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| 1 | 微波合成Zr-AC,Ni-AC和Zn-AC光催化降解印染废水(英文)显示文摘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。 | P.SURESH J.JUDITH VIJAYA L.JOHN KENNEDY | 2015 | Transactions of Nonferrous Metals Society of China2015，25，12： | 4 |
| 2 | 灰度模糊算法优化Al-SiC-Gr混合金属基复合材料的加工参数(英文)显示文摘石墨颗粒增强金属基复合材料能够提供更好的切削加工性能和摩擦性能。用灰度模糊算法优化Al-SiC-Gr混合金属基复合材料的加工参数,以获得到具有优秀综合性能的材料。当混合金属基复合材料中SiC-Gr的质量分数分别为5%、7.5%和10%时,对应的拉伸强度分别为170、210和204 MPa。另外,与另外2种材料相比,Al-10%(SiC-Gr)复合材料具有更好的切削加工性能。与其他的灰度技术相比,灰度模糊逻辑算法在输出方面提高了推理的合理性,降低了不确定性。实验结果表明,在设置的相同加工参数下,与其他的灰度技术相比,灰度模糊逻辑算法的推理合理性从0.619提高到0.891,且同时保证材料具有更好的综合性能。 | P.SURESH K.MARIMUTHU S.RANGANATHAN T.RAJMOHAN | 2014 | Transactions of Nonferrous Metals Society of China2014，24，9： | 1 |
| 3 | 查看详情显示文摘 | K.Kanagaraj P.Suresh K.Pitchumani | | 0，，： | 1 |
| 4 | Cellulase production by Aspergillus unguis in solid state fermentation显示文摘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 study.Groundnut 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. | K.Shruthi P.Suresh Yadav B.V.Siva Prasad M.Subhosh Chandra | 2019 | Journal of Forestry Research2019，30，1： | 1 |
| 5 | Optimal Confidential Mechanisms in Smart City Healthcare显示文摘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 manner.The system also concedes the freedom of a patient.IoT is a part of this system and it helps in providing care to the patients.IoTbased 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 care.Existing methodologies exhibit few shortcomings in terms of computational complexity,cost and data security.Hence,the current research article examines SHC2 security through LightWeight Cipher(LWC)with Optimal S-Box model in PRESENT cipher.This 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 optimization.The 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 models.The 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 establishments.The methodology should act practically,be an effective healthcare framework that depends on solidarity and adapts to the developing threats.Healthcare service providers integrated the IoT applications and medical services to offer individuals,a seamless technology-supported healthcare service.The proposed SHC^(2) was implemented to demonstrate its security levels in terms of time and access policies.The model was tested under different parameters such as encryption time,decryption time,access time and response time inminimum range.Then,the level of the model and throughput were analyzed by maximum value i.e.,50Mbps/sec and 95.56%for PRESENT-Authorization cipher to achieve smart city security.The proposed model achieved better results than the existing methodologies. | R.Gopi P.Muthusamy P.Suresh C.G.Gabriel Santhosh Kumar Irina V.Pustokhina Denis A.Pustokhin K.Shankar | 2022 | Computers, Materials & Continua2022，，3： | 0 |
| 6 | Deep Root Memory Optimized Indexing Methodology for Image Search Engines显示文摘Digitization has created an abundance of new information sources by altering how pictures are captured.Accessing large image databases from a web portal requires an opted indexing structure instead of reducing the contents of different kinds of databases for quick processing.This approach paves a path toward the increase of efficient image retrieval techniques and numerous research in image indexing involving large image datasets.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 effectively.Thus,retrieval becomes straightforward and rapid.This 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 cost.We focus on the schema design on a non-clustered index solution,especially cover queries.This schema provides a filter predication to make an index with a particular content of rows and an index table called filtered indexing.Finally,we include non-key columns in addition to the key columns.Experiments on two image data sets‘with and without’filtered indexing show low query cost.We compare efficiency as regards accuracy in mean average precision to measure the accuracy of retrieval with the developed coherent semantic indexing.The results show that retrieval by using deep root indexing is simple and fast. | R.Karthikeyan A.Celine Kavida P.Suresh | 2022 | Computer Systems Science & Engineering2022，40，2： | 0 |
| 7 | IoT with Evolutionary Algorithm Based Deep Learning for Smart Irrigation System显示文摘In India, water wastage in agricultural fields becomes a challengingissue and it is needed to minimize the loss of water in the irrigation process.Since 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. | P.Suresh R.H.Aswathy Sridevi Arumugam Amani Abdulrahman Albraikan Fahd N.Al-Wesabi Anwer Mustafa Hilal Mohammad Alamgeer | 2022 | Computers, Materials & Continua2022，，4： | 0 |
| 8 | Optimized Tuned Deep Learning Model for Chronic Kidney Disease Classification显示文摘In recent times,Internet of Things(IoT)and Cloud Computing(CC)paradigms are commonly employed in different healthcare applications.IoT 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 gadgets.Chronic Kidney Disease(CKD)is one of the deadliest diseases that has high mortality rate across the globe.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 FPA-DNN.The steps involved in the presented FPA-DNN model are data collection,preprocessing,Feature Selection(FS),and classification.Primarily,the IoT gadgets are utilized in the collection of a patient’s health information.The proposed FPA-DNN model deploys Oppositional Crow Search(OCS)algorithm for FS,which selects the optimal subset of features from the preprocessed data.The application of FPA helps in tuning the DNN parameters for better classification performance.The simulation analysis of the proposed FPA-DNN model was performed against the benchmark CKD dataset.The results were examined under different aspects.The 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%. | R.H.Aswathy P.Suresh Mohamed Yacin Sikkandar S.Abdel-Khalek Hesham Alhumyani Rashid A.Saeed Romany F.Mansour | 2022 | Computers, Materials & Continua2022，，2： | 0 |
| 9 | HARTIV:Human Activity Recognition Using Temporal Information in Videos显示文摘Nowadays,the most challenging and important problem of computer vision is to detect human activities and recognize the same with temporal information from video data.The 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 videos.Smarter 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 technology.In a long video,human activity may be present anywhere in the video.There can be a single ormultiple human activities present in such videos.This 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 environment.The 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 algorithm.The proposed approach has been tested on two different sports datasets including ActivityNet and THUMOS.Three 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 video.The convolutional neural network and long short-term memory are used for feature extraction of temporal action recognition from video data of sports activity.The outcomes show that the proposed method for activity recognition in the sports domain outperforms the existing methods. | Disha Deotale Madhushi Verma P.Suresh Sunil Kumar Jangir Manjit Kaur Sahar Ahmed Idris Hammam Alshazly | 2022 | Computers, Materials & Continua2022，，2： | 0 |
| 10 | Intrusion Detection System for Big Data Analytics in IoT Environment显示文摘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 traffic.Though IoT networks are popular and widely employed in real world applications,security in IoT networks remains a challenging problem.Conventional intrusion detection systems(IDS)cannot be employed in IoT networks owing to the limitations in resources and complexity.Therefore,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 networks.The 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 normalization.To manage big data,Hadoop ecosystem is employed.Besides,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 efficiency.Moreover,the beetle antenna search(BAS)with variational autoencoder(VAE),called BAS-VAE technique is applied for the detection of intrusions in the feature reduced data.The BAS algorithm is integrated into the VAE to properly tune the parameters involved in it and thereby raises the classification performance.To 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 aspects.The 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. | M.Anuradha G.Mani T.Shanthi N.R.Nagarajan P.Suresh C.Bharatiraja | 2022 | Computer Systems Science & Engineering2022，43，10： | 0 |
| 11 | Immune response differences in degradable and non-degradable alloy implants显示文摘Alloy based implants have made a great impact in the clinic and in preclinical research.Immune responses are one of the major causes of failure of these implants in the clinic.Although the immune responses toward non-degradable alloy implants are well documented,there is a poor understanding of the immune responses against degradable alloy implants.Recently,there have been several reports suggesting that degradable implants may develop substantial immune responses.This phenomenon needs to be further studied in detail to make the case for the degradable implants to be utilized in clinics.Herein,we review these new recent reports suggesting the role of innate and potentially adaptive immune cells in inducing immune responses against degradable implants.First,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 implants.Furthermore,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. | Taravat Khodaei Elizabeth Schmitzer Abhirami P.Suresh Abhinav P.Acharya | 2023 | Bioactive Materials2023，，6： | 0 |
| 12 | Optimal Deep Dense Convolutional Neural Network Based Classification Model for COVID-19 Disease显示文摘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 X-rays.However,these methods suffer from biased results and inaccurate detection of the disease.So,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 environment.The proposed methodology works on the basis of two stages such as pre-processing and prediction.Initially,CT scan images generated from prospective COVID-19 are collected from open-source system using IoT devices.The collected images are then preprocessed using Gaussian filter.Gaussian filter can be utilized in the removal of unwanted noise from the collected CT scan images.Afterwards,the preprocessed images are sent to prediction phase.In this phase,Deep Dense Convolutional Neural Network(DDCNN)is applied upon the pre-processed images.The 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 classifier.Finally,the proposed technique is used in the prediction of COVID-19 and classify the results as either COVID-19 or non-COVID-19.The projected method was implemented in MATLAB and the performances were evaluated through statistical measurements.The proposed method was contrasted with conventional techniques such as Convolutional Neural Network-Firefly Algorithm(CNN-FA),Emperor Penguin Optimization(CNN-EPO)respectively.The results established the supremacy of the proposed model. | A.Sheryl Oliver P.Suresh A.Mohanarathinam Seifedine Kadry Orawit Thinnukool | 2022 | Computers, Materials & Continua2022，，1： | 0 |
| 13 | Ba_(0.85)Ca_(0.15)Zr_(0.1)Ti_(0.88)Sn_(0.02)O_3-水泥复合材料的电热行为(英文)显示文摘寻找用于土木工程结构的多功能材料的努力引起全球研究人员的兴趣。在不同Ba_(0.85)Ca_(0.15)Zr_(0.1)Ti_(0.88)Sn_(0.02)O_3(BCZT-Sn)与水泥配比条件下,制备适合于电热应用的水泥基BCZT-Sn复合材料。水泥基复合材料的磁滞回线表现出一定的饱和特征,纯BCZT-Sn样品的磁滞回线则由于其铁电性质而饱和。此外,首次探索这种复合材料在固态制冷技术中的应用,即其电热效应(ECE)。在0~29 kV/cm的电场作用下,纯BCZT-Sn、含10%水泥及15%水泥的复合材料的热力学温度变化分别为0.71、0.64和0.50K,等温熵变分别为0.86、0.80和0.65 J/(kg·K)。水泥基复合材料的热力学温度变化与BCZT-Sn铁电陶瓷的相当。此外,具有不同陶瓷含量的复合材料的室温介电常数(er)数随着复合材料中BCZT-Sn含量的增加而增大。由于其优异的性能,这种水泥基BCZT-Sn复合材料有望用于固态制冷技术中。 | P.SURESH P.MATHIYALAGAN K.S.SRIKANTH | 2019 | Transactions of Nonferrous Metals Society of China2019，29，4： | 0 |
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