Prof. (Dr.) HEMANT KUMAR SONI | Amity University Gwalior (original) (raw)

Papers by Prof. (Dr.) HEMANT KUMAR SONI

International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), 2017

Emission and reflection of light from the objects depends on their internal energy levels and com... more Emission and reflection of light from the objects depends on their internal energy levels and composition present in the environment. The hot objects emit light while cold objects reflect or absorb light on some specific wavelengths. Spectrometer is equipment which is used to measure the spectra of light emitted. This project aims to use Ocean Optics USB 4000 spectrometer to measure the intensity spectra of light incident on its input port. The control and data acquisition of spectrometer will be developed under EPICS (Experimental Physics and Industrial Control System). The signals acquired from spectrometer will be then decomposed into small functions by using EMD (Empirical Mode Decomposition). The conspired solution for removing noise from the signals which are non-stationary and non-linear in nature is done by using EMD. Available processing techniques are either linear and stationary or nonlinear and stationary so they are unsuitable for the random signals received from the sample object. The objective of the processing technique is to bring out the useful data from the signal received after reconstruction and accurately measuring the position with the help of time of flight. This spectrometer finds several applications in Agricultural Measurements and Monitoring, Polymer Analysis, Medical Diagnostics etc. Enabling it with EPICS allows it to easily communicate and control along with other device like laser, imaging system, time synchronization etc. involved in experiment.

2018 Fourth International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB), 2018

Association rule mining is an important approach to data mining. It extracts useful and hidden in... more Association rule mining is an important approach to data mining. It extracts useful and hidden information. There are two methodologies to explore the association rules. One method is generating frequent pattern generation through apriori like algorithms whereas another methodology is by using the soft computing techniques especially genetic algorithm. Two important aspect which is most of the time unaddressed, is incremental data and multi-objective. Very few research work on incremental and multi-objective association rule mining has been done. This paper comprises of a comprehensive study of incremental data mining and a distinct study of genetic algorithms. It is observed that soft-computing technique perform better for association rules. There is also a need for Incremental algorithms which work better in the state of addition, deletion and modification of data. It is also found that strong need of Multi-objective Incremental association rule mining algorithm.

International Journal of Intelligent Engineering and Systems,, 2018

Support and confidence based Association rules mining algorithms have certain problems. Although ... more Support and confidence based Association rules mining algorithms have certain problems. Although other metrics like interest factor, comprehensibility, lift, correlation etc. are available to measure the interestingness of association rules. All the objectives are not suitable for each and every situation. All the objectives which were proposed in the literature, have some drawback, like correlation analysis gives equal importance to the items those are present and absent in transaction database. Resultant the rules generated by this, sometime mislead decision makers. Hence there is a strong need to define some new objectives for association rules that support in effective decision making. In this paper, authors proposed two novel objectives, high correlation and low correlation for 2-variables and 3-variables. These novel objectives clearly indicate that how much or how less two/three items are correlated. On the basis of this, decision makers can form their business strategies. An empirical algorithm for high and low correlative association rules generation is also proposed. With numeric evolution and experiments on the real-life data set, effectiveness has been measured and found that proposed algorithm gives better results.

International Journal of Advanced Research in Computer Science and Software Engineering, 2017

Generally association rule mining (ARM) algorithms, like the apriori algorithm, initial produce f... more Generally association rule mining (ARM) algorithms, like the apriori algorithm, initial produce frequent itemsets and afterward, from the frequent itemsets, the association rules that go beyond the minimum confidence threshold. When the data is in large volume, it takes number of scans to generate frequent items.It is a better idea if all the association rules generated directly without generating frequent items and reduce number of scanning of the database. The quality of an association rule cannot only be signified by its support or confidence. There are numerous other metrics existing to determine the quality of an association rule. Then, the concept of ARM can be present as a multi-objective optimization problem in which the objective is to find association rules while optimizing a number of such goodness and quality criteria at the same time. This point of view, evolutionary algorithms have been utilizing extensively for producing association rules. In this paper, in-depth study on various objectives for ARM and some evolutionary algorithms has been done.

INTERNATIONAL JOURNAL OF CURRENT ENGINEERING AND SCIENTIFIC RESEARCH (IJCESR), 2016

The Main objective of data mining is to find out the new, unknown and unpredictable information f... more The Main objective of data mining is to find out the new, unknown and unpredictable information from huge database, which is useful and helps in decision making. There are number of techniques used in data mining to identify frequent pattern and mining rules includes clusters analysis, anomaly detection, association rule mining etc. In this paper we discuss the main concepts of association rule mining, their stages and industries demands of data mining. The pitfalls in the existing techniques of association rule mining and future direction is also present.

2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), 2016

Data Mining is used in extracting valuable information in large volumes of data using exploration... more Data Mining is used in extracting valuable information in large volumes of data using exploration and analysis. With an enormous amount of data stored in databases and data warehouses requires powerful tools for analysis and discovery of frequent patterns and association rules. In data mining, Association Rule Mining (ARM) is one of the important areas of research, and requires more attention to explore rigorously because it is an prominent part of Knowledge Discovery in Databases (KDD). This paper present an empirical study on various algorithms for generating frequent patterns and association rules. To identifying , analyzing and understanding of the frequent patterns and related association rules from immense database, an strong tool is needed. It is observed that there is a strong need of an efficient algorithm who overcome the drawbacks of the existing algorithms. It is also found that the multiobjective association rules are more appropriate.

International Journal of Engineering and Technology (IJET), 2017

Association rule generation is a significant research area of data mining, which find out the rel... more Association rule generation is a significant research area of data mining, which find out the relation between the set of items. Significant association rule mainly based on two objectives-support and confidence. Some other metrics are also available to evaluate the goodness, effectiveness and interestingness of an association rule. Therefore, the association rule mining problem can be treated as multi-objective optimization problem. In this paper, we discuss the various objectives and their limitation. It is found that, each and every objective are not suitable in every situation. Other than this , most of the objectives are defined for 2-variables only. Simultaneously, in certain situation correlation analysis does not show the positive and negative correlation between items. Authors proposed two novel objectives, high correlation and low correlation for 2-variables and 3-variables. Through numerical analysis it is found that proposed objective clearly indicate about the positive and negative correlation among items. These objectives also gives appropriate solution in those cases, where previously defined objectives have some limitations. Simultaneously it also works in Simpson's paradox situation successfully.

International Journal of Engineering Research in Africa, 2016

Association rule mining is an iterative and interactive process of discovering valid, novel, usef... more Association rule mining is an iterative and interactive process of discovering valid, novel,
useful, understandable and hidden associations from the massive database. The Colossal databases
require powerful and intelligent tools for analysis and discovery of frequent patterns and association
rules. Several researchers have proposed the many algorithms for generating item sets and
association rules for discovery of frequent patterns, and minning of the association rules. These
proposals are validated on static data. A dynamic database may introduce some new association
rules, which may be interesting and helpful in taking better business decisions. In association rule
mining, the validation of performance and cost of the existing algorithms on incremental data are
less explored. Hence, there is a strong need of comprehensive study and in-depth analysis of the
existing proposals of association rule mining. In this paper, the existing tree-based algorithms for
incremental data mining are presented and compared on the baisis of number of scans, structure,
size and type of database. It is concluded that the Can-Tree approach dominates the other
algorithms such as FP-Tree, FUFP-Tree, FELINE Alorithm with CATS-Tree etc.This study also
highlights some hot issues and future research directions. This study also points out that there is a
strong need for devising an efficient and new algorithm for incremental data mining.

3rd International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics, 2017

Electronic commerce includes all business conduct through information and communication technolog... more Electronic commerce includes all business conduct through information and communication technology. Development of infrastructure, telecommunications, mobile technologies, the internet and social media in recent years, made a tremendous growth in business through e-commerce. Now e-commerce is a vital part of the economic development and helps in employment, FDI and GDP growth in the country. More and more companies are now on the internet and smooth the progress of transactions over the web. A large volume of data generated by these e-commerce sites which are updated very frequently. To increase the sell, customer retention and effective decision making, association rule mining play a significant role. There are number of association rule mining algorithms designed for e-commerce. A few algorithms also support the incremental and interactive association mining. In this paper, we conducted a comprehensive study of various association rule mining algorithms that support e-commerce transactions. The shortcoming of the various existing algorithms are also identified. Some plausible characteristics proposed as well for designing an efficient algorithm of e-commerce databases, which support incremental, interactive and multi-objective association rule mining.

4th International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics, 2018

Ransomware is now become a bad tool to earn money, theft data, hack the system or to stop the nor... more Ransomware is now become a bad tool to earn money, theft data, hack the system or to stop the normal functioning of the system. Ransomware is a malware that breaches the security of the system by using malicious codes. It encrypts the information and available data before noticing it. It hostage the data to earn money. Traditional vaccination system does not cure the infected system without obtaining information on ransomware. Since the data is encrypted hence cannot be recovered without encryption key. Users can avoid the infections of ransomware by updating vaccination system from time to time. However, this method has limited efficacy. This approach cannot trace modified ransomware with new pattern. Hence an active instead of a passive prevention method is urgently required. This paper explores the various ransomware attack. In this paper we converse the analysis of ransomware and the suggested action against ransomware attack. This paper also discusses ransomware removal and preventional methodology.

Journal of ICT Research and Applications, 2017

Time series data available in huge amounts can be used in decision-making. Such time series data ... more Time series data available in huge amounts can be used in decision-making. Such time series data can be converted into information to be used for forecasting. Various techniques are available for prediction and forecasting on the basis of time series data. Presently, the use of data mining techniques for this purpose is increasing day by day. In the present study, a comprehensive survey of data mining approaches and statistical techniques for rainfall prediction on time series data was conducted. A detailed comparison of different relevant techniques was also conducted and some plausible solutions are suggested for efficient time series data mining techniques for future algorithms.

I.J. Intelligent Systems and Applications, 2018

Time Series data is large in volume, highly dimensional and continuous updating. Time series data... more Time Series data is large in volume, highly dimensional and continuous updating. Time series data analysis for forecasting, is one of the most important aspects of the practical usage. Accurate rainfall forecasting with the help of time series data analysis will help in evaluating drought and flooding situations in advance. In this paper, Artificial Neural Network (ANN) technique has been used to develop one-month and two-month ahead forecasting models for rainfall prediction using monthly rainfall data of Northern India. In these model, Feed Forward Neural Network (FFNN) using Back Propagation Algorithm and Levenberg-Marquardt training function has been used. The performance of both the models has been assessed based on Regression Analysis, Mean Square Error (MSE) and Magnitude of Relative Error (MRE). Proposed ANN model showed optimistic results for both the models for forecasting and found one month ahead forecasting model perform better than two months ahead forecasting model. This paper also gives some future directions for rainfall prediction and time series data analysis research.