementation costs, data privacy concerns, and the need for scalable and adaptable AI models across diverse aquaculture environments. This review also highlights future directions for AIoT in aquaculture, emphasizing the potential for hybrid AI models, improved scalability for large-scale operations, and sustainable resource management.

The increasing global population has escalated the demand for fish products necessitating a stable supply. This can only be met through improved aquaculture practices. The automatic recognition of fish diseases from diseased underwater images is one of such practices that aims to control disease, improve fish production and optimize profits. However, due to lack of public fish disease dataset, there has been limited research towards fish disease recognition. Moreover, due to low quality of underwater images and complex underwater environments, traditional hand-designed feature extraction methods or convolutional neural networks (CNNs)-based classifiers cannot adequately recognize fish diseases in real underwater scenes. Therefore, this paper proposes a novel hybrid approach based on multilayer fusion, attention mechanism and online sequential extreme learning machine (OSELM) to recognize fish diseases in aquaculture. Feature extraction is enhanced by integrating same level features and focusing on salient features for fish disease recognition. The characteristic information of fish disease is refined by using strongly discriminative features of the infected fish regions and weakening regions of low interest using convolutional block attention module (CBAM). The module is added to the multilayer fusion network to sequentially infer attention maps along the channel and spatial dimensions for every intermediate feature map. Fish disease recognition is done by using OSELM for faster learning and improving classification performance. The models are trained, validated and tested on a custom dataset with image samples collected from various internet sources. The proposed method achieves 94.28% of accuracy, precision of 92.67%, recall of 92.17% and 92.42% of F1- score with background elimination. The proposed method can be used for fish identification in complex underwater environments in aquaculture.

This study investigates user satisfaction with e-learning systems in higher education institutions, examining the perspectives of students, lecturers, and elearning officers and heads of the department of Information Technology as key informants. A total of 375 student respondents from Diploma, Bachelor, and Masters levels, 51 lecturers, and 15 key informants across different Ugandan institutions of higher learning participated in the study, providing insights into platform usage, system usability, stability, and support mechanisms. Rigorous analysis techniques, including means, correlation coefficient calculations, were employed to explore factors influencing satisfaction. Findings revealed positive correlations between satisfaction and variables such as ease of use, ease of access, system reliability, system stability, and prompt response to queries, among others. Recommendations include improving system stability, enhancing response time to queries, providing ongoing training, addressing connectivity issues, and developing customized mobile applications. The study highlights the need for institutions to optimize e-learning experiences by leveraging feedback from users and implementing targeted interventions.

Fish is a rich supply of proteins. Due to its high demand, aquaculture has been growing steadily. However, the activity is vulnerable to many diseases. Recent developments in computer vision and internet of things have enabled the automation of aquaculture operations. The lack of public fish disease dataset and complex underwater environments have limited the advancement of automatic fish disease detection and classification. This study proposes attention-based mechanism with background removal for fish disease classification. We focus on using strongly discriminative features of the infected fish regions and weakening regions of low interest using convolutional block attention module (CBAM), which is added to the pretrained classification models to sequentially infer attention maps along the channel and spatial dimensions for every intermediate feature map. The attention maps are then multiplied to the input feature map for adaptive feature refinement. The models are trained, validated and tested on a custom dataset with image samples collected from various internet sources. The performance of the attention-based models is compared with the baseline. The results indicate that ResNet50 with CBAM achieves 89.9% of accuracy, precision of 89.9%, recall of 89.3% and 89.7% of F1-score. Conclusively, attention mechanism improves fish disease classification performance.

The COVID-19 outbreak necessitated a rapid transition to eLearning in higher education institutions worldwide, including Uganda, where infrastructural and digital literacy challenges compounded this shift. Predicting student satisfaction with eLearning systems helps institutions evaluate how well these platforms are working, assess their future potential, and make informed decisions. This supports better use of resources, prevents investment in ineffective systems, and enables timely interventions to improve teaching and learning quality. This study developed and evaluated machine learning models to predict student satisfaction based on perceptual data. Various machine learning predictive algorithms were trained on the processed and augmented dataset and tested on the original processed data, including ensemble methods (XGBoost, Random Forest, AdaBoost, Gradient Boosting), traditional classifiers (Logistic Regression, Decision Tree, Support Vector Machine, K-Nearest Neighbors), and neural networks (Multi-Layer Perceptron). Model performance was evaluated using metrics such as accuracy, precision, recall, and F1-score to identify the most effective approach for predicting student satisfaction levels. Among the evaluated models, K-Nearest Neighbors (KNN) achieved the highest mean accuracy of 88.4%, followed closely by XGBoost at 86.6%. The Friedman test indicated statistically significant differences in performance across models (χ²(8) = 39.79, p < 0.001). Post-hoc Nemenyi tests identified KNN and XGBoost as significantly outperforming several other classifiers, underscoring their effectiveness for predicting student satisfaction. These findings demonstrate the potential of machine learning models in accurately predicting student satisfaction within eLearning environments. Identifying patterns in usability, content quality, and support services can enable institutions to leverage these predictive insights to optimize resource allocation, improve instructional design, and implement timely interventions that enhance the overall quality and effectiveness of online education.