Combining statistical models using modified spectral subtraction method for embedded system

Balaji, V.; Maheswaran, S.; Babu, M. Rajesh; Kowsigan, M.; Prabhu, E; Venkatachalam, K.

doi:10.1016/j.micpro.2019.102957

Cited by 50 publications

(7 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several fields, such as the energy industry, healthcare, and internet-based vehicles have adopted blockchain to improve various processes. Existing studies on the safe sharing of data with blockchain have good improvement [5][6][7] . Although diverse studies have resolved the problems of privacy and security in with a privacy preservation method, the elements, the attributes and the scope of data to be shared are typically not deliberated.…”

mentioning

confidence: 99%

RETRACTED ARTICLE: Blockchain-enabled K-harmonic framework for industrial IoT-based systems

Baalamurugan

Prabu

Bačanin

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Industrial Internet of Things (IIoT)-based systems have become an important part of industry consortium systems because of their rapid growth and wide-ranging application. Various physical objects that are interconnected in the IIoT network communicate with each other and simplify the process of decision-making by observing and analyzing the surrounding environment. While making such intelligent decisions, devices need to transfer and communicate data with each other. However, as devices involved in IIoT networks grow and the methods of connections diversify, the traditional security frameworks face many shortcomings, including vulnerabilities to attack, lags in data, sharing data, and lack of proper authentication. Blockchain technology has the potential to empower safe data distribution of big data generated by the IIoT. Prevailing data-sharing methods in blockchain only concentrate on the data interchanging among parties, not on the efficiency in sharing, and storing. Hence an element-based K-harmonic means clustering algorithm (CA) is proposed for the effective sharing of data among the entities along with an algorithm named underweight data block (UDB) for overcoming the obstacle of storage space. The performance metrics considered for the evaluation of the proposed framework are the sum of squared error (SSE), time complexity with respect to different m values, and storage complexity with CPU utilization. The results have experimented with MATLAB 2018a simulation environment. The proposed model has better sharing, and storing based on blockchain technology, which is appropriate IIoT.

show abstract

mentioning

confidence: 99%

RETRACTED ARTICLE: Blockchain-enabled K-harmonic framework for industrial IoT-based systems

Baalamurugan

Prabu

Bačanin

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…To examine the effect of EMF radiations on the morphology of the D. Melanogaster brain, four classifiers were used to segregate it. These four classifiers were naïve Bayes [15][16][17], artificial neural network [18][19][20][21][22], support vector machine [23][24][25], and unsystematic forest.…”

Section: Segregating Open/not Open Groups Using Machine Learningmentioning

confidence: 99%

Machine Learning Technique to Detect Radiations in the Brain

Gothai¹,

Baseera²,

Prabu³

et al. 2022

Computer Systems Science and Engineering

View full text Add to dashboard Cite

The brain of humans and other organisms is affected in various ways through the electromagnetic field (EMF) radiations generated by mobile phones and cell phone towers. Morphological variations in the brain are caused by the neurological changes due to the revelation of EMF. Cellular level analysis is used to measure and detect the effect of mobile radiations, but its utilization seems very expensive, and it is a tedious process, where its analysis requires the preparation of cell suspension. In this regard, this research article proposes optimal broadcasting learning to detect changes in brain morphology due to the revelation of EMF.Here, Drosophila melanogaster acts as a specimen under the revelation of EMF. Automatic segmentation is performed for the brain to attain the microscopic images from the prejudicial geometrical characteristics that are removed to detect the effect of revelation of EMF. The geometrical characteristics of the brain image of that is microscopic segmented are analyzed. Analysis results reveal the occurrence of several prejudicial characteristics that can be processed by machine learning techniques. The important prejudicial characteristics are given to four varieties of classifiers such as naïve Bayes, artificial neural network, support vector machine, and unsystematic forest for the classification of open or nonopen microscopic image of D. melanogaster brain. The results are attained through various experimental evaluations, and the said classifiers perform well by achieving 96.44% using the prejudicial characteristics chosen by the feature selection method. The proposed system is an optimal approach that automatically identifies the effect of revelation of EMF with minimal time complexity, where the machine learning techniques produce an effective framework for image processing.

show abstract

“…24 Combining statistical models using modified spectral subtraction method for embedded system was proposed to produce an effective IoT environment related to agriculture in the future. 25 These are the literatures undergone for the above-mentioned agricultural problem. Many of the existing systems considered anyone of the parameters, temperature, soil moisture, and humidity.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel methodology for the development of an optimal agricultural crop field using Internet of Things

Manikandan¹,

Prasanna

Ashwin³

et al. 2020

Computational Intelligence

View full text Add to dashboard Cite

The Internet of Things (IoT) plays a vital role in the entity sharing and minimizing the workload of the human beings in various aspects. Nowadays the term IoT is used in various fields such as health care, automobiles, industry, agriculture, and so on. Agriculture is the main source of food to whole world. There are various problems faced by the farmers in agriculture due to shortage and wastage of water and fertilizers. In this regard, an optimal IoT model has been developed and proposed to attain an effective crop field. The proposed IoT model will monitor, record temperature, and soil moisture values, which will be continuously analyzed to achieve optimal plant growth and yield. The motor will be connected to the IoT model which automatically switch on/off based on optimal threshold temperature and soil moisture content value. A novel irrigation algorithm named differential waterflow algorithm has been proposed and deployed in the proposed IoT model for the automatic usage of the motor in the field. The proposed IoT model provides a web interface to the user through the cloud storage, so that the farmer can control and monitor the system in remote. The proposed system will reduce the water consumption and will ensure the uniform water distribution to the crops through the Poisson distribution which results in increasing yield.

show abstract

Combining statistical models using modified spectral subtraction method for embedded system

Cited by 50 publications

References 7 publications

RETRACTED ARTICLE: Blockchain-enabled K-harmonic framework for industrial IoT-based systems

RETRACTED ARTICLE: Blockchain-enabled K-harmonic framework for industrial IoT-based systems

Machine Learning Technique to Detect Radiations in the Brain

A novel methodology for the development of an optimal agricultural crop field using Internet of Things

Contact Info

Product

Resources

About