Hybridization and polyploidy are key drivers of species diversity and genome variation in Lycoris,but their cytological and evolutionary consequences remain poorly understood.Here,we investigated chromosome numbers an...Hybridization and polyploidy are key drivers of species diversity and genome variation in Lycoris,but their cytological and evolutionary consequences remain poorly understood.Here,we investigated chromosome numbers and genome sizes in 64 accessions representing the morphological diversity across the genus.Chromosome numbers ranged from 12 to 33,with seven accessions newly identified,including L.chunxiaoensis(2n=33),two putative L.guangxiensis(2n=19),and fivenatural hybrids(2n=16,18,29,33).Genome sizes varied from 18.03 Gb(L.wulingensis)to 32.62 Gb(L.caldwellii).Although no significantcorrelationwas found between genome size and chromosome number across all accessions,a strong correlation within ploidy-level groups(i.e.,diploid or aneuploid)suggested roles for post-polyploid diploidization,aneuploidy,and dysploidy in speciation.Phylogenetic analyses based on chloroplast genomes and nuclear DNA sequences revealed significantdiscordance,indicating a complex reticulate evolution and historical hybridization,which may complicate morphological classification.Chromosome number aligned more closely with morphological groups,underscoring the necessity of integrating cytological,molecular,and morphological data for accurate taxonomy,particularly in largegenome taxa.Based on this evidence,we propose a putative speciation pathway involving multiple hybridization and polyploidization events,with allopolyploidy playing a predominant role.Furthermore,our results indicate that the species L.insularis and L.longifolia are geographic populations of L.sprengeri and L.aurea,respectively,and confirmedthe distribution of L.traubii and L.albiflora in China' Mainland.These findingsoffer new insights into the mechanisms underlying speciation,interspecificrelationships,and the evolutionary history of Lycoris.展开更多
Large-scale bismuth vanadate(BiVO_(4))photoanodes are critical to the practical application of photoelectrochemical water splitting devices.However,the lack of interface-modified coatings with simultaneous low cost,sc...Large-scale bismuth vanadate(BiVO_(4))photoanodes are critical to the practical application of photoelectrochemical water splitting devices.However,the lack of interface-modified coatings with simultaneous low cost,scalability,high hole transport efficiency,low impedance,and photocorrosion resistance is a major challenge that prevents the practical application of large-size photoanodes.Here,we present a scalable nickel-chelated polydopamine conformal coating for modifying BiVO_(4)(BiVO_(4)@PDA-Ni,BPNi),achieving over 500 h of stable water oxidation at 0.6 VRHE.The excellent stability is attributed to the chelated Ni acting as hole oxidation sites for PDA,thereby suppressing the accumulated-holes-induced PDA decomposition.Additionally,the in situ generation of Ni(IV)facilitates the structural reorganization of PDA in the photoelectrochemical system,further enhancing the stability of the PDA matrix.The findings of PDA photodegradation,its autonomous metal ion capture within photoelectrochemical systems,and the rapid deactivation of BPNi photoanodes caused by vanadium(V)ions have all provided significant guidance for the enhancement of PDA.Our study demonstrates that nickel-chelated polydopamine can be applied to large-scale BiVO_(4) photoanodes to facilitate oxygen evolution.This will promote the development of large-scale photoanodes suitable for photoelectrochemical devices.展开更多
AIM: To evaluate the effect of resveratrol, alone and in combination with fenofibrate, on fructose-induced metabolic genes abnormalities in rats.METHODS: Giving a fructose-enriched diet (FED) to rats for 12 wk was use...AIM: To evaluate the effect of resveratrol, alone and in combination with fenofibrate, on fructose-induced metabolic genes abnormalities in rats.METHODS: Giving a fructose-enriched diet (FED) to rats for 12 wk was used as a model for inducing hepatic dyslipidemia and insulin resistance. Adult male albino rats (150-200 g) were divided into a control group and a FED group which was subdivided into 4 groups, a control FED, fenofibrate (FENO) (100 mg/kg), resveratrol (RES) (70 mg/kg) and combined treatment (FENO + RES) (half the doses). All treatments were given orally from the 9<sup>th</sup> week till the end of experimental period. Body weight, oral glucose tolerance test (OGTT), liver index, glucose, insulin, insulin resistance (HOMA), serum and liver triglycerides (TGs), oxidative stress (liver MDA, GSH and SOD), serum AST, ALT, AST/ALT ratio and tumor necrosis factor-α (TNF-α) were measured. Additionally, hepatic gene expression of suppressor of cytokine signaling-3 (SOCS-3), sterol regulatory element binding protein-1c (SREBP-1c), fatty acid synthase (FAS), malonyl CoA decarboxylase (MCD), transforming growth factor-β1 (TGF-β1) and adipose tissue genes expression of leptin and adiponectin were investigated. Liver sections were taken for histopathological examination and steatosis area were determined.RESULTS: Rats fed FED showed damaged liver, impairment of glucose tolerance, insulin resistance, oxidative stress and dyslipidemia. As for gene expression, there was a change in favor of dyslipidemia and nonalcoholic steatohepatitis (NASH) development. All treatment regimens showed some benefit in reversing the described deviations. Fructose caused deterioration in hepatic gene expression of SOCS-3, SREBP-1c, FAS, MDA and TGF-β1 and in adipose tissue gene expression of leptin and adiponectin. Fructose showed also an increase in body weight, insulin resistance (OGTT, HOMA), serum and liver TGs, hepatic MDA, serum AST, AST/ALT ratio and TNF-α compared to control. All treatments improved SOCS-3, FAS, MCD, TGF-β1 and leptin genes expression while only RES and FENO + RES groups showed an improvement in SREBP-1c expression. Adiponectin gene expression was improved only by RES. A decrease in body weight, HOMA, liver TGs, AST/ALT ratio and TNF-α were observed in all treatment groups. Liver index was increased in FENO and FENO + RES groups. Serum TGs was improved only by FENO treatment. Liver MDA was improved by RES and FENO + RES treatments. FENO + RES group showed an increase in liver GSH content.CONCLUSION: When resveratrol was given with half the dose of fenofibrate it improved NASH-related fructose-induced disturbances in gene expression similar to a full dose of fenofibrate.展开更多
Due to the widespread usage of social media in our recent daily lifestyles,sentiment analysis becomes an important field in pattern recognition and Natural Language Processing(NLP).In this field,users’feedback data o...Due to the widespread usage of social media in our recent daily lifestyles,sentiment analysis becomes an important field in pattern recognition and Natural Language Processing(NLP).In this field,users’feedback data on a specific issue are evaluated and analyzed.Detecting emotions within the text is therefore considered one of the important challenges of the current NLP research.Emotions have been widely studied in psychology and behavioral science as they are an integral part of the human nature.Emotions describe a state of mind of distinct behaviors,feelings,thoughts and experiences.The main objective of this paper is to propose a new model named BERT-CNN to detect emotions from text.This model is formed by a combination of the Bidirectional Encoder Representations from Transformer(BERT)and the Convolutional Neural networks(CNN)for textual classification.This model embraces the BERT to train the word semantic representation language model.According to the word context,the semantic vector is dynamically generated and then placed into the CNN to predict the output.Results of a comparative study proved that the BERT-CNN model overcomes the state-of-art baseline performance produced by different models in the literature using the semeval 2019 task3 dataset and ISEAR datasets.The BERTCNN model achieves an accuracy of 94.7%and an F1-score of 94%for semeval2019 task3 dataset and an accuracy of 75.8%and an F1-score of 76%for ISEAR dataset.展开更多
Tadalafil(TAD)and dapoxetine HCl(DAP)are recently co-formulated and both show native fluorescence.Therefore,a novel,accurate,specific and sensitive reversed-phase high performance liquid chromatographic method with fl...Tadalafil(TAD)and dapoxetine HCl(DAP)are recently co-formulated and both show native fluorescence.Therefore,a novel,accurate,specific and sensitive reversed-phase high performance liquid chromatographic method with fluorescence detection was developed and validated for their separation and quantitation in dosage form and human plasma using avanafil as an internal standard(IS).Separation was achieved using isocratic elution within 7.0 min on C18column and acetonitrile-0.15%triethylamine(40∶60,v/v;pH 4)as a mobile phase.The flow rate was 1.0 mL/min and the detection was time-programmed at 330,410 and 370 nm for TAD,DAP and IS,respectively,after excitation at 236 nm.The linear ranges from 0.01 to 30.00μg/mL for each drug with the limits of detection of 4.20 and 7.20 ng/mL for TAD and DAP,respectively.The method was validated in accordance to the International Conference on Harmonization(ICH)guidelines and was successfully applied to spiked human plasma with mean recoveries of 98.17%and 98.83%for TAD and DAP respectively.展开更多
BACKGROUND Hepatitis C virus(HCV)infection and its consequent complications are undeniably a public health burden worldwide,particularly in Egypt.Emerging evidence suggests that many lncRNAs have relevant roles in vir...BACKGROUND Hepatitis C virus(HCV)infection and its consequent complications are undeniably a public health burden worldwide,particularly in Egypt.Emerging evidence suggests that many lncRNAs have relevant roles in viral infections and antiviral responses.AIM To investigate the expression profiles of circulating lncRNAGAS5,lncRNAHEIH,lncRNABISPR and mRNABST2 in naïve,treated and relapsed HCV Egyptian patients,to elucidate relation to HCV infection and their efficacy as innovative biomarkers for the diagnosis and prognosis of HCV GT4.METHODS One hundred and thirty HCV-infected Egyptian patients and 20 healthy controls were included in this study.Serum lncRNAs and mRNABST2 were measured using quantitative real-time polymerase chain reaction(qRT-PCR).RESULTS Our results indicated that serum lncRNAGAS5 and LncRNABISPR were upregulated,whereas mRNA BST2 and LncRNA HEIH were downregulated in naïve patients.In contrast,HCV patients treated with sofosbuvir and simeprevir;with sofosbuvir and daclatasvir;or with sofosbuvir,daclatasvir and ribavirin exhibited lower levels of lncRNAGAS5 and lncRNABISPR with higher mRNABST2 compared to naïve patients.Notably,patients relapsed from sofosbuvir and simeprevir showed higher levels of these lncRNAs with lower mRNABST2 compared to treated patients.LncRNAGAS5 and lncRNABISPR were positively correlated with viral load and ALT at P<0.001,whereas mRNABST2 was negatively correlated with viral load at P<0.001 and ALT at P<0.05.Interestingly,a significant positive correlation between lncRNA HEIH and AFP was observed at P<0.001.CONCLUSION Differential expression of these RNAs suggests their involvement in HCV pathogenesis or antiviral response and highlights their promising roles in diagnosis and prognosis of HCV.展开更多
Successful blind image deconvolution algorithms require the exact estimation of the Point Spread Function size, PSF. In the absence of any priori information about the imagery system and the true image, this estimatio...Successful blind image deconvolution algorithms require the exact estimation of the Point Spread Function size, PSF. In the absence of any priori information about the imagery system and the true image, this estimation is normally done by trial and error experimentation, until an acceptable restored image quality is obtained. This paper, presents an exact estimation of the PSF size, which yields the optimum restored image quality for both noisy and noiseless images. It is based on evaluating the detail energy of the wave packet decomposition of the blurred image. The minimum detail energies occur at the optimum PSF size. Having accurately estimated the PSF, the paper also proposes a fast double updating algorithm for improving the quality of the restored image. This is achieved by the least squares minimization of a system of linear equations that minimizes some error functions derived from the blurred image. Moreover, a technique is also proposed to improve the sharpness of the deconvolved images, by constrained maximization of some of the detail wavelet packet energies. Simulation results of several examples have verified that the proposed technique manages to yield a sharper image with higher PSNR than classical approaches.展开更多
The primary goal of this research is to determine the optimal agricultural field selection that would most effectively support manufacturing producers in manufacturing production while accounting for unpredictability ...The primary goal of this research is to determine the optimal agricultural field selection that would most effectively support manufacturing producers in manufacturing production while accounting for unpredictability and reliability in their decision-making.The PFS is known to address the levels of participation and non-participation.To begin,we introduce the novel concept of a PFZN,which is a hybrid structure of Pythagorean fuzzy sets and the ZN.The PFZN is graded in terms of membership and non-membership,as well as reliability,which provides a strong advice in real-world decision support concerns.The PFZN is a useful tool for dealing with uncertainty in decision-aid problems.The PFZN is a practical way for dealing with such uncertainties in decision-aid problems.The list of aggregation operators:PFZN Einstein weighted averaging and PFZN Einstein weighted geometric,is established under the novel Pythagorean fuzzy ZNs.It is a more precise mathematical instrument for dealing with precision and uncertainty.The core of this research is to develop a numerical algorithmto tackle the uncertainty in real-life problems using PFZNs.To show the applicability and effectiveness of the proposed algorithm,we illustrate the numerical case study related to determining the optimal agricultural field.The main purpose of this work is to describe the extended EDAS approach,then compare the proposed methodology with many other methodologies now in use,and then demonstrate how the suggested methodology may be applied to real-world problems.In addition,the final ranking results that were obtained by the devised techniques weremore efficient and dependable in comparison to the results provided by other methods presented in the literature.展开更多
Face recognition technology automatically identifies an individual from image or video sources.The detection process can be done by attaining facial characteristics from the image of a subject face.Recent developments...Face recognition technology automatically identifies an individual from image or video sources.The detection process can be done by attaining facial characteristics from the image of a subject face.Recent developments in deep learning(DL)and computer vision(CV)techniques enable the design of automated face recognition and tracking methods.This study presents a novel Harris Hawks Optimization with deep learning-empowered automated face detection and tracking(HHODL-AFDT)method.The proposed HHODL-AFDT model involves a Faster region based convolution neural network(RCNN)-based face detection model and HHO-based hyperparameter opti-mization process.The presented optimal Faster RCNN model precisely rec-ognizes the face and is passed into the face-tracking model using a regression network(REGN).The face tracking using the REGN model uses the fea-tures from neighboring frames and foresees the location of the target face in succeeding frames.The application of the HHO algorithm for optimal hyperparameter selection shows the novelty of the work.The experimental validation of the presented HHODL-AFDT algorithm is conducted using two datasets and the experiment outcomes highlighted the superior performance of the HHODL-AFDT model over current methodologies with maximum accuracy of 90.60%and 88.08%under PICS and VTB datasets,respectively.展开更多
There is a growing body of literature that recognizes the importance of data mining in educational systems. This recognition makes educational data mining a new growing research community. One way to achieve the highe...There is a growing body of literature that recognizes the importance of data mining in educational systems. This recognition makes educational data mining a new growing research community. One way to achieve the highest level of quality in a higher education system is by discovering knowledge from educational data such as students’ enrollment data. Many mining tools that aim to discover exciting correlations, frequent patterns, associations, or casual structures among sets of items in educational data sets have been proposed. One of the widely used tools is association rules. In this paper, the Apriori algorithm is used to generate association rules to discover the importance and correlation between factors that influence student’s decision to enroll in higher education institutions in Sudan. The algorithm is applied using a student’s enrollment data set that was created using a questionnaire and 800 students enrolled in governmental and private sector universities as a sample. This paper classifies factors that influence enrollment into: student’s demographic factors and four categories of enrollment related factors (Student and Society, Educational Institution, Admission, and Employment related factors), and determines the most influential factors in determining student’s decision to enroll in Sudanese universities. The analysis result shows that the Educational Institution related factors (50%) and Admission related factors (40%) are strongly influencing students’ enrollment decision, while the Employment related factors (10%) and Student and Society related factors (0%) have weak influence. The factors out of the 14 Educational Institution related factors that have a high impact are: reputation, diversity of study, quality of education, education facilities, and feasibility.展开更多
Nowadays,the usage of socialmedia platforms is rapidly increasing,and rumours or false information are also rising,especially among Arab nations.This false information is harmful to society and individuals.Blocking an...Nowadays,the usage of socialmedia platforms is rapidly increasing,and rumours or false information are also rising,especially among Arab nations.This false information is harmful to society and individuals.Blocking and detecting the spread of fake news in Arabic becomes critical.Several artificial intelligence(AI)methods,including contemporary transformer techniques,BERT,were used to detect fake news.Thus,fake news in Arabic is identified by utilizing AI approaches.This article develops a new hunterprey optimization with hybrid deep learning-based fake news detection(HPOHDL-FND)model on the Arabic corpus.The HPOHDL-FND technique undergoes extensive data pre-processing steps to transform the input data into a useful format.Besides,the HPOHDL-FND technique utilizes long-term memory with a recurrent neural network(LSTM-RNN)model for fake news detection and classification.Finally,hunter prey optimization(HPO)algorithm is exploited for optimal modification of the hyperparameters related to the LSTM-RNN model.The performance validation of the HPOHDL-FND technique is tested using two Arabic datasets.The outcomes exemplified better performance over the other existing techniques with maximum accuracy of 96.57%and 93.53%on Covid19Fakes and satirical datasets,respectively.展开更多
Assuring medical images protection and robustness is a compulsory necessity nowadays.In this paper,a novel technique is proposed that fuses the wavelet-induced multi-resolution decomposition of the Discrete Wavelet Tr...Assuring medical images protection and robustness is a compulsory necessity nowadays.In this paper,a novel technique is proposed that fuses the wavelet-induced multi-resolution decomposition of the Discrete Wavelet Transform(DWT)with the energy compaction of the Discrete Wavelet Transform(DCT).The multi-level Encryption-based Hybrid Fusion Technique(EbhFT)aims to achieve great advances in terms of imperceptibility and security of medical images.A DWT disintegrated sub-band of a cover image is reformed simultaneously using the DCT transform.Afterwards,a 64-bit hex key is employed to encrypt the host image as well as participate in the second key creation process to encode the watermark.Lastly,a PN-sequence key is formed along with a supplementary key in the third layer of the EbHFT.Thus,the watermarked image is generated by enclosing both keys into DWT and DCT coefficients.The fusions ability of the proposed EbHFT technique makes the best use of the distinct privileges of using both DWT and DCT methods.In order to validate the proposed technique,a standard dataset of medical images is used.Simulation results show higher performance of the visual quality(i.e.,57.65)for the watermarked forms of all types of medical images.In addition,EbHFT robustness outperforms an existing scheme tested for the same dataset in terms of Normalized Correlation(NC).Finally,extra protection for digital images from against illegal replicating and unapproved tampering using the proposed technique.展开更多
Oral cancer is the most commonly occurring‘head and neck cancers’across the globe.Most of the oral cancer cases are diagnosed at later stages due to absence of awareness among public.Since earlier identification of ...Oral cancer is the most commonly occurring‘head and neck cancers’across the globe.Most of the oral cancer cases are diagnosed at later stages due to absence of awareness among public.Since earlier identification of disease is essential for improved outcomes,Artificial Intelligence(AI)and Machine Learning(ML)models are used in this regard.In this background,the current study introduces Artificial Intelligence with Deep Transfer Learning driven Oral Cancer detection and Classification Model(AIDTLOCCM).The primary goal of the proposed AIDTL-OCCM model is to diagnose oral cancer using AI and image processing techniques.The proposed AIDTL-OCCM model involves fuzzy-based contrast enhancement approach to perform data pre-processing.Followed by,the densely-connected networks(DenseNet-169)model is employed to produce a useful set of deep features.Moreover,Chimp Optimization Algorithm(COA)with Autoencoder(AE)model is applied for oral cancer detection and classification.Furthermore,COA is employed to determine optimal parameters involved in AE model.A wide range of experimental analyses was conducted on benchmark datasets and the results were investigated under several aspects.The extensive experimental analysis outcomes established the enhanced performance of AIDTLOCCM model compared to other approaches with a maximum accuracy of 90.08%.展开更多
With the flexible deployment and high mobility of Unmanned Aerial Vehicles(UAVs)in an open environment,they have generated con-siderable attention in military and civil applications intending to enable ubiquitous conn...With the flexible deployment and high mobility of Unmanned Aerial Vehicles(UAVs)in an open environment,they have generated con-siderable attention in military and civil applications intending to enable ubiquitous connectivity and foster agile communications.The difficulty stems from features other than mobile ad-hoc network(MANET),namely aerial mobility in three-dimensional space and often changing topology.In the UAV network,a single node serves as a forwarding,transmitting,and receiving node at the same time.Typically,the communication path is multi-hop,and routing significantly affects the network’s performance.A lot of effort should be invested in performance analysis for selecting the optimum routing system.With this motivation,this study modelled a new Coati Optimization Algorithm-based Energy-Efficient Routing Process for Unmanned Aerial Vehicle Communication(COAER-UAVC)technique.The presented COAER-UAVC technique establishes effective routes for communication between the UAVs.It is primarily based on the coati characteristics in nature:if attacking and hunting iguanas and escaping from predators.Besides,the presented COAER-UAVC technique concentrates on the design of fitness functions to minimize energy utilization and communication delay.A varied group of simulations was performed to depict the optimum performance of the COAER-UAVC system.The experimental results verified that the COAER-UAVC technique had assured improved performance over other approaches.展开更多
Smart Grid(SG)technologies enable the acquisition of huge volumes of high dimension and multi-class data related to electric power grid operations through the integration of advanced metering infrastructures,control s...Smart Grid(SG)technologies enable the acquisition of huge volumes of high dimension and multi-class data related to electric power grid operations through the integration of advanced metering infrastructures,control systems,and communication technologies.In SGs,user demand data is gathered and examined over the present supply criteria whereas the expenses are then informed to the clients so that they can decide about electricity consumption.Since the entire procedure is valued on the basis of time,it is essential to perform adaptive estimation of the SG’s stability.Recent advancements inMachine Learning(ML)andDeep Learning(DL)models enable the designing of effective stability prediction models in SGs.In this background,the current study introduces a novel Water Wave Optimization with Optimal Deep Learning Driven Smart Grid Stability Prediction(WWOODL-SGSP)model.The aim of the presented WWOODL-SGSP model is to predict the stability level of SGs in a proficient manner.To attain this,the proposed WWOODL-SGSP model initially applies normalization process to scale the data to a uniform level.Then,WWO algorithm is applied to choose an optimal subset of features from the pre-processed data.Next,Deep Belief Network(DBN)model is followed to predict the stability level of SGs.Finally,Slime Mold Algorithm(SMA)is exploited to fine tune the hyperparameters involved in DBN model.In order to validate the enhanced performance of the proposedWWOODL-SGSP model,a wide range of experimental analyses was performed.The simulation results confirmthe enhanced predictive results of WWOODL-SGSP model over other recent approaches.展开更多
Internet of Everything(IoE),the recent technological advancement,represents an interconnected network of people,processes,data,and things.In recent times,IoE gained significant attention among entrepreneurs,individual...Internet of Everything(IoE),the recent technological advancement,represents an interconnected network of people,processes,data,and things.In recent times,IoE gained significant attention among entrepreneurs,individuals,and communities owing to its realization of intense values from the connected entities.On the other hand,the massive increase in data generation from IoE applications enables the transmission of big data,from contextawaremachines,into useful data.Security and privacy pose serious challenges in designing IoE environment which can be addressed by developing effective Intrusion Detection Systems(IDS).In this background,the current study develops Intelligent Multiverse Optimization with Deep Learning Enabled Intrusion Detection System(IMVO-DLIDS)for IoT environment.The presented IMVO-DLIDS model focuses on identification and classification of intrusions in IoT environment.The proposed IMVO-DLIDS model follows a three-stage process.At first,data pre-processing is performed to convert the actual data into useful format.In addition,Chaotic Local Search Whale Optimization Algorithm-based Feature Selection(CLSWOA-FS)technique is employed to choose the optimal feature subsets.Finally,MVO algorithm is exploited with Bidirectional Gated Recurrent Unit(BiGRU)model for classification.Here,the novelty of the work is the application of MVO algorithm in fine-turning the hyperparameters involved in BiGRU model.The experimental validation was conducted for the proposed IMVO-DLIDS model on benchmark datasets and the results were assessed under distinct measures.An extensive comparative study was conducted and the results confirmed the promising outcomes of IMVO-DLIDS approach compared to other approaches.展开更多
Applied linguistics is an interdisciplinary domain which identifies,investigates,and offers solutions to language-related real-life problems.The new coronavirus disease,otherwise known as Coronavirus disease(COVID-19)...Applied linguistics is an interdisciplinary domain which identifies,investigates,and offers solutions to language-related real-life problems.The new coronavirus disease,otherwise known as Coronavirus disease(COVID-19),has severely affected the everyday life of people all over the world.Specifically,since there is insufficient access to vaccines and no straight or reliable treatment for coronavirus infection,the country has initiated the appropriate preventive measures(like lockdown,physical separation,and masking)for combating this extremely transmittable disease.So,individuals spent more time on online social media platforms(i.e.,Twitter,Facebook,Instagram,LinkedIn,and Reddit)and expressed their thoughts and feelings about coronavirus infection.Twitter has become one of the popular social media platforms and allows anyone to post tweets.This study proposes a sine cosine optimization with bidirectional gated recurrent unit-based senti-ment analysis(SCOBGRU-SA)on COVID-19 tweets.The SCOBGRU-SA technique aimed to detect and classify the various sentiments in Twitter data during the COVID-19 pandemic.The SCOBGRU-SA technique follows data pre-processing and the Fast-Text word embedding process to accomplish this.Moreover,the BGRU model is utilized to recognise and classify sen-timents present in the tweets.Furthermore,the SCO algorithm is exploited for tuning the BGRU method’s hyperparameter,which helps attain improved classification performance.The experimental validation of the SCOBGRU-SA technique takes place using a benchmark dataset,and the results signify its promising performance compared to other DL models.展开更多
Arabic is the world’s first language,categorized by its rich and complicated grammatical formats.Furthermore,the Arabic morphology can be perplexing because nearly 10,000 roots and 900 patterns were the basis for ver...Arabic is the world’s first language,categorized by its rich and complicated grammatical formats.Furthermore,the Arabic morphology can be perplexing because nearly 10,000 roots and 900 patterns were the basis for verbs and nouns.The Arabic language consists of distinct variations utilized in a community and particular situations.Social media sites are a medium for expressing opinions and social phenomena like racism,hatred,offensive language,and all kinds of verbal violence.Such conduct does not impact particular nations,communities,or groups only,extending beyond such areas into people’s everyday lives.This study introduces an Improved Ant Lion Optimizer with Deep Learning Dirven Offensive and Hate Speech Detection(IALODL-OHSD)on Arabic Cross-Corpora.The presented IALODL-OHSD model mainly aims to detect and classify offensive/hate speech expressed on social media.In the IALODL-OHSD model,a threestage process is performed,namely pre-processing,word embedding,and classification.Primarily,data pre-processing is performed to transform the Arabic social media text into a useful format.In addition,the word2vec word embedding process is utilized to produce word embeddings.The attentionbased cascaded long short-term memory(ACLSTM)model is utilized for the classification process.Finally,the IALO algorithm is exploited as a hyperparameter optimizer to boost classifier results.To illustrate a brief result analysis of the IALODL-OHSD model,a detailed set of simulations were performed.The extensive comparison study portrayed the enhanced performance of the IALODL-OHSD model over other approaches.展开更多
Recently,Internet of Things(IoT)devices produces massive quantity of data from distinct sources that get transmitted over public networks.Cybersecurity becomes a challenging issue in the IoT environment where the exis...Recently,Internet of Things(IoT)devices produces massive quantity of data from distinct sources that get transmitted over public networks.Cybersecurity becomes a challenging issue in the IoT environment where the existence of cyber threats needs to be resolved.The development of automated tools for cyber threat detection and classification using machine learning(ML)and artificial intelligence(AI)tools become essential to accomplish security in the IoT environment.It is needed to minimize security issues related to IoT gadgets effectively.Therefore,this article introduces a new Mayfly optimization(MFO)with regularized extreme learning machine(RELM)model,named MFO-RELM for Cybersecurity Threat Detection and classification in IoT environment.The presented MFORELM technique accomplishes the effectual identification of cybersecurity threats that exist in the IoT environment.For accomplishing this,the MFO-RELM model pre-processes the actual IoT data into a meaningful format.In addition,the RELM model receives the pre-processed data and carries out the classification process.In order to boost the performance of the RELM model,the MFO algorithm has been employed to it.The performance validation of the MFO-RELM model is tested using standard datasets and the results highlighted the better outcomes of the MFO-RELM model under distinct aspects.展开更多
基金supported by the ScientificFund of Nanjing Botanical Garden Men.Sun Yat-Sen(JSPKLB202519)Jiangsu Provincial Crop Germplasm Resource Bank(Lycoris)(JS-ZW-K04)Forestry Science and Technology Popularization Demonstration Project of the Central Finance[Su(2024)TG06].
文摘Hybridization and polyploidy are key drivers of species diversity and genome variation in Lycoris,but their cytological and evolutionary consequences remain poorly understood.Here,we investigated chromosome numbers and genome sizes in 64 accessions representing the morphological diversity across the genus.Chromosome numbers ranged from 12 to 33,with seven accessions newly identified,including L.chunxiaoensis(2n=33),two putative L.guangxiensis(2n=19),and fivenatural hybrids(2n=16,18,29,33).Genome sizes varied from 18.03 Gb(L.wulingensis)to 32.62 Gb(L.caldwellii).Although no significantcorrelationwas found between genome size and chromosome number across all accessions,a strong correlation within ploidy-level groups(i.e.,diploid or aneuploid)suggested roles for post-polyploid diploidization,aneuploidy,and dysploidy in speciation.Phylogenetic analyses based on chloroplast genomes and nuclear DNA sequences revealed significantdiscordance,indicating a complex reticulate evolution and historical hybridization,which may complicate morphological classification.Chromosome number aligned more closely with morphological groups,underscoring the necessity of integrating cytological,molecular,and morphological data for accurate taxonomy,particularly in largegenome taxa.Based on this evidence,we propose a putative speciation pathway involving multiple hybridization and polyploidization events,with allopolyploidy playing a predominant role.Furthermore,our results indicate that the species L.insularis and L.longifolia are geographic populations of L.sprengeri and L.aurea,respectively,and confirmedthe distribution of L.traubii and L.albiflora in China' Mainland.These findingsoffer new insights into the mechanisms underlying speciation,interspecificrelationships,and the evolutionary history of Lycoris.
基金support by National Natural Science Foundation of China(NSFC,Grant No.22379153 and 22109128)Ningbo Science And Technology Bureau:Ningbo Key Research and Development Project(2023Z147)Ningbo 3315 Program(2018A-13-C).
文摘Large-scale bismuth vanadate(BiVO_(4))photoanodes are critical to the practical application of photoelectrochemical water splitting devices.However,the lack of interface-modified coatings with simultaneous low cost,scalability,high hole transport efficiency,low impedance,and photocorrosion resistance is a major challenge that prevents the practical application of large-size photoanodes.Here,we present a scalable nickel-chelated polydopamine conformal coating for modifying BiVO_(4)(BiVO_(4)@PDA-Ni,BPNi),achieving over 500 h of stable water oxidation at 0.6 VRHE.The excellent stability is attributed to the chelated Ni acting as hole oxidation sites for PDA,thereby suppressing the accumulated-holes-induced PDA decomposition.Additionally,the in situ generation of Ni(IV)facilitates the structural reorganization of PDA in the photoelectrochemical system,further enhancing the stability of the PDA matrix.The findings of PDA photodegradation,its autonomous metal ion capture within photoelectrochemical systems,and the rapid deactivation of BPNi photoanodes caused by vanadium(V)ions have all provided significant guidance for the enhancement of PDA.Our study demonstrates that nickel-chelated polydopamine can be applied to large-scale BiVO_(4) photoanodes to facilitate oxygen evolution.This will promote the development of large-scale photoanodes suitable for photoelectrochemical devices.
文摘AIM: To evaluate the effect of resveratrol, alone and in combination with fenofibrate, on fructose-induced metabolic genes abnormalities in rats.METHODS: Giving a fructose-enriched diet (FED) to rats for 12 wk was used as a model for inducing hepatic dyslipidemia and insulin resistance. Adult male albino rats (150-200 g) were divided into a control group and a FED group which was subdivided into 4 groups, a control FED, fenofibrate (FENO) (100 mg/kg), resveratrol (RES) (70 mg/kg) and combined treatment (FENO + RES) (half the doses). All treatments were given orally from the 9<sup>th</sup> week till the end of experimental period. Body weight, oral glucose tolerance test (OGTT), liver index, glucose, insulin, insulin resistance (HOMA), serum and liver triglycerides (TGs), oxidative stress (liver MDA, GSH and SOD), serum AST, ALT, AST/ALT ratio and tumor necrosis factor-α (TNF-α) were measured. Additionally, hepatic gene expression of suppressor of cytokine signaling-3 (SOCS-3), sterol regulatory element binding protein-1c (SREBP-1c), fatty acid synthase (FAS), malonyl CoA decarboxylase (MCD), transforming growth factor-β1 (TGF-β1) and adipose tissue genes expression of leptin and adiponectin were investigated. Liver sections were taken for histopathological examination and steatosis area were determined.RESULTS: Rats fed FED showed damaged liver, impairment of glucose tolerance, insulin resistance, oxidative stress and dyslipidemia. As for gene expression, there was a change in favor of dyslipidemia and nonalcoholic steatohepatitis (NASH) development. All treatment regimens showed some benefit in reversing the described deviations. Fructose caused deterioration in hepatic gene expression of SOCS-3, SREBP-1c, FAS, MDA and TGF-β1 and in adipose tissue gene expression of leptin and adiponectin. Fructose showed also an increase in body weight, insulin resistance (OGTT, HOMA), serum and liver TGs, hepatic MDA, serum AST, AST/ALT ratio and TNF-α compared to control. All treatments improved SOCS-3, FAS, MCD, TGF-β1 and leptin genes expression while only RES and FENO + RES groups showed an improvement in SREBP-1c expression. Adiponectin gene expression was improved only by RES. A decrease in body weight, HOMA, liver TGs, AST/ALT ratio and TNF-α were observed in all treatment groups. Liver index was increased in FENO and FENO + RES groups. Serum TGs was improved only by FENO treatment. Liver MDA was improved by RES and FENO + RES treatments. FENO + RES group showed an increase in liver GSH content.CONCLUSION: When resveratrol was given with half the dose of fenofibrate it improved NASH-related fructose-induced disturbances in gene expression similar to a full dose of fenofibrate.
文摘Due to the widespread usage of social media in our recent daily lifestyles,sentiment analysis becomes an important field in pattern recognition and Natural Language Processing(NLP).In this field,users’feedback data on a specific issue are evaluated and analyzed.Detecting emotions within the text is therefore considered one of the important challenges of the current NLP research.Emotions have been widely studied in psychology and behavioral science as they are an integral part of the human nature.Emotions describe a state of mind of distinct behaviors,feelings,thoughts and experiences.The main objective of this paper is to propose a new model named BERT-CNN to detect emotions from text.This model is formed by a combination of the Bidirectional Encoder Representations from Transformer(BERT)and the Convolutional Neural networks(CNN)for textual classification.This model embraces the BERT to train the word semantic representation language model.According to the word context,the semantic vector is dynamically generated and then placed into the CNN to predict the output.Results of a comparative study proved that the BERT-CNN model overcomes the state-of-art baseline performance produced by different models in the literature using the semeval 2019 task3 dataset and ISEAR datasets.The BERTCNN model achieves an accuracy of 94.7%and an F1-score of 94%for semeval2019 task3 dataset and an accuracy of 75.8%and an F1-score of 76%for ISEAR dataset.
文摘Tadalafil(TAD)and dapoxetine HCl(DAP)are recently co-formulated and both show native fluorescence.Therefore,a novel,accurate,specific and sensitive reversed-phase high performance liquid chromatographic method with fluorescence detection was developed and validated for their separation and quantitation in dosage form and human plasma using avanafil as an internal standard(IS).Separation was achieved using isocratic elution within 7.0 min on C18column and acetonitrile-0.15%triethylamine(40∶60,v/v;pH 4)as a mobile phase.The flow rate was 1.0 mL/min and the detection was time-programmed at 330,410 and 370 nm for TAD,DAP and IS,respectively,after excitation at 236 nm.The linear ranges from 0.01 to 30.00μg/mL for each drug with the limits of detection of 4.20 and 7.20 ng/mL for TAD and DAP,respectively.The method was validated in accordance to the International Conference on Harmonization(ICH)guidelines and was successfully applied to spiked human plasma with mean recoveries of 98.17%and 98.83%for TAD and DAP respectively.
文摘BACKGROUND Hepatitis C virus(HCV)infection and its consequent complications are undeniably a public health burden worldwide,particularly in Egypt.Emerging evidence suggests that many lncRNAs have relevant roles in viral infections and antiviral responses.AIM To investigate the expression profiles of circulating lncRNAGAS5,lncRNAHEIH,lncRNABISPR and mRNABST2 in naïve,treated and relapsed HCV Egyptian patients,to elucidate relation to HCV infection and their efficacy as innovative biomarkers for the diagnosis and prognosis of HCV GT4.METHODS One hundred and thirty HCV-infected Egyptian patients and 20 healthy controls were included in this study.Serum lncRNAs and mRNABST2 were measured using quantitative real-time polymerase chain reaction(qRT-PCR).RESULTS Our results indicated that serum lncRNAGAS5 and LncRNABISPR were upregulated,whereas mRNA BST2 and LncRNA HEIH were downregulated in naïve patients.In contrast,HCV patients treated with sofosbuvir and simeprevir;with sofosbuvir and daclatasvir;or with sofosbuvir,daclatasvir and ribavirin exhibited lower levels of lncRNAGAS5 and lncRNABISPR with higher mRNABST2 compared to naïve patients.Notably,patients relapsed from sofosbuvir and simeprevir showed higher levels of these lncRNAs with lower mRNABST2 compared to treated patients.LncRNAGAS5 and lncRNABISPR were positively correlated with viral load and ALT at P<0.001,whereas mRNABST2 was negatively correlated with viral load at P<0.001 and ALT at P<0.05.Interestingly,a significant positive correlation between lncRNA HEIH and AFP was observed at P<0.001.CONCLUSION Differential expression of these RNAs suggests their involvement in HCV pathogenesis or antiviral response and highlights their promising roles in diagnosis and prognosis of HCV.
文摘Successful blind image deconvolution algorithms require the exact estimation of the Point Spread Function size, PSF. In the absence of any priori information about the imagery system and the true image, this estimation is normally done by trial and error experimentation, until an acceptable restored image quality is obtained. This paper, presents an exact estimation of the PSF size, which yields the optimum restored image quality for both noisy and noiseless images. It is based on evaluating the detail energy of the wave packet decomposition of the blurred image. The minimum detail energies occur at the optimum PSF size. Having accurately estimated the PSF, the paper also proposes a fast double updating algorithm for improving the quality of the restored image. This is achieved by the least squares minimization of a system of linear equations that minimizes some error functions derived from the blurred image. Moreover, a technique is also proposed to improve the sharpness of the deconvolved images, by constrained maximization of some of the detail wavelet packet energies. Simulation results of several examples have verified that the proposed technique manages to yield a sharper image with higher PSNR than classical approaches.
文摘The primary goal of this research is to determine the optimal agricultural field selection that would most effectively support manufacturing producers in manufacturing production while accounting for unpredictability and reliability in their decision-making.The PFS is known to address the levels of participation and non-participation.To begin,we introduce the novel concept of a PFZN,which is a hybrid structure of Pythagorean fuzzy sets and the ZN.The PFZN is graded in terms of membership and non-membership,as well as reliability,which provides a strong advice in real-world decision support concerns.The PFZN is a useful tool for dealing with uncertainty in decision-aid problems.The PFZN is a practical way for dealing with such uncertainties in decision-aid problems.The list of aggregation operators:PFZN Einstein weighted averaging and PFZN Einstein weighted geometric,is established under the novel Pythagorean fuzzy ZNs.It is a more precise mathematical instrument for dealing with precision and uncertainty.The core of this research is to develop a numerical algorithmto tackle the uncertainty in real-life problems using PFZNs.To show the applicability and effectiveness of the proposed algorithm,we illustrate the numerical case study related to determining the optimal agricultural field.The main purpose of this work is to describe the extended EDAS approach,then compare the proposed methodology with many other methodologies now in use,and then demonstrate how the suggested methodology may be applied to real-world problems.In addition,the final ranking results that were obtained by the devised techniques weremore efficient and dependable in comparison to the results provided by other methods presented in the literature.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2023R349)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.This study is supported via funding from Prince Sattam bin Abdulaziz University Project Number(PSAU/2023/R/1444).
文摘Face recognition technology automatically identifies an individual from image or video sources.The detection process can be done by attaining facial characteristics from the image of a subject face.Recent developments in deep learning(DL)and computer vision(CV)techniques enable the design of automated face recognition and tracking methods.This study presents a novel Harris Hawks Optimization with deep learning-empowered automated face detection and tracking(HHODL-AFDT)method.The proposed HHODL-AFDT model involves a Faster region based convolution neural network(RCNN)-based face detection model and HHO-based hyperparameter opti-mization process.The presented optimal Faster RCNN model precisely rec-ognizes the face and is passed into the face-tracking model using a regression network(REGN).The face tracking using the REGN model uses the fea-tures from neighboring frames and foresees the location of the target face in succeeding frames.The application of the HHO algorithm for optimal hyperparameter selection shows the novelty of the work.The experimental validation of the presented HHODL-AFDT algorithm is conducted using two datasets and the experiment outcomes highlighted the superior performance of the HHODL-AFDT model over current methodologies with maximum accuracy of 90.60%and 88.08%under PICS and VTB datasets,respectively.
文摘There is a growing body of literature that recognizes the importance of data mining in educational systems. This recognition makes educational data mining a new growing research community. One way to achieve the highest level of quality in a higher education system is by discovering knowledge from educational data such as students’ enrollment data. Many mining tools that aim to discover exciting correlations, frequent patterns, associations, or casual structures among sets of items in educational data sets have been proposed. One of the widely used tools is association rules. In this paper, the Apriori algorithm is used to generate association rules to discover the importance and correlation between factors that influence student’s decision to enroll in higher education institutions in Sudan. The algorithm is applied using a student’s enrollment data set that was created using a questionnaire and 800 students enrolled in governmental and private sector universities as a sample. This paper classifies factors that influence enrollment into: student’s demographic factors and four categories of enrollment related factors (Student and Society, Educational Institution, Admission, and Employment related factors), and determines the most influential factors in determining student’s decision to enroll in Sudanese universities. The analysis result shows that the Educational Institution related factors (50%) and Admission related factors (40%) are strongly influencing students’ enrollment decision, while the Employment related factors (10%) and Student and Society related factors (0%) have weak influence. The factors out of the 14 Educational Institution related factors that have a high impact are: reputation, diversity of study, quality of education, education facilities, and feasibility.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Small Groups Project under Grant Number(120/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R281)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4331004DSR32).
文摘Nowadays,the usage of socialmedia platforms is rapidly increasing,and rumours or false information are also rising,especially among Arab nations.This false information is harmful to society and individuals.Blocking and detecting the spread of fake news in Arabic becomes critical.Several artificial intelligence(AI)methods,including contemporary transformer techniques,BERT,were used to detect fake news.Thus,fake news in Arabic is identified by utilizing AI approaches.This article develops a new hunterprey optimization with hybrid deep learning-based fake news detection(HPOHDL-FND)model on the Arabic corpus.The HPOHDL-FND technique undergoes extensive data pre-processing steps to transform the input data into a useful format.Besides,the HPOHDL-FND technique utilizes long-term memory with a recurrent neural network(LSTM-RNN)model for fake news detection and classification.Finally,hunter prey optimization(HPO)algorithm is exploited for optimal modification of the hyperparameters related to the LSTM-RNN model.The performance validation of the HPOHDL-FND technique is tested using two Arabic datasets.The outcomes exemplified better performance over the other existing techniques with maximum accuracy of 96.57%and 93.53%on Covid19Fakes and satirical datasets,respectively.
文摘Assuring medical images protection and robustness is a compulsory necessity nowadays.In this paper,a novel technique is proposed that fuses the wavelet-induced multi-resolution decomposition of the Discrete Wavelet Transform(DWT)with the energy compaction of the Discrete Wavelet Transform(DCT).The multi-level Encryption-based Hybrid Fusion Technique(EbhFT)aims to achieve great advances in terms of imperceptibility and security of medical images.A DWT disintegrated sub-band of a cover image is reformed simultaneously using the DCT transform.Afterwards,a 64-bit hex key is employed to encrypt the host image as well as participate in the second key creation process to encode the watermark.Lastly,a PN-sequence key is formed along with a supplementary key in the third layer of the EbHFT.Thus,the watermarked image is generated by enclosing both keys into DWT and DCT coefficients.The fusions ability of the proposed EbHFT technique makes the best use of the distinct privileges of using both DWT and DCT methods.In order to validate the proposed technique,a standard dataset of medical images is used.Simulation results show higher performance of the visual quality(i.e.,57.65)for the watermarked forms of all types of medical images.In addition,EbHFT robustness outperforms an existing scheme tested for the same dataset in terms of Normalized Correlation(NC).Finally,extra protection for digital images from against illegal replicating and unapproved tampering using the proposed technique.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 1/322/42)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R161)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4310373DSR06).
文摘Oral cancer is the most commonly occurring‘head and neck cancers’across the globe.Most of the oral cancer cases are diagnosed at later stages due to absence of awareness among public.Since earlier identification of disease is essential for improved outcomes,Artificial Intelligence(AI)and Machine Learning(ML)models are used in this regard.In this background,the current study introduces Artificial Intelligence with Deep Transfer Learning driven Oral Cancer detection and Classification Model(AIDTLOCCM).The primary goal of the proposed AIDTL-OCCM model is to diagnose oral cancer using AI and image processing techniques.The proposed AIDTL-OCCM model involves fuzzy-based contrast enhancement approach to perform data pre-processing.Followed by,the densely-connected networks(DenseNet-169)model is employed to produce a useful set of deep features.Moreover,Chimp Optimization Algorithm(COA)with Autoencoder(AE)model is applied for oral cancer detection and classification.Furthermore,COA is employed to determine optimal parameters involved in AE model.A wide range of experimental analyses was conducted on benchmark datasets and the results were investigated under several aspects.The extensive experimental analysis outcomes established the enhanced performance of AIDTLOCCM model compared to other approaches with a maximum accuracy of 90.08%.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project under grant number(235/44)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2023R114)+1 种基金Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4310373DSR71)This study is supported via funding from Prince Sattam bin Abdulaziz University project number(PSAU/2023/R/1444).
文摘With the flexible deployment and high mobility of Unmanned Aerial Vehicles(UAVs)in an open environment,they have generated con-siderable attention in military and civil applications intending to enable ubiquitous connectivity and foster agile communications.The difficulty stems from features other than mobile ad-hoc network(MANET),namely aerial mobility in three-dimensional space and often changing topology.In the UAV network,a single node serves as a forwarding,transmitting,and receiving node at the same time.Typically,the communication path is multi-hop,and routing significantly affects the network’s performance.A lot of effort should be invested in performance analysis for selecting the optimum routing system.With this motivation,this study modelled a new Coati Optimization Algorithm-based Energy-Efficient Routing Process for Unmanned Aerial Vehicle Communication(COAER-UAVC)technique.The presented COAER-UAVC technique establishes effective routes for communication between the UAVs.It is primarily based on the coati characteristics in nature:if attacking and hunting iguanas and escaping from predators.Besides,the presented COAER-UAVC technique concentrates on the design of fitness functions to minimize energy utilization and communication delay.A varied group of simulations was performed to depict the optimum performance of the COAER-UAVC system.The experimental results verified that the COAER-UAVC technique had assured improved performance over other approaches.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project under grant number(180/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R140)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4310373DSR23).
文摘Smart Grid(SG)technologies enable the acquisition of huge volumes of high dimension and multi-class data related to electric power grid operations through the integration of advanced metering infrastructures,control systems,and communication technologies.In SGs,user demand data is gathered and examined over the present supply criteria whereas the expenses are then informed to the clients so that they can decide about electricity consumption.Since the entire procedure is valued on the basis of time,it is essential to perform adaptive estimation of the SG’s stability.Recent advancements inMachine Learning(ML)andDeep Learning(DL)models enable the designing of effective stability prediction models in SGs.In this background,the current study introduces a novel Water Wave Optimization with Optimal Deep Learning Driven Smart Grid Stability Prediction(WWOODL-SGSP)model.The aim of the presented WWOODL-SGSP model is to predict the stability level of SGs in a proficient manner.To attain this,the proposed WWOODL-SGSP model initially applies normalization process to scale the data to a uniform level.Then,WWO algorithm is applied to choose an optimal subset of features from the pre-processed data.Next,Deep Belief Network(DBN)model is followed to predict the stability level of SGs.Finally,Slime Mold Algorithm(SMA)is exploited to fine tune the hyperparameters involved in DBN model.In order to validate the enhanced performance of the proposedWWOODL-SGSP model,a wide range of experimental analyses was performed.The simulation results confirmthe enhanced predictive results of WWOODL-SGSP model over other recent approaches.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project under grant number(46/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R140)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4210118DSR13).
文摘Internet of Everything(IoE),the recent technological advancement,represents an interconnected network of people,processes,data,and things.In recent times,IoE gained significant attention among entrepreneurs,individuals,and communities owing to its realization of intense values from the connected entities.On the other hand,the massive increase in data generation from IoE applications enables the transmission of big data,from contextawaremachines,into useful data.Security and privacy pose serious challenges in designing IoE environment which can be addressed by developing effective Intrusion Detection Systems(IDS).In this background,the current study develops Intelligent Multiverse Optimization with Deep Learning Enabled Intrusion Detection System(IMVO-DLIDS)for IoT environment.The presented IMVO-DLIDS model focuses on identification and classification of intrusions in IoT environment.The proposed IMVO-DLIDS model follows a three-stage process.At first,data pre-processing is performed to convert the actual data into useful format.In addition,Chaotic Local Search Whale Optimization Algorithm-based Feature Selection(CLSWOA-FS)technique is employed to choose the optimal feature subsets.Finally,MVO algorithm is exploited with Bidirectional Gated Recurrent Unit(BiGRU)model for classification.Here,the novelty of the work is the application of MVO algorithm in fine-turning the hyperparameters involved in BiGRU model.The experimental validation was conducted for the proposed IMVO-DLIDS model on benchmark datasets and the results were assessed under distinct measures.An extensive comparative study was conducted and the results confirmed the promising outcomes of IMVO-DLIDS approach compared to other approaches.
基金The authors thank the Deanship of Scientific Research at King Khalid University for funding this work through Small Groups Project under grant number(120/43)Princess Nourah bint Abdulrahman UniversityResearchers Supporting Project number(PNURSP2022R281)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research atUmmAl-Qura University for supporting this work by Grant Code:(22UQU4331004DSR06).
文摘Applied linguistics is an interdisciplinary domain which identifies,investigates,and offers solutions to language-related real-life problems.The new coronavirus disease,otherwise known as Coronavirus disease(COVID-19),has severely affected the everyday life of people all over the world.Specifically,since there is insufficient access to vaccines and no straight or reliable treatment for coronavirus infection,the country has initiated the appropriate preventive measures(like lockdown,physical separation,and masking)for combating this extremely transmittable disease.So,individuals spent more time on online social media platforms(i.e.,Twitter,Facebook,Instagram,LinkedIn,and Reddit)and expressed their thoughts and feelings about coronavirus infection.Twitter has become one of the popular social media platforms and allows anyone to post tweets.This study proposes a sine cosine optimization with bidirectional gated recurrent unit-based senti-ment analysis(SCOBGRU-SA)on COVID-19 tweets.The SCOBGRU-SA technique aimed to detect and classify the various sentiments in Twitter data during the COVID-19 pandemic.The SCOBGRU-SA technique follows data pre-processing and the Fast-Text word embedding process to accomplish this.Moreover,the BGRU model is utilized to recognise and classify sen-timents present in the tweets.Furthermore,the SCO algorithm is exploited for tuning the BGRU method’s hyperparameter,which helps attain improved classification performance.The experimental validation of the SCOBGRU-SA technique takes place using a benchmark dataset,and the results signify its promising performance compared to other DL models.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R263)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4340237DSR43.
文摘Arabic is the world’s first language,categorized by its rich and complicated grammatical formats.Furthermore,the Arabic morphology can be perplexing because nearly 10,000 roots and 900 patterns were the basis for verbs and nouns.The Arabic language consists of distinct variations utilized in a community and particular situations.Social media sites are a medium for expressing opinions and social phenomena like racism,hatred,offensive language,and all kinds of verbal violence.Such conduct does not impact particular nations,communities,or groups only,extending beyond such areas into people’s everyday lives.This study introduces an Improved Ant Lion Optimizer with Deep Learning Dirven Offensive and Hate Speech Detection(IALODL-OHSD)on Arabic Cross-Corpora.The presented IALODL-OHSD model mainly aims to detect and classify offensive/hate speech expressed on social media.In the IALODL-OHSD model,a threestage process is performed,namely pre-processing,word embedding,and classification.Primarily,data pre-processing is performed to transform the Arabic social media text into a useful format.In addition,the word2vec word embedding process is utilized to produce word embeddings.The attentionbased cascaded long short-term memory(ACLSTM)model is utilized for the classification process.Finally,the IALO algorithm is exploited as a hyperparameter optimizer to boost classifier results.To illustrate a brief result analysis of the IALODL-OHSD model,a detailed set of simulations were performed.The extensive comparison study portrayed the enhanced performance of the IALODL-OHSD model over other approaches.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 2/142/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R161)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4210118DSR06).
文摘Recently,Internet of Things(IoT)devices produces massive quantity of data from distinct sources that get transmitted over public networks.Cybersecurity becomes a challenging issue in the IoT environment where the existence of cyber threats needs to be resolved.The development of automated tools for cyber threat detection and classification using machine learning(ML)and artificial intelligence(AI)tools become essential to accomplish security in the IoT environment.It is needed to minimize security issues related to IoT gadgets effectively.Therefore,this article introduces a new Mayfly optimization(MFO)with regularized extreme learning machine(RELM)model,named MFO-RELM for Cybersecurity Threat Detection and classification in IoT environment.The presented MFORELM technique accomplishes the effectual identification of cybersecurity threats that exist in the IoT environment.For accomplishing this,the MFO-RELM model pre-processes the actual IoT data into a meaningful format.In addition,the RELM model receives the pre-processed data and carries out the classification process.In order to boost the performance of the RELM model,the MFO algorithm has been employed to it.The performance validation of the MFO-RELM model is tested using standard datasets and the results highlighted the better outcomes of the MFO-RELM model under distinct aspects.
