Scopus Research — Zahraa Hashim Kareem almahanna

The evolution of wireless communication has introduced 5G-enabled healthcare systems that promise low-latency, high-speed, and scalable solutions for real-time disease detection and monitoring. Among the health conditions that benefit from such systems is Parkinson’s Disease (PD), where sound signal assessment has become a key screening tool due to the early vocal impairments associated with the disease. Accurate and fast classification of these signals is critical for enabling responsive and effective healthcare support. Traditional machine learning classifiers such as Support Vector Machines (SVM), K-Nearest Neighbours (KNN), and Random Forest (RF) are typically not capable to deal with the dynamic and non-linear nature of voice signals. Their results can weaken rapidly if they get features sets with high dimensionality or if the voice variability is too high, and this restricts their practical use for real-time healthcare systems. In order to solve these problems, the paper at hand suggests an effective classification framework which adopts Particle Swarm Optimization (PSO) method to select features and uses the Quantile Regression Forest (QRF) for classification. QRF is utilized due to its effectiveness in identifying distributional changes in non-linear data, whereas PSO assists in finding the most important subset of features in order to decrease the computational burden and increase the accuracy. The PSO-QRF model that was suggested obtained an accuracy of classification of 98.53% after feature selection, while 95.59% without it on Parkinson’s disease voice dataset. The system completes classification within 8.5 milliseconds and can handle up to 1,000 users at a time, hence, it is very suitable to be used in low-latency 5G healthcare environments. These results illustrate that the designed model is able to detect PD using voice data in real time in intelligent medical systems. © This article is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. License details: https://creativecommons.org/licenses/by-sa/4.0/

Integrating IoT with machine-learning transforms solar energy management, improving efficiency and reliability. This paper discusses building a sophisticated system that combines real-time IoT data collection with advanced machine-learning techniques to perform predictive analytics. The goal is to enhance the efficiency of solar energy generation, utilization, and deployment, thereby reducing inefficiency, costs, and carbon footprints. The study leverages mathematical modelling, IoT integration, machine-learning algorithm deployment, and comprehensive testing. Real-time data collection enhances monitoring capabilities while implementing machine-learning enables accurate prediction of energy production. This approach achieves a solar energy utilization rate of over 90%, leading to significant cost savings. The study was conducted on two systems, one of 10.45kWh at Shah Alam and the other of 14.08 kWh at Klang (in Kuala Lumpur, Malaysia), and the monitoring was conducted for a full year from September 2022 to September 2023. The results show an improvement in energy management by 12%, an increase in prediction accuracy by 8%, and the highest system reliability. The economic analysis shows significant cost reductions of up to 11%. © Published under licence by IOP Publishing Ltd.

Business intelligence is becoming more essential for supply chain administrators to make good decisions. The globalization of supply chains makes their management and control more challenging. Blockchain is a distributed digital ledger technology that guarantees traceability, transparency, and security and promises to ease global supply chain management issues. This paper proposes the Blockchain-assisted Secure Data Management Framework (BSDMF) for financial data handling for supply chain integrated business intelligence models. Analyzing, collecting, and demonstrating data could be important to a business, its supply chain performance, and sustainability. The blockchain can interrupt supply chain processes for improved finance handling, distributed management, and process automation. The study’s experimental result will help organizations deploy blockchain applications with intelligent business strategies to support supply chain management effectively. The simulation outcome has been implemented, and the recommended method achieves a computation time of fewer than 2 hours, an efficiency ratio of 97.4%, an error ratio of 94.1%, data authentication of 92.1%, and a data management ratio of 98.7%. © 2024 – IOS Press. All rights reserved.

The consensus method, being one of the most important aspects of blockchain, differs depending on the sector. The commonly used proof-of-work (PoW) consensus technique for public chain application scenarios still has challenges that are difficult to handle, such as security and high computer power. As a result, the PoW method is investigated in terms of enlarging the solution space and improving the adjustment mechanism. A consensus technique based on fuzzy random proof of work (FRMH) is presented. The FRMH algorithm improves the security of the blockchain consensus mechanism by increasing the solution space of the consensus algorithm by incorporating technologies such as a fuzzy transitive closure matrix in fuzzy mathematics. In addition, the FRMH algorithm uses a dual adjustment method to cope with machines with high computational power and thus solves the issue of high computing power being difficult to regulate on the blockchain. Through mathematics, it has been shown that the FRMH algorithm has greatly enhanced solution space and greater processing power control. © 2024 The Author(s).

One of the most critical problems facing the blockchain technology industry right now is how to protect the privacy of users’ data on the blockchain in a way that is both effective and cheap. Based on the Pedersen commitment and the Schnorr protocol, this study comes up with a secure multi-party computing protocol (BPLSM). By making the structure of the protocol and doing formal proof calculations, it has been shown that the protocol can be used in the blockchain network to combine private messages for efficient signing while keeping people’s identities secret. Furthermore, by looking at the nature and security of the protocol, it is also possible to find that the BPLSM protocol on the blockchain has a low cost of computing and a high level of information secrecy. Furthermore, it was found that the BPLSM protocol takes less time to check than the current mainstream BLS signature in a simple multi-party transaction with a fixed number of participants. © 2024 The Author(s).

The Publisher has retracted this article in agreement with the Editor-in-Chief. The article was submitted to be part of a guest-edited issue. An investigation by the publisher found a number of articles, including this one, with a number of concerns, including but not limited to compromised editorial handling and peer review process, inappropriate or irrelevant references or not being in scope of the journal or guest-edited issue. Based on the investigation's findings the publisher, in consultation with the Editor-in-Chief therefore no longer has confidence in the results and conclusions of this article. The author, Mustafa Musa Jaber disagrees with this retraction. The authors, Mohammed Hasan Ali and Sura Khalil has not responded to correspondence regarding this retraction. The Publisher has not been able to obtain a current email address for author, Ali S. Abosinnee. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

Smart logistics will encourage replacing manual systems with the Internet of Things (IoT) or automated handling equipment taking care of repetitive tasks in the enterprise management system. Opportunities to address the issues arise from the development of smart logistics. When used with other quantitative analytic tools and techniques, today’s IoT may generate vast amounts of data and reveal intricate correlations between the many transactions represented by that data. Smart logistics can benefit from the inclusion of these features. The complication and variety of consumer orders necessitate a change in warehouse operations. There is a need for real-time data and contextual data on highly tailored orders' large diversity and small batch sizes. To achieve on-time order fulfilment, the synchronization of purchase orders to support production is critical to the frequent changes in customer needs. Order fulfilment suffers as a result of inefficient and erroneous order selection. Computational intelligence techniques are used in the research to provide an advanced data analysis methodology for Industry 4.0’s smart logistics through global manufacturing. Advanced data analysis methods for Industry 4.0’s smart logistics are developed using computational intelligence approaches. However, IoT-SL can increase logistics productivity, picking accuracy, and efficiency based on data obtained from a case firm and is resilient to order unpredictability. Smart contracts, logistics planners, and asset condition monitoring are included in the paper's smart logistics system. A prototype solution is implemented to demonstrate responsibility, traceability, and obligation for asset management across the supply chain by multiple stakeholders participating in a logistics scenario. It is important to look at how IoT technologies are being used in the smart logistics industry from transportation, storage, loading/unloading, carrying, distributed processing and information transfer, thereby achieving real-time monitoring, increased logistics productivity, logistics management, increased delivery of goods and efficiency of 98.3%. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

As the amount of medical images transmitted over networks and kept on online servers continues to rise, the need to protect those images digitally is becoming increasingly important. However, due to the massive amounts of multimedia and medical pictures being exchanged, low computational complexity techniques have been developed. Most commonly used algorithms offer very little security and require a great deal of communication, all of which add to the high processing costs associated with using them. First, a deep learning classifier is used to classify records according to the degree of concealment they require. Medical images that aren't needed can be saved by using this method, which cuts down on security costs. Encryption is one of the most effective methods for protecting medical images after this step. Confusion and dispersion are two fundamental encryption processes. A new encryption algorithm for very sensitive data is developed in this study. Picture splitting with image blocks is nowdeveloped by using Zigzag patterns, rotation of the image blocks, and random permutation for scrambling the blocks.After that, this research suggests a Region of Interest (ROI) technique based on selective picture encryption. For the first step, we use an active contour picture segmentation to separate the ROI from the Region of Background (ROB). Permutation and diffusion are then carried out using a Hilbert curve and a Skew Tent map. Once all of the blocks have been encrypted, they are combined to create encrypted images. The investigational analysis is carried out to test the competence of the projected ideal with existing techniques. © 2023 Tech Science Press. All rights reserved.

Data mining plays a crucial role in extracting meaningful knowledge from large-scale data repositories, such as data warehouses and databases. Association rule mining, a fundamental process in data mining, involves discovering correlations, patterns, and causal structures within datasets. In the healthcare domain, association rules offer valuable opportunities for building knowledge bases, enabling intelligent diagnoses, and extracting invaluable information rapidly. This paper presents a novel approach called the Machine Learning based Association Rule Mining and Classification for Healthcare Data Management System (MLARMC-HDMS). The MLARMC-HDMS technique integrates classification and association rule mining (ARM) processes. Initially, the chimp optimization algorithm-based feature selection (COAFS) technique is employed within MLARMC-HDMS to select relevant attributes. Inspired by the foraging behavior of chimpanzees, the COA algorithm mimics their search strategy for food. Subsequently, the classification process utilizes stochastic gradient descent with a multilayer perceptron (SGD-MLP) model, while the Apriori algorithm determines attribute relationships. We propose a COA-based feature selection approach for medical data classification using machine learning techniques. This approach involves selecting pertinent features from medical datasets through COA and training machine learning models using the reduced feature set. We evaluate the performance of our approach on various medical datasets employing diverse machine learning classifiers. Experimental results demonstrate that our proposed approach surpasses alternative feature selection methods, achieving higher accuracy and precision rates in medical data classification tasks. The study showcases the effectiveness and efficiency of the COA-based feature selection approach in identifying relevant features, thereby enhancing the diagnosis and treatment of various diseases. To provide further validation, we conduct detailed experiments on a benchmark medical dataset, revealing the superiority of the MLARMC-HDMS model over other methods, with a maximum accuracy of 99.75%. Therefore, this research contributes to the advancement of feature selection techniques in medical data classification and highlights the potential for improving healthcare outcomes through accurate and efficient data analysis. The presented MLARMC-HDMS framework and COA-based feature selection approach offer valuable insights for researchers and practitioners working in the field of healthcare data mining and machine learning. © 2023 CRL Publishing. All rights reserved.

Computational intelligence (CI) is a group of nature-simulated computational models and processes for addressing difficult real-life problems. The CI is useful in the UAV domain as it produces efficient, precise, and rapid solutions. Besides, unmanned aerial vehicles (UAV) developed a hot research topic in the smart city environment. Despite the benefits of UAVs, security remains a major challenging issue. In addition, deep learning (DL) enabled image classification is useful for several applications such as land cover classification, smart buildings, etc. This paper proposes novel meta-heuristics with a deep learning-driven secure UAV image classification (MDLS-UAVIC) model in a smart city environment. The major purpose of the MDLS-UAVIC algorithm is to securely encrypt the images and classify them into distinct class labels. The proposed MDLS-UAVIC model follows a two-stage process: encryption and image classification. The encryption technique for image encryption effectively encrypts the UAV images. Next, the image classification process involves an Xception-based deep convolutional neural network for the feature extraction process. Finally, shuffled shepherd optimization (SSO) with a recurrent neural network (RNN) model is applied for UAV image classification, showing the novelty of the work. The experimental validation of the MDLS-UAVIC approach is tested utilizing a benchmark dataset, and the outcomes are examined in various measures. It achieved a high accuracy of 98%. © 2023 CRL Publishing. All rights reserved.

Recent economic growth and development have considerably raised energy consumption over the globe. Electric load prediction approaches become essential for effective planning, decision-making, and contract evaluation of the power systems. In order to achieve effective forecasting outcomes with minimum computation time, this study develops an improved whale optimization with deep learning enabled load prediction (IWO-DLELP) scheme for energy storage systems (ESS) in smart grid platform. The major intention of the IWO-DLELP technique is to effectually forecast the electric load in SG environment for designing proficient ESS. The proposed IWO-DLELP model initially undergoes pre-processing in two stages namely min-max normalization and feature selection. Besides, partition clustering approach is applied for the decomposition of data into distinct clusters with respect to distance and objective functions. Moreover, IWO with bidirectional gated recurrent unit (BiGRU) model is applied for the prediction of load and the hyperparameters are tuned by the use of IWO algorithm. The experiment analysis reported the enhanced results of the IWO-DLELP model over the recent methods interms of distinct evaluation measures. © 2023 Tech Science Press. All rights reserved.

Autism spectrum disorder (ASD) is regarded as a neurological disorder well-defined by a specific set of problems associated with social skills, recurrent conduct, and communication. Identifying ASD as soon as possible is favourable due to prior identification of ASD permits prompt interferences in children with ASD. Recognition of ASD related to objective pathogenic mutation screening is the initial step against prior intervention and efficient treatment of children who were affected. Nowadays, healthcare and machine learning (ML) industries are combined for determining the existence of various diseases. This article devises a Jellyfish Search Optimization with Deep Learning Driven ASD Detection and Classification (JSODL-ASDDC) model. The goal of the JSODL-ASDDC algorithm is to identify the different stages of ASD with the help of biomedical data. The proposed JSODL-ASDDC model initially performs min-max data normalization approach to scale the data into uniform range. In addition, the JSODL-ASDDC model involves JSO based feature selection (JFSO-FS) process to choose optimal feature subsets. Moreover, Gated Recurrent Unit (GRU) based classification model is utilized for the recognition and classification of ASD. Furthermore, the Bacterial Foraging Optimization (BFO) assisted parameter tuning process gets executed to enhance the efficacy of the GRU system. The experimental assessment of the JSODL-ASDDC model is investigated against distinct datasets. The experimental outcomes highlighted the enhanced performances of the JSODL-ASDDC algorithm over recent approaches. © 2023 Tech Science Press. All rights reserved.

Identifying the gender, race, age, and stature of the target during the forensic inquiry is a critical stage in various events such as accidents, bombings, terrorism, wars, and disasters. In this paper, an application has been developed that uses hand X-rays to identify and determine gender for medical applications such as special cases where diagnosing the gender is difficult, like accidents in which the hand is amputated and unknown, severe burns, and in old skeletal structures using deep learning models. For comparative purposes, GoogLeNet and ResNet-18 were employed. Gender determination using hand X-rays yielded positive results. The accuracy of gender detection in the model GoogLeNet (validation, training, test, and total) is (76.67%, 96.68%, 53.33%, and 89.5%) respectively, while the accuracy of gender detection in the model ResNet-18 (validation, training, test, and total) are (80%, 99.29%, 87.5%, 94.63%) respectively. The ResNet-18 model was adopted as the best model for gender detection and determination because high results were obtained. Simulation results showed acceptable results with high accuracy in diagnosis, where the highest gender determination rate was obtained through hand X-ray analysis at 94.63%. © 2023 IEEE.

The current work introduces a new nanostructure of Ce3+-doped NiO nanodisks fabricated by a facile hydrothermal protocol, whose characteristics were determined using XRD, SEM and EDX techniques. The results confirmed the presence of nanodisk structures, huge surface area and large pore size. Then, the surface of a screen-printed electrode was modified with the as-fabricated nanostructure to achieve a sensitive and selective electrochemical sensor (Ce3+-NiO ND/SPE) for determination of Plavix, a cardiovascular drug. Differential pulse voltammetry chronoamperometry and cyclic voltammetry were utilized to monitor the electrochemical behaviors of drug on the surface of modified electrode. The synergetic impact of Ce-doped NiO nanodisks on the Plavix oxidation was due to increased oxidation peak current and decreased oxidation over-potential. Our novel sensor under the optimized experimental and instrumental circumstances could electrochemically determine the study analyte in the range as wide as 0.01 to 700.0 µM and a limit of detection as narrow as 8.3 nM. The practical capability and sensitivity of the proposed sensor were validated by determining the Plavix in real pharmaceutical preparations, with commendable obtains. Graphical abstract: [Figure not available: see fulltext.]. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.

Epilepsy patients who are presently refractory may be monitored using a seizure prediction Brain-Computer Interface (BCI), which uses electrodes strategically implanted in the brain to anticipate and regulate the onset and duration of a seizure. Real-Time approaches to these technologies have challenges, as seen by seizures' instantaneous electrographic activity. Electroencephalographic (EEG) signals are inherently non-stationary, which means that the regular and seizure signals differ significantly among people with epilepsy. Due to the restricted number of contacts on electrodes, dynamically processed and collected characteristics cannot be employed in a prediction function without causing significant processing delays. Big data can guarantee secure storage in these situations, and it has the maximum processing capability to identify, record, and analyze time in real-Time to conduct the seizure event on the timetable. Seizure prediction and location for huge Scalp EEG recordings have been the focus of this study, which used wearable sensor data and deep learning to use cloud storage to develop the systems. A novel technique is suggested to avoid an epileptic seizure and discover the seizure origin from the utilized wearable sensors. Secondly, deep learning architectures called Clustered Autoencoder with Convolutional Neural Network (CAE-CNN), an expanded optimization methodology is presented based on the Principal Component Analysis (PCA), the Hierarchical Searching Algorithm (HSA), and the Medical Internet of Things (MIoT) has been established to define the suggested frameworks based on the collection of big data storage of the wearable sensors in real-Time, automatic computation and storage. According to clinical trials, CAE-CNN outperforms the current wearable sensor-based treatment for unresolved chronic epilepsy patients. © 2023-IOS Press. All rights reserved.

This study focuses on China’s industrial transformation and urban income inequality. It is shown that between 2011 and 2020, improvements in China’s industrial structure have a significant positive influence on lowering income gaps between urban and rural areas when used in conjunction with the empirical research approach. The mechanical study shows that the urban population impacts this causation. Rural-to-urban economic gaps have been reduced through modernisation in different parts of the country. The result remains the same even if the urban–rural consumption gap is used as a proxy for income discrepancy. The mechanism for the industrial structure upgrading model (MISUM) is proposed in this article for the modern circulation industry. Key contributions include: (1) environmental rules in these components have no impact on each other, but the updating of industrial buildings indicates a substantial location-specific dependence; (2) environmental standards have impacts on industrial structures throughout provinces; and (3) environmental standards have a long-term qualifying impact on the industrial structures. This essay focuses on combining environmental regulation with industrial expansion in different regions. In this study, government environmental requirements for industrial structural improvements are shown to be in operation. The test results show the MISUM has been described with high accuracy of 94.2%, carbon emission level of 18%, soil emission level of 11% and efficiency ratio of 97.8% compared to other methods. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

The Editor-in-Chief has retracted this article. The article was submitted to be part of a guest-edited issue. An investigation found that this article had a number of concerns. Equations 3, 8, 9, 12, 16, and 17 appear to be unrelated to article or nonsensical. Additionally, the article contains a number of irrelevant references. The publisher also found evidence of authorship manipulation. The authors have not responded to any correspondence regarding this retraction. © 2023 The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Due to the developments and increasing the manufacturing processes, the number of vehicles increased, especially smart ones, and for the purpose of reducing accidents and the dangers resulting from vehicle collisions with each other or with pedestrians. It has become necessary to create a system of communication between the vehicles themselves and the external environment through the Internet, hence the need for the Internet of vehicles (IoV), which is part of the Internet of' Things (IoT). By connecting (VANETs) with the Internet of Things, we can expand the possibilities of (IoT). Internet of vehicles (IoV) communicates between vehicles and public network and involves communication between vehicles and human, sensors and vehicles. (IoV) architecture involves four significant layers. This paper introduced also the most effective challenges and issues of (IoV) system and it's applications. © 2023 Author(s).

The Coronavirus Disease (COVID-19) pandemic has exposed the vulnerabilities of medical services across the globe, especially in underdeveloped nations. In the aftermath of the COVID-19 outbreak, a strong demand exists for developing novel computer-assisted diagnostic tools to execute rapid and cost-effective screenings in locations where many screenings cannot be executed using conventional methods. Medical imaging has become a crucial component in the disease diagnosis process, whereas X-rays and Computed Tomography (CT) scan imaging are employed in a deep network to diagnose the diseases. In general, four steps are followed in image-based diagnostics and disease classification processes by making use of the neural networks, such as network training, feature extraction, model performance testing and optimal feature selection. The current research article devises a Chaotic Flower Pollination Algorithm with a Deep Learning-Driven Fusion (CFPADLDF) approach for detecting and classifying COVID-19. The presented CFPA-DLDF model is developed by integrating two DL models to recognize COVID-19 in medical images. Initially, the proposed CFPA-DLDF technique employs the Gabor Filtering (GF) approach to pre-process the input images. In addition, a weighted voting-based ensemble model is employed for feature extraction, in which both VGG-19 and the MixNet models are included. Finally, the CFPA with Recurrent Neural Network (RNN) model is utilized for classification, showing the work's novelty. A comparative analysis was conducted to demonstrate the enhanced performance of the proposed CFPADLDF model, and the results established the supremacy of the proposed CFPA-DLDF model over recent approaches. © 2023 Tech Science Press. All rights reserved.

One of the most critical problems facing the blockchain technology industry right now is how to protect the privacy of users’ data on the blockchain in a way that is both effective and cheap. Based on the Pedersen commitment and the Schnorr protocol, this study comes up with a secure multi-party computing protocol (BPLSM). By making the structure of the protocol and doing formal proof calculations, it has been shown that the protocol can be used in the blockchain network to combine private messages for efficient signing while keeping people’s identities secret. Furthermore, by looking at the nature and security of the protocol, it is also possible to find that the BPLSM protocol on the blockchain has a low cost of computing and a high level of information secrecy. Furthermore, it was found that the BPLSM protocol takes less time to check than the current mainstream BLS signature in a simple multi-party transaction with a fixed number of participants. © 2024 selection and editorial matter, Arvind Dagur, Karan Singh, Pawan Singh Mehra & Dhirendra Kumar Shukla; individual chapters, the contributors.

The consensus method, being one of the most important aspects of blockchain, differs depending on the sector. The commonly used proof-of-work (PoW) consensus technique for public chain application scenarios still has challenges that are difficult to handle, such as security and high computer power. As a result, the PoW method is investigated in terms of enlarging the solution space and improving the adjustment mechanism. A consensus technique based on fuzzy random proof of work (FRMH) is presented. The FRMH algorithm improves the security of the blockchain consensus mechanism by increasing the solution space of the consensus algorithm by incorporating technologies such as a fuzzy transitive closure matrix in fuzzy mathematics. In addition, the FRMH algorithm uses a dual adjustment method to cope with machines with high computational power and thus solves the issue of high computing power being difficult to regulate on the blockchain. Through mathematics, it has been shown that the FRMH algorithm has greatly enhanced solution space and greater processing power control. © 2024 selection and editorial matter, Arvind Dagur, Karan Singh, Pawan Singh Mehra & Dhirendra Kumar Shukla; individual chapters, the contributors.

In this study, copper oxide (CuO) specimens were successfully prepared by the hydrothermal process at altered calcination temperatures; 350, 450, and 550°C. The synthesized samples were analyzed through X-ray powder diffraction (XRD), scanning electron microscope (SEM), Raman, Fourier-transform infrared spectroscopy (FTIR), and UV-Vis spectroscopy to analyze the impact of calcination temperature on the structural, morphological, vibration spectra, functional group, and optical properties of CuO for optoelectronic device applications. XRD confirms the pure single-phase monoclinic structure of synthesized samples with no impurity phases and has good crystallinity with the development in calcination temperature. The average crystalline size, lattice constant, and porosity were found in the range of 3.98-5.06 nm; a = 3.4357 Å, b = 3.9902 Å, c = 4.8977 Å - a = 3.0573 Å, b = 3.9573 Å, c = 4.6892 Å; and 3.37-1.03%, respectively. SEM exhibited a variation in morphology by increasing calcination temperature. Raman spectra revealed that the CuO sample calcinated at 550°C with a stone-like shape having a large grain size of 3.25 μm exhibited that Raman peak intensity and the multiphonon band became stronger and sharper and exhibited higher intensity compared to the samples calcinated at 350 and 450°C. FTIR spectra confirmed that these synthesized specimens exhibited the peaks associated with the typical stretching vibrations of the Cu-O bond between 400 and 500 cm-1 exhibiting the formation of CuO. The energy bandgap was slightly reduced from 1.61 to 1.43 eV with the increase in the calcination temperature. The optical studies revealed that the calcination temperature of 550°C improves the optical properties of CuO by tuning its optical bandgap. The modified structural, morphological, and optical characteristics of the prepared CuO samples make them an appropriate candidate for optoelectronic device applications. © 2022 Shahroz Saleem et al., published by De Gruyter.

The aim of this article is to review and analyse previous academic articles associated with car behaviour analysis for the period of 2010 to June 10, 2021 and understand the benefits of using data collection devices. Articles related to car driver behaviour and sensor utilisation are systematically searched. Three major databases – ScienceDirect, IEEE Xplore and Web of Science – were searched. A set of inclusion and exclusion criteria were developed for the search protocol. All articles were coherently classified via taxonomy. Also. The motives that have led researchers to continue their investigations are explored. The challenges and issues of driver behaviour analysis are illustrated with respect to power consumption, data analysis, detection, cost, security and privacy, sensor usage and individual challenges. The research direction of this review points towards different aspects based on the critical analysis of the different scenarios of driver behaviour studies in real time situations. Here, the critical behaviour analysis of intelligent transportation system development is addressed. The gaps in the reviewed articles include the following: sensors used during experiments, the effect of thresholds on labelling processes or data balancing and classification accuracy, the thresholds in identifying driving styles in the car-following model, insufficient experiment size (large scale or small scale) and limitations in data pre-processing. An implementation map depicting the steps of the case study is provided to give insights into the procedures and the problems they address. This review is expected to offer valid and clear points, contributing to the enhancement of driver behaviour research. © 2022 Elsevier Ltd

Indoor air quality (IAQ) refers to the conditions found within buildings that can impact respiratory health. Good IAQ conditions for hospital facilities are essential, especially for patients and medical staff. Recently, several concerns have been outlined and require an urgent solution in identifying IAQ pollutants and related thresholds and ways to provide a knowledge-based method for labelling pollution levels. To this end, a systematic review should be conducted first to construct new taxonomy research on internet of things-based IAQ sensory technology in hospital facilities to identify a research gap. Thus, the present study aims to develop an IAQ methodology that includes the recommended nine pollutants for hospitals and facilities: Carbon monoxide, Carbon dioxide, Nitrogen Dioxide, Ozone, Formaldehyde, Volatile organic compound, particulate matter (PM) and air humidity and temperature. The developed methodology utilised actual and simulated IAQ pollutant datasets to predict the pollution levels within hospital facilities in three distinct phases. In the first phase, two IAQ datasets (real and large-scale simulated datasets) are identified. The second phase includes the following: First is utilising the Interval type 2 trapezoidal-fuzzy weighted with zero inconsistency (IT2TR-FWZIC) method from the Multi-Criteria Decision Making theory for providing the required weights to the nine pollutants. Second is developing a new method, the Unified Process for Labelling Pollutants Dataset (UPLPD), consisting of six processes based on the IT2TR-FWZIC method. The UPLPD can classify the pollution levels into four levels and assign the required labels within the two datasets. Third is applying the labelled datasets to the developed machine learning model using eight algorithms. The third phase includes the model evaluation using five metrics in terms of accuracy, Area under the Curve, F1-score, precision and recall. For the actual dataset, the best three algorithms' results are Support Vector Machine, Logistic Regression and Decision Tree (DT), which achieved the highest accuracy of 99.813, 99.259 and 98.182%, respectively, with performance metrics. The simulated dataset, the Random Forest, DT and AdaBoost achieved the highest accuracy of 90.094, 88.964 and 87.735%, respectively, with performance metrics. The results satisfied the challenges and overcame the issues, and experimental results confirmed the efficacy of the predictive model. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Despite the wide range of research on pedestrian safety, previous studies have failed to analyse the real-time data of pedestrian walking misbehaviour on the basis of either pedestrian behaviour distraction or movements during specific activities to realise pedestrian safety for positive (normal) or aggressive pedestrians. Practically, pedestrian walking behaviour should be recognised, and aggressive pedestrians should be differentiated from normal pedestrians. This type of pedestrian behaviour recognition can be converted into a classification problem, which is the main challenge for pedestrian safety systems. In addressing the classification challenge, three issues should be considered: identification of factors, collection of data and exchange of data in the contexts of wireless communication and network failure. Thus, this work proposes a novel approach to pedestrian walking behaviour classification in the aforementioned contexts. Three useful phases are proposed for the methodology of this study. In the first phase involving factor identification, several factors of the irregular walking behaviour of mobile phone users are established by constructing a questionnaire that can determine users’ options (attitudes/opinions) about mobile usage whilst walking on the street. In the second phase involving data collection, four different testing scenarios are developed to acquire the real-time data of pedestrian walking behaviour by using gyroscope sensors. In the third phase involving data exchange, the proposed approach is presented on the basis of two modules. The first module for pedestrian behaviour classification uses random forest and decision tree classifiers part of machine learning techniques via wireless communication when a server becomes available. The developed module is then trained and evaluated using five category sets to obtain the best classification of pedestrian walking behaviour. The second module is based on four standard vectors for classifying pedestrian walking behaviour when a server is unavailable. Fault-tolerant pedestrian walking behaviour is identified and is initiated when failures occur in a network. Two sets of real-time data are presented in this work. The first dataset is related to the questionnaire data from 262 sampled respondents, and the second dataset comprises data on 263 sampled participants with pedestrian walking signals. Experimental results confirm the efficacy of the proposed approach relative to previous ones. © 2021, The Author(s).

In the present work, pure and Cr-doped MoO3 microrods were successfully prepared through the sol gel auto combustion method. The phase evaluation and microstructural, dielectric, and optical properties of synthesized samples were investigated by using X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and an impedance analyzer (1 MHz–3 GHz). All the samples showed hexagonal structure with space group (P63). According to Vegard’s law, lattice parameters increase with the increase in chromium (Cr3+) contents. In addition, the Williamson–Hall (W–H) plot was drawn for evaluating the micro-strain (εW-H) and crystallite size (DW-H) parameters. From microstructural analysis it was found that the size of microrods increased along with Cr3+ contents. Decreasing band gap energy was observed (from 2.98 to 2.71 eV) with increasing Cr3+ contents. The variation of the dielectric constant and tangent loss of MoO3 microrods with respect to frequency were analyzed. © 2022 by the authors.

This study attempts to address the issue that present cross-modal image synthesis algorithms do not capture the spatial and structural information of human tissues effectively. As a consequence, the resulting photos include flaws including fuzzy edges and a poor signal-to-noise ratio. The authors offer a cross-sectional technique that combines residual modules with generative adversarial networks. The approach incorporates an enhanced residual initial module and attention mechanism into the generator network, reducing the number of parameters and improving the generator's feature learning capabilities. To boost discriminant performance, the discriminator employs a multiscale discriminator. A multilevel structural similarity loss is included in the loss function to improve picture contrast preservation. On the ADNI data set, the algorithm is compared to the mainstream algorithms. The experimental findings reveal that the synthetic PET image's MAE index has dropped while the SSIM and PSNR indexes have improved. The experimental findings suggest that the proposed model may maintain picture structural information while improving image quality in both visual and objective measures. The residue initial module and attention mechanism are employed to increase the generator's capacity for learning, while the multiscale discriminator is utilized to improve the model's discriminative performance. The enhanced method in this study can maintain the structure and contrast information of the picture, according to comparative experimental findings using the ADNI dataset. The produced picture is hence more aesthetically similar to the genuine print. © 2022 Rajeev Kumar et al.

Mobile ad hoc networks (MANET) have been seen as a related advancement to Group Key Management (GKM) applications. Remembering the true objective to guarantee amass applications and disallow uncertified clients from getting to the correspondence data that cannot be anchored by a remote MANET, including IP multicast, the singular gathered data content must remain encoded by a typical shared gathering key. Key administration is required to anchor the assurance of gathering the key and to safeguard those gathering data. GKM framework is associated with the remote system condition partners with three issues: execution, security, and system versatility. This article focuses on the Unmanned Aerial Vehicle (UAV)-mobile backbone node (MBN) remote system performance. The UAV-MBN Network condition is a military system that includes a proposal to group an important administrative structure. A half-and-half gathering key administration technique, which works on each target of UAV-MBN, is included in an arrangement to start two basic remote gathering key administration difficulties: (1) operational performance and (2) multiple-enrollment development. By working with minimal small-scale key administration, this strategy can diminish the execution cost associated with the key administration along with the increment operational execution of remote GKM. Scaled-down key organization is carried out in the context of these movement units. The key administration approach also restricted the operational procedure and decreased the operation's cost in terms of key generation, figuring, and associated correspondence. Overall, the HGKM strategy that has been introduced enhances the operational process and functions effectively in reasonable remote areas. © 2022 R. Mahaveerakannan et al.

Driver behavior is a determining factor in more than 90% of road accidents. Previous research regarding the relationship between speeding behavior and crashes suggests that drivers who engage in frequent and extreme speeding behavior are overinvolved in crashes. Consequently, there is a significant benefit in identifying drivers who engage in unsafe driving practices to enhance road safety. The proposed method uses continuously logged driving data to collect vehicle operation information, including vehicle speed, engine revolutions per minute (RPM), throttle position, and calculated engine load via the on-board diagnostics (OBD) interface. Then the proposed method makes use of severity stratification of acceleration to create a driving behavior classification model to determine whether the current driving behavior belongs to safe driving or not. The safe driving behavior is characterized by an acceleration value that ranges from about ±2 m/s2 . The risk of collision starts from ±4 m/s2, which represents in this study the aggressive drivers. By measuring the in-vehicle accelerations, it is possible to categorize the driving behavior into four main classes based on real-time experiments: safe drivers, normal, aggressive, and dangerous drivers. Subsequently, the driver’s characteristics derived from the driver model are embedded into the advanced driver assistance systems. When the vehicle is in a risk situation, the system based on nRF24L01 + power amplifier/low noise amplifier PA/LNA, global positioning system GPS, and OBD-II passes a signal to the driver using a dedicated liquid-crystal display LCD and light signal. Experimental results show the correctness of the proposed driving behavior analysis method can achieve an average of 90% accuracy rate in various driving scenarios. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Driver behavior is a concerning issue in the area of intelligent transportation system (ITS). Driver behavior is a significant key player in a wide range of unpleasant events during the ride, such as accidents or crashes, traffic congestion, harsh braking, and acceleration/deceleration. Influencing factors of driver behavior have been explored in several studies. It is imperative to investigate these factors in order to provide a comprehensive analysis and to categorize them on the basis of a coherent taxonomy. With that, this study conducted a systematic review on prior studies that focused on bus driver behavior, particularly in the ITS. This study also established a taxonomy on the topic of driver behavior in multiple areas of ITS and their classifications. Different databases, namely ScienceDirect, Web of Science, and IEEE Explore, were utilized to obtain relevant articles from 2008 to 2021 (15 April). Several filtering and scanning stages were performed according to the exclusion/inclusion criteria on all 2,803 articles obtained; however, only 87 articles met the criteria. The final set of articles were categorized into a taxonomy. The first part of the taxonomy focuses on five main factors that influence driver behavior: environmental, demographic, habit, vehicle, and on-road routine factors. The second part of the taxonomy discusses the mapping of data collection methods on the basis of four categories: real-time data collection, survey, simulation, and benchmark. Discussion and analysis were provided to highlight the critical literature gaps on bus driver behavior in the ITS, involving the use of real-time data collection, which is imperative for acquiring highly accurate and sophisticated data. This multi-field systematic review has exposed new research opportunities, motivations, challenges, limitations, and recommendations and highlighted the need for the synergistic integration of interdisciplinary works. Overall, this study presented pathways solution in future direction on the basis of three sequenced phases, namely design, labeling and validation, and machine learning. This study can serve as a guide for future researchers, as it addressed the ambiguities in the ITS-driver behavior domain and provided valuable information on these ITS-driver behavior trends. © 2013 IEEE.

Pedestrian safety is a serious problem in transportation systems because pedestrian and vehicle crashes often result in fatalities amongst vulnerable road users. A vehicle-to-pedestrian (V2P) communication system allows data exchange between pedestrians and vehicles to prevent or minimise potential dangers of accidents from happening. This work aimed to analyse and review the previous work associated with information exchange in the V2P communication system and classify the existing technology utilized for this purpose. Motivation, accessible problems confronting researchers, and suggestions posed to researchers to develop this critical area of study have been among the reasons considered to enhance awareness of the field's numerous qualitative facets in reported investigations and properties. All of the papers have been divided into four categories: growth, analysis, and survey, FRAMEWORK, and data exchange in the V2P communication system. V2P communication is an area that necessitates automated solutions, instruments, and techniques that allow pedestrian detection and prediction. Pedestrian identification and data sharing on V2P have been the subject of several experiments in order to support pedestrian protection techniques. The reasons, open barriers that hinder the technology's usefulness, and authors' suggestions have been used to identify the essential characteristics of this evolving sector. This study is intended to provide researchers with new resources and enable them to focus on the holes that have been found. © 2021. All rights reserved.

Road traffic accident management is very complex and sensitive issue. Recently, more attention has delivered to research in Vehicle-to-Pedestrian (V2P) communication systems, which function for different purposes such as safety or convenience and cater to different Vulnerable Road User (VRU) groups. This paper gives a brief overview for vehicle-to-pedestrian system with focusing on different communication technologies that V2P system employ it and the different mechanisms to interact with the users. The varying characteristics of different communication architecture and applications needs to be considered in an effective V2P system. Therefore, in the field of intelligent transportation research, there is the task of improving the road user safety such as, pedestrian through the development of technological tools, which can apply to reduce the number of accidents. © 2020 IEEE.

The field of vehicular communication technology is rapidly developing. vehicular communication technologies provide great social benefits such as reduced road accident and increased road efficiency. To improve traffic safety and provide drivers with the best end-to-end transportation experience, vehicles need to be more automated which is the most important goals of modern vehicular communication technologies. Utilizing Vehicle-to- Infrastructure communication (V2I) may be a keystone to enhance and promote the various vehicular communications applications. By the exchange of information between vehicles and road infrastructures, drivers will have more ability to predict and avoid various road hazard and thus make driving experience much safer. In this paper, we will focus on the study of the main requirement and component in V2I systems and present a review of the major V2I benefits related to safety and mobility applications. © 2020 IEEE.

Data exchange in Vehicle-to-vehicle (V2V) communications systems is a field that requires automated solutions, tools and methods and the capability to facilitate early detection and even a prediction. Many studies have focused on V2V system and its classification to improve road safety, reduce traffic congestion and help streamline the vehicle flow on the road. This study aims to review and analyse literature related to data exchange in V2V communications systems. The factors considered to improve the understanding of the field's various contextual aspects were derived from published studies. We systematically searched all articles about the classification and detection of data exchange in vehicles, as well as their evaluation. Three main databases, namely, ScienceDirect, Web of Science and IEEE Xplore from 2008 to 2018, were used. These indices were considered sufficiently extensive to encompass our literature. On the basis of our inclusion and exclusion criteria, 140 articles were selected. Most articles (53/140) are studies conducted in a V2V communication system; a number of papers (51/140) covered the actual attempts to develop V2V communications; and few papers (18/140) comprised framework proposals and architectures. The last portion (18/140) of articles presented review and survey articles. V2V collision avoidance system, which is a field requiring automated solutions, tools and methods, entails the capability to facilitate early detection. Several studies have been performed on the automatic detection of V2V and their subtypes to promote accurate detection. The basic characteristics of this emerging field are identified from the aspects of motivations, open challenges that impede the technology's utility, authors' recommendations and substantial analysis of the previous studies are discussed based on seven aspect (devices, number of scenario, test location, types of sensors, number of vehicle, evaluation techniques used and types of software). A propose research methodology as new direction is provided to solve the gaps identified in the analysis. This methodology consists of four phases; investigation, develop a hardware system, study and analysis, and evaluation phases. However, research areas on V2V communication with the scope of data exchange are varied. This systematic review is expected to open opportunities for researchers and encourage them to work on the identified gaps. © 2013 IEEE.

The vehicle-to-infrastructure (V2I) communication system allows the exchange of information between vehicles and road infrastructures. It aims to avoid or reduce vehicular accidents, increase mobility, and provide other road safety benefits. This paper aimed to review and analyze the literature on data exchanges in the V2I communication system. The factors considered to improve the understanding of various contextual aspects and the characteristics of the field were motivations, open challenges, and recommendations from other researchers. We systematically searched all articles on data exchanges in the V2I communication system from the three main databases, namely ScienceDirect, Web of Science, and IEEE Xplore, from 2008 to 2018. These indices were sufficiently extensive to encompass our field of literature. A total of 70 articles were selected based on our inclusion and exclusion criteria. Most studies (42/70) covered a developed V2I communication system, while numerous articles (22/70) focused on general research on the V2I communication system. The smallest portion of articles (6/70) comprised reviews and surveys. The V2I system plays a key role in vehicular ad hoc networks but is less implemented than vehicle-to-vehicle communication owing to its deployment costs and maintenance requirements. However, numerous studies have been conducted on the V2I communication system to promote its utility. Research areas on V2I communication classification vary but are all equally vital. We expect this systematic review to help emphasize current research opportunities and thus extend and create additional research fields. © 2013 IEEE.