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Anticancer activity of Mahonia leschenaultii methanolic root extract and berberine on Dalton’s ascitic lymphoma in mice

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Asian Pacific Journal of Tropical Medicine 2019年第6期12卷 264-271页

作者： r latha TM Chozhavel rajanathan Ameer Khusro N Chidambaranathan Paul Agastian Sankaranarayanan Nagarajan research Department of Plant Biology and Biotechnology Loyola CollegeChennai-600034India K.M.College of Pharmacy UthangudiMadurai-625107Tamil NaduIndia Department of Chemistry National Institute of TechnologyImphal-795004Manipur India

Objective: To evaluate the effects of methanol root extract of Mahonia leschenaultii and berberine of Mahonia leschenaultii on Dalton’s ascitic lymphoma in Swiss Albino mice. Methods: The methanol root extracts of Mahonia leschenaultii(200 and 400 mg/kg) were given orally, and berberines(10 and 20 mg/kg) were injected intra-peritoneally for 14 successive days in tumor bearing mice. Hematological parameters(white and red blood cells, haemoglobin level, granulocytes, and agranulocytes), lipid parameters(total cholesterol and triglycerides), serum enzymes(serum glutamate pyruvate transaminases, serum glutamate oxaloacetate transaminases, and alkaline phosphatise) and mean survival and solid tumor growth were determined and compared with untreated mice. 5-fluorouracil(20 mg/kg) was used as a reference standard drug. results: Mahonia leschenaultii and berberine reduced the hematocrit significantly. Furthermore, Mahonia leschenaultii and berberine improved the survival of mice significantly and restored the affected hematological and lipid parameters similar to the normal levels. Conclusions: These observations show a strong anticancer effect of methanol root extract of Mahonia leschenaultii and berberine in suppressing Dalton’s ascitic lymphoma cancer cell growth in a mouse model by controlling haematological, lipid, serum enzymes, and other derived parameters effectively.

关键词： Mahonia leschenaultii Berberine Dalton’s ascitic lymphoma Methanolic extract

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Feature Selection Using Grey Wolf Optimization with random Differential Grouping

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Computer Systems Science & Engineering 2022年第10期43卷 317-332页

作者： r.S.latha B.Saravana Balaji Nebojsa Bacanin Ivana Strumberger Miodrag Zivkovic Milos Kabiljo Department of Computer Science and Engineering Kongu Engineering CollegeErode638060TamilnaduIndia Department of Information Technology Lebanese French UniversityErbilIraq Singidunum University Belgrade160622Serbia

Big data are regarded as a tremendous technology for processing a huge variety of data in a short time and with a large storage *** user’s access over the internet creates massive data processing over the *** data require an intelligent feature selection model by addressing huge varieties of *** feature selection techniques are only applicable to simple data *** techniques are needed in big data processing and machine learning for an efficient *** feature selection algorithms read the input features as they ***,the features are preprocessed and ***,an algorithm does not consider the *** feature selection,all features are misread as ***,a less optimal solution is *** our proposed research,we focus on the feature selection by using supervised learning techniques called grey wolf optimization(GWO)with decomposed random differential grouping(DrnDG-GWO).First,decomposition of features into subsets based on relatedness in variables is *** differential grouping is performed using a fitness value of two ***,every subset is regarded as a population in GWO *** combination of supervised machine learning with swarm intelligence techniques produces best feature optimization results in this *** the features are optimized,we classify using advanced kNN process for accurate data *** result of DrnDGGWO is compared with those of the standard GWO and GWO with PSO for feature selection to compare the efficiency of the proposed *** accuracy and time complexity of the proposed algorithm are 98%and 5 s,which are better than the existing techniques.

关键词： Feature selection data optimization supervised learning swarm intelligence decomposed random differential grouping grey wolf optimization

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Gorilla Troops Optimizer Based Fault Tolerant Aware Scheduling Scheme for Cloud Environment

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Intelligent Automation & Soft Computing 2023年第2期35卷 1923-1937页

作者： r.rengaraj K.latha Department of Information Technology Saranathan College of EngineeringTiruchirapalli620012TamilnaduIndia Department of Computer Science and Engineering Anna University(BIT Campus)Trichy620024TamilnaduIndia

In cloud computing(CC),resources are allocated and offered to the cli-ents transparently in an on-demand *** can happen in CC environment and the cloud resources are adaptable toﬂuctuations in the performance *** execution failure becomes common in the CC ***,fault-tolerant scheduling techniques in CC environment are essential for handling performance differences,resourceﬂuxes,and ***,several intelli-gent scheduling approaches have been developed for scheduling tasks in CC with no consideration of fault tolerant *** this motivation,this study focuses on the design of Gorilla Troops Optimizer Based Fault Tolerant Aware Scheduling Scheme(GTO-FTASS)in CC *** proposed GTO-FTASS model aims to schedule the tasks and allocate resources by considering fault tolerance into *** GTO-FTASS algorithm is based on the social intelligence nature of gorilla ***,the GTO-FTASS model derives aﬁtness function involving two parameters such as expected time of completion(ETC)and failure probability of executing a *** addition,the presented fault detector can trace the failed tasks or VMs and then schedule heal submodule in sequence with a remedial or retrieval scheduling *** experimental vali-dation of the GTO-FTASS model has been performed and the results are inspected under several *** comparative analysis reported the better outcomes of the GTO-FTASS model over the recent approaches.

关键词： Cloud computing gorilla troops optimizer task scheduling fault tolerant task completion time failure probability

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Machine Learning-Based Threatened Species Translocation Under Climate Vulnerability

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Intelligent Automation & Soft Computing 2023年第4期36卷 327-337页

作者： Nandhi Kesavan latha Department of Computer Science&Engineering Anna University(BIT Campus)Tiruchirappalli620024India

Climate change is the most serious causes and has a direct impact on *** to the world’s biodiversity conservation organization,rep-tile species are most affected since their biological and ecological qualities are directly linked to *** to a lack of time frame in existing works,conser-vation adoption affects the performance of existing *** proposed research presents a knowledge-driven Decision Support System(DSS)including the assisted translocation to adapt to future climate change to conserving from its *** Dynamic approach is used to develop a knowledge-driven DSS using machine learning by applying an ecological and biological variable that characterizes the model and mitigation processes for ***,the frame-work demonstrates the huge difference in the estimated signiﬁcance of climate change,the model strategy helps to recognize the probable risk of threatened spe-cies translocation to future climate *** proposed system is evaluated using various performance metrics and this framework can comfortably adapt to the decisions support to reintroduce the species for conservation in the future.

关键词： Machine learning climate change decision support system multiple regression conservation area receiver operating curve

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Sammon Quadratic recurrent Multilayer Deep Classifier for Legal Document Analytics

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Computers, Materials & Continua 2022年第8期72卷 3039-3053页

作者： Divya Mohan latha ravindran Nair School of Engineering Cochin University of Science and TechnologyKeralaIndia

In recent years,machine learning algorithms and in particular deep learning has shown promising results when used in the field of legal *** legal field is strongly affected by the problem of information overload,due to the large amount of legal material stored in textual *** text processing is essential in the legal domain to analyze the texts of the court events to automatically predict smart *** an increasing number of digitally available documents,legal text processing is essential to analyze documents which helps to automate various legal domain *** document classification is a valuable tool in legal services for enhancing the quality and efficiency of legal document *** this paper,we propose Sammon Keyword Mapping-based Quadratic Discriminant recurrent Multilayer Perceptive Deep Neural Classifier(SKM-QDrMPDNC),a system that applies deep neural methods to the problem of legal document *** SKM-QDrMPDNC technique consists of many layers to perform the keyword extraction and ***,the set of legal documents are collected from the *** the keyword extraction is performed using SammonMapping technique based on the distance *** the extracted features,Quadratic Discriminant analysis is applied to performthe document classification based on the likelihood ratio ***,the classified legal documents are obtained at the output *** process is repeated until minimum error is *** experimental assessment is carried out using various performance metrics such as accuracy,precision,recall,F-measure,and computational time based on several legal documents collected from the *** observed results validated that the proposed SKM-QDrMPDNC technique provides improved performance in terms of achieving higher accuracy,precision,recall,and F-measure with minimum computation time when compared to existing methods.

关键词： Legal document data analytics recurrent multilayer perceptive deep neural network sammon mapping quadratic discriminant analysis likelihood ratio test

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Structural and optical properties of CZTS nanoparticles prepared by a colloidal process

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rare Metals 2021年第9期40卷 2602-2609页

作者： r.Aruna-Devi M.latha S.Velumani JoséÁlvaro Chávez-Carvayar Program on Nanoscience and Nanotechnology CINVESTAV-IPNSan Pedro Zacatenco07360 Mexico CityMexico CIICAp-UAEM Av.Universidad 1001.Col.Chamilpa62209 CuernavacaMorelosMexico Department of Electrical Engineering(SEES) CINVESTAV-IPNSan Pedro Zacatenco07360 Mexico CityMexico Instituto de Investigaciones en Materiales UNAM04510 Mexico CityMexico

This research presents the influence of copper(Cu) concentration on the properties of copper zinc tin sulfide(CZTS) nanoparticles(NPs) synthesized by a colloidal method, employing oleylamine as an organic solvent. From the structural analysis, a pure phase of CZTS was obtained at an optimized molar ratio of 1.8:1.1:1:4,and secondary phases were observed as the Cu concentration increased. The increment of the NPs size from 15 to50 nm was noticed as a function of Cu ***, the optical bandgap energy was tuned from1.35 to 1.68 eV with respect to Cu concentration. The sample synthesized at the molar ratio of 1.8:1.1:1:4 showed a pure phase CZTS with desired elemental composition and bandgap. Furthermore, CZTS thin films were prepared by a doctor-blade method using synthesized NPs followed by annealing under a sulfur/nitrogen atmosphere. The annealed CZTS film showed a significant improvement in the morphological, electrical and optoelectronic ***, the annealed CZTS film can be efficiently used as an absorber layer in low-cost thin film solar cells.

关键词： CZTS Copper concentration Nanoparticles Thin film Annealing

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How New Individuals Behave in a Heterogeneous Community: A Computational Approach to Norm Assimilation Using Agent-Based Systems

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Journal of Systems Science & Complexity 2020年第4期33卷 849-881页

作者： MAHMOUD Moamin AHMAD Mohd Sharifuddin MOSTAFA Salama SUBrAMAINAN latha College of Computing and Informatics Universiti Tenaga NasionalKajangSelangorMalaysia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaParit RajaMalaysia

Heterogeneous community entails a number of social groups that adopt similar/different social *** such community,new individuals who join a new social group should be able to decide with which group they could assimilate based on their capabilities/values/***,they would be penalized by other members in the group for violating some norms which they cannot *** this approach,software agents would have better reasoning in simulating human *** this paper,the authors propose a norms assimilation theory,in which a new agent attempts to assimilate with a social group’s *** theory builds an approach to norm assimilation,analyzes the cases for an agent to decide to assimilate with a social group and develops a mathematical model to measure the assimilation cost and the agent’s *** approach is developed based on the agent’s internal belief about its ability and desire,and its external belief about the cost of assimilating with a number of social *** significance of this research is ***,the study paves the way to future design of intelligent systems,i.e.,software agents or robots,to closely mimic human social ***,the norm assimilation using agent-based system could be potentially utilized to simulate some social issues such as immigrants,new students,expatriate *** experiments that have been conducted demonstrate that an agent in the domain is able to calculate the assimilation cost and decide which social group to join.

关键词： Agent-based simulation heterogeneous community individuals norms norm assimilation normative systems social group

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GSA-based support vector neural network:a machine learning approach for crop prediction to provision sustainable farming

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International Journal of Intelligent Computing and Cybernetics 2023年第1期16卷 1-16页

作者： A.Ashwitha C.A.latha Department of ISE Ramaiah Institute of TechnologyBangaloreIndia Department of CSE AMCECBangaloreIndia and Vishveshwarya Technical UniversityBelgaumIndia Department of CSE AMC Engineering CollegeBangaloreIndia

Purpose-Automated crop prediction is needed for the following reasons:First,agricultural yields were decided by a farmer’s ability to work in a certain field and with a particular crop *** were not always able to predict the crop and its yield solely on that idea ***,seed firms frequently monitor how well new plant varieties would grow in certain ***,predicting agricultural production is critical for solving emerging food security concerns,especially in the face of global climate *** production forecasts not only assist farmers inmaking informed economic andmanagement decisions but they also aid in the prevention of *** results in farming systems’efficiency and productivity gains,as well as reduced risk from environmental ***/methodology/approach-This research paper proposes a machine learning technique for effective autonomous crop and yield prediction,which makes use of solution encoding to create solutions randomly,and then for every generated solution,fitness is evaluated to meet highest *** focus of the proposed work is to optimize the weight parameter in the input *** algorithm continues until the optimal agent or optimal weight is selected,which contributes to maximum accuracy in automated crop ***-Performance of the proposed work is compared with different existing algorithms,such as random Forest,support vector machine(SVM)and artificial neural network(ANN).The proposed method support vector neural network(SVNN)with gravitational search agent(GSA)is analysed based on different performance metrics,such as accuracy,sensitivity,specificity,CPU memory usage and training time,and maximum performance is *** limitations/implications-rather than real-time data collected by Internet of Things(IoT)devices,this research focuses solely on historical data;the proposed work does not impose IoT-based smart farming,which enhances the overall agriculture system by

关键词： Crop yield Support vector machine(SVM) Artificial neural network(ANN) SVNN Gravitational search algorithm

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Granulometric Analysis of Spots in DNA Microarray Images

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Genomics, Proteomics & Bioinformatics 2004年第4期2卷 222-236页

作者： Behara latha Balasubramanian Venkatesh Faculty of Engineering and Technology Multimedia University 75450 Melaka MalaysiaDepartment of Electrical and Computer Engineering University of New Brunswick Fredericton NB E3B 5A3 Canada.

As the topological properties of each spot in DNA microarray images may varyfrom one another, we employed granulometries to understand the shape-size content contributed due toa significant intensity value within a spot. Analysis was performed on the microarray image thatconsisted of 240 spots by using concepts from mathematical morphology. In order to find out indicesfor each spot and to further classify them, we adopted morphological multiscale openings, whichprovided microarrays at multiple scales. Successive opened microarrays were subtracted to identifythe protrusions that were smaller than the size of structuring element. Spot-wise details, in termsof probability of these observed protrusions, were computed by placing a regularly spaced grid onmicroarray such that each spot was centered in each grid. Based on the probability of sizedistribution functions of these protrusions isolated at each level, we estimated the mean size andtexture index for each spot. With these characteristics, we classified the spots in a microarrayimage into bright and dull categories through pattern spectrum and shape-size complexity *** segregated spots can be compared with those of hybridization levels.

关键词： microarray spot mathematical morphology image

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Binary Multifold Encryption Technique for Complex Cloud Systems

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Computer Systems Science & Engineering 2022年第9期42卷 1199-1213页

作者： N.Ansgar Mary T.latha Department of Information and Technology St.Xavier’s Catholic College of EngineeringTamil Nadu629001India Department of Electronics and Communication Engineering St.Xavier’s Catholic College of EngineeringTamil Nadu629001India

Data security is a major cloud computing issue due to different usertransactions in the system. The evolution of cryptography and cryptographic analysis are regarded domains of current research. deoxyribo nucleic acid (DNA) cryptography makes use of DNA as a sensing platform, which is then manipulated usinga variety of molecular methods. Many security mechanisms including knowledgebased authentication, two-factor authentication, adaptive authentication, multifactorauthentication and single password authentication have been deployed. These cryptographic techniques have been developed to ensure confidentiality, but most ofthem are based on complex mathematical calculations and equations. In the proposed approach, a novel and unique Hybrid helix scuttle-deoxy ribo nucleic acids(HHS-DNA) encryption algorithm has been proposed which is inspired by DNAcryptography and Helix scuttle. The proposed HHS-DNA is a type of multifold binary version of DNA (MF-BDNA). The major role of this paper is to present a multifold HHS-DNA algorithm to encrypt the cloud data assuring more security with lesscomplexity. The experimentation is carried out and it reduces the encryption time,cipher text size, and improves throughput. When compared with previous techniques, there is a 45% improvement in throughput, 37% fast in encryption time,54.67% cipher text size. The relevant experimental results and foregoing analysisshow that this method is of good robustness, stability, and security.

关键词： Cryptography cloud network deoxy ribo nucleic acid(DNA) encryption security cloud service provider(CSP)

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