Improving Human Face Recognition Using Deep Learning Based Image Registration And Multi-Classifier Approaches

Face detection, registration, and recognition have become a fascinating field for researchers. The motivation behind the enormous interest in the topic is the need to improve the accuracy of many real-time applications. Countless methodologies have been acknowledged and presented in the past years. The complexity of the human face visual and the significant changes based on different effects make it more challenging to design as well as implementing a powerful computational system for object recognition in addition to human face recognition. Using supervised learning often requires extensive training for the computer which results in high execution times. It is an essential step in the face recognition to apply strong preprocessing approaches such as face registration to achieve a high recognition accuracy rate. Although there are exist approaches do both detection and recognition, we believe the absence of a complete end-to-end system capable of performing recognition from an arbitrary scene is in large part due to the difficulty in alignment. Often, the face registration is ignored, with the assumption that the detector will perform a rough alignment, leading to suboptimal recognition performance. In this research, we presented an enhanced approach to improve human face recognition using a back-propagation neural network (BPNN) and features extraction based on the correlation between the training images. A key contribution of this paper is the generation of a new set called the T-Dataset from the original training data set, which is used to train the BPNN. We generated the T-Dataset using the correlation between the training images without using a common technique of image density. The correlated T-Dataset provides a high distinction layer between the training images, which helps the BPNN to converge faster and achieve better accuracy. Data and features reduction is essential in the face recognition process, and researchers have recently focused on the modern neural network. Therefore, we used using a classical conventional Principal Component Analysis (PCA) and Local Binary Patterns (LBP) to prove that there is a potential improvement even using traditional methods. We applied five distance measurement algorithms and then combined them to obtain the T-Dataset, which we fed into the BPNN. We achieved higher face recognition accuracy with less computational cost compared with the current approach by using reduced image features. We test the proposed framework on two small data sets, the YALE and AT&T data sets, as the ground truth. We achieved tremendous accuracy. Furthermore, we evaluate our method on one of the state-of-the-art benchmark data sets, Labeled Faces in the Wild (LFW), where we produce a competitive face recognition performance. In addition, we presented an enhanced framework to improve the face registration using deep learning model. We used deep architectures such as VGG16 and VGG19 to train our method. We trained our model to learn the transformation parameters (Rotation, scaling, and shifting). By leaning the transformation parameters, we will able to transfer the image back to the frontal domain. We used the LFW dataset to evaluate our method, and we achieve high accuracy

Characterization of Carbon-Fiber Reinforced Polyetherimide Thermoplastic Composites Using Mechanical and Ultrasonic Methods

Continuous fiber-reinforced thermoplastics (CFRT) have the potential for being a mass-produced material for high-performance applications. The primary challenge of using CFRT is achieving fiber wet-out due to the high viscosity of thermoplastics. This results in higher temperatures and pressures required for processing the composites. Co-mingling thermoplastic fibers with a reinforcing fiber, potentially, can enable better wetting by reducing the distance the matrix needs to flow. This could result in shorter cycle times and better consolidation at lower temperatures and pressures. In this study, a polyetherimide (PEI) fiber was comingled with carbon fibers (CF). The resultant fibers were woven into fabrics and processed through a compression-molding technique to form laminates. Control specimens were also fabricated using films of PEI layered between plies of woven carbon-fiber materials. The manufactured CFRT panels were evaluated using ultrasonic C-scans (scans in two spatial dimensions) and then characterized for mechanical properties. The specimens produced using the co-mingled fibers had the cycle time reduced significantly compared to the film CFRT, although the results from the mechanical property evaluations were mixed. The behaviors in the co-mingled laminates can be attributed to the resin- and void-content distribution and the fiber-bundle orientations in the cured composite

An efficient ionic liquid-based cloud point extraction to preconcentrate mercury in environmental samples and hair of occupational workers before spectrophotometric detection

ABSTRACT. Mercury preconcentration in environmental samples and hair of occupational workers prior to spectrophotometric detection was described using a unique, eco-friendly, and quick ionic liquid-based cloud point extraction method. The discovered method used an ionic liquid 1-butyl-3-methylimidazolium hexafluorophosphate with Triton X-114 as an extracting phase in the presence of a new chelating agent 3-(2-hydroxy-5-ethoxycarbonylphen-1-ylazo)-1,2,4-triazole at pH 7.0 to separate mercury and measure the complex spectrophotometrically at wavelength 550 nm. The effects of several analytical factors on extraction performance were investigated. With a correlation coefficient of 0.9997. The calibration graph was linear in the range of 2.0-400 µg/L. The limit of detection and preconcentration factor, respectively, were 0.5 µg/L and 100. The relative standard deviation of 100 and 300 µg/L mercury (n = 10) was 1.5 and 2.2, respectively, indicating the precision and reliability of the new IL-CPE approach. The accuracy of the proposed approach confirmed through the certified reference materials analysis. The applicability of the established technique was demonstrated successfully by the estimation of trace mercury in environmental samples and hair of occupational workers.     KEY WORDS: Mercury, Ionic liquid-based cloud point extraction, Environmental and hair samples, Spectrophotometry   Bull. Chem. Soc. Ethiop. 2022, 36(4), 767-778.                                                         DOI: https://dx.doi.org/10.4314/bcse.v36i4.

Stochastic Programming Model for Fuel Treatment Management

Due to the increased number and intensity of wild fires, the need for solutions that minimize the impact of fire are needed. Fuel treatment is one of the methods used to mitigate the effects of fire at a certain area. In this thesis, a two-stage stochastic programming model for fuel treatment management is constructed. The model optimizes the selection of areas for fuel treatment under budget and man-hour constraints. The process makes use of simulation tools like PHYGROW, which mimics the growth of vegetation after treatment, and FARSITE, which simulates the behavior of fire. The model minimizes the costs of fuel treatment as well as the potential losses when fire occurs. Texas Wild re Risk Assessment Model (TWRA) used by Texas Forest Service (TFS) is used to quantify risk at each area. The model is applied at TX 12, which is a re planning unit under the administration of TFS. Results show that the total of the expenditures on fuel treatment and the expected impact justify the efforts of fuel treatment

Performance assessment of antenna array for an unmanned air vehicle

In this paper, the performance of Linear Antenna Array Element (LAAE) has been evaluated at the Base Station (BS) with a different number of elements for Unmanned Air Vehicle UAV application. The Switched Beam (SB) and Phase Array (PA) have been used as a steering beam mechanism. The beam steering tracker is based on the GPS points of the UAV and the BS. In addition, the Misalignment angle has been analyzed for SB and PA corresponding to the maximum speed of the UAV. The compression between SB and PA in term of Bit Error Rate (BER) vs. Signal to Noise Ratio (SNR) and BER vs. Misalignment angle have been examined by using Matlab. The results show that the PA has better performance than SB in both terms under Additive White Gaussian Noise (AWGN) channel with an interference signal. When the number of the elements is eight provides longer distance than four by the factor (1.5 in SB case and 2 in PA case) and wider Misalignment angle range than twelve by factor (2 in SW case and 3 in PA case). Therefore, it is becoming a useful option for many applications

Deliberative Context-Aware Ambient Intelligence System for Assisted Living Homes

Monitoring wellbeing and stress is one of the problems covered by ambient intelligence, as stress is a significant cause of human illnesses directly affecting our emotional state. The primary aim was to propose a deliberation architecture for an ambient intelligence healthcare application. The architecture provides a plan for comforting stressed seniors suffering from negative emotions in an assisted living home and executes the plan considering the environment's dynamic nature. Literature was reviewed to identify the convergence between deliberation and ambient intelligence and the latter's latest healthcare trends. A deliberation function was designed to achieve context-aware dynamic human-robot interaction, perception, planning capabilities, reactivity, and context-awareness with regard to the environment. A number of experimental case studies in a simulated assisted living home scenario were conducted to demonstrate the approach's behavior and validity. The proposed methods were validated to show classification accuracy. The validation showed that the deliberation function has effectively achieved its deliberative objectives

Rheumatoid Arthritis Diagnosis Based on Intelligent System

التهاب المفاصل الروماتويدي  يؤثر على كثير من الناس مستهدفا المفاصل وخاصة المفاصل الصغيرة، ويستهدف جميع الأعمار حيث هو أكثر شيوعا في النساء. هذا المرض له العديد من الأعراض مشابهة لأمراض أخرى. لذلك، فمن الصعب جدا كشفه. كما أن أدوات التشخيص معقدة وغير اقتصادية. في هذا البحث، شبكة الذكاء الاصطناعي استخدمت لتشخيص والكشف المبكر عن التهاب المفاصل الروماتويدي وفقا للمعايير التي وضعتها الكلية الأمريكية للروماتيزم. أفضل أداء يحدث مع الحد الأدنى لعدد الخلايا العصبية المطلوبة عندما يكون عدد الخلايا العصبية هو 6. بحيث، فإن الأداء يساوي 10-10×3.8968. عند تقليل عدد الخلايا العصبية إلى 5 أو زيادة إلى 8، والنتيجة هي 0.0041 و  10-10×1.0611 ,على التوالي. مع ذلك، يمكن اعتبار جميع النتائج مقبولة و أن أفضل خيار لهذه التصاميم سيكون 6 خلايا عصبية من جانب التعقيد والدقة.The Rheumatoid Arthritis (RA) affects many people targeting their joints, especially small joints, and it targets all ages which it is more common in women. This disease has many symptoms similar to other diseases. Therefore, it is very hard to detect. Also, the diagnostic tools are complex and uneconomical. In this paper, artificial intelligence network used for diagnosis and early detection of RA in accordance with criteria developed by the American College of Rheumatology. The best performance occurs with the minimum number of neurons required when the number of neurons is 6. So that, the performance is equal to 3.8968x1010-.  When reducing the number of neurons to 5 or increasing to 8, the result is  0.0041 and 1.0611×10-10, respectively. However, all results can be consider acceptable and indicate that the best choice from this structure will be 6 neurons in the form of complexity and accuracy

Segmentation and measurement of lung pathological changes for COVID-19 diagnosis based on computed tomography

Coronavirus 2019 (COVID-19) spread internationally in early 2020, resulting from an existential health disaster. Automatic detecting of pulmonary infections based on computed tomography (CT) images has a huge potential for enhancing the traditional healthcare strategy for treating COVID-19. CT imaging is essential for diagnosis, the process of assessment, and the staging of COVID-19 infection. The detection in association with computed tomography faces many problems, including the high variability, and low density between the infection and normal tissues. Processing is used to solve a variety of diagnostic tasks, including highlighting and contrasting things of interest while taking color-coding into account. In addition, an evaluation is carried out using the relevant criteria for determining the alterations nature and improving a visibility of pathological changes and an accuracy of the X-ray diagnostic report. It is proposed that pre-processing methods for a series of dynamic images be used for these objectives. The lungs are segmented and parts of probable disease are identified using the wavelet transform and the Otsu threshold value. Delta maps and maps created with the Shearlet transform that have contrasting color coding are used to visualize and select features (markers). The efficiency of the suggested combination of approaches for investigating the variability of the internal geometric features (markers) of the object of interest in the photographs is demonstrated by analyzing the experimental and clinical material done in the work. The suggested system indicated that the total average coefficient obtained 97.64% regarding automatic and manual infection sectors, while the Jaccard similarity coefficient achieved 96.73% related to the segmentation of tumor and region infected by COVID-19