In mobile edge computing (MEC), it is difficult to recognise an optimum solution that can perform in limited energy by selecting the best communication path and components. This research proposed a hybrid model for energy-efficient cluster formation and a head selection (E-CFSA) algorithm based on convolutional neural networks (CNNs) and a modified k-mean clustering (MKM) method for MEC. We utilised a CNN to determine the best-transferring strategy and the most efficient partitioning of a specific task. The MKM method has more than one cluster head in each cluster to lead. It also reduces the number of reclustering cycles, which helps to overcome the energy consumption and delay during the reclustering process. The proposed model determines a training dataset by covering all the aspects of cost function calculation. This training dataset helps to train the model, which allows for efficient decision-making in optimum energy usage. In MEC, clusters have a dynamic nature and frequently change their location. Sometimes, this creates hurdles for the clusters to form a cluster head and, finally, abandons the cluster. The selected cluster heads must be recognised correctly and applied to maintain and supervise the clusters. The proposed pairing of the modified k-means method with a CNN fulfils this objective. The proposed method, existing weighted clustering algorithm (WCA), and agent-based secure enhanced performance approach (AB-SEP) are tested over the network dataset. The findings of our experiment demonstrate that the proposed hybrid model is promising in aspects of CD energy consumption, overhead, packet loss rate, packet delivery ratio, and throughput compared to existing approaches.

Mushrooms are known for their significant nutritional value and are essential to the human diet. However, the dilemmas associated with ingesting poisonous mushroom species stress the critical need for accurate identification methods. Despite many efforts to identify mushroom species, these methods are often limited in identifying them from their natural habitat. This study addresses this gap by presenting a computer vision approach that uses machine learning for accurate and reliable image-based classification of mushrooms from their natural habitat. The proposed solution aims to enhance the safety of mushroom consumption by precisely classifying mushroom species. The images of mushroom species are taken from their natural habitat to increase their applicability in real-world scenarios. The study proposed Convolutional Neural Network (CNN) models and different image augmentation techniques to accurately identify one hundred and three (103) mushroom species. Evaluation of the model from the 20% of the test dataset showed an accuracy of 96.70% and high precision-recall and F1 score for each mushroom class. The study achieved a 4.4% increase in accuracy from the state-of-the-art approaches in mushroom species identification. This research is significant to mycologists, scientists, and the general public in promoting the safe usage of mushroom species.

Maternal health risk prediction is a critical aspect of public health. This paper proposes a new parameter optimization method to improve maternal health risk prediction using the Mud Ring Algorithm (MRA). The proposed method is implemented in two stages to achieve the desired improvement. In the first stage, the MRA is used to optimize the parameter of Support Vector Machine (SVM) classifier. The MRA-optimized SVM (MRA-SVM) is then evaluated on thirteen real-world datasets to compare its performance with other state-of-the-art optimization algorithms. Subsequently, the performance of the MRA-SVM is specifically compared using a maternal health risk dataset. The maternal health risk dataset, being a medical dataset, faces a significant issue of imbalance. To address this problem, the second stage comes in turn, and the crossover oversampling technique is first employed. Furthermore, other classifiers such as Random Forest and K-Nearest Neighbor are also used for improving the prediction of the maternal health risk dataset. Experimental results indicate that our proposed method delivers highly competitive results compared to six well-known optimization algorithms, evaluated based on Accuracy, G-mean, F-Measure, MCC and Kappa metrics. Moreover, the results demonstrate that using a crossover oversampling method as a preprocessing step, along with MRA to optimize the parameters of SVM (MRA-SVM), Random Forest (MRA-RF), and K-Nearest Neighbor (MRA-KNN), increases prediction accuracy of maternal health risk dataset by 11.8%, 9.11%, and 17.08% respectively, compared to using the three classifiers without MRA and crossover oversampling. In sum, utilizing the crossover oversampling method, combined with the MRA to optimize the parameter of Random Forest classifier, leads to higher prediction performance compared to other recently published algorithms.

Unravelling mysteries of the diversity of the plant community is a crucial issue both for the development of many botanical industries as well as for the conservation of ecosystem biodiversity. Traditionally, botanists have proposed detailed dichotomous key descriptions (called also characters or concepts) about the morphology of plants and particularly of leaves that allow them to construct relationships between different plants and between plants and their environment. However, extracting these concepts is complicated, painstaking and can only be carried out by experts. One way to accelerate and broaden the use of these concepts is to automatically extract them directly from images. In this paper, we focus on one of the most basic and important concepts: the leaf arrangement. According to this concept, leaves are divided into four categories: simple, pinnnately compound, palmately compound and compound trifoliate. To accomplish this task, we follow a hierarchical scheme, reducing ambiguity between categories from the most different shapes to the most similar ones. The choice of appropriate features is performed based on botanical observations and validated by a statistical study. The method was tested on real world leaf images (the Pl@ntLeaves scans). Experimental results show its robustness for a high number of leaf species (70 species) and even in the presence of some distortions (such as rotation and partial leaf overlapping).