Opening the Black Box of Financial AI with CLEAR-Trade: A CLass-Enhanced Attentive Response Approach for Explaining and Visualizing Deep Learning-Driven Stock Market Prediction

Deep learning has been shown to outperform traditional machine learning algorithms across a wide range of problem domains. However, current deep learning algorithms have been criticized as uninterpretable "black-boxes" which cannot explain their decision making processes. This is a major shortcoming that prevents the widespread application of deep learning to domains with regulatory processes such as finance. As such, industries such as finance have to rely on traditional models like decision trees that are much more interpretable but less effective than deep learning for complex problems. In this paper, we propose CLEAR-Trade, a novel financial AI visualization framework for deep learning-driven stock market prediction that mitigates the interpretability issue of deep learning methods. In particular, CLEAR-Trade provides a effective way to visualize and explain decisions made by deep stock market prediction models. We show the efficacy of CLEAR-Trade in enhancing the interpretability of stock market prediction by conducting experiments based on S&P 500 stock index prediction. The results demonstrate that CLEAR-Trade can provide significant insight into the decision-making process of deep learning-driven financial models, particularly for regulatory processes, thus improving their potential uptake in the financial industry

Explaining the Unexplained: A CLass-Enhanced Attentive Response (CLEAR) Approach to Understanding Deep Neural Networks

In this work, we propose CLass-Enhanced Attentive Response (CLEAR): an approach to visualize and understand the decisions made by deep neural networks (DNNs) given a specific input. CLEAR facilitates the visualization of attentive regions and levels of interest of DNNs during the decision-making process. It also enables the visualization of the most dominant classes associated with these attentive regions of interest. As such, CLEAR can mitigate some of the shortcomings of heatmap-based methods associated with decision ambiguity, and allows for better insights into the decision-making process of DNNs. Quantitative and qualitative experiments across three different datasets demonstrate the efficacy of CLEAR for gaining a better understanding of the inner workings of DNNs during the decision-making process.Comment: Accepted at Computer Vision and Patter Recognition Workshop (CVPR-W) on Explainable Computer Vision, 201

Understanding Anatomy Classification Through Attentive Response Maps

One of the main challenges for broad adoption of deep learning based models such as convolutional neural networks (CNN), is the lack of understanding of their decisions. In many applications, a simpler, less capable model that can be easily understood is favorable to a black-box model that has superior performance. In this paper, we present an approach for designing CNNs based on visualization of the internal activations of the model. We visualize the model's response through attentive response maps obtained using a fractional stride convolution technique and compare the results with known imaging landmarks from the medical literature. We show that sufficiently deep and capable models can be successfully trained to use the same medical landmarks a human expert would use. Our approach allows for communicating the model decision process well, but also offers insight towards detecting biases.Comment: Accepted at ISBI, 201

Effect of biofertilizers on horticultural and yield traits in french bean var. Contender under dry temperate conditions of Kinnaur district of Himachal Pradesh

Kinnaur district is known as the dry temperate zone of Himachal Pradesh and is known for off season and quality production of vegetables.In this district of Himachal Pradesh, Natural farming is mostly done with the minimum use of chemical fertilizers. Farmers are unaware of the judicious use of farm yard manure, and biofertilizers due to which yield of the french bean is very low (50-70 q/ha). French bean is one of the most important vegetables intercropped with apple in Kinnaur District. An experiment was conducted during the summer season of 2011 at the Experimental Farm of Vegetable Research Station, Kalpa, Kinnaur, Himachal Pradesh to study the effect of Rhizobium and Phosphorus Solublizing Bacteria (PSB) on the horticultural and yield traits in french bean var. Contender. Six treatments comprising seed treatments (with and without Rhizobium), seed treatment (with and without PSB) along with the combination of 60 % dose of recommended quantity of Calcium Ammonium Nitrate and 75 % dose of recommended quantity of Single Super Phosphate and organic matter were evaluated in a Randomized Complete Block Design (RCBD) with three replications. The results revealed that T5 treatment, i.e. Rhizobium+ PSB+ Organic matter resulted in more number of pods per plant (20), pod length (18 cm) and pod yield/ha (140 q/ha)

Development of sunlight-driven eutectic phase change material nanocomposite for applications in solar water heating

Organic phase change materials (PCMs) have been utilized as latent heat energy storage medium for effective thermal management. In this work, a PCM nanocomposite, consisting of a mixture of two organic PCMs (referred to as eutectic gel PCM) and minimal amount (0.5 wt%) of nanographite (NG) as a supporting material, was prepared. Differential scanning calorimeter was used to determine the melting temperature and latent heat of pristine PCM, paraffin (61.5 °C and 161.5 J/g), eutectic gel PCM (54 °C and 158 J/g) and eutectic gel PCM nanocomposite (53.5 °C and 155 J/g). The prepared PCM nanocomposites exhibited enhanced thermal conductivity and ultrafast thermal charging characteristics. The nanocomposites were employed for two different applications: (i) providing hot water using an indigenously fabricated solar water heating (SWH) system and (ii) solar rechargeable glove that can be rapidly warmed and used. Experimental results on SWH system show that the use of PCM nanocomposites helps to increase the charging rate of PCM while reducing the discharging rate of heat by PCM to water, thus enhancing the maximum utilization of solar energy and hence improving the efficiency of the SWH system. The experimental results on solar rechargeable glove revealed that the glove has the ability to retain the temperature up to 3 hours