Missing data are a universal data quality problem in many domains, leading to misleading analysis and inaccurate decisions. Much research has been done to investigate the different mechanisms of missing data and the proper techniques in handling various data types. In the last decade, machine learning has been utilized to replace conventional methods to address the problem of missing values more efficiently. By studying and analyzing recently proposed methods using machine learning approaches, vital adoptions in accuracy, performance, and time consumed can be highlighted. This study aimed to help data analysts and researchers address the limitations of machine learning imputation methods by conducting a systematic literature review to provide a comprehensive overview of using such methods to impute missing values. Novel proposed machine learning approaches used for data imputation are analyzed and summarized to assist researchers in selecting a proper machine learning method based on several factors and settings. The review was performed on research studies published between 2016 and 2021 on adopting machine learning to impute missing values, focusing on their strengths and limitations. A total of 684 research articles from various scientific databases were analyzed using search engines, and 94 of them were selected as primary studies. Finally, several recommendations were given to guide future researchers in applying machine learning to impute missing values.

In recent years, recommendation systems have become essential for businesses to enhance customer satisfaction and generate revenue in various domains, such as e-commerce and entertainment. Deep learning techniques have significantly improved the accuracy and efficiency of these systems. However, there is a lack of literature on classification in systematic review papers that summarize the latest deep-learning techniques used in recommendation systems. Moreover, certain existing review papers have either overlooked state-of-the-art techniques or restricted their coverage to a narrow spectrum of domains. To address these research gaps, we present a systematic review paper that comprehensively analyzes the literature on deep learning techniques in recommendation systems, specifically using term classification.We analyzed relevant studies published between 2018 and February 2023, examining the techniques, datasets, domains, and measurement metrics used in these studies, utilizing a thorough SLR strategy. Our review reveals that deep learning techniques, such as graph neural networks, convolutional neural networks, and recurrent neural networks, have been widely used in recommendation systems. Furthermore, our study highlights the emerging area of research in domain classification, which has shown promising results in applying deep learning techniques to domains such as social networks, e-commerce, and e-learning. Our review paper offers insights into the deep learning techniques used across different recommendation systems and provides suggestions for future research. Our review fills a critical research gap and offers a valuable resource for researchers and practitioners interested in deep learning techniques for recommendation systems.

A personalised news recommendation system extracts news set from multiple press releases and presents the recommended news to the user. In an effort to build a better recommender system with high accuracy, this paper proposes a personalised news recommendation framework named Hybrid Personalised NEws Recommendation (HYPNER). HYPNER combines both collaborative filtering-based and content-based filtering methods. The proposed framework aims at improving the accuracy of news recommendation by resolving the issues of scalability due to large news corpus, enriching the user’s profile, representing the exact properties and characteristics of news items, and recommending diverse set of news items. Validation experiments showed that HYPNER achieved 81.56% improvement in F1-score and 5.33% in diversity as compared to an existing recommender system, SCENE.

High frequency trading, often known as HFT, is a subset of algorithmic trading, which is one of the most significant improvements to the trading environment in recent years. Algorithmic trading gives traders the ability to trade or receive orders within extremely brief time intervals, such as minutes or seconds based. The very nature of high-frequency trading necessitates the use of high-speed data feeds, which deliver information in real time as well as trade executions for high-frequency traders. Due to the unpredictable nature of the market, several different methods are utilised in order to forecast the HFT time series. However none of these methods have been demonstrated to be a consistently acceptable forecast tool. The majority of HFT trading is conducted using traditional arbitrage-based methods.The conventional method of forecasting using time series does not have the capability to take into account seasonality or outliers in a dataset. The HFT time series data is consistently expanding from day to day, thereby becoming big and enormous. To solve this problem, we require a model that is both more precise and less time-consuming. The purpose of this paper work is to investigate historical models, contemporary models and hypothetical future models that can be used to anticipate HFT time series. Within the domain of HFT forecasting where accuracy and productivity are of the utmost importance, it also becomes critical to investigate state-of-the-art technologies. In light of the inherent difficulties that classical computing methods encounter when confronted with the intricacies of time series data, our focus shifts to quantum computing as a prospective resolution. This undertaking is driven by the imperative to confront prevailing obstacles in the field of forecasting, including enhancing precision, managing extensive datasets and alleviating the consequences of noise and uncertainty.The progression of quantum technologies is expected to bring significant changes that will enhance the reliability, precision and scalability of forecasting methodologies. The synergy between quantum computing and HFT forecasting holds the promise of reshaping how we approach and derive insights from time-varying datasets. The exploration and use of quantum computing for HFT forecasting marks a significant stride toward overcoming current limitations.

Rough set theory provides the reduct and the core concepts for knowledge reduction. The cost of reduct set computation is highly influenced by the attribute set size of the dataset where the problem of finding reducts has been proven as an NP-hard problem. This paper proposes an approximate approach for reduct computation. The approach uses the discernibility matrix concept and a weighting mechanism to determine the significance of an attribute to be considered in the reduct. A second supplementary weight is used to break the tie when several attributes have the same significance. The approach is extensively experimented and evaluated on various standard domains.