Investment Recommender System Model Based on the Potential Investors' Key Decision Factors.

In this research, we propose an automatic recommender system for providing investment-type suggestions offered to investors. This system is based on a new intelligent approach using an adaptive neuro-fuzzy inference system (ANFIS) that works with four potential investors' key decision factors (KDFs), which are system value, environmental awareness factors, the expectation of high return, and expectation of low return. The proposed system provides a new model for investment recommender systems (IRSs), which is based on the data of KDFs, and the data related to the type of investment.

Corrigendum to 'Data to evaluate the usability of an interactive system based on a judgment-based model' [Data in Brief 43 (2022) 1-14/ 108418].

[This corrects the article DOI: 10.1016/j.dib.

Data to evaluate the usability of an interactive system based on a judgment-based model.

The data presented in this article are related to the published article entitled "A Judgment-Based Model for Usability Evaluating of Interactive Systems Using Fuzzy Multi Factors Evaluation (MFE)" in "" [1]. The purpose of data collection in this paper was to integrate a fuzzy multifactorial evaluation (MFE) model based on the judgment of experts in the three fields of ISPD, HCI, and AMLM. Two sets of data were used to conduct this research.

An integrated model for evaluation of big data challenges and analytical methods in recommender systems.

The study aimed to present an integrated model for evaluation of big data (BD) challenges and analytical methods in recommender systems (RSs). The proposed model used fuzzy multi-criteria decision making (MCDM) which is a human judgment-based method for weighting of RSs' properties. Human judgment is associated with uncertainty and gray information.

Experimental Analysis in Hadoop MapReduce: A Closer Look at Fault Detection and Recovery Techniques.

Hadoop MapReduce reactively detects and recovers faults after they occur based on the static heartbeat detection and the re-execution from scratch techniques. However, these techniques lead to excessive response time penalties and inefficient resource consumption during detection and recovery. Existing fault-tolerance solutions intend to mitigate the limitations without considering critical conditions such as fail-slow faults, the impact of faults at various infrastructure levels and the relationship between the detection and recovery stages.

A speech pronunciation practice system for speech-impaired children: A study to measure its success.

Background: The speech pronunciation practice (SPP) system enables children with speech impairments to practise and improve their speech pronunciation. However, little is known about the surrogate measures of the SPP system.

Aims: This research aims to measure the success and effectiveness of the SPP system using three surrogate measures: usage (frequency of use), performance (recognition accuracy) and satisfaction (children's subjective reactions), and how these measures are aligned with the success of the SPP system, as well as to each other.

Heuristics to Evaluate Interactive Systems for Children with Autism Spectrum Disorder (ASD).

In this paper, we adapted and expanded a set of guidelines, also known as heuristics, to evaluate the usability of software to now be appropriate for software aimed at children with autism spectrum disorder (ASD). We started from the heuristics developed by Nielsen in 1990 and developed a modified set of 15 heuristics. The first 5 heuristics of this set are the same as those of the original Nielsen set, the next 5 heuristics are improved versions of Nielsen's, whereas the last 5 heuristics are new.