Breast Tumors Diagnosis Using Fuzzy Inference System and Fuzzy C-Means Clustering

Many of the researches have been successful in the field of computer-aided diagnosis because of the important results the intelligent computing approaches have achieved in this field. In this paper the robust classification method is presented, that attempts to classify the tissue suspicion region as normal or not normal by using a Fuzzy Inference System (FIS) using the Fuzzy C-Mean (FCM) clustering for fuzzification of the Gray-Level Co-Occurrence Matrix (GLCM) feature and a match shape function for fuzzification of matrix shape, then by using (T-norm) generate 729 rules (243 rules based on normal DB case, 243 rules based on benign case, 243 rules based on malignant case), after that the best Eighteen rules are selected (best 6 rules based on normal DB case, best 6 rules based on benign DB case, best 6 rules based on malignant DB case) by using genetic algorithm, then make summation for each group if the summation of 6 rules based on normal DB is greater than other summation of two group (best 6 rules based on benign DB case and best 6 rules based on malignant DB case) that mean resulted of the classification step is normal. The model approved efficiency classification rate of 97.5% of input dataset image.

An approach of re-organizing input dataset to enhance the quality of emotion recognition using the bio-signals dataset of MIT

The purpose of this paper is to propose an approach of re-organizing input data to recognize emotion based on short signal segments and increase the quality of emotional recognition using physiological signals. MIT's long physiological signal set was divided into two new datasets, with shorter and overlapped segments. Three different classification methods (support vector machine, random forest, and multilayer perceptron) were implemented to identify eight emotional states based on statistical features of each segment in these two datasets. By re-organizing the input dataset, the quality of recognition results was enhanced. The random forest shows the best classification result among three implemented classification methods, with an accuracy of 97.72% for eight emotional states, on the overlapped dataset. This approach shows that, by re-organizing the input dataset, the high accuracy of recognition results can be achieved without the use of EEG and ECG signals.

A robust framework for shoulder implant X-ray image classification

PurposeMedical images are increasingly popular; therefore, the analysis of these images based on deep learning helps diagnose diseases become more and more essential and necessary. Recently, the shoulder implant X-ray image classification (SIXIC) dataset that includes X-ray images of implanted shoulder prostheses produced by four manufacturers was released. The implant's model detection helps to select the correct equipment and procedures in the upcoming surgery.Design/methodology/approachThis study proposes a robust model named X-Net to improve the predictability for shoulder implants X-ray image classification in the SIXIC dataset. The X-Net model utilizes the Squeeze and Excitation (SE) block integrated into Residual Network (ResNet) module. The SE module aims to weigh each feature map extracted from ResNet, which aids in improving the performance. The feature extraction process of X-Net model is performed by both modules: ResNet and SE modules. The final feature is obtained by incorporating the extracted features from the above steps, which brings more important characteristics of X-ray images in the input dataset. Next, X-Net uses this fine-grained feature to classify the input images into four classes (Cofield, Depuy, Zimmer and Tornier) in the SIXIC dataset.FindingsExperiments are conducted to show the proposed approach's effectiveness compared with other state-of-the-art methods for SIXIC. The experimental results indicate that the approach outperforms the various experimental methods in terms of several performance metrics. In addition, the proposed approach provides the new state of the art results in all performance metrics, such as accuracy, precision, recall, F1-score and area under the curve (AUC), for the experimental dataset.Originality/valueThe proposed method with high predictive performance can be used to assist in the treatment of injured shoulder joints.

A Comparative Study of Detection and Classification of Emotions on Social Media Using SVM and Näıve Bayes Techniques

The rapid rate of innovations and dynamics of technology has made humans life more dependent on them. In today’s synopsis Microblogging and Social networking sites like Twitter, Facebook are a part of our lives that cannot be detached from anyone. Through these social media each one of them carry their emotions and fix their opinions based on a particular situations or circumstances. This paper presents a brief comparison about Detection and Classification of Emotions on Social Media using SVM and Näıve Bayesian classifier. Twitter messages has been used as input dataset because they contain a broad, varied, and freely accessible set of emotions. The approach uses hash-tags as labels to train supervised classifiers to detect multiple classes of emotion on potentially large data sets without the need for manual intervention. We look into the usefulness of a number of features for detecting emotions, including unigrams, unigram symbol, negations and punctuations using SVM and Näıve Bayesian Classifiers.

Image Pattern Recognition in Spatial Data using Artificial Neural Network

This study predicts erosion based on the image patterns as the input data by using an ANN approach. Several simulations had been carried out to get the ANN parameter combination in producing the best accuracy through trials and errors. The results show that the accuracy of artificial neural network training is not influenced by the number of channels, namely the input dataset (erosion factors) and the dimensions of the data, but it is determined by changes in the network parameters. The best combination of parameters is 2 hidden layers, learning rate 0.001, Momentum 0.9, and RMS 0.0001 with an accuracy of 98.55%

Monthly daily-mean rainfall forecast over Indonesia using machine learning and artificial intelligence ensemble

A daily mean rainfall in a month forecast method is presented in this paper. The method provides spatial forecast over Indonesia and employs ensemble of Machine Learning and Artificial Intelligence algorithms as its forecast models. Each spatial grid in the forecast output is processed as an individual dataset. Therefore, each location in the forecast output has different stacked ensemble models as well as their model parameter settings. Furthermore, the best ensemble model is chosen for each spatial grid. The input dataset of the model consists of eight climate data (i.e., East and West Dipole Mode Index, Outgoing Longwave Radiation, Southern Oscillation Index, and Nino 1.2, 3, 4, 3.4) and monthly rainfall reanalysis data, ranging from January 1982 until December 2019. There are four assessment procedures performed on the models: daily mean rainfall establishment as a response function of climate patterns, and one-up to three-month lead forecast. The results show that, based on their performance, these non-Physical models are considerable to complement the existing forecast models.

Automatic Estimation of Interpersonal Engagement During Naturalistic Conversation Using Dyadic Physiological Measurements

Physiological responses of two interacting individuals contain a wealth of information about the dyad: for example, the degree of engagement or trust. However, nearly all studies on dyadic physiological responses have targeted group-level analysis: e.g., correlating physiology and engagement in a large sample. Conversely, this paper presents a study where physiological measurements are combined with machine learning algorithms to dynamically estimate the engagement of individual dyads. Sixteen dyads completed 15-min naturalistic conversations and self-reported their engagement on a visual analog scale every 60 s. Four physiological signals (electrocardiography, skin conductance, respiration, skin temperature) were recorded, and both individual physiological features (e.g., each participant’s heart rate) and synchrony features (indicating degree of physiological similarity between two participants) were extracted. Multiple regression algorithms were used to estimate self-reported engagement based on physiological features using either leave-interval-out crossvalidation (training on 14 60-s intervals from a dyad and testing on the 15th interval from the same dyad) or leave-dyad-out crossvalidation (training on 15 dyads and testing on the 16th). In leave-interval-out crossvalidation, the regression algorithms achieved accuracy similar to a ‘baseline’ estimator that simply took the median engagement of the other 14 intervals. In leave-dyad-out crossvalidation, machine learning achieved a slightly higher accuracy than the baseline estimator and higher accuracy than an independent human observer. Secondary analyses showed that removing synchrony features and personality characteristics from the input dataset negatively impacted estimation accuracy and that engagement estimation error was correlated with personality traits. Results demonstrate the feasibility of dynamically estimating interpersonal engagement during naturalistic conversation using physiological measurements, which has potential applications in both conversation monitoring and conversation enhancement. However, as many of our estimation errors are difficult to contextualize, further work is needed to determine acceptable estimation accuracies.

A Memory-Efficient Learning Framework for Symbol Level Precoding with Quantized NN Weights

We consider a downlink situation where the BS is equipped with four antennas (M = 4) that serve single users; and assume a single cell. We obtain the dataset from the channel realizations randomly generated from a normal distribution with zero mean and unit variance. The dataset is reshaped and converted to real number domain.<div>The input dataset is normalized by the transmit data symbol so that data entries are within the nominal range, potentially aiding the training. We generate 50,000 training samples and 2000 test samples, respectively. The transmit data symbols are modulated using a QPSK modulation scheme. The training SINR is obtained randomly from uniform distribution Γtrain∼U(Γlow, Γhigh). Stochastic gradient descent is used with the Lagrangian function as a loss metric. A parametric rectified linear unit (PReLu) activation function is used for convolutional and fully connected layers in a full-precision model and the low-bit activation function for the quantized model. After every iteration, the learning rate is reduced by a factor α= 0.65 to help the learning algorithm converge faster. <br></div>

A Memory-Efficient Learning Framework for Symbol Level Precoding with Quantized NN Weights

We consider a downlink situation where the BS is equipped with four antennas (M = 4) that serve single users; and assume a single cell. We obtain the dataset from the channel realizations randomly generated from a normal distribution with zero mean and unit variance. The dataset is reshaped and converted to real number domain.<div>The input dataset is normalized by the transmit data symbol so that data entries are within the nominal range, potentially aiding the training. We generate 50,000 training samples and 2000 test samples, respectively. The transmit data symbols are modulated using a QPSK modulation scheme. The training SINR is obtained randomly from uniform distribution Γtrain∼U(Γlow, Γhigh). Stochastic gradient descent is used with the Lagrangian function as a loss metric. A parametric rectified linear unit (PReLu) activation function is used for convolutional and fully connected layers in a full-precision model and the low-bit activation function for the quantized model. After every iteration, the learning rate is reduced by a factor α= 0.65 to help the learning algorithm converge faster. <br></div>

Comparative Study on the Deep Learning Algorithms

Deep Learning methods have paved the way for elevating the future technology that is capable of changing the world. In modern times, size of data is increasing with the level of application. Deep learning enables the huge dataset to process the highly optimized algorithms with high accuracy as well as within low time. The network architecture of deep learning works similar to human brain nerves. The network accepts the input dataset and convert the data into matrix form that passed through multiple layers in which, each layer upgrade the data to deliver the prediction or classification at the end. Researchers explored the numerous deep learning models that portrayed an inspiration for developers and benefitted in the field of voice recognition, language translation, image categorization, stock market prediction etc. The concern behind the model is to effectively resolve the numerous tasks which need to distributed representation and human intelligence. The highly advanced processors like CPU and GPU has too enhanced the deep learning application through fast matrix calculations and image processing. We will take the sample of wind dataset and used it for comparing the different Deep Neural Network (DNN) artificial algorithm. Keywords: Analysis, comparison, deep learning, training, prediction.