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Deep Root Memory Optimized Indexing Methodology for Image Search Engines

Computer Systems Science & Engineering 2022年第2期40卷 661-672页

作者： R.Karthikeyan A.Celine Kavida p.suresh Department CSE Vel Tech HighTech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062TamilnaduIndia Department of physics Vel Tech Multitech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062Tamil NaduIndia Department of ECE Veltech Rangarajan Dr Sagunthala R&D Institute of Science and TechnologyChennaiTamilnadu600062India

Digitization has created an abundance of new information sources by altering how pictures are *** large image databases from a web portal requires an opted indexing structure instead of reducing the contents of different kinds of databases for quick *** approach paves a path toward the increase of efficient image retrieval techniques and numerous research in image indexing involving large image *** retrieval usually encounters difficulties like a)merging the diverse representations of images and their Indexing,b)the low-level visual characters and semantic characters associated with an image are indirectly proportional,and c)noisy and less accurate extraction of image information(semantic and predicted attributes).This work clearly focuses and takes the base of reverse engineering and de-normalizing concept by evaluating how data can be stored ***,retrieval becomes straightforward and *** research also deals with deep root indexing with a multidimensional approach about how images can be indexed and provides improved results in terms of good performance in query processing and the reduction of maintenance and storage *** focus on the schema design on a non-clustered index solution,especially cover *** schema provides a filter predication to make an index with a particular content of rows and an index table called filtered ***,we include non-key columns in addition to the key *** on two image data sets‘with and without’filtered indexing show low query *** compare efficiency as regards accuracy in mean average precision to measure the accuracy of retrieval with the developed coherent semantic *** results show that retrieval by using deep root indexing is simple and fast.

关键词： Multi-dimensional indexing deep root hashing image retrieval filtered indexing

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Immune response differences in degradable and non-degradable alloy implants

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Bioactive Materials 2023年第6期24卷 153-170页

作者： Taravat Khodaei Elizabeth Schmitzer Abhirami p.suresh Abhinav p.Acharya Biomedical Engineering School of Biological and Health System EngineeringArizona StateUniversityTempeAZ85281USA Biological Design Arizona State UniversityTempeAZ85281USA Chemical Engineering School for the Engineering of MatterTransportand EnergyArizona StateUniversityTempeAZ85281USA Materials Science and Engineering School for the Engineering of MatterTransportand EnergyArizona State UniversityTempeAZ85281USA Center for Immunotherapy Vaccines and VirotherapyArizona State UniversityTempeAZ85281USA

Alloy based implants have made a great impact in the clinic and in preclinical *** responses are one of the major causes of failure of these implants in the *** the immune responses toward non-degradable alloy implants are well documented,there is a poor understanding of the immune responses against degradable alloy ***,there have been several reports suggesting that degradable implants may develop substantial immune *** phenomenon needs to be further studied in detail to make the case for the degradable implants to be utilized in ***,we review these new recent reports suggesting the role of innate and potentially adaptive immune cells in inducing immune responses against degradable ***,we discussed immune responses to allergen components of non-degradable implants to give a better overview on differences in the immune response between non-degradable and degradable ***,we also provide potential areas of research that can be undertaken that may shed light on the local and global immune responses that are generated in response to degradable implants.

关键词： alloy utilized implants

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Optimal Deep Dense Convolutional Neural Network Based Classification Model for COVID-19 Disease

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Computers, Materials & Continua 2022年第1期70卷 2031-2047页

作者： A.Sheryl Oliver p.suresh A.Mohanarathinam Seifedine Kadry Orawit Thinnukool Department of Computer Science and Engineering St.Joseph’s College of Engineering600119ChennaiIndia Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Faculty of Engineering Karpagam Academy of Higher EducationCoimbatore641021India Faculty of Applied Computing and Technology Noroff University CollegeKristiansand4608Norway Research Group of Embedded Systems and Mobile Application in Health Science College of ArtsMedia and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Early diagnosis and detection are important tasks in controlling the spread of COVID-19.A number of Deep Learning techniques has been established by researchers to detect the presence of COVID-19 using CT scan images and ***,these methods suffer from biased results and inaccurate detection of the ***,the current research article developed Oppositional-based Chimp Optimization Algorithm and Deep Dense Convolutional Neural Network(OCOA-DDCNN)for COVID-19 prediction using CT images in IoT *** proposed methodology works on the basis of two stages such as pre-processing and ***,CT scan images generated from prospective COVID-19 are collected from open-source system using IoT *** collected images are then preprocessed using Gaussian *** filter can be utilized in the removal of unwanted noise from the collected CT scan ***,the preprocessed images are sent to prediction *** this phase,Deep Dense Convolutional Neural Network(DDCNN)is applied upon the pre-processed *** proposed classifier is optimally designed with the consideration of Oppositional-basedChimp Optimization Algorithm(OCOA).This algorithm is utilized in the selection of optimal parameters for the proposed ***,the proposed technique is used in the prediction of COVID-19 and classify the results as either COVID-19 or *** projected method was implemented in MATLAB and the performances were evaluated through statistical *** proposed method was contrasted with conventional techniques such as Convolutional Neural Network-Firefly Algorithm(CNN-FA),Emperor penguin Optimization(CNN-EpO)*** results established the supremacy of the proposed model.

关键词： Deep learning deep dense convolutional neural network covid-19 CT images chimp optimization algorithm

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HARTIV:Human Activity Recognition Using Temporal Information in Videos

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Computers, Materials & Continua 2022年第2期70卷 3919-3938页

作者： Disha Deotale Madhushi Verma p.suresh Sunil Kumar Jangir Manjit Kaur Sahar Ahmed Idris Hammam Alshazly CSE Department G.H.Raisoni Institute of Engineering and TechnologySPPU UniversityPuneIndia CSE Department Bennett UniversityGreater NoidaIndia TML Business Services Limited PuneIndia CSE Department Anand International College of EngineeringJaipurRajasthanIndia College of Industrial Engineering King Khalid UniversityAbhaSaudi Arabia Faculty of Computers and Information South Valley UniversityQena83523Egypt

Nowadays,the most challenging and important problem of computer vision is to detect human activities and recognize the same with temporal information from video *** video datasets are generated using cameras available in various devices that can be in a static or dynamic position and are referred to as untrimmed *** monitoring is a historical necessity in which commonly occurring,regular,and out-of-the-ordinary activities can be automatically identified using intelligence systems and computer vision *** a long video,human activity may be present anywhere in the *** can be a single ormultiple human activities present in such *** paper presents a deep learning-based methodology to identify the locally present human activities in the video sequences captured by a single wide-view camera in a sports *** recognition process is split into four parts:firstly,the video is divided into different set of frames,then the human body part in a sequence of frames is identified,next process is to identify the human activity using a convolutional neural network and finally the time information of the observed postures for each activity is determined with the help of a deep learning *** proposed approach has been tested on two different sports datasets including ActivityNet and *** sports activities like swimming,cricket bowling and high jump have been considered in this paper and classified with the temporal information i.e.,the start and end time for every activity present in the *** convolutional neural network and long short-term memory are used for feature extraction of temporal action recognition from video data of sports *** outcomes show that the proposed method for activity recognition in the sports domain outperforms the existing methods.

关键词： Action recognition human activity recognition untrimmed video deep learning convolutional neural networks

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Intrusion Detection System for Big Data Analytics in IoT Environment

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Computer Systems Science & Engineering 2022年第10期43卷 381-396页

作者： M.Anuradha G.Mani T.Shanthi N.R.Nagarajan p.suresh C.Bharatiraja Department of Computer Science and Engineering St.Joseph’s College of EngineeringChennai600119India Department of Computer Science and Engineering University College of Engineering ArniThatchur632326India Department of Electronics&Communication Engineering Kings College of EngineeringPudukkottai613303India Department of Electronics&Communication Engineering K.Ramakrishnan College of EngineeringTiruchirapalli621112India Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Electrical and Electronics Engineering SRM Institute of Science and TechnologyChennai603203India

In the digital area,Internet of Things(IoT)and connected objects generate a huge quantity of data traffic which feeds big data analytic models to discover hidden patterns and detect abnormal *** IoT networks are popular and widely employed in real world applications,security in IoT networks remains a challenging *** intrusion detection systems(IDS)cannot be employed in IoT networks owing to the limitations in resources and ***,this paper concentrates on the design of intelligent metaheuristic optimization based feature selection with deep learning(IMFSDL)based classification model,called IMFSDL-IDS for IoT *** proposed IMFSDL-IDS model involves data collection as the primary process utilizing the IoT devices and is preprocessed in two stages:data transformation and data *** manage big data,Hadoop ecosystem is ***,the IMFSDL-IDS model includes a hill climbing with moth flame optimization(HCMFO)for feature subset selection to reduce the complexity and increase the overall detection ***,the beetle antenna search(BAS)with variational autoencoder(VAE),called BAS-VAE technique is applied for the detection of intrusions in the feature reduced *** BAS algorithm is integrated into the VAE to properly tune the parameters involved in it and thereby raises the classification *** validate the intrusion detection performance of the IMFSDL-IDS system,a set of experimentations were carried out on the standard IDS dataset and the results are investigated under distinct *** resultant experimental values pointed out the betterment of the IMFSDL-IDS model over the compared models with the maximum accuracy 95.25%and 97.39%on the applied NSL-KDD and UNSW-NB15 dataset correspondingly.

关键词： Big data cybersecurity IoT networks intrusion detection deep learning metaheuristics intelligent systems

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IoT with Evolutionary Algorithm Based Deep Learning for Smart Irrigation System

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Computers, Materials & Continua 2022年第4期71卷 1713-1728页

作者： p.suresh R.H.Aswathy Sridevi Arumugam Amani Abdulrahman Albraikan Fahd N.Al-Wesabi Anwer Mustafa Hilal Mohammad Alamgeer Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Electrical Sciences Adithya Institute of TechnologyCoimbatore641107India Department of Computer Science College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia&Faculty of Computer and ITSana’a UniversitySana’aYemen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabia Department of Information Systems King Khalid UniversityMuhayel AseerSaudi Arabia

In India, water wastage in agricultural fields becomes a challengingissue and it is needed to minimize the loss of water in the irrigation *** the conventional irrigation system needs massive quantity of waterutilization, a smart irrigation system can be designed with the help of recenttechnologies such as machine learning (ML) and the Internet of Things (IoT).With this motivation, this paper designs a novel IoT enabled deep learningenabled smart irrigation system (IoTDL-SIS) technique. The goal of theIoTDL-SIS technique focuses on the design of smart irrigation techniquesfor effectual water utilization with less human interventions. The proposedIoTDL-SIS technique involves distinct sensors namely soil moisture, temperature, air temperature, and humidity for data acquisition purposes. The sensordata are transmitted to the Arduino module which then transmits the sensordata to the cloud server for further process. The cloud server performs the dataanalysis process using three distinct processes namely regression, clustering,and binary classification. Firstly, deep support vector machine (DSVM) basedregression is employed was utilized for predicting the soil and environmentalparameters in advances such as atmospheric pressure, precipitation, solarradiation, and wind speed. Secondly, these estimated outcomes are fed intothe clustering technique to minimize the predicted error. Thirdly, ArtificialImmune Optimization Algorithm (AIOA) with deep belief network (DBN)model receives the clustering data with the estimated weather data as inputand performs classification process. A detailed experimental results analysisdemonstrated the promising performance of the presented technique over theother recent state of art techniques with the higher accuracy of 0.971.

关键词： Smart irrigation precision agriculture internet of things deep learning machine learning

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Optimal Confidential Mechanisms in Smart City Healthcare

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Computers, Materials & Continua 2022年第3期70卷 4883-4896页

作者： R.Gopi p.Muthusamy p.suresh C.G.Gabriel Santhosh Kumar Irina V.pustokhina Denis A.pustokhin K.Shankar Department of Computing Science and Engineering Dhanalakshmi Srinivasan Engineering CollegePerambalur621212India School of Computing Science and Engineering Galgotias UniversityNCRDelhi203201India Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Electrical and Electronics Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia Department of Computer Applications Alagappa UniversityKaraikudi630001India

Smart City Healthcare(SHC2)system is applied in monitoring the patient at home while it is also expected to react to their needs in a timely *** system also concedes the freedom of a *** is a part of this system and it helps in providing care to the *** healthcare devices are trustworthy since it almost certainly recognizes the potential intensifications at very early stage and alerts the patients and medical experts to such an extent that they are provided with immediate *** methodologies exhibit few shortcomings in terms of computational complexity,cost and data ***,the current research article examines SHC2 security through LightWeight Cipher(LWC)with Optimal S-Box model in pRESENT *** procedure aims at changing the sub bytes in which a single function is connected with several bytes’information to upgrade the security level through Swam *** key contribution of this research article is the development of a secure healthcare model for smart city using SHC2 security via LWC and Optimal S-Box *** study used a nonlinear layer and single 4-bit S box for round configuration after verifying SHC2 information,constrained by Mutual Authentication(MA).The security challenges,in healthcare information systems,emphasize the need for a methodology that immovably concretes the *** methodology should act practically,be an effective healthcare framework that depends on solidarity and adapts to the developing *** service providers integrated the IoT applications and medical services to offer individuals,a seamless technology-supported healthcare *** proposed SHC^(2) was implemented to demonstrate its security levels in terms of time and access *** model was tested under different parameters such as encryption time,decryption time,access time and response time inminimum ***,the level of the model and throughput were analyzed by maximum value i.e.,50Mbp

关键词： Smart city healthcare security block cipher LWC

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Optimized Tuned Deep Learning Model for Chronic Kidney Disease Classification

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Computers, Materials & Continua 2022年第2期70卷 2097-2111页

作者： R.H.Aswathy p.suresh Mohamed Yacin Sikkandar S.Abdel-Khalek Hesham Alhumyani Rashid A.Saeed Romany F.Mansour Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAlMajmaah11952Saudi Arabia Department of Mathematics College of ScienceTaif UniversityTaif21944Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

In recent times,Internet of Things(IoT)and Cloud Computing(CC)paradigms are commonly employed in different healthcare *** gadgets generate huge volumes of patient data in healthcare domain,which can be examined on cloud over the available storage and computation resources in mobile *** Kidney Disease(CKD)is one of the deadliest diseases that has high mortality rate across the *** current research work presents a novel IoT and cloud-based CKD diagnosis model called Flower pollination Algorithm(FpA)-based Deep Neural Network(DNN)model abbreviated as *** steps involved in the presented FpA-DNN model are data collection,preprocessing,Feature Selection(FS),and ***,the IoT gadgets are utilized in the collection of a patient’s health *** proposed FpA-DNN model deploys Oppositional Crow Search(OCS)algorithm for FS,which selects the optimal subset of features from the preprocessed *** application of FpA helps in tuning the DNN parameters for better classification *** simulation analysis of the proposed FpA-DNN model was performed against the benchmark CKD *** results were examined under different *** simulation outcomes established the superior performance of FpA-DNN technique by achieving the highest sensitivity of 98.80%,specificity of 98.66%,accuracy of 98.75%,F-score of 99%,and kappa of 97.33%.

关键词： Deep learning chronic kidney disease IoT cloud computing feature selection classification

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Cellulase production by Aspergillus unguis in solid state fermentation

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Journal of Forestry Research 2019年第1期30卷 205-212页

作者： K.Shruthi p.suresh Yadav B.V.Siva prasad M.Subhosh Chandra Department of Microbiology Yogi Vemana University

Lignocellulosic substrates are a good carbon source and provide rich growth media for a variety of microorganisms which prodLuce industrially important enzymes. Cellulases are a group of hydrolytic enzymes such as filter paperase (Fpase), carboxymethyl cellulase(CMCase) andβ-glucosidase-responsible for release of sugars in the bioconversion of the lignocellulosic biomass into a variety of value-added products. This study examined cellulase production by a newly isolated Aspergillus unguis on individual lignocellulosic substrates in solid state fermentation (SSF). The maximum peak production of enzymes varied from one substrate to another, however,based on the next best solid support and local availability of groundnut fodder supported maximum enzyme yields compared with other solid supports used in this *** fodder supported significant production of Fpase (5.9 FpU/g of substrate), CMCase (1.1 U/g of substrate) andβ-glucosidase activity (6.5 U/g of substrate) in SSF. Considerable secretion of protein (27.0 mg/g of substrate) on groundnut fodder was recorded. Constant increment of protein content in groundnut fodder due to cultivation of A. unguis is an interesting observation and it has implications for the improvement of nutritive value of groundnut fodder for cattle.

关键词： Lignocellulosic substrates Aspergillus unguis Cellulase Solid state fermentation

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微波合成Zr-AC,Ni-AC和Zn-AC光催化降解印染废水(英文)

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Transactions of Nonferrous Metals Society of China 2015年第12期25卷 4216-4225页

作者： p.suresh J.JUDITH VIJAYA L.JOHN KENNEDY Catalysis and Nanomaterials Research Laboratory Department of ChemistryLoyola College Materials Division School of Advanced Sciences Vellore Institute of Technology University

采用微波照射法成功制备新型氧化锆、氧化镍和氧化锌负载活性炭纳米颗粒。采用XRD、HR-SEM和BET对合成纳米颗粒进行表征。利用UV-Vis漫反射光谱法研究Zr-AC、Ni-AC和Zn-AC复合材料的光学性能。验证了紫外光照射下印染废水的光催化效率... 详细信息

采用微波照射法成功制备新型氧化锆、氧化镍和氧化锌负载活性炭纳米颗粒。采用XRD、HR-SEM和BET对合成纳米颗粒进行表征。利用UV-Vis漫反射光谱法研究Zr-AC、Ni-AC和Zn-AC复合材料的光学性能。验证了紫外光照射下印染废水的光催化效率。每隔一定时间观察印染废水的化学需氧量,以计算化学需氧量的去除率。结果表明,Zn-AC复合材料具有明显的光催化性,这归因于紫外光区域吸光度的增大、染料分子的有效吸附能力、辅助电荷转移和电子-空穴对抑制重组。利用Zn-AC复合材料降解印染废水可以得到最大的降解率(82%化学需氧量去除率)。在Zn-AC复合材料表面设计一个可能实现的协同机制。Zn-AC复合材料重复使用5次后,其催化活性无明显降低。

关键词：纳米结构半导体印染废水光学性能催化降解

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