Complex hand gesture interactions among dynamic sign words may lead to misclassification, which affects the recognition accuracy of the ubiquitous sign language recognition system. This paper proposes to augment the feature vector of dynamic sign words with knowledge of hand dynamics as a proxy and classify dynamic sign words using motion patterns based on the extracted feature vector. In this method, some double-hand dynamic sign words have ambiguous or similar features across a hand motion trajectory, which leads to classification errors. Thus, the similar/ambiguous hand motion trajectory is determined based on the approximation of a probability density function over a time frame. Then, the extracted features are enhanced by transformation using maximal information correlation. These enhanced features of 3D skeletal videos captured by a leap motion controller are fed as a state transition pattern to a classifier for sign word classification. To evaluate the performance of the proposed method, an experiment is performed with 10 participants on 40 double hands dynamic ASL words, which reveals 97.98% accuracy. The method is further developed on challenging ASL, SHREC, and LMDHG data sets and outperforms conventional methods by 1.47%, 1.56%, and 0.37%, respectively.
Most of the available American Sign Language (ASL) words share similar characteristics. These characteristics are usually during sign trajectory which yields similarity issues and hinders ubiquitous application. However, recognition of similar ASL words confused translation algorithms, which lead to misclassification. In this paper, based on fast fisher vector (FFV) and bi-directional Long-Short Term memory (Bi-LSTM) method, a large database of dynamic sign words recognition algorithm called bidirectional longshort term memory-fast fisher vector (FFV-Bi-LSTM) is designed. This algorithm is designed to train 3D hand skeletal information of motion and orientation angle features learned from the leap motion controller (LMC). Each bulk features in the 3D video frame is concatenated together and represented as an highdimensional vector using FFV encoding. Evaluation results demonstrate that the FFV-Bi-LSTM algorithm is suitable for accurately recognizing dynamic ASL words on basis of prosodic and angle cues. Furthermore, comparison results demonstrate that FFV-Bi-LSTM can provide better recognition accuracy of 98% and 91.002% for randomly selected ASL dictionary and 10 pairs of similar ASL words, in leave-one-subjectout cross-validation on the constructed dataset. The performance of our FFV-Bi-LSTM is further evaluated on ASL data set, leap motion dynamic hand gestures data set (LMDHG), and Semaphoric hand gestures contained in the Shape Retrieval Contest (SHREC) dataset. We improve the accuracy of the ASL data set, LMDHG, and SHREC data sets by 2%, 2%, and 3.19% respectively.
Adaptive Neuro-fuzzy Inference System (ANFIS) remains one of the promising AI techniques to handle data over-fitting and as well, improves generalization. Presently, many ANFIS optimization techniques have been synergized and found effective at some points through trial and error procedures. In this work, we tune ANFIS using Grid partition algorithm to handle unseen data effectively with fast convergence. This model is initialized using a careful selection of effective parameters that discriminate climate conditions; minimum temperature, maximum temperature, average temperature, wind speed and relative humidity. These parameters are used as inputs for ANFIS, whereas confirmed cases of COVID-19 is chosen as dependent values for two consecutive months and first ten days of December for new COVID-19 confirmed cases according to the Department of disease control (DDC) Thailand. The proposed ANFIS model provides outstanding achievement to predict confirmed cases of COVID-19 with R 2 of 0.99. Furthermore, data set trend analysis is done to compare fluctuations of daily climatic parameters, to satisfy our proposition, and illustrates the serious effect of these parameters on COVID-19 epidemic virus spread.
Addressing crime detection, cyber security and multi-modal gaze estimation in biometric information recognition is challenging. Thus, trained artificial intelligence (AI) algorithms such as Support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) have been proposed to recognize distinct and discriminant features of biometric information (intrinsic hand features and demographic cues) with good classification accuracy. Unfortunately, due to nonlinearity in distinct and discriminant features of biometric information, accuracy of SVM and ANFIS is reduced. As a result, optimized AI algorithms ((ANFIS) with subtractive clustering (ANFIS-SC) and SVM with error correction output code (SVM-ECOC)) have shown to be effective for biometric information recognition. In this paper, we compare the performance of the ANFIS-SC and SVM-ECOC algorithms in their effectiveness at learning essential characteristics of intrinsic hand features and demographic cues based on Pearson correlation coefficient (PCC) feature selection. Furthermore, the accuracy of these algorithms are presented, and their recognition performances are evaluated by root mean squared error (RMSE), mean absolute percentage error (MAPE), scatter index (SI), mean absolute deviation (MAD), coefficient of determination (R 2 ), Akaike's Information Criterion (AICc) and Nash-Sutcliffe model efficiency index (NSE). Evaluation results show that both SVM-ECOC and ANFIS-SC algorithms are suitable for accurately recognizing soft biometric information on basis of intrinsic hand measurements and demographic cues. Moreover, comparison results demonstrated that ANFIS-SC algorithms can provide better recognition accuracy, with RMSE, AICc, MAPE, R 2 and NSE values of ≤ 3.85, 2.39E+02, 0.18%, ≥ 0.99 and ≥ 99, respectively.
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