Digital Mammogram Inferencing System Using Intuitionistic Fuzzy Theory

Mishra, Susmita; Prakash, Manu

doi:10.32604/csse.2022.020439

Cited by 13 publications

(9 citation statements)

References 27 publications

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“…For example, the feature matching-based visual SLAM algorithm needs to continuously compute a large feature, which makes it unsuitable for use on devices with limited hardware capabilities. It also frequently requires GPU acceleration, but in augmented reality systems, the function of the GPU is to render models [20]. The plane detection algorithm is improved by PROSAC algorithm, and the experiment process feature point cloud is reduced, but it is still necessary to design a more rapid sparse point cloud-based fast plane recognition detection algorithm.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Monocular Visual SLAM for Markerless Tracking Algorithm to Augmented Reality

Yang¹,

Jia²,

Yu³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Section: Conclusion and Discussionmentioning

confidence: 99%

Monocular Visual SLAM for Markerless Tracking Algorithm to Augmented Reality

Yang¹,

Jia²,

Yu³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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“…Table 3 offers a detailed classification result analysis of the MOFSS-ODL model under five distinct runs [26][27][28][29]. The experimental results reported that the MOFSS-ODL model has accomplished effective classification performance.…”

Section: Experimental Validationmentioning

confidence: 99%

Evolutionary Algorithm Based Feature Subset Selection for Students Academic Performance Analysis

Babu¹,

MathuSoothana²,

Kumar³

2023

Intelligent Automation &Amp; Soft Computing

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Educational Data Mining (EDM) is an emergent discipline that concentrates on the design of self-learning and adaptive approaches. Higher education institutions have started to utilize analytical tools to improve students' grades and retention. Prediction of students' performance is a difficult process owing to the massive quantity of educational data. Therefore, Artificial Intelligence (AI) techniques can be used for educational data mining in a big data environment. At the same time, in EDM, the feature selection process becomes necessary in creation of feature subsets. Since the feature selection performance affects the predictive performance of any model, it is important to elaborately investigate the outcome of students' performance model related to the feature selection techniques. With this motivation, this paper presents a new Metaheuristic Optimization-based Feature Subset Selection with an Optimal Deep Learning model (MOFSS-ODL) for predicting students' performance. In addition, the proposed model uses an isolation forest-based outlier detection approach to eliminate the existence of outliers. Besides, the Chaotic Monarch Butterfly Optimization Algorithm (CBOA) is used for the selection of highly related features with low complexity and high performance. Then, a sailfish optimizer with stacked sparse autoencoder (SFO-SSAE) approach is utilized for the classification of educational data. The MOFSS-ODL model is tested against a benchmark student's performance data set from the UCI repository. A wide-ranging simulation analysis portrayed the improved predictive performance of the MOFSS-ODL technique over recent approaches in terms of different measures. Compared to other methods, experimental results prove that the proposed (MOFSS-ODL) classification model does a great job of predicting students' academic progress, with an accuracy of 96.49%.

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“…Generally, the design of UWSN is considered in various simulation tools such as Network Simulator, OPNET, OMNET, MATLAB, QUALNET, etc., [27][28][29][30][31][32][33]. In this proposed network, USWN is designed in QualNet simulator with various design parameters of UWSN as user friendly.…”

Section: Design and Simulation Parametersmentioning

confidence: 99%

Performance Analysis of Underwater Wireless Sensor Network by Deploying FTP, CBR, and VBR as Applications

Sathish

Ravikumar

Srinivasulu

et al. 2022

Journal of Computer Networks and Communications

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Oceans cover more than 75% of the planet’s land surface, making it the most water-rich place on the Earth. We know very little about oceans because of the extraordinary activities that take place in the depths. Underwater wireless sensors are devices that are able to monitor and record the physical and environmental parameters of their surroundings, as well as transmit these data in a continuous manner to one of the source sensors. The network that is formed by the collection of these underwater wireless sensors is referred to as an underwater wireless sensor network (UWSN). The analysis of performance parameters is thought to be most effectively done with this particular technology. In this paper, we will investigate various performance parameters in a random waypoint mobility model by shifting the maximum speed of a node and altering the number of nodes in the model. These parameters include average transmission delay, average jitter, average pathloss, percentage of utilization, and energy consumed in transmit, receive, and idle modes. The QualNet 7.1 simulator is utilized in order to conduct analyses and performance studies.

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Digital Mammogram Inferencing System Using Intuitionistic Fuzzy Theory

Cited by 13 publications

References 27 publications

Monocular Visual SLAM for Markerless Tracking Algorithm to Augmented Reality

Monocular Visual SLAM for Markerless Tracking Algorithm to Augmented Reality

Evolutionary Algorithm Based Feature Subset Selection for Students Academic Performance Analysis

Performance Analysis of Underwater Wireless Sensor Network by Deploying FTP, CBR, and VBR as Applications

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