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Feature selection techniques for microarray datasets:a comprehensive review,taxonomy,and future directions

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Frontiers of information technology & Electronic Engineering 2022年第10期23卷 1451-1478页

作者： Kulanthaivel BALAKRISHNAN Ramasamy DHANALAKSHMI Department of Computer Science and Engineering Indian Institute of Information TechnologyTiruchirappalli 620012India

For optimal results,retrieving a relevant feature from a microarray dataset has become a hot topic for researchers involved in the study of feature selection(FS)techniques.The aim of this review is to provide a thorough description of various,recent FS techniques.This review also focuses on the techniques proposed for microarray datasets to work on multiclass classification problems and on different ways to enhance the performance of learning algorithms.We attempt to understand and resolve the imbalance problem of datasets to substantiate the work of researchers working on microarray datasets.An analysis of the literature paves the way for comprehending and highlighting the multitude of challenges and issues in finding the optimal feature subset using various FS techniques.A case study is provided to demonstrate the process of implementation,in which three microarray cancer datasets are used to evaluate the classification accuracy and convergence ability of several wrappers and hybrid algorithms to identify the optimal feature subset.

关键词： Feature selection High dimensionality Learning techniques Microarray dataset

Codimension-2 bifurcation in a discrete predator-prey system with constant yield predator harvesting

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生物数学学报:英文版 2023年第5期16卷 165-191页

作者： Anuraj Singh Vijay Shankar Sharma ABV-Indian Institute of Information Technology and Management Gwalior Madhya PradeshIndia

This work investigates the bifurcation analysis in a discrete-time Leslie-Gower predatorprey model with constant yield predator harvesting.The stability analysis for the fixed points of the discretized model is shown briefy.In this study,the model undergoes codimension-1 bifurcation such as fold bifurcation(limit point),flip bifurcation(perioddoubling)and Neimark-Sacker bifurcation at a positive fixed point.Further,the model exhibits codimension-2 bifurcations,including Bogdanov-Takens bifurcation and generalized fip bifurcation at the fixed point.For each bifurcation,by using the critical normal form coefficient method,various critical states are calculated.To validate our analytical findings,the bifurcation curves of fixed points are drawn by using MATCONTM.The system exhibits interesting rich dynamics including limit cycles and chaos.Moreover,it has been shown that the predator harvesting may control the chaos in the system.

关键词： Flip bifurcation Neimark-Sacker bifurcation generalized fip bifurcation Bogdanov-Takens bifurcation harvesting

VLSI scaling methods and low power CMOS buffer circuit

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Journal of Semiconductors 2013年第9期34卷 92-99页

作者： Vijay Kumar Sharma Manisha Pattanaik Department of information technology ABV-Indian Institute of Information Technology & Management

Device scaling is an important part of the very large scale integration （VLSI） design to boost up the success path of VLSI industry, which results in denser and faster integration of the devices. As technology node moves towards the very deep submicron region, leakage current and circuit reliability become the key issues. Both are increasing with the new technology generation and affecting the performance of the overall logic circuit. The VLSI designers must keep the balance in power dissipation and the circuit＇s performance with scaling of the devices. In this paper, different scaling methods are studied first. These scaling methods are used to identify the effects of those scaling methods on the power dissipation and propagation delay of the CMOS buffer circuit. For mitigating the power dissipation in scaled devices, we have proposed a reliable leakage reduction low power transmission gate （LPTG） approach and tested it on complementary metal oxide semiconductor （CMOS） buffer circuit. All simulation results are taken on HSPICE tool with Berkeley predictive technology model （BPTM） BSIM4 bulk CMOS files. The LPTG CMOS buffer reduces 95.16% power dissipation with 84.20% improvement in figure of merit at 32 nm technology node. Various process, voltage and temperature variations are analyzed for proving the robustness of the proposed approach. Leakage current uncertainty decreases from 0.91 to 0.43 in the CMOS buffer circuit that causes large circuit reliability.

关键词： LPTG buffer power dissipation propagation delay scaling nanoscale CMOS figure of merit

粉末冶金Al-5.6Zn-2Mg合金在热压缩过程中加工硬化特性及本构模型的比较

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Journal of Central South University 2024年第2期31卷 346-368页

作者： KATIKA Harikrishna DAMODA R.K. DAVIDSON M.J. SEETHARAM R. KASAGANI Veera Venkata Nagaraju Department of Mechanical Engineering National Institute of TechnologyWarangal 506004India Department of Mechanical Engineering The Indian Institute of Information Technology Design and ManufacturingKurnool 518007India

在300~5000℃、应变速率为0.1~0.0001 s−1的条件下,在万能试验机上对Al-5.6Zn-2Mg铝合金进行热压缩试验,以确定动态再结晶开始时的加工硬化速率曲线σ_(c)(ε_(c))l以及关键特征σ_(c)(ε_(c))l、σ_(p)(ε_(p))和σ_(ss)与Z系数之间的... 详细信息

在300~5000℃、应变速率为0.1~0.0001 s−1的条件下,在万能试验机上对Al-5.6Zn-2Mg铝合金进行热压缩试验,以确定动态再结晶开始时的加工硬化速率曲线σ_(c)(ε_(c))l以及关键特征σ_(c)(ε_(c))l、σ_(p)(ε_(p))和σ_(ss)与Z系数之间的相关性。使用了四个本构模型,Arrhenius模型、改进的Johnson-Cook模型(MJC)、改进的Zerilli-Armstrong模型(MZA)和开发的人工神经网络(ANN)。结果表明,ANN型模型和Arrhenius模型的相对误差的绝对平均值最低,分别为0.486%和3.36%,MZA型模型和MJC型模型的相对误差的绝对平均值较高,分别为8.84%和3.93%。由于Arrhenius模型能够处理各种因素之间的非线性关系,因此它被认为是最合适的预测模型,但在材料性质未知或实验数据有限的情况下,MJC模型可能是一种更简单的替代方法。MZA模型不适合估计热压缩时的流变应力。此外,训练最好的神经网络模型的预测性能最好,相对误差的绝对平均值为0.486%,R值为0.99。

关键词：加工硬化速率曲线本构建模热压缩 EBSD分析流变曲线

An Effective Density Based Approach to Detect Complex Data Clusters Using Notion of Neighborhood Difference

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International Journal of Automation and computing 2017年第1期14卷 57-67页

作者： S. Nagaraju Manish Kashyap Mahua Bhattachraya Visual information Processing Lab Indian Institute of Information Technology and Management Gwalior India

The density based notion for clustering approach is used widely due to its easy implementation and ability to detect arbitrary shaped clusters in the presence of noisy data points without requiring prior knowledge of the number of clusters to be identified. Density-based spatial clustering of applications with noise （DBSCAN） is the first algorithm proposed in the literature that uses density based notion for cluster detection. Since most of the real data set, today contains feature space of adjacent nested clusters, clearly DBSCAN is not suitable to detect variable adjacent density clusters due to the use of global density parameter neighborhood radius Y,.ad and minimum number of points in neighborhood Np~,. So the efficiency of DBSCAN depends on these initial parameter settings, for DBSCAN to work properly, the neighborhood radius must be less than the distance between two clusters otherwise algorithm merges two clusters and detects them as a single cluster. Through this paper： 1） We have proposed improved version of DBSCAN algorithm to detect clusters of varying density adjacent clusters by using the concept of neighborhood difference and using the notion of density based approach without introducing much additional computational complexity to original DBSCAN algorithm. 2） We validated our experimental results using one of our authors recently proposed space density indexing （SDI） internal cluster measure to demonstrate the quality of proposed clustering method. Also our experimental results suggested that proposed method is effective in detecting variable density adjacent nested clusters.

关键词： Density based clustering neighborhood difference density-based spatial clustering of applications with noise （DBSCAN）,space density indexing （SDI） core object.

Rheological fluid motion in tube by metachronal waves of cilia

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Applied Mathematics and Mechanics(English Edition) 2017年第3期38卷 393-410页

作者： S.MAITI S.K.PANDEY Department of Mathematical Sciences Indian Institute of Technology (BHU) Department of Mathematics The LNM Institute of Information Technology

This paper presents a theoretical study of a non-linear rheological fluid transport in an axisymmetric tube by cilia. An attempt has been made to explain the role of cilia motion in the transport of fluid through the ductus efferent of the male reproductive tract. The Ostwald-de Waele power-law viscous fluid is considered to represent the rheological fluid. We analyze pumping by means of a sequence of cilia beats from rowto-row of cilia in a given row of cells and from one row of cells to the next(metachronal wave movement). For this purpose, we consider the conditions that the corresponding Reynolds number is small enough for inertial effects to be negligible, and the wavelengthto-diameter ratio is large enough so that the pressure can be considered uniform over the cross section. Analyses and computations of the fluid motion reveal that the time-average flow rate depends on ε, a non-dimensional measure involving the mean radius a of the tube and the cilia length. Thus, the flow rate significantly varies with the cilia length.Moreover, the flow rate has been reported to be close to the estimated value 6 × 10ml/h for human efferent ducts if ε is near 0.4. The estimated value was suggested by Lardner and Shack(Lardner, T. J. and Shack, W. J. Cilia transport. Bulletin of Mathematical Biology, 34, 325–335(1972)) for human based on the experimental observations of flow rates in efferent ducts of other animals, e.g., rat, ram, and bull. In addition, the nature of the rheological fluid, i.e., the value of the fluid index n strongly influences various flow-governed characteristics. An interesting feature of this paper is that the pumping improves the thickening behavior for small values of ε or in free pumping(?P = 0) and pumping(?P > 0) regions.

关键词： non-Newtonian fluid cilia movement metachronal wave volumetric flow flow reversal velocity at wave crest and trough

Analysis of community question-answering issues via machine learning and deep learning:State-of-the-art review

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CAAI Transactions on Intelligence technology 2023年第1期8卷 95-117页

作者： Pradeep Kumar Roy Sunil Saumya Jyoti Prakash Singh Snehasish Banerjee Adnan Gutub Department of Computer Science and Engineering Indian Institute of Information TechnologySuratIndia Indian Institute of Information Technology DharwadIndia Department of Computer Science and Engineering National Institute of TechnologyPatnaIndia The York Management School University of YorkYorkUK Computer Engineering Department College of Computer&Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia

Over the last couple of decades,community question-answering sites(CQAs)have been a topic of much academic interest.Scholars have often leveraged traditional machine learning(ML)and deep learning(DL)to explore the ever-growing volume of content that CQAs engender.To clarify the current state of the CQA literature that has used ML and DL,this paper reports a systematic literature review.The goal is to summarise and synthesise the major themes of CQA research related to(i)questions,(ii)answers and(iii)users.The final review included 133 articles.Dominant research themes include question quality,answer quality,and expert identification.In terms of dataset,some of the most widely studied platforms include Yahoo!Answers,Stack Exchange and Stack Overflow.The scope of most articles was confined to just one platform with few cross-platform investigations.Articles with ML outnumber those with DL.Nonetheless,the use of DL in CQA research is on an upward trajectory.A number of research directions are proposed.

关键词： answer quality community question answering deep learning expert user machine learning question quality

Active Learning Query Strategies for Classification,Regression,and Clustering:A Survey

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维普期刊数据库

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Journal of Computer Science & technology 2020年第4期35卷 913-945页

作者： Punit Kumar Atul Gupta Indian Institute of Information Technology Design and ManufacturingJabalpurMadhya Pradesh 482005India

Generally,data is available abundantly in unlabeled form,and its annotation requires some cost.The labeling,as well as learning cost,can be minimized by learning with the minimum labeled data instances.Active learning(AL),learns from a few labeled data instances with the additional facility of querying the labels of instances from an expert annotator or oracle.The active learner uses an instance selection strategy for selecting those critical query instances,which reduce the generalization error as fast as possible.This process results in a refined training dataset,which helps in minimizing the overall cost.The key to the success of AL is query strategies that select the candidate query instances and help the learner in learning a valid hypothesis.This survey reviews AL query strategies for classification,regression,and clustering under the pool-based AL scenario.The query strategies under classification are further divided into:informative-based,representative-based,informative-and representative-based,and others.Also,more advanced query strategies based on reinforcement learning and deep learning,along with query strategies under the realistic environment setting,are presented.After a rigorous mathematical analysis of AL strategies,this work presents a comparative analysis of these strategies.Finally,implementation guide,applications,and challenges of AL are discussed.

关键词： active learning active learning query strategy active classification active regression active clustering deep active learning

SEIHCRD Model for COVID-19 Spread Scenarios,Disease Predictions and Estimates the Basic Reproduction Number,Case Fatality Rate,Hospital,and ICU Beds Requirement

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Computer Modeling in Engineering & Sciences 2020年第12期125卷 991-1031页

作者： Avaneesh Singh Manish Kumar Bajpai PDPM Indian Institute of Information Technology Design and ManufacturingJabalpur482005India

We have proposed a new mathematical method,the SEIHCRD model,which has an excellent potential to predict the incidence of COVID-19 diseases.Our proposed SEIHCRD model is an extension of the SEIR model.Three-compartments have added death,hospitalized,and critical,which improves the basic understanding of disease spread and results.We have studiedCOVID-19 cases of six countries,where the impact of this disease in the highest are Brazil,India,Italy,Spain,the United Kingdom,and the United States.After estimating model parameters based on available clinical data,the modelwill propagate and forecast dynamic evolution.Themodel calculates the Basic reproduction number over time using logistic regression and the Case fatality rate based on the selected countries’age-category scenario.Themodel calculates two types of Case fatality rate one is CFR daily,and the other is total CFR.The proposed model estimates the approximate time when the disease is at its peak and the approximate time when death cases rarely occur and calculate how much hospital beds and ICU beds will be needed in the peak days of infection.The SEIHCRD model outperforms the classic ARXmodel and the ARIMA model.RMSE,MAPE,andRsquaredmatrices are used to evaluate results and are graphically represented using Taylor and Target diagrams.The result shows RMSE has improved by 56%–74%,and MAPE has a 53%–89%improvement in prediction accuracy.

关键词： COVID-19 coronavirus SIER model SEIHCRD model parameter estimation mathematical model India Brazil United Kingdom United States Spain Italy hospital beds ICU beds basic reproduction number case fatality rate