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Social media communications offer valuable feedback to firms about their brands. We present a targeted approach to Twitter sentiment analysis for brands using supervised feature engineering and the dynamic architecture for artificial neural networks. The proposed approach addresses challenges associated with the unique characteristics of the Twitter language and brand-related tweet sentiment class distribution. We demonstrate its effectiveness on Twitter data sets related to two distinctive brands. The supervised feature engineering for brands offers final tweet feature representations of only seven dimensions with greater feature density. Reducing the dimensionality of the representations reduces the complexity of the classification problem and feature sparsity. Two sets of experiments are conducted for each brand in three-class and five-class tweet sentiment classification. We examine five-class classification to target the mild sentiment expressions that are of particular interest to firms and brand management practitioners. We compare the proposed approach to the performances of two state-of-the-art Twitter sentiment analysis systems from the academic and commercial domains. The results indicate that it outperforms these state-of-the-art systems by wide margins, with classification F 1 -measures as high as 88 percent and excellent recall of tweets expressing mild sentiments. Furthermore, they demonstrate the tweet feature representations, though consisting of only seven dimensions, are highly effective in capturing indicators of Twitter sentiment expression. The proposed approach and vast majority of features identified through supervised feature engineering are applicable across brands, allowing researchers and brand management practitioners to quickly generate highly effective tweet feature representations for Twitter sentiment analysis on other brands.

The diagnosis of faults in grid-connected photovoltaic (GCPV) systems is a challenging task due to their complex nature and the high similarity between faults. To address this issue, we propose a wrapper approach called the salp swarm algorithm (SSA) for feature selection. The main objective of SSA is to extract only the most important features from the raw data and eliminate unnecessary ones to improve the classification accuracy of supervised machine learning (SML) classifiers. Subsequently, the selected features are used to train supervised machine learning (SML) techniques in distinguishing between various operating modes. To evaluate the efficiency of the technique, we used healthy and faulty data from GCPV systems that have been injected with frequent faults, 20 different types of faults were introduced, including line-to-line, line-to-ground, connectivity faults, and those affecting the operation of bay-pass diodes. These faults present diverse conditions, such as simple and multiple faults in the PV arrays and mixed faults in both arrays. The performances of the developed SSA-SML are compared with those using principal component analysis (PCA) and kernel PCA (KPCA) based SML techniques through different criteria (i.e., accuracy, recall, precision, F1 score, and computation time). The experimental findings demonstrated that the proposed diagnosis paradigm outperformed the other techniques and achieved a high diagnostic accuracy (an average accuracy greater than 99%) while significantly reducing computation time.

This study proposed a performance-interpretable deep learning model for rolling bearing fault diagnosis that integrates an intelligent fusion of sound and vibration signals and self-supervised learning via an interpretable attention mechanism. A deep learning decoder framework with a compressed attention mechanism encoder is developed to automatically learn the correlation between sound and vibration signals and the fusion method, eliminating the need for manual feature extraction and multi-model construction. By introducing the dynamic attention mechanism, the strength of the correlation between sound and vibration signals can be sensed in real-time to adapt to different scenarios flexibly. When the correlation is strong, due to the high similarity between the signals, a complex feature weight fusion strategy is employed to extract and fuse the essential features of different modalities more efficiently, enabling this fusion to mutually enhance the expressive power of the features for feature fusion. Whereas, when the correlation is weak, the correlation between the signals is low and forcing a complex fusion may introduce more noise and redundant information, therefore a hybrid input strategy is used. The CAME-Transformer Decoder (CAME-TD) model dynamically updates the correlation thresholds and fusion strategies using regularized loss constraints to ensure adaptation to multimodal signal differences. During model training, visual analysis of the attention mechanism role weights and feature learning helps in parameter optimization and performance evaluation. The experimental results demonstrate the effectiveness of the proposed methodology, with an improvement in fault diagnosis performance under various operating and noise conditions compared to a single signal input. Moreover, the CAME-TD model not only achieves considerable diagnostic performance but also enhances interpretability, providing a new approach for rolling bearing fault diagnosis.

Human Gait Recognition (HGR) is a biometric approach, widely used for security purposes from the past few decades. In HGR, the change in an individual walk along with wearing clothes and carrying bag are major covariant controls which impact the performance of a system. Moreover, recognition under various view angles is another key challenge in HGR. In this work, a novel fully automated method is proposed for HGR under various view angles using deep learning. Four primary steps are involved such as: preprocessing of original video frames, exploiting pre-trained Densenet-201 CNN model for features extraction, reduction of additional features from extracted vector based on a hybrid selection method, and finally recognition using supervised learning methods. The extraction of CNN features is a key step in which our target is to extract the most active features. To achieve this goal, we fuse the features of both second last and third last layers in a parallel process. At a later stage, best features are selected by the Firefly algorithm and Skewness based approach. These selected features are serially combined and fed to One against All Multi Support Vector Machine (OAMSVM) for final recognition. Three different angles 18 0 , 36 0 and 54 0 of the CASIA B dataset are selected for the evaluation process and accuracy of 94.3%, 93.8% and 94.7% is achieved respectively. Results show significant improvement in accuracy and recall rate as compared to the existing state-of-the-art techniques.

Weakly supervised learning applies image tag labels to train convolutional neural networks to locate defect. In industrial vision system, metal surface is anisotropic under light in all directions and it is inevitable to cause local overexposure due to the natural reflection of active strong light, especially on the cylindrical metal surface. In this paper, injector valve is taken as the representative of cylindrical metal workpieces. Since the variety and complexity of cylindrical metal workpiece defects which cause pixel-level annotation require expensive manual work. This problem hinders the application of convolutional neural network in industries. In order to solve these above challenges, this paper proposed an end-to-end weakly supervised learning framework named Integrated Residual Attention Convolutional Neural Network (IRA-CNN). IRA-CNN only uses image tag annotation for training and performs defect classification and defect segmentation simultaneously. Weakly supervised learning is achieved by extracting category-related spatial features from defect classification scores. IRA-CNN is composed of multiple Integrated Residual Attention Block (IRA-Block) as the feature extractor which improves the accuracy and achieves real-time performance. IRA-Block adds Integrated Attention Module (IAM) which includes channel attention submodule and spatial attention submodule. The channel attention submodule adaptively extracts the channel attention feature map to improve its bilateral nonlinearity and the robustness. IAM can be well integrated into the IRA-CNN makes the neural network suppress the interference of useless background area and highlight the defect area. Satisfied performance is achieved by the proposed method in our own defect dataset which could meet the requirements in the industrial process. Experimental results show that the method has good generalization ability. The accuracy of defect classification reaches 97.84% and the segmentation accuracy is significantly improved compared with the benchmark method.

Computer Vision (CV) domain is widely used in the current era of automation and visual surveillance for the detection and classification of different objects in a diverse environment. The automatic machine inspection of different objects in the scenes is based on internal and external parameters like features that provide a huge amount of information related to the nature of an object in the scene. In this work, we propose a new automated method based on classical and deep learning feature selection. The proposed object classification method follows three steps. The data augmentation is performed in the first step to make the balance database. Later, Pyramid HOG (PHOG) and Central Symmetric LBP (CS-LBP) features are serially fused along with deep learning-based extracted features. The deep learning features are extracted from the pre-trained CNN model name Inception V3. In the third step, a new technique name Joint Entropy along with KNN (JEKNN) is employed to select the best features. The best-selected features are finally classified by well-known supervised learning methods and choose the best one based on higher accuracy. The proposed method is evaluated on Caltech101 balanced dataset and achieved maximum accuracy of 90.4% on Ensemble classifier which outperforms as compare to existing techniques.

The rapid growth and adaptation of medical information to identify significant health trends and help with timely preventive care have been recent hallmarks of the modern healthcare data system. Heart disease is the deadliest condition in the developed world. Cardiovascular disease and its complications, including dementia, can be averted with early detection. Further research in this area is needed to prevent strokes and heart attacks. An optimal machine learning model can help achieve this goal with a wealth of healthcare data on heart disease. Heart disease can be predicted and diagnosed using machine-learning-based systems. Active learning (AL) methods improve classification quality by incorporating user–expert feedback with sparsely labelled data. In this paper, five (MMC, Random, Adaptive, QUIRE, and AUDI) selection strategies for multi-label active learning were applied and used for reducing labelling costs by iteratively selecting the most relevant data to query their labels. The selection methods with a label ranking classifier have hyperparameters optimized by a grid search to implement predictive modelling in each scenario for the heart disease dataset. Experimental evaluation includes accuracy and F-score with/without hyperparameter optimization. Results show that the generalization of the learning model beyond the existing data for the optimized label ranking model uses the selection method versus others due to accuracy. However, the selection method was highlighted in regards to the F-score using optimized settings.

Coronary heart disease is one of the major causes of deaths around the globe. Predicating a heart disease is one of the most challenging tasks in the field of clinical data analysis. Machine learning (ML) is useful in diagnostic assistance in terms of decision making and prediction on the basis of the data produced by healthcare sector globally. We have also perceived ML techniques employed in the medical field of disease prediction. In this regard, numerous research studies have been shown on heart disease prediction using an ML classifier. In this paper, we used eleven ML classifiers to identify key features, which improved the predictability of heart disease. To introduce the prediction model, various feature combinations and well-known classification algorithms were used. We achieved 95% accuracy with gradient boosted trees and multilayer perceptron in the heart disease prediction model. The Random Forest gives a better performance level in heart disease prediction, with an accuracy level of 96%.

Millions of people worldwide are affected by Parkinson’s disease (PD), which significantly worsens their quality of life. Currently, the diagnosis is based on assessment of motor symptoms, but interest toward non-motor symptoms is increasing, as well. Among them, idiopathic hyposmia (IH) is associated with an increased risk of developing PD in healthy adults. In this work, a wearable inertial device, named SensFoot V2, was used to acquire motor data from 30 healthy subjects, 30 people with IH, and 30 PD patients while performing tasks from the MDS-UPDRS III for lower limb assessment. The most significant and non-correlated extracted parameters were selected in a feature array that can identify differences between the three groups of people. A comparative classification analysis was performed by applying three supervised machine learning algorithms. The system resulted able to distinguish between healthy and patients (specificity and recall equal to 0.967), and the people with IH can be identified as a separate class within a three-group classification (accuracy equal to 0.78). Thus, the system could support the clinician in objective assessment of PD. Further, identification of IH together with changes in motor parameters could be a non-invasive two-step approach to investigate the early onset of PD.

With the continuous evolve of E-commerce systems, online reviews are mainly considered as a crucial factor for building and maintaining a good reputation. Moreover, they have an effective role in the decision making process for end users. Usually, a positive review for a target object attracts more customers and lead to high increase in sales. Nowadays, deceptive or fake reviews are deliberately written to build virtual reputation and attracting potential customers. Thus, identifying fake reviews is a vivid and ongoing research area. Identifying fake reviews depends not only on the key features of the reviews but also on the behaviors of the reviewers. This paper proposes a machine learning approach to identify fake reviews. In addition to the features extraction process of the reviews, this paper applies several features engineering to extract various behaviors of the reviewers. The paper compares the performance of several experiments done on a real Yelp dataset of restaurants reviews with and without features extracted from users behaviors. In both cases, we compare the performance of several classifiers; KNN, Naive Bayes (NB), SVM, Logistic Regression and Random forest. Also, different language models of n-gram in particular bi-gram and tri-gram are taken into considerations during the evaluations. The results reveal that KNN(K=7) outperforms the rest of classifiers in terms of f-score achieving best f-score 82.40%. The results show that the f-score has increased by 3.80%when taking the extracted reviewers behavioral features into consideration.