This research investigates the use of machine learning methods to forecast students' academic performance in a school setting. Students' data with behavioral, academic, and demographic details were used in implementations with standard classical machine learning models including multi-layer perceptron classifier (MLPC). MLPC obtained 86.46% maximum accuracy for test set across all implementations. Under 10-fold cross validation, MLPC obtained 79.58% average accuracy for test set while for train set, it was 99.65%. MLP's better performance over other machine learning models strongly suggest the potential use of neural networks as data-efficient models. Feature selection approach played a crucial role in improving the performance and multiple evaluation approaches were used in order to compare with existing literature. Explainable machine learning methods were utilized to demystify the black box models and to validate the feature selection approach.

The transition from traditional power grids to smart grids, significant increase in the use of renewable energy sources, and soaring electricity prices has triggered a digital transformation of the energy infrastructure that enables new, data driven, applications often supported by machine learning models. However, the majority of the developed machine learning models rely on univariate data. To date, a structured study considering the role meta-data and additional measurements resulting in multivariate data is missing. In this paper we propose a taxonomy that identifies and structures various types of data related to energy applications. The taxonomy can be used to guide application specific data model development for training machine learning models. Focusing on a household electricity forecasting application, we validate the e ff ectiveness of the proposed taxonomy in guiding the selection of the features for various types of models. Finally, using a feature importance techniques, we explain individual feature contributions to the forecasting accuracy.1. Introduction The transition from traditional power grids to smart grids, significant increase in the use of renewable energy sources, and soaring electricity prices has led to an increase in complexity [1], particularly with the adoption of smart meters (SMs), energy management systems (EMSes), and intelligent electronic devices (IEDs) at the low voltage (L V) level. These devices enable innovative energy [2] and non-energy applications [3, 4], such as energy cost optimization and matching consumption with self-production from renewable energy sources. On the distribution system operator (DSO) side of the L V grid, reliability and latency are the main challenges, and complete ob-servability of the L V grid for each substation is crucial.

Autism and Attention-Deficit Hyperactivity Disorder (ADHD) are two of the most commonly observed neurodevelopmental conditions in childhood. Providing a specific computational assessment to distinguish between the two can prove difficult and time intensive. Given the high prevalence of their co-occurrence, there is a need for scalable and accessible methods for distinguishing the co-occurrence of autism and ADHD from individual diagnoses. The first step is to identify a core set of features that can serve as the basis for behavioral feature extraction. We trained machine learning models on data from the National Survey of Children's Health to identify behaviors to target as features in automated clinical decision support systems. A model trained on the binary task of distinguishing either developmental delay (autism or ADHD) vs. neither achieved sensitivity >92% and specificity >94%, while a model trained on the 4-way classification task of autism vs. ADHD vs. both vs. none demonstrated >65% sensitivity and >66% specificity. While the performance of the binary model was respectable, the relatively low performance in the differential classification of autism and ADHD highlights the challenges that persist in achieving specificity within clinical decision support tools for developmental delays. Nevertheless, this study demonstrates the potential of applying behavioral questionnaires not traditionally used for clinical purposes towards supporting digital screening assessments for pediatric developmental delays.

Like any other useful technology, cryptocurrencies are sometimes used for criminal activities. While transactions are recorded on the blockchain, there exists a need for a more rapid and scalable method to detect addresses associated with fraudulent activities. We present RiskSEA, a scalable risk scoring system capable of effectively handling the dynamic nature of large-scale blockchain transaction graphs. The risk scoring system, which we implement for Ethereum, consists of 1. a scalable approach to generating node2vec embedding for entire set of addresses to capture the graph topology 2. transaction-based features to capture the transactional behavioral pattern of an address 3. a classifier model to generate risk score for addresses that combines the node2vec embedding and behavioral features. Efficiently generating node2vec embedding for large scale and dynamically evolving blockchain transaction graphs is challenging, we present two novel approaches for generating node2vec embeddings and effectively scaling it to the entire set of blockchain addresses: 1. node2vec embedding propagation and 2. dynamic node2vec embedding. We present a comprehensive analysis of the proposed approaches. Our experiments show that combining both behavioral and node2vec features boosts the classification performance significantly, and that the dynamic node2vec embeddings perform better than the node2vec propagated embeddings.

Chandola et al (2009) subdivided anomalies into three types: point anomalies (an object is considered anomalous when compared against the rest of objects), contextual anomalies (an object is anomalous in a specific context), and collective anomalies (a collection of objects is anomalous with respect to the entire dataset). The analysis of anomalies has a wide range of applications, such as in network security (Ahmed et al, 2016a), bioinformatics (Spinosa and Carvalho, 2005), fraud detection (Ahmed et al, 2016b), and fault detection and isolation (Hwang et al, 2009). Anomaly analysis consists of two equally important tasks: anomaly detection and anomaly explanation. A wealth of'shallow' machine learning based methods, i.e., not based on deep learning, have been proposed to detect anomalies (Chandola et al, 2009). More recently, many deep learning based anomaly detection methods have also been developed (Pang et al, 2021). However, deep learning based anomaly detection methods are notoriously known as not being interpretable, in the sense that generally both the model itself is non-transparent and the resulting anomaly scores are challenging to interpret without the use of a post-hoc explainer.

PsychoGenics CEO Emer Leahy of Paramus, New Jersey, explains how the first potential AI-discovered treatment for schizophrenia was developed through machine learning. Fox News Digital spoke with her. As the world of artificial intelligence continues to evolve, a New Jersey biotech company is taking AI capabilities to the next level. After decades of working with AI-driven phenotypic platforms in an attempt to develop drugs for mental illness, PsychoGenics has had a breakthrough with one compound that aims to treat schizophrenia. PsychoGenics president and CEO Emer Leahy spoke to Fox News Digital in a recent on-camera interview, explaining that she and her team are closer than ever to developing what she said is the first-ever AI-discovered drug.

Sender-receiver interactions, and specifically persuasion games, are widely researched in economic modeling and artificial intelligence, and serve as a solid foundation for powerful applications. However, in the classic persuasion games setting, the messages sent from the expert to the decision-maker are abstract or well-structured application-specific signals rather than natural (human) language messages, although natural language is a very common communication signal in real-world persuasion setups. This paper addresses the use of natural language in persuasion games, exploring its impact on the decisions made by the players and aiming to construct effective models for the prediction of these decisions. For this purpose, we conduct an online repeated interaction experiment. At each trial of the interaction, an informed expert aims to sell an uninformed decision-maker a vacation in a hotel, by sending her a review that describes the hotel. While the expert is exposed to several scored reviews, the decision-maker observes only the single review sent by the expert, and her payoff in case she chooses to take the hotel is a random draw from the review score distribution available to the expert only. The expert’s payoff, in turn, depends on the number of times the decision-maker chooses the hotel. We also compare the behavioral patterns in this experiment to the equivalent patterns in similar experiments where the communication is based on the numerical values of the reviews rather than the reviews’ text, and observe substantial differences which can be explained through an equilibrium analysis of the game. We consider a number of modeling approaches for our verbal communication setup, differing from each other in the model type (deep neural network (DNN) vs. linear classifier), the type of features used by the model (textual, behavioral or both) and the source of the textual features (DNN-based vs. hand-crafted). Our results demonstrate that given a prefix of the interaction sequence, our models can predict the future decisions of the decision-maker, particularly when a sequential modeling approach and hand-crafted textual features are applied. Further analysis of the hand-crafted textual features allows us to make initial observations about the aspects of text that drive decision making in our setup.

Social reviews have dominated the web and become a plausible source of product information. People and businesses use such information for decision-making. Businesses also make use of social information to spread fake information using a single user, groups of users, or a bot trained to generate fraudulent content. Many studies proposed approaches based on user behaviors and review text to address the challenges of fraud detection. To provide an exhaustive literature review, social fraud detection is reviewed using a framework that considers three key components: the review itself, the user who carries out the review, and the item being reviewed. As features are extracted for the component representation, a feature-wise review is provided based on behavioral, text-based features and their combination. With this framework, a comprehensive overview of approaches is presented including supervised, semi-supervised, and unsupervised learning. The supervised approaches for fraud detection are introduced and categorized into two sub-categories; classical, and deep learning. The lack of labeled datasets is explained and potential solutions are suggested. To help new researchers in the area develop a better understanding, a topic analysis and an overview of future directions is provided in each step of the proposed systematic framework.

E-commerce websites use machine learned ranking models to serve shopping results to customers. Typically, the websites log the customer search events, which include the query entered and the resulting engagement with the shopping results, such as clicks and purchases. Each customer search event serves as input training data for the models, and the individual customer engagement serves as a signal for customer preference. So a purchased shopping result, for example, is perceived to be more important than one that is not. However, new or under-impressed products do not have enough customer engagement signals and end up at a disadvantage when being ranked alongside popular products. In this paper, we propose a novel method for data curation that aggregates all customer engagements within a day for the same query to use as input training data. This aggregated customer engagement gives the models a complete picture of the relative importance of shopping results. Training models on this aggregated data leads to less reliance on behavioral features. This helps mitigate the cold start problem and boosted relevant new products to top search results. In this paper, we present the offline and online analysis and results comparing the individual and aggregated customer engagement models trained on e-commerce data.

Sender-receiver interactions, and specifically persuasion games, are widely researched in economic modeling and artificial intelligence, and serve as a solid foundation for powerful applications. However, in the classic persuasion games setting, the messages sent from the expert to the decision-maker are abstract or well-structured application-specific signals rather than natural (human) language messages, although natural language is a very common communication signal in real-world persuasion setups. This paper addresses the use of natural language in persuasion games, exploring its impact on the decisions made by the players and aiming to construct effective models for the prediction of these decisions. For this purpose, we conduct an online repeated interaction experiment. At each trial of the interaction, an informed expert aims to sell an uninformed decision-maker a vacation in a hotel, by sending her a review that describes the hotel. While the expert is exposed to several scored reviews, the decision-maker observes only the single review sent by the expert, and her payoff in case she chooses to take the hotel is a random draw from the review score distribution available to the expert only. The expert's payoff, in turn, depends on the number of times the decision-maker chooses the hotel. We consider a number of modeling approaches for this setup, differing from each other in the model type (deep neural network (DNN) vs. linear classifier), the type of features used by the model (textual, behavioral or both) and the source of the textual features (DNN-based vs. hand-crafted). Our results demonstrate that given a prefix of the interaction sequence, our models can predict the future decisions of the decision-maker, particularly when a sequential modeling approach and hand-crafted textual features are applied.