Human Values in the Structure of the Arabic Language: A Descriptive Analytical Study

This study aims at discovering the relationship between the structure of the Arabic language and the traits, characteristics, ethics and values of the Arabs which are originally connected to the Islamic religion. The study is built on the assumption that there is large harmony between the structure of the Arabic language on one hand and the human characteristics which Islam came to affirm on the other. The reflections of these values are clearly present in the Arabic language as if to say that the structure of the Arabic language is in harmony with these human values just as the Islamic Shariah does. This is why God chooses this language to carry the message of this true religion, which is the religion of pure human instinct. This study also reaches another conclusion which is that the Arabic language has characteristics that are non-present in any other language in the world

QuSecNets: Quantization-based Defense Mechanism for Securing Deep Neural Network against Adversarial Attacks

Adversarial examples have emerged as a significant threat to machine learning algorithms, especially to the convolutional neural networks (CNNs). In this paper, we propose two quantization-based defense mechanisms, Constant Quantization (CQ) and Trainable Quantization (TQ), to increase the robustness of CNNs against adversarial examples. CQ quantizes input pixel intensities based on a "fixed" number of quantization levels, while in TQ, the quantization levels are "iteratively learned during the training phase", thereby providing a stronger defense mechanism. We apply the proposed techniques on undefended CNNs against different state-of-the-art adversarial attacks from the open-source \textit{Cleverhans} library. The experimental results demonstrate 50%-96% and 10%-50% increase in the classification accuracy of the perturbed images generated from the MNIST and the CIFAR-10 datasets, respectively, on commonly used CNN (Conv2D(64, 8x8) - Conv2D(128, 6x6) - Conv2D(128, 5x5) - Dense(10) - Softmax()) available in \textit{Cleverhans} library

Development of low-overhead soft error mitigation technique for safety critical neural networks applications

Deep Neural Networks (DNNs) have been widely applied in healthcare applications. DNN-based healthcare applications are safety-critical systems that require highreliability implementation due to a high risk of human death or injury in case of malfunction. Several DNN accelerators are used to execute these DNN models, and GPUs are currently the most prominent and the dominated DNN accelerators. However, GPUs are prone to soft errors that dramatically impact the GPU behaviors; such error may corrupt data values or logic operations, which result in Silent Data Corruption (SDC). The SDC propagates from the physical level to the application level (SDC that occurs in hardware GPUs’ components) results in misclassification of objects in DNN models, leading to disastrous consequences. Food and Drug Administration (FDA) reported that 1078 of the adverse events (10.1%) were unintended errors (i.e., soft errors) encountered, including 52 injuries and two deaths. Several traditional techniques have been proposed to protect electronic devices from soft errors by replicating the DNN models. However, these techniques cause significant overheads of area, performance, and energy, making them challenging to implement in healthcare systems that have strict deadlines. To address this issue, this study developed a Selective Mitigation Technique based on the standard Triple Modular Redundancy (S-MTTM-R) to determine the model’s vulnerable parts, distinguishing Malfunction and Light-Malfunction errors. A comprehensive vulnerability analysis was performed using a SASSIFI fault injector at the CNN AlexNet and DenseNet201 models: layers, kernels, and instructions to show both models’ resilience and identify the most vulnerable portions and harden them by injecting them while implemented on NVIDIA’s GPUs. The experimental results showed that S-MTTM-R achieved a significant improvement in error masking. No-Malfunction have been improved from 54.90%, 67.85%, and 59.36% to 62.80%, 82.10%, and 80.76% in the three modes RF, IOA, and IOV, respectively for AlexNet. For DenseNet, NoMalfunction have been improved from 43.70%, 67.70%, and 54.68% to 59.90%, 84.75%, and 83.07% in the three modes RF, IOA, and IOV, respectively. Importantly, S-MTTMR decreased the percentage of errors that case misclassification (Malfunction) from 3.70% to 0.38% and 5.23% to 0.23%, for AlexNet and DenseNet, respectively. The performance analysis results showed that the S-MTTM-R achieved lower overhead compared to the well-known protection techniques: Algorithm-Based Fault Tolerance (ABFT), Double Modular Redundancy (DMR), and Triple Modular Redundancy (TMR). In light of these results, the study revealed strong evidence that the developed S-MTTMR was successfully mitigated the soft errors for the DNNs model on GPUs with lowoverheads in energy, performance, and area indicated a remarkable improvement in the healthcare domains’ model reliability

An Empirical Study on the Effect of Perceived Usefulness and Ease of Use on Purchase Intention Through Mobile Devices in Pakistan: A Mediating Role of Online Trust

In Pakistan, electronic commerce market is in developing phase, and people are not much familiar with technology. Customers feel fear for providing debit/credit card information to online retailers. This research proposes a theoretical model in which online trust is used as a mediator among perceived ease of use, perceived usefulness and intention to purchase through mobile devices. In this study Smartphone, cellular phones and tablets with internet connection conceived as mobile devices. Data were collected through questionnaire from the students and employees of different universities in Pakistan and analyzed by partial least squares structural equation modeling (PLS-SEM) method. Results suggest that PEOU have an insignificant effect while PU has a positive effect on online trust and purchase intention through mobile devices. Also, online trust does not mediate between PEOU and purchase Intention through mobile devices while it partially mediates between PU and purchase intention through mobile devices. Keywords: Electronic Commerce, Online trust, Intention to purchase through mobile devices, Perceived ease of use, perceived usefulness

In silico elucidation of potential drug target sites of the Thumb Index Fold Protein, Wnt-8b

Purpose: The involvement of Wnt-8b in Wnt signaling pathway leads to various  cancers. The purpose of this study was to determine the therapeutic compounds from the available library by targeting Wnt-8b using molecular docking analyses.Methods: Threading and comparative modeling approaches were employed to predict the 3D structure of Wnt-8b. Sixty-eight models were evaluated using molprobity, ERRAT and rampage evaluation tools and the model having 82.456 % overall quality value was selected for further analyses. The acyl group was added to the suitable model to satisfy the hydrophobic nature of the Wnt-8b. Literature-derivedcompounds were selected for comparative molecular docking studies using GOLD, AutoDock and AutoDock Vina. Furthermore, docked complexes were analyzed and visualized using Chimera and Ligplot.Results: The compound ZINC04029462 exhibited high binding potential with Wnt-8b and palmitoleic acid and was found common among top 20 compounds of each tool. His-183, Val-185, Ser-186, Gly-187, Ser-188 and Thr-190 residues commonly interacted with compounds and palmitoleic acid and considered as potential interacting residues.Conclusion: Common interacting residues from top 20 compounds of each tool suggest that these compounds may be utilized to inhibit aberrant expression of Wnt-8b. The common inhibitor ZINC04029462 may act as a lead compound for further drug designing against Wnt family.Keywords: Wnt-8b, Cancer, Homology modeling, Molecular docking, AutoDoc

Plant Disease Detection and Classification by Deep Learning

Plant diseases affect the growth of their respective species, therefore their early identification is very important. Many Machine Learning (ML) models have been employed for the detection and classification of plant diseases but, after the advancements in a subset of ML, that is, Deep Learning (DL), this area of research appears to have great potential in terms of increased accuracy. Many developed/modified DL architectures are implemented along with several visualization techniques to detect and classify the symptoms of plant diseases. Moreover, several performance metrics are used for the evaluation of these architectures/techniques. This review provides a comprehensive explanation of DL models used to visualize various plant diseases. In addition, some research gaps are identified from which to obtain greater transparency for detecting diseases in plants, even before their symptoms appear clearly

Plant Disease Classification: A Comparative Evaluation of Convolutional Neural Networks and Deep Learning Optimizers

Recently, plant disease classification has been done by various state-of-the-art deep learning (DL) architectures on the publicly available/author generated datasets. This research proposed the deep learning-based comparative evaluation for the classification of plant disease in two steps. Firstly, the best convolutional neural network (CNN) was obtained by conducting a comparative analysis among well-known CNN architectures along with modified and cascaded/hybrid versions of some of the DL models proposed in the recent researches. Secondly, the performance of the best-obtained model was attempted to improve by training through various deep learning optimizers. The comparison between various CNNs was based on performance metrics such as validation accuracy/loss, F1-score, and the required number of epochs. All the selected DL architectures were trained in the PlantVillage dataset which contains 26 different diseases belonging to 14 respective plant species. Keras with TensorFlow backend was used to train deep learning architectures. It is concluded that the Xception architecture trained with the Adam optimizer attained the highest validation accuracy and F1-score of 99.81% and 0.9978 respectively which is comparatively better than the previous approaches and it proves the novelty of the work. Therefore, the method proposed in this research can be applied to other agricultural applications for transparent detection and classification purposes

Weed Detection by Faster RCNN Model: An Enhanced Anchor Box Approach

To apply weed control treatments effectively, the weeds must be accurately detected. Deep learning (DL) has been quite successful in performing the weed identification task. However, various aspects of the DL have not been explored in previous studies. This research aimed to achieve a high average precision (AP) of eight classes of weeds and a negative (non-weed) class, using the DeepWeeds dataset. In this regard, a DL-based two-step methodology has been proposed. This article is the second stage of the research, while the first stage has already been published. The former phase presented a weed detection pipeline and consisted of the evaluation of various neural networks, image resizers, and weight optimization techniques. Although a significant improvement in the mean average precision (mAP) was attained. However, the Chinee apple weed did not reach a high average precision. This result provided a solid ground for the next stage of the study. Hence, this paper presents an in-depth analysis of the Faster Region-based Convolutional Neural Network (RCNN) with ResNet-101, the best-obtained model in the past step. The architectural details of the Faster RCNN model have been thoroughly studied to investigate each class of weeds. It was empirically found that the generation of anchor boxes affects the training and testing performance of the Faster RCNN model. An enhancement to the anchor box scales and aspect ratios has been attempted by various combinations. The final results, with the addition of 64 × 64 scale size, and aspect ratio of 1:3 and 3:1, produced the best classification and localization of all classes of weeds and a negative class. An enhancement of 24.95% AP was obtained in Chinee apple weed. Furthermore, the mAP was improved by 2.58%. The robustness of the approach has been shown by the stratified k-fold cross-validation technique and testing on an external dataset