Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a

One of the significant stages in computer vision is image segmentation which is fundamental for different applications, for example, robot control and military target recognition, as well as image analysis of remote sensing applications. Studies have dealt with the process of improving the classification of all types of data, whether text or audio or images, one of the latest studies in which researchers have worked to build a simple, effective, and high-accuracy model capable of classifying emotions from speech data, while several studies dealt with improving textual grouping. In this study, we seek to improve the classification of image division using a novel approach depending on two methods used to segment the images. The first

     The widespread use of images, especially color images and rapid advancement of computer science, have led to an emphasis on securing these images and defending them against intruders. One of the most popular ways to protect images is to use encryption algorithms that convert data in a way that is not recognized by someone other than the intended user. The Advanced Encryption Standard algorithm (AES) is one of the most protected encryption algorithms. However, due to various types of theoretical and practical assaults, like a statistical attack, differential analysis, and brute force attack, its security is under attack.

In this paper, a modified AES coined as (M-AES) is proposed to improve the efficiency

Alongside the development of high-speed rail, rail flaw detection is of great importance to ensure railway safety, especially for improving the speed and load of the train. Several conventional inspection methods such as visual, acoustic, and electromagnetic inspection have been introduced in the past. However, these methods have several challenges in terms of detection speed and accuracy. Combined inspection methods have emerged as a promising approach to overcome these limitations. Nondestructive testing (NDT) techniques in conjunction with artificial intelligence approaches have tremendous potential and viability because it is highly possible to improve the detection accuracy which has been proven in various conventional nondestr

The seizure epilepsy is risky because it happens randomly and leads to death in some cases. The standard epileptic seizures monitoring system involves video/EEG (electro-encephalography), which bothers the patient, as EEG electrodes are attached to the patient’s head.

Seriously, helping or alerting the patient before the seizure is one of the issue that attracts the researchers and designers attention. So that there are spectrums of portable seizure detection systems available in markets which are based on non-EEG signal.

The aim of this article is to provide a literature survey for the latest articles that cover many issues in the field of designing portable real-time seizure detection that includes the use of multiple

Let be a Banach space, be a nonempty closed convex subset of , and be self
nonexpansive map. The sequence generated by the iterative method
, where be a contractive mapping
and is a sequence in We generalize the mapping to non-sel -Strongly
Pseudocontractive .

     Obstacle avoidance is one of the major tasks needed to be carefully focused by the autonomous system designers. In this digital era, most industries are moving towards autonomous systems. Obstacle avoidance is considered as a primary concern for any autonomous system. In this paper, a prototype of an autonomous vehicle is presented, which is capable of obstacle avoidance using an ultrasonic sensor for its movement and avoidance. An Arduino microcontroller is used to achieve the desired operation. In order to achieve the desired task of the proposed system, a proper methodology is followed which combines appropriate selection of hardware components as well as logic design of actions for obstacle avoidance. The proposed system can

In this work chemical vapor deposition method (CVD) for the production of carbon nanotubes (CNTs) have been improved by the addition of S. Steel mesh container (SSMC) inside which the catalyst (Fe/Al2O3) was placed. Scanning electron microscopy (SEM) investigation method used to study nanotubes produced, showed that high yield of two types of (CNTs) obtained, single wall carbon nanotube (SWCNTs) with diameter and length of less than 50nm and several micrometers respectively and nanocoil tubes with a diameter and length of less than 100nm and several micrometers respectively. The chemical analysis of (CNTs) reveals that the main component is carbon (94%) and a little amount of Al (0.32%), Fe (2.22%) the reminder is oxygen. It was also fou

In this paper Alx Ga1-x As:H films have been prepared by using new deposition method based on combination of flash- thermal evaporation technique. The thickness of our samples was about 300nm. The Al concentration was altered within the 0 x 40.
The results of X- ray diffraction analysis (XRD) confirmed the amorphous structure of all AlXGa1-x As:H films with x  40 and annealing temperature (Ta)<200°C. the temperature dependence of the DC conductivity GDC with various Al content has been measured for AlXGa1-x As:H films.
We have found that the thermal activation energy Ea depends of Al content and Ta, thus the value of Ea were approximately equal to half the value of optical gap.