articles by Jaber. S Alzahrani
classifier results. The experimental result analysis of the MDFO-EMTRR system was performed on be... more classifier results. The experimental result analysis of the MDFO-EMTRR system was performed on benchmark datasets and attained maximum accuracy of 99.74%.
classifier results. The experimental result analysis of the MDFO-EMTRR system was performed on be... more classifier results. The experimental result analysis of the MDFO-EMTRR system was performed on benchmark datasets and attained maximum accuracy of 99.74%.
In recent times, numerous decision-making procedures are not only based on the decision-making of... more In recent times, numerous decision-making procedures are not only based on the decision-making of choices, but also public perceptions of possible solutions. In a multi-criteria-based decision-making system, user preferences have been deeply considered. Sentiment analysis, on either side, is similar to natural language processing dedicated to the creation of methods capable of assessing evaluations and determining their intensity. The main aim of this research is to make efficient decisions using social media tweets. The proposed method uses the SentiRank method and neutrosophic set theory to make decisions and rank the reviews. Novel multi-criteria-based neutrosophic theory is used in this research for decision-making. An assembled neutral vocabulary, and the adapted VADER, are used to create Neutro-VADER, a novel version. Every evaluation of a product feature is given a positive, neutral, or negative scores of sentiment by the Neutro-VADER. A unique idea at this level is to use the positive, neutral, and negative scores on emotion to represent reality, uncertainty, and falsehood participation levels of a neutrosophic number. The testing findings support the value of sentiment data through reviews in the ranking procedure. The performance metrics used in the systems are precision, recall, and F1 measures and accuracy for evaluating the aspect detection module. The system performs better in food, service, and pricing categories, whereas the anecdotes group gives bad results. F1 and accuracy level shows better results in the proposed system by using SentiRank and the neutrosophic set theory method.
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Arabic is one of the most spoken languages across the globe. However, there are fewer studies con... more Arabic is one of the most spoken languages across the globe. However, there are fewer studies concerning Sentiment Analysis (SA) in Arabic. In recent years, the detected sentiments and emotions expressed in tweets have received significant interest. The substantial role played by the Arab region in international politics and the global economy has urged the need to examine the sentiments and emotions in the Arabic language. Two common models are available: Machine Learning and lexicon-based approaches to address emotion classification problems. With this motivation, the current research article develops a Teaching and Learning Optimization with Machine Learning Based Emotion Recognition and Classification (TLBOML-ERC) model for Sentiment Analysis on tweets made in the Arabic language. The presented TLBOML-ERC model focuses on recognising emotions and sentiments expressed in Arabic tweets. To attain this, the proposed TLBOML-ERC model initially carries out data pre-processing and a Continuous Bag Of Words (CBOW)-based word embedding process. In addition, Denoising Autoencoder (DAE) model is also exploited to categorise different emotions expressed in Arabic tweets. To improve the efficacy of the DAE model, the Teaching and Learning-based Optimization (TLBO) algorithm is utilized to optimize the parameters. The proposed TLBOML-ERC method was experimentally validated with the help of an Arabic tweets dataset. The obtained results show the promising performance of the proposed TLBOML-ERC model on Arabic emotion classification.
Facial expression is commonly utilized by humans to deliver their mood and emotional state to oth... more Facial expression is commonly utilized by humans to deliver their mood and emotional state to other people. Facial expression recognition (FER) becomes a hot research area in recent days, and it is a tedious process owing to the presence of high intra-class variation. The conventional methods for FEC are mainly based on handcrafted features with a classification model trained on image or video datasets. Since the facial datasets involve large variations in the images and comprise partial faces, it is needed to design automated FER models. The latest advancements in artificial intelligence (AI) and deep learning (DL) models find useful for better understanding of facial emotions related to face images. In this aspect, this paper presents an intelligent FER using optimal deep transfer learning (IFER-DTFL) model. The proposed IFER-DTFL technique aims to detect the face and identify the facial expressions automatically. The IFER-DTFL technique encompasses a three state process: face detection, feature extraction, and expression classification. In addition, a mask RCNN model is used for the detection of faces. Moreover, the Adam optimizer with Densely Connected Networks (DenseNet121) model is employed for feature extraction process. Furthermore, the weighted kernel extreme learning machine (WKELM) model is utilized to classify the facial expressions. A comprehensive set of simulations were carried out on benchmark dataset and the results are inspected under varying aspects. The experimental results pointed out the supremacy of the IFER-DTFL technique over the other recent techniques interms of several performance measures.
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Osteosarcoma is a type of malignant bone tumor that is reported across the globe. Recent advancem... more Osteosarcoma is a type of malignant bone tumor that is reported across the globe. Recent advancements in Machine Learning (ML) and Deep Learning (DL) models enable the detection and classification of malignancies in biomedical images. In this regard, the current study introduces a new Biomedical Osteosarcoma Image Classification using Elephant Herd Optimization and Deep Transfer Learning (BOIC-EHODTL) model. The presented BOIC-EHODTL model examines the biomedical images to diagnose distinct kinds of osteosarcoma. At the initial stage, Gabor Filter (GF) is applied as a pre-processing technique to get rid of the noise from images. In addition, Adam optimizer with MixNet model is also employed as a feature extraction technique to generate feature vectors. Then, EHO algorithm is utilized along with Adaptive Neuro-Fuzzy Classifier (ANFC) model for recognition and categorization of osteosarcoma. EHO algorithm is utilized to fine-tune the parameters involved in ANFC model which in turn helps in accomplishing improved classification results. The design of EHO with ANFC model for classification of osteosarcoma is the novelty of current study. In order to demonstrate the improved performance of BOIC-EHODTL model, a comprehensive comparison was conducted between the proposed and existing models upon benchmark dataset and the results confirmed the better performance of BOIC-EHODTL model over recent methodologies.
Intelligent Transportation System (ITS) is one of the revolutionary technologies in smart cities ... more Intelligent Transportation System (ITS) is one of the revolutionary technologies in smart cities that helps in reducing traffic congestion and enhancing traffic quality. With the help of big data and communication technologies, ITS offers real-time investigation and highly-effective traffic management. Traffic Flow Prediction (TFP) is a vital element in smart city management and is used to forecast the upcoming traffic conditions on transportation network based on past data. Neural Network (NN) and Machine Learning (ML) models are widely utilized in resolving real-time issues since these methods are capable of dealing with adaptive data over a period of time. Deep Learning (DL) is a kind of ML technique which yields effective performance on data classification and prediction tasks. With this motivation, the current study introduces a novel Slime Mould Optimization (SMO) model with Bidirectional Gated Recurrent Unit (BiGRU) model for Traffic Prediction (SMOBGRU-TP) in smart cities. Initially, data preprocessing is performed to normalize the input data in the range of [0, 1] using minmax normalization approach. Besides, BiGRU model is employed for effective forecasting of traffic in smart cities. Moreover, the novelty of the work lies in using SMO algorithm to effectively adjust the hyperparameters of BiGRU method. The proposed SMOBGRU-TP model was experimentally validated and the simulation results established the model's superior performance in terms of prediction compared to existing techniques.
different datasets and the outcomes confirmed the supremacy of the proposed model over other rece... more different datasets and the outcomes confirmed the supremacy of the proposed model over other recent approaches.
Multi-Agent System (MAS) gained significant interest amongst researchers since it provides multip... more Multi-Agent System (MAS) gained significant interest amongst researchers since it provides multiple benefits through several application areas. MAS involves a network
Wireless Sensor Networks (WSN) interlink numerous Sensor Nodes (SN) to support Internet of Things... more Wireless Sensor Networks (WSN) interlink numerous Sensor Nodes (SN) to support Internet of Things (loT) services. But the data gathered from SNs can be divulged, tempered, and forged. Conventional WSN data processes manage the data in a centralized format at terminal gadgets. These devices are prone to attacks and the security of systems can get compromised. Blockchain is a distributed and decentralized technique that has the ability to handle security issues in WSN. The security issues include transactions that may be copied and spread across numerous nodes in a peer-peer network system. This breaches the mutual trust and allows data immutability which in turn permits the network to go on. At some instances, few nodes die or get compromised due to heavy power utilization. The current article develops an Energy Aware Chaotic Pigeon Inspired Optimization based Clustering scheme for Blockchain assisted WSN technique abbreviated as EACPIO-CB technique. The primary objective of the proposed EACPIO-CB model is to proficiently group the sensor nodes into clusters and exploit Blockchain (BC) for inter-cluster communication in the network. To select Cluster Heads (CHs) and organize the clusters, the presented EACPIO-CB model designs a fitness function that involves distinct input parameters. Further, BC technology enables the communication between one CH and the other and with the Base Station (BS) in the network. The authors conducted comprehensive set of simulations and the outcomes were investigated under different aspects. The 6548 CMC, 2022, vol.73, no.3 simulation results confirmed the better performance of EACPIO-CB method over recent methodologies.
The recent technological developments have revolutionized the functioning of Wireless Sensor Netw... more The recent technological developments have revolutionized the functioning of Wireless Sensor Network (WSN)-based industries with the development of Internet of Things (IoT). Internet of Drones (IoD) is a division under IoT and is utilized for communication amongst drones. While drones are naturally mobile, it undergoes frequent topological changes. Such alterations in the topology cause route election, stability, and scalability problems in IoD. Encryption is considered as an effective method to transmit the images in IoD environment. The current study introduces an Atom Search Optimization based Clustering with Encryption Technique for Secure Internet of Drones (ASOCE-SIoD) environment. The key objective of the presented ASOCE-SIoD technique is to group the drones into clusters and encrypt the images captured by drones. The presented ASOCE-SIoD technique follows ASO-based Cluster Head (CH) and cluster construction technique. In addition, signcryption technique is also applied to effectually encrypt the images captured by drones in IoD environment. This process enables the secure transmission of images to the ground station. In order to validate the efficiency of the proposed ASOCE-SIoD technique, several experimental analyses were conducted and the outcomes were inspected under different aspects. The comprehensive comparative analysis results established the superiority of the proposed ASOCE-SIoD model over recent approaches.
maximum accuracy of 99.73%. The outcomes inferred the supremacy of the proposed SFODTL-AHCR model... more maximum accuracy of 99.73%. The outcomes inferred the supremacy of the proposed SFODTL-AHCR model over other approaches.
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Unmanned Aerial Vehicle (UAV) is treated as an effective technique for gathering high resolution ... more Unmanned Aerial Vehicle (UAV) is treated as an effective technique for gathering high resolution aerial images. The UAV based aerial image collection is highly preferred due to its inexpensive and effective nature. However, automatic classification of aerial images poses a major challenging issue in the design of UAV, which could be handled by the deep learning (DL) models. This study designs a novel UAV assisted DL based image classification model (UAVDL-ICM) for Industry 4.0 environment. The proposed UAVDL-ICM technique involves an ensemble of voting based three DL models, namely Residual network (ResNet), Inception with ResNetv2, and Densely Connected Networks (DenseNet). Also, the hyperparameter tuning of these DL models takes place using a genetic programming (GP) approach. Finally, Oppositional Water Wave Optimization (OWWO) with Fully Connected Deep Neural networks (FCDNN) is employed for the classification of aerial images. A wide range of simulations takes place and the results are examined in terms of different parameters. A detailed comparative study highlighted the betterment of the UAVDL-ICM technique compared to other recent approaches.
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articles by Jaber. S Alzahrani
minimize the make span. This model has been built using Microsoft Excel spreadsheets and
solved using @Risk solver. A set of experiments have been also conducted to examine the
accuracy of the model and its effectiveness has been proven.