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Energy Efficient Cluster-Based Optimal Resource management in Iot Environment

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computers, materials & Continua 2022年第1期70卷 1247-1261页

作者： J.V.Anchitaalagammai t.Jayasankar P.Selvaraj mohamed Yacin Sikkandar m.Zakarya mohamed Elhoseny k.Shankar department of computer science and Engineering Velammal College of Engineering and TechnologyMadurai625009India department of Electronics and Communication Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India department of Information technology SRMInstitute of Science and TechnologyKattankulathur603203Tamil NaduIndia department of medical Equipment technology College of Applied Medical SciencesMajmaah UniversityAlMajmaah11952Saudi Arabia department of mathematics College of ScienceKing Khalid UniversityP.O.Box 9004Abha61413Saudi Arabia department of mathematics Faculty of ScienceAl-Azhar UniversityAssiut71524Egypt College of computer Information technology American University in the EmiratesDubai503000United Arab Emirates department of computer Applications Alagappa UniversityKaraikudiIndia

Internet of things(Iot)is a technological revolution that redefined communication and computation of modern *** generally refers to a network of gadgets linked via wireless network and communicates via *** management,especially energy management,is a critical issue when designing Iot *** studies reported that clustering and routing are energy efficient solutions for optimal management of resources in Iot *** this point of view,the current study devises a new Energy-Efficient Clustering-based Routing technique for Resource management i.e.,EECBRm in Iot *** proposed EECBRm model has three stages namely,fuzzy logic-based clustering,Lion Whale Optimization with tumbling(LWOt)-based routing and cluster maintenance *** proposed EECBRmmodel was validated through a series of experiments and the results were verified under several *** model was compared with existing methods in terms of energy efficiency,delay,number of data transmission,and network *** simulated,in comparison with other methods,EECBRm model yielded excellent results in a significant ***,the efficiency of the proposed model is estab.ished.

关键词： Iot environment clustering routing resource management energy efficiency

Controlling Spatiotemporal Chaos Using Nonlinear Feedback Functional method

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Chinese Physics Letters 1997年第11期14卷 823-826页

作者： FANG Jin-qing m.k.Ali FANG Qing China Institute of Atomic Energy Beijing 102413 department of Physics The University of LethbridgeLethbridgeAlberta TlK 3M4Canada Beijing Institute of television technology Beijing 100031

Nonlinear feedback functional method for controlling spatiotemporal chaos is *** a key point,some typical kinds of nonlinear feedback functions are *** efficiency and robustness of the method based on the flexibility ... 详细信息

关键词： Nonlinear nonlinear Chaos

Nonlocal-means Image Denoising technique Using Robust m-Estimator

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Journal of computer science & technology 2010年第3期25卷 623-631页

作者： Dinesh J. Peter V. k. Govindan ab.aham t. mathew department of computer science National Institute of Technology Calicut department of Electrical Engineering National Institute of Technology Calicut

Edge preserved smoothing techniques have gained importance for the purpose of image processing applications A good edge preserving filter is given by nonlocal-means filter rather than any other linear model based approaches. this paper explores a different approach of nonlocal-means filter by using robust m-estimator function rather than the exponential function for its weight calculation. Here the filter output at each pixel is the weighted average of pixels with surrounding neighborhoods using the chosen robust m-estimator function. the main direction of this paper is to identify the best robust m-estimator function for nonlocal-means denoising algorithm. In order to speed up the computation, a new patch classification method is followed to eliminate the uncorrelated patches from the weighted averaging process. this patch classification approach compares favorab.y to existing techniques in respect of quality versus computational time. Validations using standard test images and brain atlas images have been analyzed and the results were compared with the other known methods. It is seen that there is reason to believe that the proposed refined technique has some notab.e points.

关键词： image processing denoising technique nonlocal-means filter robust m-estimators

Security and Privacy Challenges in SDN-Enab.ed Iot Systems: Causes, Proposed Solutions,and Future Directions

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computers, materials & Continua 2024年第8期80卷 2511-2533页

作者： Ahmad Rahdari Ahmad Jalili mehdi Esnaashari mehdi Gheisari Alisa A.Vorobeva Zhaoxi Fang Panjun Sun Viktoriia m.korzhuk Ilya Popov Zongda Wu Hamid tahaei Institute of Artificial Intelligence Shaoxing UniversityShaoxing312000China department of computer Engineering Gonbad Kavous UniversityGonbad-e Kavus49717-99151Iran Faculty of computer Engineering K.N.Toosi University of TechnologyTehran196976-4499Iran department of computer science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India Secure Information technologies department National Research University ITMOSt.Petersburg197101Russia School of Electrical and computer Engineering Shiraz UniversityShiraz71946–84334Iran department of computer science and Engineering Islamic Azad UniversityDamavand***Iran department of R&D Shenzhen BKD Co.Ltd.Shenzhen518000China

Software-Defined Networking(SDN)represents a significant paradigm shift in network architecture,separating network logic from the underlying forwarding devices to enhance flexibility and centralize ***-rently,the Internet of things(Iot)connects numerous devices to the Internet,enab.ing autonomous interactions with minimal human ***,implementing and managing an SDN-Iot system is inherently complex,particularly for those with limited resources,as the dynamic and distributed nature of Iot infrastructures creates security and privacy challenges during SDN *** findings of this study underscore the primary security and privacy challenges across application,control,and data planes.A comprehensive review evaluates the root causes of these challenges and the defense techniques employed in prior works to estab.ish sufficient secrecy and privacy *** investigations have explored cutting-edge methods,such as leveraging blockchain for transaction recording to enhance security and privacy,along with applying machine learning and deep learning approaches to identify and mitigate the impacts of Denial of Service(DoS)and Distributed DoS(DDoS)***,the analysis indicates that encryption and hashing techniques are prevalent in the data plane,whereas access control and certificate authorization are prominently considered in the control plane,and authentication is commonly employed within the application ***,this paper outlines future directions,offering insights into potential strategies and technological advancements aimed at fostering a more secure and privacy-conscious SDN-based Iot ecosystem.

关键词： Security privacy-preserving software-defined network internet of things

Effective Return Rate Prediction of Blockchain Financial Products Using machine Learning

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computers, materials & Continua 2023年第1期74卷 2303-2316页

作者： k.kalyani Velmurugan Subbiah Parvathy Hikmat A.m.ab.eljab.r t.Satyanarayana murthy Srijana Acharya Gyanendra Prasad Joshi Sung Won kim PG and Research department of computer science Marudupandiyar College(Affiliated to Bharathidasan UniversityTiruchirappalli)Thanjavur613403India department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126TamilnaduIndia department of computer science Faculty of Information TechnologyApplied Science Private UniversityAmmanJordan department of Information technology Chaitanya Bharathi Institute of TechnologyHyderabadTelangana500075India department of Convergence science Kongju National UniversityGongju32588Korea department of computer science and Engineering Sejong UniversitySeoul05006Korea department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

In recent times,financial globalization has drastically increased in different ways to improve the quality of services with advanced *** successful applications of bitcoin Blockchain(BC)techniques enab.e the stockholders to worry ab.ut the return and risk of financial *** stockholders focused on the prediction of return rate and risk rate of financial ***,an automatic return rate bitcoin prediction model becomes essential for BC financial *** newly designed machine learning(mL)and deep learning(DL)approaches pave the way for return rate predictive *** study introduces a novel Jellyfish search optimization based extreme learning machine with autoencoder(JSO-ELmAE)for return rate prediction of BC financial *** presented JSO-ELmAE model designs a new ELmAE model for predicting the return rate of financial ***,the JSO algorithm is exploited to tune the parameters related to the ELmAE model which in turn boosts the classification *** application of JSO technique assists in optimal parameter adjustment of the ELmAE model to predict the bitcoin return *** experimental validation of the JSO-ELmAE model was executed and the outcomes are inspected in many *** experimental values demonstrated the enhanced performance of the JSO-ELmAE model over recent state of art approaches with minimal RmSE of 0.1562.

关键词： Financial products blockchain return rate prediction model machine learning parameter optimization

Secured Cloud Data migration technique by Competent Probab.listic Public key Encryption

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China Communications 2020年第5期17卷 168-190页

作者： m.G.Aruna k.G.mohan Aassociate professor department of computer science and engineeringm s engineering collegeaffiliated to vtubengalurukarnataka562110india professor and head department of computer science and engineeringpresidency universitybengalurukarnataka560064india

Cloud computing, a recently emerged paradigm faces major challenges in achieving the privacy of migrated data, network security, etc. too many cryptographic technologies are raised to solve these issues based on identity, attributes and prediction algorithms yet;these techniques are highly prone to attackers. this would raise a need of an effective encryption technique, which would ensure secure data migration. With this scenario, our proposed methodology Efficient Probab.listic Public key Encryption(EPPkE) is optimized with Covariance matrix Adaptation Evolution Strategies(CmA-ES). It ensures data integrity through the Luhn algorithm with BLAkE 2b encapsulation. this enab.es an optimized security to the data which is migrated through cloud. the proposed methodology is implemented in Open Stack with Java Language. It achieves better results by providing security compared to other existing techniques like RSA, IBA, ab., PBE, etc.

关键词： Luhn algorithm encryption efficient probab.listic public key encryption(EPPkE) covariance matrix adaptation evolution strategies(CmA-ES) trusted third party(ttP)

Continuity Adjustment for Control Charts for Attributes

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Acta mathematicae Applicatae Sinica 2003年第3期19卷 397-404页

作者： L.k.Chan t.k.mak B.tao department of management sciences City University of Hong Kong 83 Tat Chee AvenueKowloonHong KongDepartment of Decision Sciences & M.I.S.Concordia University 1445 De Maisonneuve Blvd.WestMontrealH3G 1M8 CanadaAcademy of Mathematics and System SciencesChinese Academy of SciencesBeijing 100081China

A unified approach is proposed for making a continuity adjustment on some control charts for attributes, e.g., np-chart and c-chart, through adding a uniform (0, 1) random observation to the conventional sample statistic (e.g., and c i ). the adjusted sample statistic then has a continuous distribution. Consequently, given any type I risk α (the probab.lity that the sample statistic is on or beyond the control limits), control charts achieving the exact value of α can be readily constructed. Guidelines are given for when to use the continuity adjustment control chart, the conventional Shewhart control chart (with ±3 standard deviations control limits), and the control chart based on the exact distribution of the sample statistic before adjustment.

关键词： Control charts for attributes continuity adjustments np, p, and c control charts statistical process control

Gaussian Support Vector machine Algorithm Based Air Pollution Prediction

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computers, materials & Continua 2022年第4期71卷 683-695页

作者： k.S.Bhuvaneshwari J.Uma k.Venkatachalam mehedi masud mohamed ab.uhawwash t.Logeswaran department of computer Sceince and Engineering Karpagam College of EngineeringCoimbatore641032India department of Electrical and Electronics Engineering M.Kumarasamy College of EngineeringKarur639113India department of Applied Cybernetics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic department of computer science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia department of mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt department of Computational mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA department of Electrical and Electronics Engineering Kongu Engineering CollegeErode638060India

Air pollution is one of the major concerns considering detriments to human *** type of pollution leads to several health problems for humans,such as asthma,heart issues,skin diseases,bronchitis,lung cancer,and throat and eye *** pollution also poses serious issues to the *** from the vehicle industry is the cause of greenhouse effect and CO2 ***,real-time monitoring of air pollution in these areas will help local authorities to analyze the current situation of the city and take necessary *** monitoring process has become efficient and dynamic with the advancement of the Internet of things and wireless sensor *** is the main issue in WSNs;if the sensor node location is unknown,then coverage and power and routing are not *** study concentrates on localization-based air pollution prediction systems for real-time monitoring of smart *** systems comprise two phases considering the prediction as heavy or light traffic area using the Gaussian support vector machine algorithm based on the air pollutants,such as Pm2.5 particulate matter,Pm10,nitrogen dioxide(NO2),carbon monoxide(CO),ozone(O3),and sulfur dioxide(SO2).the sensor nodes are localized on the basis of the predicted area using the meta-heuristic algorithms called fast correlation-based elephant herding *** dataset is divided into training and testing parts based on 10 *** evaluation on predicting the air pollutant for localization is performed with the training *** error prediction in localizing nodes is 9.83 which is lesser than existing solutions and accuracy is 95%.

关键词： Air pollution monitoring air pollutant SVm Gaussian EHO fast correlation WSN localization

multi-lab.l Emotion Classification of COVID–19 tweets with Deep Learning and topic modelling

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computer Systems science & Engineering 2023年第6期45卷 3005-3021页

作者： k.Anuratha m.Parvathy department of Information technology Sri Sai Ram Institute of TechnologyChennaiTamilnaduIndia department of computer science and Engineering Sethu Institute of TechnologyMaduraiTamilnaduIndia

the COVID-19 pandemic has become one of the severe diseases in recent *** it majorly affects the common livelihood of people across the universe,it is essential for administrators and healthcare professionals to be aware of the views of the community so as to monitor the severity of the spread of the *** public opinions are been shared enormously in microblogging med-ia like twitter and is considered as one of the popular sources to collect public opinions in any topic like politics,sports,entertainment etc.,this work presents a combination of Intensity Based Emotion Classification Convolution Neural Net-work(IBEC-CNN)model and Non-negative matrix Factorization(NmF)for detecting and analyzing the different topics discussed in the COVID-19 tweets as well the intensity of the emotional content of those *** topics were identified using NmF and the emotions are classified using pretrained IBEC-CNN,based on predefined intensity *** research aimed at identifying the emotions in the Indian tweets related to COVID-19 and producing a list of topics discussed by the users during the COVID-19 *** the twitter Application Programming Interface(twitter API),huge numbers of COVID-19 tweets are retrieved during January and July *** extracted tweets are ana-lyzed for emotions fear,joy,sadness and trust with proposed Intensity Based Emotion Classification Convolution Neural Network(IBEC-CNN)model which is *** classified tweets are given an intensity score varies from 1 to 3,with 1 being low intensity for the emotion,2 being the moderate and 3 being the high *** identify the topics in the tweets and the themes of those topics,Non-negative matrix Factorization(NmF)has been *** of emotions of COVID-19 tweets has identified,that the count of positive tweets is more than that of count of negative tweets during the period considered and the negative tweets related to COVID-19 is less than 5%.Also,more than 75%neg

关键词： twitter topic detection emotion classification COVID-19 corona virus non-negative matrix factorization(NmF) convolutional neural network(CNN) sentiment classification healthcare