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Human Action recognition Using Difference of Gaussian and Difference of Wavelet

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Big Data Mining and Analytics 2023年第3期6卷 336-346页

作者： Gopampallikar Vinoda reddy Kongara Deepika Lakshmanan malliga Duraivelu Hemanand Chinnadurai Senthilkumar Subburayalu Gopalakrishnan Yousef Farhaoui Department of Computer Science and Engineering(AI&ML) CMR Technical CampusHyderabad 501401India. Department of Information Technology Kakatiya Institute of Technology and ScienceWarangal 506015India. Department of Electronics and Communication Engineering Malla Reddy Engineering College for Women(Autonomous)Hyderabad 500100India. Department of Computer Science and Engineering S.A.Engineering College(Autonomous)Thiruverkadu 600077India. Department of Electronics and Communication Engineering Saveetha Institute of Medical and Technical Sciences(SIMATS)Chennai 602105India. Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India. IDMS Team STI LaboratoryFaculty of Sciences and TechniquesMoulay Ismail University of MeknèsErrachidia 25003Morocco.

Human Action recognition(HAr)attempts to recognize the human action from images and *** major challenge in HAr is the design of an action descriptor that makes the HAr system robust for different environments.A novel action descriptor is proposed in this study,based on two independent spatial and spectral *** proposed descriptor uses a Difference of Gaussian(DoG)filter to extract scale-invariant features and a Difference of Wavelet(DoW)filter to extract spectral *** create a composite feature vector for a particular test action picture,the Discriminant of Guassian(DoG)and Difference of Wavelet(DoW)features are *** Discriminant Analysis(LDA),a widely used dimensionality reduction technique,is also used to eliminate duplicate ***,a closest neighbor method is used to classify the *** and UCF 11 datasets were used to run extensive simulations of the suggested strategy,and the accuracy assessed after the simulations were run on Weizmann datasets for five-fold cross validation is shown to perform *** average accuracy of DoG+DoW is observed as 83.6635%while the average accuracy of Discrinanat of Guassian(DoG)and Difference of Wavelet(DoW)is observed as 80.2312%and 77.4215%,*** average accuracy measured after the simulation of proposed methods over UCF 11 action dataset for five-fold cross validation DoG+DoW is observed as 62.5231%while the average accuracy of Difference of Guassian(DoG)and Difference of Wavelet(DoW)is observed as 60.3214%and 58.1247%,*** the above accuracy observations,the accuracy of Weizmann is high compared to the accuracy of UCF 11,hence verifying the effectiveness in the improvisation of recognition accuracy.

关键词： human action recognition difference of Gaussian difference of wavelet linear discriminant analysis Weizmann UCF 11 accuracy

An Intelligent Heuristic Manta-ray Foraging Optimization and Adaptive Extreme Learning Machine for Hand Gesture Image recognition

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Big Data Mining and Analytics 2023年第3期6卷 321-335页

作者： Seetharam Khetavath Navalpur Chinnappan Sendhilkumar Pandurangan Mukunthan Selvaganesan Jana Lakshmanan malliga Subburayalu Gopalakrishnan Sankuru ravi Chand Yousef Farhaoui Department of Electronics and Communication Engineering Chaitanya(Deemed to be University)Warangal 506001India. Department of Electronics and Communication Engineering Sri Indu College of Engineering&TechnologySherigudaHyderabad 501510India. Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India. Department of Electronics and Communication Engineering Malla Reddy Engineering College for Women(Autonomous)Telangana 500100India. Department of Electronics and Communication Engineering Nalla Narasimha Reddy Education Society’s Group of Institutions-Integrated CampusHyderabad 500088India. STI Laboratory the IDMS TeamFaculty of Sciences and TechniquesMoulay Ismail University of MeknèsErrachidia 52000Morocco.

The development of hand gesture recognition systems has gained more attention in recent days,due to its support of modern human-computer ***,sign language recognition is mainly developed for enabling communication between deaf and dumb *** conventional works,various image processing techniques like segmentation,optimization,and classification are deployed for hand gesture ***,it limits the major problems of inefficient handling of large dimensional datasets and requires more time consumption,increased false positives,error rate,and misclassification ***,this research work intends to develop an efficient hand gesture image recognition system by using advanced image processing *** image segmentation,skin color detection and morphological operations are performed for accurately segmenting the hand gesture ***,the Heuristic Manta-ray Foraging Optimization(HMFO)technique is employed for optimally selecting the features by computing the best fitness ***,the reduced dimensionality of features helps to increase the accuracy of classification with a reduced error ***,an Adaptive Extreme Learning Machine(AELM)based classification technique is employed for predicting the recognition *** results validation,various evaluation measures have been used to compare the proposed model’s performance with other classification approaches.

关键词： hand gesture recognition skin color detection morphological operations Multifaceted Feature Extraction(MFE)model Heuristic Manta-ray Foraging Optimization(HMFO) Adaptive Extreme Learning Machine(AELM)

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人源大麻素受体CB2的晶体结构

科学新闻 2020年第2期 33-33页

作者：刘志杰 Xiaoting Li Tian Hua Kiran Vemuri Jo-Hao Ho Yiran Wu Lijie Wu Petr Popov Othman Benchama Nikolai Zvonok K'ara Locke Lu Qu Gye Won Han malliga r.iyer resat Cinar Nathan J.Coffey Jingjing Wang Meng Wu sevolod Katritch Suwen Zhao George Kunos Laura M.Bohn Alexandros Makriyannis raymond C.Stevens 上海科技大学

文章简介大麻素受体CB2主要分布在人体的免疫系统,选择性调控大麻素受体CB2对炎症、纤维化及神经退行性疾病等具有较好的治疗潜力,同时可避免由大麻素受体CB1引起的精神活性。本研究报道了大麻素受体CB2与新型拮抗剂AM0257复合物2.8埃... 详细信息

文章简介大麻素受体CB2主要分布在人体的免疫系统,选择性调控大麻素受体CB2对炎症、纤维化及神经退行性疾病等具有较好的治疗潜力,同时可避免由大麻素受体CB1引起的精神活性。本研究报道了大麻素受体CB2与新型拮抗剂AM0257复合物2.8埃分辨率的晶体结构。经结构对比分析,发现同处于拮抗状态的CB2与CB1不但结构差别极大,拮抗状态的CB2-AM0257复合物结构

关键词：大麻素受体 CB2 晶体结构

Pharmacological potentials of phenolic compounds from Prosopis spp.-a review

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Journal of Coastal Life Medicine 2014年第11期 918-924页

作者： Dhananjaya Seturaman Prabha Hans-Uwe Dahms Prabha malliga Department of Marine Biotechnology National Facility for Marine Cyanobacteria(sponsored by DBTGovt.of India)Bharathidasan UniversityTiruchirappalli-620024India Kaohsiung Medical University Department of Biomedical Science and Environmental Biology No.100Shin-Chuan 1st RoadKaohsiung 80708Taiwan R.O.C

The shrub Prosopsis juliflora(Sw.)*** several medicinal properties due to its different chemical compounds such as alkaloids,flavonoids,terpenoids,saponins and phenolic compounds distributed in different parts of the plant body,such as woody parts(roots,stem,branches and bark)as well as leaves and pollen have been used for the extraction of medically active *** juliflora containing a diverse group of secondary metabolites has unique and multifactorial medicinal *** utilization of this abundant resource provides a viable option for producing bioactive natural products that may serve as lead substances for the chemical and pharmaceutical *** review highlights accessible bioactive sources from this plant that are of potential interest for industrial applications.

关键词： industrial applications secondary metabolites phenolic compounds chemical compounds active substances natural products

The Design, Development and Testing of Multimedia Courseware Template for History

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通讯和计算机（中英文版） 2010年第2期7卷 23-36页

作者： rossafri bin Mohamad Balakrishnan Muniandy malliga Govindasamy Center for Instructional Technology and Multimedia Universiti Sains Malaysia Penang 11800 Malaysia