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.     

Intelligent system for visual web content analytics: A new approach and case study

The accuracy of searches for visual data elements, as well as other types of information, depends on the terms used by the user in the input query to retrieve the relevant results and to reduce the irrelevant ones. Most of the results that are returned are relevant to the query terms, but not to their meaning. For example, certain types of web contents hold hidden information that traditional search engines are unable to retrieve. Searching for the mathematical construct of 1/x using Google will not result in the retrieval of the documents that contain the mathematically equivalent expressions (i.e. x^?1). Because conventional search engines fall short of providing math-search capabilities. One of these capabilities is the ability of these search engines to detect the mathematical equivalence between users’ quires and math contents. In addition, users sometimes need to use slang terms, either to retrieve slang-based visual data (e.g. social media content) or because they do not know how to write using classical form. To solve such a problem, this paper proposed an AI-based system for analysing multilingual slang web contents so as to allow a user to retrieve web slang contents that are relevant to the user’s query. The proposed system presents an approach for visual data analytics, and it also enables users to analyse hundreds of potential search results/web pages by starting an informed friendly dialogue and presenting innovative answers.     

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%.

     

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%.     

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.     

Clams as pollution bioindicators in Kuwait''s marine environment: metal accumulation and depuration

This study has developed and verified clam transplantation for marine pollution monitoring in Kuwait and has assessed its reliability for monitoring pollution by heavy metals through laboratory and field transplantation experiments. In field experiments, live specimens of the clam Circenita callipyga were transplanted, either suspended in seawater or buried in sand, to 11 coastal sites in Kuwait; subsamples of transplanted clams were recovered at intervals over a 6-month period. In laboratory experiments, heavy metal accumulation for 36 days and depuration (body's release of pollutants) for 60 days in small and big clams were investigated. Clams subsampled in laboratory and field experiments were analyzed for Hg, Cu, V, Cd and Pb. Results showed the ability of clams to survive under the transplantation conditions. Accumulation of biofouling materials was a problem at only two transplantation sites and was overcome by periodic cleaning. Statistical analysis of laboratory experimental data showed significant accumulation of all test metals and significant depuration of Hg, Cu, V and Cd. Rates of metal accumulation and depuration differed in relation to clam size class. Field experiments indicated statistically significant increases in Hg and Cu concentrations in the transplanted clams at most stations, no change in Cd concentrations and an increase or, occasionally, a decrease in V and Pb concentrations.