Optimizing artificial neural networks using LevyChaotic mapping on Wolf Pack optimization algorithm for detect driving sleepiness

Artificial Neural Networks (ANNs) are utilized to solve a variety of problems in many domains. In this type of network, training and selecting parameters that define networks architecture play an important role in enhancing the accuracy of the network's output; Therefore, Prior to training, those parameters must be optimized. Grey Wolf Optimizer (GWO) has been considered one of the efficient developed approaches in the Swarm Intelligence area that is used to solve real-world optimization problems. However, GWO still faces a problem of the slump in local optimums in some places due to insufficient diversity. This paper proposes a novel algorithm Levy Flight- Chaotic Chen mapping on Wolf Pack Algorithm in Neural Network. It efficiently exploits the search regions to detect driving sleepiness and balance the exploration and exploitation operators, which are considered implied features of any stochastic search algorithm. Due to the lack of dataset availability, a dataset of 15 participants has been collected from scratch to evaluate the proposed algorithm's performance. The results show that the proposed algorithm achieves an accuracy of 99.3%

Deteção automática de defeitos em couro

Dissertação de mestrado em Informatics EngineeringEsta dissertação desenvolve-se em torno do problema da deteção de defeitos em couro. A deteção de defeitos em couro é um problema tradicionalmente resolvido manualmente, usando avaliadores ex perientes na inspeção do couro. No entanto, como esta tarefa é lenta e suscetível ao erro humano, ao longo dos últimos 20 anos tem-se procurado soluções que automatizem a tarefa. Assim, surgiram várias soluções capazes de resolver o problema eficazmente utilizando técnicas de Machine Learning e Visão por Computador. No entanto, todas elas requerem um conjunto de dados de grande dimensão anotado e balanceado entre as várias categorias. Assim, esta dissertação pretende automatizar o processo tradicio nal, usando técnicas de Machine Learning, mas sem recorrer a datasets anotados de grandes dimensões. Para tal, são exploradas técnicas de Novelty Detection, as quais permitem resolver a tarefa de inspeção de defeitos utilizando um conjunto de dados não supervsionado, pequeno e não balanceado. Nesta dis sertação foram analisadas e testadas as seguintes técnicas de novelty detection: MSE Autoencoder, SSIM Autoencoder, CFLOW, STFPM, Reverse, and DRAEM. Estas técnicas foram treinadas e testadas com dois conjuntos de dados diferentes: MVTEC e Neadvance. As técnicas analisadas detectam e localizam a mai oria dos defeitos das imagens do MVTEC. Contudo, têm dificuldades em detetar os defeitos das imagens do dataset da Neadvance. Com base nos resultados obtidos, é proposta a melhor metodologia a usar para três diferentes cenários. No caso do poder computacional ser baixo, SSIM Autoencoder deve ser a técnica usada. No caso onde há poder computational suficiente e os exemplos a analisar são de uma só cor, DRAEM deve ser a técnica escolhida. Em qualquer outro caso, o STFPM deve ser a opção escolhida.This dissertation develops around the leather defects detection problem. The leather defects detec tion problem is traditionally manually solved, using experient assorters in the leather inspection. However, as this task is slow and prone to human error, over the last 20 years the searching for solutions that automatize this task has continued. In this way, several solutions capable to solve the problem effi ciently emerged using Machine Learning and Computer Vision techniques. Nonetheless, they all require a high-dimension dataset labeled and balanced between all categories. Thus, this dissertation pretends to automatize the traditional process, using the Machine Learning techniques without requiring a large dimensions labelled dataset. To this end, there will be explored Novelty Detection techniques, that in tend to solve the leather inspection task using an unsupervised small and non-balanced dataset. This dissertation analyzed and tested the following Novelty Detection techniques: MSE Autoencoder, SSIM Autoencoder, CFLOW, STFPM, Reverse, and DRAEM. These techniques are trained and tested in two distinct datasets: MVTEC and Neadvance. The analyzed techniques detect and localize most MVTEC defects. However, they have difficulties in defect detection on Neadvance samples. Based on the ob tained results, it is proposed the best methodology to use for three distinct scenarios. In the case where the computational power available is low, SSIM Autoencoder should be the technique to use. In the case where there is enough computational power and the samples to inspect have the same color, DRAEM should be the chosen technique. In any other case, the STFPM should be the chosen option

Application of AI in Modeling of Real System in Chemistry

In recent years, discharge of synthetic dye waste from different industries leading to aquatic and environmental pollution is a serious global problem of great concern. Hence, the removal of dye prediction plays an important role in wastewater management and conservation of nature. Artificial intelligence methods are popular owing due to its ease of use and high level of accuracy. This chapter proposes a detailed review of artificial intelligence-based removal dye prediction methods particularly multiple linear regression (MLR), artificial neural networks (ANNs), and least squares-support vector machine (LS-SVM). Furthermore, this chapter will focus on ensemble prediction models (EPMs) used for removal dye prediction. EPMs improve the prediction accuracy by integrating several prediction models. The principles, advantages, disadvantages, and applications of these artificial intelligence-based methods are explained in this chapter. Furthermore, future directions of the research on artificial intelligence-based removal dye prediction methods are discussed

Riskit: investment risk assessment platform

Mestrado em IPB-ESTG e ASSOCIAÇÃO DE POLITÉCNICOS DO NORTE (APNOR): Instituto Politécnico do Cávado e do Ave, P. Porto, Instituto Politécnico de Viana do CasteloThe current society is volatile, influenced by macro social, economic, geopolitical, and natural phenomena that have a global and deeply interconnected impact. As a result, as unpredictability increases, access to information and decision-support tools becomes increasingly vital in all aspects of social life. The capital market (and companies) is at the forefront of these phenomena, given its volatility and extreme exposure to these macro events. In this scenario, the objective was to develop a platform that predicts insolvencies. The Riskit: Insolvency Predictor is a web-based platform aimed at assisting the scientific community and investors in predicting the possibility of companies becoming insolvent based on specific financial indicators. Methodologically, a dataset of 15,000 Portuguese companies was randomly extracted from the Iberian Balance Sheet Analysis System (SABI) database1. An analysis was conducted, resulting in the selection of 11 financial indicators used for predictions. To make predictions, the authors conducted a comprehensive study of models commonly used for this type of forecasting and also experimented with some machine-learning models that are not frequently mentioned in the literature. The evaluation of the application’s performance in predicting insolvencies is measured by a series of performance benchmarks calculated with the help of a confusion matrix. It was found that models frequently mentioned in the literature do not always have better performance. The main objectives of this project were achieved, providing both the scientific community and investors with a tool that predicts insolvency using a set of financial indicators and demonstrating the value of machine-learning models for making these predictions. The application can be visited at https://riskit.ipb.pt/

Deep Learning Detected Nutrient Deficiency in Chili Plant

Chili is a staple commodity that also affects the Indonesian economy due to high market demand. Proven in June 2019, chili is a contributor to Indonesia's inflation of 0.20% from 0.55%. One factor is crop failure due to malnutrition. In this study, the aim is to explore Deep Learning Technology in agriculture to help farmers be able to diagnose their plants, so that their plants are not malnourished. Using the RCNN algorithm as the architecture of this system. Use 270 datasets in 4 categories. The dataset used is primary data with chili samples in Boyolali Regency, Indonesia. The chili we use are curly chili. The results of this study are computers that can recognize nutrient deficiencies in chili plants based on image input received with the greatest testing accuracy of 82.61% and has the best mAP value of 15.57%

Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit

Code intelligence leverages machine learning techniques to extract knowledge from extensive code corpora, with the aim of developing intelligent tools to improve the quality and productivity of computer programming. Currently, there is already a thriving research community focusing on code intelligence, with efforts ranging from software engineering, machine learning, data mining, natural language processing, and programming languages. In this paper, we conduct a comprehensive literature review on deep learning for code intelligence, from the aspects of code representation learning, deep learning techniques, and application tasks. We also benchmark several state-of-the-art neural models for code intelligence, and provide an open-source toolkit tailored for the rapid prototyping of deep-learning-based code intelligence models. In particular, we inspect the existing code intelligence models under the basis of code representation learning, and provide a comprehensive overview to enhance comprehension of the present state of code intelligence. Furthermore, we publicly release the source code and data resources to provide the community with a ready-to-use benchmark, which can facilitate the evaluation and comparison of existing and future code intelligence models (https://xcodemind.github.io). At last, we also point out several challenging and promising directions for future research

Radial Basis Function Neural Network in Identifying The Types of Mangoes

Mango (Mangifera Indica L) is part of a fruit plant species that have different color and texture characteristics to indicate its type. The identification of the types of mangoes uses the manual method through direct visual observation of mangoes to be classified. At the same time, the more subjective way humans work causes differences in their determination. Therefore in the use of information technology, it is possible to classify mangoes based on their texture using a computerized system. In its completion, the acquisition process is using the camera as an image processing instrument of the recorded images. To determine the pattern of mango data taken from several samples of texture features using Gabor filters from various types of mangoes and the value of the feature extraction results through artificial neural networks (ANN). Using the Radial Base Function method, which produces weight values, is then used as a process for classifying types of mangoes. The accuracy of the test results obtained from the use of extraction methods and existing learning methods is 100%

Application of machine learning to predict quality of Portuguese wine based on sensory preferences

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceTechnology has been broadly used in the wine industry, from vineyards to purchases, improving means or understanding customers' preferences. Numerous companies are using machine learning solutions to leverage their business. Henceforth, the sensory properties of wines constitute a significant element to determine wine quality, that combined with the accuracy of predictive models attained by classification methods, could be helpful to support winemakers enhance their outcomes. This research proposes a supervised machine learning approach to predict the quality of Portuguese wines based on sensory characteristics such as acidity, intensity, sweetness, and tannin. Additionally, this study includes red and white wines, implements, and compare the effectiveness of three classification algorithms. The conclusions promote understanding the importance of the sensory characteristics that influence the wine quality throughout customers' perception.Tecnologia vem sendo amplamente empregada na indústria do vinho. Desde melhoria em processos de cultivo à compreensão de mercado por meio da análise de preferência de consumidores. Tendo em vista à atual dinâmica dos mercados, empresas estão gradualmente a considerar soluções que implementam conceitos de aprendizagem de máquina e tragam diferencial competitivo para potencializar o negócio. Doravante, propriedades sensoriais são importantes elementos para determinação da qualidade do vinho, que aliado à precisão obtida por modelos preditivos podem auxiliar produtores de vinho a melhorar produtos e resultados. O presente estudo propõe a elaboração de modelos de aprendizado supervisionado, baseado em algoritmos de classificação a fim de prever qualidade de vinhos portugueses a partir de dados sensoriais detetados por consumidores como acidez, intensidade, açúcar e taninos. A pesquisa inclui vinhos tintos e brancos; implementa e compara a efetividade de três algoritmos de classificação. Não obstante, o estudo permite compreender como dados sensoriais fornecidos por consumidores podem determinar a qualidade de vinhos, bem como perceber quais características contribuem no processo de avaliação