Template Matching Based Sign Language Recognition System for Android Devices

An android based sign language recognition system for selected English vocabularies was developed with the explicit objective to examine the specific characteristics that are responsible for gestures recognition. Also, a recognition model for the process was designed, implemented, and evaluated on 230 samples of hand gestures.  The collected samples were pre-processed and rescaled from 3024 ×4032 pixels to 245 ×350 pixels. The samples were examined for the specific characteristics using Oriented FAST and Rotated BRIEF, and the Principal Component Analysis used for feature extraction. The model was implemented in Android Studio using the template matching algorithm as its classifier. The performance of the system was evaluated using precision, recall, and accuracy as metrics. It was observed that the system obtained an average classification rate of 87%, an average precision value of 88% and 91% for the average recall rate on the test data of hand gestures.  The study, therefore, has successfully classified hand gestures for selected English vocabularies. The developed system will enhance the communication skills between hearing and hearing-impaired people, and also aid their teaching and learning processes. Future work include exploring state-of-the-art machining learning techniques such Generative Adversarial Networks (GANs) for large dataset to improve the accuracy of results. Keywords— Feature extraction; Gestures Recognition; Sign Language; Vocabulary, Android device

DESIGN AND SIMULATION OF AN EFFICIENT MODEL FOR CREDIT CARDS FRAUD DETECTION

In this study a model which can improve the accuracy and reliability of credit card fraud detection was proposed. This is with a few to mitigating contentious issues regarding online transaction of credit card, such as  amount of transactions that have resulted in payment default and the number of credit card fraud cases that have been recorded, all of which have put the economy in jeopardy.   To address this challenge,sample dataset was sourced from online repository database of Kaggle. The feature extraction on the data was performed using Principal Component Analysis (PCA). The credit card fraud detection model was designed using Neuro-fuzzy logic technique, clustering was done using Hierarchical Density Based Spatial Clustering of Application with Noise (HDBSCAN) .The simulation of the proposed model was done in Python programming environment.The performance evaluation of the model was carried out by comparing the proposed model with Neuro-Fuzzy (NF) technique using performance metrics such as precision, recall, F1-score and accuracy.  The simulation result showed that the proposed model (NF + HDBSCAN) had precision of 98.75%, recall of 98.70%, F1-Score of 97.65% and accuracy 99.75% . NF had Precision of 94.60%, recall of 94.50%, F1-Score of 95.50% and accuracy 95.70% using training dataset. Likewise, when test dataset were used, the proposed (NF + HDBSCAN) had precision of 93.50%, recall of 95.50%, F1-Score of 94.50% and accuracy 95.50%. NF had Precision of 92.50%, recall of 93.00%, F1-Score of 94.00% and accuracy 93.50%.  The simulation results of the proposed model was viable, reliable and showed possibility of being designed as module which could be  integrated into the existing credit card design for lowering fraud rate and assisting fraud investigators

TEXTURE MODELING AND SIMULATION FOR SYNTHETIC PALM VEIN IMAGE GENERATION SYSTEM

Unavailability of large-scale palm vein databases due to their intrusiveness have posed challenges in exploring this technology for large-scale applications. Hence, this research modelled and generated synthetic palm vein images from only a couple of initial samples using statistical features. Variations were introduced to the three optimized statistical features (S5; the original images were employed as training images and the best variation in the first experiment  as training images, S4; the best variation in the first experiment  as training images while the original images were used as testing images, S3; mean vectors, covariance matrices and correlation coefficient, S2; mean vectors and covariance matrices, S1; mean vectors, Non-Synthetic; acquired image) which were used to generate synthetic palm vein images employing statistical and Genetic Algorithm (GA) approaches and were evaluated based on Equal Error Rate (EER), Average Recognition Accuracy (ARA) and Average Recognition Time (ART). The results obtained from the experiment showed that EERs were 0.22, 0.51, 0.58 and 4.36 for S3, S2, S1 and NS respectively. S3 had superior ARA (99.83%) compared with S2 (99.77 %), S1 (99.70 %) and NS (98.33 %). The ARTs obtained were 84.97s, 75.55s, 84.04s and 681.74s for S1, S2, S3 and NS respectively with S2 (75.55s) having significantly least value. Furthermore, EER, ARA and ART for S4 were 0.43, 99.00%, and 12.13s, respectively while the corresponding values for S5 were 1.43, 97.50%, and 680.13s, respectively. The research outcome justifies the extraction of mean vectors, covariance matrices and correlation coefficient

Entrapped chemically synthesized gold nanoparticles combined with polyethylene glycol and chloroquine diphosphate as an improved antimalarial drug

Objective(s): Drug delivery is an engineering technology to control the release and delivery of therapeutic agents to target organs, tissues, and cells. Metallic nanoparticles, such as gold nanoparticles (AuNPs) have exceptional properties which enable efficient drug transport into different cell types with reduced side effects and cytotoxicity to other tissues.Materials and Methods: AuNPs were synthesized by adopting the Turkevich method to reduce tetra chloroauric (III) acid (HAuCl4) solution with sodium citrate. A factorial design of 24 was used to investigate the influence of temperature, stirring speed, and the volume of citrate and gold salt on the size of AuNPs synthesis. The produced chemical-AuNPs (CN-AuNPs) were characterized using ultraviolet-visible spectroscopy and dynamic light scattering (DLS) which was conjugated with polyethylene glycol (PEG) loaded with chloroquine diphosphate. The latter were characterized with transmission electron microscopy (TEM), Energy dispersive x-ray spectroscopy (EDS), selected area electron diffraction (SAED) patterns and Fourier transmission infrared spectroscopy. The antimalarial activities of the three formulations were tested on Plasmodium-infected mice. Moreover, the evaluation of curative potentials of the formulations was carried out via parasite counts. The anemic and pathological conditions of nano-encapsulation were investigated for their cytotoxicity level. Results: The CN-AuNPs show surface plasmon resonance absorption ranging from 526 to 529 nm with smaller particle size at the lower citrate volume. The TEM image of CN-AuNPs with polyethylene glycol (PEG) and CN-AuNPs-PEG encapsulated with chloroquine diphosphate revealed spherical shape with EDS showing the appearance of gold (Au) at 2.0, 2.1, and 9.9 KeV. The SAED also revealed that the AuNPs were crystalline in nature. The in vitro time-dependent encapsulation release showed an extension of time release, compared to CN-AuNPs-PEG with parasitemia clearance at the same level of cytotoxicity. Conclusion: Therefore, although improved activity of the CN-AuNPs-PEG encapsulating was achieved but its cytotoxicity still is a limitation