Knowledge graph-based convolutional network coupled with sentiment analysis towards enhanced drug recommendation

Recommending appropriate drugs to patients based on their history and symptoms is a complex real-world problem. Knowing whether a drug is useful without its consumption by a variety of people followed by proper evaluation is a challenge. Modern-day recommender systems can assist in this provided they receive large data to learn. Public reviews on various drugs are available for knowledge sharing. These reviews assist in recommending the best and most appropriate option to the user. The explicit feedback underpins the entire recommender system. This work develops a novel knowledge graph-based convolutional network for recommending drugs. The knowledge graph is coupled with sentiment analysis extracted from the public reviews on drugs to enhance drug recommendations. For each drug that has been used previously, sentiments have been analyzed to determine which one has the most effective reviews. The knowledge graph effectively captures user-item relatedness by mining its associated attributes. Experiments are performed on public benchmarks and a comparison is made with closely related state-of-the-art works. Based on the obtained results, the current work performs better than the past contributions by achieving up to 98.7% Area Under Curve (AUC) score

EmoPercept: EEG-based emotion classification through perceiver

Emotions play an important role in human cognition and are commonly associated with perception, logical decision making, human interaction, and intelligence. Emotion and stress detection is an emerging topic of interest and importance in the research community. With the availability of portable, cheap, and reliable sensor devices, researchers are opting to use physiological signals for emotion classification as they are more prone to human deception, as compared to audiovisual signals. In recent years, deep neural networks have gained popularity and have inspired new ideas for emotion recognition based on electroencephalogram (EEG) signals. Recently, widespread use of transformer-based architectures has been observed, providing state-of-the-art results in several domains, from natural language processing to computer vision, and object detection. In this work, we investigate the effectiveness and accuracy of a novel transformer-based architecture, called perceiver, which claims to be able to handle inputs from any modality, be it an image, audio, or video. We utilize the perceiver architecture on raw EEG signals taken from one of the most widely used publicly available EEG-based emotion recognition datasets, i.e., DEAP, and compare its results with some of the best performing models in the domain

On the Utility of Parents\u27 Historical Data to Investigate the Causes of Autism Spectrum Disorder: A Data Mining-Based Framework

Objective: Autism Spectrum Disorder (ASD) is acknowledged as a challenge that influences the learning ability of adolescents and also negatively impacts their families. Autism may be caused due to environmental exposure or genetically inherited disorder, however, no definitive or universally customary reasons are known. This makes the issue fairly challenging. Material and methods: This work focuses on identifying the reasons of ASD utilizing computational methods. For this, data is collected that focuses on parental history for finding the trigged features by reviewing antenatal, perinatal, and infant hazard factors of ASD. Afterwards, ML techniques are applied on the collected instances to develop a predictive model and identify the reasons to ASD. While collecting the data, samples are obtained for ASD and non-ASD individuals both. A total of 115 features are obtained from each subject. The collected dataset has 47% samples of the subjects with ASD. Dimensionality reduction, and four feature selection methods are applied on the data to eliminate noise and least valued features. The data is verified using two clustering techniques, i.e., k-means and k-medoid. To validate the clustering results five clustering validation indices are used. Later, three classifiers, i.e. k-nearest neighbor (k-NN), Support Vector Machine (SVM), and Artificial Neural Network (ANN) are trained to predict cases with ASD. The frequent items mining technique and the descriptive analysis of the clustered data are utilized to identify the factors that may cause ASD. Results: The proposed framework enables to identify the features that may contribute towards ASD. Whereas, for the classification part, SVM classifier performs better than others do with an average accuracy of 98.34% in predicting the ASD cases. Conclusion: The results identified stress as the dominant feature and environmental factors, like frequent use of canned food and plastic/steel bottles during fertilization period that may contribute towards ASD

Photopyroelectric spectroscopy of Sb2O3 - ZnO ceramics

Photopyroelectric spectroscopy is used to study the band-gap energy of the ceramic (ZnO + xSb2O3), x = 0.1 - 1.5 mol% and the ceramic (ZnO + 0.4 mol%  Bi2O3 + xSb2O3), x = 0 - 1.5 mol% sintered at isothermal temperature, 1280 °C, for 1 and 2 hours. The wavelength of incident light, modulated at 9 Hz, is kept in the visible range and the photopyroelectric spectrum with reference to doping level is discussed. The band-gap energy is reduced from 3.2 eV, for pure ZnO, to 2.86, 2.83 eV for the samples without Bi2O3at 0.1 mol% of Sb2O3 for 1 and 2 hours of sintering time, respectively. It is reduced to 2.83, 2.80 eV for the samples with Bi2O3 at 0 mol% of Sb2O3 for 1 and 2 hours of sintering time, respectively. The steepness factor σA which characterizes the slop of exponential optical absorption is discussed with reference to the doping level. The phase constitution is determined by XRD analysis; microstructure and compositional analysis of the selected areas are analyzed using SEM and EDX

Photopyroelectric spectrum of MNO2 Doped Bi2O3 - TiO2 - ZnO ceramic combination

Photopyroelectric spectroscopy is used to study the energy band-gap of the ceramic (ZnO 0.25 Bi2O3 +0.25 TiO2 + x MnO2), x = 0 - 1.3 mol%, sintered at isothermal temperature 1190 and 1270°C for 2 hours in air. The wavelength of incident light, modulated at 9 Hz, is kept in the range of 300 to 800 nm and the photopyroelectric spectrum with reference to the doping level is discussed. The energy band-gap is estimated from the plot (phv)2 vs hv and is 2.80 eV for the samples without MnO2 at both sintering temperatures. It decreases to 2.08 eV with a further increase of MnO2. The phase constitution is determined by XRD analysis. Microstructure and compositional analysis of the selected areas are analyzed using SEM and EDX. The maximum relative density, 91.4 %, and the grain size, 47 µm, were observed in this ceramics combination

Effect of sintering temperature on the photothermal spectrum of Bi2O3 - TiO2 - Co3O4 - ZnO ceramics

Photopyroelectric spectroscopy is used to study the band‐gap energy of the ceramic ZnO+0.5 Bi2O3+0.5 TiO2+0.4 Co3O4 (mol%), sintered at the isothermal temperature 1180 and 1210, 1240, 1270, 1300° C for 1 and 2 hours in air. The wavelength of incident light, modulated at 9 Hz, is kept in the range 300 to 800 nm and the photopyroelectric spectrum with reference to the doping level is discussed. The band‐gap energy is estimated from the plot (ρhν)2 vs hν and is 2.30 eV at 1180° C for 1 hour sintering time and is reduced to 2.15 eV at 1300° C sintering temperature. Eg is constant at about 2.8 eV at all sintering temperatures for 2 hours sintering time. The steepness factor σA (in A region) and σB (in B region) which characterizes the slop of exponential optical absorption is discussed with reference to the sintering temperature. The phase constitution is determined by XRD analysis. Microstructure and compositional analysis of the selected areas are analyzed using SEM and EDAX. The maximum relative density 87.5% and the grain size 44.6 μm are observed in this ceramics combination

A novel binary chaotic genetic algorithm for feature selection and its utility in affective computing and healthcare

© 2020, Springer-Verlag London Ltd., part of Springer Nature. Genetic algorithm (GA) is a nature-inspired algorithm to produce best possible solution by selecting the fittest individual from a pool of possible solutions. Like most of the optimization techniques, the GA can also stuck in the local optima, producing a suboptimal solution. This work presents a novel metaheuristic optimizer named as the binary chaotic genetic algorithm (BCGA) to improve the GA performance. The chaotic maps are applied to the initial population, and the reproduction operations follow. To demonstrate its utility, the proposed BCGA is applied to a feature selection task from an affective database, namely AMIGOS (A Dataset for Affect, Personality and Mood Research on Individuals and Groups) and two healthcare datasets having large feature space. Performance of the BCGA is compared with the traditional GA and two state-of-the-art feature selection methods. The comparison is made based on classification accuracy and the number of selected features. Experimental results suggest promising capability of BCGA to find the optimal subset of features that achieves better fitness values. The obtained results also suggest that the chaotic maps, especially sinusoidal chaotic map, perform better as compared to other maps in enhancing the performance of raw GA. The proposed approach obtains, on average, a fitness value twice as better than the one achieved through the raw GA in the identification of the seven classes of emotions

Extended ICA and M-CSP with BiLSTM towards improved classification of EEG signals

Mental stress is an issue that creates functional limitations in the workplace. Chronic stress leads to a number of psychophysiological sicknesses. For instance, it raises the risk of depression, heart attack, and stroke. According to the most recent findings in neuroscience, the human brain is the primary focus of mental stress. Perception of biological motion in the human brain determines the risky and stressful situations. Neural signaling of the human brain is used as an objective measure for determining the stress level of a subject. The oscillations of electroencephalography (EEG) signals are utilized for classifying human stress. EEG signals have a higher temporal resolution and are rapidly distorted with unwanted noise, resulting in a variety of artifacts. This study utilizes Extended Independent Component Analysis based approach for artifacts removal. A Multiclass Common Spatial Pattern-based moving window technique is proposed here to obtain the most distinguishable time segment of EEG trials. BiLSTM is used to improve classification results. In order to validate the model performance, two publically available datasets (i.e., DEAP and SEED) are utilized. Employing these datasets, the proposed model achieves state-of-the-art results (93.1, 96.84%) for EEG signal classification to identify stress