Advanced Driver-Assistance System with Traffic Sign Recognition for Safe and Efficient Driving

Advanced Driver-Assistance Systems (ADAS) coupled with traffic sign recognition could lead to safer driving environments. This study presents a sophisticated, yet robust and accurate traffic sign detection system using computer vision and ML, for ADAS. Unavailability of large local traffic sign datasets and the unbalances of traffic sign distribution are the key bottlenecks of this research.  Hence, we choose to work with support vector machines (SVM) with a custom-built unbalance dataset, to build a lightweight model with excellent classification accuracy.  The SVM model delivered optimum performance with the radial basis kernel, C=10, and gamma=0.0001. In the proposed method, same priority was given to processing time (testing time) and accuracy, as traffic sign identification is time critical. The final accuracy obtained was 87% (with confidence interval 84%-90%) with a processing time of 0.64s (with confidence interval of 0.57s-0.67s) for correct detection at testing, which emphasizes the effectiveness of the proposed method

Fuzzy adaptive emperor penguin optimizer for global optimization problems

The Emperor Penguin Optimizer (EPO) is a recently developed population-based metaheuristic algorithm that simulates the huddling behaviour of emperor penguins. Mixed results have been observed in the performance of EPO in solving general optimization problems. Within the EPO, two parameters need to be tuned (namely f and l) to ensure a good balance between exploration (i.e., roaming unknown locations) and exploitation (i.e., manipulating the current known best). Since the search contour varies depending on the optimization problem, the tuning of parameters f and l is problem-dependent, and there is no one-size-fits-all approach. To alleviate this parameter tuning problem, an adaptive mechanism can be introduced in EPO. This research work proposes a fuzzy adaptive variant of EPO, namely FAEPO, to solve this problem. As the name suggests, FAEPO can adaptively tune the parameters f and l throughout the search based on three measures (i.e., quality, success rate, and diversity of the current search) via fuzzy decisions. A test suite of twelve benchmark test functions and three global optimization problems: Team Formation Optimization (TFO), Low Autocorrelation Binary Sequence (LABS), and Modified Condition/ Decision coverage (MC/DC) test case generation problem were solved using the proposed algorithm. The respective solution results of the competing metaheuristic algorithms were compared. The experimental results demonstrate that FAEPO significantly improved the performance especially of its predecessor (EPO), an improved variant of EPO (i.e., IEPO), and a fuzzy-based variant of ChOA (i.e., FChOA) and gives superior performance against the competing metaheuristic algorithms. Moreover, the proposed FAEPO requires 50% less fitness function evaluation in each iteration than the ancestor EPO and exhibits competitive performance in terms of convergence and computational time against its predecessor (EPO) and other competing meta-heuristic algorithms with a 90% confidence level

Analysis of Recurrent Neural Networks for Henon Simulated Time-Series Forecasting

Forecasting of chaotic time-series has increasingly become a challenging subject. Non-linear models such as recurrent neural networks have been successfully applied in generating short term forecasts, but perform poorly in long term forecasts due to the vanishing gradient problem when the forecasting period increases. This study proposes a robust model that can be applied in long term forecasting of henon chaotic time-series whilst reducing the vanishing gradient problem through enhancing the models ability in learning of long-term dependencies. The proposed hybrid model is tested using henon simulated chaotic time-series data. Empirical analysis is performed using quantitative forecasting metrics and comparative model performance on the generated forecasts. Performance evaluation results confirm that the proposed recurrent model performs long term forecasts on henon chaotic time-series effectively in terms of error metrics compared to existing forecasting models

A classifier mechanism for host based intrusion detection and prevention system in cloud computing environment

Distributed denial-of-service (DDoS) attacks are incidents in a cloud computing environment that cause major performance disturbances. Intrusion-detection and prevention system (IDPS) are tools to protect against such incidents, and the correct placement of ID/IP systems on networks is of great importance for optimal monitoring and for achieving maximum effectiveness in protecting a system. Even with such systems in place, however, the security level of general cloud computing must be enhanced. More potent attacks attempt to take control of the cloud environment itself; such attacks include malicious virtual-machine (VM) hyperjacking as well as traditional network-security threats such as traffic snooping (which intercepts network traffic), address spoofing and the forging of VMs or IP addresses. It is difficult to manage a host-based IDPS (H-IDPS) because information must be configured and managed for every host, so it is vital to ensure that security analysts fully understand the network and its context in order to distinguish between false positives and real problems. For this, it is necessary to know the current most important classifiers in machine learning, as these offer feasible protection against false-positive alarms in DDoS attacks. In order to design a more efficient classifier, it is necessary to develop a system for evaluating the classifier. In this thesis, a new mechanism for an H-IDPS classifier in a cloud environment has desigend. The mechanism’s design is based on the hybrid Antlion Optimization Algorithm (ALO) with Multilayer Perceptron (MLP) to protect against DDoS attacks. To implement the proposed mechanism, we demonstrate the strength of the classifier using a dimensionally reduced dataset using NSL-KDD. Furthermore, we focus on a detailed study of the NSL-KDD dataset that contains only selected records. This selected dataset provides a good analysis of various machine-learning techniques for H-IDPS. The evaluation process H-IDPS system shows the increases of intrusion detection accuracy and decreases the false positive alarms when compared to other related works. This is epitomized by the skilful use of the confusion matrix technique for organizing classifiers, visualizing their performance, and assessing their overall behaviour

A fast efficient local search-based algorithm for multi-objective supply chain configuration problem

Supply chain configuration (SCC) plays an important role in supply chain management. This paper focuses on a multi-objective SCC (MOSCC) problem for minimizing both the cost of goods sold and the lead time simultaneously. Some existing population-based methods use the evolution of a population to obtain the optimal Pareto set, but they are time-consuming. In this paper, an Efficient Local Search-based algorithm with rank (ELSrank) is designed to solve the MOSCC problem. Firstly, instead of use of population, two solutions (xA and xB) are generated by the greedy strategy, which have the minimal cost and the minimal time, respectively. They approximately locate in two sides of the Pareto front (PF). Secondly, with the consideration of the problem characteristics, a local search (LS) is proposed to find competitive solutions among the common neighborhood of two given solutions. If xA and xB are chosen to execute the proposed LS, solutions along the link path (the approximate PF) of xA and xB can be found. This way, the solutions along the whole PF can be found. The comparative experiments are conducted on six instances from the real-life MOSCC problems, and the results show that ELSrank performs better than other start-of-the-art algorithms, especially on the large scale problem instances

Informational Paradigm, management of uncertainty and theoretical formalisms in the clustering framework: A review

Fifty years have gone by since the publication of the first paper on clustering based on fuzzy sets theory. In 1965, L.A. Zadeh had published “Fuzzy Sets” [335]. After only one year, the first effects of this seminal paper began to emerge, with the pioneering paper on clustering by Bellman, Kalaba, Zadeh [33], in which they proposed a prototypal of clustering algorithm based on the fuzzy sets theory

Project portfolio selection problems: a review of models, uncertainty approaches, solution techniques, and case studies

Project portfolio selection has been the focus of many scholars in the last two decades. The number of studies on the strategic process has significantly increased over the past decade. Despite this increasing trend, previous studies have not been yet critically evaluated. This paper, therefore, aims to presents a comprehensive review of project portfolio selection and optimization studies focusing on the evaluation criteria, selection approach, solution approach, uncertainty modeling, and applications. This study reviews more than 140 papers on project portfolio selection research topic to identify the gaps and to present future trends. The findings show that not only the financial criteria but also social and environmental aspects of project portfolios have been focused by researchers in project portfolio selection in recent years. In addition, meta-heuristics and heuristics approach to finding the solution of mathematical models have been the critical research by scholars. Expert systems, artificial intelligence, and big data science have not been considered in project portfolio selection in the previous studies. In future, researchers can investigate the role of sustainability, resiliency, foreign investment, and exchange rates in project portfolio selection studies, and they can focus on artificial intelligence environments using big data and fuzzy stochastic optimization techniques

Modelo de ensembles multiníveis para classificadores

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Ciência da Computação, Florianópolis, 2018.Um comitê de máquinas, ou ensemble, é uma combinação de diversos classificadores por meio de uma estratégia pré-estabelecida. Seu uso tem sido comum na literatura para garantir um aumento de generali- zação nos problemas de classificação. Entretanto, é fundamental o uso de uma boa estratégia de diversidade para assegurar a qualidade dos resultados. Para tanto, a presente pesquisa propõe a construção de um modelo multinível, onde a decisão final é realizada por meio da com- binação das saídas de ensembles. A esse modelo refere-se aqui como comitê de ensembles. O tema da presente dissertação buscou avançar o estado da arte ao propor uma estratégia para a realização do comitê de ensembles. Propôs-se ainda a combinação de ensembles que tenham em sua formação classificadores com similaridades entre si. Dessa forma, cada ensemble do comitê especializa-se em determinado paradigma de aprendizagem (família). Busca-se com isso um aumento ainda maior da diversidade. A aplicação do modelo proposto (nível 2) ocorreu em bases de dados públicas com diferentes características e sua avaliação foi mensurada por meio da acurácia, área sob a curva ROC (AUC) e tempo de execução. Os resultados mostraram semelhanças de desem- penho dos níveis 0 e 1. O modelo proposto conseguiu um crescimento médio de até 14% e 10% em relação à, respectivamente, acurácia e área sob a curva ROC dos níveis 0 e 1. A família que apresentou os me- lhores resultados foi a Bayesiana. Os resultados demonstraram que o desempenho da família bayesiana foi 949 vezes mais rápido no tempo de execução que o comitê de ensembles com os resultados de acurácia e área sob a curva ROC mais estáveis e levemente superior às demais famílias (nível 1). Por fim, a análise estatística, com um nível de sig- nificância de 5% (a = 0, 05), comprovou o bom desempenho do comitê de ensembles em quase todas as comparações em relação aos demais níveis tanto em termos de acurácia quanto de área sob a curva ROC, embora com um alto tempo de execução.Abstract : A committee machine, or ensemble, is a combination of several classifi- ers by means of a pre-established strategy. Its use has been common in the literature to ensure an increase the generalization in classification problems. However, a good diversity strategy is essential to ensure the quality of results. Therefore, the present research proposes the cons- truction of a multi-level model, where the final decision is made through the combination of ensembles outputs. This model is referred to here as an committee ensembles. The theme of this dissertation sought to advance the state of the art by proposing a strategy for the accomplish- ment of the committee ensembles. It s also proposed the combination of ensembles that have in their formation classifiers with similarities among themselves. Therefore, each committee ensemble specializes in a particular learning paradigm (family). An increase in diversity is thus sought. The validation of the proposed method (level 2) use public da- tabases with different characteristics and its evaluation was measured by means of accuracy, area under the ROC curve (AUC) and processing time. The results showed similarities of performance of levels 0 and 1. The proposed model achieved an average growth of up to 14% and 10% in relation to, respectively, accuracy and area under the ROC curve of levels 0 and 1. The family that presented the best results was Bayesian. The results showed that the performance of the Bayesian family was 949 times faster in the execution time than committee ensembles with the results of accuracy and area under the ROC curve more stable and slightly superior to the other families (level 1). Our results are statisti- cally analyzed with a significance level of 5% (a = 0.05), which proved the increased good performance of the ensembles committee in almost all comparisons in relation to other levels both in terms of accuracy and area under the ROC curve, although with a high execution time

Reconhecimento de padrão em pacientes com esclerose sistêmica por sistemas

Medical images are used to gather information for diagnosis and/or follow-up of patients state. Image acquisition technologies have evolved along the years and the most common are: computed tomography (CT), ultrasonography, magnetic resonance imaging (MRI), nuclear medicine and x-ray. Chest X-ray images invariably have intensity gradations and uncertainties, which leads to the choice of the fuzzy systems (FS) approach. The objective of this study was to optimize images of simple chest X-ray that are used in pulmonary involvement diagnoses' confirmation and follow-up in patients with systemic sclerosis (SS). Pattern recognition using fuzzy methodology, with emphasis on the images segmentation by the intuitionist fuzzy sets (IFS), led to the creation of the pulmonary fibrosis intensity index (P F II) using fuzzy sets (FS). This index, associated with results from pulmoray function tests (PFT), forced vital capacity (F V C) and carbon monoxide difusion capacity (DLCO), assisted on the clinical follow-up of patients with SS. The techniques and methods implemented allowed the development of the SisRPIP - Pattern Recognition System in Pulmonary Imaging. The viability of the SisRPIP was verified with 40 patients' plain chest radiographs already diagnosed with SS, and the results and methodology used, are presented in this thesis.As imagens médicas objetivam captar informações para diagnóstico e/ou acompanhamento do paciente. As tecnologias de aquisição de imagem evoluíram e se apresentam como: tomografia computadorizada (TC), ultrassonografia, ressonância magnética (RMP), medicina nuclear e a radiografia simples (raio-X). As imagens de radiografias simples de tórax possuem invariavelmente graduações de intensidade e incertezas, o que conduz à escolha da abordagem dos sistemas fuzzy (SF). Objetiva-se otimizar a visualização de imagens de radiografias simples de tórax na confirmação e acompanhamento do diagnóstico do acometimento pulmonar em pacientes com esclerose sistêmica (ES). O reconhecimento de padrão pela metodologia fuzzy com destaque na segmentação das imagens pelos conjuntos intuicionistas fuzzy (CIF), conduziu a criação do índice de intensidade de fibrose pulmonar (IIF P) pelos conjuntos fuzzy (CF) que associado às variáveis advindas dos exames de função pulmonar, capacidade vital forçada (CV F) e capacidade de difusão do monóxido de carbono (DLCO), contribuiu para o acompanhamento clínico de pacientes com ES. As técnicas e métodos implementados, propiciou o desenvolvimento do SisRPIP - Sistema de Reconhecimento de Padrão em Imagens Pulmonares. A viabilidade do SisRPIP foi verificada em radiografias simples de tórax de 40 pacientes já diagnosticados com ES e, os resultados, assim como a metodologia utilizada são apresentados nesta Tese

An online framework for civil unrest prediction using tweet stream based on tweet weight and event diffusion

Twitter is one of most popular Internet-based social networking platform to share feelings, views, and opinions. In recent years, many researchers have utilized the social dynamic property of posted messages or tweets to predict civil unrest in advance. However, existing frameworks fail to describe the low granularity level of tweets and how they work in offline mode. Moreover, most of them do not deal with cases where enough tweet information is not available. To overcome these limitations, this article proposes an online framework for analyzing tweet stream inpredicting future civil unrest events. The framework filters tweet stream and classifies tweets using linear Support Vector Machine (SVM) classifier. After that, the weight of the tweet is measured and distributed among extracted locations to update the overall weight in each location in a day in a fully online manner. The weight history is then used to predict the status of civil unrest in a location. The significant contributions of this article are (i) A new keyword dictionary with keyword score to quantify sentiment in extracting the low granularity level of knowledge (ii) A new diffusion model for extracting locations of interest and distributing the sentiment among the locations utilizing the concept of information diffusion and location graph to handle locations with insufficient information (iii) Estimating the probability of civil unrest and determining the stages of unrest in upcoming days. The performance of the proposed framework has been measured and compared with existing logistic regression based predictive framework. The results showed that the proposed framework outperformed the existing framework in terms of F1 score, accuracy, balanced accuracy, false acceptance rate, false rejection rate, and Matthews correlation coefficient