There has been a growing interest in the field of neural networks for prediction in recent years. In this research, a public dataset including the sales history of a retail store is investigated to forecast the sales of furniture. To this aim, several forecasting models are applied. First, some classical time-series forecasting techniques such as Seasonal Autoregressive Integrated Moving Average (SARIMA) and Triple Exponential Smoothing are utilized. Then, more advanced methods such as Prophet, Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) are applied. The performances of the models are compared using different accuracy measurement methods (e.g., Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE)). The results show the superiority of the Stacked LSTM method over the other methods. In addition, the results indicate the good performances of the Prophet and CNN models. 

Time-series forecasting of seasonal items sales using machine learning - A comparative analysis

Data availability and accessibility have brought in unseen changes in the finance systems and new theoretical and computational challenges. For example, in contrast to classical stochastic control theory and other analytical approaches for solving financial decision-making problems that rely heavily on model assumptions, new developments from reinforcement learning (RL) can make full use of a large amount of financial data with fewer model assumptions and improve decisions in complex economic environments. This paper reviews the developments and use of Deep Learning(DL), RL, and Deep Reinforcement Learning (DRL)methods in information-based decision-making in financial industries. Therefore, it is necessary to understand the variety of learning methods, related terminology, and their applicability in the financial field. First, we introduce Markov decision processes, followed by Various algorithms focusing on value and policy-based methods that do not require any model assumptions. Next, connections are made with neural networks to extend the framework to encompass deep RL algorithms. Finally, the paper concludes by discussing the application of these RL and DRL algorithms in various decision-making problems in finance, including optimal execution, portfolio optimization, option pricing, hedging, and market-making. The survey results indicate that RL and DRL can provide better performance and higher efficiency than traditional algorithms while facing real economic problems in risk parameters and ever-increasing uncertainties. Moreover, it offers academics and practitioners insight and direction on the state-of-the-art application of deep learning models in finance. 

How are reinforcement learning and deep learning algorithms used for big data based decision making in financial industries-A review and research agenda

Recently, offensive content has become increasingly popular for harassing and criticizing people on numerous social media platforms. This paper proposes an offensive text classification algorithm named LSTM-BOOST employing Long Short-Term Memory(LSTM) model with ensemble learning to recognize offensive Bengali texts in various social media platforms. The proposed LSTM-BOOST model uses the modified AdaBoost algorithm employing principal component analysis(PCA) along with LSTM networks. In the LSTM-Boost model, the dataset is divided into three categories, and PCA and LSTM networks are applied to each part of the dataset to obtain the most significant variance and reduce the weighted error of the weak hypothesis of the model. Furthermore, different classifiers are used for baseline experiment and the model is evaluated on various word embedding vector methods. Our investigation found that the LSTM-BOOST algorithms outperform most of the baseline architecture, leading F1-score of 92.61% on the Bengali offensive text from Social Platforms(BHSSP) dataset. 

How can we manage Offensive Text in Social Media - A Text Classification Approach using LSTM-BOOST

Abstract Autism spectrum is a brain development condition that impairs an individual’s capacity to communicate socially and manifests through strict routines and obsessive–compulsive behavior. Applied behavior analysis (ABA) is the gold-standard treatment for autism spectrum disorder (ASD). However, as the number of ASD cases increases, there is a substantial shortage of licensed ABA practitioners, limiting the timely formulation, revision, and implementation of treatment plans and goals. Additionally, the subjectivity of the clinician and a lack of data-driven decision-making affect treatment quality. We address these obstacles by applying two machine learning algorithms to recommend and personalize ABA treatment goals for 29 study participants with ASD. The patient similarity and collaborative filtering methods predicted ABA treatment with an average accuracy of 81–84%, with a normalized discounted cumulative gain of 79–81% (NDCG) compared to clinician-prepared ABA treatment recommendations. Additionally, we assess the two models’ treatment efficacy (TE) by measuring the percentage of recommended treatment goals mastered by the study participants. The proposed treatment recommendation and personalization strategy are generalizable to other intervention methods in addition to ABA and for other brain disorders. This study was registered as a clinical trial on November 5, 2020 with trial registration number CTRI/2020/11/028933. 

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Machine learning-based ABA treatment recommendation and personalization for autism spectrum disorder: an exploratory study

In order to deduce more appropriate and useful recommendation, it is imperative to amalgamate additional information such as the features of the user and the item along with the usual user-item correlation based on rating. In this research, a content-based recommender system, MATURE is further developed, which relies on the additional information related to the user-item to recommend the items for a user who has current mandatory needs to accommodate as opposed to the past preferences. The MATURE-driven recommendations mandatorily include and satisfy the user's mandatory requirements at a given time. This distinguishes it from the existing algorithms, where recommendations are primarily based on the past preferences and fail to satisfy current mandatory requirements of the user, which are opposite to the user's past preferences. Arguably, MATURE is first recommender system, which promisingly undertakes the given mandatory requirements while recommending the most appropriate item set with pertinent justification of the recommendation. This paper expands the applicability of MATURE and focuses on its application as a food recommender system with mandatory nutrient values, required to be consumed daily. 

MATURE-Food: Food Recommender System for MAndatory FeaTURE Choices A system for enabling Digital Health

The past decade has seen significant development in the number of personalized recommendation applications on the World Wide Web. It aims to assist users to retrieve relevant items from a large repository of contents by providing items or services of likely interest based on examined evidence of the users’ preferences and desires. However, this vision is complex due to the huge amount of information aka media-rich information available on the web. Most of the systems formulated so far use the metadata linked with the digital contents, but such systems fail to generate significant recommendations results. In these circumstances, a semantic personalized recommendation system (SPRS) plays an important role to take away the semantic gap between high-level semantic contents and low-level media features. The proposed system recommends personalized sets of videos to users depending on their previous activity on the site and exploits a domain ontology and user items content to the domain concepts. To evaluate the performance of the framework, items’ prediction is executed by utilizing the proposed framework, and performance is determined by comparing the predicted and actual ratings of the items in terms of Predictive Accuracy Metrics, precision, and recall.

Vivek Kumar

Papers

Deep learning based semantic personalized recommendation system