Deep-Learning-Based Automatic Mineral Grain Segmentation and Recognition

Deep Learning Utilization in Agriculture: Detection of Rice Plant Diseases Using an Improved CNN Model

Rice is considered one the most important plants globally because it is a source of food for over half the world’s population. Like other plants, rice is susceptible to diseases that may affect the quantity and quality of produce. It sometimes results in anywhere between 20–40% crop loss production. Early detection of these diseases can positively affect the harvest, and thus farmers would have to be knowledgeable about the various disease and how to identify them visually. Even then, it is an impossible task for farmers to survey the vast farmlands on a daily basis. Even if this is possible, it becomes a costly task that will, in turn, increases the price of rice for consumers. Machine learning algorithms fitted to drone technology combined with the Internet of Things (IoT) can offer a solution to this problem. In this paper, we propose a Deep Convolutional Neural Network (DCNN) transfer learning-based approach for the accurate detection and classification of rice leaf disease. The modified proposed approach includes a modified VGG19-based transfer learning method. The proposed modified system can accurately detect and diagnose six distinct classes: healthy, narrow brown spot, leaf scald, leaf blast, brown spot, and bacterial leaf blight. The highest average accuracy is 96.08% using the non-normalized augmented dataset. The corresponding precision, recall, specificity, and F1-score were 0.9620, 0.9617, 0.9921, and 0.9616, respectively. The proposed modified approach achieved significantly better results compared with similar approaches using the same dataset or similar-size datasets reported in the extant literature.

Diabetic Retinopathy Detection from Fundus Images of the Eye Using Hybrid Deep Learning Features

Diabetic Retinopathy (DR) is a medical condition present in patients suffering from long-term diabetes. If a diagnosis is not carried out at an early stage, it can lead to vision impairment. High blood sugar in diabetic patients is the main source of DR. This affects the blood vessels within the retina. Manual detection of DR is a difficult task since it can affect the retina, causing structural changes such as Microaneurysms (MAs), Exudates (EXs), Hemorrhages (HMs), and extra blood vessel growth. In this work, a hybrid technique for the detection and classification of Diabetic Retinopathy in fundus images of the eye is proposed. Transfer learning (TL) is used on pre-trained Convolutional Neural Network (CNN) models to extract features that are combined to generate a hybrid feature vector. This feature vector is passed on to various classifiers for binary and multiclass classification of fundus images. System performance is measured using various metrics and results are compared with recent approaches for DR detection. The proposed method provides significant performance improvement in DR detection for fundus images. For binary classification, the proposed modified method achieved the highest accuracy of 97.8% and 89.29% for multiclass classification.

Arabic Hate Speech Detection Using Deep Recurrent Neural Networks

With the vast number of comments posted daily on social media and other platforms, manually monitoring internet activity for possible national security risks or cyberbullying is an impossible task. However, with recent advances in machine learning (ML), the automatic monitoring of such posts for possible national security risks and cyberbullying becomes feasible. There is still the issue of privacy on the internet; however, in this study, only the technical aspects of designing an automated system that could monitor and detect hate speech in the Arabic language were targeted, which many companies, such as Facebook, Twitter, and others, could use to prevent hate speech and cyberbullying. For this task, a unique dataset consisting of 4203 comments classified into seven categories, including content against religion, racist content, content against gender equality, violent content, offensive content, insulting/bullying content, normal positive comments, and normal negative comments, was designed. The dataset was extensively preprocessed and labeled, and its features were extracted. In addition, the use of deep recurrent neural networks (RNNs) was proposed for the classification and detection of hate speech. The proposed RNN architecture, called DRNN-2, consisted of 10 layers with 32 batch sizes and 50 iterations for the classification task. Another model consisting of five hidden layers, called DRNN-1, was used only for binary classification. Using the proposed models, a recognition rate of 99.73% was achieved for binary classification, 95.38% for the three classes of Arabic comments, and 84.14% for the seven classes of Arabic comments. This accuracy was high for the classification of a complex language, such as Arabic, into seven different classes. The achieved accuracy was higher than that of similar methods reported in the recent literature, whether for binary classification, three-class classification, or seven-class classification, as discussed in the literature review section.