Papers by pradeep mallick
Mathematics
Data clustering is a process of arranging similar data in different groups based on certain chara... more Data clustering is a process of arranging similar data in different groups based on certain characteristics and properties, and each group is considered as a cluster. In the last decades, several nature-inspired optimization algorithms proved to be efficient for several computing problems. Firefly algorithm is one of the nature-inspired metaheuristic optimization algorithms regarded as an optimization tool for many optimization issues in many different areas such as clustering. To overcome the issues of velocity, the firefly algorithm can be integrated with the popular particle swarm optimization algorithm. In this paper, two modified firefly algorithms, namely the crazy firefly algorithm and variable step size firefly algorithm, are hybridized individually with a standard particle swarm optimization algorithm and applied in the domain of clustering. The results obtained by the two planned hybrid algorithms have been compared with the existing hybridized firefly particle swarm optim...
Personal and Ubiquitous Computing, 2020
Microarray data analysis is a major challenging field of research in recent days. Machine learnin... more Microarray data analysis is a major challenging field of research in recent days. Machine learning-based automated gene data classification is an essential aspect for diagnosis of gene related any malfunctions and diseases. As the size of the data is very large, it is essential to design a suitable classifier that can process huge amount of data. Deep learning is one of the advanced machine learning techniques to mitigate these types of problems. Due the presence of more number of hidden layers, it can easily handle the big amount of data. We have presented a method of classification to understand the convergence of training deep neural network (DNN). The assumptions are taken as the inputs do not degenerate and the network is over-parameterized. Also the number of hidden neurons is sufficiently large. Authors in this piece of work have used DNN for classifying the gene expressions data. The dataset used in the work contains the bone marrow expressions of 72 leukemia patients. A five-layer DNN classifier is designed for classifying acute lymphocyte (ALL) and acute myelocytic (AML) samples. The network is trained with 80% data and rest 20% data is considered for validation purpose. Proposed DNN classifier is providing a satisfactory result as compared to other classifiers. Two types of leukemia are classified with 98.2% accuracy, 96.59% sensitivity, and 97.9% specificity. The different types of computer-aided analyses of genes can be helpful to genetic and virology researchers as well in future generation.
International journal of engineering & technology, Apr 3, 2018
Conservative nature of Vegas creates less opportunity to get fair share of bandwidth then Reno in... more Conservative nature of Vegas creates less opportunity to get fair share of bandwidth then Reno in wired network. On the other hand, aggressive nature of Reno helps to achieve more share of bandwidth. Both Reno and Vegas assumes that congestion occurs in the forward rather than in reverse path. In asymmetric network the path characteristics of forward and backward is different. In this work, we propose a network model and analyzed the Inter-protocol fairness between TCP Reno and TCP Vegas with some queue management techniques such as Droptail and random early detection (RED) in asymmetric network where the forward and backward path has different characteristics. The simulation experiment results using NS2 indicates that use of RED can achieve better fairness than Droptail in asymmetric network.
Emerging Trends and Applications in Cognitive Computing, 2019
The basic aim of risk management is to recognize, assess, and prioritize risk in order to assure ... more The basic aim of risk management is to recognize, assess, and prioritize risk in order to assure that the uncertainty should not deviate from the intended purpose of the business goals. Risk can take place from various sources, which includes uncertainty in financial markets, recessions, inflation, interest rates, currency fluctuations, etc. Various methods used for this management of risk are faced with various decisions such as the market price, historical data, statistical methodologies, etc. For stock prices, the information derives from the historical data where the next price depends only upon the current price and some of the outside factors. Financial market is very risky to invest money, but the proper prediction with handling the risk will benefit a lot. Various types of risk in the financial market and the appropriate solutions to overcome the risk are analyzed in this study.
Cognitive Informatics and Soft Computing, 2020
Stress and depression are now a global problem. 25% of world's population is facing this problem.... more Stress and depression are now a global problem. 25% of world's population is facing this problem. With the growing use of sensor equipped intelligent wearables, physiological parameters can be easily extracted and effectively analyzed to predict it at an early stage. Stress management is a complex problem, as to predict stress; there exist several parameters to be considered. Choosing the right parameter is a challenging task, to predict the stress more accurately. In this work, to select the most efficient parameters the unsupervised algorithm, K-Means is used; and after getting the right parameters, a radial basis function based neural network is utilized to group the captured data to be stressed or non-stressed. The model also identifies the type of the stress. The work is validated in Python based environment and gives a promising result, in terms of accuracy.
Cognitive Informatics and Soft Computing, 2020
This work deals the comparison of perturbation and observation (P and O) as well as asymmetrical ... more This work deals the comparison of perturbation and observation (P and O) as well as asymmetrical fuzzy logic controller (AFLC)-based soft computing approach as a maximum power point tracking (MPPT) means for photovoltaic (PV) network. The simulation of PV power system has been realized using both algorithms for different solar insolations. In this work, Cuk converter has been interfaced between PV array and load for smooth MPPT operations. The grid-tied PV system with unity power factor has estimated using MATLAB/SIMULINK approach. Simulation results explain the effectiveness and robustness of AFLC-based MPPT formulation.
Advances in Systems, Control and Automations, 2021
The customer review is always beneficial for a company for making its product at the next level. ... more The customer review is always beneficial for a company for making its product at the next level. There are thousands of products and their reviews which are impossible to analysis manually. Perspective analysis or sentiment analysis is an automated process which retrieves subjective opinion from the review and categorized as positive and negative. Through which an organization can apprehend that where their invention goes on. Living life with uncomfortable looks has a negative impact on our daily life. So, day by day increasing the use of anti-aging products is appreciable. Major portion of such data are available on Web sites like twitter, Facebook, Linked-In and also from e-commerce sites like Amazon, Flip-kart, and various types of blogs. Hence, this research paper aims to guide a sentimental analysis approach to customer reviews of anti-aging products from Amazon. Classify each review as an affirmative review; otherwise, neglect review by adopting several machine learning strategies. This research builds a model using supervised and ensemble machine learning approaches. The method of ensemble machine learning approach applied in this analysis is voting which altogether seven different classifiers: Gaussian Naive Bayes, logistic regression, random forest, bagging using decision tree algorithm, and boosting using extreme gradient boosting (XGB), SVM, k-nearest neighbor algorithm and give output according to best accuracy. The data have also been analyzed using text blob and VADER and accuracy score also compares with this model. The experiment in this research done using Python and NLTK and have created different scenarios for the evaluation of proposed methodology against the classifiers. The scenarios in feature extraction steps such as by using different types of N-grams
Cureus, 2021
Introduction Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease, w... more Introduction Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease, with a global prevalence of 20%-40%. Approximately 40%-60% of patients with type 2 diabetes mellitus (DM2) experience NAFLD; out of which 20%-40% cases may have higher severity. Due to the scarcity of available reports from the eastern part of India, we aimed to evaluate the effects of dapagliflozin, a sodium-glucose cotransporter-2 inhibitor used in these types of cases. Material and methods The study included consecutive patients with DM2 and NAFLD, treated with dapagliflozin at 10 mg daily for six months. All patients underwent detailed anthropometric, biochemical, abdominal ultrasonography, and transient elastography studies at baseline and after therapy as well as a comparative analysis. Results In the 100 patients included in our study, the male patients outnumbered the female patients (male-tofemale ratio, 4.27:-1) and the mean age at presentation was 44.11 ± 8.24 years. The mean body mass index significantly decreased over the course of the therapy, from 27.31± 1.87 kg/m 2 at baseline to 26.21 ± 1.51 kg/m 2 after the therapy. The patients' transaminitis, dyslipidemia, and glycemic status significantly improved over the course of the therapy. We also observed significant (p < 0.05) improvement in hepatic steatosis by the end of the treatment. Although transient elastography by FibroScan-measured hepatic fibrosis score (Echosens, Paris, France) significantly decreased from 6.95 ± 1.42 to 6 ± 1.44 kPa, hepatic fibrosis did not improve significantly (p ≥ 0.05) following therapy. Conclusion Although dapagliflozin improved body mass index, transaminitis, dyslipidemia, glycemic status, and hepatic steatosis, it had a minimal effect on hepatic fibrosis.
Applied Sciences, 2020
There is a consistent rise in chronic diseases worldwide. These diseases decrease immunity and th... more There is a consistent rise in chronic diseases worldwide. These diseases decrease immunity and the quality of daily life. The treatment of these disorders is a challenging task for medical professionals. Dimensionality reduction techniques make it possible to handle big data samples, providing decision support in relation to chronic diseases. These datasets contain a series of symptoms that are used in disease prediction. The presence of redundant and irrelevant symptoms in the datasets should be identified and removed using feature selection techniques to improve classification accuracy. Therefore, the main contribution of this paper is a comparative analysis of the impact of wrapper and filter selection methods on classification performance. The filter methods that have been considered include the Correlation Feature Selection (CFS) method, the Information Gain (IG) method and the Chi-Square (CS) method. The wrapper methods that have been considered include the Best First Search (...
Sensors, 2020
Disease diagnosis is a critical task which needs to be done with extreme precision. In recent tim... more Disease diagnosis is a critical task which needs to be done with extreme precision. In recent times, medical data mining is gaining popularity in complex healthcare problems based disease datasets. Unstructured healthcare data constitutes irrelevant information which can affect the prediction ability of classifiers. Therefore, an effective attribute optimization technique must be used to eliminate the less relevant data and optimize the dataset for enhanced accuracy. Type 2 Diabetes, also called Pima Indian Diabetes, affects millions of people around the world. Optimization techniques can be applied to generate a reliable dataset constituting of symptoms that can be useful for more accurate diagnosis of diabetes. This study presents the implementation of a new hybrid attribute optimization algorithm called Enhanced and Adaptive Genetic Algorithm (EAGA) to get an optimized symptoms dataset. Based on readings of symptoms in the optimized dataset obtained, a possible occurrence of diab...
International Journal of Computer and Communication Technology, 2014
Demand of information security is increasing day by day with the exponential growth of Internet. ... more Demand of information security is increasing day by day with the exponential growth of Internet. The content of message is kept secret in cryptography, where as steganography message is embedded into the cover image. In this paper a system is developed in which cryptography and steganography are used as integrated part along with newly developed enhanced security model. In cryptography the process of encryption is carried out using symmetric block ciphers with linear algebraic equation to encrypt a message [1] and the obtained cipher text is hidden in to the cover image which makes the system highly secured. Least Significant Bit (LSB) technique is used for message hiding which replaces the least significant Bits of pixel selected to the hide the information. A large number of commercial steganographic programs use LSB as the method of choice for message hiding in 24-bit,8bit-color images, and gray scale images. It is observed from the simulation study that both methods together enh...
International Journal of Computer and Communication Technology, 2012
Content Based Image Retrieval (CBIR) operates on a totally different principle from keyword index... more Content Based Image Retrieval (CBIR) operates on a totally different principle from keyword indexing. Primitive features characterizing image content, such as color, texture, and shape are computed for both stored and query images, and used to identify the images most closely matching the query. There have been many approaches to decide and extract the features of images in the database. Towards this goal we propose a technique by which the color content of images is automatically extracted to form a class of meta-data that is easily indexed. The color indexing algorithm uses the back-projection of binary color sets to extract color regions from images. This technique uses equalized histogram image bins of red, green and blue color. The feature vector is composed of mean, standard deviation and variance of 16 histogram bins of each color space. The new proposed methods are tested on the database of 600 images and the results are in the form of precision and recall.
Journal of Big Data, 2020
Glioblastoma (GBM) is a stage 4 malignant tumor in which a large portion of tumor cells are repro... more Glioblastoma (GBM) is a stage 4 malignant tumor in which a large portion of tumor cells are reproducing and dividing at any moment. These tumors are life threatening and may result in partial or complete mental and physical disability. In this study, we have proposed a classification model using hybrid deep belief networks (DBN) to classify magnetic resonance imaging (MRI) for GBM tumor. DBN is composed of stacked restricted Boltzmann machines (RBM). DBN often requires a large number of hidden layers that consists of large number of neurons to learn the best features from the raw image data. Hence, computational and space complexity is high and requires a lot of training time. The proposed approach combines DTW with DBN to improve the efficiency of existing DBN model. The results are validated using several statistical parameters. Statistical validation verifies that the combination of DTW and DBN outperformed the other classifiers in terms of training time, space complexity and cla...
International Journal of Engineering & Technology, 2018
Countering digital dangers, particularly assault detection, is a testing region of research in th... more Countering digital dangers, particularly assault detection, is a testing region of research in the field of data affirmation. Intruders utilize polymorphic instruments to disguise the assault payload and dodge the detection methods. Many supervised and unsupervised learning comes closer from the field of machine learning and example acknowledgments have been utilized to expand the adequacy of intrusion detection systems (IDSs). Supervised learning approaches utilize just marked examples to prepare a classifier, however getting adequate named tests is lumbering, and requires the endeavors of area specialists. Notwithstanding, un-marked examples can without much of a stretch be acquired in some genuine issues. Contrasted with super-vised learning approaches, semi-supervised learning (SSL) addresses this issue by considering expansive number of unlabeled examples together with the marked examples to fabricate a superior classifier. In today’s age security is a big issue and every day w...
Advanced Composites Letters, 2018
Composites are widely used in several applications ranging from automotive to aircraft industry d... more Composites are widely used in several applications ranging from automotive to aircraft industry due to their high strength to weight ratio. More often than not drilling on these composite laminates are conducted to serve some functional or aesthetic requirement. Delamination caused due to drilling pose a severe problem to the integrity of the structure. It is often not possible to develop an exact mathematical model to predict the delamination associated with such drilling. So, in this paper, an empirical model is developed based on the extensive experiments performed on polyester composite reinforced with chopped fibreglass. To account for the various parameters a Box-Behnken design of experiments is conducted for four parameters (material thickness, drill diameter, spindle speed, and feed rate) each having threedistinct levels. Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) techniques are then used for predicting the global optimum (minimum delamination factor). The ...
Handbook of Research on Emerging Perspectives in Intelligent Pattern Recognition, Analysis, and Image Processing
Fruit and vegetables market is getting highly selective and requiring their suppliers to distribu... more Fruit and vegetables market is getting highly selective and requiring their suppliers to distribute the fruits of high standards of quality and good appearance. So the growing need to supply quality fruits within a short period of time has given rise to development of Automated Grading of fresh market fruits. The objective of this chapter is to classify apples into three grades based on its attributes such as color, size and weight. Initially apple image database is created. Next each image is analyzed using image processing software where images are first preprocessed and useful features like color and size are extracted from the images. Fuzzy logic is used for classification. Color, size features are represented as a fuzzy variables which are used for classification. The apples of different classes are graded into three grades viz. Grade1, Grade2 and Grade3 on the basis of combination of parameters mentioned above.
Research Advances in the Integration of Big Data and Smart Computing
Though image segmentation is a fundamental task in image analysis; it plays a vital role in the a... more Though image segmentation is a fundamental task in image analysis; it plays a vital role in the area of image processing. Its value increases in case of medical diagnostics through medical images like X-ray, PET, CT and MRI. In this chapter, an attempt is taken to analyze an MRI brain image. It has been segmented for a particular patch in the brain MRI image that may be one of the tumors in the brain. The purpose of segmentation is to partition an image into meaningful regions with respect to a particular application. Image segmentation is a method of separating the image from the background, read the contents and isolating it. In this chapter both the concept of clustering and thresholding technique have been used. The standard methods such as Sobel, Prewitt edge detectors is applied initially. Then the result is optimized using GA for efficient minimization of the objective function and for improved classification of clusters. Further the segmented result is passed through a Gaussian filter to obtain a smoothed image.
Journal of Minerals and Materials Characterization and Engineering, 2013
Two low-grade siliceous manganese ores such 1) siliceous crystalline and 2) siliceous cherty type... more Two low-grade siliceous manganese ores such 1) siliceous crystalline and 2) siliceous cherty types from north Orissa, India was mineralogically characterized and investigated for their possible upgradation. Both the Mn-ore types were subjected to different physical beneficiation techniques under identical conditions and results reported. The results revealed that in the case of low-grade siliceous crystalline type Mn-ore a feed having 26% Mn could be upgraded to more than 45% Mn by dry magnetic separation with 69% recovery at 1.00 tesla magnetic intensity. But the cherty type Mnore could not respond well to any of the beneficiation techniques, particularly dry magnetic separation, because of poor liberation even at a size fraction below 100 mesh, though the other type gives best result at this size fraction.
International Journal of Computational Vision and Robotics, 2010
In this paper, a novel algorithm for object tracking in image sequences using local colour histog... more In this paper, a novel algorithm for object tracking in image sequences using local colour histogram method (LCHM) is presented. In order to represent the object to be tracked, the proposed local colour histogram model divides the image into distinct blocks of same size and encodes the colour distribution of each local block. The histogram of each local block of the query image is compared in parallel with the corresponding local block of the test image(s) and the similarity measure is computed using a metric and compared against a threshold. Histogram matching is performed by distance measures like histogram intersection, Euclidean distance and histogram quadratic distance and their performance for detecting the presence of the object in the image is compared. Experimental results show that for retrieval of visually similar object from the image sequences, the local histogram method gives good retrieval precision with speed.
Computers
Botanical plants suffer from several types of diseases that must be identified early to improve t... more Botanical plants suffer from several types of diseases that must be identified early to improve the production of fruits and vegetables. Mango fruit is one of the most popular and desirable fruits worldwide due to its taste and richness in vitamins. However, plant diseases also affect these plants’ production and quality. This study proposes a convolutional neural network (CNN)-based metaheuristic approach for disease diagnosis and detection. The proposed approach involves preprocessing, image segmentation, feature extraction, and disease classification. First, the image of mango leaves is enhanced using histogram equalization and contrast enhancement. Then, a geometric mean-based neutrosophic with a fuzzy c-means method is used for segmentation. Next, the essential features are retrieved from the segmented images, including the Upgraded Local Binary Pattern (ULBP), color, and pixel features. Finally, these features are given into the disease detection phase, which is modeled using ...
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Papers by pradeep mallick