Analysis of OFDM system using DCT-PTS-SLM based approach for multimedia applications

Hardas, Bhalchandra M.; Pokle, Sanjay B.

doi:10.1007/s10586-018-2140-0

Cited by 15 publications

(10 citation statements)

References 16 publications

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“…Here, B & e represents coefficient that is utilized for reducing the attack power linearly from B to 0 as well as Itr indicates the iteration count. Since the attack power of sailfish reduces over the hunting time [24], this reduction promotes the convergence of the search. Once ATP is greater, e.g., larger than 0.5, the location of each sardine is upgraded.…”

Section: Sfo-ssae Based Classification Modelmentioning

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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Section: Sfo-ssae Based Classification Modelmentioning

confidence: 99%

Evolutionary Algorithm Based Feature Subset Selection for Students Academic Performance Analysis

Babu¹,

MathuSoothana²,

Kumar³

2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

show abstract

“…30 The explanations can provide trust for the medical professionals in employing the prediction model in health care scenarios. 31 Individuals with an eGFR less than 60 mL/min per 173 m 2 (stage 3 and above) were judged to have chronic renal illness, while those with a greater eGFR were regarded to be controls (corresponding to chronic kidney disease stage 0-2). Neelakandan et al 6,7 ecology of chronic kidney disease eGFR was determined using serum creatinine and a collaboration equation.…”

Section: Related Workmentioning

confidence: 99%

An explainable deep learning model for prediction of early‐stage chronic kidney disease

Arumugham,

Sankaralingam,

Jayachandran

et al. 2023

Computational Intelligence

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Chronic kidney disease (CKD) is a major public health concern with rising prevalence and huge costs associated with dialysis and transplantation. Early prediction of CKD can reduce the patient's risk of CKD progression to end‐stage kidney failure. Artificial intelligence offers more intelligent and expert healthcare services in disease diagnosis. In this work, a deep learning model is built using deep neural networks (DNN) with an adaptive moment estimation optimization function to predict early‐stage CKD. The health care applications require interpretability over the predictions of the black‐box model to build conviction towards the model's prediction. Hence, the predictions of the DNN‐CKD model are explained by the local interpretable model‐agnostic explainer (LIME). The diagnostic patient data is trained on five layered DNN with three hidden layers. Over the unseen data, the DNN‐CKD model yields an accuracy of 98.75% and a roc_auc score of 98.86% in detecting CKD risk. The explanation revealed by the LIME algorithm echoes the influence of each feature on the prediction made by the DNN‐CKD model over the given CKD data. With its interpretability and accuracy, the proposed system may effectively help medical experts in the early diagnosis of CKD.

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“…The group member gathers clue information in the search. Few clues are left in the event of accomplishing best possible clue in another location, on the other hand, the dataset on them are applied to enrich the search technique 21 . In the study, the left clue location is saved in the memory matrix

M

, whereby the location of individual is stored in the

X

matrixes.…”

Section: The Proposed Modelmentioning

confidence: 99%

Improved search and rescue optimization based load balancing scheme of CCTV footage on edge computing environment

Deshpande

Dinesh

Umredkar

et al. 2022

Concurrency and Computation

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Nowadays, data centers and servers are rising in size by order of magnitude due to increasing CCTV footages. Therefore, load balancing (LB) becomes a primary concern in scalable computing system involves mobile edge cloud computing environment. In mobile edge cloud computing system, a mobile user offloading their task to neighboring edge server for supporting realistic application. But while user is situated in a hot spot, various edge servers are over-loaded as a result of sudden offloading task from the mobile user. The LB process can be considered as an optimization problem, which can be resolved by the use of metaheuristic optimization algorithms. This article introduces an Improved Search and Rescue Optimization based LB Scheme for Edge Computing Environment (ISROLBS-ECE). The presented ISROLBS-ECE technique majorly concentrates on the distribution of offloading tasks to neighboring edge servers effectively. In addition, the ISROLBS-ECE technique involves the hybridization of chaotic concept into the conventional search and rescue optimization (SRO) approach. In the ISROLBS-ECE technique, the moderately lightest and task arrival nodes are employed as target node for allocating new task whereas rest of the nodes are not provisionally allocated tasks to accomplish dynamic balancing of the network. The experimental validation of the ISROLBS-ECE technique is tested and the outcomes are investigated under various aspects. The comparative study shows the better performance of the ISROLBS-ECE approach over other recent techniques.

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Analysis of OFDM system using DCT-PTS-SLM based approach for multimedia applications

Cited by 15 publications

References 16 publications

Evolutionary Algorithm Based Feature Subset Selection for Students Academic Performance Analysis

Evolutionary Algorithm Based Feature Subset Selection for Students Academic Performance Analysis

An explainable deep learning model for prediction of early‐stage chronic kidney disease

Improved search and rescue optimization based load balancing scheme of CCTV footage on edge computing environment

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