Rounak Das

National Institute of Technology Durgapur

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Sayantani Saha

Maulana Abul Kalam Azad University of Technology, West Bengal

Sarmistha Neogy

Indian Statistical Institute

Tamal Dutta

Institute of Inorganic Chemistry of the Slovak Academy of Sciences

Nilanjan Chattaraj

National Institute of Technology Durgapur

S. Mahesh Kumar

Institute of Technology Management

Suman Datta

Georgia Institute of Technology

Arindam Biswas

Indian Institute of Engineering Science and Technology, Shibpur

Bodhisattwa Rakshit

Swati Bhaumik

Institute of Technology Management

Cooperative Institutions

Institute of Technology Management

Jadavpur University

Indian Institute of Technology Kharagpur

University of Notre Dame

Institute of Inorganic Chemistry of the Slovak Academy of Sciences

National Institute of Technology Durgapur

Indian Institute of Science Bangalore

Birla Institute of Technology and Science, Pilani

Maulana Abul Kalam Azad University of Technology, West Bengal

University of Engineering & Management

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Detection-Accuracy Enhancement of COVID-19 from Multi-Class Lung Diseases by Instrumenting CLAHE Integrated Deep Learning Technique

Bodhisattwa Rakshit Rounak Das Nilanjan Chattaraj

In this article we have investigated a detection-accuracy enhancement technique of COVID-19 from multi-class lung diseases by instrumenting the CLAHE integrated deep learning technique. The image population is distributed upon disjoint sets of the patients suffering from diseases like (a) Viral-Pneumonia, (b) Lung Opacity, (c) COVID-19 and (d) Normal persons. We have used the CLAHE algorithm to pre-process those images and incorporated the ConvNet algorithm with the help of the transfer learning strategy to perform the analysis. The study reveals that the processing of the X-ray images by CLAHE technique followed by ConvNet algorithm can enhance the performance of the above mentioned four exclusive classes of lung images. Keeping the X-ray image processing using the CLAHE technique intact, we have further explored the diagnosis methods by using several CNN models such as, InceptionResNetV2, InceptionV3 and DenseNet121. In our study, out of 2400 chest X-ray images we have used 80% for the training and 20% for the validation purpose. The comparative study of the performance matrices explicitly shows the enhancement in multi-class detection accuracy. The study also shows that the CLAHE integrated DenseNet121 model provides the best performance exhibiting a maximum accuracy of 98.33% for detecting COVID-19 from multiple diseases. Also we have compared the performance of the present technique with the earlier reported approaches.

Adaptive histogram equalization

Disjoint sets

10.1109/isssc56467.2022.10051405

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A Novel Architecture for Different DSP Applications Using Field Programmable Gate Array

International Journal of Microelectronics Engineering (2016)

Arindam Biswas S. Mahesh Kumar Tamal Dutta Swati Bhaumik Rounak Das

This paper presents a reconfigurable processor for different digital signal processing applications.The performance of the proposed architecture has been evaluated by taking different dsp applications like Low pass filter, high pass filter, finite impulse response (FIR) filter and FFT module.We designed the architecture of the processor and realizing the architecture using adder, multiplier, delay unit and validate it in the FPGA, which show that the hardware scheme is feasible for practical application.The experimental results clearly reveal the novelty of the architecture for dsp applications.This paper investigates the potential use of FPGAs for implementing efficient "Reconfigurable Processor" for different dsp applications.The proposed processor is based on parallel re-configurable which is implemented on FPGA.FPGAs have become an important component for implementing these functions with respect to cost, performance and flexibility.The general purpose SPARTAN 3AN FPGA kit has been employed for developing reconfigurable processor, with all the coding done using the hardware description language VERILOG.

Gate array

Macrocell array

Programmable logic array

10.5121/ijme.2016.2202

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A cloud security framework for a data centric WSN application

Sayantani Saha Rounak Das Suman Datta Sarmistha Neogy

Data centric applications like remote healthcare application designed on a WSN-Cloud computing integrated architecture requires attention to its secure data management mechanism. A huge amount of data is generated in a health care application, which needs to be managed efficiently so that fast and reliable data communication and access can take place among the end users. In this research work we have proposed a flexible security model especially for data centric applications in cloud computing based scenario. This security model ensures data confidentiality, data integrity and fine grained access control to the application data. The objective of this work is to propose a secure framework to assist users at remote locations by considering the interoperability among different security solutions.

Data Security

Data access

Data integrity

10.1145/2833312.2849559

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Delta Authorization Concept for Dynamic Access Control Model in Cloud Environment

Advances in intelligent systems and computing (2018)

Sayantani Saha Rounak Das Sarmistha Neogy

Identity management

Data access

10.1007/978-981-10-8183-5_5

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