Abstract Along with the rapid development of intelligent mobile terminals, applications on landmark recognition attract increasingly attentions by world wide researchers in the past several years. Although promising achievements have been presented, designing a robust recognition system with an accurate recognition rate and fast response speed is still challenging. To address these issues, we propose a novel landmark recognition algorithm in this paper using the spatial pyramid kernel based bag-of-words (SPK-BoW) histogram approach with the feedforward artificial neural networks (FNN) and the sparse representation classifier (SRC). In the proposed algorithm, the SPK-BoW approach is first employed to extract features and construct an overcomplete dictionary for landmark image representation. Then, the FNN trained with the extreme learning machine (ELM) algorithm combined with the SRC is implemented for landmark image recognition. We conduct experiments using the Nanyang Technological University (NTU) campus landmark database to show that the proposed method achieves a high recognition rate than ELM and a lower response time than the sparse representation technique.

Landmark recognition with sparse representationclassification and extreme learning machine

Background/Objectives: Research in recent years has probed integration amongst research field of Software Engineering & Semantic Web technology, addressing the advantages of applying Semantic techniques to the field of Software Engineering. Prolifically published studies have further substantiated the benefits of ontologies to the field of Software Engineering, which clearly motivate us to explore further opportunities available in this collaborated field. This paper is a survey expounding such opportunities while discussing the role of ontologies as a Software Life-Cycle support technology. Method/Statistical Analysis: Survey centred on providing an overview of the state-of-art of all the ontologies available for Software Engineering followed by their categorization based on software life cycle phases and their application scope. Findings: Characterization of ontologies as a Software Life-cycle support technology, instigated by the increasing need to investigate the interplay between Semantic Web & Software Engineering with the ultimate goal of enabling & improving Software Engineering capabilities. Application/Improvements: This paper discusses the practical and potential applications of ontologies in the field of Software Engineering followed by the issues and challenges that will keep this field dynamic and lively for years to come.

Ontologies for Software Engineering: Past, Present and Future

Ant Colony Optimization (ACO) algorithms tend to fall into local optimal and have insufficient astringency when applied to solve Traveling Salesman Problem (TSP). To address this issue, a novel game-based ACO (NACO) is proposed in this report. NACO consists of two ACOs: Ant Colony System (ACS) and Max-Min Ant System (MMAS). First, an entropy-weighted learning strategy is proposed. By improving diversity adaptively, the optimal solution precision can be optimized. Then, to improve the astringency, a nucleolus game strategy is set for ACS colonies. ACS colonies under cooperation share pheromone distribution and distribute cooperative profits through nucleolus. Finally, to jump out of the local optimum, mean filtering is introduced to process the pheromone distribution when the algorithm stalls. From the experimental results, it is demonstrated that NACO has well performance in terms of both the solution precision and the astringency.

A novel ant colony optimization based on game for traveling salesman problem

Predictive Model for Dyslexia from Fixations and Saccadic Eye Movement Events.

Various studies have shown that the ant colony optimization (ACO) algorithm has a good performance in approximating complex combinatorial optimization problems such as traveling salesman problem (TSP) for real-world applications. However, disadvantages such as long running time and easy stagnation still restrict its further wide application in many fields. In this study, a saltatory evolution ant colony optimization (SEACO) algorithm is proposed to increase the optimization speed. Different from the past research, this study innovatively starts from the perspective of near-optimal path identification and refines the domain knowledge of near-optimal path identification by quantitative analysis model using the pheromone matrix evolution data of the traditional ACO algorithm. Based on the domain knowledge, a near-optimal path prediction model is built to predict the evolutionary trend of the path pheromone matrix so as to fundamentally save the running time. Extensive experiment results on a traveling salesman problem library (TSPLIB) database demonstrate that the solution quality of the SEACO algorithm is better than that of the ACO algorithm, and it is more suitable for large-scale data sets within the specified time window. This means it can provide a promising direction to deal with the problem about slow optimization speed and low accuracy of the ACO algorithm.

https://www.mdpi.com/2227-7390/10/6/925/pdf?version=1647330050

A New Fast Ant Colony Optimization Algorithm: The Saltatory Evolution Ant Colony Optimization Algorithm

Dyslexia is a learning disorder characterized by lack of reading and /or writing skills, difficulty in rapid word naming and also poor in spelling. Dyslexic individuals have great difficulty to read and interpret words or letters. Research work is carried out to classify dyslexic from non-dyslexics by various approaches such as machine learning, image processing, understanding the brain behavior through psychology, studying the differences in anatomy of brain. In addition to it several assistive tools are developed to support dyslexics. In this work, brain images are used for screening individuals who have high risk to dyslexia. This work also motivates the application of machine learning in distributed environment. The proposed predictive model uses the machine-learning algorithm Support Vector Machine (SVM). The model is designed in Apache SPARK framework to support voluminous data. The prediction accuracy of 92.5% is achieved using SVM. 

/pdf/prediction-of-dyslexia-using-support-vector-machine-in-380hkzwpkj.pdf

Prediction of dyslexia using support vector machine in distributed environment

Towards a Multi-level Upper Ontology/ foundation Ontology Framework as Background Knowledge for Ontology Matching Problem

Nowadays, the Extreme learning machine (ELM) is playing a key role in machine intelligence and big data analytics due to its various advantages such as fast training rate, universal classification/regression and the capability of approximation. The standard ELM uses the Moore–Penrose generalized pseudo-inverse for solving the hidden layer activation matrix and also it identifies the output weights. Because of that, the standard ELM takes more time to train the features from the dataset. In ELM, scalability also considered as a one of the major concern while processing the large dataset. In order to overcome this concern, the Automotive Rank based ELM (AR-ELM) is proposed to obtain an effective tensor decomposition for diminishing the training time. Besides, the Bayesian approach is considered in this AR-ELM to remove the redundancy from the decomposed samples of the tensor. The major objective of this proposed AR-ELM is to process the large amount of dataset without depending on memory capacities. The recognition accuracy is improved by eliminating redundant information. The key idea of the ARELM is to reduce the training time while processing the huge dataset. The implementation and simulation of the ARELM is done in Spark Python 3.7. The performance of the AR-ELM is analysed in terms of accuracy, precision, recall and training time. The proposed methodology is compared with three existing methodologies such as basic ELM, ELM-TUCKER and ELM-PARAFAC. The recognition accuracy of the AR-ELM methodology with Hardlims activation function is 0.8879 for letter recognition dataset, it is high when compared to the basic ELM, ELM-TUCKER and ELM-PARAFAC that are 0.8102, 0.8375 and 0.834 respectively.

/pdf/automotive-rank-based-elm-using-iterative-decomposition-1kt31c7om8.pdf

Automotive Rank Based ELM Using Iterative Decomposition

In this paper we present an extension of existing Nearest-Neighbor heuristics to an algorithm called k-Repetitive-Nearest-Neighbor. The idea is to start with a tour of k nodes and then perform a Nearest-Neighbor search from there on. After doing this for all permutations of k nodes the result gets selected as the shortest tour found. Experimental results show that for 2-RNN the solutions quality remains relatively stable between about 10% to 40% above the optimum.

Ramesh Ragala

Papers

Prediction of dyslexia using support vector machine in distributed environment

Towards a Multi-level Upper Ontology/ foundation Ontology Framework as Background Knowledge for Ontology Matching Problem

Automotive Rank Based ELM Using Iterative Decomposition

k-RNN: Extending NN-heuristics for the TSP

k-RNN: Extending NN-heuristics for the TSP.