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Deep Neural Artificial Intelligence for IoT Based Tele Health Data Anal

Computers, Materials & Continua 2022年第3期70卷 4467-4483页

作者： Nithya Rekha Sivakumar ahmed zohair Ibrahim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Tele health utilizes information and communication mechanisms to convey medical information for providing clinical and educational *** makes an effort to get the better of issues of health service delivery involving time factor,space and laborious terrains,validating cost-efficiency and finer ingress in both developed and developing *** health has been categorized into either real-time electronic communication,or store-andforward *** recent years,a third-class has been perceived as remote healthcare monitoring or tele health,presuming data obtained via Internet of Things(IOT).Although,tele health data analytics and machine learning have been researched in great depth,there is a dearth of studies that entirely concentrate on the progress of ML-based techniques for tele health data analytics in the IoT healthcare *** by this fact,in this work a method called,Weighted Bayesian and Polynomial Taylor Deep Network(WB-PTDN)is proposed to improve health prediction in a computationally efficient and accurate ***,the Independent Component Data Arrangement model is designed with the objective of normalizing the data obtained from the Physionet ***,with the normalized data as input,Weighted Bayesian Feature Extraction is applied to minimize the dimensionality involved and therefore extracting the relevant features for further health risk ***,to obtain reliable predictions concerning tele health data analytics,First Order Polynomial Taylor DNN-based Feature Homogenization is proposed that with the aid of First Order Polynomial Taylor function updates the new results based on the result analysis of old values and therefore provides increased transparency in decision *** comparison of proposed and existing methods indicates that the WB-PTDN method achieves higher accuracy,true positive rate and lesser response time for IoT based tele health data analytics than the traditional methods.

关键词： Telehealth internet of things independent component weighted bayesian first order polynomial taylor deep neural network

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Pyogenic liver abscess caused by Fusobacterium in a 21-year-old immunocompetent male

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World Journal of Gastroenterology 2015年第12期21卷 3731-3735页

作者： zohair ahmed Saurabh K Bansal Sonu Dhillon Department of Internal Medicine University of Illinois Peoria Campus OSF Saint Francis Medical Center Department of Gastroenterology and Hepatology University of Illinois Peoria Campus OSF Saint Francis Medical Center

A 21-year-old male with no significant past medical history, presented with right upper quadrant(RUQ) abdominal pain along with fevers and chills. Lab work revealed leukocytosis, anemia, and slightly elevated alkaline phosphatase. Viral serology for hepatitis B virus, hepatitis C virus, and human immunodeficiency virus were negative and he was *** tomography imaging revealed hepatic abscesses, the largest measuring 9.5 cm. Empiric antibiotics were started and percutaneous drains were placed in the abscesses. Anaerobic cultures from the abscesses grew Fusobacterium nucleatum. This is a gram negative anaerobic bacteria; a normal flora of the oral cavity. Fusobacterium is most commonly seen in Lemiere's disease, which is translocation of oral bacteria to the internal jugular vein causing a thrombophlebitis and subsequent spread of abscesses. Our patient did not have Lemiere's, and is the first case described of fusobacterium pyogenic liver abscess in a young immunocompetent male with good oral hygiene. This case was complicated by sepsis, empyema, and subsequent abscesses located outside the liver. These abscesses' have the propensity to flare abruptly and can be fatal. This case not only illustrates fusobacterium as a rare entity for pyogenic liver abscess, but also the need for urgent diagnosis and treatment. It is incumbent on physicians to diagnose and drain any suspicious hepatic lesions. While uncommon, such infections may develop without any overt source and can progress rapidly. Prompt drainage with antibiotic therapy remains the cornerstone of therapy.

关键词： Hepatic abscess Pyogenic Fusobacterium Liver Immun

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Kernel Granulometric Texture Analysis and Light RES-ASPP-UNET Classification for Covid-19 Detection

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Computers, Materials & Continua 2022年第4期71卷 651-666页

作者： A.Devipriya P.Prabu K.Venkatachalam ahmed zohair Ibrahim Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Computer Science CHRIST(Deemed to be University)Bangalore560029India Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec Králové50003Hradec KrálovéCzech Republic Computer Sciences Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhKSA.P.o.Box:84428Postal Code:11671

This research article proposes an automatic frame work for detectingCOVID -19 at the early stage using chest X-ray image. It is an undeniable factthat coronovirus is a serious disease but the early detection of the virus presentin human bodies can save lives. In recent times, there are so many research solutions that have been presented for early detection, but there is still a lack in needof right and even rich technology for its early detection. The proposed deeplearning model analysis the pixels of every image and adjudges the presence ofvirus. The classifier is designed in such a way so that, it automatically detectsthe virus present in lungs using chest image. This approach uses an imagetexture analysis technique called granulometric mathematical model. Selectedfeatures are heuristically processed for optimization using novel multi scaling deep learning called light weight residual–atrous spatial pyramid pooling(LightRES-ASPP-Unet) Unet model. The proposed deep LightRES-ASPPUnet technique has a higher level of contracting solution by extracting majorlevel of image features. Moreover, the corona virus has been detected usinghigh resolution output. In the framework, atrous spatial pyramid pooling(ASPP) method is employed at its bottom level for incorporating the deepmulti scale features in to the discriminative mode. The architectural workingstarts from the selecting the features from the image using granulometricmathematical model and the selected features are optimized using LightRESASPP-Unet. ASPP in the analysis of images has performed better than theexisting Unet model. The proposed algorithm has achieved 99.6% of accuracyin detecting the virus at its early stage.

关键词： Deep residual learning convolutional neural network COVID-19 X-ray principal component analysis granulo metrics texture analysis

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Aspect-Based Sentiment Analysis for Polarity Estimation of Customer Reviews on Twitter

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Computers, Materials & Continua 2021年第5期67卷 2203-2225页

作者： Ameen Banjar zohair ahmed Ali Daud Rabeeh Ayaz Abbasi Hussain Dawood Department of Information Systems and Technology College of Computer Science and EngineeringUniversity of JeddahJeddah21589Saudi Arabia Air University Islamabad44000Pakistan Department of Computer Science Islamabad44000Pakistan Department of Computer and Network Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21589 Saudi Arabia

Most consumers read online reviews written by different users before making purchase decisions,where each opinion expresses some ***,sentiment analysis is currently a hot topic of *** particular,aspect-based sentiment analysis concerns the exploration of emotions,opinions and facts that are expressed by people,usually in the form of *** is crucial to consider polarity calculations and not simply categorize reviews as positive,negative,or ***,the available lexicon-based method accuracy is affected by limited *** of the available polarity estimation techniques are too general and may not reect the aspect/topic in question if reviews contain a wide range of information about different *** paper presents a model for the polarity estimation of customer reviews using aspect-based sentiment analysis(ABSA-PER).ABSA-PER has three major phases:data preprocessing,aspect co-occurrence calculation(CAC)and polarity estimation.A multi-domain sentiment dataset,Twitter dataset,and trust pilot forum dataset(developed by us by dened judgement rules)are used to verify *** outcomes show that ABSA-PER achieves better accuracy,i.e.,85.7%accuracy for aspect extraction and 86.5%accuracy in terms of polarity estimation,than that of the baseline methods.

关键词： Natural language processing sentiment analysis aspect co-occurrence calculation sentiment polarity customer reviews twitte

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Heart-Net: AMulti-Modal Deep Learning Approach for Diagnosing Cardiovascular Diseases

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Computers, Materials & Continua 2024年第9期80卷 3967-3990页

作者： DeemaMohammed Alsekait ahmed Younes Shdefat AymanNabil Asif Nawaz Muhammad Rizwan Rashid Rana zohair ahmed Hanaa Fathi Diaa Salama Abd Elminaam Department of Computer Science and Information Technology Applied CollegePrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia College of Engineering and Technology American University of the Middle EastEgaila15453Kuwait Faculty of Computers Science Misr International UniversityCairo11800Egypt University Institute of Information Technology—PMAS Arid Agriculture University Rawalpjndi46000Pakistan Department of Computer Science International Islamic UniversityIslamabad04436Pakistan Jadara Research Center Jadara UniversityIrbid21110Jordan Information System Department Faculty of Computers and Artificial IntelligenceBenha UniversityBanha13511Egypt MEU Research Unit Middle East UniversityAmman11831Jordan

Heart disease remains a leading cause of morbidity and mortality worldwide,highlighting the need for improved diagnostic *** diagnostics face limitations such as reliance on single-modality data and vulnerability to apparatus faults,which can reduce accuracy,especially with poor-quality ***,these methods often require significant time and expertise,making them less accessible in resource-limited *** technologies like artificial intelligence and machine learning offer promising solutions by integrating multi-modality data and enhancing diagnostic precision,ultimately improving patient outcomes and reducing healthcare *** study introduces Heart-Net,a multi-modal deep learning framework designed to enhance heart disease diagnosis by integrating data from Cardiac Magnetic Resonance Imaging(MRI)and Electrocardiogram(ECG).Heart-Net uses a 3D U-Net for MRI analysis and a Temporal Convolutional Graph Neural Network(TCGN)for ECG feature extraction,combining these through an attention mechanism to emphasize relevant *** is performed using Optimized *** approach improves early detection,reduces diagnostic errors,and supports personalized risk assessments and continuous health *** proposed approach results show that Heart-Net significantly outperforms traditional single-modality models,achieving accuracies of 92.56%forHeartnetDataset Ⅰ(HNET-DSⅠ),93.45%forHeartnetDataset Ⅱ(HNET-DSⅡ),and 91.89%for Heartnet Dataset Ⅲ(HNET-DSⅢ),mitigating the impact of apparatus faults and image quality *** findings underscore the potential of Heart-Net to revolutionize heart disease diagnostics and improve clinical outcomes.

关键词： Heart diseases magnetic resonance imaging electrocardiogram deep learning classification

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Integrated Approach of Brain Disorder Analysis by Using Deep Learning Based on DNA Sequence

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Computer Systems Science & Engineering 2023年第6期45卷 2447-2460页

作者： ahmed zohair Ibrahim P.Prakash V.Sakthivel P.Prabu Computer Sciences Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhP.O.Box 8442811671KSA School of Computer Scence and Engineering Vellore Institute of TechnologyChennai600127India Department of Computer Science CHRIST(Deemed to be University)Bangalore560029India

In order to research brain problems using MRI,PET,and CT neuroimaging,a correct understanding of brain function is *** has been considered in earlier times with the support of traditional *** learning process has also been widely considered in these genomics data processing *** this research,brain disorder illness incliding Alzheimer’s disease,Schizophrenia and Parkinson’s diseaseis is analyzed owing to misdetection of disorders in neuroimaging data examined by means fo traditional ***,deep learning approach is incorporated here for classification purpose of brain disorder with the aid of Deep Belief Networks(DBN).Images are stored in a secured manner by using DNA sequence based on JPEG Zig Zag Encryption algorithm(DBNJZZ)*** suggested approach is executed and tested by using the performance metric measure such as accuracy,root mean square error,Mean absolute error and mean absolute percentage *** DBNJZZ gives better performance than previously available methods.

关键词： Deep belief networks zig zag deep learning mean absolute percentage error mean absolute error root mean square error DNA genomics

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Recent advances in vaccination of non-responders to standard dose hepatitis B virus vaccine

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World Journal of Hepatology 2015年第24期7卷 2503-2509页

作者： Saqib Walayat zohair ahmed Daniel Martin Srinivas Puli Michael Cashman Sonu Dhillon Department of Gastroenterology andHepatology University of Illinois PeoriaCampusOSF Saint Francis Medical Center Department of Internal Medicine Universityof Illinois Peoria CampusOSF Saint Francis Medical Center

Hepatitis B virus(HBV) infection is a global health problem. It is estimated there are more than 2 billion individuals exposed to the virus and 250 million are chronically infected. Hepatitis B is the cause of more than 600000 annual deaths due to cirrhosis and hepatocellular carcinoma. An effective vaccine exists and preventative initiatives center around universal vaccination especially in those at highest risk. Effective vaccination algorithms have led to a significant decline in the development of new infections and its devastating consequences. The vaccine is administered intramuscularly in three doses, with 95% showing long lasting serologic immunity. An additional fourth dose or a repeated higher dose three course regimen is given to those that fail to show immunity. Despite these additional regimens, some remain vulnerable to hepatitis B and are deemed nonresponders. Individuals with chronic disease states such as kidney disease, liver disease, diabetes mellitus, as well as those with a genetic predisposition, and those on immunomodulation therapy, have the highest likelihood of non-response. Various strategies have been developed to elicit an immune response in these individuals. These include increased vaccination dose, intradermal administration, alternative adjuvants, alternative routes of administration, co-administration with other vaccines, and other novel therapies. These alternative strategies can show improved response and lasting immunity. In summary, HBV vaccination is a major advance of modern medicine and all individuals at risk should be sought and vaccinated with subsequent adequate titers demonstrated.

关键词： Hepatitis B vaccine Non-responders Intradermal vac

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Progression from low-grade dysplasia to malignancy in patients with Barrett's esophagus diagnosed by two or more pathologists

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World Journal of Gastroenterology 2016年第39期22卷 8831-8843页

作者： Harsha Moole Jaymon Patel zohair ahmed Abhiram Duvvuri Sreekar Vennelaganti Vishnu Moole Sowmya Dharmapuri Raghuveer Boddireddy Pratyusha Yedama Naveen Bondalapati Achuta Uppu Prashanth Vennelaganti Srinivas Puli Department of Medicine University of Illinois College of Medicine at Peoria Division of Gastro-enterology and Hepatology Kansas City Veteran Affairs Medical Center Department of Medicine Apogee Medical Group Department of Medicine NTR University of Health Sciences Department of Medicine Barnes Jewish Christian Medical Group Christian Hospital Department of Medicine Albert Einstein College of Medicine Division of Gastroenterology and Hepatology University of Kansas Medical Center Division of Gastroenterology and Hepatology University of Illinois College of Medicine at Peoria

AIM To evaluate annual incidence of low grade dysplasia(LGD) progression to high grade dysplasia(HGD) and/or esophageal adenocarcinoma(EAC) when diagnosis was made by two or more expert *** Studies evaluating the progression of LGD to HGD or EAC were included. The diagnosis of LGD must be made by consensus of two or more expert gastrointestinal pathologists. Articles were searched in Medline, Pubmed, and Embase. Pooled proportions were calculated using fixed and random effects model. Heterogeneity among studies was assessed using the I2 statistic. RESULTS Initial search identified 721 reference articles, of which 53 were selected and reviewed. Twelve studies(n = 971) that met the inclusion criteria were included in this analysis. Among the total original LGD diagnoses in the included studies, only 37.49% reached the consensus LGD diagnosis after review by two or more expert pathologists. Total follow up period was 1532 patient-years. In the pooled consensus LGD patients, the annual incidence rate(AIR) of progression to HGD and or EAC was 10.35%(95%CI: 7.56-13.13) and progression to EAC was 5.18%(95%CI: 3.43-6.92). Among the patients down staged from original LGD diagnosis to No-dysplasia Barrett's esophagus, the AIR of progression to HGD and EAC was 0.65%(95%CI: 0.49-0.80). Among the patients down staged to Indefinite for dysplasia, the AIR of progression to HGD and EAC was 1.42%(95%CI: 1.19-1.65). In patients with consensus HGD diagnosis, the AIR of progression to EAC was 28.63%(95%CI: 13.98-43.27). CONCLUSION When LGD is diagnosed by consensus agreement of two or more expert pathologists, its progression towards malignancy seems to be at least three times the current estimates, however it could be up to 20 times the current estimates. Biopsies of all Barrett's esophagus patients with LGD should be reviewed by two expert gastroenterology pathologists. Follow-up strict surveillance programs should be in place for these patients.

关键词： Barrett’s esophagus Low grade dysplasia High grade dysplasia Esophageal adenocarcinoma Annual incidence of progression Systematic review Meta-analysis

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Review of acute ischaemic stroke in Pakistan:progress in management and future perspectives

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Stroke & Vascular Neurology 2017年第1期2卷 30-39页

作者： Ali zohair Nomani Sumaira Nabi Mazhar Badshah Shahzad ahmed Department of Neurology Pakistan Institute of Medical SciencesIslamabadPakistan

Ischaemic stroke is a major cause of neurological morbidity and *** objective of this review article is to summarise facts pertaining to acute ischaemic stroke and its various aspects in a developing country like Pakistan,where resources are limited and the healthcare system is *** large-scale epidemiological studies are available to determine the true incidence of stroke in *** reviewed the available literature on stroke from Pakistan and through this article we primarily aim to present the current acute ischaemic stroke management in Pakistan in juxtaposition to that of the developed *** also intend to highlight areas for future development and improvement in *** routine practice in Pakistan is that of using stat dose of aspirin in emergency(ER)at large with only a handful of centres offering thrombolytic therapy with recombinant tissue plasminogen activator for acute ischaemic *** too is faced with the problem of long window periods before the patient reaches a proper stroke care *** facilities of interventional therapies like arterial thrombolysis and endovascular surgery are non-existent and rehabilitation facilities *** opportunities for training physicians in acute stroke are also *** in children is underdiagnosed and that in women not availing facilities at stroke care *** basic research has gained pace regarding local demographic data,advanced research and genetic studies are extremely *** field of stroke neurology needs to grow at a substantial pace in Pakistan to be at par with the developed world.

关键词： routine centre Pakistan

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High HbA1c is associated with higher risk of ischaemic stroke in Pakistani population without diabetes

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Stroke & Vascular Neurology 2016年第3期1卷 133-139页

作者： Ali zohair Nomani Sumaira Nabi Shahzad ahmed Mansoor Iqbal Haris Majid Rajput Suhail Rao Department of Neurology Pakistan institute of Medical SciencesIslamabadPakistan

Context:The role of glycated haemoglobin(HbA1c)in the prediction of ischaemic stroke in individuals without diabetes is ***:We performed a study to analyse the role of HbA1c in the risk prediction of ischaemic stroke in Pakistani population without *** further studied the difference between HbA1c values of individuals with diabetes and without diabetes with *** and design:Single centre,case–*** and methods:In phase I,a total of 233 patients without diabetes with ischaemic stroke and 245 as controls were *** of HbA1c levels,lipid profiles and blood pressure recordings with ischaemic stroke was *** phase II,comparison was done between diabetics and nondiabetics with *** analysis:Comparison of the mean variables was performed with Student’s *** regression analysis with ischaemic stroke as the dependent variable was performed for phase ***:In phase I,the ischaemic stroke group had significantly higher HbA1c levels(5.9±2.9%vs 5.5±1.6%)compared with controls(p0.05).Conclusions:Higher HbA1c indicated a significantly increased risk for ischaemic *** HbA1c value above 5.6%(prediabetic range)predicted future risk of stroke and efforts to maintain glucose level within the normal range(≤5.6%)in individuals with high cardiovascular risk are important.

关键词： diabetes cholesterol cardiovascular

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