Artificial intelligence has many applications in various industries, including agriculture. It can help overcome challenges by providing efficient solutions, especially in the early stages of development. When working with tree leaves to identify the type of disease, diseases often show up through changes in leaf color. Therefore, it is crucial to improve the color brightness before using them in intelligent agricultural systems. Color improvement should achieve a balance where no new colors appear, as this could interfere with accurate identification and diagnosis of the disease. This is considered one of the challenges in this field. This work proposes an effective model for olive disease diagnosis, consisting of five modules: image enhancement, feature extraction, clustering, and deep neural network. In image enhancement, noise reduction, balanced colors, and CLAHE are applied to LAB color space channels to improve image quality and visual stimulus. In feature extraction, raw images of olive leaves are processed through triple convolutional layers, max pooling operations, and flattening in the CNN convolutional phase. The classification process starts by dividing the data into clusters based on density, followed by the use of a deep neural network. The proposed model was tested on over 3200 olive leaf images and compared with two deep learning algorithms (VGG16 and Alexnet). The results of accuracy and loss rate show that the proposed model achieves (98%, 0.193), while VGG16 and Alexnet reach (96%, 0.432) and (95%, 1.74), respectively. The proposed model demonstrates a robust and effective approach for olive disease diagnosis that combines image enhancement techniques and deep learning-based classification to achieve accurate and reliable results.

Biometrics is defined as identifying people by their physiological characteristic, such as iris pattern, fingerprint, and face, or by some aspects of their behavior, such as voice, signature, and gesture. Considerable attention has been drawn on these issues during the last several decades. And many biometric systems for commercial applications have been successfully developed. Recently, the vein pattern biometric becomes increasingly attractive for its uniqueness, stability, and noninvasiveness. A vein pattern is the physical distribution structure of the blood vessels underneath a person’s skin. The palm vein pattern is very ganglion and it shows a huge number of vessels. The attitude of the palm vein vessels stays in the same location for the whole life and its pattern is definitely unique. In our work, the matching filter method is proposed for the palm vein image enhancement. New palm vein features extraction methods, global feature extracted based on wavelet coefficients and locality preserving projections (WLPP), and local feature based on local binary pattern variance and locality preserving projections (LBPV_LPP) have been proposed. Finally, the nearest neighbour matching method has been proposed that verified the test palm vein images. The experimental result shows that the EER to the proposed method is 0.1378%.

Tire defects are crucial for safe driving. Specialized experts or expensive tools such as stereo depth cameras and depth gages are usually used to investigate these defects. In image processing, feature extraction, reduction, and classification are presented as three challenging and symmetric ways to affect the performance of machine learning models. This paper proposes a hybrid system for cracked tire detection based on the adaptive selection of correlation features and deep belief neural networks. The proposed system has three steps: feature extraction, selection, and classification. First, the oriented gradient histogram extracts features from the tire images. Second, the proposed adaptive correlation feature selection selects important features with a threshold value adapted to the nature of the images. The last step of the system is to predict the image category based on the deep belief neural networks technique. The proposed model is tested and evaluated using real images of cracked and normal tires. The experimental results show that the proposed solution performs better than the current studies in effectively classifying tire defect images. The proposed hybrid cracked tire detection system based on adaptive correlation feature selection and Deep Belief Neural Networks’ performance provided better classification accuracy (88.90%) than that of Belief Neural Networks (81.6%) and Convolution Neural Networks (85.59%).

Palm vein recognition systems are one of the newest biometric technologies to have arisen in the recent past. Palm vein authentication uses the vascular patterns of an individual's palm of the hand as personal identification data. Veins and other subcutaneous features in the human hand present large, robust, stable and largely hidden patterns. In the proposed work, we present a palm vein verification system. In the proposed work, the Gaussian-Second-Derivative (GSD) is proposed for enhancement the palm vein images. Secondly, a new feature extraction method based on Gabor filter and Fisher Discriminated Analysis (FDA) is proposed called Gabor Fisher Vein Feature (GFVF). Finally, the Cosine Distance method is proposed for verify the tested palm vein. The EER to the proposed system is 0.0333%.

With the development of technology, recognition has become now a very important and main thing due to the large number of fraud and forgery operations that occur. The traditional methods such as signature and recognition based on knowledge do not meet the safety requirements due to the ease of forging and stealing them, as biometric technologies have emerged as an alternative to them due to the high security provided and accuracy. Biometrics rely on behavioral and physiological characteristics such as recognition based on palm veins to provide maximum protection and avoid unauthorized access to our personal property. In this paper, we will talk about authentication through palm veins. They are biological properties that do not change over time, and each person has his own pattern of veins, even symmetric twins have different patterns. These veins are not seen with the eye, which is difficult to forge and steal as the pattern of veins is extracted by near infrared rays palm veins images are considered one of the most promising technologies that meet the advanced safety requirements of modern privacy policies for highly sensitive systems. The authentication system through palm veins generally content from four steps, namely acquisition of image, preprocessing, extraction of feature and feature matching. In each stage, there is a set of methods used in this research. We will talk about these methods used in the palm vein by using the cassia database.