AES-Based Encoding and Decoding Images using MATLAB
A Novel Technique of Sign Language Recognition System using Machine Learning for Differently Abled Person
Implementation of Machine Learning Techniques for Depression in Text Messages: A Survey
A Study of Ransomware Attacks on Windows Platform
Techniques of Migration in Live Virtual Machine and its Challenges
Efficient Agent Based Priority Scheduling and LoadBalancing Using Fuzzy Logic in Grid Computing
A Survey of Various Task Scheduling Algorithms In Cloud Computing
A Viable Solution to Prevent SQL Injection Attack Using SQL Injection
A Computational Intelligence Technique for Effective Medical Diagnosis Using Decision Tree Algorithm
Integrated Atlas Based Localisation Features in Lungs Images
The strength of an Information Retrieval System lies on its ability to retrieve relevant information or documents according to user's intent by considering a high level of precision and a low level of irrelevant recall of results. A recent development to actualize this dream is the application of ontology. Therefore, Ontology-Based Information Retrieval is becoming an interesting area in the current research trend of ontology and semantic web. However, the sufficiency of developing domain ontology alone to efficiently and effectively take care of information retrieval becomes a research issue. Thus, to address the research gap, a technique called Query Expansion has been identified as a veritable tool. Query Expansion is a process of expanding initial user's query term(s) with the aid of a technology such as wordNet to return relevant results according to user's intent. But returns of query results using the existing wordNet is challenging in normal or inflected terms, such as synonyms or polysemy (word mismatch). Therefore, this paper proposes improved query expansion algorithm as framework to effectively and efficiently develop ontology based information retrieval system.
Twitter is a social networking platform that has become popular in recent years. It has become a versatile information dissemination tool used by individuals, businesses, celebrities, and news organizations. It allows users to share messages called tweets with one another. These messages can contain different types of information from personal opinions of users, advertisement of products belonging to all kinds of businesses to the news. Tweets can also contain messages that are racist, bigotry, offensive, and of extremist views as shown by research. Manual identification of such tweets is impossible as hundreds of millions of tweets are posted every day and hence a solution to automate the identification of these types of tweets through classification is required for the Twitter administrators or an intelligence and security analyst. This paper presents a comparative study of traditional machine learning algorithms and deep learning algorithms for the task of tweet classification to detect different categories of abusive languages with the aim to determine which algorithm performs best in detecting abusive language that is prevalent on social media. Two approaches for building feature vectors were explored. Feature vectors based on the bag-of-words method and feature vectors based on word embeddings. These two methods of feature representation were evaluated in this paper using tweet messages representing five abusive language categories. The experiments show that the deep learning algorithms trained with word embeddings outperformed all the other machine learning algorithms that were trained with feature vectors based on the bag-of-words approach.
This paper proposes a road surface condition monitoring device. The design features the use of a programmed accelerometer sensor deployed to respond to vehicular vibrations as a function of the vehicle's acceleration due to gravity (g-force). Furthermore, a database was created and hosted online to store the traces acquired over the different test surfaces. The test results show that the proposed system successfully sensed the utilized road surfaces, and effectively logged the acquired traces into the created database. This device will be beneficial to road maintenance agencies for road surface monitoring, and it can be installed in both manned and unmanned vehicles to enhance road navigation. In addition, the stored traces can be freely accessed and used by researchers working in related areas.
Examinations are means of assessing the knowledge or skills that students have acquired, after having been taught over a period of time. Anomalies in student results are noteworthy observations that require additional clarifications. Manual detection of anomalies in results leads to human errors and wastage of manpower. This paper describes how extreme learning machines can be used to automatically detect anomalies in student results. The results show that using extreme learning machines almost always produces better or equal results compared to decision trees.
The introduction of Computational Intelligence (CI) algorithms in the area of optimizations have been given significant attention in science and engineering allied disciplines. This is because they always find answers to a problem while maintaining perfect stability among its components. However, these algorithms sometimes suffer from premature convergence and fitness stagnation, which usually originates from the lack of explorative search capability of its perturbation operator. This paper presents a comparative performance of a Cultural Artificial Bee Colony (called the CABCA) algorithm and Cultural Artificial Fish Swarms Algorithm (called the mCAFAC). In both algorithms (CABCA and mCAFAC), the normative and situational knowledge is employed to guide the direction and step size of the population (ABC and AFSA). Four variants of each ABC and AFSA were developed using different configurations of cultural knowledge in Matlab/Simulink environment. A collection of twelve optimization benchmark functions was used to test the performance, and it was found that the modified algorithms (CABCA and mCAFAC) outperformed their respective standard (ABC and AFSA) algorithms.