Introduction to TRIMET along with its properties and scope

Amiripalli, Shanmuk Srinivas; Kumar, Anthati Kalyan; Tulasi, B.

doi:10.1063/1.4940280

Cited by 9 publications

(6 citation statements)

References 4 publications

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“…The future scope is this is a comparative study not much is to be improved. Optimization can also be achieved by TGO model [11][12][13][14][15][16][17].…”

Section: Literature Surveymentioning

confidence: 99%

Mineral Rock Classification Using Convolutional Neural Network

Amiripalli

Rao

Behara

et al. 2021

Recent Trends in Intensive Computing

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The main aim of the research is to build a model that can effectively predict the type of mineral rocks. Rocks can be predicted by observing it is colour, shape and chemical composition. On-site technicians need to apply different techniques on rock sample in order to predict rock type. Technicians need to apply different techniques on rock samples, so it is a time-consuming process, and sometimes the predictions may be accurate, and sometimes predictions may be false. When predictions are false, it might show a negative impact in several ways for workers and organization as well. We considered an image dataset of rock types, namely Biotite, Bornite, Chrysocolla, Malachite, Muscovite, Pyrite, and Quartz. We applied CNN (Convolutional Neural Network) Algorithm to get a better prediction of different mineral rocks. Nowadays, CNN is mainly used for image classification and image recognition tasks.

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“…The future scope is this is a comparative study not much is to be improved. Optimization can also be achieved by TGO model [11][12][13][14][15][16][17].…”

Section: Literature Surveymentioning

confidence: 99%

Mineral Rock Classification Using Convolutional Neural Network

Amiripalli

Rao

Behara

et al. 2021

Recent Trends in Intensive Computing

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show abstract

“…When the heads of the cluster are decided and the number of control messages received decreases, the same heads of the cluster stay for another round as heads of the cluster, and the third round begins and new ones are selected [13]. e algorithm that clusters homogeneous nodes that have equal energy is provided in [20].…”

Section: Related Workmentioning

confidence: 99%

“…In what follows, algorithms are explained in the simulation section which are compared to the proposed method and some other related works. FBUC [20] is the first algorithm that was proved to be an enhancement on EAUCF [21]. Unlike EAUCF, at the very beginning of the clustering process, FBUC designates a threshold so that the system can decide which of the sensor nodes can be chosen for the impermanent cluster head based on fuzzy logic to develop the random figure in a way so that it can interact with its neighboring nodes.…”

Section: Related Workmentioning

confidence: 99%

Protect Mobile Travelers Information in Sensitive Region Based on Fuzzy Logic in IoT Technology

Memon

Shaikh

Hasan

et al. 2020

Security and Communication Networks

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The Internet of Things (IoT) is susceptible to several identities, primarily based on attacks. However, these attacks are controlling for IoT due to extraordinary growth in consumers’ density and slight analysis with low power access nodes. In this work, we explore the possible flaws associated with security for IoT environment insensitively meant for transfer conditions. We proposed a novel design aimed at detecting a spoofing attack that inspects the probability distributions of received power founded for the regions designed for mobile (moving) users. Additionally, we examine the influence on the Confidentiality Scope of targeted consumers in the absence and presence of observer. Our approaches were done through simulation results used for three diverse regions. Grounded on outcomes, we suggest an algorithm called MTFLA, which will guarantee detection and protection techniques intended to protect vastly sensitive areas, i.e., wherever the chance of an attack is maximized. We provide a comparison among various security algorithms prepared for the energy consumption of different patterns. Simulation results revealed that the proposed algorithm for protection (MTFL) is verified to be energy-proficient (secure garnering). It decreases the energy prerequisite for encrypting the data. We evaluated our techniques over simulation results for sensitive region information built on fuzzy logic.

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“…Caliskan et al [34] presented deep learning-based strategy where internal parameter spaces are divided into different partitions and each partition is optimized individually using L-BFGS optimization. This deep learning scheme uses auto encoder with soft max classifier [35].…”

Section: Literature Surveymentioning

confidence: 99%

Logistic regression and Random forest-based hybrid classifier with recursive feature elimination technique for diabetes classification

L¹

2020

IJATCSE

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Diabetes mellitus is a chronic metabolic ailment being considered as one of the deadliest diseases in the world. Millions of cases and deaths have been reported due to diabetes. Initial forecast of diabetes condition helps in reducing the death rate significantly that happen due to it. Current advancements in biomedical techniques have facilitated to store the electronic health record datasets which can be analyzed for better diagnosis. In order to explore these datasets, data mining techniques are considered as promising techniques which examines the data rapidly and provides a desired outcome. In this work, the data mining technique was adopted for diabetes classification using machine learning techniques. The proposed approach comprises of several steps such as missing value imputation, attribute selection and classification which are performed using mean missing value imputation, logistic regression &Recursive Feature Elimination for feature selection and random forest for classification, respectively. The evaluation results demonstrate that projected methods achieve97.39% prediction accuracy which shows a significant improvement in contrast to prevailing current approaches.

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Introduction to TRIMET along with its properties and scope

Cited by 9 publications

References 4 publications

Mineral Rock Classification Using Convolutional Neural Network

Mineral Rock Classification Using Convolutional Neural Network

Protect Mobile Travelers Information in Sensitive Region Based on Fuzzy Logic in IoT Technology

Logistic regression and Random forest-based hybrid classifier with recursive feature elimination technique for diabetes classification

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