White supremacist hate speech is one of the most recently observed harmful content on social media. The critical influence of these radical groups is no longer limited to social media and can negatively affect society by promoting racial hatred and violence. Traditional channels of reporting hate speech have proved inadequate due to the tremendous explosion of information and the implicit nature of hate speech. Therefore, it is necessary to detect such speech automatically and in a timely manner. This research investigates the feasibility of automatically detecting white supremacist hate speech on Twitter using deep learning and natural language processing techniques. Two deep learning models are investigated in this research. The first approach utilizes a bidirectional Long Short-Term Memory (BiLSTM) model along with domain-specific word embeddings extracted from white supremacist corpus to capture the semantic of white supremacist slangs and coded words. The second approach utilizes one of the most recent language models, which is Bidirectional Encoder Representations from Transformers (BERT). The BiLSTM model achieved 0.75 F1-score and BERT reached a 0.80 F1-score. Both models are tested on a balanced dataset combined from Twitter and a Stormfront dataset compiled from white supremacist forum.

Epilepsy is a nervous system disorder. Encephalography (EEG) is a generally utilized clinical approach for recording electrical activity in the brain. Although there are a number of datasets available, most of them are imbalanced due to the presence of fewer epileptic EEG signals compared with non-epileptic EEG signals. This research aims to study the possibility of integrating local EEG signals from an epilepsy center in King Abdulaziz University hospital into the CHB-MIT dataset by applying a new compatibility framework for data integration. The framework comprises multiple functions, which include dominant channel selection followed by the implementation of a novel algorithm for reading XLtek EEG data. The resulting integrated datasets, which contain selective channels, are tested and evaluated using a deep-learning model of 1D-CNN, Bi-LSTM, and attention. The results achieved up to 96.87% accuracy, 96.98% precision, and 96.85% sensitivity, outperforming the other latest systems that have a larger number of EEG channels.

Cloud services offer doctors and data scientists access to medical data from multiple locations using different devices (laptops, desktops, tablets, smartphones, etc.). Therefore, cyber threats to medical data at rest, in transit and when used by applications need to be pinpointed and prevented preemptively through a host of proven cryptographical solutions. The presented work integrates adaptive key generation, neural-based confusion and non-XOR, namely DNA diffusion, which offers a more extensive and unique key, adaptive confusion and unpredictable diffusion algorithm. Only authenticated users can store this encrypted image in cloud storage. The proposed security framework uses logistics, tent maps and adaptive key generation modules. The adaptive key is generated using a multilayer and nonlinear neural network from every input plain image. The Hopfield neural network (HNN) is a recurrent temporal network that updates learning with every plain image. We have taken Amazon Web Services (AWS) and Simple Storage Service (S3) to store encrypted images. Using benchmark evolution metrics, the ability of image encryption is validated against brute force and statistical attacks, and encryption quality analysis is also made. Thus, it is proved that the proposed scheme is well suited for hosting cloud storage for secure images.

Person re-identification systems (person Re-ID) have recently gained more attention between computer vision researchers. They are playing a key role in intelligent visual surveillance systems and have widespread applications like applications for public security. The person Re-ID systems can identify if a person has been seen by a non-overlapping camera over large camera network in an unconstrained environment. It is a challenging issue since a person appears differently under different camera views and faces many challenges such as pose variation, occlusion and illumination changes. Many methods had been introduced for generating handcrafted features aimed to handle person Re-ID problem. In recent years, many studies have started to apply deep learning methods to enhance the person Re-ID performance due the deep learning yielded significant results in computer vision issues. Therefore, this paper is a survey of the recent studies that proposed to improve the person Re-ID systems using deep learning. The public datasets that are used for evaluating these systems are discussed. Finally, the paper addresses future directions and current issues that must be considered toward improving the person Re-ID systems.

Flat foot is a postural deformity in which the plantar part of the foot is either completely or partially contacted with the ground. In recent clinical practices, X-ray radiographs have been introduced to detect flat feet because they are more affordable to many clinics than using specialized devices. This research aims to develop an automated model that detects flat foot cases and their severity levels from lateral foot X-ray images by measuring three different foot angles: the Arch Angle, Meary’s Angle, and the Calcaneal Inclination Angle. Since these angles are formed by connecting a set of points on the image, Template Matching is used to allocate a set of potential points for each angle, and then a classifier is used to select the points with the highest predicted likelihood to be the correct point. Inspired by literature, this research constructed and compared two models: a Convolutional Neural Network-based model and a Random Forest-based model. These models were trained on 8000 images and tested on 240 unseen cases. As a result, the highest overall accuracy rate was 93.13% achieved by the Random Forest model, with mean values for all foot types (normal foot, mild flat foot, and moderate flat foot) being: 93.38 precision, 92.56 recall, 96.46 specificity, 95.42 accuracy, and 92.90 F-Score. The main conclusions that were deduced from this research are: (1) Using transfer learning (VGG-16) as a feature-extractor-only, in addition to image augmentation, has greatly increased the overall accuracy rate. (2) Relying on three different foot angles shows more accurate estimations than measuring a single foot angle.

Water pollution due to the discharge of untreated industrial effluents is a serious environmental and public health issue. The presence of organic pollutants such as polycyclic aromatic hydrocarbons (PAHs) causes worldwide concern because of their mutagenic and carcinogenic effects on aquatic life, human beings, and the environment. PAHs are pervasive atmospheric compounds that cause nervous system damage, mental retardation, cancer, and renal kidney diseases. This research presents the first usage of palm kernel shell biochar (PKSB) (obtained from agricultural waste) for PAH removal from industrial wastewater (oil and gas wastewater/produced water). A batch scale study was conducted for the remediation of PAHs and chemical oxygen demand (COD) from produced water. The influence of operating parameters such as biochar dosage, pH, and contact time was optimized and validated using a response surface methodology (RSM). Under optimized conditions, i.e., biochar dosage 2.99 g L⁻¹, pH 4.0, and contact time 208.89 min, 93.16% of PAHs and 97.84% of COD were predicted. However, under optimized conditions of independent variables, 95.34% of PAH and 98.21% of COD removal was obtained in the laboratory. The experimental data were fitted to the empirical second-order model of a suitable degree for the maximum removal of PAHs and COD by the biochar. ANOVA analysis showed a high coefficient of determination value (R² = 0.97) and a reasonable second-order regression prediction. Additionally, the study also showed a comparative analysis of PKSB with previously used agricultural waste biochar for PAH and COD removal. The PKSB showed significantly higher removal efficiency than other types of biochar. The study also provides analysis on the reusability of PKSB for up to four cycles using two different methods. The methods reflected a significantly good performance for PAH and COD removal for up to two cycles. Hence, the study demonstrated a successful application of PKSB as a potential sustainable adsorbent for the removal of micro-pollutants from produced water.