Machine Learning Shrewd Approach For An Imbalanced Dataset Conversion Samples

The imbalance data applies to at least one of the classes, which are typically exceeded by the other ones. The Machine Learning Algorithm (Classifier) trained with an imbalance dataset predicts the majority class (frequently occurring) ‎more than the other minority classes (rarely occurring). Training with an imbalance dataset poses challenges for classifiers; ‎however, applying suitable techniques for reducing class imbalance issues can enhance the classifier’s performance. We take an ‎imbalanced dataset from an educational context. Initially, all shortcomings regarding classification of imbalanced dataset have ‎been examined. After that, we apply data-level algorithms for class balancing and compare the performance of classifiers. The ‎performance of the classifier is measured using the underlying information in their confusion matrices such as accuracy, ‎precision, recall, and f-measure. It shows that classification with an imbalance dataset may produce higher accuracy but low ‎precision and recall for the minority class. The analysis confirms that both undersampling and oversampling are effective for ‎balancing datasets, however, oversampling dominates.

Ultrastructure of antennal sensillae of the samsum ant, Pachycondyla sennaarensis (Hymenoptera: Formicidae)

Black ant (Samsum), Pachycodyla sennarrensis, stings and injects venom and inflicts allergy (a rare clinical problem) due to its local and systemic reaction, which is considered as a health hazard amongst Saudi society. Thus, black ant is a source of serious concern for the government and experts as well.  Ultramorphological variations, distribution, differential sensillae counts (DSC) and total sensillae counts (TSC), were identified and estimated as a complementary part of the peripheral nervous system on the antennae of worker samsum ant, P. sennarrensis in order to understand its behavioral ecology. Based on scanning electron micrographs, four types of sensillae constituted with three trichoid types, which is an abundant form with a high distribution density at the apex, but a low density at subsequent proximal flagellomere of the antenna and a placoid type of sensillae (a rare form mostly found in the middle of the flagellum, that is, on the 4th, 5th and 6th flagellomere) were categorised. It is documented that nonporous trichoid type of sensillae are mechanoreceptors and thermoreceptors, whereas, the placoid types are olfactory receptors. Present findings in an indigenous species in Saudi Arabia may help in understanding the ecological behaviour of the ant, which subsequently may form the basis in producing its effective control measure in future.Key words: Samsum ants, Pachycondyla sennarrensis, ultrastructure, antenna, sensillae

Decreasing maize production-consumption gap by intercropping with upland rice using different planting densities under deficit irrigation

A two-year field experiment was conducted in 2018 and 2019 at Gemmiza Agricultural Research Station (Lat. 31.03° N, Long. 30.88° E, 8 m a.s.l.); Gharbia Governorate; Egypt. The aim was to use untraditional sowing method to intercrop maize with upland rice using three maize planting densities (25, 37.5 and 50% of its recommended density) and application of two deficit irrigation treatments (irrigation every 9 and 12 days), in addition to irrigation every 6 days (control) and to study its effect on the yield of both intercrops, competitive relationships and farmer’s income. The results indicated that the highest value of rice yield and its components were found under irrigation every 6 days and 25% maize planting density intercropped with rice. Whereas, the highest value of maize yield and its components were found under irrigation every 9 days and 50% maize planting density intercropped with rice, which also obtained the highest land and water equivalent ratios, area time equivalent ratio, and land equivalent coefficient. Furthermore, the highest total income and monetary advantage index were obtained under irrigation every 9 days and 50% maize planting density intercropped with rice. Thus, these results implied that intercropping maize with upland rice can solve part of the maize production-consumption gap through increasing its production without using additional lands or water.  Keywords: Land and water equivalent ratios, percentage of land saved, area time equivalent ratio, land equivalent coefficient, monetary advantage index, Rice, Maize, Egyp

A qualitative study of airborne minerals and associated organic compounds in southeast of Cairo, Egypt

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This study is concerned with the identification of the mineralogical composition of dust fall samples collected from southeast of Cairo, Egypt. The mineralogical identification was conducted by means of the polarizing microscope, infra-red spectroscopy (IR), and X-ray diffraction (XRD). The relationship between the mineralogical composition of dust fall samples and 10 rock samples from the surrounding terrains were investigated. The major mineralogical species existing in the atmosphere of the study area are: carbonates mainly in the form of calcite in addition to the appearance of the dolomite form in traces overall the study area, but with considerable observation in the southern region; quartz which is less than calcite in its abundance; sulphates in the form of gypsum which may also be present as traces in the anhydrite form. Trace constitution of feldspars; clay minerals in the form of kaolinite, illite, and montimorillonite; and halite are also observable in the same samples. Organic compounds are present in the atmosphere of the area mainly as alkanes with presence of traces of phosphines. This study qualitatively shows the mineralogy of air particulate over rock processing area and the obtained results indicates that the main pollution source in the study area is the industrial activities with minor contribution of the natural sources, especially erosion and dust carried by winds from the surrounding terrains Cairo in the southern direction. This study provides useful results for the contribution of rock processing activities to the mineral composition of atmospheric particulates

Treatment of Taman Beringin landfill leachate using the column technique

Landfill leachate is currently a major environmental concern because it contains high concentrations of organic and inorganic contaminants. Leachate treatment using natural materials, such as aquifer sand, peat, and the commercial material BIRM (Burgess Iron Removal Media), was performed through column experiments. Aquifer sand was taken from Kg Teluk, Kelantan, peat was taken from Peatland Paradise, and BIRM was bought from a supplier. The heavy metals (Fe3+, Cr, Ni, and Cu) from natural leachate were selected for this experiment. The concentrations of Fe, Cr, Ni, and Cu before the experiment were 11, 1.27, 4.535, and 3.293 mg L–1, respectively. The physical and chemical parameters of leachate and surface water at the Taman Beringin Landfill have been studied to understand the impact of pollution in the area. The results show that leachate samples at the bottom of the landfills have the highest pollution. Both the physical and chemical parameters of leachate exceed the limits of Interim National Water Quality Standards for Malaysia. Experimental test results were also analyzed in terms of breakthrough curves and percentage of heavy metal removal. The results show that the BIRM sample has a higher adsorption capacity for heavy metals, including Fe, compared with aquifer sand and peat

Advanced Diagnostic Technique for Alzheimer’s Disease using MRI Top-Ranked Volume and Surface-based Features

Background: Alzheimer’s disease (AD) is the most dominant type of dementia that has not been treated completely yet. Few Alzheimer‘s patients are correctly diagnosed on time. Therefore, diagnostic tools are needed for better and more efficient diagnoses. Objective: This study aimed to develop an efficient automated method to differentiate Alzheimer’s patients from normal elderly and present the essential features with accurate Alzheimer’s diagnosis.Material and Methods: In this analytical study, 154 Magnetic Resonance Imaging (MRI) scans were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, preprocessed, and normalized by the head size for extracting features (volume, cortical thickness, Sulci depth, and Gyrification Index Features (GIF). Relief-F algorithm, t-test, and one way-ANOVA were used for feature ranking to obtain the most effective features representing the AD for the classification process. Finally, in the classification step, four classifiers were used with 10 folds cross-validation as follows: Gaussian Support Vector Machine (GSVM), Linear Support Vector Machine (LSVM), Weighted K-Nearest Neighbors (W-KNN), and Decision Tree algorithm. Results: The LSVM classifier and W-KNN produce a testing accuracy of 100% with only seven features. Additionally, GSVM and decision tree produce a testing accuracy of 97.83% and 93.48%, respectively.  Conclusion: The proposed system represents an automatic and highly accurate AD detection with a few reliable and effective features and minimum time