295 research outputs found
Comparative study on functional and structural properties of the Barbary sheep and European mouflon haemoglobins
No full textOn the use of multivariate analysis and land evaluation for potential agricultural development of the northwestern coast of Egypt
No full textThe development of the agricultural sector is considered the backbone of sustainable development in Egypt. While the developing countries of the world face many challenges regarding food security due to rapid population growth and limited agricultural resources, this study aimed to assess the soils of Sidi Barrani and Salloum using multivariate analysis to determine the land capability and crop suitability for potential alternative crop uses, based on using principal component analysis (PCA), agglomerative hierarchical cluster analysis (AHC) and the Almagra model of MicroLEIS. In total, 24 soil profiles were dug, to represent the geomorphic units of the study area, and the soil physicochemical parameters were analyzed in laboratory. The land capability assessment was classified into five significant classes (C1 to C5) based on AHC and PCA analyses. The class C1 represents the highest capable class while C5 is assigned to lowest class. The results indicated that about 7% of the total area was classified as highly capable land (C1), which is area characterized by high concentrations of macronutrients (N, P, K) and low soil salinity value. However, about 52% of the total area was assigned to moderately high class (C2), and 29% was allocated in moderate class (C3), whilst the remaining area (12%) was classified as the low (C4) and not capable (C5) classes, due to soil limitations such as shallow soil depth, high salinity, and increased erosion susceptibility. Moreover, the results of the Almagra soil suitability model for ten crops were described into four suitability classes, while about 37% of the study area was allocated in the highly suitable class (S2) for wheat, olive, alfalfa, sugar beet and fig. Furthermore, 13% of the area was categorized as highly suitable soil (S2) for citrus and peach. On the other hand, about 50% of the total area was assigned to the marginal class (S4) for most of the selected crops. Hence, the use of multivariate analysis, mapping land capability and modeling the soil suitability for diverse crops help the decision makers with regard to potential agricultural development
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Feature selection in wind speed forecasting systems based on meta-heuristic optimization
No full textTechnology for anticipating wind speed can improve the safety and stability of power networks with heavy wind penetration. Due to the unpredictability and instability of the wind, it is challenging to accurately forecast wind power and speed. Several approaches have been developed to improve this accuracy based on processing time series data. This work proposes a method for predicting wind speed with high accuracy based on a novel weighted ensemble model. The weight values in the proposed model are optimized using an adaptive dynamic grey wolf-dipper throated optimization (ADGWDTO) algorithm. The original GWO algorithm is redesigned to emulate the dynamic group-based cooperative to address the difficulty of establishing the balance between exploration and exploitation. Quick bowing movements and a white breast, which distinguish the dipper throated birds hunting method, are employed to improve the proposed algorithm exploration capability. The proposed ADGWDTO algorithm optimizes the hyperparameters of the multi-layer perceptron (MLP), K-nearest regressor (KNR), and Long Short-Term Memory (LSTM) regression models. A dataset from Kaggle entitled Global Energy Forecasting Competition 2012 is employed to assess the proposed algorithm. The findings confirm that the proposed ADGWDTO algorithm outperforms the literature’s state-of-the-art wind speed forecasting algorithms. The proposed binary ADGWDTO algorithm achieved average fitness of 0.9209 with a standard deviation fitness of 0.7432 for feature selection, and the proposed weighted optimized ensemble model (Ensemble using ADGWDTO) achieved a root mean square error of 0.0035 compared to state-of-the-art algorithms. The proposed algorithm’s stability and robustness are confirmed by statistical analysis of several tests, such as one-way analysis of variance (ANOVA) and Wilcoxon’s rank-sum.Full Tex
Advanced Meta-Heuristics, Convolutional Neural Networks, and Feature Selectors for Efficient COVID-19 X-Ray Chest Image Classification
No full textThe chest X-ray is considered a significant clinical utility for basic examination and diagnosis. The human lung area can be affected by various infections, such as bacteria and viruses, leading to pneumonia. Efficient and reliable classification method facilities the diagnosis of such infections. Deep transfer learning has been introduced for pneumonia detection from chest X-rays in different models. However, there is still a need for further improvements in the feature extraction and advanced classification stages. This paper proposes a classification method with two stages to classify different cases from the chest X-ray images based on a proposed Advanced Squirrel Search Optimization Algorithm (ASSOA). The first stage is the feature learning and extraction processes based on a Convolutional Neural Network (CNN) model named ResNet-50 with image augmentation and dropout processes. The ASSOA algorithm is then applied to the extracted features for the feature selection process. Finally, the Multi-layer Perceptron (MLP) Neural Network's connection weights are optimized by the proposed ASSOA algorithm (using the selected features) to classify input cases. A Kaggle chest X-ray images (Pneumonia) dataset consists of 5,863 X-rays is employed in the experiments. The proposed ASSOA algorithm is compared with the basic Squirrel Search (SS) optimization algorithm, Grey Wolf Optimizer (GWO), and Genetic Algorithm (GA) for feature selection to validate its efficiency. The proposed (ASSOA + MLP) is also compared with other classifiers, based on (SS + MLP), (GWO + MLP), and (GA + MLP), in performance metrics. The proposed (ASSOA + MLP) algorithm achieved a classification mean accuracy of (99.26%). The ASSOA + MLP algorithm also achieved a classification mean accuracy of (99.7%) for a chest X-ray COVID-19 dataset tested from GitHub. The results and statistical tests demonstrate the high effectiveness of the proposed method in determining the infected cases
General characteristics of current in front of Port Said, Egypt
No full textThis paper is a preliminary investigation of the general characteristics of the current in front of the coastal Mediterranean city: Port Said, Egypt. The study of the current regime in front of Port Said helps environmental engineers to tackle problems as marine port sedimentation and shoreline changes. Surface and bottom current recordings at a single offshore station of depth 104 m located at 31° 34.90′ N, 32° 30.01′ E have been subject to statistical analysis. The measurements showed unexpectedly that bottom currents were relatively stronger than surface currents during May-99
Advanced Ensemble Model for Solar Radiation Forecasting Using Sine Cosine Algorithm and Newton's Laws
No full textAs research in alternate energy sources is growing, solar radiation is catching the eyes of the research community immensely. Since solar energy generation depends on uncontrollable natural variables, without proper forecasting, this energy source cannot be trusted. For this forecasting, the use of machine learning algorithms is one of the best choices. This paper proposed an optimized solar radiation forecasting ensemble model consisting of pre-processing and training ensemble phases. The training ensemble phase works on an advanced sine cosine algorithm (ASCA) using Newton’s laws of gravity and motion for objects (agents). ASCA uses sine and cosine functions to update the agent’s position/velocity components by considering its mass. The training ensemble model is then developed using the k-nearest neighbors (KNN) regression. The performance of the proposed ensemble model is measured using a dataset from Kaggle (Solar Radiation Prediction, Task from NASA Hackathon). The proposed ASCA algorithm is evaluated in comparison with the Particle Swarm Optimizer (PSO), Whale Optimization Algorithm (WOA), Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Squirrel Search Algorithm (SSA), Harris Hawks Optimization (HHO), Hybrid Greedy Sine Cosine Algorithm with Differential Evolution (HGSCADE), Hybrid Modified Sine Cosine Algorithm with Cuckoo Search Algorithm (HMSCACSA), Marine Predators Algorithm (MPA), Chimp Optimization Algorithm (ChOA), and Slime Mould Algorithm (SMA). Obtained results of the proposed ensemble model are compared with those of state-of-the-art models, and significant superiority of the proposed ensemble model is confirmed using statistical analysis such as ANOVA and Wilcoxon’s rank-sum tests.Full Tex
Petrogenesis and evolution of the Dineibit El-Qulieb hyperaluminous leucogranite, Southeastern Desert, Egypt: petrological and geochemical constraints
No full textSpread of TEM, VIM, SHV, and CTX-M β-Lactamases in Imipenem-Resistant Gram-Negative Bacilli Isolated from Egyptian Hospitals
No full textCarbapenem-resistant Gram-negative bacilli resulting from β-lactamases have been reported to be an important cause of nosocomial infections and are a critical therapeutic problem worldwide. This study aimed to describe the prevalence of imipenem-resistant Gram-negative bacilli isolates and detection of blaVIM, blaTEM, blaSHV, blaCTX-M-1, and blaCTX-M-9 genes in these clinical isolates in Egyptian hospitals. The isolates were collected from various clinical samples, identified by conventional methods and confirmed by API 20E. Antibiotic susceptibility testing was determined by Kirby-Bauer technique and interpreted according to CLSI. Production of blaVIM, blaTEM, blaSHV, and blaCTX-M genes was done by polymerase chain reaction (PCR). Direct sequencing from PCR products was subsequently carried out to identify and confirm these β-lactamases genes. Out of 65 isolates, (46.1%) Escherichia coli, (26.2%) Klebsiella pneumoniae, and (10.7%) Pseudomonas aeruginosa were identified as the commonest Gram-negative bacilli. 33(50.8%) were imipenem-resistant isolates. 22 isolates (66.7%) carried blaVIM, 24(72.7%) had blaTEM, and 5(15%) showed blaSHV, while 12(36%), 6(18.2%), and 0(0.00%) harbored blaCTX-M-1, blaCTX-M-9, and blaCTX-M-8/25, respectively. There is a high occurrence of β-lactamase genes in clinical isolates and sequence analysis of amplified genes showed differences between multiple SNPs (single nucleotide polymorphism) sites in the same gene among local isolates in relation to published sequences
An Optimization Methodology for Container Handling Using Genetic Algorithm
No full textAbstractContainer handling problems at container terminals are considered as NP-hard combinatorial optimization problems. In this paper, we propose an optimization methodology for solving container handling problems using genetic algorithm. The proposed methodology is applied on a real case study data of container terminal at Port-said Port in Egypt. The computational results show the effectiveness of the proposed methodology for container terminal where 56% reduction in ship service time (loading/unloading) in port is achieved
Recent research approaches for wind power forecasting, m and s indicate minutes and seconds, respectively.
No full textRecent research approaches for wind power forecasting, m and s indicate minutes and seconds, respectively.</p
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