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Smart Healthcare Activity Recognition Using Statistical Regression and Intelligent Learning

Computers, Materials & Continua 2024年第1期78卷 1189-1205页

作者： K.Akilandeswari Nithya Rekha Sivakumar Hend Khalid Alkahtani Shakila Basheer sara abdelwahab ghorashi Department of Computer Sciences Government Arts College(Autonomous)Salem636007India Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia

In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and *** exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and *** many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during ***,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare *** first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction ***,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable *** attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced *** that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and *** evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several *** quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.

关键词： Internet of Things smart health care monitoring human activity recognition intelligent agent learning statistical partial regression support vector

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MIoT Based Skin Cancer Detection Using Bregman Recurrent Deep Learning

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Computers, Materials & Continua 2022年第12期73卷 6253-6267页

作者： Nithya Rekha Sivakumar sara abdelwahab ghorashi Faten Khalid Karim Eatedal Alabdulkreem Amal Al-Rasheed Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Mobile clouds are the most common medium for aggregating,storing,and analyzing data from the medical Internet of Things(MIoT).It is employed to monitor a patient’s essential health signs for earlier disease diagnosis and *** the various disease,skin cancer was the wide variety of cancer,as well as enhances the endurance *** recent years,many skin cancer classification systems using machine and deep learning models have been developed for classifying skin tumors,including malignant melanoma(MM)and other skin ***,accurate cancer detection was not performed with minimum time *** order to address these existing problems,a novel Multidimensional Bregman Divergencive Feature Scaling Based Cophenetic Piecewise Regression Recurrent Deep Learning Classification(MBDFS-CPRRDLC)technique is introduced for detecting cancer at an earlier *** MBDFS-CPRRDLC performs skin cancer detection using different layers such as input,hidden,and output for feature selection and *** patient information is composed of *** patient information was stored in mobile clouds server for performing predictive *** collected data are sent to the recurrent deep learning *** the first hidden layer,the feature selection process is carried out using the Multidimensional Bregman Divergencive Feature Scaling technique to find the significant features for disease identification resulting in decreases time *** by,the disease classification is carried out in the second hidden layer using cophenetic correlative piecewise regression for analyzing the testing and training *** process is repeatedly performed until the error gets *** this way,disease classification is accurately performed with higher *** evaluation is carried out for factors namely Accuracy,precision,recall,F-measure,as well as cancer detection time,by the amount of patient *** observed result confirms that the pr

关键词： MIoT skin cancer detection recurrent deep learning classification multidimensional bregman divergencive scaling cophenetic correlative piecewise regression

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Heuristic and Bent Key Exchange Secured Energy Efficient Data Transaction for Traffic Offloading in Mobile Cloud

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Computers, Materials & Continua 2020年第12期65卷 1925-1943页

作者： Nithya Rekha Sivakumar sara ghorashi Mona Jamjoom Mai Alduaili Computer Science Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

In today’s world,smart phones offer various applications namely face detection,augmented-reality,image and video processing,video gaming and speech *** the increasing demand for computing resources,these applications become more *** Computing(CC)environment provides access to unlimited resource pool with several features,including on demand self-service,elasticity,wide network access,resource pooling,low cost,and ease of *** Cloud Computing(MCC)aimed at overcoming drawbacks of smart phone *** task remains in combining CC technology to the mobile devices with improved battery life and therefore resulting in significant *** remote execution,recent studies suggested downloading all or part of mobile application from mobile *** the other hand,in offloading process,mobile device energy consumption,Central Processing Unit(CPU)utilization,execution time,remaining battery life and amount of data transmission in network were related to one or more constraints by frameworks *** address the issues,a Heuristic and Bent Key Exchange(H-BKE)method can be considered by both ways to optimize energy consumption as well as to improve security during ***,an energy efficient offloading model is designed using Reactive Heuristic Offloading algorithm where,the secondary users are allocated with the unused primary users’***,a novel AES algorithm is designed that uses a Bent function and Rijndael variant with the advantage of large block size is hard to interpret and hence is said to ensure security while accessing primary users’unused spectrum by the secondary *** are conducted for efficient offloading in mobile cloud and performance valuations are carried on the way to demonstrate that our projected technique is successful in terms of time consumption,energy consumption along with the security aspects covered during offloading in MCC.

关键词： Cloud computing mobile cloud computing heuristic bent key exchange reactive offloading

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