Identification of Speed and Unique Letter of Handwriting Using Wavelet and Neural Networks

Handwriting  stroke  reflects  the  personality  and emotional    condition.    Graphology    is    scientific    method    to evaluation  personality  through  handwriting.  There  are  many features  in  graphology  to  identify  personality.  Several  previous researches  used  page  margins,  spacing,  baseline,  vertical  zone, font  size,  and  the  type  of  unique  letter  t.  Other  research  also identify the personality of signatures. This research uses feature writing  speed  and  the  type  of  letters  a,  d,  i  m,  and  t  to  identify personalities   using   structural   analysis   and   artificial   neural networks.  To  improve  accuracy,  image  writing  extracted  using wavelet transform. The system is built with the approach of the structure  and  symbol  has  been  implemented  in  software.  The results show a unique type of letter recognition by 74%, and the speed  feature  by  60%  recognition.  Variations  training  data greatly affect recognition results

Paraphrase Detection Using Manhattan's Recurrent Neural Networks and Long Short-Term Memory

Natural Language Processing (NLP) is a part of artificial intelligence that can extract sentence structures from natural language. Some discussions about NLP are widely used, such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) to summarize papers with many sentences in them. Siamese Similarity is a term that applies repetitive twin network architecture to machine learning for sentence similarity. This architecture is also called Manhattan LSTM, which can be applied to the case of detecting paraphrase sentences. The paraphrase sentence must be recognized by machine learning first. Word2vec is used to convert sentences to vectors so they can be recognized in machine learning. This research has developed paraphrase sentence detection using Siamese Similarity with word2vec embedding. The experimental results showed that the amount of training data is dominant to the new data compared to the number of times and the variation in training data. Obtained data accuracy, 800,000 pairs provide accuracy reaching 99% of training data and 82.4% of new data. These results are better than the accuracy of the new data, with half of the training data only yielding 64%. While the amount of training data did not effect on training data

Dynamic Hand Gesture Recognition Using Temporal-Stream Convolutional Neural Networks

Movement recognition is a hot issue in machine learning. The gesture recognition is related to video processing, which gives problems in various aspects. Some of them are separating the image against the background firmly. This problem has consequences when there are incredibly different settings from the training data. The next challenge is the number of images processed at a time that forms motion. Previous studies have conducted experiments on the Deep Convolutional Neural Network architecture to detect actions on sequential model balancing each other on frames and motion between frames. The challenge of identifying objects in a temporal video image is the number of parameters needed to do a simple video classification so that the estimated motion of the object in each picture frame is needed. This paper proposed the classification of hand movement patterns with the Single Stream Temporal Convolutional Neural Networks approach. This model was robust against extreme non-training data, giving an accuracy of up to 81,7%. The model used a 50 layers ResNet architecture with recorded video training

Post-Stroke identification of EEG signals using recurrent neural networks and long short-term memory

Stroke often causes disability, so patients need rehabilitation for recovery. Therefore, it is necessary to measure its effectiveness. An Electroencephalogram (EEG) can capture the improvement of activity in the brain in stroke rehabilitation. Therefore, the focus is on the identification of several post-rehabilitation conditions. This paper proposed identifying post-stroke EEG signals using Recurrent Neural Networks (RNN) to process sequential data. Memory control in the use of RNN adopted Long Short-Term Memory. Identification was provided out on two classes based on patient condition, particularly "No Stroke" and "Stroke". EEG signals are filtered using Wavelet to get the waves that characterize a stroke. The four waves and the average amplitude are features of the identification model. The experiment also varied the weight correction, i.e., Adaptive Moment Optimization (Adam) and Stochastic Gradient Descent (SGD). This research showed the highest accuracy using Wavelet without amplitude features of 94.80% for new data with Adam optimization model. Meanwhile, the feature configuration tested effect shows that the use of the amplitude feature slightly reduces the accuracy to 91.38%. The results also show that the effect of the optimization model, namely Adam has a higher accuracy of 94.8% compared to SGD, only 74.14%. The number of hidden layers showed that three hidden layers could slightly increase the accuracy from 93.10% to 94.8%. Therefore, wavelets as extraction are more significant than other configurations, which slightly differ in performance. Adam's model achieved convergence in earlier times, but the speed of each iteration is slower than the SGD model. Experiments also showed that the optimization model, number of epochs, configuration, and duration of the EEG signal provide the best accuracy settings

Spoken Word and Speaker Recognition Using MFCC and Multiple Recurrent Neural Networks

Identification of spoken word and speaker has been featured in many kinds of research. The problem or obstacle that persists is in the pronunciation of a particular word. So it is the noise that causes the difficulty of words to be identified. Furthermore, every human has different pronunciation habits and is influenced by several variables, such as amplitude, frequency, tempo, and rhythmic. This study proposed the identification of spoken sounds by using specific word input to determine the patterns of the speaker and spoken using Mel-frequency Cepstrum Coefficients (MFCC) and Multiple Recurrent Neural Networks (RNN). The Mel coefficient of MFCC is used as an input feature for identifying spoken words and speakers using RNN and Long Short Term Memory (LSTM). Multiple RNN works spoken word and speaker in parallel. The results obtained by multiple RNN have an accuracy of 87.74%, while single RNNs have 80.58% using Adam of new data. In order to test our model computational regularly, the experiment tested K-fold Cross-Validation of datasets for spoken and speakers with an average accuracy of 86.07%, which means the model to be able to learn on the dataset without being affected by the order or selection of test data

Semantic Classification of Scientific Sentence Pair Using Recurrent Neural Network

One development of Natural Language Processing is the semantic classification of sentences and documents. The challenge is finding relationships between words and between documents through a computational model. The development of machine learning makes it possible to try out various possibilities that provide classification capabilities. This paper proposes the semantic classification of sentence pairs using Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM). Each couple of sentences is turned into vectors using Word2Vec. Experiments carried out using CBOW and Skip-Gram to get the best combination. The results are obtained that word embedding using CBOW produces better than Skip-Gram, although it is still around 5%. However, CBOW slows slightly at the beginning of iteration but is stable towards convergence. Classification of all six classes, namely Equivalent, Similar, Specific, No Alignment, Related, and Opposite. As a result of the unbalanced data set, the retraining was conducted by eliminating a few classes member from the data set, thus providing an accuracy of 73% for non-training data. The results showed that the Adam model gave a faster convergence at the start of training compared to the SGD model, and AdaDelta, which was built, gave 75% better accuracy with an F1-Score of 67%

Hand Movement Identification Using Single-Stream Spatial Convolutional Neural Networks

Human-robot interaction can be through several ways, such as through device control, sounds, brain, and body, or hand gesture. There are two main issues: the ability to adapt to extreme settings and the number of frames processed concerning memory capabilities. Although it is necessary to be careful with the selection of the number of frames so as not to burden the memory, this paper proposed identifying hand gesture of video using Spatial Convolutional Neural Networks (CNN). The sequential image's spatial arrangement is extracted from the frames contained in the video so that each frame can be identified as part of one of the hand movements. The research used VGG16, as CNN architecture is concerned with the depth of learning where there are 13 layers of convolution and three layers of identification. Hand gestures can only be identified into four movements, namely 'right', 'left', 'grab', and 'phone'. Hand gesture identification on the video using Spatial CNN with an initial accuracy of 87.97%, then the second training increased to 98.05%. Accuracy was obtained after training using 5600 training data and 1120 test data, and the improvement occurred after manual noise reduction was performed

Classification of Post-Stroke EEG Signal Using Genetic Algorithm and Recurrent Neural Networks

Stroke is caused by a sudden burst of blood vessels in the brain, causing speech difficulties, memory loss, and also paralysis. The identification of electrical activity in the brain of post-stroke patients from EEG signals is an attempt to evaluate rehabilitation. EEG signal recording involves multiple channels with overlapping information. Therefore the importance of channel optimization is to reduce processing time and reduce the computational burden. Besides, that channel optimization can have an overfitting effect due to excessive utilization of EEG channels. This paper proposed the optimization of EEG channels for the identification of post-stroke patients using Genetic Algorithms and Recurrent Neural Networks. Data was taken from 75 subjects with a recording duration of 180 seconds in a seated state. The data was segmented and extracted using Wavelet to get the frequency of the Alpha, Theta, Mu, Delta, and Amplitude changes. The next step is the channel optimization process using Genetic Algorithms. The method applied to get a combination of channels that qualifies. Then, the EEG signal identification proceeds of the optimization of the channels used Recurrent Neural Network. The result showed that applying the Genetic Algorithm afforded 12 channels configuration with 90.00% of accuracy; meanwhile, used all channels gave a 72.22% result. Therefore, channel optimization is essential to reduce redundancy and increase recognition

Game Simulasi Gerakan Pasien Cedera Bahu Menggunakan Jaringan Saraf Tiruan Backpropagation

Bahu merupakan bagian dari lengan yang mudah mengalami cedera. Cedera pada bahu antara lain peradangan sendi, pergeseran tulang (dislokasi), dan bahu kaku (frozen shoulder) serta pasca stroke. Selain itu, penyebab bahu cedera karena olahraga yang menitikberatkan lengan sebagai tumpuan. Latihan terapi yang terjadwal merupakan upaya merehabilitasi bahu untuk memulihkan dan mengembalikan fungsi bahu. Namun kegiatan rehabilitasi medik memerlukan jangka waktu lama dan terkesan monoton yang berakibat menurunnya motivasi pasien dalam menjalani latihan terapi. Sementara itu, perkembangan teknologi yang memudahkan berbagai aspek kehidupan khususnya dibidang kesehatan menjadikan video game dan perangkat sensor Kinect dapat diterapkan sebagai media dalam simulasi latihan terapi cedera bahu. Penelitian ini telah membangun game simulasi sebagai visualisasi untuk mendukung latihan terapi cedera bahu dan kemampuan dalam memprediksi pemulihan cedera bahu pasien yang terbagi atas tiga kelas yaitu “Meningkat”, “Tetap”, dan “Menurun”. Pasien melakukan gerakan untuk mengontrol game dengan mengangkat lengan menjauhi garis tengah terhadap bidang frontal pada tubuh atau disebut sebagai gerakan Shoulder Active Abduction. Gerakan dilakukan oleh salah satu lengan cedera yang akan menghasilkan nilai sudut bervariasi dengan rentang 0°-180°. Gerakan yang dilakukan direkam sensor Kinect yang dapat memvisualisasikan peta gerakan kerangka tubuh atau disebut matchstick skeleton. Keluaran dari sensor Kinect berupa nilai koordinat yang direpresentasikan ke dalam nilai sudut. Data latih diperoleh dari lima naracoba yang menghasilkan nilai sudut berbeda. Nilai-nilai sudut dilakukan pelatihan menggunakan Backpropagation yang selanjutnya menghasilkan nilai akurasi. Hasil pelatihan dengan learning rate 0,01 menunjukan akurasi sebesar 82% untuk prediksi data yang sudah dilatih, sedangkan pengujian data baru menunjukan akuarasi sebesar 66,7%.Bahu merupakan bagian dari lengan yang mudah mengalami cedera. Cedera pada bahu antara lain peradangan sendi, pergeseran tulang (dislokasi), dan bahu kaku (frozen shoulder) serta pasca stroke. Selain itu, penyebab bahu cedera karena olahraga yang menitikberatkan lengan sebagai tumpuan. Latihan terapi yang terjadwal merupakan upaya merehabilitasi bahu untuk memulihkan dan mengembalikan fungsi bahu. Namun kegiatan rehabilitasi medik memerlukan jangka waktu lama dan terkesan monoton yang berakibat menurunnya motivasi pasien dalam menjalani latihan terapi. Sementara itu, perkembangan teknologi yang memudahkan berbagai aspek kehidupan khususnya dibidang kesehatan menjadikan video game dan perangkat sensor Kinect dapat diterapkan sebagai media dalam simulasi latihan terapi cedera bahu. Penelitian ini telah membangun game simulasi sebagai visualisasi untuk mendukung latihan terapi cedera bahu dan kemampuan dalam memprediksi pemulihan cedera bahu pasien yang terbagi atas tiga kelas yaitu “Meningkat”, “Tetap”, dan “Menurun”. Pasien melakukan gerakan untuk mengontrol game dengan mengangkat lengan menjauhi garis tengah terhadap bidang frontal pada tubuh atau disebut sebagai gerakan Shoulder Active Abduction. Gerakan dilakukan oleh salah satu lengan cedera yang akan menghasilkan nilai sudut bervariasi dengan rentang 0°-180°. Gerakan yang dilakukan direkam sensor Kinect yang dapat memvisualisasikan peta gerakan kerangka tubuh atau disebut matchstick skeleton. Keluaran dari sensor Kinect berupa nilai koordinat yang direpresentasikan ke dalam nilai sudut. Data latih diperoleh dari lima naracoba yang menghasilkan nilai sudut berbeda. Nilai-nilai sudut dilakukan pelatihan menggunakan Backpropagation yang selanjutnya menghasilkan nilai akurasi. Hasil pelatihan dengan learning rate 0,01 menunjukan akurasi sebesar 82% untuk prediksi data yang sudah dilatih, sedangkan pengujian data baru menunjukan akuarasi sebesar 66,7%

Emotion brain-computer interface using wavelet and recurrent neural networks

Brain-Computer Interface (BCI) has an intermediate tool that is usually obtained from EEG signal information. This paper proposed the BCI to control a robot simulator based on three emotions for five seconds by extracting a wavelet function in advance with Recurrent Neural Networks (RNN). Emotion is amongst variables of the brain that can be used to move external devices. BCI's success depends on the ability to recognize one person’s emotions by extracting their EEG signals. One method to appropriately recognize EEG signals as a moving signal is wavelet transformation. Wavelet extracted EEG signal into theta, alpha, and beta wave, and consider them as the input of the RNN technique. Connectivity between sequences is accomplished with Long Short-Term Memory (LSTM). The study also compared frequency extraction methods using Fast Fourier Transform (FFT). The results showed that by extracting EEG signals using Wavelet transformations, we could achieve a confident accuracy of 100% for the training data and 70.54% of new data. While the same RNN configuration without pre-processing provided 39% accuracy, even adding FFT would only increase it to 52%. Furthermore, by using features of the frequency filter, we can increase its accuracy from 70.54% to 79.3%. These results showed the importance of selecting features because of RNNs concern to sequenced its inputs. The use of emotional variables is still relevant for instructions on BCI-based external devices, which provide an average computing time of merely 0.235 seconds