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Chinese Physics B 2020年第10期29卷 566-572页

作者： Deepika Raju Kumar Ritesh Kumar Kamdeo Prasad Yadav pratyush vaibhav Seema Sharma Rakesh Kumar Singh Santosh Kumar College o f Commerce Arts and SciencePatna.BiharIndia Jaypee University o f Engineering and Technology GunaMadhya PradeshIndia Anugrah Narayan College PatnaBiharIndia Aryabhatta Knowledge University PatnaBiharIndia

Zn0.90Ni0.10O nanoparticles have been synthesized by single-bath two-electrode electrodeposition at constant voltage. X-ray diffraction, UV vis and photoluminescence studies reveal that a single-phase polycrystalline hcp wurtzite crystal structure of ZnO is evolved. The material consists of a large number of defects such as oxygen vacancy (Ov) and zinc interstitial (Zi). The magnetization study reveals that the sample exhibits room-temperature global ferromagnetism and the ferromagnetic ordering seems to be defect induced via bound magnetic polaron mechanism, and double exchange is also expected to have played role. Interesting optoelectronic properties have been found in the synthesized sample and the material seems to be a potential candidate to be used as a UV sensor. Such a transition metal doped ZnO based dilute magnetic semiconducting system exhibiting room-temperature ferromagnetism is likely to be first of its kind in the sense that such materials have not yet been reported to be synthesized by the simple method of electrodeposition to the best of our knowledge on the basis of ample literature review.

关键词： dilute magnetic semiconductors(DMS) bound magnetic polaron photoluminescence ferromagnetism

Application of Response Surface Methodology for Analysing and Optimizing the Finned Solar Air Heater

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Journal of Thermal Science 2024年第3期33卷 985-1009页

作者： Vineet SINGH Vinod Singh YADAV vaibhav TRIVEDI Manoj KUMAR Niraj KUMAR Department of Mechanical Engineering School of Engineering&TechnologyIFTM UniversityMoradabadUttar PradeshIndia Department of Mechanical Engineering NITUttarakhandPauriUttarakhandIndia Department of Electronics and Communication Engineering Jaypee Institute of Information TechnologyNoidaUttar Pradesh201309India

In this research paper,a solar air heater with triangular fins has been experimentally analysed and optimized.Initially,an experimental set-up of a solar air heater having triangular fins has been developed at the location of 28.10°N,78.23°E.The heat transfer rate through fins and fins efficiency has been determined by the Finite Difference Method model equations.The experimental data and modeled data of response parameters have been optimized in MINITAB-17 software by the Response Surface Methodology tool.For creating the response surface design,three input parameters have been selected namely solar intensity,Reynolds number,and fin base-to-height ratio.The range of solar intensity,Reynolds number,and fin base-to-height ratio is 600 to 1000W/m^(2),4000 to 6000,and 0.4 to 0.8 respectively.The response surface design has been analyzed by calculating the outlet temperature,friction factor,Nusselt number,fin efficiency,thermal performance factor,and exergy efficiency.The optimum settings of input parameters:solar intensity is 1000 W/m^(2);Reynolds number is 4969.7,and the fin base to height ratio is 0.6060,on which these response:namely outlet temperature of 92.531℃,friction factor of 0.2350,Nusselt number of 127.761,thermal efficiency of 50.836%,thermal performance factor of 1.4947,and exergy efficiency of 8.762%.

关键词： solar air heater optimization fins exergy response surface methodology air

Evaluation of artificial intelligence models for osteoarthritis of the knee using deep learning algorithms for orthopedic radiographs

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World Journal of Orthopedics 2022年第6期13卷 603-614页

作者： Anjali Tiwari Murali Poduval vaibhav Bagaria Department of Orthopedics Sir H.N.Reliance Foundation Hospital and Research CentreMumbai 400004India Lifesciences Engineering Tata Consultancy ServicesMumbai 400096India Department of Orthopedics Columbia Asia HospitalMumbai 400004India

BACKGROUND Deep learning,a form of artificial intelligence,has shown promising results for interpreting radiographs.In order to develop this niche machine learning(ML)program of interpreting orthopedic radiographs with accuracy,a project named deep learning algorithm for orthopedic radiographs was conceived.In the first phase,the diagnosis of knee osteoarthritis(KOA)as per the standard Kellgren-Lawrence(KL)scale in medical images was conducted using the deep learning algorithm for orthopedic radiographs.AIM To compare efficacy and accuracy of eight different transfer learning deep learning models for detecting the grade of KOA from a radiograph and identify the most appropriate ML-based model for the detecting grade of KOA.METHODS The study was performed on 2068 radiograph exams conducted at the Department of Orthopedic Surgery,Sir HN Reliance Hospital and Research Centre(Mumbai,India)during 2019-2021.Three orthopedic surgeons reviewed these independently,graded them for the severity of KOA as per the KL scale and settled disagreement through a consensus session.Eight models,namely ResNet50,VGG-16,InceptionV3,MobilnetV2,EfficientnetB7,DenseNet201,Xception and NasNetMobile,were used to evaluate the efficacy of ML in accurately classifying radiographs for KOA as per the KL scale.Out of the 2068 images,70%were used initially to train the model,10%were used subsequently to test the model,and 20%were used finally to determine the accuracy of and validate each model.The idea behind transfer learning for KOA grade image classification is that if the existing models are already trained on a large and general dataset,these models will effectively serve as generic models to fulfill the study’s objectives.Finally,in order to benchmark the efficacy,the results of the models were also compared to a first-year orthopedic trainee who independently classified these models according to the KL scale.RESULTS Our network yielded an overall high accuracy for detecting KOA,ranging from 54%to 93%.The most successful of these was the De

关键词： Osteoarthritis Artificial intelligence Knee Computer vision Machine leaning Deep learning

Thermal Comfort Analysis of Personalized Conditioning System and Performance Assessment with Different Radiant Cooling Systems

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Energy and Built Environment

Energy and Built Environment 2023年第1期4卷 111-121页

作者： vaibhav Rai Khare Ravi Garg Jyotirmay Mathur Vishal Garg Malaviya National Institute of Technology JaipurIndia International Institute of Information Technology HyderabadIndia

The radiant systems consume less energy and provide better thermal comfort as compared to the conventional system,but the main drawback of radiant technology is inability to cater the latent load and condensation.This problem has engaged researchers to push their emphasis on Personalized Conditioning System(PCS).It forms a micro-climate region near the occupant.This study is aimed at PCS combined with a radiant conditioning system,which evaluates the performance of the PCS and compares it with different radiant cooling systems.A radiant cubicle was used to investigate the performance of the PCS for both heating&cooling modes.For heating mode,the Standard Effective Temperature(SET)was calculated with the help of thermal comfort surveys which were in a range of 23.4℃ to 27.3℃.For cooling mode,four different cases compared the performance of the system which are:Conventional Air Conditioning System(CACS),Radiant Cooled Ceiling System(RCCS),Personalized Radiant Conditioning System(PRCS),and both PRCS-RCCS coupled.It was observed that PRCS alone reaches the thermal comfort criteria with low energy consumption.The energy-saving of RCCS-PRCS was achieved 38%when compared with CACS.It can be concluded that PRCS is a new step in the field of thermal comfort.

关键词： Radiant cooling system Personalized cooling system Thermal comfort Standard effective temperature

Chi-Square and PCA Based Feature Selection for Diabetes Detection with Ensemble Classifier

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Intelligent Automation & Soft Computing 2023年第5期36卷 1931-1949页

作者： vaibhav Rupapara Furqan Rustam Abid Ishaq Ernesto Lee Imran Ashraf School of Computing and Information Sciences Florida International UniversityUSA Department of Computer Science Khwaja Fareed University of Engineering and Information TechnologyRahim Yar Khan64200Pakistan Department of Computer Science Broward CollegeBroward CountyFloridaUSA Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-si38541Korea

Diabetes mellitus is a metabolic disease that is ranked among the top 10 causes of death by the world health organization.During the last few years,an alarming increase is observed worldwide with a 70%rise in the disease since 2000 and an 80%rise in male deaths.If untreated,it results in complications of many vital organs of the human body which may lead to fatality.Early detection of diabetes is a task of signiﬁcant importance to start timely treatment.This study introduces a methodology for the classiﬁcation of diabetic and normal people using an ensemble machine learning model and feature fusion of Chi-square and principal component analysis.An ensemble model,logistic tree classiﬁer(LTC),is proposed which incorporates logistic regression and extra tree classiﬁer through a soft voting mechanism.Experiments are also performed using several well-known machine learning algorithms to analyze their performance including logistic regression,extra tree classiﬁer,AdaBoost,Gaussian naive Bayes,decision tree,random forest,and k nearest neighbor.In addition,several experiments are carried out using principal component analysis(PCA)and Chi-square(Chi-2)fea-tures to analyze the inﬂuence of feature selection on the performance of machine learning classiﬁers.Results indicate that Chi-2 features show high performance than both PCA features and original features.However,the highest accuracy is obtained when the proposed ensemble model LTC is used with the proposed fea-ture fusion framework-work which achieves a 0.85 accuracy score which is the highest of the available approaches for diabetes prediction.In addition,the statis-tical T-test proves the statistical signiﬁcance of the proposed approach over other approaches.

关键词： Diabetes mellitus prediction feature fusion ensemble classiﬁer principal component analysis chi-square

Clinical practice of early extubation after liver transplantation

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Hepatobiliary & Pancreatic Diseases International 2012年第6期11卷 577-585页

作者： vaibhav Rastogi First Affiliated Hospital Zhejiang University School of Medicine

BACKGROUND: Anesthetic practices such as early tracheal extubation facilitate postoperative recovery. Early extubation after liver transplantation has been adopted by some centers in the recent two decades. No universal clinical guidelines are used and questions remain. This review aimed to address the current status of early extubation after liver transplantation. DATA SOURCES: A literature search of MEDLINE and ISI Web of Knowledge databases was performed using terms such as liver transplantation, early extubation, immediate tracheal extubation fast tracking or fast track anesthesia and postoperative tracheal extubation. Additional papers were identified by a manual search of the references in the key articles. RESULTS: Review of the available literature provided an insight into the definition, evolution, advantages and risks of early extubation, and anesthetic techniques that prompt early extubation in liver transplant patients. Early extubation has proved to be feasible and safe in these patients, but the outcomes are still uncertain. CONCLUSIONS: Early extubation after liver transplantation is feasible, safe and cost-effective in the majority of patients and has been increasingly accepted as an option for conventional postoperative ventilation. Comprehensive and individualized evaluation of the patient’s condition before extubation by an experienced anesthesiologist is the cornerstone of success Understanding of its effect on the outcome remains incomplete In the future, additional trials are required to establish universal early extubation guidelines and to determine its benefits for patients and practitioners.

关键词： liver transplantation extubation ventilation anesthesia

Malicious Traffic Detection in IoT and Local Networks Using Stacked Ensemble Classifier

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Computers, Materials & Continua 2022年第4期71卷 489-515页

作者： R.D.Pubudu L.Indrasiri Ernesto Lee vaibhav Rupapara Furqan Rustam Imran Ashraf School of Computing and Mathematics Charles Sturt UniversityAustralia Department of Computer Science Broward CollegeBroward CountyFloridaUSA School of Computing and Information Sciences Florida International UniversityUSA Department of Computer Science Khwaja Fareed University of Engineering and Information TechnologyRahim Yar KhanPakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-si38541Korea

Malicious traffic detection over the internet is one of the challenging areas for researchers to protect network infrastructures from any malicious activity.Several shortcomings of a network system can be leveraged by an attacker to get unauthorized access through malicious traffic.Safeguard from such attacks requires an efficient automatic system that can detect malicious traffic timely and avoid system damage.Currently,many automated systems can detect malicious activity,however,the efficacy and accuracy need further improvement to detect malicious traffic from multi-domain systems.The present study focuses on the detection of malicious traffic with high accuracy using machine learning techniques.The proposed approach used two datasets UNSW-NB15 and IoTID20 which contain the data for IoT-based traffic and local network traffic,respectively.Both datasets were combined to increase the capability of the proposed approach in detecting malicious traffic from local and IoT networks,with high accuracy.Horizontally merging both datasets requires an equal number of features which was achieved by reducing feature count to 30 for each dataset by leveraging principal component analysis(PCA).The proposed model incorporates stacked ensemble model extra boosting forest(EBF)which is a combination of tree-based models such as extra tree classifier,gradient boosting classifier,and random forest using a stacked ensemble approach.Empirical results show that EBF performed significantly better and achieved the highest accuracy score of 0.985 and 0.984 on the multi-domain dataset for two and four classes,respectively.

关键词： Stacked ensemble PCA malicious traffic detection classification machine learning

Predicting Future Cryptocurrency Prices Using Machine Learning Algorithms

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Journal of Data Analysis and Information Processing 2023年第4期11卷 400-419页

作者： vaibhav Saha Grade 10 Calcutta International School Kolkata West Bengal

Cryptocurrency price prediction has garnered significant attention due to the growing importance of digital assets in the financial landscape. This paper presents a comprehensive study on predicting future cryptocurrency prices using machine learning algorithms. Open-source historical data from various cryptocurrency exchanges is utilized. Interpolation techniques are employed to handle missing data, ensuring the completeness and reliability of the dataset. Four technical indicators are selected as features for prediction. The study explores the application of five machine learning algorithms to capture the complex patterns in the highly volatile cryptocurrency market. The findings demonstrate the strengths and limitations of the different approaches, highlighting the significance of feature engineering and algorithm selection in achieving accurate cryptocurrency price predictions. The research contributes valuable insights into the dynamic and rapidly evolving field of cryptocurrency price prediction, assisting investors and traders in making informed decisions amidst the challenges posed by the cryptocurrency market.

关键词： Cryptocurrency Price Prediction Machine Learning Algorithms Feature Engineering Performance Metrics

Behavior of ring footing resting on reinforced sand subjected to eccentric-inclined loading

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Journal of Rock Mechanics and Geotechnical Engineering 2018年第2期10卷 347-357页

作者： vaibhav Sharma Arvind Kumar Dr.B.R.Ambedkar National Institute of Technology Jalandhar144011India

Ring footings are suitable for the structures like tall transmission towers, chimneys, silos and oil storages.These types of structures are susceptible to horizontal loads(wind load) in addition to their dead weight.In the literature, very little or no effort has been made to study the effect of ring footing resting on reinforced sand when subjected to eccentric, inclined and/or eccentric-inclined loadings. This paper aims to study the behavior of ring footing resting on loose sand and/or compacted randomly distributed fiberreinforced sand(RDFS) when subjected to eccentric(0 B, 0.05 B and 0.1 B, where B is the outer diameter of ring footing), inclined(0°,5°,10°, 15°,-5°,-10° and-15°)and eccentric-inclined loadings by using a finite element(FE) software PLAXIS 3 D. The behavior of ring footing is studied by using a dimensionless factor called reduction factor(RF). The numerical model used in the PLAXIS 3 D has been validated by conducting model plate load tests. Moreover, an empirical expression using regression analysis has been presented which will be helpful in plotting a load-settlement curve for the ring footing.

关键词： Soil reinforcement Ring footing Numerical method Randomly distributed fibers Load inclination Load eccentricity Model tests Reduction factor(RF)

Shock wave mitigation using zig-zag structures and cylindrical obstructions

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Defence Technology（防务技术） 2021年第6期17卷 1840-1851页

作者： Arun Kumar R vaibhav Pathak Department of Mechanical Engineering Indian Institute of Technology Jodhpur(IITJ)JodhpurRajasthan342037India

The present study focuses on the mitigation of shock wave using novel geometric passages in the flow field.The strategy is to produce multiple shock reflections and diffractions in the passage with minimum flow obstruction,which in turn is expected to reduce the shock wave strength at the target location.In the present study the interaction of a plane shock front(generated from a shock tube)with various geometric designs such as,1)zig-zag geometric passage,2)staggered cylindrical obstructions and 3)zigzag passage with cylindrical obstructions have been investigated using computational technique.It is seen from the numerical simulation that,among the various designs,the maximum shock attenuation is produced by the zig-zag passage with cylindrical obstructions which is then followed by zig-zag passage and staggered cylindrical obstructions.A comprehensive investigation on the shock wave reflection and diffraction phenomena happening in the proposed complex passages have also been carried out.In the new zig-zag design,the initial shock wave undergoes shock wave reflection and diffraction process which swaps alternatively as the shock front moves from one turn to the other turn.This cyclic shock reflection and diffraction process helps in diffusing the shock wave energy with practically no obstruction to the flow field.It is found that by combining the shock attenuation ability of zig-zag passage(using shock reflection and diffraction)with the shock attenuation ability of cylindrical blocks(by flow obstruction),a drastic attenuation in shock strength can be achieved with moderate level of flow blocking.

关键词： Blast wave mitigation Blockage ratio Geometric obstructions Shock wave reflection and diffraction Zig-zag structure