Sanjay Chakraborty

• B.Tech, M.Tech, PhD
• Associate Professor at Techno International New Town

This paper aims to develop a new deep learning model (DeepPneuNet) and evaluate its performance in predicting Pneumonia infection diagnosis based on patients' chest x‐ray images. We have collected 5856 chest x‐ray images that are labeled as either “pneumonia” or “normal” from a public forum. Before applying the DeepPneuNet model, a necessary featur...

Attackers are now using sophisticated techniques, like polymorphism, to change the attack pattern for each new attack. Thus, the detection of novel attacks has become the biggest challenge for cyber experts and researchers. Recently, anomaly and hybrid approaches are used for the detection of network attacks. Detecting novel attacks, on the other h...

In the ever-evolving world of cricket, the T20 format has captured the imaginations of fans worldwide, intensifying the anticipation for match outcomes with each passing delivery. This study explores the realm of predictive analytics, leveraging the power of machine learning to alleviate the suspense by forecasting T20 cricket match winners before...

The goal of artificial intelligence (AI), a field with a solid scientific foundation, is to enable machines to simulate human intelligence and problem-solving abilities. AI focuses on the study, development, and application of complex algorithms and computational models, with a particular focus on deep learning techniques. The application of artifi...

Attackers are now using sophisticated techniques, like polymorphism, to change the attack pattern for each new attack. Thus, the detection of novel attacks has become the biggest challenge for cyber experts and researchers. Recently, anomaly and hybrid approaches are used for the detection of network attacks. Detecting novel attacks, on the other h...

Regression analysis is intended to assist investors in identifying practical trends from historical data that aid in the formulation of their investment decisions. Stock market prediction is a technique for estimating future stock and other financial value prices for a corporation. Regression analysis is one of the most effective tools for predicti...

In this paper, we present a set of studies of detecting facemask to prevent COVID-19 spread using machine learning, deep learning, and artificial intelligence. It also explains some recent studies based on the hybrid concepts of deep learning and image processing techniques in this domain. This paper is mainly focused on the set of studies that ana...

The purpose of this paper is to show concisely how we can promote chatbots in the medical sector and cure infectious diseases. We can create awareness through the users and the users can get proper medical solutions to prevent disease. We created a preliminary training model and a study report to improve human interaction in databases in 2021. Thro...

Microarray technology has been successfully used in many biology studies to solve the protein–protein interaction (PPI) prediction computationally. For normal tissue, the cell regulation process begins with transcription and ends with the translation process. However, when cell regulation activity goes wrong, cancer occurs. Microarray data can prec...

Artificial intelligence is a core specialization of making smart machines, especially computer programs. It relates to the familiar task of using computers to understand human intelligence. AI is broadly classified to study computations that permit perception, reason, and action. This paper gives an overview idea of a Python-based personal assistan...

The main objective of this paper is to detect the infection rate of the SARS-Cov-2 virus among patients who are suffering from COVID with different symptoms. In this work, some data inputs from the intended patients (like contact with any COVID infected person and any COVID patient within 1 km.) are collected in the form of a questionnaire and then...

In this paper, a quantum image edge extraction technique is developed with the help of the classical Robinson operator. A novel enhanced quantum representation (NEQR) technique is used to represent the quantum image. A quantum methodology is proposed to implement the Robinson masks of eight directions and perform convolution operations with the qua...

Technologically the automobile sector is rising fast and the road vehicles on road is stepping up every day. On other hand, the safety of vehicles and riders also matters. We must ensure adequate safety in each vehicle. Our paper aims to provide safety features to a vehicle with real-time location access of the vehicle. Our prototype will consist o...

Incremental clustering is nothing but a process of grouping new incoming or incremental data into classes or clusters. It mainly clusters the randomly new data into a similar group of clusters. The existing K-means and DBSCAN clustering algorithms are inefficient to handle the large dynamic databases because, for every change in the incremental dat...

Scientists and analysts of machine learning and data mining have a problem when it comes to high-dimensional data processing. Variable selection is an excellent method to address this issue. It removes unnecessary and repetitive data, reduces computation time, improves learning accuracy, and makes the learning strategy or data easier to comprehend....

The main objective of this paper is to detect the infection rate of the SARS-Cov-2 virus amongst patients who are suffering from COVID with different symptoms. In this work, some data inputs from the intended patients (like contact with any COVID infected person, any COVID patient within 1KM, etc.) are collected in the form of a questionnaire and t...

Artificial Intelligence is a core specialization of making smart machines, especially computer programs. It relates to the familiar task of using computers to understand human intelligence. AI is broadly classified to study computations that permit perception, reason, and action. This paper gives an overview idea of a python-based personal assistan...

Technologically the automobile sector is rising fast and the road vehicles on road is stepping up everyday. On other hand, the safety of vehicles and riders also matters. We must ensure adequate safety in each vehicle. Our paper aims to provide safety features to a vehicle with real-time location access of the vehicle. Our prototype will consist of...

In this paper, we present a set of studies of detecting facemask to prevent COVID-19 spread using machine learning, deep learning, and artificial intelligence. It also explains some recent studies based on the hybrid concepts of deep learning and image processing techniques in this domain. This paper is mainly focused on the set of studies that ana...

Data mining is a process of discovering some necessary hidden patterns from a large chunk of data that can be stored in multiple heterogeneous resources. It has an enormous use to make strategic decisions by business executives after analyzing the hidden truth of data. Data mining one of the steps in the knowledge-creation process. A data mining sy...

Machine learning is a subset of AI. It’s a research project aimed at gathering computer programs capable of performing intelligent actions based on prior facts or experiences. Most of us utilize various machine learning techniques every day when we use Netflix, YouTube, Spotify recommendation algorithms, and Google and Yahoo search engines and voic...

A subset of accessible variants data is chosen for the learning approaches during the variant selection procedure. It includes the important one with the fewest dimensions and contributes the most to learner accuracy. The benefit of variant selection would be that essential information about a particular variant isn’t lost, but if just a limited nu...

Using supervised learning-based data classification and incremental clustering, an unknown example can be classified using the most common class among K-nearest examples. The KNN classifier claims, “Tell me who your neighbors are, and it will tell you who you are”. The supervised learning-based data classification and incremental clustering techniq...

Data modelling, which is based on mathematics, statistics, and numerical analysis, is used to look at clustering. Clusters in machine learning allude to hidden patterns; unsupervised learning is used to find clusters, and the resulting system is a data concept. As a result, clustering is the unsupervised discovery of a hidden data concept. The comp...

Data mining and machine learning are the most expressive research and application domain. All real-time application directly or indirectly depends on data mining and machine learning. There are many relevant fields, like data analysis in finance, retail, telecommunications sector, analyzing biological data, other scientific uses, and intrusion dete...

COVID‐19 has already affected the world with this deadly virus, resulting in over 3.5 lakh deaths. The behavior of this virus is extraordinarily peculiar and mutates frequently. So, the scientific community faces the problems to analyze and forecast the virus's growth and transmission capability. The combined effort of powerful Artificial intellige...

The amalgamation of 'Quantum computing' with image processing represents the various ways of handling images for different purposes. In this paper,an image denoising scheme based on quantum wavelet transform is proposed.A noisy image is embedded into the wavelet co-efficients of the original image. As a result,it affects the visual quality of the o...

Background COVID-19 (Coronavirus Disease-19), a disease caused by the SARS-CoV-2 virus, has been declared as a pandemic by the World Health Organization on March 11, 2020. Over 15 million people have already been affected worldwide by COVID-19, resulting in more than 0.6 million deaths. Protein–protein interactions (PPIs) play a key role in the cel...

Quantum image processing reduces the gap between quantum computing and image processing fields. The principles of quantum computing explore the image processing in various ways of handling (capture, manipulate, extract) images of different formats and for different purposes. In this paper, an image denoising scheme based on quantum wavelet transfor...

Ensemble methods are algorithms that combine various models together to give higher accuracy than individual models. The ensemble methods used here are majority voting, XGBoost, and random forest. Several decision trees are combined using voting classifier, Random forest tree, and XGBoost. These are considered as the best universal models which are...

Quantum machine learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning. It generally exposes the synthesis of important machine learning algorithms in a quantum framework. Dimensionality reduction of a dataset with a suitable feature selection strategy is one of the most important ta...

Emotion detection is a booming research field in Brain Computer Interfacing where researchers are trying to find the various mental statuses of people in different situations such as, while watching movies, listening music etc. The interfaces like mouse and keyboards are used to communicate computers via human hands similarly an emerging field call...

In today’s world of enormous amounts of data, it is very important to effectively extract knowledge from it. This can be accomplished by feature subset selection. Feature subset selection is the method of selecting a minimum number of features with the help of which our machine can learn and accurately predict which class a particular data belongs...

Search-based methods that use matrix- or vector-based representations of the dataset are commonly employed to solve the problem of feature selection. These methods are more generalized and easy to apply. Recently, a set of algorithms have started using graph-based representation of the dataset instead of the traditional representations. These metho...

Traditional machine learning shares several benefits with quantum information processing field. The study of machine learning with quantum mechanics is called quantum machine learning. Data clustering is an important tool for machine learning where quantum computing plays a vital role in its inherent speed up capability. In this paper, a hybrid qua...

Cluster analysis has been widely used in several disciplines, such as statistics, software engineering, biology, psychology and other social sciences in order to identify natural groups in large amount of data. K-means is one of the most popular clustering algorithms. In spite of several advances in K-means clustering algorithm, it suffers in some...

Feature selection is the tool required to study data with high dimensions in an easy way. It involves extracting attributes from a dataset having a large number of attributes in such a way so as the reduced attribute set can describe the dataset in a manner similar to that of the entire attribute set. Reducing the features of the data and selecting...

Brain-computer interfacing (BCI) is a bridging technology between a human brain and a device that enables signals from the brain to direct some external activity, such as control of a cursor or a prosthetic limb. In practice, brain signals are captured by the popular EEG technique and then the scalp voltage level is transferred into corresponding c...

Brain-computer interfacing (BCI) is a communication bridge between human brain and computer. BCI system consisted of 4 sections (Signal acquisition, Signal processing, Feature extraction and classifications, Application Interface). In this survey paper, we try to elaborate the entire structure of BCI process especially emphasizing on feature extrac...

Feature selection remains one of the most important steps for usability of a model for both supervised and unsupervised classification. For a dataset, with n features, the number of possible feature subsets is 2^n. Even for a moderate size of n, there is a combinatorial explosion in the search space. Feature selection is a NP-Hard problem, hence fi...

Image processing on quantum platform is a hot topic for researchers now a day. Inspired from the idea of quantum physics, researchers are trying to shift their focus from classical image processing towards quantum image processing. This paper starts with a brief review of the principles which underlie quantum computing, and also deals with some of...

Brain-Computer Interfacing(BCI) helps physically disabled people to control multidimensional cursor movement in a real-life scenario. Noninvasive BCI techniques play a major role for this purpose. In this paper, we have proposed three algorithms of cursor movement using three well-known clustering methods (Minimum distance, DB-Scan and Gaussian Mix...

Emotion detection is one of the popular research topics in “Brain–Computer Interfacing” where researchers are trying to find the various emotional states of people. EEG signal is widely used for detecting different categories of emotions. The EEG signal is captured through multiple electrode channels, very few of them are useful for emotion detecti...

The book, to the best of the author’s knowledge, is dealing with some traditional and modern aspects of ―Machine learning based human brain-computer interaction‖. It provides an in-depth analysis of the machine learning models and algorithms and demonstrates their applications in real world problems with respect to brain-computer interaction. The b...

Trend analysis of datasets is one kind of text mining procedure which is a part of natural language processing. Product trend analysis is basically the method of analyzing the reviews given by the customer to a particular product. In this paper, we have used various classifier's algorithms (such as, Naive Bayes, K-NN and SVM) to determine the posit...

Feature selection plays a very important role in all pattern recognition tasks. It has several benefits in terms of reduced data collection effort, better interpretability of the models and reduced model building and execution time. A lot of problems in feature selection have been shown to be NP – Hard. There has been significant research in featur...

Color image segmentation is one of the very useful applications in the field of image processing at present. The main aim of this article is to deal with the application of quantum computation aspects to image processing task. The benefit of using quantum version of its corresponding classical image processing task is the speedup and efficiency. In...

Now a days, ’Brain Computer Interface’ is one of the fastest growing technologies in which researchers are trying to communicate between human brain and external devices effectively.Generally, brain signal can be captured by EEG technique and the scalp voltage is measured in timely manner.The signal is then transferred to the external devices for m...

Image representation in a multilevel quantum system is always an important issue now a day. This paper initially proposes two approaches which help to represent color images in a ternary quantum system based on the modified concept of famous FRQI model and normalized amplitude based quantum representation model. But these approaches are complicated...

Security is one of the major concerns in generic computing system which is used in our day to day life to deal with various aspects like education, banking, communication, entertainment etc. Security is obtained to prevent threats that usually affect the end users of other areas as well (like grid computing, cloud computing etc.). Malicious code de...

Image processing on quantum platform is a hot topic for researchers now a day. Inspired from the idea of quantum physics, researchers are trying to shift their focus from classical image processing towards quantum image processing. Storing and representation of images in a binary and ternary quantum system is always one of the major issues in quant...

Thousands of human lives are lost every year around the globe, apart from significant damage on property, animal life, etc., due to natural disasters (e.g., earthquake, flood, tsunami, hurricane and other storms, landslides, cloudburst, heat wave, forest fire). In this paper, we focus on reviewing the application of data mining and analytical techn...

Clustering is a powerful tool which has been used in several applications (such as, fraud detection, pattern matching etc.). It is known to all that quantum counterpart is more superior to classical one on some specific areas. Quantum computation can solve certain problems much faster than classical computation. This paper mainly proposes a quantum...

Security is one of the major concerns in cloud computing now-a-days. Malicious code deployment is the main cause of threat in today’s cloud paradigm. Antivirus software unable to detect many modern malware threats which causes serious impacts in basic cloud operations. This paper counsels a new model for malware detection on cloud architecture. Thi...

Distributing workloads across multiple computing resources are one of the major challenges in a cloud computing environment. This paper is being discussed over the basic obstacles of load balancing in cloud environment. The paper looks beyond the problems faced by the cloud system to overcome those through probable improvised techniques. This is a...

The advent of Web 2.0 has led to an increase in the amount of sentimental content available in the Web. Such content is often found in social media web sites in the form of movie or product reviews, user comments, testimonials, messages in discussion forums etc. Timely discovery of the sentimental or opinionated web content has a number of advantag...

Image processing on quantum platform is a hot topic for researchers now a day. Inspired from the idea of quantum physics, researchers are trying to shift their focus from classical image processing towards quantum image processing. Storing and representation of images in a binary and ternary quantum system is always one of the major issues in quant...

Ternary quantum logic plays a very important role for building high speed and efficient futuristic computers. It has several advantages over classical computing and binary quantum circuits. In this paper, the realization of basic ternary circuits for adder/subtractor, encoder and priority encoder are proposed and designed. These circuits are very e...

The advent of Web 2.0 has led to an increase in the amount of sentimental content available in the Web. Such content is often found in social media web sites in the form of movie or product reviews, user comments, testimonials, messages in discussion forums etc. Timely discovery of the sentimental or opinionated web content has a number of advantag...

Thousands of human lives are lost every year around the globe, apart from significant damage on property, animal life, etc., due to natural disasters (e.g., earthquake, flood, tsunami, hurricane and other storms, landslides, cloudburst, heat wave, forest fire). In this paper, we focus on reviewing the application of data mining and analytical techn...

In this paper, we describe a set of packet control algorithms to be deployed in intermediate system routers. They improve TCP routine in wireless networks with packet stoppage variations and long sudden packet delays. The NS-2 replication results show that the proposed algorithms reduce the adverse effect of spurious fast re transmits and timeouts...

Machine learning methods are increasingly being used in conjunction with conventional meteorological observations in the synoptic analysis and conventional weather forecast to extract information of relevance for agriculture and food security of the human society in India. Density based clustering approach is incrementally used to predict the futur...

Over the years, a variety of web services have started using server-side scripting to deliver results back to a client as a paid or free service; one such server-side scripting language is Java Server Pages (JSP). Also Extensible markup language (XML), is being adopted by most web developers as a tool to describe data.Therefore, we present a conver...

Data compression technique helps us to reduce the size of such large volumes of data that reduces network bandwidth and the storage spaces as well. So text compression is a very important concept in Data Management. The research aim of this paper is to present a new lossless data compression technique for English text compression. It is basically a...

“Clustering” the significance and application of this technique is spread over various fields. Clustering is an unsupervised process in data mining, that is why the proper evaluation of the results and measuring the compactness and separability of the clusters are important issues. The procedure of evaluating the results of a clustering algorithm i...

Data mining is a popular concept of mined necessary data from a large set of data. Data mining using clustering is a powerful way to analyze data and gives prediction. In this paper non structural time series data is used to forecast daily average temperature, humidity and overall weather conditions of Kolkata city. The air pollution data have been...

The incremental K-means clustering algorithm has already been proposed and analysed in paper [Chakraborty and Nagwani, 2011]. It is a very innovative approach which is applicable in periodically incremental environment and dealing with a bulk of updates. In this paper the performance evaluation is done for this incremental K-means clustering algori...

Network & internet security is the burning question of today’s world and they are deeply related to each other for secure successful data transmission. Network security approach is totally based on the concept of network security services. In this paper, a new system of network security service is implemented which is more secure than conventional...

Clustering is a powerful tool which has been used in several forecasting works, such as time series forecasting, real time storm detection, flood forecasting and so on. In this paper, a generic methodology for weather forecasting is proposed by the help of incremental K-means clustering algorithm. Weather forecasting plays an important role in day...

Incremental K-means and DBSCAN are two very important and popular clustering techniques for today"s large dynamic databases (Data warehouses, WWW and so on) where data are changed at random fashion. The performance of the incremental K-means and the incremental DBSCAN are different with each other based on their time analysis characteristics. Both...

Incremental K-means and DBSCAN are two very important and popular clustering techniques for today‟s large dynamic databases (Data warehouses, WWW and so on) where data are changed at random fashion. The performance of the incremental K-means and the incremental DBSCAN are different with each other based on their time analysis characteristics. Both...

Study of this paper describes the incremental behaviours of partitioning based K-means clustering. This incremental clustering is designed using the cluster’s metadata captured from the K-Means results. Experimental studies shows that this clustering outperformed when the number of clusters increased, number of objects increased, length of the clus...

This paper describes the incremental behaviours of Density based clustering. It specially focuses on the Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and its incremental approach.DBSCAN relies on a density based notion of clusters.It discovers clusters of arbitrary shapes in spatial databases with noise.In incremen...