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Deep Learning-Based Mask Identification System Using ResNet Transfer Learning Architecture

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Computer Systems Science & Engineering 2024年第2期48卷 341-362页

作者： Arpit Jain nageswara rao Moparthi A.Swathi Yogesh Kumar Sharma Nitin Mittal Ahmed Alhussen Zamil S.Alzamil MohdAnul Haq Department of Computer Science and Engineering KoneruLakshmaiah Education FoundationVaddeswaram522302India Department of Computer Science and Engineering Sreyas Institute of Engineering and TechnologyHyderabad500068India University Centre for Research and Development Chandigarh UniversityMohali140413India Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia

Recently,the coronavirus disease 2019 has shown excellent attention in the global community regarding health and the economy.World Health Organization(WHO)and many others advised controlling Corona Virus Disease in 2019.The limited treatment resources,medical resources,and unawareness of immunity is an essential horizon to unfold.Among all resources,wearing a mask is the primary non-pharmaceutical intervention to stop the spreading of the virus caused by Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2)droplets.All countries made masks mandatory to prevent infection.For such enforcement,automatic and effective face detection systems are crucial.This study presents a face mask identification approach for static photos and real-time movies that distinguishes between images with and without masks.To contribute to society,we worked on mask detection of an individual to adhere to the rule and provide awareness to the public or organization.The paper aims to get detection accuracy using transfer learning from Residual Neural Network 50(ResNet-50)architecture and works on detection localization.The experiment is tested with other popular pre-trained models such as Deep Convolutional Neural Networks(AlexNet),Residual Neural Networks(ResNet),and Visual Geometry Group Networks(VGG-Net)advanced architecture.The proposed system generates an accuracy of 98.4%when modeled using Residual Neural Network 50(ResNet-50).Also,the precision and recall values are proved as better when compared to the existing models.This outstanding work also can be used in video surveillance applications.

关键词： Transfer learning depth analysis convolutional neural networks(CNN) COVID-19

Predicting the success rate of a start-up using LSTM with a swish activation function

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Journal of Control and Decision 2022年第3期9卷 355-363页

作者： Ramakrishna Allu Venkata nageswara rao Padmanabhuni Department of Computer Science and Engineering GITAM(Deemed to be university)VisakhapatnamAndhra PradeshIndia

The researchers emphasised more small-scale start-ups which required improvement in the economy decreased the failure rate by avoiding the valuable resources.The resources were required to be used without wasting them lead to the development of the economy and reduces the rate of unemployment.Therefore,to minimise resource wastage and to avoid the risk of failure the researchers considered certain factors that affected the failure and success of smallscale industries.The present research uses the Crunch Base dataset that predicts the success or failure of a start-up by using the Long Short Term Memory(LSTM).The LSTM unit Recurrent Neural Network(RNN)uses the Swish activation function in Feed Forward Neural(FFN)Network for the classification.The proposed LSTM obtained better accuracy of 71.64%when compared with existing methods such as RNN that attained 65.67%and Artificial Neural Network(ANN)of 69.7%.

关键词： Crunch base dataset gross domestic product long short term memory recurrent neural network start-up companies

Field and numerical modelling on the stability of underground strata in longwall workings

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能源地球科学(英文)

能源地球科学(英文) 2023年第1期4卷 1-12页

作者： G.Budi K nageswara rao Punit Mohanty Department of Mining Engineering Indian Institute of Technology(ISM)Dhanbad826004India CSIR-Central Institute of Mining and Fuel Research Dhanbad826001India

The longwall method is one of the feasible and efficient underground mining methods for greater depth of workings.Recently,Singareni Collieries Company Limited(SCCL),the largest coal-producing government company in the Southern part of India,has deployed a high capacity(2×1152T capacity)power support system in its Adriyala Longwall Project(ALP)at a depth of 375 m.There was a concern about the stability of the longwall workings and the protective pillars.Thus,research work was carried out with the geotechnical instruments and numerical modelling tools to analyze the stability while retreating the longwall panels.The outcomes revealed that the convergence in the gate roads increased with the longwall face advancement and the area of exposure.The pressure of the powered support legs on the dip side was less than the rise side legs,which implies a stable roof condition over the longwall face.An abutment zone was identified ahead of the line of extraction up to 10e25 m and presumed from the Factor of Safety(FoS)criterion that within this zone,the longwall pillar possibly will be of better stability at every stage of extraction.The results obtained from this study would be helpful for the mining engineers to understand the behaviour of underground strata in longwall workings.

关键词： Adriyala longwall Project(ALP) Strata behaviour Field and numerical modelling

SRN-LEACH: an energy-efficient leach protocol with random number stabilisation for WSNs

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Journal of Control and Decision 2023年第1期10卷 82-89页

作者： ravi Kumar Sanapala Srineevasa rao Duggirala Department of Electronics and Communication Engineering Jawaharlal Nehru Technological UniversityHyderabadTelanganaIndia

The power supplies of wireless sensor networks (WSNs) are not replaceable orrechargeable forsensors. For reducing the communication protocols’ impact onthe sensor network’s energy dissipation,various protocols or mechanisms havebeen proposed. Therefore, this article proposesa stable random number-basedLEACH protocol to stabilize the random number generation toimprove the CHselection efficiency. The wireless sensor network’s average node energy (ANE)ismultiplied by the random number that relies on the nodes’ energy. A nodegrade algorithm(NGA) based relay selection is introduced for inter andintracluster communication for energy efficiencyenhancement. The proposed NGA calculates the grade of a node using residual energy,distance from CHs,and its traffic parameters. Based on the simulation results, the proposedSRN-LEACH protocol shows superior performance as compared to existing protocolsincludingreduced energy dissipation, improved communication quality, andNLT. The proposed methodattains a minimum average delay of 0.136ms, 0.95% PDR, 148kbps TP rate, and 98% NLT.

关键词： Wireless sensor networks LEACH protocol cluster head average node energy node grade algorithm relay selection

An Efficient Long Short-Term Memory Model for Digital Cross-Language Summarization

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Computers, Materials & Continua 2023年第3期74卷 6389-6409页

作者： Y.C.A.Padmanabha Reddy Shyam Sunder Reddy Kasireddy nageswara rao Sirisala Ramu Kuchipudi Purnachand Kollapudi Department of CSE B V Raju Institute of TechnologyNarsapurMedakT.S502313India Department of IT Vasavi College of EngineeringHyderabadT.S500089India Department of CSE K.S.R.M College of EngineeringKadapaA.P516003India Department of IT C.B.I.TGandipetHyderabadTelangana500075India

The rise of social networking enables the development of multilingual Internet-accessible digital documents in several languages.The digital document needs to be evaluated physically through the Cross-Language Text Summarization(CLTS)involved in the disparate and generation of the source documents.Cross-language document processing is involved in the generation of documents from disparate language sources toward targeted documents.The digital documents need to be processed with the contextual semantic data with the decoding scheme.This paper presented a multilingual crosslanguage processing of the documents with the abstractive and summarising of the documents.The proposed model is represented as the Hidden Markov Model LSTM Reinforcement Learning(HMMlstmRL).First,the developed model uses the Hidden Markov model for the computation of keywords in the cross-language words for the clustering.In the second stage,bi-directional long-short-term memory networks are used for key word extraction in the cross-language process.Finally,the proposed HMMlstmRL uses the voting concept in reinforcement learning for the identification and extraction of the keywords.The performance of the proposed HMMlstmRL is 2%better than that of the conventional bi-direction LSTM model.

关键词： Text summarization reinforcement learning hidden markov model cross-language multilingual

Blockchain Assisted Intrusion Detection System Using Differential Flower Pollination Model

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Computers, Materials & Continua 2022年第12期73卷 4695-4711页

作者： Mohammed Altaf Ahmed Sara A Althubiti Dronamraju nageswara rao ELaxmi Lydia Woong Cho Gyanendra Prasad Joshi Sung Won Kim Department of Computer Engineering College of Computer Engineering&SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Science and Engineering Chitkara University Institute of Engineering and TechnologyChitkara UniversityPunjab140401India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

Cyberattacks are developing gradually sophisticated,requiring effective intrusion detection systems(IDSs)for monitoring computer resources and creating reports on anomalous or suspicious actions.With the popularity of Internet of Things(IoT)technology,the security of IoT networks is developing a vital problem.Because of the huge number and varied kinds of IoT devices,it can be challenging task for protecting the IoT framework utilizing a typical IDS.The typical IDSs have their restrictions once executed to IoT networks because of resource constraints and complexity.Therefore,this paper presents a new Blockchain Assisted Intrusion Detection System using Differential Flower Pollination with Deep Learning(BAIDS-DFPDL)model in IoT Environment.The presented BAIDS-DFPDLmodelmainly focuses on the identification and classification of intrusions in the IoT environment.To accomplish this,the presented BAIDS-DFPDL model follows blockchain(BC)technology for effective and secure data transmission among the agents.Besides,the presented BAIDSDFPDLmodel designs Differential Flower Pollination based feature selection(DFPFS)technique to elect features.Finally,sailfish optimization(SFO)with Restricted Boltzmann Machine(RBM)model is applied for effectual recognition of intrusions.The simulation results on benchmark dataset exhibit the enhanced performance of the BAIDS-DFPDL model over other models on the recognition of intrusions.

关键词： Internet of things feature selection intrusion detection blockchain security deep learning

A rapid and sensitive liquid chromatography-tandem mass spectrometric assay for duloxetine in human plasma:Its pharmacokinetic application

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Journal of Pharmaceutical Analysis 2013年第1期3卷 36-44页

作者： Ramakrishna Gajula Rambabu Maddela Vasu Babu ravi Jaswanth Kumar Inamadugu nageswara rao Pilli Wellquest Clinical Research Laboratories

This paper describes a simple, rapid and sensitive liquid chromatography tandem mass spectrometry assay for the determination of duloxetine in human plasma. A duloxetine stable labeled isotope （duloxetine ds） was used as an internal standard. Analyte and the internal standard were extracted from 100 btL of human plasma via solid phase extraction technique using Oasis HLB cartridges. The chromatographic separation was achieved on a Cl8 column by using a mixture of acetonitrile 5 mM ammonium acetate buffer （83：17, v/v） as the mobile phase at a flow rate of 0.9 mL/min. The calibration curve obtained was linear （r2≥0.99） over the concentration range of 0.05 101 ng/mL. Multiple-reaction monitoring mode （MRM） was used for quantification of ion transitions at rn/z 298.3/154.1 and 303.3/159.1 for the drug and the internal standard, respectively. Method validation was performed as per FDA guidelines and the results met the acceptance criteria. A run time of 2.5 min for each sample made it possible to analyze more than 300 plasma samples per day. The proposed method was found to be applicable to clinical studies.

关键词： Duloxetine in humanplasma Solid-phase extraction（SPE） Liquidchromatography-tandem mass spectro-metry Method validation Pharmacokinetic studies

Multilayer Neural Network Based Speech Emotion Recognition for Smart Assistance

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Computers, Materials & Continua 2023年第1期74卷 1523-1540页

作者： Sandeep Kumar MohdAnul Haq Arpit Jain C.Andy Jason nageswara rao Moparthi Nitin Mittal Zamil S.Alzamil Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramAP522502India Department of Computer Science College of Computer and Information SciencesMajmaah University11952Al-MajmaahSaudi Arabia Department of Computer Science and Engineering Teerthanker Mahaveer UniversityMoradabadUttar Pradesh244001India Department of Electronics and Communication Engineering Sreyas Institute of Engineering and TechnologyHyderabad500068India University Centre for Research and Development Chandigarh UniversityMohali140413PunjabIndia

Day by day,biometric-based systems play a vital role in our daily lives.This paper proposed an intelligent assistant intended to identify emotions via voice message.A biometric system has been developed to detect human emotions based on voice recognition and control a few electronic peripherals for alert actions.This proposed smart assistant aims to provide a support to the people through buzzer and light emitting diodes(LED)alert signals and it also keep track of the places like households,hospitals and remote areas,etc.The proposed approach is able to detect seven emotions:worry,surprise,neutral,sadness,happiness,hate and love.The key elements for the implementation of speech emotion recognition are voice processing,and once the emotion is recognized,the machine interface automatically detects the actions by buzzer and LED.The proposed system is trained and tested on various benchmark datasets,i.e.,Ryerson Audio-Visual Database of Emotional Speech and Song(RAVDESS)database,Acoustic-Phonetic Continuous Speech Corpus(TIMIT)database,Emotional Speech database(Emo-DB)database and evaluated based on various parameters,i.e.,accuracy,error rate,and time.While comparing with existing technologies,the proposed algorithm gave a better error rate and less time.Error rate and time is decreased by 19.79%,5.13 s.for the RAVDEES dataset,15.77%,0.01 s for the Emo-DB dataset and 14.88%,3.62 for the TIMIT database.The proposed model shows better accuracy of 81.02%for the RAVDEES dataset,84.23%for the TIMIT dataset and 85.12%for the Emo-DB dataset compared to Gaussian Mixture Modeling(GMM)and Support Vector Machine(SVM)Model.

关键词： Speech emotion recognition classifier implementation feature extraction and selection smart assistance

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 unit.Renewable energy sources are playing a significant role in the modern energy system with rapid development.In renewable sources like fuel combustion and solar energy,the generated voltages change due to their environmental changes.To develop energy resources,electric power generation involved huge awareness.The power and output voltages are plays important role in our work but it not considered in the existing system.For 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 system.The input information is collected from two input sources.After that,input layer transfer information to hidden layer 1 fuzzy PI is employed for controlling voltage in GPIC-MDCNNC Model.Hidden layer 1 is transferred to hidden layer 2.Gaussian activation is employed for determining the output voltage with help of the controller.At last,the output layer offers the last value in GPIC-MDCNNC Model.The designed method was confirmed using one and multiple sources by stable and unpredictable input voltages.GPIC-MDCNNC Model increases the performance of grid-connected renewable energy systems by enhanced voltage value compared with state-of-the-art works.The 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