Soil moisture estimation is an important task to enable precision agriculture in creating optimal plans for irrigation, fertilization, and harvest. It is common to utilize statistical and machine learning models to estimate soil moisture from traditional data sources such as weather forecasts, soil properties, and crop properties. However, there is a growing interest in utilizing aerial and geospatial imagery to estimate soil moisture. Although these images capture high-resolution crop details, they are expensive to curate and challenging to interpret. Imagine, an AI-enhanced software tool that predicts soil moisture using visual cues captured by smartphones and statistical data given by weather forecasts. This work is a first step towards that goal of developing a multi-modal approach for soil moisture estimation. In particular, we curate a dataset consisting of real-world images taken from ground stations and their corresponding weather data. We also propose MIS-ME - Meteorological & Image based Soil Moisture Estimator, a multi-modal framework for soil moisture estimation. Our extensive analysis shows that MIS-ME achieves a MAPE of 10.14%, outperforming traditional unimodal approaches with a reduction of 3.25% in MAPE for meteorological data and 2.15% in MAPE for image data, highlighting the effectiveness of tailored multi-modal approaches. Our code and dataset will be available at https://github.com/OSU-Complex-Systems/MIS-ME.git.

Modeling the interactions between drugs, targets, and diseases has significant implications for drug discovery, precision medicine and personalized treatments. Current computational approaches consider pairwise interaction, including drug-target or drug-disease interaction individually. On the other hand, within human metabolic systems, the interaction of drugs with protein targets in cells influences target activities. Moving beyond binary relationships and exploring tighter relationships together as triple is essential to understanding drugs' mechanism of action (MoAs). Moreover, considering the heterogeneity of drugs, targets, and diseases, along with their distinct characteristics, it is critical to model these complex interactions appropriately. To address these challenges, we develop a novel Heterogeneous Graph Triplet Attention Network (HeTan)by modeling the interconnectedness of all entities in a heterogeneous graph. HeTAN introduces a novel triplet message passing and triplet-wise attention mechanism within this heterogeneous graph structure. In contrast to focusing only on pairwise attention as the importance of an entity for the other, we define triplet attention to model the importance of pairs for the other in the drug-target-disease triplet prediction problem. We perform extensive experiments on real-world datasets and our results show that HeTAN outperforms several baselines, demonstrating its superior performance in uncovering novel drug-target-disease relationships.

Online social media, periodically serves as a platform for cascading polarizing topics of conversation. The inherent community structure present in online social networks (homophily) and the advent of fringe outlets like Gab have created online "echo chambers" that amplify the effects of polarization, which fuels detrimental behavior. Recently, in October 2018, Gab made headlines when it was revealed that Robert Bowers, the individual behind the Pittsburgh Synagogue massacre, was an active member of this social media site and used it to express his anti-Semitic views and discuss conspiracy theories. Thus to address the need of automated data-driven analyses of such fringe outlets, this research proposes novel methods to discover topics that are prevalent in Gab and how they cascade within the network. Specifically, using approximately 34 million posts, and 3.7 million cascading conversation threads with close to 300k users; we demonstrate that there are essentially five cascading patterns that manifest in Gab and the most "viral" ones begin with an echo-chamber pattern and grow out to the entire network. Also, we empirically show, through two models viz. Susceptible-Infected and Bass, how the cascades structurally evolve from one of the five patterns to the other based on the topic of the conversation with upto 84% accuracy.

Actionability is a mode of revealing actionable knowledge in the form of Action Rules from large datasets. Action rule imparts in the form of recommendations as how a data object can change from one value to another more desirable value. The towering production of data in the recent years, due to increased usage of web, social media and IoT, has led to the age of big data. Also, abundant usage of cloud storages and cloud based services causes the data to be spread around the globe. This requires more time and space for a single computer to cope with such widespread data. Ecosystems like Hadoop MapReduce, Spark have been introduced to store, process and retrieve back the data efficiently in a distributed fashion. Data mining finds substantial improvements over such distributed frameworks to process huge volume of data and acquire knowledge from them in a short span of time. In this paper, we present an approach SARGS: Specific Action Rule discovery based on Grabbing Strategy, to build more specific Action Rules using Apache Spark framework and evaluate the results with our previous Hadoop MapReduce system (MR-Random Forest Algorithm for Distributed Action Rules Discovery). Also, we propose a novel approach to distribute data in a distributed environment to get more optimal Action Rules and upgraded ARoGS algorithm to get more specific Action Rules.