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IOT Assisted Biomedical Monitoring Sensors for Healthcare in Human

Computer Systems Science & Engineering 2023年第6期45卷 2853-2868页

作者： S.Periyanayagi v.nandini K.Basarikodi v.Sumathy Department of ECE Ramco Institute of TechnologyRajapalayam626117India Department of CSE Sona College of TechnologySalem636005India Department of Mathematics Ramco Institute of TechnologyRajapalayamIndia Government College of Engineering DharmapuriIndia

The Internet of Things(IoT)is a concept that refers to the deployment of Internet Protocol(IP)address sensors in health care systems to monitor patients’*** has the ability to access the Internet and collect data from *** decisions are made after evaluating the information of illness people ***’health and well-being can be monitored through IoT medical *** is possible to trace the origins of biological,medical equipment and *** reliability is a major concern in user activity and fitness trackers in day-to-day *** fundamental challenge is to measure the efficiency of the human system *** to maintain tabs on the well-being of humans;this paper recommends the use of wireless body area networks(WBANs)and artificial neural networks(ANN)to create an IoT-based healthcare framework for hospital information systems(IoT-HF-HIS).Our evaluation system uses a server to estimate how much computing power is needed for modeling,and simulations of the framework have been done using data rate and latency requirements are implementing the energy-aware technology presented in this *** proposed framework implements several hospital information system case studies by building a time-saving simulation *** the world’s population ages,more and more people suffer from physical and emotional *** the recommended strategy regularly has been proven user-friendly,reliable,and cost-effective,with an overall performance of 95.2%.

关键词： IoT WBANs ANN healthcare biomedical sensors humans

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Metaheuristic Based Clustering with Deep Learning Model for Big Data Classification

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Computer Systems Science & Engineering 2023年第1期44卷 391-406页

作者： R.Krishnaswamy Kamalraj Subramaniam v.nandini K.vijayalakshmi Seifedine Kadry Yunyoung Nam Department of Electronics and Communication Engineering University College of Engineering AriyalurAriyalur621704India Department of Biomedical Engineering Faculty of EngineeringKarpagam Academy of Higher EducationCoimbatore641021India Department of Computer Science and Engineering Sona College of TechnologySalem636005India Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai600077India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Recently,a massive quantity of data is being produced from a distinct number of sources and the size of the daily created on the Internet has crossed two *** the same time,clustering is one of the efficient techniques for mining big data to extract the useful and hidden patterns that exist in ***-based clustering techniques have gained significant attention owing to the fact that it helps to effectively recognize complex patterns in spatial *** data clustering is a trivial process owing to the increasing quantity of data which can be solved by the use of Map Reduce *** this motivation,this paper presents an efficient Map Reduce based hybrid density based clustering and classification algorithm for big data analytics(MR-HDBCC).The proposed MR-HDBCC technique is executed on Map Reduce tool for handling the big *** addition,the MR-HDBCC technique involves three distinct processes namely pre-processing,clustering,and *** proposed model utilizes the Density-Based Spatial Clustering of Applications with Noise(DBSCAN)techni-que which is capable of detecting random shapes and diverse clusters with noisy *** improving the performance of the DBSCAN technique,a hybrid model using cockroach swarm optimization(CSO)algorithm is developed for the exploration of the search space and determine the optimal parameters for density based ***,bidirectional gated recurrent neural network(BGRNN)is employed for the classification of big *** experimental validation of the proposed MR-HDBCC technique takes place using the benchmark dataset and the simulation outcomes demonstrate the promising performance of the proposed model interms of different measures.

关键词： Big data data classification clustering mapreduce dbscan algorithm

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A Discrete Cosine Adaptive Harmonic Wavelet Packet and Its Application to Signal Compression

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Journal of Signal and Information Processing 2010年第1期1卷 63-76页

作者： nandini Basumallick S. v. Narasimhan 不详

A new adaptive Packet algorithm based on Discrete Cosine harmonic wavelet transform (DCHWT), (DCAHWP) has been proposed. This is realized by the Discrete Cosine Harmonic Wavelet transform (DCHTWT) which exploits the good properties of DCT viz., energy compaction (low leakage), frequency resolution and computational simplicity due its real nature, compared to those of DFT and its harmonic wavelet version. Hence the proposed wavelet packet is advantageous both in terms of performance and computational efficiency compared to those of existing DFT harmonic wavelet packet. Further, the new DCAHWP also enjoys the desirable properties of a Harmonic wavelet transform over the time domain WT, viz., built in decimation without any explicit antialiasing filtering and easy interpolation by mere concatenation of different scales in frequency (DCT) domain with out any image rejection filter and with out laborious delay compensation required. Further, the compression by the proposed DCAHWP is much better compared to that by adaptive WP based on Daubechies-2 wavelet (DBAWP). For a compression factor (CF) of 1/8, the ratio of the percentage error energy by proposed DCAHWP to that by DBAWP is about 1/8 and 1/5 for considered 1-D signal and speech signal, respectively. Its compression performance is better than that of DCHWT, both for 1-D and 2-D signals. The improvement is more significant for signals with abrupt changes or images with rapid variations (textures). For compression factor of 1/8, the ratio of the percentage error energy by DCAHWP to that by DCHWT, is about 1/3 and 1/2, for the considered 1-D signal and speech signal, respectively. This factor for an image considered is 2/3 and in particular for a textural image it is 1/5.

关键词： Adaptive Harmonic Wavelet Packets Discrete Cosine Transform Signal Compression

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