International Journal of Forensic Software Engineering (9 papers in press)
A hybrid approach to find cloned objects in copy move forged images
by Ashish Kumar Chakraverti, Vijay Dhir
Abstract: In this paper, we have proposed a new hybrid approach to detecting
the cloned object in copy move forged image. Presently images are a very
powerful medium to communicate the messages across the world wide web.
Social media is used to spread the image as the message in the way of the
nuclear reaction. With the help of highly efficient and advanced image editing
tools and techniques, images are modified intentionally and then spread over the
world wide web. Due to the availability of these advance and efficient image
editing tools, the genuineness of images is now highly questionable. There
are so many types of image forgery, one of the very popular image forgeries
is the copy-move forgery. In this forgery, images are forged by making the
clone of objects of the same image with the help of copy past method. There
are so many techniques to detect similar objects in the forged image. In our
proposed work we have integrated two states of art techniques named Adhoc
method and Principle Component Analysis (PCA) based Scale Invariant Feature
Transform (SIFT) method and created a new hybrid technique to detect the
cloned object in the forged image. Our technique is implemented in the two-
step. In the first step, we have preprocessed the object image by the proposed
Dynamic Block Size Technique based Local Contrast Modification and Contrast
Limited Adaptive Histogram Equalization (DBST-LCM CLAHE) up to desired
Peak Signal to Noise Ratio (PSNR) and grey level. After that in the second step,
we have integrated Adhoc, and PCA based SIFT algorithm to design a hybrid
approach. We have tested our proposed method over CoMoFoD image database.
Our proposed method shows the better performance in comparison of the state of
art techniques regarding false positive rate(FPR), true positive rate(TPR), and
Keywords: Image Processing; Image Enhancement; Adhoc; PSNR; Imagernforgery; SIFT; PCA; CLAHE; Noise.
Design of Framework for Ontological Component Retrieval from Software Component Repositories
by Iqbaldeep Kaur
Abstract: Modern component retrieval approaches are based on the context and domain of the software component in addition to using various classification and retrieval techniques like keyword search, attribute based search and semantic search. This work proposes Ontological Retrieval framework for software components that makes the use of ontology for effective retrieval of components. Ontological Component Retrieval framework has been created to facilitate the users and developers to retrieve software components matching precisely to their needs. A well-framed set of queries ranging from little to specific knowledge about the component requirement has been used to validate the results
Keywords: Ontology; Software Component; Context Search; Semantic web services; Retrieval; Novelty Ratio; Lucene-Indexed Search; Reusability.
Performing Opinion Mining and Analytical Study for Cashless Transactions
by Sonakshi Vij, Amita Jain, Devendra Tayal
Abstract: Cashless economy and digital payments are one of the hot topics of research that are gaining enormous popularity day by day. It is primarily due to their ability to combat issues of corruption, back money, illegal transactions and counterfeit money. With the increase in number of options of digital payments, people are inclining themselves towards ease of payment. This paper focuses on two aspects: firstly it provides an analytical study performed in the field of cashless transactions and digital payments. Secondly, it finds out the opinion of the people about cashless transactions using a hybrid approach of type 2 fuzzy logic and hesitant fuzzy sets for polarity assignment. A sentiment analysis on Cashless Economy has been carried out using data obtained from Twitter in the form of Tweets.
Keywords: Cashless Economy; Digital Payments; Hesitant Fuzzy Sets; Opinion Mining; Sentiment Analysis; Type 2 Fuzzy Logic.
State Estimation based Target Tracking and Applications of Multi Sensor Data Fusion
by David Kondru, Mehmet Celenk, Xiaoping Shen
Abstract: Detection, discrimination and tracking of a target under a given dynamic environment is one of the main challenges of an integrated sensory system. A combination of two or more sensors will always provide a better position estimate rather than a single sensor. The advantages of multi-sensor data fusion over a conventional single sensor tracking are presented in this paper. Using state estimators from simple linear rustic filters to a complex nonlinear filter, the tracking of target performing three different motions with sensor noises are presented in this paper. RADAR and the infrared search and track (IRST) are the two sensors considered based on which a complete mathematical modelling and simulation of the sensor measurements and tracking methodologies are utilised. The extension of the paper also presents the image fusion techniques using a widely known technique known as principle component analysis method. The RMS errors in position as well as image error measurements are performed that shows the superiority of the multi sensor data fusion process.
Keywords: Kalman filter; Particle filter; measurement fusion; state vector fusion; Image fusion; Principle Component Analysis.
Data Preprocessing Based On Missing Value and Discretization
by Neeta Yadav, Neelendra Badal
Abstract: In the real world, data is not available in the appropriate form for mining or extracting information from this. Generally, real-world data is incomplete, inconsistent and dirty so it is very necessary to process data smartly according to the requirement of the dataset. Preprocessing is one of the most crucial steps in data mining and most of the time spent in this about 60% of the time. Unprocessed data takes lots of time in mining. End-user wasted lots of time in getting the desired result. So it is very necessary to process data according to the specific dataset by applying techniques of processing and thereby it reduces the overall mining time, the end user gets the desired result more fastly.
In this paper preprocessing of missing value and discretization has been done. Preprocessing of missing value handle by three techniques that is a deletion, Replacement by mean or averages, and prediction method. From these three techniques user opt the best technique for handling missing value, which gives maximum accuracy and takes less time for preprocessing. After handling the missing value, discretization is done for data reduction so it minimizes the preprocessing time.
Keywords: Deletion; Discretization; Missing value; Prediction; Replacement by Mean.
Feasibility Predictability Model for Software Test Automation Projects in DevOps Setting
by Jayasri Angara, Srinivas Prasad, Sridevi Gutta
Abstract: DevOps is an outgrowth of agile practice and evolved to manage the continuous change. The goal is to shorten the project timelines, increase productivity, without impacting business and quality. Automation has become one of the key enablers for success. However, test automation gets little time. This poses a challenge to managers whether to automate the test function or not. Managers need to take swift go/no-go decision. The objective of this paper is to develop a predictability model for test automation project feasibility. Authors conducted a literature and practitioners survey and identified 21 key factors which determine the viability of a project. Authors surveyed 38 test automation projects and created a dataset. A custom simulation model was developed, augmenting the dataset with 23,407 more records. Authors attempted to predict the success using machine learning algorithms. Further, factor analysis was conducted to reduce the number of factors for operational simplicity.
Keywords: DevOps; Agile Test Automation; Project Feasibility Prediction Algorithm; Machine Learning; Logistic Regression.
Smart Design for Automation System
by GIRISH VARMA VEGESNA, Vijaya Nagarjana Devi Duvvuri, RAVI VEMAGIRI, SOWJANYA SWATHI NAMBHATLA, MOUNIKA KAKOLLU
Abstract: With the successful advancements of IoT, a new innovative product idea for the successful adjustment of brightness of available home appliances according to the user requirement is modulated. This product permits the user to adjust the brightness accordingly to their required level of extent. It concentrates on reducing the flow of electricity and banking the electricity with the help of well-equipped capacitors and eye strain. Even if the user forgets to turn off the light, he can be capable to operate through his device from any remote area. This innovative product, by interacting with sensors detects the status of the home appliance and reports the up-to-date product status daily. It also proclaims the status of the storage block in the hardware, whether started/not started to the user.
Keywords: eye strain; capacitors; handy; sensors; status; storage block.
Parameter correlation analysis and minimum volume design of a helical gear implementing PSO algorithm
by Edmund S. Maputi, Rajesh Arora
Abstract: Gears are important mechanical elements in the assembly and functionality of a machine. The application of optimisation techniques in engineering design and gear technology has increased due to the accessibility and advancement in computational resources. Furthermore, current research trends reflect a keen interest in volume minimisation of gear systems. In this research work, a helical gear volume model is investigated. Particle swarm optimisation algorithm is applied and the results are compared and validated using an analytical method and geometric modelling software. Optimal design theory is discussed including the interrelation of concepts such as design, analysis and optimisation reflecting the need to analyse the influence of parameters on objectives. Experimental runs using genetic algorithm, firefly algorithm and teaching learning-based algorithm were also performed with statistical analysis. Parameter variation studies were also performed on each variable against the objective, minimum volume. The results of this research work, shows that module, pinion tooth number and face width are important parameters for the minimisation of gear weight with 38%, 32% and 12% contribution to variation, respectively.
Keywords: optimization; design; gear; parameters; correlation.
Requirements Complexity Ranking using Natural Language Processing and Complexity Class Correlation with Defect Severity
by Mukundan Sundararajan, Priti Srikrishnan, Kiran Nayak
Abstract: This paper addresses the risks to delivery schedule and product quality from non-periodic temporal detection of high severity defects in software projects. The non-periodicity in time and lack of a time boundary in detecting severe defects primarily stems from subjective scheduling of development and testing of product features. One solution is to objectively determine complexity and ranking of requirements to drive the project development and test sequence that uncovers high severity defects early in the life cycle phases. Requirements complexity is strongly correlated with defect severity as measurements show. Applying natural language processing, key words are identified in the given set of requirements, their weights measured to determine the complexity class distribution and ranking that drives the scheduling. The complexity and defect correlation-based sequencing mitigates the risks by discovery of high severity defects in a temporal saw tooth pattern providing the project team sufficient time to fix defects and mitigate the risks.
Keywords: Requirements complexity; Requirements ranking; Requirement complexity classes; Natural language processing; NLP; Defect severity correlation; defect spatial distribution; defect temporal distribution.