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Solar photovoltaic (PV) systems, integral for sustainable energy, face challenges in forecasting due to the unpredictable nature of environmental factors influencing energy output. This study explores five distinct machine learning (ML) models which are built and compared to predict energy production based on four independent weather variables: wind speed, relative humidity, ambient temperature, and solar irradiation. The evaluated models include multiple linear regression (MLR), decision tree regression (DTR), random forest regression (RFR), support vector regression (SVR), and multi-layer perceptron (MLP). These models were hyperparameter tuned using chimp optimization algorithm (ChOA) for a performance appraisal. The models are subsequently validated on the data from a 264 kWp PV system, installed at the Applied Science University (ASU) in Amman, Jordan. Of all 5 models, MLP shows best root mean square error ( RMSE ), with the corresponding value of 0.503, followed by mean absolute error ( MAE ) of 0.397 and a coefficient of determination ( R 2 ) value of 0.99 in predicting energy from the observed environmental parameters. Finally, the process highlights the fact that fine-tuning of ML models for improved prediction accuracy in energy production domain still involves the use of advanced optimization techniques like ChOA, compared with other widely used optimization algorithms from the literature.

The intermittent and stochastic nature of Renewable Energy Sources (RESs) necessitates accurate power production prediction for effective scheduling and grid management. This paper presents a comprehensive review conducted with reference to a pioneering, comprehensive, and data-driven framework proposed for solar Photovoltaic (PV) power generation prediction. The systematic and integrating framework comprises three main phases carried out by seven main comprehensive modules for addressing numerous practical difficulties of the prediction task: phase I handles the aspects related to data acquisition (module 1) and manipulation (module 2) in preparation for the development of the prediction scheme; phase II tackles the aspects associated with the development of the prediction model (module 3) and the assessment of its accuracy (module 4), including the quantification of the uncertainty (module 5); and phase III evolves towards enhancing the prediction accuracy by incorporating aspects of context change detection (module 6) and incremental learning when new data become available (module 7). This framework adeptly addresses all facets of solar PV power production prediction, bridging existing gaps and offering a comprehensive solution to inherent challenges. By seamlessly integrating these elements, our approach stands as a robust and versatile tool for enhancing the precision of solar PV power prediction in real-world applications.

Water-in-diesel (W/D) emulsion fuel is a potentially viable diesel fuel that can simultaneously enhance engine performance and reduce exhaust emissions in a current diesel engine without requiring engine modifications or incurring additional costs. In a consistent manner, the current study examines the impact of adding water, in the range of 5–30% wt. (5% increment) and 2% surfactant of polysorbate 20, on the performance in terms of brake torque (BT) and exhaust emissions of a four-cylinder four-stroke diesel engine. The relationship between independent factors, including water addition and engine speed, and dependent factors, including different exhaust released emissions and BT, was initially generated using machine learning support vector regression (SVR). Subsequently, a robust and modern optimization of the sea-horse optimizer (SHO) was run through the SVR model to find the optimal water addition and engine speed for improving the BT and lowering exhaust emissions. Furthermore, the SVR model was compared to the artificial neural network (ANN) model in terms of R-squared and mean square error (MSE). According to the experimental results, the BT was boosted by 3.34% compared to pure diesel at 5% water addition. The highest reduction in carbon monoxide (CO) and unburned hydrocarbon (UHC) was 9.57% and 15.63%, respectively, at 15% of water addition compared to diesel fuel. The nitrogen oxides (NOx) emissions from emulsified fuel were significantly lower than those from pure diesel, with a maximum decrease of 67.14% at 30% water addition. The suggested SVR-SHO model demonstrated superior prediction reliability, with a significant R-Squared of more than 0.98 and a low MSE of less than 0.003. The SHO revealed that adding 15% water to the W/D emulsion fuel at an engine speed of 1848 rpm yielded the optimum BT, CO, UHC, and NOx values of 49.5 N.m, 0.5%, 57 ppm, and 369 ppm, respectively. Finally, these outcomes have important implications for the potential of the SVR-SHO approach to minimize engine exhaust emissions while maximizing engine performance.

Water-in-diesel (W/D) emulsion fuel is a potential alternative fuel that can simultaneously lower NOx exhaust emissions and improves combustion efficiency. Additionally, there are no additional costs or engine modifications required when using W/D emulsion fuel. The proportion of water added and engine speed is crucial factors influencing engine behavior. This study aims to examine the impact of the W/D emulsion diesel fuel on engine performance and NOx pollutant emissions using a compression ignition (CI) engine. The emulsion fuel had water content ranging from 0 to 30% with a 5% increment, and 2% surfactant was employed. The tests were performed at speeds ranging from 1000 to 3000 rpm. All W/D emulsion fuel was compared to a standard of pure diesel in all tests. A four-cylinder, four-stroke, water-cooled, direct-injection diesel engine test bed was used for the experiments. The performance and exhaust emissions of the diesel engine were measured at full load and various engine speeds using a dynamometer and an exhaust gas analyzer, respectively. The second purpose of this study is to illustrate the application of two optimizers, grey wolf optimizer (GWO) and intelligent grey wolf optimizer (IGOW), along with using multivariate polynomial regression (MPR) to identify the optimum (W/D) emulsion blend percentage and engine speed to enhance the performance, reduce fuel consumption, and reduce NOX exhaust emissions of a diesel engine operating. The engine speed and proportion of water in the fuel mixture were the independent variables (inputs), while brake power (BP), brake thermal efficiency (BTE), brake-specific fuel consumption (BSFC), and NOx were the dependent variables (outcomes). It was experimentally observed that utilizing emulsified gasoline generally enhances engine performance and decreases emissions in general. Experimentally, at 5% water content and 2000 rpm, the BSFC has a minimal value of 0.258 kJ/kW·h. Under the same conditions, the maximum BP of 11.6 kW and BTE of 32.8% were achieved. According to the IGWO process findings, adding 9% water to diesel fuel and running the engine at a speed of 1998 rpm produced the highest BP (11.2 kW) and BTE (33.3%) and the lowest BSFC (0.259 kg/kW·h) and reduced NOx by 14.3% compared with the CI engine powered by pure diesel. The accuracy of the model is high, as indicated by a correlation coefficient R2 exceeding 0.97 and a mean absolute error (MAE) less than 0.04. In terms of the optimizer, the IGWO performs better than GWO in determining the optimal water addition and engine speed. This is attributed to the IGWO has excellent exploratory capability in the early stages of searching.

Automated glasses detection is a cardinal component in facial/ocular analysis that powers forensic, surveillance and biometric authentication systems. Throughout literature, glasses detection was always experimented by either utilizing hand‐crafted or deep learning features. Nevertheless, in both cases, highly standard face/ocular images were needed to derive the suggested technique. Both working methods performed reasonably well, but the results were bonded to the quality of the facial image and extracted features, where a slight shift and/or rotation in the input face image negatively affects the results. In addition, the obtained performance is even worse on real‐world (non‐standard) images, especially when compared to recent achievements in other computer vision research areas. In this paper, we present a robust deep learning approach for glasses detection from selfie photos full/partial frontal body non‐standard images captured in real‐life uncontrolled environments that do not utilize any facial landmarks. To the best of our knowledge this paper is the first to experiment detecting glasses from selfie photos, using a robust deep learning approach. Experimental results on various benchmark facial analysis datasets demonstrated the superior performance of the proposed technique with 96% accuracy.

Biodiesel is considered to be a promising alternative option to diesel fuel. The main contribution of the current work is to improve compression ignition engine performance, fueled by several biodiesel blends. Three metrics were used to evaluate the output performance of the compression ignition engine, as follows: brake torque (BT), brake specific fuel consumption (BSFC), and brake thermal efficiency (BTE), by varying two input parameters (engine speed and fuel type). The engine speeds were in the 1200–2400 rpm range. Three biodiesel blends, containing 20 vol.% of vegetable oil and 80 vol.% of pure diesel fuel, were prepared and tested. In all the experiments, pure diesel fuel was employed as a reference for all biodiesel blends. The experimental results revealed the following findings: although all types of biodiesel blends have low calorific value and slightly high viscosity, as compared to pure diesel fuel, there was an improvement in both BT and brake power (BP) outputs. An increase in BSFC by 7.4%, 4.9%, and 2.5% was obtained for palm, sunflower, and corn biodiesel blends, respectively, as compared to that of pure diesel. The BTE of the palm oil biodiesel blend was the lowest among other biodiesel blends. The suggested work strategy includes two stages (modeling and parameter optimization). In the first stage, a robust fuzzy model is created, depending on the experimental results, to simulate the output performance of the compression ignition engine. The particle swarm optimization (PSO) algorithm is used in the second stage to determine the optimal operating parameters. To confirm the distinction of the proposed strategy, the obtained outcomes were compared to those attained by response surface methodology (RSM). The coefficient of determination (R2) and the root-mean-square-error (RMSE) were used as comparison metrics. The average R2 was increased by 27.7% and 29.3% for training and testing, respectively, based on the fuzzy model. Using the proposed strategy in this work (integration between fuzzy logic and PSO) may increase the overall performance of the compression ignition engine by 2.065% and 8.256%, as concluded from the experimental tests and RSM.

Wireless Sensor Networks (WSNs) are increasingly being used in mission-critical infrastructures. In such applications, they are evaluated on the risk of cyber intrusions that can target the already constrained resources. Traditionally, Intrusion Detection Systems (IDS) in WSNs have been based on machine learning techniques; however, these models fail to capture the nonlinear, temporal, and topological dependencies across the network nodes. As a result, they often suffer degradation in detection accuracy and exhibit poor adaptability against evolving threats. To overcome these limitations, this study introduces a hybrid deep learning-based IDS that integrates multi-scale convolutional feature extraction, dual-stage attention fusion, and graph convolutional reasoning. Moreover, bidirectional long short-term memory components are embedded into the unified framework. Through this combination, the proposed architecture effectively captures the hierarchical spatial–temporal correlations in the traffic patterns, thereby enabling precise discrimination between normal and attack behaviors across several intrusion classes. The model has been evaluated on a publicly available benchmarking dataset, and it has been found to attain higher classification capability in multiclass scenarios. Furthermore, the model outperforms conventional IDS-focused approaches. In addition, the proposed design aims to retain suitable computational efficiency, making it appropriate for edge and distributed deployments. Consequently, this makes it an effective solution for next-generation WSN cybersecurity. Overall, the findings emphasize that combining topology-aware learning with multi-branch attention mechanisms offers a balanced trade-off between interpretability, accuracy, and deployment efficiency for resource-constrained WSN environments.

A series of tests were carried out to assess the environmental effects of biodiesel blends made of different vegetable oil, such as corn, sunflower, and palm, on exhaust and noise diesel engine emissions. Biodiesel blends with 20% vegetable oil biodiesel and 80% diesel fuel by volume were developed. The tests were conducted in a stationary diesel engine test bed consisting of a single-cylinder, four-stroke, and direct injection engine at variable engine speed. A prediction framework in terms of polynomial regression (PR) was first adopted to determine the correlation between the independent variables (engine speed, fuel type) and the dependent variables (exhaust emissions, noise level, and brake thermal efficiency). After that, a regression model was optimized by the grey wolf optimization (GWO) algorithm to update the current positions of the population in the discrete searching space, resulting in the optimal engine speed and fuel type for lower exhaust and noise emissions and maximizing engine performance. The following conclusions were drawn from the experimental and optimization results: in general, the emissions of unburned hydrocarbon (UHC), carbon dioxide (CO2), and carbon monoxide (CO) from all the different types of biodiesel blends were lower than those of diesel fuel. In contrast, the concentration of nitrogen oxides (NOx) emitted by all the types of biodiesel blends increased. The noise level produced by all the forms of biodiesel, especially palm biodiesel fuel, was lowered when compared to pure diesel. All the tested fuels had a high noise level in the middle frequency band, at 75% engine load, and high engine speeds. On average, the proposed PR-GWO model exhibited remarkable predictive reliability, with a high square of correlation coefficient (R2) of 0.9823 and a low root mean square error (RMSE) of 0.0177. Finally, the proposed model achieved superior outcomes, which may be utilized to predict and maximize engine performance and minimize exhaust and noise emissions.

Cancer is a general term for a wide range of diseases that can affect any part of the body due to the rapid creation of abnormal cells that grow outside their normal boundaries. Liver cancer is one of the common diseases that cause the death of more than 600,000 each year. Early detection is important to diagnose and reduce the incidence of death. Examination of liver lesions is performed with various medical imaging modalities such as Ultrasound (US), Computer tomography (CT), and Magnetic resonance imaging (MRI). The improvements in medical imaging and image processing techniques have significantly enhanced the interpretation of medical images. Computer-Aided Diagnosis (CAD) systems based on these techniques play a vital role in the early detection of liver disease and hence reduce liver cancer death rate. Moreover, CAD systems can help physician, as a second opinion, in characterising lesions and making the diagnostic decision. Thus, CAD systems have become an important research area. Particularly, these systems can provide diagnostic assistance to doctors to improve overall diagnostic accuracy. The traditional methods to characterise liver lesions and differentiate normal liver tissues from abnormal ones are largely dependent on the radiologists experience. Thus, CAD systems based on the image processing and artificial intelligence techniques gained a lot of attention, since they could provide constructive diagnosis suggestions to clinicians for decision making. The liver lesions are characterised through two ways: (1) Using a content-based image retrieval (CBIR) approach to assist the radiologist in liver lesions characterisation. (2) Calculating the high-level features that describe/ characterise the liver lesion in a way that is interpreted by humans, particularly Radiologists/Clinicians, based on the hand-crafted/engineered computational features (low-level features) and learning process. However, the research gap is related to the high-level understanding and interpretation of the medical image contents from the low-level pixel analysis, based on mathematical processing and artificial intelligence methods. In our work, the research gap is bridged if a relation of image contents to medical meaning in analogy to radiologist understanding is established. This thesis explores an automated system for the classification and characterisation of liver lesions in CT scans. Firstly, the liver is segmented automatically by using anatomic medical knowledge, histogram-based adaptive threshold and morphological operations. The lesions and vessels are then extracted from the segmented liver by applying AFCM and Gaussian mixture model through a region growing process respectively. Secondly, the proposed framework categorises the high-level features into two groups; the first group is the high-level features that are extracted from the image contents such as (Lesion location, Lesion focality, Calcified, Scar, ...); the second group is the high-level features that are inferred from the low-level features through machine learning process to characterise the lesion such as (Lesion density, Lesion rim, Lesion composition, Lesion shape,...). The novel Multiple ROIs selection approach is proposed, in which regions are derived from generating abnormality level map based on intensity difference and the proximity distance for each voxel with respect to the normal liver tissue. Then, the association between low-level, high-level features and the appropriate ROI are derived by assigning the ability of each ROI to represents a set of lesion characteristics. Finally, a novel feature vector is built, based on high-level features, and fed into SVM for lesion classification. In contrast with most existing research, which uses low-level features only, the use of high-level features and characterisation helps in interpreting and explaining the diagnostic decision. The methods are evaluated on a dataset containing 174 CT scans. The experimental results demonstrated that the efficacy of the proposed framework in the successful characterisation and classification of the liver lesions in CT scans. The achieved average accuracy was 95:56% for liver lesion characterisation. While the lesion's classification accuracy was 97:1% for the entire dataset. The proposed framework is developed to provide a more robust and efficient lesion characterisation framework through comprehensions of the low-level features to generate semantic features. The use of high-level features (characterisation) helps in better interpretation of CT liver images. In addition, the difference-of-features using multiple ROIs were developed for robust capturing of lesion characteristics in a reliable way. This is in contrast to the current research trend of extracting the features from the lesion only and not paying much attention to the relation between lesion and surrounding area. The design of the liver lesion characterisation framework is based on the prior knowledge of the medical background to get a better and clear understanding of the liver lesion characteristics in medical CT images.