Artificial intelligence (AI) has become a significantly growing field in the environmental sector due to its ability to solve problems, make decisions, and recognize patterns. The significance of AI in wildlife acoustic monitoring is particularly important because of the vast amounts of data that are available in this field, which can be leveraged for computer vision and interpretation. Despite the increasing use of AI in wildlife ecology, its future in acoustic wildlife monitoring remains uncertain. To assess its potential and identify future needs, a scientific literature review was conducted on 54 works published between 2015 and March 2022. The results of the review showed a significant rise in the utilization of AI techniques in wildlife acoustic monitoring over this period, with birds (N = 26) gaining the most popularity, followed by mammals (N = 12). The most commonly used AI algorithm in this field was Convolutional Neural Network, which was found to be more accurate and beneficial than previous categorization methods in acoustic wildlife monitoring. This highlights the potential for AI to play a crucial role in advancing our understanding of wildlife populations and ecosystems. However, the results also show that there are still gaps in our understanding of the use of AI in wildlife acoustic monitoring. Further examination of previously used AI algorithms in bioacoustics research can help researchers better understand patterns and identify areas for improvement in autonomous wildlife monitoring. In conclusion, the use of AI in wildlife acoustic monitoring is a rapidly growing field with a lot of potential. While significant progress has been made in recent years, there is still much to be done to fully realize the potential of AI in this field. Further research is needed to better understand the limitations and opportunities of AI in wildlife acoustic monitoring, and to develop new algorithms that can improve the accuracy and usefulness of this technology.

Learning styles, learners’ preferred ways to learn, can be applied to design personalized learning courses for the individual learner. One of those applications is the recommender system. This study ran a systematic literature review on the recommender system utilizing learning styles from two dependable academic libraries, IEEE Xplore and ScienceDirect, which included six reviewed topics; research objective, research methodology, educational recommendation, learning styles theory, learning style identification, and recommendation algorithm. Finally, the obtained information was examined and summarized in order to develop a unique strategy for leveraging the utilization of learning style theory for a recommender system to improve learning efficiency.

Nowadays, renewable energy sources in a micro‐grid (MG) system have increased challenges in terms of the irregularly and fluctuation of the photovoltaic and wind turbine units. It is necessary to develop battery energy storage. The MG central controller is helping to develop it in the MG system for improving the time of availability. Thus, reducing the total energy expenses of MG and improving the renewable energy sources (battery energy storage) are considered together with the operation management of the MG system. This study proposes fitness‐based modified game particle swarm optimisation (FMGPSO) algorithm to optimise the total costs of operation and pollutant emissions in the MG and multi‐MG system. The optimal size of battery energy storage is also considered. A non‐dominated sorting genetic algorithm‐III, a multi‐objective covariance matrix adaptation evolution strategy, and a speed‐constrained multi‐objective particle swarm optimisation are compared with the proposed FMGPSO to show the performance. The results of the simulation show that the FMGPSO outperforms both the comparison algorithms for the minimisation operation management problem of the MG and the multi‐MG system.

Information and communication technology considerably impacts students’ engagement with self-regulated learning (SRL) methodologies. However, there has been a lack of any comprehensive visualization of the SRL process, making it difficult to interpret student behaviors. To address this issue, the REXX platform is used in this study to visualize SRL outputs. While REXX has previously been used to present educational metrics more comprehensively and personally in the learning management system (LMS) framework, research on understanding student behavior through the learning analytics platform (LAP) remains unexplored. This study focused on transforming REXX from an LMS to an LAP to capture detailed features of individual student profiles, thereby reflecting specific SRL characteristics. We collected profile data from 215 high school students via an e-learning web application and used K-means clustering to categorize their behaviors. The method yielded a Davies–-Bouldin score of 0.9718, a silhouette score of 0.54, and a Calinski–Harabasz score of 124.1805. This study addresses both teaching and learning strategies for educators and students. It represents a considerable step toward understanding student behavior in the e-learning environment. However, we recommend integrating machine learning models to enhance automated learning strategies alongside this baseline framework.

Plant diseases and nutrient deficiencies pose significant challenges to food production, making it crucial to identify them accurately and quickly, as their symptoms can often be similar. Prompt and precise detection is essential to implement effective measures that prevent crop losses. While computer vision techniques have demonstrated effectiveness in classification, their high computational demands have limited their adoption by farmers in the field. In this study, a Corn leaf Nutrition Deficiency and Disease network (CNDD-net) is designed based on the ResNet framework, incorporating a depth-wise separable convolution and a convolutional block attention module for a lightweight, high-performance model. The models underwent training, validation, and testing using a corn leaf nutrition deficiencies and diseases data set with seven classes implementing five-fold cross-validation. The performance of the models is assessed using average accuracy, GFLOPs, number of parameters, and model size. Following experiments involving the manipulation of the position of the attention module, the number of feature maps, and the depth of the network, the model was finalised. The CNDD-net design has a model size of 0.24 MB with 48,041 parameters and a GFLOPs of 0.18, providing an average accuracy of 96.71%. Compared to conventional models, this research demonstrates optimal performance and computational complexity, offering an efficient, lightweight solution to identify nutritional deficiencies and diseases of corn leaf, thus supporting sustainable agriculture.

Accurately identifying individual wildlife is critical to effective species management and conservation efforts. However, it becomes particularly challenging when distinctive features, such as spot shape and size, serve as primary discriminators, as in the case of Sika deer. To address this challenge, we employed four different Convolutional Neural Network (CNN) base models (EfficientNetB7, VGG19, ResNet152, Inception_v3) within a Siamese Network Architecture that used triplet loss functions for the identification and re-identification of Sika deer. Subsequently, we then determined the best-performing model based on its ability to capture discriminative features. From this model, we extracted embeddings representing the learned features. We then applied a Support Vector Machine (SVM) to these embeddings to classify individual Sika deer. We analyzed 5169 image datasets consisting of images of seven individual Sika deers captured with three camera traps deployed on farmland in Hokkaido, Japan, for over 60 days. During our analysis, ResNet152 performed exceptionally well, achieving a training accuracy of 0.97, and a validation accuracy of 0.96, with mAP scores for the training and validation datasets of 0.97 and 0.96, respectively. We extracted 128 dimensional embeddings of ResNet152 and performed Principal Component Analysis (PCA) for dimensionality reduction. PCA1 and PCA2, which together accounted for over 80% of the variance collectively, were selected for subsequent SVM analysis. Utilizing the Radial Basis Function (RBF) kernel, which yielded a cross-validation score of 0.96, proved to be most suitable for our research. Hyperparameter optimization using the GridSearchCV library resulted in a gamma value of 10 and C value of 0.001. The OneVsRest SVM classifier achieved an impressive overall accuracy of 0.97 and 0.96, respectively, for the training and validation datasets. This study presents a precise model for identifying individual Sika deer using images and video frames, which can be replicated for other species with unique patterns, thereby assisting conservationists and researchers in effectively monitoring and protecting the species.