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Search Results: 1 - 10 of 3826 matches for " Sanjay Chakraborty "
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Capacity Calculation and Sub-Optimal Power Allocation Scheme for OFDM-Based Systems  [PDF]
Somnath Das, Abhishek Chakraborty, Sanjay Kumar
Communications and Network (CN) , 2012, DOI: 10.4236/cn.2012.44034
Abstract: For emerging cellular wireless systems, the mitigation of inter-cell interference is the key to achieve a high capacity and good user experience. This paper is devoted to the performance analysis of interference mitigation techniques for the downlink in an orthogonal frequency division multiple access (OFDMA) network, with a focus on the Long Term Evolution-Advanced (LTE-A) standard. Here we have derived a general closed-form equation of system capacity taking multiple cells into consideration and then we have investigated a coordination technique for interference mitigation. For the given interference constraint, how power should be transmitted into each OFDM sub-carrier for prevailing channel condition such that the total transmission rate of the base station can be maximized.
Canonical PSO Based k-Means Clustering Approach for Real Datasets
Lopamudra Dey,Sanjay Chakraborty
Computer Science , 2014,
Abstract: "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 is known as cluster validity measure. Different types of indexes are used to solve different types of problems and indices selection depends on the kind of available data.This paper first proposes Canonical PSO based K-means clustering algorithm and also analyses some important clustering indices (intercluster, intracluster) and then evaluates the effects of those indices on real-time air pollution database,wholesale customer, wine, and vehicle datasets using typical K-means, Canonical PSO based K-means, simple PSO based K-means,DBSCAN, and Hierarchical clustering algorithms.This paper also describes the nature of the clusters and finally compares the performances of these clustering algorithms according to the validity assessment. It also defines which algorithm will be more desirable among all these algorithms to make proper compact clusters on this particular real life datasets. It actually deals with the behaviour of these clustering algorithms with respect to validation indexes and represents their results of evaluation in terms of mathematical and graphical forms.
A New Advanced User Authentication and Confidentiality Security Service
Sanjay Majumder,Sanjay Chakraborty,Suman Das
Computer Science , 2014, DOI: 10.5120/16257-5904
Abstract: 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 network security services. This technique is mainly deals with two essential network security services, one is user authentication and other is data confidentiality. For user authentication this paper introduces Graphical Username & Voice Password approaches which provides better security than conventional username & password authentication process. In data confidentiality section this paper introduces two layer private key for both message encryption & decryption which is mainly applicable on 8 bit plain text data. This paper also provides the hints of introducing other two network security services (integrity and non-repudiation) as a future work.
Analysis and Study of Incremental DBSCAN Clustering Algorithm
Sanjay Chakraborty,N. K. Nagwani
Computer Science , 2014,
Abstract: 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 incremental approach, the DBSCAN algorithm is applied to a dynamic database where the data may be frequently updated. After insertions or deletions to the dynamic database, the clustering discovered by DBSCAN has to be updated. And we measure the new cluster by directly compute the new data entering into the existing clusters instead of rerunning the algorithm.It finally discovers new updated clusters and outliers as well.Thus it describes at what percent of delta change in the original database the actual and incremental DBSCAN algorithms behave like same.DBSCAN is widely used in those situations where large multidimensional databases are maintained such as Data Warehouse.
Performance Evaluation of Incremental K-means Clustering Algorithm
Sanjay Chakraborty,N. K. Nagwani
Computer Science , 2014,
Abstract: 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 algorithm using air pollution database. This paper also describes the comparison on the performance evaluations between existing K-means clustering and incremental K-means clustering using that particular database. It also evaluates that the particular point of change in the database upto which incremental K-means clustering performs much better than the existing K-means clustering. That particular point of change in the database is known as "Threshold value" or "% delta change in the database". This paper also defines the basic methodology for the incremental K-means clustering algorithm.
Can JSP Code be Generated Using XML Tags?
Neha Bothra,Kritika Jain,Sanjay Chakraborty
Computer Science , 2015, DOI: 10.15864/ajac.v2i3.138
Abstract: 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 conversion method which uses predefined XML tags as input and generates the corresponding JSP code. However, the end users are required to have a basic experience with web pages. This conversion method aims to reduce the time and effort spent by the user (web developer) to get acquainted with JSP. The conversion process abstracts the user from the intricacies of JSP and enables him to focus on the business logic.
Weather forecasting using Convex hull & K-Means Techniques An Approach
Ratul Dey Sanjay Chakraborty Lopamudra Dey
Computer Science , 2015,
Abstract: 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 taken from West Bengal Pollution Control Board to build the original dataset on which the prediction approach of this paper is studied and applied. This paper describes a new technique to predict the weather conditions using convex hull which gives structural data and then apply incremental K-means to define the appropriate clusters. It splits the total database into four separate databases with respect to different weather conditions. In the final step, the result will be calculated on the basis of priority based protocol which is defined based on some mathematical deduction.
Performance Comparison of Incremental K-means and Incremental DBSCAN Algorithms
Sanjay Chakraborty,N. K. Nagwani,Lopamudra Dey
Computer Science , 2014,
Abstract: 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 algorithms are efficient compare to their existing algorithms with respect to time, cost and effort. In this paper, the performance evaluation of incremental DBSCAN clustering algorithm is implemented and most importantly it is compared with the performance of incremental K-means clustering algorithm and it also explains the characteristics of these two algorithms based on the changes of the data in the database. This paper also explains some logical differences between these two most popular clustering algorithms. This paper uses an air pollution database as original database on which the experiment is performed.
Weather Forecasting using Incremental K-means Clustering
Sanjay Chakraborty,N. K. Nagwani,Lopamudra Dey
Computer Science , 2014,
Abstract: 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 to day applications.Weather forecasting of this paper is done based on the incremental air pollution database of west Bengal in the years of 2009 and 2010. This paper generally uses typical K-means clustering on the main air pollution database and a list of weather category will be developed based on the maximum mean values of the clusters.Now when the new data are coming, the incremental K-means is used to group those data into those clusters whose weather category has been already defined. Thus it builds up a strategy to predict the weather of the upcoming data of the upcoming days. This forecasting database is totally based on the weather of west Bengal and this forecasting methodology is developed to mitigating the impacts of air pollutions and launch focused modeling computations for prediction and forecasts of weather events. Here accuracy of this approach is also measured.
Subclinical haemorrhagic tendency exists in patients with β-thalassaemia major in early childhood
Abhishek Maiti,Amartya Chakraborti,Puranjoy Chakraborty,Sanjay Mishra
Australasian Medical Journal , 2012,
Abstract: BackgroundAlterations of coagulation profile have been reported in patients with β-thalassaemia major (β-TM).MethodTo investigate this in the paediatric population, we studied haemostatic parameters in pre-transfusion blood samples from 50 non-splenectomised transfusion-dependent children with β-TM (mean age 6±2.5 years) and in blood from 25 healthy controls.ResultsLaboratory evaluation showed thrombocytopenia in 40%, prolongation of prothrombin time (PT) in 12% and prolongation of activated partial thromboplastin time (APTT) in 6% of the patients. Mean values for PT, APTT and platelet count (PC) were all raised in the patient population compared with the controls. The alteration of coagulation status was significant for PT (p value <0.005) and APTT (p value <0.0001). However, the change for PC was not significant (p value >0.05). No significant liner correlation could be identified between PT, APTT, PC of the patients and interval between transfusions (in days) or days since last transfusion.ConclusionThe findings from this study suggest that a subclinical haemorrhagic tendency exists in patients with β-TM at a very early age. The intrinsic pathway appears to be more affected than the extrinsic pathway.
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