The COVID-19 pandemic has highlighted a significant research gap in the field of molecular diagnostics. This has brought forth the need for AI-based edge solutions that can provide quick diagnostic results whilst maintaining data privacy, security and high standards of sensitivity and specificity. This paper presents a novel proof-of-concept method to detect nucleic acid amplification using ISFET sensors and deep learning. This enables the detection of DNA and RNA on a low-cost and portable lab-on-chip platform for identifying infectious diseases and cancer biomarkers. We show that by using spectrograms to transform the signal to the time-frequency domain, image processing techniques can be applied to achieve the reliable classification of the detected chemical signals. Transformation to spectrograms is beneficial as it makes the data compatible with 2D convolutional neural networks and helps gain significant performance improvement over neural networks trained on the time domain data. The trained network achieves an accuracy of 84% with a size of 30kB making it suitable for deployment on edge devices. This facilitates a new wave of intelligent lab-on-chip platforms that combine microfluidics, CMOS-based chemical sensing arrays and AI-based edge solutions for more intelligent and rapid molecular diagnostics.
Day traders in the financial markets are under constant pressure to make rapid decisions and limit capital losses in response to fluctuating market prices. As such, their emotional state can greatly influence their decision-making, leading to suboptimal outcomes in volatile market conditions. Despite the use of risk control measures such as stop loss and limit orders, it is unclear if these strategies have a substantial impact on the emotional state of traders. In this paper, we aim to determine if the use of limit orders and stop loss has a significant impact on the emotional state of traders compared to when these risk control measures are not applied. The paper provides a technical framework for valence-arousal classification in financial trading using EEG data and deep learning algorithms. We conducted two experiments: the first experiment employed predetermined stop loss and limit orders to lock in profit and risk objectives, while the second experiment did not employ limit orders or stop losses. We also proposed a novel hybrid neural architecture that integrates a Conditional Random Field with a CNN-BiLSTM model and employs Bayesian Optimization to systematically determine the optimal hyperparameters. The best model in the framework obtained classification accuracies of 85.65% and 85.05% in the two experiments, outperforming previous studies. Results indicate that the emotions associated with Low Valence and High Arousal, such as fear and worry, were more prevalent in the second experiment. The emotions associated with High Valence and High Arousal, such as hope, were more prevalent in the first experiment employing limit orders and stop loss. In contrast, High Valence and Low Arousal (calmness) emotions were most prominent in the control group which did not engage in trading activities. Our results demonstrate the efficacy of our proposed framework for emotion classification in financial trading and aid in the risk-related decision-making abilities of day traders. Further, we present the limitations of the current work and directions for future research.
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Emotion recognition is an important field of research in Brain Computer Interactions. As technology and the understanding of emotions are advancing, there are growing opportunities for automatic emotion recognition systems. Neural networks are a family of statistical learning models inspired by biological neural networks and are used to estimate functions that can depend on a large number of inputs that are generally unknown. In this paper we seek to use this effectiveness of Neural Networks to classify user emotions using EEG signals from the DEAP (Koelstra et al (2012)) dataset which represents the benchmark for Emotion classification research. We explore 2 different Neural Models, a simple Deep Neural Network and a Convolutional Neural Network for classification. Our model provides the state-of-the-art classification accuracy, obtaining 4.51 and 4.96 percentage point improvements over (Rozgic et al (2013)) classification of Valence and Arousal into 2 classes (High and Low) and 13.39 and 6.58 percentage point improvements over (Chung and Yoon(2012)) classification of Valence and Arousal into 3 classes (High, Normal and Low). Moreover our research is a testament that Neural Networks could be robust classifiers for brain signals, even outperforming traditional learning techniques.
So I've made this deck, its basically all the drug classification graphs/flowcharts from KD Tripathi turned into flashcards. They're very very no frills, no cream cards. Just image occlusion done on those flowcharts. All the cards are arranged according to the chapters. They will not help you a lot with NEET/NEXT/USMLE, but just do them a month before your Uni and you'll be pretty much set for both theory + viva.
Recent years have witnessed a remarkable performance of attention mechanisms for learning representative and prototypical features for tasks such as the classification of distinct sounds and images. Classification of environmental sounds is also an equally challenging task to the classification of speech and music. The presence of semantically irrelevant and silent frames are two major issues that persist in environmental sound classification (ESC). This paper presents a linear self-attention (LSA) mechanism with a learnable memory unit that encodes temporal and spectral characteristics of the spectrogram used while training the deep ESC model. The memory unit can be easily designed using two linear layers followed by a normalization layer. Unlike traditional self-attention mechanisms, the proposed LA mechanism has a linear computational cost. The efficacy of the proposed method is evaluated on two benchmark ESC datasets, viz. ESC-10 and DCASE-2019 Task-1A datasets. The experiments and results show that the model trained with the proposed attention mechanism efficiently learns temporal and spectral information from spectrogram of a signal. The performance of the proposed deep ESC model is comparable or superior to state-of-the-art attention-based deep ESC models.
Fourier transform infrared (FT-IR) spectroscopy historically is a powerful tool for the taxonomic classification of bacteria by genus, species, and strain when they are grown under carefully controlled conditions. Relatively few reports have investigated the determination and classification of pathogens such as the National Institute of Allergy and Infectious Diseases (NIAID) Category A Bacillus anthracis spores and cells (BA), Yersinia species, Francisella tularensis (FT), and Category B Brucella species from FT-IR spectra. We investigated the multivariate statistics classification ability of the FT-IR spectra of viable pathogenic and non-pathogenic NIAID Category A and B bacteria. The impact of different growth media, growth time and temperature, rolling circle filter of the data, and wavelength range were investigated for their microorganism differentiation capability. Viability of the bacteria was confirmed by agar plate growth after the FT-IR experimental procedures were performed. Principal component analysis (PCA) was reduced to maps of two PC vectors in order to distill the FT-IR spectral features into manageable, visual presentations. The PCA results of the strains of BA, FT, Brucella, and Yersinia spectra from conditions of varying growth media and culture time were readily separable in two-dimensional (2D) PC plots. FT spectra were separated from those of the three other genera. The BA pathogenic spore strains 1029, LA1, and Ames were clearly differentiated from the rest of the dataset. Yersinia rhodei, Y. enterocolitica, and Y. pestis species were distinctly separated from the remaining dataset and could also be classified by growth media. Different growth media produced distinct subsets in the FT, BA, and Yersinia spp. regions in the 2D PC plots. Various 2D PC plots provided differential degrees of separation with respect to the four viable bacterial genera including the BA sub-categories of pathogenic spores, vegetative cells, and nonpathogenic vegetative cells. This work provided evidence that FT-IR spectroscopy can indeed separate the four major pathogenic bacterial genera of NIAID Category A and B biological threat agents including details according to the growth conditions and statistical parameters.
Detection of volatile organic compound (VOC) vapors, which are known to have carcinogenic effects, is extremely important and necessary in many areas. In this work, the sensing properties of a cobalt phthalocyanine (CoPc) thin film at six different VOC vapors (methanol, ethanol, butanol, isopropyl alcohol, acetone, and ammonia) concentrations from 50 to 450 ppm are investigated. In this sense, it is observed that the interaction between the VOC vapors and the CoPc surface is not selective. It is shown that using machine learning algorithms the present sensor, which is poorly selective, can be transformed into a more efficient one with better detection ability. As a feature, 10 seconds of responses taken from the steady state region are used without any additional processing technique. Among classification algorithms, k-nearest neighbor (KNN) reaches the highest accuracy of 96.7%. This feature is also compared with the classical steady state response feature. Classification results indicate that the feature based on 10 seconds of responses taken from the steady state region is much better than that based on the classical steady state response feature. e24fc04721
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