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A Contemporary review on Drought Modeling Using Machine Learning Approaches

Computer Modeling in Engineering & Sciences 2021年第8期128卷 447-487页

作者： Karpagam Sundararajan Lalit Garg Kathiravan Srinivasan Ali Kashif Bashir Jayakumar Kaliappan Ganapathy Pattukandan Ganapathy Senthil Kumaran selvaraj T.Meena School of Information Technology and Engineering Vellore Institute of TechnologyVellore632014India Faculty of Information and Communication Technology University of MaltaMsidaMSD2080Malta Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterM156BHUK School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014India Centre for Disaster Mitigation and Management Vellore Institute of TechnologyVellore632014India Department of Manufacturing Engineering School of Mechanical EngineeringVellore Institute of TechnologyVellore632014India School of Civil Engineering Vellore Institute of TechnologyVellore632014India

Drought is the least understood natural disaster due to the complex relationship of multiple contributory factors. Itsbeginning and end are hard to gauge, and they can last for months or even for years. India has faced many droughtsin the last few decades. Predicting future droughts is vital for framing drought management plans to sustainnatural resources. The data-driven modelling for forecasting the metrological time series prediction is becomingmore powerful and flexible with computational intelligence techniques. Machine learning (ML) techniques havedemonstrated success in the drought prediction process and are becoming popular to predict the weather, especiallythe minimum temperature using backpropagation algorithms. The favourite ML techniques for weather forecastinginclude support vector machines (SVM), support vector regression, random forest, decision tree, logistic regression,Naive Bayes, linear regression, gradient boosting tree, k-nearest neighbours (KNN), the adaptive neuro-fuzzyinference system, the feed-forward neural networks, Markovian chain, Bayesian network, hidden Markov models,and autoregressive moving averages, evolutionary algorithms, deep learning and many more. This paper presentsa recent review of the literature using ML in drought prediction, the drought indices, dataset, and performancemetrics.

关键词： Drought forecasting machine learning drought indices stochastic models fuzzy logic dynamic method hybrid method

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Chaotic Flower Pollination with Deep Learning Based COVID-19 Classification Model

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Computers, Materials & Continua 2023年第3期74卷 6195-6212页

作者： T.Gopalakrishnan Mohamed Yacin Sikkandar raed Abdullah Alharbi P.selvaraj Zahraa H.Kareem Ahmed Alkhayyat Ali Hashim Abbas Department of Information Technology Manipal Institute of Technology BengaluruManipal Academy of Higher EducationManipalIndia Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Public Health Department College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Computing Technologies College of Engineering and TechnologyFaculty of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203TamilnaduIndia Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University CollegeBabylonIraq College of Technical Engineering The Islamic UniversityNajafIraq College of Information Technology Imam Ja’afar Al-Sadiq UniversityAl-Muthanna66002Iraq

The Coronavirus Disease(COVID-19)pandemic has exposed the vulnerabilities of medical services across the globe,especially in underdeveloped *** the aftermath of the COVID-19 outbreak,a strong demand exists for developing novel computer-assisted diagnostic tools to execute rapid and cost-effective screenings in locations where many screenings cannot be executed using conventional *** imaging has become a crucial component in the disease diagnosis process,whereas X-rays and Computed Tomography(CT)scan imaging are employed in a deep network to diagnose the *** general,four steps are followed in image-based diagnostics and disease classification processes by making use of the neural networks,such as network training,feature extraction,model performance testing and optimal feature *** current research article devises a Chaotic Flower Pollination Algorithm with a Deep Learning-Driven Fusion(CFPADLDF)approach for detecting and classifying *** presented CFPA-DLDF model is developed by integrating two DL models to recognize COVID-19 in medical ***,the proposed CFPA-DLDF technique employs the Gabor Filtering(GF)approach to pre-process the input *** addition,a weighted voting-based ensemble model is employed for feature extraction,in which both VGG-19 and the MixNet models are ***,the CFPA with recurrent Neural Network(rNN)model is utilized for classification,showing the work’s novelty.A comparative analysis was conducted to demonstrate the enhanced performance of the proposed CFPADLDF model,and the results established the supremacy of the proposed CFPA-DLDF model over recent approaches.

关键词： Deep learning medical imaging fusion model chaotic models ensemble model COVID-19 detection

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Energy Efficient Cluster-Based Optimal resource Management in IoT Environment

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Computers, Materials & Continua 2022年第1期70卷 1247-1261页

作者： J.V.Anchitaalagammai T.Jayasankar P.selvaraj Mohamed Yacin Sikkandar M.Zakarya Mohamed Elhoseny K.Shankar Department of Computer Science and Engineering Velammal College of Engineering and TechnologyMadurai625009India Department of Electronics and Communication Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India Department of Information Technology SRMInstitute of Science and TechnologyKattankulathur603203Tamil NaduIndia Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAlMajmaah11952Saudi Arabia Department of Mathematics College of ScienceKing Khalid UniversityP.O.Box 9004Abha61413Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityAssiut71524Egypt College of Computer Information Technology American University in the EmiratesDubai503000United Arab Emirates Department of Computer Applications Alagappa UniversityKaraikudiIndia

Internet of Things(IoT)is a technological revolution that redefined communication and computation of modern *** generally refers to a network of gadgets linked via wireless network and communicates via *** management,especially energy management,is a critical issue when designing IoT *** studies reported that clustering and routing are energy efficient solutions for optimal management of resources in IoT *** this point of view,the current study devises a new Energy-Efficient Clustering-based routing technique for resource Management i.e.,EECBrM in IoT *** proposed EECBrM model has three stages namely,fuzzy logic-based clustering,Lion Whale Optimization with Tumbling(LWOT)-based routing and cluster maintenance *** proposed EECBrMmodel was validated through a series of experiments and the results were verified under several *** model was compared with existing methods in terms of energy efficiency,delay,number of data transmission,and network *** simulated,in comparison with other methods,EECBrM model yielded excellent results in a significant ***,the efficiency of the proposed model is established.

关键词： IoT environment clustering routing resource management energy efficiency

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Assessing methods for multiple imputation of systematic missing data in marine fisheries time series with a new validation algorithm

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Aquaculture and Fisheries 2023年第5期8卷 587-599页

作者： Ivan F.Benavides Marlon Santacruz Jhoana P.romero-Leiton Carlos Barreto John Josephraj selvaraj Instituto de Estudios del Pacífico Universidad Nacional de ColombiaTumaco CampusKilometro 30-31CajapíVía Nacional Tumaco-PastoTumacoNarino522020Colombia Ingeniería en Produccion Acuícola Universidad de NarinoCra.22#30-63Torre 4 apto.703San Juan de PastoNarino520004Colombia Facultad de ingeniería Universidad CesmagPasto520004Colombia Datambiente 520001Colombia Autoridad Nacional de Pesca y Acuicultura(AUNAP) 520001Colombia

Time series from fisheries often contain multiple missing *** is a severe limitation that prevents using the data for research on population dynamics,stock assessment,forecasting,and,hence,decision-making around marine *** methods have been proposed to impute missing data in univariate time ***,their performances depend not only on the amount of missing data but also on the data *** study compares the performance of twelve imputation methods on the time series of marine fishery landings for six species in the Colombian Pacific *** other studies,we validate the precision of the imputations in the same target time series that include missing data,using the Known Sub-Sequence Algorithm(KSSA),a novelty validation approach that simulates missing data in known sub-sequences of the target time *** results showed that the best methods for imputation are Seasonal Decomposition with Kalman filters and Structural Models with Kalman filters fitted by maximum *** also show that validating the imputation methods with other time series different to the target time series,leads to wrong imputation methods *** is noteworthy that these methods and also the validation framework are mainly suited to time series with non-random distribution of missing data,this is,missing data produced systematically in chunks or clusters with predictable frequency,which are common in marine sciences.

关键词： Data gaps Fish populations Artisanal fishing Time series imputation

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Application of Principal Component Analysis as Properties and Sensory Assessment Tool for Legume Milk Chocolates

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American Journal of Computational Mathematics 2023年第1期13卷 136-152页

作者： Preethini selvaraj Arrivukkarasan Sanjeevirayar Anhuradha Shanmugam Biochemistry Laboratory Department of Chemical Engineering Annamalai University Annamalai Nagar India

Principal component analysis (PCA) was employed to examine the effect of nutritional and bioactive compounds of legume milk chocolate as well as the sensory to document the extend of variations and their significance with plant sources. PCA identified eight significant principle components, that reduce the size of the variables into one principal component in physiochemical analysis interpreting 73.5% of the total variability with/and 78.6% of total variability explained in sensory evaluation. Score plot indicates that Double Bean milk chocolate in-corporated with MOL and CML in nutritional profile have high positive correlations. In nutritional evaluation, carbohydrates and fat content shows negative/minimal correlations whereas no negative correlations were found in sensory evaluation which implies every sensorial variable had high correlation with each other.

关键词： Principal Component Analysis Legume Milk Chocolate Bioactive Plant Source Nutritional and Sensory Properties

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Formulation and Optimization of Constituent in Legumes-Based Milk Chocolate Fortified with Citrus Peel Powder

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Food and Nutrition Sciences 2022年第6期13卷 600-617页

作者： Preethini selvaraj Anhuradha Shanmugam Arrivukkarasan Sanjeevirayar Biochemistry Laboratory Department of Chemical Engineering Annamalai University Annamalai Nagar India

The present study was employed to optimize the process for development of legumes-milk based chocolate using peanut (PN) and yellow-pea (YP) milk using response surface methodology (rSM). Different combinations of legumes-milk chocolate at various ratios of PN:YP milk with fixed concentration of other regular ingredients were prepared, and the finest combination (1:1) was selected on the basis of their sensory and nutritional properties. PN and YP milk, Jaggery (JG), Butter (BT), and Citrus Peel Powder (CPP) served as independent variables while the dependent variables were allocated to the regression equation to determine folic acid (r2 = 93.15) along with protein content (r2 = 93.11) and vitamin C (r2 = 90.57). The nutritional parameters such as Folic acid, Protein and Vitamin C content were found to be optimum in the milk chocolate. The optimized concentrations of PNM, YPM, JG, BT and CPP were found to be 5.0 ml, 5.5 ml, 18.1 g, 2.9 g and 0.53 g respectively. Addition of JG had an interactive effect on folic acid in YP milk (p rceived in legume based milk chocolate than the control chocolate due to supplement of CPP.

关键词： Bioactive Compounds Chocolate Citrus Peel Ingredients Legumes Optimization Sensory

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Analysis of Gene Expression Patterns during Seed Coat Development in Arabidopsis

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Molecular Plant 2011年第6期4卷 1074-1091页

作者： Gillian Dean YongGuo Cao DaoQuan Xiang Nicholas J. Provart Larissa ramsay Abdul Ahad rick White Gopalan selvaraj raju Datla George Haughn Department of Botany University of British Columbia 6270 University Blvd Vancouver BC V6T 1Z4 Canada National research Council of Canada Plant Biotechnology Institute 110 Gymnasium Place Saskatoon SK S7N owg Canada Department of Cell and Systems Biology/Centre for the Analysis of Genome Evolution and Function University of Toronto 25 Willcocks St Toronto ON M5S 3B2 Canada Department of Statistics University of British Columbia 6356 Agricultural Road Vancouver BC V6T 1Z2 Canada

The seed coat is important for embryo protection, seed hydration, and dispersal. Seed coat composition is also of interest to the agricultural sector, since it impacts the nutritional value for humans and livestock alike. Although some seed coat genes have been identified, the developmental pathways controlling seed coat development are not completely elucidated, and a global genetic program associated with seed coat development has not been reported. This study uses a combination of genetic and genomic approaches in Arabidopsis thaliana to begin to address these knowledge gaps. Seed coat development is a complex process whereby the integuments of the ovule differentiate into specialized cell types. In Arabidopsis, the outermost layer of cells secretes mucilage into the apoplast and develops a secondary cell wall known as a columella. The layer beneath the epidermis, the palisade, synthesizes a secondary cell wall on its inner tangential side. The innermost layer （the pigmented layer or endothelium） produces proanthocyanidins that condense into tannins and oxidize, giving a brown color to mature seeds. Genetic separation of these cell layers was achieved using the ap2-7 and tt16-1 mutants, where the epidermis/palisade and the endothelium do not develop respectively. This genetic ablation was exploited to examine the developmental programs of these cell types by isolating and collecting seed coats at key tran- sitions during development and performing global gene expression analysis. The data indicate that the developmental programs of the epidermis and the pigmented layer proceed relatively independently. Global expression datasets that can be used for identification of new gene candidates for seed coat development were generated. These dataset provide a comprehensive expression profile for developing seed coats in Arabidopsis, and should provide a useful resource and reference for other seed systems.

关键词： Seed coat microarray APETALA2 TrANSPArENT TESTA16 mucilage pectin secondary cell wall pigmentedlayer proanthocyanidin.

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Statistical Tests of Hypothesis Based Color Image retrieval

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Journal of Data Analysis and Information Processing 2016年第2期4卷 90-99页

作者： K. Seetharaman S. selvaraj Department of Computer Science & Engineering Annamalai University Annamalai Nagar India Department of Computer Applications Research Scholar Manonmaniam Sundaranar University Tirunelveli India

This paper proposes a novel method based on statistical tests of hypotheses, such as F-ratio and Welch’s t-tests. The input query image is examined whether it is a textured or structured. If it is structured, the shapes are segregated into various regions according to its nature;otherwise, it is treated as textured image and considered the entire image as it is for the experiment. The aforesaid tests are applied regions-wise. First, the F-ratio test is applied, if the images pass the test, then it is proceeded to test the spectrum of energy, i.e. means of the two images. If the images pass both tests, then it is concluded that the two images are the same or similar. Otherwise, they differ. Since the proposed system is distribution-based, it is invariant for rotation and scaling. Also, the system facilitates the user to fix the number of images to be retrieved, because the user can fix the level of significance according to their requirements. These are the main advantages of the proposed system.

关键词： F-ratio Test Welch’s Test Tests of Hypotheses Mean Average Precision Target Image Query Image

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Synthesis, Characterization and Metal Ion Adsorption Studies on Novel Aromatic Poly(Azomethine Amide)s Containing Thiourea Groups

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Open Journal of Polymer Chemistry 2014年第1期4卷 1-11页

作者： Lingam ravikumar.selvaraj Kalaivani Thangaraj Vidhyadevi Arukkani Murugasen selvaraj Dinesh Kirupha Subramanian Sivanesan Department of Applied Science and Technology Anna University Chennai India Department of Chemistry C.B.M. College Bharathiar University Coimbatore India

A series of novel ladder-type poly(azomethine amide)s (PAMs) were prepared from new azomethine containing carboxylic acid monomer (TCA) with simple aromatic diamines and aromatic diamines with thiourea groups (TDAs) by means of phosphorylation polycondensation reaction. Molecular weights of the polyamides were evaluated viscometrically, and the inherent viscosities were in the range of 0.31 - 0.58 g/dl. These ladder-type polymers were not freely soluble in common organic solvents. Structure of monomers and polymers were confirmed using FT-Ir, 1H-NMr and 13C-NMr spectroscopic analysis. removal of Cu2+ and Cd2+ from aqueous solutions by adsorption onto the polyamides was investigated. The effect of pH, initial metal ion concentrations and contact time were studied in batch experiments. The polyamides were found to be highly effective adsorbents for the removal of Cu2+ and Cd2+ metal ions from aqueous solutions. In a mixture of metal ions, the selectivity order was found to be Pb(II) > Cu(II) > Cr(IV) > Cd(II).

关键词： Polyamide Azomethine Phenyl Thiourea Ladder Type Heavy Metal Ion removal

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Quantitative detection of citrate for early stage diagnosing of prostate cancer:Discriminating normal to cancer in prostate tissues

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Chinese Chemical Letters 2024年第2期35卷 501-506页

作者： selvaraj Muthusamy Kanagaraj rajalakshmi Palanisamy Kannan Yun-Sik Nam Dongwei Zhu Yeonggil Seo Jong-Won Song Dong Nyoung Heo Il Keun Kwon Yuanguo Xu School of Chemistry and Chemical Engineering Jiangsu UniversityZhenjiang 212013China College of Biological Chemical Sciences and EngineeringJiaxing UniversityJiaxing 314001China Advanced Analysis Center Korea Institute of Science & TechnologySeongbuk-guSeoul02792Republic of Korea School of Medicine Jiangsu UniversityZhenjiang 212013China Department of Chemistry Education Daegu UniversityGyeongsan-siGyeongsangbuk-do 38453Republic of Korea Department of Dental Materials School of DentistryKyung Hee UniversitySeoulRepublic of Korea

Prostate cancer(PC)biomarker-citrate detection is clinically important to diagnose PC in early *** iodide(Q)conjugated indole-phenylboronic acid(IB)was designed as a red-emissive QIB probe for the detection of citrate through Lewis acid-base reaction and intramolecular charge transfer(ICT)sensing *** acid acts as Lewis acid as well as citrate(Lewis base)recognition *** probe reacted with citrate,showing enhanced red *** the probe has excellent water solubility and great biocompatibility,practical application in biological systems is *** was monitored precisely in the mitochondria organelle(in vitro)of living cells with a positive charge on ***,endogenous(in situ)citrate was detected quantitatively to discriminate non-cancerous and PC mice,observed strong and lower(negligible)emission intensity on non-cancerous and cancerous prostate tissues,***,the concentration of citrate is higher in healthy prostate compared with PC ***,the analysis of sliced prostate tissues can give PC-related information for clinical diagnosis to prevent and treat PC in the initial ***,we believe that the present probe is a promising biochemical reagent in diagnosing PC.

关键词： Prostate cancer Biomarker-citrate Boronic acid Lewis acid/base-ICT red-emissive Mitochondria targeting Prostate tissue

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