ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY
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Driver Drowsiness Detection Using Gray Wolf Optimizer Based on Face and Eye Tracking
Get PDFIt is critical today to provide safe and collision-free transport. As a result, identifying the driver’s drowsiness before their capacity to drive is jeopardized. An automated hybrid drowsiness classification method that incorporates the artificial neural network (ANN) and the gray wolf optimizer (GWO) is presented to discriminate human drowsiness and fatigue for this aim. The proposed method is evaluated in alert and sleep-deprived settings on the driver drowsiness detection of video dataset from the National Tsing Hua University Computer Vision Lab. The video was subjected to various video and image processing techniques to detect the drivers’ eye condition. Four features of the eye were extracted to determine the condition of drowsiness, the percentage of eyelid closure (PERCLOS), blink frequency, maximum closure duration of the eyes, and eye aspect ratio (ARE). These parameters were then integrated into an ANN and combined with the proposed method (gray wolf optimizer with ANN [GWOANN]) for drowsiness classification. The accuracy of these models was calculated, and the results demonstrate that the proposed method is the best. An Adadelta optimizer with 3 and 4 hidden layer networks of (13, 9, 7, and 5) and (200, 150, 100, 50, and 25) neurons was utilized. The GWOANN technique had 91.18% and 97.06% accuracy, whereas the ANN model had 82.35% and 86.76%
A New Design Approach for a Compact Microstrip Diplexer with Good Passband Characteristics
Get PDFThis paper presents an efficient theoretical design approach of a very compact microstrip diplexer for modern wireless communication system applications. The proposed basic resonator is made of coupled lines, simple transmission line and a shunt stub. The coupled lines and transmission line make a U-shape resonator while the shunt stub is loaded inside the U-shape cell to save the size significantly, where the overall size of the presented diplexer is only 0.008 λg2 . The configuration of this resonator is analyzed to increase intuitive understanding of the structure and easier optimization. The first and second resonance frequencies are f o1 = 895 MHz and f o2 = 2.2 GHz. Both channels have good properties so that the best simulated insertion loss at the first channel (0.075 dB) and the best simulated common port return losses at both channels (40.3 dB and 31.77 dB) are achieved. The presented diplexer can suppress the harmonics acceptably up to 3 GHz (3.3 fo1 ). Another feature is having 31% fractional bandwidth at the first channel
Some Enzymatic and Non-enzymatic Antioxidants Response under Nickel and Lead Stress for Some Fabaceae Trees
Get PDFThis study investigates the effects of soil contamination by nickel and lead on some enzymatic and non-enzymatic antioxidants in addition to the nitrate reductase (NR) enzyme activity for Gleditsia triacanthos, Leucaena leucocephala, and Robinia pseudoacacia plant species. The results of this study show a significant increase in peroxidase enzyme activity and a significant decrease in catalase enzyme activity, proline, total carotenoids, and total carbohydrate content of leaves of the three species with increasing the concentration of Ni and Pb except for the total carbohydrate, which increased only for L. leucocephala species. Each NR enzyme activity and ascorbic acid content are increased significantly with increasing the concentration of Ni and Pb for G. triacanthos, L. leucocephala, and on the contrary, they decreased significantly for R. pseudoacacia species. From the result, we can conclude a general increase or decrease in leaves content of some antioxidants content for all the species, whereas there is some peculiarity according to the plant species regarding other contents, which in turn reflects different mechanisms of these species to tolerant heavy metal stress
 
Detection of Sperm DNA Integrity and Some Immunological Aspects in Infertile Males
Get PDFImmunoinfertility caused by anti-sperm antibodies (ASAs) represents about 10–20% of infertility among couples, which interfere with sperm motility and ability to penetrate cervical mucus, sperm-oocyte binding, fertilization, and embryo development. In addition, deoxyribonucleic acid (DNA) damages are increasingly found with infertile cases affecting male reproduction potency and progeny. This study aims to assess the semen, presence of ASAs, and DNA fragmentation index in normozoospermic patients. A total number of 116 cases with an average age of 20–51 years old, and duration of infertility at 4.70 ± 2.77 are classified into 77 and 39 primary and secondary types of infertility, respectively. Mixed agglutination reaction test was used to estimate the ASAs in semen (direct method) and in seminal plasma and blood serum (indirect method), for both immunoglobulins IgG and IgA. Acridine orange test was used to detect DNA fragmentation index. The results showed a significant difference (P > 0.05) for those with a secondary type of infertility at means 24.37 and 31.48 for IgG, and 14.46 ± 1.76 and 6.86 ± 0.39 for IgA by both direct and indirect methods, respectively. The direct method showed a significant difference only for the sperm tail, while that for indirect method was in sperm mid-piece. The mean of DFI for all cases was 38.25 ± 2.08, at 41.61 ± 2.19 and 31.63 ± 4.29, for both primary and secondary cases, respectively. The percentage of ASAs revealed no significant difference with DFI, except in some parts of sperm
Driver Drowsiness Detection Using Gray Wolf Optimizer Based on Voice Recognition
Get PDFGlobally, drowsiness detection prevents accidents. Blood biochemicals, brain impulses, etc., can measure tiredness. However, due to user discomfort, these approaches are challenging to implement. This article describes a voice-based drowsiness detection system and shows how to detect driver fatigue before it hampers driving. A neural network and Gray Wolf Optimizer are used to classify sleepiness automatically. The recommended approach is evaluated in alert and sleep-deprived states on the driver tiredness detection voice real dataset. The approach used in speech recognition is mel-frequency cepstral coefficients (MFCCs) and linear prediction coefficients (LPCs). The SVM algorithm has the lowest accuracy (71.8%) compared to the typical neural network. GWOANN employs 13-9-7-5 and 30-20-13-7 neurons in hidden layers, where the GWOANN technique had 86.96% and 90.05% accuracy, respectively, whereas the ANN model achieved 82.50% and 85.27% accuracy, respectively
Train Support Vector Machine Using Fuzzy C-means Without a Prior Knowledge for Hyperspectral Image Content Classification
Get PDFIn this paper, a new cooperative classification method called auto-train support vector machine (SVM) is proposed. This new method converts indirectly SVM to an unsupervised classification method. The main disadvantage of conventional SVM is that it needs a priori knowledge about the data to train it. To avoid using this knowledge that is strictly required to train SVM, in this cooperative method, the data, that is, hyperspectral images (HSIs), are first clustered using Fuzzy C-means (FCM); then, the created labels are used to train SVM. At this stage, the image content is classified using the auto-trained SVM. Using FCM, clustering reveals how strongly a pixel is assigned to a class thanks to the fuzzification process. This information leads to gaining two advantages, the first one is that no prior knowledge about the data (known labels) is needed and the second one is that the training data selection is not done randomly (the training data are selected according to their degree of membership to a class). The proposed method gives very promising results. The method is tested on two HSIs, which are Indian Pines and Pavia University. The results obtained have a very high accuracy of the classification and exceed the existing manually trained methods in the literature
Machine Learning Algorithms for Detecting and Analyzing Social Bots Using a Novel Dataset
Get PDFSocial media is internet-based technology and an electronic form of communication that facilitates sharing of ideas, documents, and personal information. Twitter is a microblogging platform and is the most effective social service for posting microblogs and likings, commenting, sharing, and communicating with others. The problem we are shedding light on in this paper is the misuse of bots on Twitter. The purpose of bots is to automate specific repetitive tasks instead of human interaction. However, bots are misused to influence people’s minds by spreading rumors and conspiracy related to controversial topics. In this paper, we initiate a new benchmark created on a 1.5M Twitter profile. We train different supervised machine learning on our benchmark to detect bots on Twitter. In addition to increasing benchmark scalability, various autofeature selections are utilized to identify the most influential features and remove the less influential ones. Furthermore, over-under-sampling is applied to reduce the imbalance effect on the benchmark. Finally, our benchmark compared with other stateof-the-art benchmarks and achieved a 6% higher area under the curve than other datasets in the case of generalization, improving the model performance by at least 2% by applying over-/undersampling
Wound Healing Properties and Structural Analysis of Four Geographical Areas’ Natural Clays
Get PDFClays are fine particle materials that harden after drying. The difference in their structure is the key to their efficacy and their subsequent application. The current study aims to evaluate the wound healing property of four countries (C1:Iraq, C2:Turkey, C3:Azerbaijan and C4:Russia) clay samples by excision model using Sprague dawley rats also the chemical analysis of the samples was performed using X-ray diffraction (XRD) and X-ray Fluorescence (XRF) methods. Results revealed that the best wound healing activities were given by C1, C3, C4 and C2 respectively with healing percentages (76%, 71%, 62%, and 60%), respectively. XRD results revealed the presence of Calcium carbonate and CalciumMagnesium carbonate in C1, Dolomite and Calcium-Magnesium carbonate in C2, Cobalt Tantalum Sulfide in C3, Finally Quartz and Silicon Oxide in C4. On the other hand, XRF analysis showed the appearance of different major and trace elements with different quantities in each clay type. We conclude that different countries clays enclose wound healing property with diverse ranges and this diversity is due to their chemical and mineral structures
Data Analytics and Techniques: A Review
Get PDFBig data of different types, such as texts and images, are rapidly generated from the internet and other applications. Dealing with this data using traditional methods is not practical since it is available in various sizes, types, and processing speed requirements. Therefore, data analytics has become an important tool because only meaningful information is analyzed and extracted, which makes it essential for big data applications to analyze and extract useful information. This paper presents several innovative methods that use data analytics techniques to improve the analysis process and data management. Furthermore, this paper discusses how the revolution of data analytics based on artificial intelligence algorithms might provide improvements for many applications. In addition, critical challenges and research issues were provided based on published paper limitations to help researchers distinguish between various analytics techniques to develop highly consistent, logical, and information-rich analyses based on valuable features. Furthermore, the findings of this paper may be used to identify the best methods in each sector used in these publications, assist future researchers in their studies for more systematic and comprehensive analysis and identify areas for developing a unique or hybrid technique for data analysis
Classification of Different Shoulder Girdle Motions for Prosthesis Control Using a Time-Domain Feature Extraction Technique
Get PDFAbstract—The upper limb amputation exerts a significant burden on the amputee, limiting their ability to perform everyday activities, and degrading their quality of life. Amputee patients’ quality of life can be improved if they have natural control over their prosthetic hands. Among the biological signals, most commonly used to predict upper limb motor intentions, surface electromyography (sEMG), and axial acceleration sensor signals are essential components of shoulder-level upper limb prosthetic hand control systems. In this work, a pattern recognition system is proposed to create a plan for categorizing high-level upper limb prostheses in seven various types of shoulder girdle motions. Thus, combining seven feature groups, which are root mean square, four-order autoregressive, wavelength, slope sign change, zero crossing (ZC), mean absolute value, and cardinality. In this article, the time-domain features were first extracted from the EMG and acceleration signals. Then, the spectral regression (SR) and principal component analysis dimensionality reduction methods are employed to identify the most salient features, which are then passed to the linear discriminant analysis (LDA) classifier. EMG and axial acceleration signal datasets from six intact-limbed and four amputee participants exhibited an average classification error of 15.68 % based on SR dimensionality reduction using the LDA classifier