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.

The kinship Verification system analyzes the facial Features of two people to determine whether they are related or not. To identify the kinship, different Features can be extracted from the faces. In this paper, to evaluate a kinship verification system for the first-generation kinship (father-son, father-daughter, mother-son, and mother-daughter), texture and color Features are tested, and Feature fusion, as well as examining several different classifiers is considered. In this regard, two proposed approaches have been proposed: (1) fusing effective Features and evaluate different classifiers for kinship verification and (2) using NRML metric learning to generate a distinctive Feature vector to increase kinship verification efficiency. The proposed methods for the two databases KinFaceW-I and KinFaceW-II have been analyzed and evaluated in different cases. The results of the evaluations show that the fusion of Features and the use of NRML metric learning have been able to improve the performance of the kinship verification system. In addition to the two proposed approaches, Feature extraction from the whole image as well as image blocks is proposed and the results are presented. The results indicate that using the block-wise method for Feature extraction can be effective in improving the final kinship verification results.

In this paper, we propose five data fusion schemes for the Internet of Things (IoT) scenario, which are Relief and Perceptron (Re-P), Relief and Genetic Algorithm Particle Swarm Optimization (Re- GAPSO), Genetic Algorithm and Artificial Neural Network (GA-ANN), Rough and Perceptron (Ro-P) and Rough and GAPSO (Ro-GAPSO). All the schemes consist of four stages, including preprocessing the data set based on curve fitting, reducing the data dimension and identifying the most effective Feature sets according to data correlation, training classification algorithms, and finally predicting new data based on classification algorithms. The results derived from five compound schemes are investigated and compared with each other with three metrics, namely, Quality of Train (QoT) Accuracy (Ac) and Storage Capacity (SC). While the Re-P scheme is only capable of separating classes that are linearly separable, Re-GAPSO one is a dynamic method, appropriate for constantly changing problems of the real life. On the other hand, GA-ANN is a Wrapper method and despite Relief can adapt itself to the machine learning algorithm. Meanwhile, Ro-P scheme is useful for analyzing vague and imprecise information and, unlike GA-ANN, has less calculative costs. Among these five schemes, Ro-GAPSO is a more precise one, which has less calculative cost and does not become stuck in local minima. Experimental results show that Re-P outperforms other proposed and existing methods in terms of computational time complexity.

In an age where preserving knowledge and information from books and documents is crucial, traditional manual scanning methods are tedious and error-prone. It involves a lot of human intervention and, as a result, sometimes results in erroneous digitization, which makes the downstream tasks, such as optical character recognition, difficult. Therefore, innovative techniques are required to be proposed that not only reduce human effort in terms of digitization but also give highly accurate results over the recently proposed state-of-the-art techniques. We proposed a novel computer vision-based algorithm that combines Gray-Level Co-occurrence Matrix (GLCM) Features with Thepade's 10-ary texture Features (TSBTC) for video frame classification. This hybrid approach significantly enhances frame selection accuracy, ensures high-quality digitization, and accommodates multiple languages and document types. We also proposed a dataset of 54,000 diverse images to demonstrate our algorithm's effectiveness in real-world scenarios and compare it to existing methods, making a valuable contribution to document digitization. The proposed dataset can be utilized for several document image analysis tasks.

These days, the most important areas of research in many different applications, with different tools, are focused on how to get awareness. One of the serious applications is the awareness of the behavior and activities of patients. The importance is due to the need of ubiquitous medical care for individuals. That the doctor knows the patient's physical condition, sometimes is very important. Of course, there are other important applications for this information. There are a variety of methods and tools for measurement, gathering, and analysis of the physical behaviors and activities’ information. One of the most successful tools for this aim are ubiquitous intelligent electronic devices, specifically smartphones, and smart watches. There are many sensors in these devices, some of which can be used to understand the activities of daily living. As an output result, these sensors produce many raw data. Thus, it is needed to process these information and recognize the individual behavior of the output of this processing. In this paper, the basic components of the analysis phase for this process have been proposed. Simulations validate the benefits and superiority of this method.

In this paper, Feature fusion technique is employed for improvement of recognition of handwritten digits.By merging three different Feature vectors, given a specific weight for each of vectors, the Genetic Algorithm and Particle Swarm Optimization processes were applied to calculate the optimum weights.The main objective in this study was to compare the calculated weights according to each of the optimization techniques to that of classifiers combination in order to achieve a higher recognition rate and time for Persian Handwritten digits.A database containing 60’000 training samples and 20’000 test samples is used for the process.

Hyper-spectral image classification is a popular topic in the field of remote sensing. Hyperspectral images (HSI) have rich spectral information and spatial information. Traditional hyperspectral image (HSI) classification methods typically use the spectral Features and do not make full use of the spatial or other Features of the HSI. In general, the classification approaches classify input data by considering the spectral information of the data to produce a classification map in order to discriminate different classes of interest. The pixel-wise classification approaches classify each pixel autonomously without considering information about spatial structures, further enhancement of classification results can be obtain by considering spatial dependences between pixels. However, how to fuse and utilize spectral-spatial Features more efficiently is a challenging task. So the combination of spectral information and spatial information has become an effective means to obtain good classification results. Specifically, firstly, the principal component analysis (PCA) algorithm is used to extract the first principal component in the original hyperspectral image. Secondly, the residual network Gabor, GLCM and MP are introduced for each band to extract the spatial information of the image. Thirdly, the image is classified by using SVM to get the final classification result. In this paper, we have used the neural network classifier in the classification of hyperspectral images by integrating spectral and spatial properties in two methods stack and the method based on binary graphs. In spite of the traditional stack method, the use of local binary graph method to properly integrate spectral and spatial information is a desirable method for the simultaneous use of spectral information along with spatial information (Feature fusion) in hyperspectral image classification. In each of these methods, the neural network classifier is applied to the spectral and spatial Features separately and then compared with the performance of the support vector machine classifier in similar conditions. The classification results show that the proposed method can outperform other traditional classification techniques

Purpose: In recent years, some studies have examined the gait patterns of neurodegenerative diseases utilizing signal processing techniques and machine learning algorithms. The aim of this study was to provide an automated system for distinguishing Huntington's disease, amyotrophic lateral sclerosis (ALS), and Parkinson's disease from healthy control group using dynamic analysis of gait pattern (more precisely, stride time). In addition, we examined the effect of fusion of Features obtained from the left and right feet. Methods: First, polar-based measures were extracted from lagged Poincaré maps. The optimal latency of the map was estimated using the mutual information algorithm. Then, five Featurelevel fusion strategies were presented. The classification was performed using the feed-forward neural network; while the effect of changing the network parameter was also investigated. The proposed system was evaluated using the data available in the Physionet database, which includes 16 records of the control group (14 females and 2 males; 20-74 years), 20 records of Huntington's disease (14 females and 6 males; 29-71 years), 13 records of ALS (3 women and 10 men; 36-70 years) and 15 records of Parkinson's disease (5 women and 10 men; 44-80 years). Results: Using the fourth fusion strategy, the maximum accuracy of 93. 47% was obtained in separating the control and Huntington groups. Applying the second fusion algorithm, the control/Huntington and control/Parkinson groups were separated with the accuracy rate of 92. 92% and 91. 93%, respectively. The highest accuracy of the first fusion algorithm was 91. 72% in classifying the control group and ALS. The third fusion algorithm was able to provide a 91. 13% classification accuracy in separating the control and Huntington groups. The performance of the algorithm in separating patient groups was weaker. Conclusion: The proposed system performed well compared to previously published algorithms. Further studies on intelligent classification algorithms and the development of the suggested method could pave the way for preclinical diagnosis of neurodegenerative diseases.

Philadelphia chromosome can be founding 95% of patients with chronic myeloid leukemia (CML) by cytogenetic studies. The fused bcr/abl is transcribed in two types of chimeric mRNA. RT-PCR amplification of these two transcripts have been designed to give two different size products. This assay can detect one positive bcr/abl expressing cell in a back ground of 106 negative bcr/abl cells. The power of this assay is the detection of minimal residual disease (MRD) between 6-12 months following bone marrow transplantation (BMT) is an independent and significant factor that predicts the relapse in future. The aim of optimization was to detect β2 microglobulin (β 2M) mRNA. Detection of β2 mRNA and absence of bcr/abl indicates that the patient is negative for MRD and as a result, there is molecular remission in addition to clinical remission. To monitor MRD we tested a patient's blood sample who had tolerated allogenic BMT 7 years ago. bcr/abl wasn't detected in this patient and only β2 M was observed. This result confirmed the absence of MRD in this case. To effectively monitor minimal leukemic activity after BMT, we used a competitive RT- PCR to quantify expression of the characteristic bcr/abl fusion gene mRNA in patients with CML.