Ahmad Hasasneh

Neonatal cerebral atrophy is a serious form of structural brain anomaly observed in up to 15% of newborns admitted to neonatal intensive care units (NICUs). Early and accurate assessment is clinically important because it can inform prognosis, guide neuroprotective and rehabilitative management, and support long-term developmental monitoring. This systematic review, conducted and reported in accordance with the PRISMA 2020 statement, searched four electronic databases (PubMed/MEDLINE, Scopus, Web of Science, and IEEE Xplore) from inception to August 2026, yielding 312 records; after two-stage screening by two independent reviewers, 90 peer-reviewed studies were included and assessed for reporting quality using the CLAIM checklist. The review found that deep learning methods for neonatal brain MRI are advancing rapidly in tissue segmentation, brain age estimation, injury detection, and anomaly detection; however, no included study directly addressed the diagnosis of neonatal cerebral atrophy, and no validated atrophy-specific model, dataset, or benchmark currently exists. Transferable methodological foundations were identified in convolutional and U-Net architectures, Transformers, and Mamba-based state-space models, the latter offering linear-complexity long-range modeling that is potentially advantageous for high-resolution 3D neonatal MRI; these architectures are presented as promising research options that still require direct comparative validation. Publicly available neonatal MRI datasets lack radiologist-confirmed atrophy labels, and explainable AI methods remain largely unvalidated in neonatal neuroimaging. The review concludes by defining priority directions: neonatal-specific 3D architectures, age-normalized volumetric biomarkers, externally validated explainable pipelines, and curated atrophy-labeled datasets.

Precise weather forecasting plays a critical role in sectors such as agriculture, transport, energy management, and climate change adaptation, and machine learning and deep learning algorithms have been widely used for data-driven time series forecasting problems. In this work, we explore the application of machine learning and deep learning models for multi-weather variable forecasting in a dataset recorded over a period of ten years (2015–2025) for five weather stations in Palestine. The dataset comprises measurements for temperature, relative humidity, wind speed, precipitation, atmospheric pressure, and sunshine hours. To avoid the issue of temporal leakage, a chronological training, validation, and test set splitting approach was used in the evaluation experiments. The models used in this study include ARIMA, SARIMA, Random Forest, XGBoost, CNN, LSTM, GRU, ConvLSTM, CNN-GRU, and CNN-LSTM with station embeddings. Our experimental results indicate that the XGBoost model achieved the highest performance in predicting temperature and relative humidity (R² = 0.953 and R² = 0.670, respectively), while deep learning methods exhibited high accuracy across several weather features. The CNN-LSTM model was successfully able to learn temporal–spatial patterns via station embeddings, while recurrent neural networks performed impressively in forecasting sunshine hours and atmospheric pressure.

Weeds continue to pose a serious challenge to agriculture, reducing both the productivity and quality of crops. In this paper, we explore how modern deep learning, specifically Vision Transformers (ViTs), can help address this issue through fast and accurate weed classification. We developed a transformer-based model trained on the DeepWeeds dataset, which contains images of nine different weed species collected under various environmental conditions, such as changes in lighting and weather. By leveraging the ViT architecture, the model is able to capture complex patterns and spatial details in high-resolution images, leading to improved prediction accuracy. We also examined the effects of model optimization techniques, including fine-tuning and the use of pre-trained weights, along with different strategies for handling class imbalance. While traditional oversampling actually hurt performance, dropping accuracy to 94%, using class weights alongside strong data augmentation boosted accuracy to 96.9%. Overall, our ViT model outperformed standard Convolutional Neural Networks, achieving 96.9% accuracy on the held-out test set. Attention-based saliency maps were inspected to confirm that predictions were driven by weed regions, and model consistency under location shift and capture perturbations was assessed using the diverse acquisition sites in DeepWeeds. These findings show that with the right combination of model architecture and training strategies, Vision Transformers can offer a powerful solution for smarter weed detection and more efficient farming practices.

With the increasing use of emails in our daily lives, they have become a prime target of phishing attacks, posing a significant threat to users. Attackers pretend to be trusted sources and use email phishing attacks to trick people into clicking malicious links or opening attachments. The aim of these attacks is to obtain sensitive information, such as financial information, login credentials, and personally identifiable information. Emails have attributes including the URL, sender, subject, receiver(s), and body. This paper proposes a hybrid intelligence model that integrates machine learning algorithms (ML) and natural language processing (NLP) techniques for email phishing detection. Three ML algorithms are employed: logistic regression, decision tree, and random forest. In addition, a customized ChatGPT model has been developed to receive email classification results from the hybrid model. This model educates users on recognizing phishing emails by explaining email classifications, highlighting keywords, and offering security tips. The proposed approach to detecting phishing emails raises awareness and educates users on recognizing and reporting email phishing attacks.

Remote Access Trojans (RATs) pose a serious cybersecurity risk due to their stealthy control over compromised systems. This study presents a detection framework that integrates host, network, and newly engineered behavioral features to enhance the identification of RATs. Two sets of experiments were performed: (i) using the original dataset only, and (ii) using an extended dataset with ten engineered features and importance analysis. The framework was evaluated on a public Kaggle dataset of an RAT and benign traffic. Eight machine learning classifiers were tested, including three baseline methods, four ensemble approaches, and a neural network. Results show that the engineered hybrid feature set substantially improves detection performance. Among the tested algorithms, Random Forest and MLP achieved the strongest performance, with accuracies of 98% and 97%, respectively, while Gradient Boosting and LightGBM also performed competitively. Performance was assessed using multiple metrics, and to gain deeper insight into model learning behavior, learning curves and Precision–Recall curves were analyzed. The results demonstrate how well hybrid feature modeling, neural networks, and ensemble machine learning techniques may improve RAT identification. In future work, exploring the use of explainable ML methods may improve the detection capabilities.

The early detection and diagnosis of neonatal problems are critical to ensuring that an infant receives timely medical attention, which greatly enhances health outcomes. In this study, we propose a novel deep learning framework that listens to an infant’s cry to identify and diagnose six separate conditions: one being healthy and the other five comprising sepsis, respiratory distress syndrome, jaundice, hyperbilirubinemia, and vomiting. The study utilizes a rich dataset of infant cry recordings from which key acoustic features such as spectrograms, Mel-spectrograms, and Gammatone Frequency Cepstral Coefficients (GFCCs) are extracted. A sophisticated Vision Transformer (ViT) model was developed and meticulously fine-tuned to achieve an impressive 99% classification accuracy through cross-validation. To enhance the model’s interpretability, powerful explainable artificial intelligence (XAI) methods such as LRP, LIME, and attention imaging were implemented to clarify the reasoning behind the model’s outputs. Through cross-validation tests, the model’s trustworthiness and extensive generalizability were assessed. The findings underscore the promising capabilities of employing transformer-based deep learning frameworks along with multimodal acoustic features and explanatory methods to improve cry analysis in infants and their usable scopes in pediatric medicine.