The healthcare sector plays a significant role in the industry, where a client looks for the highest amount of care and services, no matter the cost. However, this sector has not satisfied society's presumption, even if this industry consumes a considerable percentage of the national budget. In the past, medical experts have been looking for smart medical solutions. This work focuses on accurate and early detection of illness from various medical images. Early detection not only aids in the development of better medications but can also save a life in the long run. Deep learning provides an excellent solution for early medical imaging in healthcare. This paper proposed a Stacked-based BiLSTM with Resnet50 Model using an AdaSwarm optimizer to classify and analyze the medical illnesses from the different medical image datasets. For this study, four medical datasets were used as benchmarks: Covid19, Pneumonia, Ma, and Lung Cancer. Accuracy, AUC, ROC, and F1 Score performance metrics are used to evaluate the prosed model from other models. The proposed model gives a mean ACCURACY, AUC, ROC, and F1 Score on these four datasets are 98%, 99%, 97%, and 98%, respectively.

Presumption

Health sector

Medical services

F1 score

Healthcare Industry

10.1109/tensymp54529.2022.9864353

Cite

Citations (2)

Antlion optimizer tuned PID controller based on Bode ideal transfer function for automobile cruise control system

Journal of Industrial Information Integration (2018)

Rosy Pradhan Santosh Kumar Majhi Jatin Kumar Pradhan Bibhuti Bhusan Pati

10.1016/j.jii.2018.01.002

Cite

Citations (52)

Formal Analysis of Virtual Machine Migration and Identification of Faults

International Journal of Knowledge-Based Organizations (2017)

Santosh Kumar Majhi Sunil Kumar Dhal

Infrastructure as a service (IaaS) cloud supports flexible and agile execution of applications by creating virtualized execution environment namely, virtual machines (VMs) with on-demand infrastructural resources. In such environment, VM migration is used as a tool to facilitate system maintenance, load balancing and fault tolerance. The use of VM migration is to establish the portfolio of using dynamic and scalable infrastructure services offered by the service providers. In this paper, we study the VM migration process and investigate the potential faults which can occur during migration. Also, the state changes of a VM throughout its lifetime has been systematically analyzed and modeled as concurrent state machines. The potential faults are presented considering the live migration process of VM and accordingly VM state changes. In addition, a methodology for identifying the migration faults has been presented.

Live migration

Process migration

Identification

10.4018/ijkbo.2018010102

Cite

Citations (0)

MANET ROUTING PROTOCOLS: A REVIEW ON PERFORMANCE ISSUES AND ITS IMPLICATIONS

Akash Mohanty Santosh Kumar Majhi Gopikrishna Panda Ashis Kumar

Mobile Ad-Hoc Network (MANET) is wireless networks in which the nodes are free to move arbitrarily i. e. it does not need any infrastructure support. As the nodes are not fixed, biggest problem about routing among nodes. As large numbers of MANET protocols are there but no single protocol is used in all conditions. This paper gives an overview of such routing protocols by representing there functionality and characteristics along with merits and demerits. Some of the recent activity about MANET have discussed. Keyword: MANET, ROUTING, PROTOCOL, WIRLESS.

Interior gateway protocol

Zone Routing Protocol

Source

Cite

Citations (0)

Fuzzy clustering using salp swarm algorithm for automobile insurance fraud detection

Journal of Intelligent & Fuzzy Systems (2019)

Santosh Kumar Majhi Subho Bhatachharya Rosy Pradhan Shubhra Biswal

In this paper, a hybrid fuzzy clustering techniques using Salp Swarm Algorithm (SSA) is proposed. The proposed fuzzy clustering method is used to optimize the cluster centroids obtained as an under sampling method. The performance of the proposed fuzzy clustering method is compared with some well-k nown clustering algorithms to shows the superiority of the proposed clustering algorithm. In addition, a novel hybrid Automobile Insurance Fraud Detection System is proposed in which undersampling of the majority class is performed by using the proposed fuzzy clustering algorithm which eliminates the outliers from the majority class samples. The balanced dataset for automobile fraud detection obtained after undersampling undergoes classification. Different classifiers used for this purpose are Random Forest Classifier, Logistic Regression Classifier and XGBoost Classifier. The performance of each of the three classifiers is evaluated by considering different performance metrics such as sensitivity, accuracy and specificity. The proposed fuzzy clustering method along with XGBoost outperforms the other methods presented.

10.3233/jifs-169944

Cite

Citations (56)

Classification of Phishing Websites Using Moth-Flame Optimized Neural Network

Advances in intelligent systems and computing (2018)

Santosh Kumar Majhi Pragati Mahapatra

Phishing

10.1007/978-981-13-1951-8_5

Cite

Citations (6)