Recognizing arabic handwritten characters using deep learning and genetic algorithms

Automated techniques for Arabic content recognition are at a beginning period contrasted with their partners for the Latin and Chinese contents recognition. There is a bulk of handwritten Arabic archives available in libraries, data centers, historical centers, and workplaces. Digitization of these documents facilitates (1) to preserve and transfer the country’s history electronically, (2) to save the physical storage space, (3) to proper handling of the documents, and (4) to enhance the retrieval of information through the Internet and other mediums. Arabic handwritten character recognition (AHCR) systems face several challenges including the unlimited variations in human handwriting and the leakage of large and public databases. In the current study, the segmentation and recognition phases are addressed. The text segmentation challenges and a set of solutions for each challenge are presented. The convolutional neural network (CNN), deep learning approach, is used in the recognition phase. The usage of CNN leads to significant improvements across different machine learning classification algorithms. It facilitates the automatic feature extraction of images. 14 different native CNN architectures are proposed after a set of try-and-error trials. They are trained and tested on the HMBD database that contains 54,115 of the handwritten Arabic characters. Experiments are performed on the native CNN architectures and the best-reported testing accuracy is 91.96%. A transfer learning (TF) and genetic algorithm (GA) approach named “HMB-AHCR-DLGA” is suggested to optimize the training parameters and hyperparameters in the recognition phase. The pre-trained CNN models (VGG16, VGG19, and MobileNetV2) are used in the later approach. Five optimization experiments are performed and the best combinations are reported. The highest reported testing accuracy is 92.88%.

     

Intelligent Machine Learning with Metaheuristics Based Sentiment Analysis and Classification

Sentiment Analysis (SA) is one of the subfields in Natural Language Processing (NLP) which focuses on identification and extraction of opinions that exist in the text provided across reviews, social media, blogs, news, and so on. SA has the ability to handle the drastically-increasing unstructured text by transforming them into structured data with the help of NLP and open source tools. The current research work designs a novel Modified Red Deer Algorithm (MRDA) Extreme Learning Machine Sparse Autoencoder (ELMSAE) model for SA and classification. The proposed MRDA-ELMSAE technique initially performs preprocessing to transform the data into a compatible format. Moreover, TF-IDF vectorizer is employed in the extraction of features while ELMSAE model is applied in the classification of sentiments. Furthermore, optimal parameter tuning is done for ELMSAE model using MRDA technique. A wide range of simulation analyses was carried out and results from comparative analysis establish the enhanced efficiency of MRDA-ELMSAE technique against other recent techniques.     

Modeling and Analysis of UAV-Assisted Mobile Network with Imperfect Beam Alignment

With the rapid development of emerging 5G and beyond (B5G), Unmanned Aerial Vehicles (UAVs) are increasingly important to improve the performance of dense cellular networks. As a conventional metric, coverage probability has been widely studied in communication systems due to the increasing density of users and complexity of the heterogeneous environment. In recent years, stochastic geometry has attracted more attention as a mathematical tool for modeling mobile network systems. In this paper, an analytical approach to the coverage probability analysis of UAV-assisted cellular networks with imperfect beam alignment has been proposed. An assumption was considered that all users are distributed according to Poisson Cluster Process (PCP) around base stations, in particular, Thomas Cluster Process (TCP). Using this model, the impact of beam alignment errors on the coverage probability was investigated. Initially, the Probability Density Function (PDF) of directional antenna gain between the user and its serving base station was obtained. Then, association probability with each tier was achieved. A tractable expression was derived for coverage probability in both Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) condition links. Numerical results demonstrated that at low UAVs altitude, beam alignment errors significantly degrade coverage performance. Moreover, for a small cluster size, alignment errors do not necessarily affect the coverage performance.     

Self-care independence in children with neurological disorders: An interactional model of adaptive demands and executive dysfunction.

Objective: To describe a neuropsychological assessment model that proposes executive functioning as a key moderator in the development of self-care independence. Setting: Inpatient and outpatient rehabilitation facilities. Participants: Transition-age youths with congenital and acquired neurological disorders. Intervention: Transition to the adaptive roles and expectations of adulthood can be challenging for adolescents with neurological disorders and other chronic medical conditions. These individuals frequently encounter functional problems related to additional time requirements, new life skill demands, and increased need for organization and planning. In addition, the neuropsychological consequences of these disorders often include deficits in motor speed and coordination as well as executive control (including planning, organization, and working memory). Consideration of the integrity of executive function skills and the presence of atypical adaptive demands is crucial during planning for transition of individuals into self-care independence and development of an approach to assessment and intervention. Conclusions: Rehabilitation psychologists have the potential to improve the quality of life of adolescents with neurological disorders as they transition into adulthood by considering the "executive burden" posed to the individuals by various combinations of executive dysfunction and atypical adaptive demands. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Photocatalytic degradation of diclofenac using hybrid MIL-53(Al)@TiO2 and MIL-53(Al)@ZnO catalysts

In this work, TiO₂ and ZnO were incorporated successfully into a MIL-53(Al) metal–organic framework (MOF) to form nanocomposites via a facile post-modification technique. The hybrid MIL-53(Al)@TiO₂ and MIL-53(Al)@ZnO were characterized by several characterization tests. The X-ray diffraction (XRD), Fourier-transform infrared (FTIR), and field-emission scanning electron microscopy (FE-SEM) analyses showed evidence of the successful incorporation of TiO₂ and ZnO within the MIL-53(Al) framework. The thermal gravimetric analysis (TGA) analysis demonstrated the excellent thermal stability of MIL-53(Al)@TiO₂ and MIL-53(Al)@ZnO, while diffuse reflectance spectroscopy (DRS) determined the direct optical band gaps of MIL-53(Al)@ZnO and MIL-53(Al)@TiO₂ to be 3.24 and 3.34 eV, respectively. The composites were also tested for the photocatalytic degradation of diclofenac (DCF) as a micropollutant. The DCF degradation efficiency of the photocatalysts was ranked in the following order: MIL-53(Al)@TiO₂ > MIL-53(Al) > TiO₂ > ZnO > MIL-53(Al)@ZnO. The incorporation of TiO₂ enhanced the optical properties of MIL-53 (Al), which was confirmed with the superior photodegradation efficiency of MIL-53(Al)@TiO₂ (>85% in 2 h) as compared to the pristine MIL-53(Al) (around 80% in 2 h). The improvement in the photodegradation of the hybrid-MOF is mostly associated with the possible dual function of the adsorption and photodegradation mechanisms. The reusability of MIL-53(Al) and its composites was inspected over 3 cycles of photodegradation experiments with DCF. The photocatalytic activity of MIL-53(Al)@TiO₂ remained unchanged (>90%), while for MIL-53(Al) and MIL-53(Al)@ZnO a slight drop was observed over three cyclic degradation experiments. Fluorescence measurements revealed that the hydroxyl radical is an important reactive oxygen species produced by all the photocatalysts that aid in the photodegradation of DCF. Furthermore, the kinetic modelling of the photoreaction identified a second-order kinetics for all catalysts. Experiments with scavengers showed that hydroxyl radicals played a major role in the photocatalytic process, and it was found that only 2 h of treatment was sufficient to obtain a considerable chemical oxygen demand (COD) reduction of 58%.     

Structural,ferroelectric, magnetic and magnetoelectric properties of (1-x) Ba0.85Ca0.15Zr0.1Ti0.9O3 -x La0.67Sr0.33MnO3 lead-free magnetoelectric composites

The influence of an additional La_0.67Sr_0.33MnO₃ (LSMO) magnetic phase on the structural, ferromagnetic, ferroelectric, and magnetoelectric properties of Ba_0.85Ca_0.15Zr_0.1Ti_0.9O₃ (BCZT) ferroelectric phase was studied for composites of (1-x)BCZT -xLSMO (x = 0, 25, 50, 75 and 100%). The ferroelectric BCZT sample showed a perovskite single phase formation with a tetragonal crystal structure of the P4mm space group, and the magnetic phase of LSMO presented a rhombohedral crystal structure of R3c space group as shown by XRD. The composite sample with 25% LSMO exhibited large ferroelectric and piezoelectric properties with remnant, saturation polarization, and coercive electric field P_r ~7.74 μC/cm², P_s ~11.69 μC/cm² and E_C ~12.22 kV/cm with a piezoelectric coefficient d₃₃ ~ 231 pC/N. The magnetic characterization for the composites showed that the sample containing 75% of LSMO revealed the highest remnant, saturation magnetization, and coercive field of M_r ~1.358 emu/g, M_s ~19.17 emu/g, and H_C ~33.19 Oe, respectively. Moreover, it revealed the largest magnetoelectric coupling coefficient α_ME ~2.51 mV/cm.Oe with high coupling quality at a lower applied magnetic field. The results highlight the value of these composites as lead-free room temperature magnetoelectric sensors and actuators.     

Intelligent Transmission Control for Efficient Operations in SDN

Although the Software-Defined Network (SDN) is a well-controlled and efficient network but the complexity of open flow switches in SDN causes multiple issues. Many solutions have been proposed so far for the prevention of errors and mistakes in it but yet, there is still no smooth transmission of pockets from source to destination specifically when irregular movements follow the destination host in SDN, the errors include packet loss, data compromise etc. The accuracy of packets received at their desired destination is possible if networks for pockets and hosts are monitored instead of analysis of network snapshot statistically for the state, as these approaches with open flow switches, discover bugs after their occurrence. This article proposes a design to achieve the said objective by defining the Intelligent Transmission Control Layer (ITCL) layer. It monitors all the connections of end hosts at their specific locations and performs necessary settlements when the connection state changes for one or multiple hosts. The layer informs the controller regarding any state change at one period and controller collects information of end nodes reported via ITCL. Then, updates flow tables accordingly to accommodate a location-change scenario with a route-change policy. ICTL is organized on prototype-based implementation using the popular POX platform. In this paper, it has been discovered that ITCL produces efficient performance in the trafficking of packets and controlling different states of SDN for errors and packet loss.     

Energetic Control of Redox-Active Polymers toward Safe Organic Bioelectronic Materials

Avoiding faradaic side reactions during the operation of electrochemical devices is important to enhance the device stability, to achieve low power consumption, and to prevent the formation of reactive side-products. This is particularly important for bioelectronic devices, which are designed to operate in biological systems. While redox-active materials based on conducting and semiconducting polymers represent an exciting class of materials for bioelectronic devices, they are susceptible to electrochemical side-reactions with molecular oxygen during device operation. Here, electrochemical side reactions with molecular oxygen are shown to occur during organic electrochemical transistor (OECT) operation using high-performance, state-of-the-art OECT materials. Depending on the choice of the active material, such reactions yield hydrogen peroxide (H₂O₂), a reactive side-product, which may be harmful to the local biological environment and may also accelerate device degradation. A design strategy is reported for the development of redox-active organic semiconductors based on donor–acceptor copolymers that prevents the formation of H₂O₂ during device operation. This study elucidates the previously overlooked side-reactions between redox-active conjugated polymers and molecular oxygen in electrochemical devices for bioelectronics, which is critical for the operation of electrolyte-gated devices in application-relevant environments.