Estimation of Organizational Competitiveness by a Hybrid of One-Dimensional Convolutional Neural Networks and Self-Organizing Maps Using Physiological Signals for Emotional Analysis of Employees

Alanazi, Saad; Alruwaili, Madallah; Ahmad, Fahad; Alaerjan, Alaa; Alshammari, Nasser

doi:10.3390/s21113760

Cited by 13 publications

(3 citation statements)

References 61 publications

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“…It is the reason they are regularly alluded to as TDCNNs. Another option, an adjusted rendition of a TDCNN called an ODCNN, has been created recently [40].…”

Section: Forward_propagation Return Empathetic_concern Degree_of_empa...mentioning

confidence: 99%

Prediction of Emotional Empathy in Intelligent Agents to Facilitate Precise Social Interaction

et al. 2023

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The research area falls under the umbrella of affective computing and seeks to introduce intelligent agents by simulating emotions artificially and encouraging empathetic behavior in them, to foster emotional empathy in intelligent agents with the overarching objective of improving their autonomy. Raising the emotional empathy of intelligent agents to boost their autonomic behavior can increase their independence and adaptability in a socially dynamic context. As emotional intelligence is a subset of social intelligence, it is essential for successful social interaction and relationships. The purpose of this research is to develop an embedded method for analyzing empathic behavior in a socially dynamic situation. A model is proposed for inducing emotional intelligence through a deep learning technique, employing multimodal emotional cues, and triggering appropriate empathetic responses as output. There are 18 categories of emotional behavior, and each one is strongly influenced by multimodal cues such as voice, facial, and other sensory inputs. Due to the changing social context, it is difficult to classify emotional behavior and make predictions based on modest changes in multimodal cues. Robust approaches must be used to be sensitive to these minor changes. Because a one-dimensional convolutional neural network takes advantage of feature localization to minimize the parameters, it is more efficient in this exploration. The study’s findings indicate that the proposed method outperforms other popular ML approaches with a maximum accuracy level of 98.98 percent when compared to currently used methods.

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“…It is the reason they are regularly alluded to as TDCNNs. Another option, an adjusted rendition of a TDCNN called an ODCNN, has been created recently [40].…”

Section: Forward_propagation Return Empathetic_concern Degree_of_empa...mentioning

confidence: 99%

Prediction of Emotional Empathy in Intelligent Agents to Facilitate Precise Social Interaction

et al. 2023

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“…It is worth noting that keyword factorial analysis reveals new information. Unsupervised classification, commonly known as k-means clustering, seeks to divide data into meaningful or usable clusters [48,49]. Hierarchical clustering (HC) is a technique that clusters items based on their similarity.…”

Section: Co-word Analysis: the Conceptual Structure Of A Fieldmentioning

confidence: 99%

Bibliometric Analysis of Publications on the Omicron Variant from 2020 to 2022 in the Scopus Database Using R and VOSviewer

Ejaz

Zeeshan

Ahmad

et al. 2022

IJERPH

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Human respiratory infections caused by coronaviruses can range from mild to deadly. Although there are numerous studies on coronavirus disease 2019 (COVID-19), few have been published on its Omicron variant. In order to remedy this deficiency, this study undertook a bibliometric analysis of the publishing patterns of studies on the Omicron variant and identified hotspots. Automated transportation, environmental protection, improved healthcare, innovation in banking, and smart homes are just a few areas where machine learning has found use in tackling complicated problems. The sophisticated Scopus database was queried for papers with the term “Omicron” in the title published between January 2020 and June 2022. Microsoft Excel 365, VOSviewer, Bibliometrix, and Biblioshiny from R were used for a statistical analysis of the publications. Over the study period, 1917 relevant publications were found in the Scopus database. Viruses was the most popular in publications for Omicron variant research, with 150 papers published, while Cell was the most cited source. The bibliometric analysis determined the most productive nations, with USA leading the list with the highest number of publications (344) and the highest level of international collaboration on the Omicron variant. This study highlights scientific advances and scholarly collaboration trends and serves as a model for demonstrating global trends in Omicron variant research. It can aid policymakers and medical researchers to fully grasp the current status of research on the Omicron variant. It also provides normative data on the Omicron variant for visualization, study, and application.

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“…However, a significant portion of the required data is inaccessible or incomplete, due to data source retrieval or significant data loss. Therefore, a model with a high degree of accuracy must be created using a limited number of input parameters [26,27]. Various methodologies, including deterministic and probabilistic approaches, have been utilized to estimate the global ranking of cloud services.…”

Section: Introductionmentioning

confidence: 99%

InteliRank: A Four-Pronged Agent for the Intelligent Ranking of Cloud Services Based on End-Users’ Feedback

Din

Alshammari

Alanazi

et al. 2022

Sensors

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Cloud Computing (CC) provides a combination of technologies that allows the user to use the most resources in the least amount of time and with the least amount of money. CC semantics play a critical role in ranking heterogeneous data by using the properties of different cloud services and then achieving the optimal cloud service. Regardless of the efforts made to enable simple access to this CC innovation, in the presence of various organizations delivering comparative services at varying cost and execution levels, it is far more difficult to identify the ideal cloud service based on the user’s requirements. In this research, we propose a Cloud-Services-Ranking Agent (CSRA) for analyzing cloud services using end-users’ feedback, including Platform as a Service (PaaS), Infrastructure as a Service (IaaS), and Software as a Service (SaaS), based on ontology mapping and selecting the optimal service. The proposed CSRA possesses Machine-Learning (ML) techniques for ranking cloud services using parameters such as availability, security, reliability, and cost. Here, the Quality of Web Service (QWS) dataset is used, which has seven major cloud services categories, ranked from 0–6, to extract the required persuasive features through Sequential Minimal Optimization Regression (SMOreg). The classification outcomes through SMOreg are capable and demonstrate a general accuracy of around 98.71% in identifying optimum cloud services through the identified parameters. The main advantage of SMOreg is that the amount of memory required for SMO is linear. The findings show that our improved model in terms of precision outperforms prevailing techniques such as Multilayer Perceptron (MLP) and Linear Regression (LR).

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Estimation of Organizational Competitiveness by a Hybrid of One-Dimensional Convolutional Neural Networks and Self-Organizing Maps Using Physiological Signals for Emotional Analysis of Employees

Cited by 13 publications

References 61 publications

Prediction of Emotional Empathy in Intelligent Agents to Facilitate Precise Social Interaction

Prediction of Emotional Empathy in Intelligent Agents to Facilitate Precise Social Interaction

Bibliometric Analysis of Publications on the Omicron Variant from 2020 to 2022 in the Scopus Database Using R and VOSviewer

InteliRank: A Four-Pronged Agent for the Intelligent Ranking of Cloud Services Based on End-Users’ Feedback

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