Prabhat Ranjan

• Doctor of Philosophy
• Professor (Associate) at Central University of South Bihar

As cloud computing systems grow more complex, traditional access control models like Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC) often fail to provide the required granularity and adaptability to secure dynamic, distributed environments. This paper proposes an innovative Operation-Centric Access Control (OCAC) model d...

In the rapidly evolving realm of cloud computing, the surging adoption and its profound role in modern IT infrastructures necessitate a vigilant exploration of emerging security challenges and threats. While prior studies have categorized threats, the need for integrated solutions is evident, and an integrated study is evident, with only a limited...

In this review, we explore into the critical realm of access control in cloud computing, a fundamental component ensuring data and resource confidentiality, integrity, and availability. The increasing prevalence of cloud computing has led to a growing need for solid and effective access control solutions. However, the emergence of connected devices...

Bitcoin, a secure and transparent peer-to-peer payment system, is the origin of blockchain technology. Blockchain has the capability to stores data records in a secure and immutable way without the centralized control. It achieves a goal through a novel decentralized consensus that provides a platform in trustless environment. Consensus mechanism p...

High dimensional data have brobdingnagian number of features, but not all features are useful. Irrelevant and redundant features may even reduce the classification accuracy. Feature selection is a process of selecting a subset of relevant features to decrease the dimensionality of data. When applied on high dimensional datasets (Big Data) the featu...

Meteorological drought in India arises due to significant deficiency of rainfall for abnormal periods over an area. The large spatial and temporal variability of Indian summer monsoon rainfall (ISMR) over the Eastern Gangetic Plain (EGP) of India triggers meteorological drought (further leading to agricultural and hydrological drought), with widesp...

Particle swarm optimisation (PSO) is a popular nature inspired computing method due to its fast and accurate performance, exploration and exploitation capability, cognitive and social behaviour and has fewer parameters to adjust. Recently, an improved binary PSO (IBPSO) was proposed by Chuang et al. (2008) to avoid getting trapped in local optimum...

Feature selection is the utmost requirement to deal with high dimensional datasets. Fuzzy logic and particle swarm optimization are the two very popular soft computing methods which have used for feature selection. In this paper different variants of PSO are summarized to explore the latest development in PSO. The survey has been grouped in three c...

Visual analytics uses interactive visualizations in order to incorporate user’s knowledge and cognitive capability into data analytics processes. The progressive visual analytic paradigm simplifies the analytic process when it comes to large datasets. It uses the interactive sequential pattern mining algorithm which reports patterns as it finds the...

Technological developments have reshaped the scientific thinking, since observation from experiments and real world are massive. Each experiment is able to produce information about the huge number of variables (High dimensional). Unique characteristics of high dimensionality impose various challenges to the traditional learning methods. This paper...

Making software with high quality assurance is becoming difficult due to software testing and maintenance is costly and time consuming phases of software development life cycle (SDLC). This takes more than half the cost of software development. Researchers are already proposed methods to reduce the cost and time of software testing. However, these...

Dimensionality reduction of high dimensional data still perceives challenges and hence, it is pertinent to introduce new methods or revamp existing methods. In this study, a new ternary particle swarm optimization (TPSO) algorithm has been proposed, in which particle is a string of "trit", which is the smallest unit of information. Ternary string i...

Feature selection is the utmost requirement to deal with high dimensional datasets. In this paper novel fuzzy ternary PSO (FTPSO) method has been proposed. The analysis of proposed algorithm shows that map reduce concept has decreased the processing time of algorithm. Experimental results show the benefits of proposed MRFTPSO.

Particle swarm optimization and fuzzy logic have shown their fruits for many years across the fields of science. Fuzzy logic acts as an intelligent layer to any conventional system. Recently fuzzy logic has been used to improve the performance of particle swarm optimization (PSO). This paper presents a novel fuzzy rule based binary PSO (FRBPSO) for...

In this era of big data, a huge volume of data is produced. Storage and analysis of such data is not possible by traditional techniques. In this paper, a good method to implement the MapReduce Apriori algorithm using vertical layout of database along with power set and concept of Set Theory of Intersection have been proposed. The vertical layout ha...

The aim of the proposed methodology is to improve the quality and reduce the testing efforts of the software. The methodology consists of three approaches namely pre development, during development and post development. The first approach finds the fault prone module in software. The second approach finds the fault in different phases of the softwa...

Rainfall and corresponding Runoff estimation are substantially dependent on various geographic, climatic, and biotic features of the catchment or basin under study and these factors often induce a linear, non-linear or highly complex relation between rainfall and runoff. The few of key factors include precipitation, percolation, infiltration, evapo...

Software testing is one of the important and crucial phases of software development life cycle. In context of time, cost and effective testing the prime need is test case optimization. In present testing scenario the meta-heuristic methods are used for optimization problem and also provide good optimized result. The most popular algorithm used meta...

Outlier detection is a great area of interest in the field of data mining. It has been observed that there exist several application domains in which direct mapping is possible between outliers in data and real world anomalies. Outlier detection is an important research topic in various application domains and knowledge disciplines. This paper pres...

High Dimensional data spaces are very common in areas like medicine, biology, bioinformatics, web data, text documents. This high dimensionality brings different challenges when applied with algorithms such as slowness, sensitivity to initial values, either early or slow convergence etc. Various algorithm for the large data set has been proposed in...

The arrangement of number in several ways gives us amazing & surprising objects, objects that we try to define historically mathematically, or relate them to religious context. This paper also presents such a magical arrangement of numbers called Magic Square, this paper is mainly concerned with the algorithm for finding the magic squares. In this...

Particle swarm optimization (PSO) is a population based optimization technique, inspired by social behavior of animal and birds, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a brief overview of the basic concepts of clustering techniques proposed in last four decades and a quick review of different...

This paper throws a light on the available clustering techniques and algorithms, k-means is used to cluster standard and satellite image in RGB and HSV color space. Normally satellite images comes with data and noises, in order to extract meaningful information efficiently there is a need of image clustering and performance of clustering based on p...

Data outbreak following the flourishing of technologies like cloud computing, social networks, and many more stimulate emergence of new paradigm called “Big Data”, which not only improved the decision-making, but also provided a means to easy privacy violation of an individual. Existing data anonymization techniques adhering to certain privacy mode...

Regression testing is unavoidable maintenance activity that is performed several times in software development life cycle. Optimization of regression test case is required to minimize the test case (which will in-turn reduce the time and cost of testing) and to find the fault in early testing activity. The two widely used regression test case optim...

High dimensionality is the problem for many research areas. There are huge number of dimensionality reduction methods are available. Broadly they are grouped into two categories feature selection and feature extraction. Feature selection methods select a subset of features based on some criteria while feature extraction methods transform the data i...

The early information from requirement and design phase is very effective in software testing phase and it is also admitted by software requirement engineers (SRE) and software designer’s community. However still most of the regression testing techniques work is performed by taking conventional information from software code. It will be more benefi...

Information Technology has produced huge amounts of data and these data need to be processed to extract information hidden in it. Feature selection techniques often come handy to process these data efficiently. In this paper, a novel approach for feature selection GA-CFS is proposed. The approach is based on Genetic Algorithm (GA), Correlation base...

Virtual Output Queuing (VOQ) is used to overcome the head-ofline (HoL) blocking problem in input-queued (IQ) packet switches. There are a lot of research has been devoted to design iterative arbitration algorithms to find maximum throughput of this architecture. In this paper approximating maximum size matching (MSM) algorithm called Selective Requ...

Metrics plays an important role in the software modularization. Several metrics have been discussed so far. This paper describes a metrics based on control structures which is a minor modification of McCabe's approach. Proposed generalized approach is a control structure based complexity (CSBC) which is used to compute the complexity by counting th...

To generate efficient test data is one of the major problems during testing phase. This task is complex and very time consuming and researchers proposed different methods for generating the test data. This paper proposed a technique that uses the source code of the program, transforms it into Control Flow Graph (CFG), thereafter calculate the outde...

In this paper, an agent-based open and adaptive system development process has been proposed which continuously change and evolve to meet new requirements. The proposed methodology is based on a model-based technique that provides a specific model for the type of information to be gathered and uses this model to drive the domain specific analysis p...

Agent oriented software development is generally motivated by the need of open and adaptive systems development that continuously change and evolve to meet new requirements. In this paper, we propose model-based technique that provides a specific model for the type of information to be gathered and uses this model to drive the domain specific analy...

It is unlikely that a single analysis model will be able to analyze the requirements for the complete system. For capturing and analyzing the overall requirement separate models and analyst experts are needed. It is necessary to model at different level of abstraction so that both domain experts and developers can get an idea of the overall system...

This paper provides three criterias of clustering within a placement cell multi-agent system. Our goal is to group roles with similar objectives. Optimize the system performance by minimizing the overall interaction, data transmission and competition of shared resource between roles/agents. This paper presents a novel systematic approach to optimiz...

An agent-based system is a complex software system with functional and non-functional requirements. Non-functional requirements (NFRs) are crucial software requirements that have been specified early in the software development process while eliciting the functional requirements (FRs) in agent oriented software development. Some of the functional a...

Many well known agent-oriented software analysis method have been proposed such as GAIA, ROADMAP, PROMETHUS and TROPOS methodology. Out of the numerous proposed methods, selecting a particular analysis method is again a problem, as every method has its own advantages and disadvantages. There is an ongoing effort to find such a comprehensive agent o...

Agent oriented software development is generally motivated by the need of open and adaptive systems development that continuously change and evolve to meet new requirements. In this paper, we propose an agent-based open and adaptive system development process. The proposed methodology is based on model-based techniques and provides a specific model...

In this paper, we propose a system performance-efficient clustering and mapping algorithm for agent-based system. Optimal cluster size is obtained by user-defined performance parameter (η).This presents a novel systematic approach to optimize the system performance by exploiting the relationships and dependencies among roles as well as clustering o...