Preventing Cyber Attacks using Artificial Intelligence
Exploring Advantages and Challenges of Chatbot Integration in Human-Robot Interaction
Counterfeit Currency Detection using Machine Learning
Artificial Intelligence use in Lung Cancer Screening to Improve Patient Recovery
An Analysis of Various Crypto Coins and their Suitability for Real-Time Applications
Design and Evaluation of Parallel Processing Techniques for 3D Liver Segmentation and Volume Rendering
Ensuring Software Quality in Engineering Environments
New 3D Face Matching Technique for an Automatic 3D Model Based Face Recognition System
Algorithmic Cost Modeling: Statistical Software Engineering Approach
Prevention of DDoS and SQL Injection Attack By Prepared Statement and IP Blocking
Cryptanalysis is one of the most challenging research areas in the field of information security. Often, this includes how to find the key which has been used for hiding the message and thus to arrive to the original information. In order to avoid others’ attacks, one should first have enough knowledge and experience of the existing cryptanalytic attacks on various cryptographic systems. These attacks and their avoidance requirements can be described based on information available to opponent, computational time requisites, memory requirements, etc. Security analysis of the existing ciphers is very helpful to better understand the requirements for designing secure and efficient ciphers. This paper main objective is to propose a design for a general cryptanalysis platform for pedagogical purposes. Besides educational benefits expected on information security side, other benefits of practicing with certain software development methods will also be investigated. The whole work can be considered to be under the general title of ethical hacking. In order to make a solid ground for the research, the paper starts by surveying different cryptanalysis techniques for various cipher systems. The paper also reports on the progress of our ongoing work.
Software documentation is one of the important factors in the software maintenance. Documentation illustrates the written form of data which can be easily understandable by the software engineers. Traceability links are the links which are used to decrease the distance between the software developers and the software documentation. Previously there was a technique called AdDoc that automatically detects the changes in the documentation. In this paper we propose a method called Information Retrieval (IR). Information retrieval is well known method for the automating traceability recovery based on the similarity among the software artifacts. IR combines both textual and structural information for the traceability recovery in the software documentation. Synonymy problem can be decreased by the information retrieval method and can retrieve the correct link between the source code and the documentation. In this work, the performance of the information retrieval method is comparatively high than the previous technique.
Nowadays detecting and resolving lexical ambiguities are difficult tasks in the business process models. Business process models represent all functions of a business activity in a sequential order. So business process models should not hold any terminological issues there has been lack of techniques to handle the problem of ambiguity in words due to synonyms and homonyms. In existing work, a technique called word sense disambiguation based on babelnet was used to detect and resolve the lexical ambiguities. Word sense disambiguation is a method for finding correct meaning of an ambiguous word. Babelnet is one of the widely used lexical resource that combine both wordnet and Wikipedia to identify the different meanings of the ambiguous words automatically. In addition to the existing work, the authors proposed a domain driven disambiguation approach that uses wordnet domain to find the domain information about a word automatically to detect and resolve the lexical ambiguity in business process models.
The prediction software defect components are an economically important activity and so has received a good deal of attention. However, making sense of the many, and sometimes seemingly inconsistent, a result is difficult. To improve the performance of software defect prediction, this research proposed the mixture of genetic algorithm and bagging technique. The thesis contains two phase. The first phase is feature selection; the features are selected using genetic algorithm, the bagging technique is used for class imbalance problem. The second phase is defect prediction; Software defects are predicted using K-Means and an Expectation Maximization (EM) algorithm. K-Means is a simple and popular approach that is widely used to cluster/classify data. EM algorithm is known to be an appropriate optimization for finding compact clusters. EM guarantees elegant convergence. EM algorithm assigns an object to a cluster according to a weight representing the probability of membership. The proposed method is evaluated using the data sets from NASA metric data repository. The proposed method is evaluated based on evaluation measurement such as accuracy and error rate. The experimental results demonstrate that our approach outperforms other competing approaches.
Software project scheduling is a problem faced by software project managers. Different evolutionary algorithms will give different results i.e, different schedules. Project scheduling problem includes identifying every task and the dependency among the tasks. The skills necessary by the people to execute those tasks and also the dependency among the tasks will be analyzed. Project scheduling problem also needs to estimate the effort and cost prior to the development of the project. Even after estimating the skills required, number of employees needed, and cost of resources the schedules that are provided may not be accurate because of using evolutionary algorithm which is using repair mechanism. Repair mechanism unnecessarily reduces the dedications which may leads to failure of the software product. To overcome the problems like reducing the dedications unnecessarily and producing different schedules for the same project, the techniques like mutation, encoding and fitness will be used. Those are implemented in the improved evolutionary algorithm which will provide the results like hit rate and fitness values. Hit rate and fitness values will be useful to know that the schedule obtained by using the parameters what we are considered to get the fitness is accurate one or not.
Current software development approaches need to cope with new design challenges, in which the incessant complexity of software system require more scalable systems that can be adapted better. Hence, the evolution of such systems and their architectures depend on how stable a design is against new requirements and on the desired quality level. As in previous columns, Software Stability results in a key to deal with many challenges that might influence the system. We have seen in previous articles in this series, what the problems associated with traditional software architecture approaches are, when it comes to scalability and stability in particular and how they negatively impact the software, over due course of time. Modern software development approaches require one to produce highly scalable, adaptable and stable systems and platforms, that in many cases could be more reactive against changes (e.g.: Self-adaptable systems). Thus, the underlying architecture behind such systems should be flexible enough and adaptable to realize the idea of stability.
