Depression has become one of the most widespread mental health disorders across the globe. Depression is a state of mind which affects how we think, feel, and act. The number of suicides caused by ...depression has been on the rise for the last several years. This issue needs to be addressed. Considering the rapid growth of various social media platforms and their effect on society and the psychological context of a being, it’s becoming a platform for depressed people to convey feelings and emotions, and to study their behavior by mining their social activity through social media posts. The key objective of our study is to explore the possibility of predicting a user’s mental condition by classifying the depressive from non-depressive ones using Twitter data. Using textual content of the user’s tweet, semantic context in the textual narratives is analyzed by utilizing deep learning models. The proposed model, however, is a hybrid of two deep learning architectures, Convolutional Neural Network (CNN) and bi-directional Long Short-Term Memory (biLSTM) that after optimization obtains an accuracy of 94.28% on benchmark depression dataset containing tweets. CNN-biLSTM model is compared with Recurrent Neural Network (RNN) and CNN model and also with the baseline approaches. Experimental results based on various performance metrics indicate that our model helps to improve predictive performance. To examine the problem more deeply, statistical techniques and visualization approaches were used to show the profound difference between the linguistic representation of depressive and non-depressive content.
Toroidal dipole is a localized electromagnetic excitation that plays an important role in determining the fundamental properties of matter due to its unique potential to excite nearly nonradiating ...charge–current configuration. Toroidal dipoles are recently discovered in metamaterial systems where it is shown that these dipoles manifest as poloidal currents on the surface of a torus and are distinctly different from the traditional electric and magnetic dipoles. Here, an active toroidal metamaterial switch is demonstrated in which the toroidal dipole can be dynamically switched to the fundamental electric dipole or magnetic dipole, through selective inclusion of active elements in a hybrid metamolecule design. Active switching of nonradiating toroidal configuration into highly radiating electric and magnetic dipoles can have significant impact in controlling the electromagnetic excitations in free space and matter that can have potential applications in designing efficient lasers, sensors, filters, and modulators.
Toroidal dipole excitation can be dynamically switched to the fundamental electric dipole or magnetic dipole, through selective inclusion of the active elements in a mirrored configuration of Fano resonators. Optical switching between various multipole excitations that range from nonradiating to strongly radiating configuration presents an innovative approach to implement more than one electromagnetic feature in a single device.
•Analysis of 84 different models in noisy imbalanced and incomplete datasets.•MICE and KNN imputation techniques outperform in incomplete imbalanced datasets.•A result shows SMOTE-ENN better in noisy ...imbalanced datasets.•MICE-SMOTE-ENN performs better in noisy imbalanced and incomplete datasets.
Class imbalance creates a considerable impact on the classification of instances using traditional classifiers. Class imbalance, along with other difficulties, creates a significant impact on recognizing instances of minority class. Researchers work in various directions to mitigate class imbalance effect along with noise as well as missing values in datasets. However, combined studies of noisy class imbalance along with incomplete datasets have not been performed yet. This article contains a detailed analysis of 84 different machine learning models to deal with noisy binary class imbalanced and incomplete data using AUC, G-Mean, and F1-score as performance metrics. This article contains a detailed experiment considering missing value imputation and oversampling techniques. The article contains three comparisons: first missing value imputation techniques in incomplete and binary class imbalanced data, second, resampling techniques in noisy binary class imbalanced data, and third, combined techniques in noisy binary class imbalanced and incomplete data. We conclude that MICE and KNN techniques perform well with an increase in the imbalanced dataset's missing value from the first comparison. In second comparison, the SMOTE-ENN technique performs better than state-of-art in noisy binary class imbalanced datasets, and in the third comparison, we conclude that MICE with SMOTE-ENN technique perform well compared to the rest of the techniques.
Abstract
The revolutionary 5G cellular systems represent a breakthrough in the communication network design to provide a single platform for enabling enhanced broadband communications, virtual ...reality, autonomous driving, and the internet of everything. However, the ongoing massive deployment of 5G networks has unveiled inherent limitations that have stimulated the demand for innovative technologies with a vision toward 6G communications. Terahertz (0.1-10 THz) technology has been identified as a critical enabler for 6G communications with the prospect of massive capacity and connectivity. Nonetheless, existing terahertz on-chip communication devices suffer from crosstalk, scattering losses, limited data speed, and insufficient tunability. Here, we demonstrate a new class of phototunable, on-chip topological terahertz devices consisting of a broadband single-channel 160 Gbit/s communication link and a silicon Valley Photonic Crystal based demultiplexer. The optically controllable demultiplexing of two different carriers modulated signals without crosstalk is enabled by the topological protection and a critically coupled high-quality (
Q
) cavity. As a proof of concept, we demultiplexed high spectral efficiency 40 Gbit/s signals and demonstrated real-time streaming of uncompressed high-definition (HD) video (1.5 Gbit/s) using the topological photonic chip. Phototunable silicon topological photonics will augment complementary metal oxide semiconductor (CMOS) compatible terahertz technologies, vital for accelerating the development of futuristic 6G and 7G communication era driving the real-time terabits per second wireless connectivity for network sensing, holographic communication, and cognitive internet of everything.
Abstract
Cervical cancer is one of the most common cancers among women in the world. As at the earlier stage, cervical cancer has fewer symptoms. Cancer research is vital as the prognosis of cancer ...enables clinical applications for patients. In this study, we demonstrate a new approach that applies an ensemble approach to machine learning models for the automatic diagnosis of cervical cancer. The dataset used in the study is the cervical cancer dataset available at the University of California Irvine database repository. Initially, missing values are imputed (k-nearest neighbors) and then the data are balanced (oversampled). Two feature selection approaches are used to extract the most significant features. The proposed stacking architecture, applied for the first time on the cervical cancer dataset, used time elapse of 5.6 s and achieved an area under the curve score of 99.7% performing better than the methods used in previous works. The objective of the study is to propose a computational model that can predict the diagnosis of cervical cancer efficiently. Further, the proposed learning architecture is gauged with several ensemble approaches like random forest, gradient boosting, voting ensemble and weighted voting ensemble to perceive the enhancement.
In the area of Materials Science and Engineering, the tetrahedron comprising of processing, microstructure, properties and performance as four vertex corners is always key to develop new materials ...and to convert them to a useful shape for end application with the best properties possible ...
The magic of the unification of different categories of materials to develop superior materials (composites) with improved functionality was recognized way back by our forefathers and was utilized ...very well by mother nature to support the dynamic functionality and requirements of both static and moving living organisms ...
Breast cancer is the second largest cause of mortality among women. Breast cancer patients in developed nations have a relative survival rate of more than 5-years due to early detection and ...treatment. Deep learning approaches can help enhance the identification of breast cancer cells, lower the risk of detection mistakes, and minimize the time it takes to diagnose breast cancer using human methods. This paper examines the accuracy of artificial neural networks, Restricted Boltzmann Machine, Deep Autoencoders, and Convolutional Neural Networks (CNN) for post-operative survival analysis of breast cancer patients. A thorough examination of each network's operation and design is carried out to determine which network outperforms the other, followed by an analysis based on the network's prediction accurateness. The experimental results assert that all the deep learning techniques can predict the survival of breast cancer patients. The accuracy score achieved by Restricted Boltzmann Machine performed is the highest (0.97), followed by deep Autoencoders that attained an accuracy score of 0.96. CNN achieved a 92% accuracy score, while artificial neural networks attained the least accuracy score (0.89). The prediction performance of models has been evaluated using distinct parameters like accuracy, the area under the curve, F1 Score, Matthew’s correlation coefficient, sensitivity, and specificity. Also, the models have been validated using fivefold cross-validation techniques. However, there is still a need for complete analysis and research using deep learning methods to determine the design that provides superior accuracy.
Abstract
A class imbalance problem plays a vital role while dealing with classes with rare number of instances. Noisy class imbalanced datasets create considerable effect on the machine learning ...classification of classes. Data resampling techniques commonly used for handling class imbalance problem show insignificant behavior in noisy imbalanced datasets. To cure curse of data resampling technique in noisy class imbalanced data, we have proposed improved hybrid bag-boost with proposed resampling technique model. This model contains proposed resampling technique used for handling noisy imbalanced datasets. Proposed resampling technique comprises K-Means SMOTE (Synthetic Minority Oversampling TEchnique) as an oversampling technique and edited nearest neighbor (ENN) undersampling technique used as noise removal. This resampling technique is used to mitigate noise in imbalanced datasets at three levels, i.e. first clusters datasets using K-Means clustering technique, SMOTE inside clusters for handling imbalance by inducing synthetic instances of class in minority and lastly, using ENN technique to remove instances that create noise afterwards. Experiments were performed using 11 binary imbalanced datasets by varying attribute noise percentages, and by using area under receiver operating curve as performance metrics. Experimental results confirmed that proposed model shows better results than the rest. Moreover, it is also confirmed that proposed technique performs better with an increased noise percentage in binary imbalanced datasets.
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