Clinical keratoconus (KCN) detection is a challenging and time-consuming task. In the diagnosis process, ophthalmologists must revise demographic and clinical ophthalmic examinations. The latter include slit-lamb, corneal topographic maps, and Pentacam indices (PI). We propose an Ensemble of Deep Transfer Learning (EDTL) based on corneal topographic maps. We consider four pretrained networks, SqueezeNet (SqN), AlexNet (AN), ShuffleNet (SfN), and MobileNet-v2 (MN), and fine-tune them on a dataset of KCN and normal cases, each including four topographic maps. We also consider a PI classifier. Then, our EDTL method combines the output probabilities of each of the five classifiers to obtain a decision based on the fusion of probabilities. Individually, the classifier based on PI achieved 93.1% accuracy, whereas the deep classifiers reached classification accuracies over 90% only in isolated cases. Overall, the average accuracy of the deep networks over the four corneal maps ranged from 86% (SfN) to 89.9% (AN). The classifier ensemble increased the accuracy of the deep classifiers based on corneal maps to values ranging (92.2% to 93.1%) for SqN and (93.1% to 94.8%) for AN. Including in the ensemble-specific combinations of corneal maps’ classifiers and PI increased the accuracy to 98.3%. Moreover, visualization of first learner filters in the networks and Grad-CAMs confirmed that the networks had learned relevant clinical features. This study shows the potential of creating ensembles of deep classifiers fine-tuned with a transfer learning strategy as it resulted in an improved accuracy while showing learnable filters and Grad-CAMs that agree with clinical knowledge. This is a step further towards the potential clinical deployment of an improved computer-assisted diagnosis system for KCN detection to help ophthalmologists to confirm the clinical decision and to perform fast and accurate KCN treatment.
Traffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Most systems of network traffic identification are based on features. These features may be static signatures, port numbers, statistical characteristics, and so on. Current methods of data flow classification are effective, they still lack new inventive approaches to meet the needs of vital points such as real-time traffic classification, low power consumption, ), Central Processing Unit (CPU) utilization, etc. Our novel Fast Deep Packet Header Inspection (FDPHI) traffic classification proposal employs 1 Dimension Convolution Neural Network (1D-CNN) to automatically learn more representational c
... Show MoreBackground: As photochemical reaction that can stiffen the cornea, CXL is the only promising method of preventing progression of keratectasia such as KC and secondary ectasia following refractive surgery. The aim of CXL is to stabilize the underlying condition with a small chance of visual improvement. Objective: To show the sequences of changes in visual acuity and topographic outcomes during 1 year post CXL for patients with progressive Keratoconus.Type of the study: Cross sectional studyMethods: CXL procedure was done for 45 eyes with progressive KC. The following parameters had been monitored pre operatively, 1, 3, 6 and 12 months postoperatively: K apex, K2, corneal thickness at thinnest location, anterior and posterior elevation po
... Show MoreThe mind map represents a system to solve problems including design problems, because it depends on employing the complex and purposeful mental activity, which starts from a desire based on the search and investigation in order to reach integrated results of scientific thinking, utilizing the objective view of the scientific and practical aspects from various angles. This visual system can develop thinking and shorten the retrieval time of the previous information in order to employ it in the solution finding system, that it evokes past experiences and relates them to current situations, then enables the user to choose a suitable solution for the problems. This visual, educational and circulatory means, at the same time, has speci
... Show MoreIn this research, annealed nanostructured ZnO catalyst water putrefaction system was built using sun light and different wavelength lasers as stimulating light sources to enhance photocatalytic degradation activity of methylene blue (MB) dye as a model based on interfacial charges transfer. The structural, crystallite size, morphological, particle size, optical properties and degradation ability of annealed nanostructured ZnO were characterized by X-Ray Diffraction (XRD), Atomic Force Microscopy (AFM) and UV-VIS Spectrometer, respectively. XRD results demonstrated a pure crystalline hexagonal wurtzite with crystalline size equal to 23 nm. From AFM results, the average particle size was 79.25nm. All MB samples and MB with annealed nanostr
... Show MoreThese days, it is crucial to discern between different types of human behavior, and artificial intelligence techniques play a big part in that. The characteristics of the feedforward artificial neural network (FANN) algorithm and the genetic algorithm have been combined to create an important working mechanism that aids in this field. The proposed system can be used for essential tasks in life, such as analysis, automation, control, recognition, and other tasks. Crossover and mutation are the two primary mechanisms used by the genetic algorithm in the proposed system to replace the back propagation process in ANN. While the feedforward artificial neural network technique is focused on input processing, this should be based on the proce
... Show MoreThe fast evolution of cyberattacks in the Internet of Things (IoT) area, presents new security challenges concerning Zero Day (ZD) attacks, due to the growth of both numbers and the diversity of new cyberattacks. Furthermore, Intrusion Detection System (IDSs) relying on a dataset of historical or signature‐based datasets often perform poorly in ZD detection. A new technique for detecting zero‐day (ZD) attacks in IoT‐based Conventional Spiking Neural Networks (CSNN), termed ZD‐CSNN, is proposed. The model comprises three key levels: (1) Data Pre‐processing, in this level a thorough cleaning process is applied to the CIC IoT Dataset 2023, which contains both malicious and t
The oxidation desulphurization assisted by ultrasound waves was applied to the desulphurization of heavy naphtha. Hydrogen peroxide and acetic acid were used as oxidants, ultrasound waves as phase dispersion, and activated carbon as solid adsorbent. When the oxidation desulphurization (ODS) process was followed by a solid adsorption step, the performance of overall Sulphur removal was 89% for heavy naphtha at the normal condition of pressure and temperature. The process of (ODS) converts the compounds of Sulphur to sulfoxides/ sulfones, and these oxidizing compounds can be removed by activated carbon to produce fuel with low Sulphur content. The absence of any components (hydrogen peroxide, acetic acid, ultrasound waves and activated car
... Show MoreThe research aimed at identifying the effect of using constructive learning model on academic achievement and learning soccer dribbling Skill in 2nd grade secondary school students. The researcher used the experimental method on (30) secondary school students; 10 selected for pilot study, 20 were divided into two groups. The experimental group followed constructive learning model while the controlling group followed the traditional method. The experimental program lasted for eight weeks with two teaching sessions per week for each group. The data was collected and treated using SPSS to conclude the positive effect of using constructive learning model on developing academic achievement and learning soccer dribbling Skill in 2nd grade seconda
... Show MoreA strong sign language recognition system can break down the barriers that separate hearing and speaking members of society from speechless members. A novel fast recognition system with low computational cost for digital American Sign Language (ASL) is introduced in this research. Different image processing techniques are used to optimize and extract the shape of the hand fingers in each sign. The feature extraction stage includes a determination of the optimal threshold based on statistical bases and then recognizing the gap area in the zero sign and calculating the heights of each finger in the other digits. The classification stage depends on the gap area in the zero signs and the number of opened fingers in the other signs as well as
... Show MoreChange detection is a technology ascertaining the changes of
specific features within a certain time Interval. The use of remotely
sensed image to detect changes in land use and land cover is widely
preferred over other conventional survey techniques because this
method is very efficient for assessing the change or degrading trends
of a region. In this research two remotely sensed image of Baghdad
city gathered by landsat -7and landsat -8 ETM+ for two time period
2000 and 2014 have been used to detect the most important changes.
Registration and rectification the two original images are the first
preprocessing steps was applied in this paper. Change detection using
NDVI subtractive has been computed, subtrac