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Vitamin D Deficiency and Its Relationship with Child-Pugh Class in Patients with Chronic Liver Disease

Journal of Clinical and Translational Hepatology 2018年第2期6卷 135-140页

作者： Zubia Jamil Sharmin Arif Anum Khan Asghar Aurangzeb Durrani nayyar yaqoob Department of Medicine Foundation University Medical CollegeIslamabadPakistan Department of Medicine Fauji Foundation HospitalRawalpindiPakistan

Background and Aims:Skeletal manifestation in liver dis-eases represents the minimally scrutinized part of the disease *** D deficiency has a central role in developing hepatic osteodystrophy in patients with chronic liver *** study aimed to investigate vitamin D levels and their relationship with disease advancement in these ***:Vitamin D levels were checked in 125 chronic liver disease *** patients were classified in three stages according to Child-Pugh score:A,B and *** relationship of vitamin D levels with Child-Pugh score and other variables in the study was assessed by the contingency *** and logistic regression analyses were also carried out to find additional predictors of low vitamin D ***:Among the patients,88%had either insufficient or deficient stores of vitamin D,while only 12%had sufficient vitamin D levels(p>0.05).Vitamin D levels were notably related to Child-Pugh class(contingency coefficient=0.5,p<0.05).On univariate and multinomial regression analyses,age,female sex,MELD and Child-Pugh class were predictors of low vitamin D ***,model of end-stage liver disease score and Child-Pugh score were negatively correlated to vitamin D levels(p<0.05).Conclusions:Vitamin D deficiency is notably re-lated to age,female sex and model of end-stage liver disease score,in addition to Child-Pugh class of liver ***-min D levels should be routinely checked in patients with ad-vanced liver cirrhosis(Child-Pugh class B and C)and this deficiency must be addressed in a timely manner to improve general well-being of cirrhotic patients.

关键词： Vitamin D deficiency Liver cirrhosis Osteodystrophy Liver disease Skeletal manifestations

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A Novel Approach to Breast Tumor Detection: Enhanced Speckle Reduction and Hybrid Classification in Ultrasound Imaging

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Computers, Materials & Continua 2024年第5期79卷 1875-1901页

作者： K.Umapathi S.Shobana Anand nayyar Judith Justin R.Vanithamani Miguel Villagómez Galindo Mushtaq Ahmad Ansari Hitesh Panchal Department of Biomedical Engineering KIT-Kalaignarkarunanidhi Institute of TechnologyCoimbatore641402India Graduate School Faculty of Information TechnologyDuy Tan UniversityDa Nang550000Viet Nam Department of Biomedical and Instrumentation Engineering Avinashilingam Institute for Home Science and Higher Education forWomenCoimbatore641043India UniversidadMichoacana de SanNicolás de HidalgoAvenida Francisco J.Múgica S/N Ciudad Universitaria MoreliaCP58030Mexico Department of Pharmacology and Toxicology College of PharmacyKing Saud UniversityRiyadh11451Saudi Arabia Department of Mechanical Engineering Gujarat Technological UniveristyNigam NagarChandkhedaAhmedabadGujarat382424India

Breast cancer detection heavily relies on medical imaging, particularly ultrasound, for early diagnosis and effectivetreatment. This research addresses the challenges associated with computer-aided diagnosis (CAD) of breastcancer fromultrasound images. The primary challenge is accurately distinguishing between malignant and benigntumors, complicated by factors such as speckle noise, variable image quality, and the need for precise segmentationand classification. The main objective of the research paper is to develop an advanced methodology for breastultrasound image classification, focusing on speckle noise reduction, precise segmentation, feature extraction, andmachine learning-based classification. A unique approach is introduced that combines Enhanced Speckle ReducedAnisotropic Diffusion (SRAD) filters for speckle noise reduction, U-NET-based segmentation, Genetic Algorithm(GA)-based feature selection, and Random Forest and Bagging Tree classifiers, resulting in a novel and efficientmodel. To test and validate the hybrid model, rigorous experimentations were performed and results state thatthe proposed hybrid model achieved accuracy rate of 99.9%, outperforming other existing techniques, and alsosignificantly reducing computational time. This enhanced accuracy, along with improved sensitivity and specificity,makes the proposed hybrid model a valuable addition to CAD systems in breast cancer diagnosis, ultimatelyenhancing diagnostic accuracy in clinical applications.

关键词： Ultrasound images breast cancer tumor classification segmentation deep learning lesion detection

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Role of artificial intelligence in gastrointestinal surgery

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Artificial Intelligence in Cancer 2024年第2期5卷 1-9页

作者： Ankit Shukla Rajesh Chaudhary Nishant nayyar Department of Surgery Dr Rajendra Prasad Government Medical CollegeKangra 176001Himachal PradeshIndia Department of Renal Transplantation Dr Rajendra Prasad Government Medical CollegeKangra 176001India Department of Radiology Dr Rajendra Prasad Government Medical CollegeKangra 176001Himachal PradeshIndia

Artificial intelligence is rapidly evolving and its application is increasing day-byday in the medical *** application of artificial intelligence is also valuable in gastrointestinal diseases,by calculating various scoring systems,evaluating radiological images,preoperative and intraoperative assistance,processing pathological slides,prognosticating,and in treatment *** field has a promising future and can have an impact on many management *** this minireview,we aimed to determine the basics of artificial intelligence,the role that artificial intelligence may play in gastrointestinal surgeries and malignancies,and the limitations thereof.

关键词： Machine learning Gastrointestinal surgery Radiology Artificial neural network Deep learning

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Drugs used for pain management in gastrointestinal surgery and their implications

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World Journal of Gastrointestinal Pharmacology and Therapeutics 2024年第5期15卷 15-23页

作者： Ankit Shukla Rajesh Chaudhary Nishant nayyar Bhanu Gupta Department of Surgery Dr Rajendra Prasad Government Medical CollegeKangra 176001Himachal PradeshIndia Department of Renal Transplant Surgery Dr Rajendra Prasad Government Medical CollegeKangra 176001India Department of Radiology Dr Rajendra Prasad Government Medical CollegeKangra 176001Himachal PradeshIndia Department of Anaesthesia Dr Rajendra Prasad Government Medical CollegeTandaKangra 176001Himachal PradeshIndia

Pain is the predominant symptom troubling *** management is one of the most important aspects in the management of surgical patients leading to early recovery from surgical procedures or in patients with chronic diseases or *** groups of drugs are used for dealing with this;however,they have their own implications in the form of adverse effects and *** this article,we review the concerns of different pain-relieving medicines used postoperatively in gastrointestinal surgery and for malignant and chronic diseases.

关键词： Acute pain Acute post operative pain Pain score Pain after GI surgery Analgesia Spinal anaesthesia Epidural anaesthesia Intravenous anaesthesia Regional anaesthesia Pain management

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System Performance of Wireless Sensor Network Using LoRa–Zigbee Hybrid Communication

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Computers, Materials & Continua 2021年第8期68卷 1615-1635页

作者： Van-Truong Truong Anand nayyar Showkat Ahmad Lone Faculty of Electrical and Electronic Engineering Duy Tan UniversityDa Nang550000Vietnam Institute of Research and Development Duy Tan UniversityDa Nang550000Vietnam Graduate School Duy Tan UniversityDa Nang 550000Vietnam Faculty of Information Technology Duy Tan UniversityDa NangVietnam Department of Basic Science College of Science and Theoretical StudiesSaudi Electronic UniversityJeddah CampusKSA

Wireless sensor network(WSN)is considered as the fastest growing technology pattern in recent years because of its applicability in varied *** sensor nodes with different sensing functionalities are deployed in the monitoring area to collect suitable data and transmit it to the *** communications in heterogeneous WSNs,is a critical issue that needs to be *** this research paper,we study the system performance of a heterogeneous WSN using LoRa–Zigbee hybrid ***,two Zigbee sensor clusters and two LoRa sensor clusters are used and combined with two Zigbee-to-LoRa converters to communicate in a network managed by a LoRa *** overall system integrates many different sensors in terms of types,communication protocols,and accuracy,which can be used in many applications in realistic environments such as on land,under water,or in the *** addition to this,a synchronous management software on ThingSpeak Web server and Blynk app is *** the proposed system,the token ring protocol in Zigbee network and polling mechanism in LoRa network is *** system can operate with a packet loss rate of less than 0.5%when the communication range of the Zigbee network is 630 m,and the communication range of the LoRa network is 3.7 *** the basis of the digital results collected on the management software,this study proves tremendous improvements in the system performance.

关键词： Wireless sensor network Zigbee LoRa communication protocol converter packet loss

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Multiway Relay Based Framework for Network Coding in Multi-Hop WSNs

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Computers, Materials & Continua 2023年第1期74卷 1199-1216页

作者： Vinod Kumar Menaria Anand nayyar Sandeep Kumar Ketan Kotecha Department of IT&C(DoIT&C) Govt of RajasthanJaipurIndia Graduate School Faculty of Information TechnologyDuy Tan UniversityDa NangVietnam Department of Computer Science and Engineering CHRIST(Deemed to be University)Bangalore560074India Symbiosis Centre for Applied Artificial Intelligence Symbiosis International UniversityPuneIndia

In today’s information technology(IT)world,the multi-hop wireless sensor networks(MHWSNs)are considered the building block for the Internet of Things(IoT)enabled communication systems for controlling everyday tasks of organizations and industry to provide quality of service(QoS)in a stipulated time slot to end-user over the *** city(SC)is an example of one such application which can automate a group of civil services like automatic control of traffic lights,weather prediction,surveillance,etc.,in our daily *** IoT-based networks with multi-hop communication and multiple sink nodes provide efficient communication in terms of performance parameters such as throughput,energy efficiency,and end-to-end delay,wherein low latency is considered a challenging issue in next-generation networks(NGN).This paper introduces a single and parallels stable server queuing model with amulti-class of packets and native and coded packet flowto illustrate the simple chain topology and complexmultiway relay(MWR)node with specific neighbor ***,for improving data transmission capacity inMHWSNs,an analytical framework for packet transmission using network coding at the MWR node in the network layer with opportunistic listening is performed by considering bi-directional network flow at the MWR ***,the accuracy of the proposed multi-server multi-class queuing model is evaluated with and without network coding at the network layer by transmitting data *** results of the proposed analytical framework are validated and proved effective by comparing these analytical results to simulation results.

关键词： Multi-hop wireless sensor networks network coding multiway relay node throughput multi-server multi-class queuing models

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LOA-RPL:Novel Energy-Efficient Routing Protocol for the Internet of Things Using Lion Optimization Algorithm to Maximize Network Lifetime

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Computers, Materials & Continua 2021年第10期69卷 351-371页

作者： Sankar Sennan Somula Ramasubbareddy Anand nayyar Yunyoung Nam Mohamed Abouhawwash Department of Computer Science Engineering Sona College of TechnologySalem636005India VNR Vignana Jyothi Institute of Engineering&Technology Hyderabad500090India Graduate School Duy Tan UniversityDa NangVietnam550000Vietnam Faculty of Information Technology Duy Tan UniversityDa NangVietnam550000Vietnam Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Electrical and Computer Engineering Michigan State UniversityEast LansingMI48824USA

Energy conservation is a significant task in the Internet of Things(IoT)because IoT involves highly resource-constrained *** is an effective technique for saving energy by reducing duplicate *** a clustering protocol,the selection of a cluster head(CH)plays a key role in prolonging the lifetime of a ***,most cluster-based protocols,including routing protocols for low-power and lossy networks(RPLs),have used fuzzy logic and probabilistic approaches to select the CH ***,early battery depletion is produced near the *** overcome this issue,a lion optimization algorithm(LOA)for selecting CH in RPL is proposed in this ***-RPL comprises three processes:cluster formation,CH selection,and route establishment.A cluster is formed using the Euclidean *** selection is performed using *** establishment is implemented using residual energy *** extensive simulation is conducted in the network simulator ns-3 on various parameters,such as network lifetime,power consumption,packet delivery ratio(PDR),and *** performance of LOA-RPL is also compared with those of RPL,fuzzy rule-based energyefficient clustering and immune-inspired routing(FEEC-IIR),and the routing scheme for IoT that uses shuffled frog-leaping optimization algorithm(RISARPL).The performance evaluation metrics used in this study are network lifetime,power consumption,PDR,and *** proposed LOARPL increases network lifetime by 20%and PDR by 5%–10%compared with RPL,FEEC-IIR,and ***-RPL is also highly energy-efficient compared with other similar routing protocols.

关键词： Internet of things cluster head clustering protocol optimization algorithm lion optimization algorithm network lifetime routing protocol wireless sensor networks energy consumption low-power and lossy networks

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Stock Prediction Based on Technical Indicators Using Deep Learning Model

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Computers, Materials & Continua 2022年第1期70卷 287-304页

作者： Manish Agrawal Piyush Kumar Shukla Rajit Nair Anand nayyar Mehedi Masud Department of Computer Science&Engineering University Institute of TechnologyRajiv Gandhi Proudyogiki VishwavidyalayaBhopal-462033Madhya PradeshIndia Faculty of Department of Computer Science&Engineering University Institute of TechnologyRajiv Gandhi Proudyogiki VishwavidyalayaBhopal-462033Madhya PradeshIndia Faculty of Department of Computer Science&Engineering School of Engineering and TechnologyJagran Lakecity UniversityBhopal462026Madhya PradeshIndia Graduate School Duy Tan UniversityDa Nang550000Vietnam Faculty of Information Technology Duy Tan UniversityDa Nang550000Vietnam Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable *** stock data is usually non-stationary,and attributes are non-correlative to each *** traditional Stock Technical Indicators(STIs)may incorrectly predict the stockmarket *** study the stock market characteristics using STIs and make efficient trading decisions,a robust model is *** paper aims to build up an Evolutionary Deep Learning Model(EDLM)to identify stock trends’prices by using *** proposed model has implemented the Deep Learning(DL)model to establish the concept of *** analysis of the dataset of three most popular banking organizations obtained from the live stock market based on the National Stock exchange(NSE)-India,a Long Short Term Memory(LSTM)is *** datasets encompassed the trading days from the 17^(th) of Nov 2008 to the 15^(th) of Nov *** work also conducted exhaustive experiments to study the correlation of various STIs with stock price *** model built with an EDLM has shown significant improvements over two benchmark ML models and a deep learning *** proposed model aids investors in making profitable investment decisions as it presents trend-based forecasting and has achieved a prediction accuracy of 63.59%,56.25%,and 57.95%on the datasets of HDFC,Yes Bank,and SBI,*** indicate that the proposed EDLA with a combination of STIs can often provide improved results than the other state-of-the-art algorithms.

关键词： Long short term memory evolutionary deep learning model national stock exchange stock technical indicators predictive modelling prediction accuracy

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URL Phishing Detection Using Particle Swarm Optimization and Data Mining

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Computers, Materials & Continua 2022年第12期73卷 5625-5640页

作者： Saeed M.Alshahrani nayyar Ahmed Khan Jameel Almalki Waleed Al Shehri College of Computing and IT-Shaqra University Shaqra155572Saudi Arabia Department of Computer Science College of Computer in Al-LeithUmm Al-Qura UniversityMakkahSaudi Arabia

The continuous destruction and frauds prevailing due to phishing URLs make it an indispensable area for *** techniques are adopted in the detection process,including neural networks,machine learning,or hybrid techniques.A novel detection model is proposed that uses data mining with the Particle Swarm Optimization technique(PSO)to increase and empower the method of detecting phishing *** selection based on various techniques to identify the phishing candidates from the URL is *** this approach,the features mined from the URL are extracted using data mining *** features are selected on the basis of URL *** classification of these features identified by the data mining rules is done using PSO *** selection of features with PSO optimization makes it possible to identify phishing *** a large number of rule identifiers,the true positive rate for the identification of phishing URLs is maximized in this *** experiments show that feature selection using data mining and particle swarm optimization helps tremendously identify the phishing URLs based on the structure of the URL ***,it can minimize processing time for identifying the phishing website ***,the approach can be beneficial to identify suchURLs over the existing contemporary detecting models proposed before.

关键词： Phishing particle swarm optimization feature selection data mining classification cloud application

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Predicting Resource Availability in Local Mobile Crowd Computing Using Convolutional GRU

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Computers, Materials & Continua 2022年第3期70卷 5199-5212页

作者： Pijush Kanti Dutta Pramanik Nilanjan Sinhababu Anand nayyar Mehedi Masud Prasenjit Choudhury Department of Computer Science&Engineering National Institute of TechnologyDurgapurIndia Reliability Engineering Centre Indian Institute of Technology KharagpurIndia Graduate School Duy Tan UniversityDa Nang550000Vietnam Faculty of Information Technology Duy Tan UniversityDa Nang550000Vietnam Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

In mobile crowd computing(MCC),people’s smart mobile devices(SMDs)are utilized as computing *** the ever-growing computing capabilities of today’s SMDs,a collection of them can offer significantly high-performance computing *** a localMCC,the SMDs are typically connected to a local Wi-Fi *** and institutions can leverage the SMDs available within the campus to form local MCCs to cater to their computing needs without any financial and operational *** it offers an economical and sustainable computing solution,users’mobility poses a serious issue in the QoS of *** address this,before submitting a job to an SMD,we suggest estimating that particular SMD’s availability in the network until the job is *** this,we propose a convolutional GRU-based prediction model to assess how long an SMD is likely to be available in the network from any given point of *** experimental purposes,we collected real users’mobility data(in-time and outtime)with respect to a Wi-Fi access *** build the prediction model,we presented a novel feature extraction method to be applied to the time-series *** experimental results prove that the proposed convolutional GRU model outperforms the conventional GRU model.

关键词： Resource selection resource availability mobile grid mobile cloud ad-hoc cloud crowd computing deep learning GRU CNN RNN

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