Personal identification based on mobile-based keystroke dynamics

This paper is addressing the personal identification problem by using mobile-based keystroke dynamics of touch mobile phone. The proposed approach consists of two main phases, namely feature selection and classification. The most important features are selected using Genetic Algorithm (GA). Moreover, Bagging classifier used the selected features to identify persons by matching the features of the unknown person with the labeled features. The outputs of all Bagging classifiers are fused to determine the final decision. In this experiment, a keystroke dynamics database for touch mobile phones is used. The database, which consists of four sets of features, is collected from 51 individuals and consists of 985 samples collected from males and females with different ages. The results of the proposed model conclude that the third subset of features achieved the best accuracy while the second subset achieved the worst accuracy. Moreover, the fusion of all classifiers of all ensembles will improve the accuracy and achieved results better than the individual classifiers and individual ensembles

One-dimensional vs. two-dimensional based features: Plant identification approach

The number of endangered species has been increased due to shifts in the agricultural production, climate change, and poor urban planning. This has led to investigating new methods to address the problem of plant species identification/classification. In this paper, a plant identification approach using 2D digital leaves images was proposed. The approach used two features extraction methods based on one-dimensional (1D) and two-dimensional (2D) and the Bagging classifier. For the 1D-based methods, Principal Component Analysis (PCA), Direct Linear Discriminant Analysis (DLDA), and PCA + LDA techniques were applied, while 2DPCA and 2DLDA algorithms were used for the 2D-based method. To classify the extracted features in both methods, the Bagging classifier, with the decision tree as a weak learner was used. The five variants, i.e. PCA, PCA + LDA, DLDA, 2DPCA, and 2DLDA, of the approach were tested using the Flavia public dataset which consists of 1907 colored leaves images. The accuracy of these variants was evaluated and the results showed that the 2DPCA and 2DLDA methods were much better than using the PCA, PCA + LDA, and DLDA. Furthermore, it was found that the 2DLDA method was the best one and the increase of the weak learners of the Bagging classifier yielded a better classification accuracy. Also, a comparison with the most related work showed that our approach achieved better accuracy under the same dataset and same experimental setup

One-dimensional vs. two-dimensional based features: Plant identification approach

The number of endangered species has been increased due to shifts in the agricultural production, climate change, and poor urban planning. This has led to investigating new methods to address the problem of plant species identification/classification. In this paper, a plant identification approach using 2D digital leaves images was proposed. The approach used two features extraction methods based on one-dimensional (1D) and two-dimensional (2D) and the Bagging classifier. For the 1D-based methods, Principal Component Analysis (PCA), Direct Linear Discriminant Analysis (DLDA), and PCA + LDA techniques were applied, while 2DPCA and 2DLDA algorithms were used for the 2D-based method. To classify the extracted features in both methods, the Bagging classifier, with the decision tree as a weak learner was used. The five variants, i.e. PCA, PCA + LDA, DLDA, 2DPCA, and 2DLDA, of the approach were tested using the Flavia public dataset which consists of 1907 colored leaves images. The accuracy of these variants was evaluated and the results showed that the 2DPCA and 2DLDA methods were much better than using the PCA, PCA + LDA, and DLDA. Furthermore, it was found that the 2DLDA method was the best one and the increase of the weak learners of the Bagging classifier yielded a better classification accuracy. Also, a comparison with the most related work showed that our approach achieved better accuracy under the same dataset and same experimental setup

Utilization of desalinated brackish water residues for cultivation of the marine fish species,Dicentrarchus labrax, Sparus aurata, and Sciaenops ocellatus

The utilization of brackish water residues from desalination units for fish mariculture in Palestine could represent an environmental friendly alternative of disposing brine water which could have an adverse effect on wild life. In this study, three juvenile marine fish species (Gilt-head bream S. aurata, European sea bass D. labrax, and red drum S. ocellatus) with average weights of 0.7-4.9 g, were acclimated and reared in brine brackish water residues collected from Jericho desalination units with salinities of 6.5 ‰ and 11 ‰ diluted sea water as control for 3-7 months. Fish were fed 56 % rich protein diet. Brine brackish water contained high levels of Cl-1 (3369 mgL-1), Na+1(3735 mgL-1), K+1(300 mgL-1), SO4 -2 (716 mgL-1) with the divalent ions Mg+2 (57.3 mgL-1) and Ca+2 (276 mgL-1).The experimental well showed more than the maximum allowable concentration of Cr (14.49 μgL-1), Ag (5.3 μgL-1) and Mn (27.88 μgL-1) for water quality of fisheries use. The sea bass with an average weight of 0.76 g (at a salinity of 6.5 ‰) showed an acceptable growth performance parameters and reached a percentage weight gain (% WG) of 6345.23 % and a survival rate of 77.5 % compared to control groups at 11 ‰ that reached at the same time a % WG of 6543.78 % and a survival rate of 82 % after 30 weeks. The red drum juveniles reached a % WG of 2661.6 % and 2673.92 % after 15 weeks at 6.5 ‰ and 11 ‰ respectively while sea bream reached a % WG of 241.63 % and 772.44 % after 15 weeks at 6.5 ‰ and 11 ‰ respectively. Survival rate was only 5 % at both salinities for the two species. In a further study sea bass fingerlings with an average weight of 20.5 g were reared in brackish water of 6.5 ‰ salinity for 7 weeks and were fed superior fish meal with fish oil. They received diets of 2.1 %, 3.0 %, and 4.0 % of body weight. Sea bass fingerlings which received diets of 2.1 %, had the highest FCE (0.82) and PER (1.46) with the lowest FCR (1.22). These results confirm that the most suitable fish for cultivation utilizing brackish water from Jericho desalination units with salinity of 6.5 ‰ is sea bass D. labrax, while the other two species need some modification to acclimatize to this water. Furthermore, the best food % for optimum growth of sea bass at this salinity is 2.1 % after 7 weeks. Brackish water from Jericho desalination units are suitable for cultivation of these species, but may need some modification as adding salts to fish diet or water and reducing toxicity of some high trace metals present by a suitable method

Optimized superpixel and AdaBoost classifier for human thermal face recognition

Infrared spectrum-based human recognition systems offer straightforward and robust solutions for achieving an excellent performance in uncontrolled illumination. In this paper, a human thermal face recognition model is proposed. The model consists of four main steps. Firstly, the grey wolf optimization algorithm is used to find optimal superpixel parameters of the quick-shift segmentation method. Then, segmentation-based fractal texture analysis algorithm is used for extracting features and the rough set-based methods are used to select the most discriminative features. Finally, the AdaBoost classifier is employed for the classification process. For evaluating our proposed approach, thermal images from the Terravic Facial infrared dataset were used. The experimental results showed that the proposed approach achieved (1) reasonable segmentation results for the indoor and outdoor thermal images, (2) accuracy of the segmented images better than the non-segmented ones, and (3) the entropy-based feature selection method obtained the best classification accuracy. Generally, the classification accuracy of the proposed model reached to 99% which is better than some of the related work with around 5%

Latent effect of gamma irradiation on reproductive potential and ultrastructure of males' testes of Culex pipiens (Diptera; Culicidae)

AbstractLaboratory male pupae of Culex pipiens were exposed to 23, 41, 74 and 128 Gy doses of gamma radiation according to the LD25, LD50, LD75 and LD90 calculation, respectively. The inherited deleterious effects of gamma radiation were observed in the F1, F2 and F3 generations. Levels of sterility index in the F1 and F2 were higher than those of untreated control but in the F3 generation there was a semi-sterility compared with the control. Ultrastructure of normal males' testes of C. pipiens was studied using transmission electron microscopy. Histopathological responses were observed in the irradiated testes of C. pipiens. Gamma radiation had greatly affected the testes, such as (i) rupture, necrosis, degeneration and small vacuoles were reported in the testicular wall (ii) an abnormal distribution of the developmental stages of spermatogonia and spermatocytes leading to a general decrease in the rate of spermatogenesis; and (iii) deformity of sperm inhibitting the movements and the fertility of the sperm led to the decrease in the reproductive potential of C. pipiens. Consequently, these radiation doses are consistent with those used in the already established Sterile Insect Technique (SIT) programmes against Culex pipiens

Automatic cattle identification using graph matching based on local invariant features

Cattle muzzle classification can be considered as a biometric identifier important to animal traceability systems to ensure the integrity of the food chain. This paper presents a muzzle-based classification system that combines local invariant features with graph matching. The proposed approach consists of three phases; namely feature extraction, graph matching, and matching refinement. The experimental results showed that our approach is superior than existing works as ours achieves an all correct identification for the tested images. In addition, the results proved that our proposed method achieved this high accuracy even if the testing images are rotated in various angles.info:eu-repo/semantics/publishedVersio

Diagnostic and Prognostic Significance of Lipid Profiles in Holstein Dairy Cattle with Displaced Abomasum: Before and After Surgical Operation

The study aimed to describe the pattern of changes in clinical findings and lipid metabolism profiles in dairy cattle with displacement of the abomasum (DA) from day 0 until day 30 after operation. The study was conducted on DA cattle (n= 25) belonged to dairy farms in Hokkaido area, Japan. Cows were examined and sampled at days 0 (operation), 7 and 30. They were clinically and biochemically examined to estimate BCS and many serum biochemical constituents such as lecithin:cholesterol acyltransferase (LCAT) and apolipoprotein B-100 (apoB-100), β-hydroxybutyric acid (BHBA), non-esterified fatty acids (NEFAs) and aspartate amino transferase (AST). Based on blood BHBA at day 0, DA cows were classified into three categories; DA only (<1.2 mmol/l), DA with subclinical ketosis (DA SCK) (1.2-2.4 mmol/l) and DA with clinical ketosis (DA CK) (≥2.5 mmol/l). The changes in the pattern of serum biochemical constituents throughout this study indicated recovery of diseased cows and significant effect of surgical operation. Serum biochemical constituents returned to their physiological values indicating that these cows were restoring their normal physiological status. This was reflected through a significant (P＜0.05) elevation of LCAT, apoB-100, and cholesterol and a significant (P＜0.05) reduction in AST, NEFAs and BHBA (Not in DA group), in all DA groups particularly at day 30 when their values compared with those at day 0. The current study also recorded no remarkable changes (P＞0.05) between the diseased groups except for NEFA and BHBA (at day 0 between DA group and the other two groups) at any of the three sampling days

Particle Swarm Optimization : a tutorial

Optimization algorithms are necessary to solve many problems such as parameter tuning. Particle Swarm Optimization (PSO) is one of these optimization algorithms. The aim of PSO is to search for the optimal solution in the search space. This paper highlights the basic background needed to understand and implement the PSO algorithm. This paper starts with basic definitions of the PSO algorithm and how the particles are moved in the search space to find the optimal or near optimal solution. Moreover, a numerical example is illustrated to show how the particles are moved in a convex optimization problem. Another numerical example is illustrated to show how the PSO trapped in a local minima problem. Two experiments are conducted to show how the PSO searches for the optimal parameters in one-dimensional and two-dimensional spaces to solve machine learning problems

Exploring the impact of rotating rectangular plasmonic nano-hole arrays on the transmission spectra and its application as a plasmonic sensor

Plasmonic nano-structures play a significant role in most recent photonic devices and applications. In this paper, we investigate the optical transmission spectra of rotatable periodic nano-metric apertures with different dimensions. This investigation includes monitoring the modification of both the transmission resonance wavelengths and peak transmittance at different dimensions and orientations of the nano-holes. The obtained results provide better insight to the interaction of light with periodic plasmonic nano-hole arrays. We find that nano-holes dimension/orientation can totally suppress an optical transmission, tune its resonance wavelengths, and change its peak values. Furthermore, we present the surface plasmonic resonance sensing as an application for the reported nano-hole array