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Chinese Journal of Chemical Engineering 2020年第3期28卷 854-863页

作者： samikannu prabu Hong-wen Wang Department of Chemistry Chung-Yuan Christian UniversityZhongli 320TaiwanChina

The electrodeposition of aluminum(Al)was studied using two electrolyte solutions,such as anhydrous AlCl3-urea and hydrated AlCl3·6 H2 O-urea.A systematic examination using cell voltages 1.0–2.0 V was carried out at temperatures((50–100)±2)°C.A needle-shaped cathode was employed for the deposition of aluminum.A dendrite and particulate microstructure of Al were observed on the needle-shaped *** improved condition for the manufacturing of small sizes and high purity of aluminum deposits was *** Al with a current efficiency(yield)of 84%–99%was obtained from those of non-aqueous electrolytes and only of 8.6%–9.3%from those of hydrated *** electrical conductivities of electrolytes remained considerable at((50–100)±2)°*** improved aluminum powders were used for the reaction with *** aluminum reacts with water at room temperature,producing pure H2 with 100%*** electrodeposited aluminum metal can be used as an excellent energy carrier.

关键词： Electrodepositions aluminum Molten salt

Efficient Communication in Wireless Sensor Networks Using Optimized Energy Efficient Engroove Leach Clustering Protocol

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Tsinghua Science and Technology 2024年第4期29卷 985-1001页

作者： N.Meenakshi Sultan Ahmad A.V.prabu J.Nageswara Rao Nashwan Adnan Othman Hikmat A.M.Abdeljaber R.Sekar Jabeen Nazeer SRM Institute of Science and Technology KattankulathurChennai 603203India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj 11942Saudi Arabia University Center for Research and Development(UCRD) Department of Computer Science and EngineeringChandigarh UniversityGharuanMohali 140413India Department of ECE Koneru Lakshmaiah Education FoundationVaddeswaramGuntur 522502India Department of Computer Science and Engineering Lakireddy Balireddy College of Engineering(A)NTR District 521230India Department of Computer Engineering College of EngineeringKnowledge UniversityErbil 44001Iraq Department of Computer Science Faculty of Information TechnologyApplied Science Private UniversityAmman 11937Jordan Department of Electronics and Communication Engineering(ECE) School of EngineeringPresidency UniversityBangalore 834001India

The Wireless Sensor Network(WSN)is a network that is constructed in regions that are inaccessible to human *** widespread deployment of wireless micro sensors will make it possible to conduct accurate environmental monitoring for a use in both civil and military *** make use of these data to monitor and keep track of the physical data of the surrounding environment in order to ensure the sustainability of the *** data have to be picked up by the sensor,and then sent to the sink node where they may be *** nodes of the WSNs are powered by batteries,therefore they eventually run out of *** energy restriction has an effect on the network life span and environmental *** objective of this study is to further improve the Engroove Leach(EL)protocol’s energy efficiency so that the network can operate for a very long time while consuming the least amount of *** lifespan of WSNs is being extended often using clustering and routing *** Meta Inspired Hawks Fragment Optimization(MIHFO)system,which is based on passive clustering,is used in this study to do *** cluster head is chosen based on the nodes’residual energy,distance to neighbors,distance to base station,node degree,and node *** on distance,residual energy,and node degree,an algorithm known as Heuristic Wing Antfly Optimization(HWAFO)selects the optimum path between the cluster head and Base Station(BS).They examine the number of nodes that are active,their energy consumption,and the number of data packets that the BS *** overall experimentation is carried out under the MATLAB *** the analysis,it has been discovered that the suggested approach yields noticeably superior outcomes in terms of throughput,packet delivery and drop ratio,and average energy consumption.

关键词： wireless sensor networks energy efficient engroove leach protocol meta inspired Hawks fragment optimization heuristic wing antfly optimization

Techno-economic analysis on oxy-fuel based steam turbine power system using municipal solid waste and coals with ultrasonicator sulfur removal

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Waste Disposal and Sustainable Energy 2022年第2期4卷 131-147页

作者： Pradeep Sahu V.prabu Department of Chemical Engineering Indian Institute of Technology GuwahatiAssam781039India

Municipal solid waste(MSW)is a carbon–neutral energy source and possesses a moderate heating value;hence,it can be used as an alternative fuel for *** use high ash and high sulfur Indian coals efficiently,a techno economic analysis is performed for electricity generation using supercritical and subcritical based steam turbines operating in the oxy-fuel co-combustion mode of MSW with Indian *** impact of the capture of direct and indirect greenhouse gasses such as CO_(2),NO_(x)and SO_(x)on the net thermal efficiency of the power plants is *** supercritical based steam turbine achieved a higher net thermal efficiency by 8.8%using MSW based feedstock compared to sub-critical *** co-combustion mode reduced the levelized cost of electricity(LCOE)by 48–73$/***-economic analysis for sulfur removal in coal using ultrasonication technology has not yet been reported in the *** incorporation of an ultrasonicator(a pre-combustion sulfur remover)and a duct sorbent injector(a post-combustion SO_(x)absorber)increased the LCOE by 1.39–2.75$/*** high sulfur coals,the SO_(x)emissions decreased from 224.79 mg/m^(3)to 9.2 mg/m^(3).

关键词： Municipal solid waste Coal Steam turbine power plant Economic analysis Co-combustion

Gaussian PI Controller Network Classifier for Grid-Connected Renewable Energy System

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Intelligent Automation & Soft Computing 2023年第1期35卷 983-995页

作者： Ravi samikannu K.Vinoth Narasimha Rao Dasari Senthil Kumar Subburaj Department of Electrical Computer and Telecommunications Engineering Botswana International University of Science and TechnologyBotswana Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennaiTamil NaduIndia Department of Electrical Power Engineering College of EngineeringDefence UniversityEthiopia Department Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and TechnologySembakkamTamil NaduIndia

Multi-port converters are considered as exceeding earlier period decade owing to function in a combination of different energy sources in a single processing *** energy sources are playing a significant role in the modern energy system with rapid *** renewable sources like fuel combustion and solar energy,the generated voltages change due to their environmental *** develop energy resources,electric power generation involved huge *** power and output voltages are plays important role in our work but it not considered in the existing *** considering the power and voltage,Gaussian PI Controller-Maxpooling Deep Convolutional Neural Network Classifier(GPIC-MDCNNC)Model is introduced for the grid-connected renewable energy *** input information is collected from two input *** that,input layer transfer information to hidden layer 1 fuzzy PI is employed for controlling voltage in GPIC-MDCNNC *** layer 1 is transferred to hidden layer *** activation is employed for determining the output voltage with help of the *** last,the output layer offers the last value in GPIC-MDCNNC *** designed method was confirmed using one and multiple sources by stable and unpredictable input ***-MDCNNC Model increases the performance of grid-connected renewable energy systems by enhanced voltage value compared with state-of-the-art *** control technique using GPIC-MDCNNC Model increases the dynamics of hybrid energy systems connected to the grid.

关键词： Multi-port converters renewable sources fuzzy PI controller gaussian activation function fuel cell

Deep Learning Based Modeling of Groundwater Storage Change

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Computers, Materials & Continua 2022年第3期70卷 4599-4617页

作者： Mohd Anul Haq Abdul Khadar Jilani P.prabu College of Computer and Information Sciences Majmaah University Almajmaah 11952Saudi Arabia CHRIST(Deemed to be University) BangaloreIndia

The understanding of water resource changes and a proper projection of their future availability are necessary elements of sustainable water *** GWS change and future water resource availability are crucial,especially under changing climatic *** methods for in situ groundwater well measurement are a significant challenge due to data *** present investigation utilized the Long Short Term Memory(LSTM)networks to monitor and forecast Terrestrial Water Storage Change(TWSC)and Ground Water Storage Change(GWSC)based on Gravity Recovery and Climate Experiment(GRACE)datasets from 2003-2025 for five basins of Saudi *** attempt has been made to assess the effects of rainfall,water used,and net budget modeling of *** of GRACE-derived TWSC and GWSC estimates indicates that all five basins show depletion of water from 2003-2020 with a rate ranging from−5.88±1.2 mm/year to−14.12±1.2 mm/year and−3.5±1.5 to−10.7±1.5,*** based on the developed LSTM model indicates that the investigated basins are likely to experience serious water depletion at rates ranging from−7.78±1.2 to−15.6±1.2 for TWSC and−4.97±1.5 to−12.21±1.5 for GWSC from *** interesting observation was a minor increase in rainfall during the study period for three basins.

关键词： LSTM forecasting time series tensorflow keras modeling

Realtime Object Detection Through M-ResNet in Video Surveillance System

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Intelligent Automation & Soft Computing 2023年第2期35卷 2257-2271页

作者： S.prabu J.M.Gnanasekar Department of Computer Science&Engineering Karpaga Vinayaga College of Engineering and TechnologyChengalpattuTamilnadu603308India Department of Computer Science&Engineering Sri Venkateshwara College of Engineering&TechnologyChennai602001TamilnaduIndia

Object detection plays a vital role in the video surveillance *** enhance security,surveillance cameras are now installed in public areas such as trafﬁc signals,roadways,retail malls,train stations,and ***,monitor-ing the video continually at a quicker pace is a challenging *** a consequence,security cameras are useless and need human *** primary difﬁculty with video surveillance is identifying abnormalities such as thefts,accidents,crimes,or other unlawful *** anomalous action does not occur at a high-er rate than usual *** detect the object in a video,ﬁrst we analyze the images pixel by *** digital image processing,segmentation is the process of segregating the individual image parts into *** performance of segmenta-tion is affected by irregular illumination and/or low *** factors highly affect the real-time object detection process in the video surveillance *** this paper,a modiﬁed ResNet model(M-Resnet)is proposed to enhance the image which is affected by insufﬁcient *** results provide the comparison of existing method output and modiﬁcation architecture of the ResNet model shows the considerable amount improvement in detection objects in the video *** proposed model shows better results in the metrics like preci-sion,recall,pixel accuracy,etc.,andﬁnds a reasonable improvement in the object detection.

关键词： Object detection ResNet video survilence image processing object quality

Enhanced Attention-Based Encoder-Decoder Framework for Text Recognition

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Intelligent Automation & Soft Computing 2023年第2期35卷 2071-2086页

作者： S.prabu K.Joseph Abraham Sundar School of Computing SASTRA Deemed to be UniversityThanjavur613401India

Recognizing irregular text in natural images is a challenging task in computer *** existing approaches still face difﬁculties in recognizing irre-gular text because of its diverse *** this paper,we propose a simple yet powerful irregular text recognition framework based on an encoder-decoder *** proposed framework is divided into four main ***,in the image transformation module,a Thin Plate Spline(TPS)transformation is employed to transform the irregular text image into a readable text ***-ondly,we propose a novel Spatial Attention Module(SAM)to compel the model to concentrate on text regions and obtain enriched feature ***,a deep bi-directional long short-term memory(Bi-LSTM)network is used to make a con-textual feature map out of a visual feature map generated from a Convolutional Neural Network(CNN).Finally,we propose a Dual Step Attention Mechanism(DSAM)integrated with the Connectionist Temporal Classiﬁcation(CTC)-Attention decoder to re-weights visual features and focus on the intra-sequence relationships to generate a more accurate character *** effectiveness of our proposed framework is veriﬁed through extensive experiments on various benchmarks datasets,such as SVT,ICDAR,CUTE80,and *** perfor-mance of the proposed text recognition framework is analyzed with the accuracy *** that our proposed method outperforms the existing approaches on both regular and irregular ***,the robustness of our approach is evaluated using the grocery datasets,such as GroZi-120,Web-Market,SKU-110K,and Freiburg Groceries datasets that contain complex text ***,our framework produces superior performance on grocery datasets.

关键词： Deep learning text recognition text normalization attention mechanism convolutional neural network(CNN)

Brain Image Classification Using Time Frequency Extraction with Histogram Intensity Similarity

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Computer Systems Science & Engineering 2022年第5期41卷 645-660页

作者： Thangavel Renukadevi Kuppusamy Saraswathi P.prabu K.Venkatachalam Department of Computer Technology-UG Kongu Engineering CollegeErodeIndia Department of Computer Science CHRIST(Deemed to be University)BengaluruIndia Department of Computer Science and Engineering CHRIST(Deemed to be University)BengaluruIndia

Brain medical image classification is an essential procedure in Computer-Aided Diagnosis(CAD)*** methods depend specifically on the local or global *** fusion methods have also been developed,most of which are problem-distinct and have shown to be highly favorable in medical ***,intensity-specific images are not *** recent deep learning methods ensure an efficient means to design an end-to-end model that produces final classification accuracy with brain medical images,compromising *** solve these classification problems,in this paper,Histogram and Time-frequency Differential Deep(HTF-DD)method for medical image classification using Brain Magnetic Resonance Image(MRI)is *** construction of the proposed method involves the following ***,a deep Convolutional Neural Network(CNN)is trained as a pooled feature mapping in a supervised manner and the result that it obtains are standardized intensified pre-processed features for ***,a set of time-frequency features are extracted based on time signal and frequency signal of medical images to obtain time-frequency ***,an efficient model that is based on Differential Deep Learning is designed for obtaining different *** proposed model is evaluated using National Biomedical Imaging Archive(NBIA)images and validation of computational time,computational overhead and classification accuracy for varied Brain MRI has been done.

关键词： Histogram differential deep learning convolutional neural network

Kernel Granulometric Texture Analysis and Light RES-ASPP-UNET Classification for Covid-19 Detection

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Computers, Materials & Continua 2022年第4期71卷 651-666页

作者： A.Devipriya P.prabu K.Venkatachalam Ahmed Zohair Ibrahim Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Computer Science CHRIST(Deemed to be University)Bangalore560029India Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec Králové50003Hradec KrálovéCzech Republic Computer Sciences Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhKSA.P.o.Box:84428Postal Code:11671

This research article proposes an automatic frame work for detectingCOVID -19 at the early stage using chest X-ray image. It is an undeniable factthat coronovirus is a serious disease but the early detection of the virus presentin human bodies can save lives. In recent times, there are so many research solutions that have been presented for early detection, but there is still a lack in needof right and even rich technology for its early detection. The proposed deeplearning model analysis the pixels of every image and adjudges the presence ofvirus. The classifier is designed in such a way so that, it automatically detectsthe virus present in lungs using chest image. This approach uses an imagetexture analysis technique called granulometric mathematical model. Selectedfeatures are heuristically processed for optimization using novel multi scaling deep learning called light weight residual–atrous spatial pyramid pooling(LightRES-ASPP-Unet) Unet model. The proposed deep LightRES-ASPPUnet technique has a higher level of contracting solution by extracting majorlevel of image features. Moreover, the corona virus has been detected usinghigh resolution output. In the framework, atrous spatial pyramid pooling(ASPP) method is employed at its bottom level for incorporating the deepmulti scale features in to the discriminative mode. The architectural workingstarts from the selecting the features from the image using granulometricmathematical model and the selected features are optimized using LightRESASPP-Unet. ASPP in the analysis of images has performed better than theexisting Unet model. The proposed algorithm has achieved 99.6% of accuracyin detecting the virus at its early stage.

关键词： Deep residual learning convolutional neural network COVID-19 X-ray principal component analysis granulo metrics texture analysis

Grid Search for Predicting Coronary Heart Disease by Tuning Hyper-Parameters

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Computer Systems Science & Engineering 2022年第11期43卷 737-749页

作者： S.prabu B.Thiyaneswaran M.Sujatha C.Nalini Sujatha Rajkumar Department of ECE Mahendra Institute of TechnologyNamakkalTamilnadu637503India Department of ECE Sona College of TechnologySalemTamilnadu636005India Department of ECE Koneru Lakshmaiah Education FoundationVijayawadaAndhra Pradesh522502India Department of Information Technology Kongu Engineering CollegePerunduraiTamilnadu638060India Department of Embedded Technology Vellore Institute of TechnologyVelloreTamilnadu632014India

Diagnosing the cardiovascular disease is one of the biggest medical difficulties in recent *** cardiovascular(CHD)is a kind of heart and blood vascular *** this sort of cardiac illness leads to more precise decisions for cardiac *** Grid Search Optimization(GSO)machine training models is therefore a useful way to forecast the sickness as soon as *** state-of-the-art work is the tuning of the hyperparameter together with the selection of the feature by utilizing the model search to minimize the false-negative *** models with a cross-validation approach do the required *** Selection based on the use of statistical and correlation matrices for multivariate *** Random Search and Grid Search models,extensive comparison findings are produced utilizing retrieval,F1 score,and precision *** models are evaluated using the metrics and kappa statistics that illustrate the three models’*** study effort focuses on optimizing function selection,tweaking hyperparameters to improve model accuracy and the prediction of heart disease by examining Framingham datasets using random forestry *** the hyperparameter in the model of grid search thus decreases the erroneous rate achieves global optimization.

关键词： Grid search coronary heart disease(CHD) machine learning feature selection hyperparameter tuning