Segmentation of retinal blood vessels is the first step for several computer aided-diagnosis systems (CAD), not only for ocular disease diagnosis such as diabetic retinopathy (DR) but also of non-ocular disease, such as hypertension, stroke and cardiovascular diseases. In this paper, a supervised learning-based method, using a multi-layer perceptron neural network and carefully selected vector of features, is proposed. In particular, for each pixel of a retinal fundus image, we construct a 24-D feature vector, encoding information on the local intensity, morphology transformation, principal moments of phase congruency, Hessian, and difference of Gaussian values. A post-processing technique depending on mathematical morphological operators is used to optimise the segmentation. Moreover, the selected feature vector succeeded in outfitting the symmetric features that provided the final blood vessel probability as a binary map image. The proposed method is tested on three known datasets: Digital Retinal Image for Extraction (DRIVE), Structure Analysis of the Retina (STARE), and CHASED_DB1 datasets. The experimental results, both visual and quantitative, testify to the robustness of the proposed method. This proposed method achieved 0.9607, 0.7542, and 0.9843 in DRIVE, 0.9632, 0.7806, and 0.9825 on STARE, 0.9577, 0.7585 and 0.9846 in CHASE_DB1, with respectable accuracy, sensitivity, and specificity performance metrics. Furthermore, they testify that the method is superior to seven similar state-of-the-art methods.

In modern textile industry, Tissue online Automatic Inspection (TAI) is becoming an attractive alternative to Human Vision Inspection (HVI). HVI needs a high level of attention nevertheless leading to low performance in terms of tissue inspection. Based on the co-occurrence matrix and its statistical features, as an approach for defects textile identification in the digital image, TAI can potentially provide an objective and reliable evaluation on the fabric production quality. The goal of most TAI systems is to detect the presence of faults in textiles and accurately locate the position of the defects. The motivation behind the fabric defects identification is to enable an on-line quality control of the weaving process. In this paper, we proposed a method based on texture analysis and neural networks to identify the textile defects. A feature extractor is designed based on Gray Level Co-occurrence Matrix (GLCM). A neural network is used as a classifier to identify the textile defects. The numerical simulation showed that the error recognition rates were 100% for the training and 100%, 91% for the best and worst testing respectively.

The process of recognition and identification of plant species is very time-consuming as it has been mainly carried out by botanists. The focus of computerized living plant's identification is on stable feature's extraction of plants. Leaf-based features are preferred over fruits, also the long period of its existence than fruits. In this preliminary study, we study and propose neural networks and Mutual information for identification of two, three Fig cultivars (Ficus Carica L.) in Syria region. The identification depends on image features of Fig tree leaves. A feature extractor is designed based on Mutual Information computation. The Neural Networks is used with two hidden layers and one output layer with 3 nodes that correspond to varieties (classes) of FIG leaves. The proposal technique is a tester on a database of 84 images leaves with 28 images for each variety (class). The result shows that our technique is promising, where the recognition rates 100%, and 92% for the training and testing respectively for the two cultivars with 100% and 90 for the three cultivars. The preliminary results obtained indicated the technical feasibility of the proposed method, which will be applied for more than 80 varieties existent in Syria

Diagnosis is a crucial precautionary step in research studies of the coronavirus disease, which shows indications similar to those of various pneumonia types. The COVID-19 pandemic has caused a significant outbreak in more than 150 nations and has significantly affected the wellness and lives of many individuals globally. Particularly, discovering the patients infected with COVID-19 early and providing them with treatment is an important way of fighting the pandemic. Radiography and radiology could be the fastest techniques for recognizing infected individuals. Artificial intelligence strategies have the potential to overcome this difficulty. Particularly, transfer learning MobileNetV2 is a convolutional neural network architecture that can perform well on mobile devices. In this study, we used MobileNetV2 with transfer learning and augmentation data techniques as a classifier to recognize the coronavirus disease. Two datasets were used: the first consisted of 309 chest X-ray images (102 with COVID-19 and 207 were normal), and the second consisted of 516 chest X-ray images (102 with COVID-19 and 414 were normal). We assessed the model based on its sensitivity rate, specificity rate, confusion matrix, and F1-measure. Additionally, we present a receiver operating characteristic curve. The numerical simulation reveals that the model accuracy is 95.8% and 100% at dropouts of 0.3 and 0.4, respectively. The model was implemented using Keras and Python programming.

Orthogonal multiple access schemes based on assignment of communication resource blocks among multiple contenders, although widely available, still necessitate an upper limit on the number of concurrent users for minimization of multiple-user interference. The feature thwarts efforts to cater for pressing connectivity demands posed by modern-day cellular communication networks. Non-orthogonal multiple access, regarded as a key advancement towards realization of high-speed 5G wireless communication networks, enables multiple users to access the same set of resource blocks non-orthogonally in terms of power with controllable interference, thereby allowing for overall performance enhancement. Owing to the combinatorial nature of the underlying optimization problem involving user pairing/grouping scheme, power control and decoding order, the computational complexity in determining optimal and sub-optimal solutions remains considerably high. This work proposes three novel alternative approaches (Randomly, 2-Opt and Hybrid) for arriving at a near-optimal solution for the problem of user pairing/grouping. The algorithms not only offer reduced computational complexity but also outperform orthogonal multiple access and existing schemes reported in the literature for uplink non-orthogonal multiple access systems.

In many locations, reverse transcription polymerase chain reaction (RT-PCR) tests are used to identify COVID-19. It could take more than 48 h. It is a key factor in its seriousness and quick spread. Images from chest X-rays are utilized to diagnose COVID-19. Which generally deals with the issue of imbalanced classification. The purpose of this paper is to improve CNN’s capacity to display Chest X-ray pictures when there is a class imbalance. CNN Training has come to an end while chastening the classes for using more examples. Additionally, the training data set uses data augmentation. The achievement of the suggested method is assessed on an image’s two data sets of chest X-rays. The suggested model’s efficiency was analyzed using criteria like accuracy, specificity, sensitivity, and F1 score. The suggested method attained an accuracy of 94% worst, 97% average, and 100% best cases, respectively, and an F1-score of 96% worst, 98% average and 100% best cases, respectively.

Fifth-generation wireless communications provide several benefits, including high throughput, lower latency, massive connectivity, considerable improvement in the number of users, higher base station capacity, and achieved quality of service. Non-orthogonal multiple access, an effective approach for sharing the same radio resources, has been highlighted as a viable technology in the fifth-generation wireless networks to achieve the demands of available bandwidth, user connectivity, and application latency. Non-orthogonal multiple access and heterogeneous networks have recently emerged as promising network infrastructures for enhancing the spectrum capacity and accommodating more users by sharing the same resources with high throughput. This potential capability has made the non-orthogonal multiple access–enabled heterogeneous networks a new research topic in the modern era. In this survey, the concept of non-orthogonal multiple access and its significance in different emerging technologies has been well explored. Furthermore, this survey covers a systematic overview of the state-of the-art techniques based on non-orthogonal multiple access–enabled heterogeneous networks and devising taxonomy for uplink non-orthogonal multiple access–enabled heterogeneous networks. In addition, this survey provides critical insights and identifies several open research challenges considering the uplink non-orthogonal multiple access–enabled heterogeneous networks.

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