Browsing by Author "Pradhan A."

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Adsorptive removal of surfactant using dolochar: A kinetic and statistical modeling approach
(2019) Shami S.; Dash R.R.; Verma A.K.; Dash A.K.; Pradhan A.
Disturbingly high rates of consumption of surfactants in household and industries have led to mark them as emerging contaminants in the environment. In the present work, removal of sodium dodecyl sulfate (SDS), an anionic surfactant, using an industrial waste (dolochar) was explored. The adsorbent material was characterized with the help of Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD). Kinetic evaluation was performed using first, pseudo-first, second, and pseudo-second order models. Adsorption of SDS over dolochar was expressed best by pseudo-second order kinetic model with regression coefficient (R2) of.99. Three input parameters including adsorbent dose (20�10�g/L), initial concentration (30�100�mg/L) of the surfactant, and contact time (2�60�min) were chosen for optimization using response surface methodology based on Box�Behnken design (BBD) approach. A total of 15 experiments were run to examine the effect of these variables on removal of SDS by dolochar in a multivariate system. A regression analysis indicated the experimental data fitted well to a quadratic polynomial model with coefficient of regression (R2) as.99. ANOVA and lack-of-fit test depicted the precision and efficiency of the model. The optimized conditions for SDS removal were found to be adsorbent dose 16.62�g/L, contact time 40�min, and initial concentration 47�mg/L with removal efficiency as 98.91%. Practitioner points: Daily ablutions and use of personal care products introduce a number of surfactants and recalcitrant compounds into the environment. Adsorption is a handy and easy to operate treatment technique to remove graywater pollutants. Kinetic and statistical modeling may be recommended as one of the most prominent tools to understand the removal mechanism. Decentralized treatment of graywater using industrial wastes is recommended as sustainable solution in the developing nations. � 2019 Water Environment Federation
Comparative performance study of wore segmentation techniques for handwritten Odia documents
(2017) Pradhan A.; Behera S.; Panda G.; Majhi B.
Word segmentation of handwritten documents is a vital step in the Optical Character Recognition system as its accuracy greatly influences the overall recognition performance. In the literature, various methods have been proposed for word segmentation of handwritten documents of various languages. However, it is observed that for Odia, which is an important Indian language, very little work has been reported on word segmentation. Hence, the objective of this paper is to employ two standard existing methods to segment words of Odia handwritten documents and compare the segmentation performance of these methods with the lone Water Reservoir Algorithm available in the literature and finally rank those methods based on their segmentation performance. It is observed that out of three methods, the Tree Structure method performs the best comparing four different performance measures. � 2016 IEEE.
Distributed Multi-authority Attribute-Based Encryption Using Cellular Automata
(2019) Pradhan A.; Sethi K.; Mohapatra S.; Bera P.
Cellular automata (CA) has attracted the attention of research communities for its applications in the design of symmetric and public-key cryptosystems. The strength of cellular automata lies in its inherent data parallelism, which can help accelerate access control mechanisms, and its information scrambling capabilities, which can enhance the security of the system. Also, the cryptosystems designed using CA do not involve number-theoretic methodologies that incur large computational overhead like traditional cryptosystems. However, existing CA-based cryptosystems encompass a limited set from the set of all possible transition rules indicating the existence of CA cryptosystems which are possibly unbreakable but have not been explored sufficiently. Thus, they have not yet been considered for applications involving fine-grained access control for heterogeneous access to the data. In this paper, we propose a secure distributed multi-authority attribute-based encryption using CA, which has potential applications in cloud systems. Our cryptosystem adopts the concept of multi-authority attribute-based access control where the encryption and attribute distribution use reversible CA, and policy satisfiability is achieved by Turing-complete CA in a distributed environment. We illustrate the practical usability of our proposed cryptosystem, in terms of efficiency and security, by extensive experimental results. � 2019, Springer Nature Switzerland AG.
Empirical mode decomposition and Hessian LLE in Fluorescence spectral signal analysis for Cervical cancer detection
(2025) Deo B.S.; Nayak S.; Pal M.; Panigrahi P.K.; Pradhan A.
Cervical cancer is a significant cause of female mortality worldwide. The timely and accurate identification of different stages of cervical cancer has the capacity to significantly improve both treatment efficacy and patient survival duration. Fluorescence spectroscopy acts as a significantly sensitive technique for identifying the biochemical changes that occur during the advancement of cancer. Fluorescence spectral data was collected from a diverse set of 110 human cervix samples in our study. The spectral data underwent an initial preprocessing step that included data normalization. Subsequently, empirical mode decomposition (EMD) was utilized to decompose the signal into several intrinsic mode functions within the spectral domain. Thereafter, various nonlinear dimensionality reduction methods, including Isomap, Local Linear Embedding (LLE), and Hessian LLE, were applied to extract more informative features in a lower-dimensional representation. Furthermore, a 1D convolutional neural network (CNN) was employed to categorize the lower dimensional spectral signals into three classes: normal, pre-cancerous, and cancerous. The proposed methodology attained the best evaluation metrics using the Hessian LLE dimensionality reduction technique. A mean classification accuracy, precision, recall, F1-score, and specificity of 98.72%, 98.02%, 98.61%, 98.00%, and 99.30%, respectively, were obtained through a 5-fold cross-validation technique. The combination of fluorescence spectroscopy and machine learning holds promise for detecting cancer at earlier stages than current diagnostic methods. � 2024 Elsevier Ltd
Performance evaluation of various cutting fluids using MQL technique in hard turning of AISI 4340 alloy steel
(2020) Das A.; Patel S.K.; Biswal B.B.; Sahoo N.; Pradhan A.
The present study emphasizes on various machinability aspects using different cutting fluids. The effect of cutting fluids on various machining forces, tool flank wear and chip thickness are carried out using the minimal quantity lubrication technique (MQL). This method predicts minimal health risks and economical aspect compared to other techniques. It is experimentally observed that depth of cut and speed was the principal cutting parameter affecting the cutting force and feed force and speed as the principal cutting parameter for the radial force for compressed air and water soluble coolant. Instead, depth of cut, feed and speed were the principal cutting parameters influencing cutting force, feed force and radial force, respectively using nanofluid. In the flank wear analysis, nanofluid performed the best followed by water soluble coolant and compressed air. Finally the mathematical models were developed for the machining forces with 95% confidence level using Minitab 16 which is frequently used for statistical data analysis. ANOVA analysis, Anderson-Darling test and normal probability plot was used to inspect the effectiveness, adequacy, authenticity and statistical significance of the developed model. � 2019
A scalable attribute based encryption for secure data storage and access in cloud
(2019) Sethi K.; Pradhan A.; Punith R.; Bera P.
Today a large volume of data is stored in cloud that requires fine grained accessibility for heterogeneous users. Cipher-text policy attribute based encryption (CP-ABE) has evolved into a promising solution for secure data storage with fine grained access control. In CP-ABE, the ciphertext is associated with an access policy (set of rules) and users can access the data if their attributes satisfies the access policy. However, existing CP-ABE schemes fail to perform in presence of large number of users and hierarchical relationships among them. Moreover, a majority of the CP-ABE schemes require large computational overhead for light weight applications. In this paper, we present a hierarchical attribute based cryptosystem by introducing hierarchical dependency between the users and thereby achieving multi-layer verification for fine grained data access. Moreover, our proposed cryptosystem is seamless to user revocation. The efficiency and security of our proposed cryptosystem have been analyzed and reported. Further we implement the proposed cryptosystem in Charm to demonstrate its practicality. � 2019 IEEE.