Fruit Categorization is a classification problem that the agricultural fruit industry needs to solve in order to reduce the post-harvesting losses that occur during the traditional system of manual grading. Fruit grading which involves categorization is an important step in obtaining high fruit quality and market demand. There are various Feature selection challenges in agriculture produced especially fruit grading to build an appropriate machine Learning approach to solve the problem of reducing losses. In this paper, we describe different Features, a machine Learning technique that has been recently applied to different fruit classification problems producing a promising result. We discuss the Feature extraction method, technique used in image classification applications for fruit prediction. A proposed multiclass fruit classification model is theoretically described and their most distinguishing Features and technique is then presented at the end of this paper.

With the explosion of Learning materials available on personal Learning environments (PLEs) in the recent years, it is difficult for learners to discover the most appropriate materials according to keyword searching method. Recommender systems (RSs) that are used to support activity of learners in PLE can deliver suitable material to learners. This technology suffers from the cold-start and sparsity problems. On the other hand, in most researches, less attention has been paid to latent Features of products. For improving the quality of recommendations and alleviating sparsity problem, this research proposes a latent Feature based recommendation approach. Since usually there isn’t adequate information about the observed Features of learner and material, latent Features are introduced for addressing sparsity problem. First preference matrix (PM) is used to model the interests of learner based on latent Features of Learning materials in a multidimensional information model. Then, we use genetic algorithm (GA) as a supervised Learning task whose fitness function is the mean absolute error (MAE) of the RS. GA optimizes latent Features weight for each learner based on his/her historical rating. The method outperforms the previous algorithms on accuracy measures and can alleviate the sparsity problem. The main contributions are optimization of latent Features weight using genetic algorithm and alleviating the sparsity problem to improve the quality of recommendation.

Nowadays, we face a huge number of high-dimensional data in different applications and technologies. To tackle the challenge, various Feature selection methods have been recently proposed for reducing the computational complexity of the Learning algorithms as well as simplifying the Learning models. Maintaining the geometric structures and considering the discriminative information in data are two important factors that should be borne in mind particularly for unsupervised Feature selection methods. In this paper, our aim is to propose a new unsupervised Feature selection approach by considering global and local similarities and discriminative information. Furthermore, this unsupervised framework incorporates cluster analysis to consider the underlying structure of the samples. Moreover, the correlation of Features and clusters is computed by an
 , -norm regularized regression to eliminate the redundant and irrelevant Features. Finally, a unified objective function is presented as well as an efficient iterative optimization algorithm to solve the corresponding problem with some theoretical analysis of the convergence and the complexity of the algorithm. We compare the proposed approach with the state-of-the-art method based on clustering results on the various standard datasets including biology, image, voice, and artificial data. The experimental results have presented the strength and performance improvement of the proposed method by outperforming the well-known methods.

Facial Feature recognition (FFR) has witnessed a remarkable surge in recent years, driven by its extensive applications in identity recognition, security, and intelligent imaging. The UTKFace dataset plays a pivotal role in advancing FFR by providing a rich dataset of facial images with accurate age, gender, and race labels. This paper proposes a novel multi-task Learning (MTL) model that leverages the powerful Efficient-Net architecture and incorporates attention-based Learning with two key innovations. First, we introduce an age-specific loss function that minimizes the impact of errors in less critical cases while focusing the Learning process on accurate age estimation within sensitive age ranges. This innovation is trained using the UTKFace dataset and is specifically optimized to improve accuracy in age estimation across different age groups. Second, we present an enhanced attention mechanism that guides the model to prioritize Features that contribute to more robust FFR. This mechanism is trained on the diverse and challenging images of UTKFace and is capable of identifying subtle and discriminative Features in faces for more accurate gender, race, and age recognition. Furthermore, our proposed method achieves a 30% reduction in model parameters compared to the baseline network while maintaining accuracy. Extensive comparisons with existing state-of-the-art methods demonstrate the efficiency and effectiveness of our proposed approach. Using the UTKFace dataset as the evaluation benchmark, our model achieves a 0.62% improvement in gender recognition accuracy, a 2.35% improvement in race recognition accuracy, and a noteworthy 3.23-year reduction in mean absolute error for age estimation.