CORRELATION OF ARTIFICIAL INTELLIGENCE TECHNIQUES WITH SOFT COMPUTING IN VARIOUS AREAS

Artificial Intelligence (AI) is a part of computer science concerned with designing intelligent computer systems that exhibit the characteristics used to associate with intelligence in human behavior. Basically, it define as a field that study and design of intelligent agents. Traditional AI approach deals with cognitive and biological models that imitate and describe human information processing skills. This processing skills help to perceive and interact with their environment. But in modern era developers can build system that assemble superior information processing needs of government and industry by choosing from large areas of mature technologies. Soft Computing (SC) is an added area of AI. It focused on the design of intelligent systems that process uncertain, imprecise and incomplete information. It applied in real world problems frequently to offer more robust, tractable and less costly solutions than those obtained by more conventional mathematical techniques. This paper reviews correlation of artificial intelligence techniques with soft computing in various areas

Real time ear recognition using deep learning

Automatic identity recognition of ear images represents an active area of interest within the biometric community. The human ear is a perfect source of data for passive person identification. Ear images can be captured from a distance and in a covert manner; this makes ear recognition technology an attractive choice for security applications and surveillance in addition to related application domains. Differing from other biometric modalities, the human ear is neither affected by expressions like faces are nor do need closer touching like fingerprints do. In this paper, a deep learning object detector called faster region based convolutional neural networks (Faster R-CNN) is used for ear detection. A convolutional neural network (CNN) is used as feature extraction. principal component analysis (PCA) and genetic algorithm are used for feature reduction and selection respectively and a fully connected artificial neural network as a matcher. The testing proved the accuracy of 97.8% percentage of success with acceptable speed and it confirmed the accuracy and robustness of the proposed system

Abnormal gait detection by means of LSTM

This article presents a system focused on the detection of three types of abnormal walk patterns caused by neurological diseases, specifically Parkinsonian gait, Hemiplegic gait, and Spastic Diplegic gait. A Kinect sensor is used to extract the Skeleton from a person during its walk, to then calculate four types of bases that generate different sequences from the 25 points of articulations that the Skeleton gives. For each type of calculated base, a recurrent neural network (RNN) is trained, specifically a Long short-term memory (LSTM). In addition, there is a graphical user interface that allows the acquisition, training, and testing of trained networks. Of the four trained networks, 98.1% accuracy is obtained with the database that was calculated with the distance of each point provided by the Skeleton to the Hip-Center point

The classification of skateboarding trick images by means of transfer learning and machine learning models

The evaluation of tricks executions in skateboarding is commonly executed manually and subjectively. The panels of judges often rely on their prior experience in identifying the effectiveness of tricks performance during skateboarding competitions. This technique of classifying tricks is deemed as not a practical solution for the evaluation of skateboarding tricks mainly for big competitions. Therefore, an objective and unbiased means of evaluating skateboarding tricks for analyzing skateboarder’s trick is nontrivial. This study aims at classifying flat ground tricks namely Ollie, Kickflip, Pop Shove-it, Nollie Frontside Shove-it, and Frontside 180 through the camera vision and the combination of Transfer Learning (TL) and Machine Learning (ML). An amateur skateboarder (23 years of age with ± 5.0 years’ experience) executed five tricks for each type of trick repeatedly on an HZ skateboard from a YI action camera placed at a distance of 1.26 m on a cemented ground. The features from the image obtained are extracted automatically via 18 TL models. The features extracted from the models are then fed into different tuned ML classifiers models, for instance, Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Random Forest (RF). The grid search optimization technique through five-fold cross-validation was used to tune the hyperparameters of the classifiers evaluated. The data (722 images) was split into training, validation, and testing with a stratified ratio of 60:20:20, respectively. The study demonstrated that VGG16 + SVM and VGG19 + RF attained classification accuracy (CA) of 100% and 98%, respectively on the test dataset, followed by VGG19 + k-NN and also DenseNet201 + k-NN that achieved a CA of 97%. In order to evaluate the developed pipelines, robustness evaluation was carried out via the form of independent testing that employed the augmented images (2250 images). It was found that VGG16 + SVM, VGG19 + k-NN, and DenseNet201 + RF (by average) are able to yield reasonable CA with 99%, 98%, and 97%, respectively. Conclusively, based on the robustness evaluation, it can be ascertained that the VGG16 + SVM pipeline able to classify the tricks exceptionally well. Therefore, from the present study, it has been demonstrated that the proposed pipelines may facilitate judges in providing a more accurate evaluation of the tricks performed as opposed to the traditional method that is currently applied in competitions

Text Classification of Public Feedbacks using Convolutional Neural Network Based on Differential Evolution Algorithm

Online feedback is an effective way of communication between government departments and citizens. However, the daily high number of public feedbacks has increased the burden on government administrators. The deep learning method is good at automatically analyzing and extracting deep features of data, and then improving the accuracy of classification prediction. In this study, we aim to use the text classification model to achieve the automatic classification of public feedbacks to reduce the work pressure of administrator. In particular, a convolutional neural network model combined with word embedding and optimized by differential evolution algorithm is adopted. At the same time, we compared it with seven common text classification models, and the results show that the model we explored has good classification performance under different evaluation metrics, including accuracy, precision, recall, and F1-score

The Real-Time Classification of Competency Swimming Activity Through Machine Learning

Every year, an average of 3,536 people die from drowning in America. The significant factors that cause unintentional drowning are people’s lack of water safety awareness and swimming proficiency. Current industry and research trends regarding swimming activity recognition and commercial motion sensors focus more on lap swimming utilized by expert swimmers and do not account for freeform activities. Enhancing swimming education through wearable technology can aid people in learning efficient and effective swimming techniques and water safety. We developed a novel wearable system capable of storing and processing sensor data to categorize competitive and survival swimming activities on a mobile device in real-time. This paper discusses the sensor placement, the hardware and app design, and the research process utilized to achieve activity recognition. For our studies, the data we have gathered comes from various swimming skill levels, from beginner to elite swimmers. Our wearable system uses angle-based novel features as inputs into optimal machine learning algorithms to classify flip turns, traditional competitive strokes, and survival swimming strokes. The machine-learning algorithm was able to classify all activities at .935 of an F-measure. Finally, we examined deep learning and created a CNN model to classify competitive and survival swimming strokes at 95% ac- curacy in real-time on a mobile device

A Survey of Hyper-parameter Optimization Methods in Convolutional Neural Networks

Konvolüsyonel Sinir Ağları (KSA), katmanlarının en az bir tanesinde matris çarpımı yerine konvolüsyon işleminin kullanıldığı çok katmanlı yapay sinir ağlarının bir türüdür. Özellikle bilgisayarlı görü çalışmalarında çok başarılı sonuçlar elde edilse de KSA hala birçok zorluk içermektedir. Daha başarılı sonuçlar elde etmek için geliştirilen mimarilerin giderek daha derinleşmesi ve kullanılan görüntülerin giderek daha yüksek kalitede olmasıyla daha fazla hesaplama maliyetleri ortaya çıkmaktadır. Hem bu hesaplama maliyetlerinin düşürülmesi, hem de başarılı sonuçlar elde edilebilmesi, güçlü donanımların kullanılmasına ve kurulan ağın hiper-parametrelerin optimize edilmesine bağlıdır. Bu çalışmada, Genetik Algoritma, Parçacık Sürü Optimizasyonu, Diferansiyel Evrim ve Bayes Optimizasyonu gibi yöntemler ile KSA optimizasyonu gerçekleştirilen çalışmalar incelendi. Bu çalışmalarda optimize edilen hiper-parametreler, tanımlanan değer aralıkları ve elde edilen sonuçlar incelendi. Buna göre, KSA’ nın performansında en etkili hiper-parametrelerin filtre sayısı, filtre boyutu, katman sayısı, seyreltme oranı, öğrenme oranı ve yığın boyutu olduğu görülmüştür. Aynı veri kümelerinin kullanıldığı çalışmalar, elde edilen doğruluk değerleri açısından karşılaştırıldığında çoğu veri kümesi için en iyi doğruluk oranlarının popülasyon tabanlı yöntemlerden Genetik Algoritma ve Parçacık Sürü Optimizasyonu kullanılan çalışmalarda elde edildiği görülmüştür. Bu üst-sezgiseller ile elde edilen modellerin performanslarının “state of the art” modellerle yarışabilir durumda hatta bazen daha iyi oldukları görülmüştür. Yine üst-sezgisel kullanılan bazı çalışmalarda üretilen modellerin aşırı büyümesi engellenmiş; basit ve kolay eğitilebilir modeller üretilmiştir. Hesaplama maliyeti açısından çok avantajlı bu basit modeller ile literatürdeki karmaşık modellere çok yakın sonuçlar elde edilebilmiştir.Convolutional neural networks (CNN) are special types of multi-layer artificial neural networks in which convolution method is used instead of matrix multiplication in at least one of its layers. Although satisfactory results have been achieved by CNN especially in computer vision studies, they still have some difficulties. As the proposed network architectures become deeper with the aim of much better accuracy and the resolution of the input images increases, this results in a need for more computational power. Reducing the computational cost while at the same time still having high accuracy rates depend on the use of powerful equipments and the selection of hyper-parameter values in CNN. In this study, we examined methods like Genetic Algorithms, Particle Swarm Optimization, Differential Evolution and Bayes Optimization that has been used extensively to optimize CNN hyper-parameters, and also listed the hyper-parameters selected to be optimized in those studies, ranges of those parameter values and the results obtained by each of those studies. These studies reveal that the number of layers, number and size of the kernels at each layer, learning rate and the batch size parameters are among the hyper-parameters that affect the performance of the CNNs the most. When the studies that use the same datasets are compared in terms of accuracy, Genetic Algorithms and Particle Swarm Optimization which are both population-based methods achieve the best results for the majority of the datasets. It is also shown that the performance of the models found in these studies are competitive or sometimes better than those of the “state of the art” models. In addition, the CNNs produced in these studies are prevented from being overgrown by imposing limits on the hiper-parameter values. Thus simpler and easier to train models have been obtained. These computationally advantageous simpler models were able to achieve competitive results compared to complicated models