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| 1 | Circulating micro RNA, mi R-122 and mi R-221 signature in Egyptian patients with chronic hepatitis C related hepatocellular carcinoma显示文摘AIM: To explore the potential usefulness of serum miR-122 and miR-221 as non-invasive diagnostic markers of hepatitis C virus(HCV)-related hepatocellular carcinoma(HCC).METHODS: This prospective study was conducted on 90 adult patients of both sex with HCV-related chronic liver disease and chronic hepatitis C related HCC. In addition to the 10 healthy control individuals, patients were stratified into; interferon-na?ve chronic hepatitis C(CH)(n = 30), post-hepatitis C compensated cirrhosis(LC)(n = 30) and treatment-naive HCC(n = 30). All patients and controls underwent full clinical assessment and laboratory investigations in addition to the evaluation of the level of serum miR NA expression by RT-PCR.RESULTS: There was a significant fold change in serum mi RNA expression in the different patient groups when compared to normal controls; mi R-122 showed significant fold increasing in both CH and HCC and significant fold decrease in LC. On the other hand, mi R-221 showed significant fold elevation in both CH and LC groups and significant fold decrease in HCC group(P = 0.01). Comparing fold changes in miR NAs in HCC group vs non HCC group(CH and Cirrhosis), there was non-significant fold elevation in miR-122(P = 0.21) and significant fold decreasing in miR-221 in HCC vs non-HCC(P = 0.03). ROC curve analysis for miR-221 yielded 87% sensitivity and 40% specificity for the differentiation of HCC patients from non-HCC at a cutoff 1.82. CONCLUSION: Serum miR-221 has a strong potential to serve as one of the novel non-invasive biomarkers of HCC. | Hassan El-Garem Ayman Ammer Hany Shehab Olfat Shaker Mohammed Anwer Wafaa El-Akel Heba Omar | 2014 | World Journal of Hepatology2014，6，11： | 18 |
| 2 | 鼓形与圆柱形杆齿式纵轴流脱粒滚筒功耗对比试验显示文摘为降低功耗,同时减少脱粒滚筒堵塞,提高水稻联合收获机收获效率,设计了一种鼓形杆齿式纵轴流脱粒滚筒,并对其功耗进行了仿真与试验研究。以具有相同外部尺寸的圆柱形杆齿式纵轴流脱粒滚筒为对照,以脱粒滚筒旋转轴总力矩为试验指标,进行了基于离散元法的对比试验,结果显示:相同喂入量下,2种结构的脱粒滚筒旋转轴总力矩存在明显差异,鼓形滚筒旋转轴总力矩小于圆柱形滚筒,且随喂入量增大,差异越大。与仿真条件一致的2种脱粒滚筒结构功耗对比台架试验结果表明,在喂入量为0.8~1.6 kg/s时,随喂入量增加,滚筒功耗增大,与相同外部尺寸的圆柱形滚筒相比,鼓形结构的脱粒滚筒功耗平均降低5%~15%。 | 谢干 张国忠 付建伟 周勇 王洋 高原 王伟康 Mohamed Anwer | 2021 | 华中农业大学学报2021，40，1： | 5 |
| 3 | Evaluation of anti-resistant activity of Auklandia(Saussurea lappa) root against some human pathogens显示文摘Objective:The antimicrobial activity of the ethanol extract of the Auklandia(Saussurea lappa)root plant was investigated to verify its medicinal use in the treatment of microbial infections.Methods:The antimicrobial activity of the ethanol extract was tested against clinical isolates ofsome multidrug-resistant bacteria using the agar well diffusion method.Commercial antibioticswere used as positive reference standards to determine the sensitivity of the clinical isolates.Results:The extracts showed significant inhibitory activity against clinical isolates of methicillinresistantStaphylococcus aureus,Pseudomonas aeruginosa,Escherichia coli,Klebsiella pneumonia,Extended Spectrum Beta-Lactemase,Acinetobacter baumannii.The minimum inhibitory concentration values obtained using the agar dilution test ranged from 2.0μg/μL-12.0μg/μL.In the contrary the water extract showed no activity at all against the tested isolates.Furthermore,theresults obtained by examining anti-resistant activity of the plant ethanolic extract showed thatat higher concentration of the plant extract(12μg)all tested bacteria isolates were inhibited with variable inhibition zones similar to those obtained when we applied lower extract concentrationusing the well diffusion assay.Conclusion:The results demonstrated that the crude ethanolicextract of the Auklandia(Saussurea lappa)root plant has a wide spectrum of activity suggestingthat it may be useful in the treatment of infections caused by the above clinical isolates(humanpathogens). | Sidgi Syed Anwer Hasson Mohammed Saeed Al-Balushi KhazinaAlharthy JumaZaidAl-Busaidi MunaSulimanAldaihani Mohammed Shafeeq Othman Elias Antony Said Omar Habal Talal Abdullah Sallam Ali Abdullah Aljabri Mohamed AhmedIdris | 2013 | Asian Pacific Journal of Tropical Biomedicine2013，3，7： | 2 |
| 4 | 基于薄膜传感器的横轴流脱粒滚筒实时喂入量测量系统设计显示文摘为实现水稻联合收割机脱粒滚筒实时喂入量监测,基于薄膜传感器设计了一种脱粒滚筒喂入量测量系统,其原理为通过薄膜传感器测出滚筒顶盖侧边因喂入量变化而产生的受力变化。以额定喂入量为0.8 kg/s的小型横轴流脱粒滚筒为试验对象,以成熟的晚籼98和传奇丰两优1号水稻为主要试验材料,在转速分别为650、800、950、1100 r/min、喂入量为0.2~0.8 kg/s的条件下开展台架试验,结果显示:薄膜传感器采集的实时信号与实时喂入量显著相关,对喂入量和传感器信号进行线性关系拟合,拟合效果较好。试验结果表明,设计的测量系统可以通过传感器信号对喂入量进行测量。 | 赵胜华 张国忠 张仕杰 付建伟 谢干 MOHAMED Anwer | 2020 | 华中农业大学学报2020，39，2： | 2 |
| 5 | Energy Aware Data Collection with Route Planning for 6G Enabled UAV Communication显示文摘With technological advancements in 6G and Internet of Things(IoT), the incorporation of Unmanned Aerial Vehicles (UAVs) and cellularnetworks has become a hot research topic. At present, the proficient evolution of 6G networks allows the UAVs to offer cost-effective and timelysolutions for real-time applications such as medicine, tracking, surveillance,etc. Energy efficiency, data collection, and route planning are crucial processesto improve the network communication. These processes are highly difficultowing to high mobility, presence of non-stationary links, dynamic topology,and energy-restricted UAVs. With this motivation, the current research paperpresents a novel Energy Aware Data Collection with Routing Planning for6G-enabled UAV communication (EADCRP-6G) technique. The goal of theproposed EADCRP-6G technique is to conduct energy-efficient cluster-baseddata collection and optimal route planning for 6G-enabled UAV networks.EADCRP-6G technique deploys Improved Red Deer Algorithm-based Clustering (IRDAC) technique to elect an optimal set of Cluster Heads (CH) andorganize these clusters. Besides, Artificial Fish Swarm-based Route Planning(AFSRP) technique is applied to choose an optimum set of routes for UAVcommunication in 6G networks. In order to validated whether the proposedEADCRP-6G technique enhances the performance, a series of simulationswas performed and the outcomes were investigated under different dimensions.The experimental results showcase that the proposed model outperformed allother existing models under different evaluation parameters. | Mesfer Al Duhayyim Marwa Obayya Fahd N.Al-Wesabi Anwer Mustafa Hilal Mohammed Rizwanullah Majdy M.Eltahir | 2022 | Computers, Materials & Continua2022，，4： | 1 |
| 6 | Deep Learning Enabled Computer Aided Diagnosis Model for Lung Cancer using Biomedical CT Images显示文摘Early detection of lung cancer can help for improving the survival rate of the patients.Biomedical imaging tools such as computed tomography(CT)image was utilized to the proper identification and positioning of lung cancer.The recently developed deep learning(DL)models can be employed for the effectual identification and classification of diseases.This article introduces novel deep learning enabled CAD technique for lung cancer using biomedical CT image,named DLCADLC-BCT technique.The proposed DLCADLC-BCT technique intends for detecting and classifying lung cancer using CT images.The proposed DLCADLC-BCT technique initially uses gray level co-occurrence matrix(GLCM)model for feature extraction.Also,long short term memory(LSTM)model was applied for classifying the existence of lung cancer in the CT images.Moreover,moth swarm optimization(MSO)algorithm is employed to optimally choose the hyperparameters of the LSTM model such as learning rate,batch size,and epoch count.For demonstrating the improved classifier results of the DLCADLC-BCT approach,a set of simulations were executed on benchmark dataset and the outcomes exhibited the supremacy of the DLCADLC-BCT technique over the recent approaches. | Mohammad Alamgeer Hanan Abdullah Mengash Radwa Marzouk Mohamed K Nour Anwer Mustafa Hilal Abdelwahed Motwakel Abu Sarwar Zamani Mohammed Rizwanullah | 2022 | Computers, Materials & Continua2022，，10： | 1 |
| 7 | Biocontrol efficiency of Bacillus thuringiensis toxins against root-knot nematode, Meloidogyne incognita显示文摘 | Mohammed S H Saedy E Anwer M Enan M R Ibrahim N E Ghareeb A Moustafa S A | 2008 | Journal of Cell and Molecular Biology2008，7，1： | 1 |
| 8 | Deep Learning Empowered Cybersecurity Spam Bot Detection for Online Social Networks显示文摘Cybersecurity encompasses various elements such as strategies,policies,processes,and techniques to accomplish availability,confidentiality,and integrity of resource processing,network,software,and data from attacks.In this scenario,the rising popularity of Online Social Networks(OSN)is under threat from spammers for which effective spam bot detection approaches should be developed.Earlier studies have developed different approaches for the detection of spam bots in OSN.But those techniques primarily concentrated on hand-crafted features to capture the features of malicious users while the application of Deep Learning(DL)models needs to be explored.With this motivation,the current research article proposes a Spam Bot Detection technique using Hybrid DL model abbreviated as SBDHDL.The proposed SBD-HDL technique focuses on the detection of spam bots that exist in OSNs.The technique has different stages of operations such as pre-processing,classification,and parameter optimization.Besides,SBD-HDL technique hybridizes Graph Convolutional Network(GCN)with Recurrent Neural Network(RNN)model for spam bot classification process.In order to enhance the detection performance of GCN-RNN model,hyperparameters are tuned using Lion Optimization Algorithm(LOA).Both hybridization of GCN-RNN and LOA-based hyperparameter tuning process make the current work,a first-of-its-kind in this domain.The experimental validation of the proposed SBD-HDL technique,conducted upon benchmark dataset,established the supremacy of the technique since it was validated under different measures. | Mesfer Al Duhayyim Haya Mesfer Alshahrani Fahd NAl-Wesabi Mohammed Alamgeer Anwer Mustafa Hilal Mohammed Rizwanullah | 2022 | Computers, Materials & Continua2022，，3： | 1 |
| 9 | Artificial Intelligence Based Optimal Functional Link Neural Network for Financial Data Science显示文摘In present digital era,data science techniques exploit artificial intelligence(AI)techniques who start and run small and medium-sized enterprises(SMEs)to have an impact and develop their businesses.Data science integrates the conventions of econometrics with the technological elements of data science.It make use of machine learning(ML),predictive and prescriptive analytics to effectively understand financial data and solve related problems.Smart technologies for SMEs enable allows the firm to get smarter with their processes and offers efficient operations.At the same time,it is needed to develop an effective tool which can assist small to medium sized enterprises to forecast business failure as well as financial crisis.AI becomes a familiar tool for several businesses due to the fact that it concentrates on the design of intelligent decision making tools to solve particular real time problems.With this motivation,this paper presents a new AI based optimal functional link neural network(FLNN)based financial crisis prediction(FCP)model forSMEs.The proposed model involves preprocessing,feature selection,classification,and parameter tuning.At the initial stage,the financial data of the enterprises are collected and are preprocessed to enhance the quality of the data.Besides,a novel chaotic grasshopper optimization algorithm(CGOA)based feature selection technique is applied for the optimal selection of features.Moreover,functional link neural network(FLNN)model is employed for the classification of the feature reduced data.Finally,the efficiency of theFLNNmodel can be improvised by the use of cat swarm optimizer(CSO)algorithm.A detailed experimental validation process takes place on Polish dataset to ensure the performance of the presented model.The experimental studies demonstrated that the CGOA-FLNN-CSO model has accomplished maximum prediction accuracy of 98.830%,92.100%,and 95.220%on the applied Polish dataset Year I-III respectively. | Anwer Mustafa Hilal Hadeel Alsolai Fahd NAl-Wesabi Mohammed Abdullah Al-Hagery Manar Ahmed Hamza Mesfer Al Duhayyim | 2022 | Computers, Materials & Continua2022，，3： | 1 |
| 10 | ECG data compression using optimal non-orthogonal wavelet transform显示文摘 | Anwer Al-Shrouf Mohammed Abo-Zahhad | 2000 | Medical Engineering & Physics2000，22，： | 1 |
| 11 | Optimal Deep Convolutional Neural Network for Vehicle Detection in Remote Sensing Images显示文摘Object detection(OD)in remote sensing images(RSI)acts as a vital part in numerous civilian and military application areas,like urban planning,geographic information system(GIS),and search and rescue functions.Vehicle recognition from RSIs remained a challenging process because of the difficulty of background data and the redundancy of recognition regions.The latest advancements in deep learning(DL)approaches permit the design of effectual OD approaches.This study develops an Artificial Ecosystem Optimizer with Deep Convolutional Neural Network for Vehicle Detection(AEODCNN-VD)model on Remote Sensing Images.The proposed AEODCNN-VD model focuses on the identification of vehicles accurately and rapidly.To detect vehicles,the presented AEODCNN-VD model employs single shot detector(SSD)with Inception network as a baseline model.In addition,Multiway Feature Pyramid Network(MFPN)is used for handling objects of varying sizes in RSIs.The features from the Inception model are passed into theMFPNformultiway andmultiscale feature fusion.Finally,the fused features are passed into bounding box and class prediction networks.For enhancing the detection efficiency of the AEODCNN-VD approach,AEO based hyperparameter optimizer is used,which is stimulated by the energy transfer strategies such as production,consumption,and decomposition in an ecosystem.The performance validation of the presentedmethod on benchmark datasets showed promising performance over recent DL models. | Saeed Masoud Alshahrani Saud S.Alotaibi Shaha Al-Otaibi Mohamed Mousa Anwer Mustafa Hilal Amgad Atta Abdelmageed Abdelwahed Motwakel Mohamed I.Eldesouki | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 12 | Indoor Electromagnetic Radiation Intensity Relationship to Total Energy of Household Appliances显示文摘The rapid technological developments in the modern era have led to increased electrical equipment in our daily lives,work,and homes.From this standpoint,the main objective of this study is to evaluate the potential relationship between the intensity of electromagnetic radiation and the total energy of household appliances in the living environment within the building by measuring and analyzing the strength of the electric field and the entire electromagnetic radiation flux density of electrical devices operating at frequencies(5 Hz to 1 kHz).The living room was chosen as a center for measurement at 15 homes in three different environmental regions(urban,suburbs,and open areas).The three measurement methods are(Mode 1:people in a sitting position with electrical appliances on.Mode 2:People in a standing position with electrical appliances on.Mode 3:People are in the upright positionwhile turning off the electrical devices)in the living room.These measurement methods and their results reinforce the importance of this research.The results showed that the average electric field strengthmeasured inMode 2 ismuch greater than the two methods,and we also found less electromagnetic radiation in Mode 3 than in the two modes.All results remain within the recommended overall exposure developed by the International Committee for the Prevention of Non-Ionizing Radiation and the International Electrotechnical Commission. | Murad A.A.Almekhlafi Lamia Osman Widaa Fahd N.Al-Wesabi Mohammad Alamgeer Anwer Mustafa Hilal Manar Ahmed Hamza Abu Sarwar Zamani Mohammed Rizwanullah | 2022 | Computers, Materials & Continua2022，，3： | 0 |
| 13 | An Optimal Text Watermarking Method for Sensitive Detecting of Illegal Tampering Attacks显示文摘Due to the rapid increase in the exchange of text information via internet networks,the security and authenticity of digital content have become a major research issue.The main challenges faced by researchers are how to hide the information within the text to use it later for authentication and attacks tampering detection without effects on the meaning and size of the given digital text.In this paper,an efficient text-based watermarking method has been proposed for detecting the illegal tampering attacks on theArabic text transmitted online via an Internet network.Towards this purpose,the accuracy of tampering detection and watermark robustness has been improved of the proposed method as compared with the existing approaches.In the proposed method,both embedding and extracting of the watermark are logically implemented,which causes no change in the digital text.This is achieved by using the third level and alphanumeric strategy of the Markov model as a text analysis technique for analyzing the Arabic contents to obtain its features which are considered as the digital watermark.This digital watermark will be used later to detecting any tampering of illegal attack on the received Arabic text.An extensive set of experiments using four data sets of varying lengths proves the effectiveness of our approach in terms of detection accuracy,robustness,and effectiveness under multiple random locations of the common tampering attacks. | Anwer Mustafa Hilal Fahd N.Al-Wesabi Mohammed Alamgeer Manar Ahmed Hamza Mohammad Mahzari Murad A.Almekhlafi | 2022 | Computers, Materials & Continua2022，，3： | 0 |
| 14 | Intelligent Deep Learning Based Automated Fish Detection Model for UWSN显示文摘An exponential growth in advanced technologies has resulted in the exploration of Ocean spaces.It has paved the way for new opportunities that can address questions relevant to diversity,uniqueness,and difficulty of marine life.Underwater Wireless Sensor Networks(UWSNs)are widely used to leverage such opportunities while these networks include a set of vehicles and sensors to monitor the environmental conditions.In this scenario,it is fascinating to design an automated fish detection technique with the help of underwater videos and computer vision techniques so as to estimate and monitor fish biomass in water bodies.Several models have been developed earlier for fish detection.However,they lack robustness to accommodate considerable differences in scenes owing to poor luminosity,fish orientation,structure of seabed,aquatic plantmovement in the background and distinctive shapes and texture of fishes from different genus.With this motivation,the current research article introduces an Intelligent Deep Learning based Automated Fish Detection model for UWSN,named IDLAFD-UWSN model.The presented IDLAFD-UWSN model aims at automatic detection of fishes from underwater videos,particularly in blurred and crowded environments.IDLAFD-UWSN model makes use of Mask Region Convolutional Neural Network(Mask RCNN)with Capsule Network as a baseline model for fish detection.Besides,in order to train Mask RCNN,background subtraction process using GaussianMixtureModel(GMM)model is applied.This model makes use of motion details of fishes in video which consequently integrates the outcome with actual image for the generation of fish-dependent candidate regions.Finally,Wavelet Kernel Extreme Learning Machine(WKELM)model is utilized as a classifier model.The performance of the proposed IDLAFD-UWSN model was tested against benchmark underwater video dataset and the experimental results achieved by IDLAFD-UWSN model were promising in comparison with other state-of-the-art methods under different aspects with the maximum accuracy of 98%and 97%on the applied blurred and crowded datasets respectively. | Mesfer Al Duhayyim Haya Mesfer Alshahrani Fahd NAl-Wesabi Mohammed Alamgeer Anwer Mustafa Hilal Manar Ahmed Hamza | 2022 | Computers, Materials & Continua2022，，3： | 0 |
| 15 | CryptoNight Mining Algorithm with YAC Consensus for Social Media Marketing Using Blockchain显示文摘Social media is a platform in which user can create,share and exchange the knowledge/information.Social media marketing is to identify the different consumer’s demands and engages them to create marketing resources.The popular social media platforms are Microsoft,Snapchat,Amazon,Flipkart,Google,eBay,Instagram,Facebook,Pin interest,and Twitter.The main aim of social media marketing deals with various business partners and build good relationship with millions of customers by satisfying their needs.Disruptive technology is replacing old approaches in the social media marketing to new technology-based marketing.However,this disruptive technology creates some issues like fake news,insecure,inconsistency,inaccuracy and so on.These issues contribute economic instability in the society,diminishing the level of trustworthy.To overcome these issues,this paper we present blockchain as disruptive technology for social media marketing.Blockchain plays a vital role on social media marketing by providing secure to the company page in the website.The properties of disruptive potential of blockchain on social media marketing is transparency,security,reliability and immutability.This paper presents a new framework for disruptive technology in blockchain social media marketing using fusion of CryptoNight mining algorithm with YAC consensus algorithm[BCDSMM-CNYAC].This mining algorithm provides high CPU efficiency,high dimensionality of secure and detecting falsifying data attack in the social media marketing.For the data analysis we proposed ANOVA analysis method regarding to the factors of age,time,frequency visiting times of social media platform.For reliability analysis of data Cronbach’s alpha tests are implemented. | Anwer Mustafa Hil Fahd N.Al-Wesabi Hadeel Alsolai Ola Abdelgney Omer Ali Nadhem Nemri Manar Ahmed Hamza Abu Sarwar Zamani Mohammed Rizwanullah | 2022 | Computers, Materials & Continua2022，，5： | 0 |
| 16 | Modeling of Artificial Intelligence Based Traffic Flow Prediction with Weather Conditions显示文摘Short-term traffic flow prediction (TFP) is an important area inintelligent transportation system (ITS), which is used to reduce traffic congestion. But the avail of traffic flow data with temporal features and periodicfeatures are susceptible to weather conditions, making TFP a challengingissue. TFP process are significantly influenced by several factors like accidentand weather. Particularly, the inclement weather conditions may have anextreme impact on travel time and traffic flow. Since most of the existing TFPtechniques do not consider the impact of weather conditions on the TF, it isneeded to develop effective TFP with the consideration of extreme weatherconditions. In this view, this paper designs an artificial intelligence based TFPwith weather conditions (AITFP-WC) for smart cities. The goal of the AITFPWC model is to enhance the performance of the TFP model with the inclusionof weather related conditions. The proposed AITFP-WC technique includesElman neural network (ENN) model to predict the flow of traffic in smartcities. Besides, tunicate swarm algorithm with feed forward neural networks(TSA-FFNN) model is employed for the weather and periodicity analysis. Atlast, a fusion of TFP and WPA processes takes place using the FFNN modelto determine the final prediction output. In order to assess the enhancedpredictive outcome of the AITFP-WC model, an extensive simulation analysisis carried out. The experimental values highlighted the enhanced performanceof the AITFP-WC technique over the recent state of art methods. | Mesfer Al Duhayyim Amani Abdulrahman Albraikan Fahd N.Al-Wesabi Hiba M.Burbur Mohammad Alamgeer Anwer Mustafa Hilal Manar Ahmed Hamza Mohammed Rizwanullah | 2022 | Computers, Materials & Continua2022，，5： | 0 |
| 17 | 埃及重症监护护士临床领导力行为的调查研究显示文摘目的本研究旨在调查埃及医院重症监护护士的临床领导力行为现状,并比较私立和公立医院护士临床领导力水平的差异。方法2019年1—3月,在埃及1所公立教学医院和1所私立医院共选取365名具有护理学学士学位的重症监护护士进行横断面调查。采用社会人口学特征调查表和临床领导力行为问卷(the Clinical Leadership Behaviors Questionnaire,CLB-Q)收集数据,将各维度及总分的条目均分转换为百分制得分,以便进行比较和分析。结果结果显示,护士CLB-Q得分为(77.11±11.87)分,其中沟通维度得分最高(91.84±7.38)。私立医院重症监护护士的CLB-Q得分为(90.48±5.53)分,高于公立教学医院护士得分(68.29±4.21),差异有统计学意义(P<0.001)。有5~10年工作经验的护士在CLB-Q总分及自我认知、倡导和授权、决策、质量与安全、团队合作和临床卓越维度得分较其他组别护士得分高(P<0.01)。单身护士的CLB-Q总分及倡导和授权、决策、质量与安全以及临床卓越维度得分较已婚护士高(P<0.01)。结论护理管理者应促进重症监护护士加强临床实践,鼓励他们参加质量和安全方面的培训项目,以培养其临床领导力。公立医院的重症监护护士尤其需要全面提升临床领导力水平。 | Heba Mohamed Al Anwer Ashour Maram Ahmed Banakhar Naglaa Abd Elaziz Elseesy | 2022 | International Journal of Nursing Sciences2022，9，3： | 0 |
| 18 | Privacy Preserving Image Encryption with Deep Learning Based IoT Healthcare Applications显示文摘Latest developments in computing and communication technologies are enabled the design of connected healthcare system which are mainly based on IoT and Edge technologies.Blockchain,data encryption,and deep learning(DL)models can be utilized to design efficient security solutions for IoT healthcare applications.In this aspect,this article introduces a Blockchain with privacy preserving image encryption and optimal deep learning(BPPIEODL)technique for IoT healthcare applications.The proposed BPPIE-ODL technique intends to securely transmit the encrypted medical images captured by IoT devices and performs classification process at the cloud server.The proposed BPPIE-ODL technique encompasses the design of dragonfly algorithm(DFA)with signcryption technique to encrypt the medical images captured by the IoT devices.Besides,blockchain(BC)can be utilized as a distributed data saving approach for generating a ledger,which permits access to the users and prevents third party’s access to encrypted data.In addition,the classification process includes SqueezeNet based feature extraction,softmax classifier(SMC),and Nadam based hyperparameter optimizer.The usage of Nadam model helps to optimally regulate the hyperparameters of the SqueezeNet architecture.For examining the enhanced encryption as well as classification performance of the BPPIE-ODL technique,a comprehensive experimental analysis is carried out.The simulation outcomes demonstrate the significant performance of the BPPIE-ODL technique on the other techniques with increased precision and accuracy of 0.9551 and 0.9813 respectively. | Mohammad Alamgeer Saud S.Alotaibi Shaha Al-Otaibi Nazik Alturki Anwer Mustafa Hilal Abdelwahed Motwakel Ishfaq Yaseen Mohamed I.Eldesouki | 2022 | Computers, Materials & Continua2022，，10： | 0 |
| 19 | Integration of Fog Computing for Health Record Management Using Blockchain Technology显示文摘Internet of Medical Things (IoMT) is a breakthrough technologyin the transfer of medical data via a communication system. Wearable sensordevices collect patient data and transfer them through mobile internet, thatis, the IoMT. Recently, the shift in paradigm from manual data storage toelectronic health recording on fog, edge, and cloud computing has been noted.These advanced computing technologies have facilitated medical services withminimum cost and available conditions. However, the IoMT raises a highconcern on network security and patient data privacy in the health caresystem. The main issue is the transmission of health data with high security inthe fog computing model. In today’s market, the best solution is blockchaintechnology. This technology provides high-end security and authenticationin storing and transferring data. In this research, a blockchain-based fogcomputing model is proposed for the IoMT. The proposed technique embedsa block chain with the yet another consensus (YAC) protocol building securityinfrastructure into fog computing for storing and transferring IoMT data inthe network. YAC is a consensus protocol that authenticates the input datain the block chain. In this scenario, the patients and their family membersare allowed to access the data. The empirical outcome of the proposedtechnique indicates high reliability and security against dangerous threats.The major advantages of using the blockchain model are high transparency,good traceability, and high processing speed. The technique also exhibitshigh reliability and efficiency in accessing data with secure transmission. Theproposed technique achieves 95% reliability in transferring a large number offiles up to 10,000. | Mesfer AI Duhayyim Fahd N.Al-Wesabi Radwa Marzouk Abdalla Ibrahim Abdalla Musa Noha Negm Anwer Mustafa Hilal Manar Ahmed Hamza Mohammed Rizwanullah | 2022 | Computers, Materials & Continua2022，，5： | 0 |
| 20 | An Optimized Deep Learning Model for Emotion Classification in Tweets显示文摘The task of automatically analyzing sentiments from a tweet has more use now than ever due to the spectrum of emotions expressed from national leaders to the average man.Analyzing this data can be critical for any organization.Sentiments are often expressed with different intensity and topics which can provide great insight into how something affects society.Sentiment analysis in Twittermitigates the various issues of analyzing the tweets in terms of views expressed and several approaches have already been proposed for sentiment analysis in twitter.Resources used for analyzing tweet emotions are also briefly presented in literature survey section.In this paper,hybrid combination of different model’s LSTM-CNN have been proposed where LSTMis Long Short TermMemory andCNNrepresents ConvolutionalNeural Network.Furthermore,the main contribution of our work is to compare various deep learning and machine learning models and categorization based on the techniques used.The main drawback of LSTM is that it’s a timeconsuming process whereas CNN do not express content information in an accurate way,thus our proposed hybrid technique improves the precision rate and helps in achieving better results.Initial step of our mentioned technique is to preprocess the data in order to remove stop words and unnecessary data to improve the efficiency in terms of time and accuracy also it shows optimal results when it is compared with predefined approaches. | Chinu Singla Fahd NAl-Wesabi Yash Singh Pathania Badria Sulaiman Alfurhood Anwer Mustafa Hilal Mohammed Rizwanullah Manar Ahmed Hamza Mohammad Mahzari | 2022 | Computers, Materials & Continua2022，，3： | 0 |
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