In the field of natural language processing (NLP), natural language understanding (NLU) plays a critical role in transforming human languages into machine-interpretable formats. This study explores the methodologies, and resources of Arabic Question Answering Systems (QASs), particularly emphasizing NLU’s role in question analysis and classification. This key module is essential for semantically and logically interpreting questions, which are crucial for obtaining accurate, quality, context-sensitive answers in QASs. Our findings reveal that advanced deep learning models are highly effective in accurately processing complex languages. Simultaneously, machine learning algorithms maintain significance in classification tasks. This study also identified the potential of rule-based and hybrid approaches, suggesting that future research should focus on these, alongside evolving evaluation methods, to match the rapid developments in NLP. Arabic QAS faces unique challenges, including Arabic complex syntax, dialectal diversity, scarce tools and resources, and the need for benchmark datasets and standardized testing frameworks. Future trends lean towards comprehensive datasets for deep learning, standardized testing for Arabic QASs, and a hybrid approach in question classification, incorporating additional factors such as interrogative words, tense, aspect, and multiple question detection. This comprehensive survey examined the question analysis and classification of Arabic QASs, underscoring the specific challenges and limitations of Arabic language. This study highlights NLU’s crucial role of NLU in system efficiency, aiming to fill the existing literature gaps, it proposes new research directions, contributing to a deeper understanding of Arabic QASs and emphasizing the continuous need for innovation in NLU within QASs.

Overall prediction of oral cavity squamous cell carcinoma (OCSCC) remains inadequate, as more than half of patients with oral cavity cancer are detected at later stages. It is generally accepted that the differential diagnosis of OCSCC is usually difficult and requires expertise and experience. Diagnosis from biopsy tissue is a complex process, and it is slow, costly, and prone to human error. To overcome these problems, a computer-aided diagnosis (CAD) approach was proposed in this work. A dataset comprising two categories, normal epithelium of the oral cavity (NEOR) and squamous cell carcinoma of the oral cavity (OSCC), was used. Feature extraction was performed from this dataset using four deep learning (DL) models (VGG16, AlexNet, ResNet50, and Inception V3) to realize artificial intelligence of medial things (AIoMT). Binary Particle Swarm Optimization (BPSO) was used to select the best features. The effects of Reinhard stain normalization on performance were also investigated. After the best features were extracted and selected, they were classified using the XGBoost. The best classification accuracy of 96.3% was obtained when using Inception V3 with BPSO. This approach significantly contributes to improving the diagnostic efficiency of OCSCC patients using histopathological images while reducing diagnostic costs.

Solar energy utilization in the industry has grown substantially, resulting in heightened recognition of renewable energy sources from power plants and intelligent grid systems. One of the most important challenges in the solar energy field is detecting anomalies in photovoltaic systems. This paper aims to address this by using various machine learning algorithms and regression models to identify internal and external abnormalities in PV components. The goal is to determine which models can most accurately distinguish between normal and abnormal behavior of PV systems. Three different approaches have been investigated for detecting anomalies in solar power plants in India. The first model is based on a physical model, the second on a support vector machine (SVM) regression model, and the third on an SVM classification model. Grey wolf optimizer was used for tuning the hyper model for all models. Our findings will clarify that the SVM classification model is the best model for anomaly identification in solar power plants by classifying inverter states into two categories (normal and fault).

The sudden increase in patients with severe COVID-19 has obliged doctors to make admissions to intensive care units (ICUs) in health care practices where capacity is exceeded by the demand. To help with difficult triage decisions, we proposed an integration system Xtreme Gradient Boosting (XGBoost) classifier and Analytic Hierarchy Process (AHP) to assist health authorities in identifying patients’ priorities to be admitted into ICUs according to the findings of the biological laboratory investigation for patients with COVID-19. The Xtreme Gradient Boosting (XGBoost) classifier was used to decide whether or not they should admit patients into ICUs, before applying them to an AHP for admissions’ priority ranking for ICUs. The 38 commonly used clinical variables were considered and their contributions were determined by the Shapley’s Additive explanations (SHAP) approach. In this research, five types of classifier algorithms were compared: Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighborhood (KNN), Random Forest (RF), and Artificial Neural Network (ANN), to evaluate the XGBoost performance, while the AHP system compared its results with a committee formed from experienced clinicians. The proposed (XGBoost) classifier achieved a high prediction accuracy as it could discriminate between patients with COVID-19 who need ICU admission and those who do not with accuracy, sensitivity, and specificity rates of 97%, 96%, and 96% respectively, while the AHP system results were close to experienced clinicians’ decisions for determining the priority of patients that need to be admitted to the ICU. Eventually, medical sectors can use the suggested framework to classify patients with COVID-19 who require ICU admission and prioritize them based on integrated AHP methodologies.

Temperature forecasting is an area of ongoing research because of its importance in all life aspects. However, because a variety of climate factors controls the temperature, it is a never-ending challenge. The numerical weather prediction (NWP) model has been frequently used to forecast air temperature. However, because of its deprived grid resolution and lack of parameterizations, it has systematic distortions. In this study, a gray wolf optimizer (GWO) and a support vector machine (SVM) are used to ensure accuracy and stability of the next day forecasting for minimum and maximum air temperatures in Seoul, South Korea, depending on local data assimilation and prediction system (LDAPS; a model of local NWP over Korea). A total of 14 LDAPS models forecast data, the daily maximum and minimum air temperatures of in situ observations, and five auxiliary data were used as input variables. The LDAPS model, the multimodal array (MME), the particle swarm optimizer with support vector machine (SVM-PSO), and the conventional SVM were selected as comparison models in this study to illustrate the advantages of the proposed model. When compared to the particle swarm optimizer and traditional SVM, the Gray Wolf Optimizer produced more accurate results, with the average RMSE value of SVM for T max and T min Forecast prediction reduced by roughly 51 percent when combined with GWO and 31 percent when combined with PSO. In addition, the hybrid model (SVM-GWO) improved the performance of the LDAPS model by lowering the RMSE values for T max Forecast and T min Forecast forecasting from 2.09 to 0.95 and 1.43 to 0.82, respectively. The results show that the proposed hybrid (GWO-SVM) models outperform benchmark models in terms of prediction accuracy and stability and that the suggested model has a lot of application potentials.

As one kind of skin cancer, melanoma is very dangerous. Dermoscopy-based early detection and reorganization strategy is critical for melanoma therapy. Skin imaging is essential in order to diagnose the tumor type; this had to be accomplished by taking a biopsy from patients. This procedure is very uncomfortable for patients and requires time and highly qualified dermatologists to precisely differentiate between different types of skin cancer. In order to solve these problems, many efforts focused on developing automatic image analysis systems that reduce the suffering of patients, time, and tumor classification error. Here we report a novel strategy based on deep learning technique, which achieved very high skin melanoma diagnosis accuracy. Different pretrained architectures (VGG16, Inception_v3, and ResNet-50) were proposed and applied on 10,135 dermoscopy skin images. A comparison between these models was accomplished with hair removal and nonremoval algorithms. Experimental results showed that the removal of hair had a significant effect on the skin imaging as it helped to improve the training accuracy results. The VGG16 model achieved the highest accuracy (96.9%) with hair removal algorithm, followed by Inception_v3 and ResNet-50 model with an accuracy of 94.2% and 91.8% respectively.