Missing data is common problem faced by researchers and data scientists. Therefore, it is required to handle them appropriately in order to get better and accurate results of data analysis. Objective of this research paper is to provide better understanding of data missingness mechanism, data imputation methods, and to assess performance of the widely used data imputation methods for numeric dataset. It will help practitioners and data scientists to select appropriate method of data imputation for numeric dataset while performing data mining task. In this paper, we comprehensively compare seven data imputation methods namely mean imputation, median imputation, kNN imputation, predictive mean matching, Bayesian Linear Regression (norm), Linear Regression, non-Bayesian (norm.nob), and random sample. We have used five different numeric datasets obtained from UCI machine learning repository for analyzing and comparing performance of the data imputation methods. Performance of the data imputation methods is assessed using Normalized Root Mean Square Error (RMSE) method. The results of analysis show that kNN imputation method outperforms the other methods. It has also been found that performance of the data imputation method is independent of the dataset and percentage of missing values in the dataset.

Recent advances in computer and technology resulted into ever increasing set of documents. The need is to classify the set of documents according to the type. Laying related documents together is expedient for decision making. Researchers who perform interdisciplinary research acquire repositories on different topics. Classifying the repositories according to the topic is a real need to analyze the research papers. Experiments are tried on different real and artificial datasets such as NEWS 20, Reuters, emails, research papers on different topics. Term Frequency-Inverse Document Frequency algorithm is used along with fuzzy K-means and hierarchical algorithm. Initially experiment is being carried out on small dataset and performed cluster analysis. The best algorithm is applied on the extended dataset. Along with different clusters of the related documents the resulted silhouette coefficient, entropy and F-measure trend are presented to show algorithm behavior for each data set.

Purpose: This study intends to find the industries that have leveraged Robotic Process Automation (RPA) technology and elucidate the extent of the adoption of RPA in various industry domains with benefits. The identification of tasks eligible for RPA itself is a challenge. Therefore, the study further brings out the challenges faced in various industry verticals and postulates the future direction of research and applications in RPA.Design/methodology/approach: The study focuses on articles from popular databases such as SCOPUS, Web of Science and Google scholar. PRISMA methodology is used for systematic literature review and 113 papers are shortlisted for study. Three questions are framed to carry out the review and set the research direction.Findings: It is evident from this study that RPA has been widely used in banking and related areas with moderate use in healthcare and manufacturing leading to operational efficiency and productivity. However, there are a lot more opportunities in other domains that need to be taped by leveraging technology advancements and a research agenda has been devised by postulating future directions.Originality/value: The study brings out a new comprehensive perspective as regards RPA implementation across domains. There is no promising study found that gathers three-dimensional aspects of the meta-themes applications, benefits and challenges. The study summarizes the research agenda and projects the industry domains that have not yet explored, the benefits of RPA. This will be a good reference article for those who develop RPA techniques and organizations that have plans to go for RPA.

We present a bibliometric analysis of the evolving field of digital image forensics (DIF) from 2014 to early 2020. The study analyses and discusses the results obtained from the highly indexed Scopus database for various subject areas, renowned authors, funding agencies, and affiliations using a mind map approach. We adapt the PRISMA methodology formerly applied for systematic reviews to refine our search results. The results of this study indicate an increasing awareness of DIF in countries like China, India, and Italy along with an increase in publications since 2018, specifically, with the application of deep learning techniques for DIF.

Purpose: This study explores privacy challenges in recommender systems (RSs) and how they have leveraged privacy-preserving technology for risk mitigation. The study also elucidates the extent of adopting privacy-preserving RSs and postulates the future direction of research in RS security.Design/methodology/approach: The study gathered articles from well-known databases such as SCOPUS, Web of Science and Google scholar. A systematic literature review using PRISMA was carried out on the 41 papers that are shortlisted for study. Two research questions were framed to carry out the review.Findings: It is evident from this study that privacy issues in the RS have been addressed with various techniques. However, many more challenges are expected while leveraging technology advancements for fine-tuning recommenders, and a research agenda has been devised by postulating future directions.Originality/value: The study unveils a new comprehensive perspective regarding privacy preservation in recommenders. There is no promising study found that gathers techniques used for privacy protection. The study summarizes the research agenda, and it will be a good reference article for those who develop privacy-preserving RSs.

The exploration of interconnected devices, embedded devices, sensors, and various network-connected devices helps to communicate each other and exchange communications. These devices overcome with security threats related to privacy and data exchange over billions of devices are interconnected. Lightweight block ciphers aim to provide a feasible solution for power-constrained devices which includes Radio-frequency identification (RFID) tags, ubiquitous computing, wireless sensor network, aggregation network and IoT. In this paper, we have implemented a lightweight block cipher compact, secure, and lightweight (CSL). It operates on 64-bit block size, and key size varies from 64-bit to 128-bit key for encryption and decryption. The hardware implementation of CSL algorithm was thrived using field programmable gate array (FPGA) architecture. A pipelined design of compact S-boxes implemented on Digilent Nexys 4 DDR Artix™-7 board. Our experimental results of CSL consumes 1145 LUTs (Lookup Tables) and has fewer memory requirements. CSL shows resistant against various cryptanalytic attacks.