scholarly journals Application of AI in Human Resource Management and Gen Y‟s Reaction

2019 ◽  
Vol 8 (4) ◽  
pp. 10325-10331
Keyword(s):  
Intelligent Agents ◽  
Working Population ◽  
Gen Y ◽  
Health Group ◽  
Hr Roles ◽  
Functional Areas ◽  
Deep Learning Model ◽  
Clear Majority ◽  
Hr Systems

HR is facing the transformation challenge and disruptive impact of AI in its functions. In this study, we shall bring into light certain challenges faced by AI in HR and the technological counter solutions to it that have been reinstated. Many companies like IBM, have reengineered its HR service delivery strategy to render intelligent agents to the services being provided toits managers and associates, answer their queries and suggest decisive aspects of employees roles, careers, rewards, compensation and learning. Also United Health Group is on building a graph database which uses AI to identify improvements in efficiency and quality of services. Such instances create the need to study the reaction of the Gen y which composes more than the 50% of the working population i.e population under 25 and around 65% is under the age of 35 and their reaction to the transformation in HR systems to it quotes Forbes study1,7, 27 . A qualitative study was carried out and Gen Y professionals were interviewed to seek the feedback. The HR themes and functional areas were identified which are perceived as areas where application of AI is possible. A deep-learning model using Neuroph Studio was created to capture the perception of the young working professionals. The study brings out the statistics to a clear majority of the sample to believe that AI must be implemented in the current HR systems. The research is useful to current practitioners of profession of HR. The study implies that AI will enter HR roles and working population is ready for it. The study is good indicator for artificial intelligence developers as areas of application are identified

scholarly journals Ensemble-Based Out-of-Distribution Detection

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 567
Author(s):  
Donghun Yang ◽  
Kien Mai Mai Ngoc ◽  
Iksoo Shin ◽  
Kyong-Ha Lee ◽  
Myunggwon Hwang
Keyword(s):  
Detection Method ◽  
State Of The Art ◽  
Metric Learning ◽  
Feature Space ◽  
Confidence Score ◽  
Distance Metric Learning ◽  
Current State ◽  
Overall Performance ◽  
Deep Learning Model

To design an efficient deep learning model that can be used in the real-world, it is important to detect out-of-distribution (OOD) data well. Various studies have been conducted to solve the OOD problem. The current state-of-the-art approach uses a confidence score based on the Mahalanobis distance in a feature space. Although it outperformed the previous approaches, the results were sensitive to the quality of the trained model and the dataset complexity. Herein, we propose a novel OOD detection method that can train more efficient feature space for OOD detection. The proposed method uses an ensemble of the features trained using the softmax-based classifier and the network based on distance metric learning (DML). Through the complementary interaction of these two networks, the trained feature space has a more clumped distribution and can fit well on the Gaussian distribution by class. Therefore, OOD data can be efficiently detected by setting a threshold in the trained feature space. To evaluate the proposed method, we applied our method to various combinations of image datasets. The results show that the overall performance of the proposed approach is superior to those of other methods, including the state-of-the-art approach, on any combination of datasets.

scholarly journals Assessing Time of Eating in Commensality Research

2021 ◽  
Vol 18 (6) ◽  
pp. 2941
Author(s):  
Henrik Scander ◽  
Maria Lennernäs Wiklund ◽  
Agneta Yngve
Keyword(s):  
Quality Of Life ◽  
Systematic Reviews ◽  
Time Use ◽  
Scoping Review ◽  
Search Term ◽  
Working Population ◽  
Nutritional Health ◽  
The Family ◽  
Use Studies

Commensal meals seem to be related to a better nutritional and metabolic health as well as an improved quality of life. The aim of this paper was to examine to what extent research was performed using the search term commensality related to assessment of timing of meals. A scoping review was performed, where 10 papers were identified as specifically addressing the assessment of timing of commensality of meals. Time use studies, questionnaires, and telephone- and person-to-person interviews were used for assessing meal times in relation to commensality. Four of the studies used a method of time use registration, and six papers used interviews or questionnaires. Common meals with family members were the most common, and dinners late at night were often preferred for commensal activities among the working population. In conclusion, the family meal seemed to be the most important commensal meal. It is clear from the collected papers and from previous systematic reviews that more studies of commensal meals in general and about timing aspects in particular and in relation to nutritional health are essential to provide a solid background of knowledge regarding the importance of timing in relation to commensal meals.

scholarly journals Opportunities and challenges of using socially intelligent agents: increasing interaction and school participation for children suffering from a long-term illness

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Eva Mårell-Olsson ◽  
Thomas Mejtoft ◽  
Sofia Tovedal ◽  
Ulrik Söderström
Keyword(s):  
Intelligent Agents ◽  
Design Methodology ◽  
Holistic Approach ◽  
Negative Effects ◽  
Content Type ◽  
Medical Needs ◽  
Societal Benefits ◽  
School Participation

PurposeChildren suffering from cancer or cardiovascular disease, who need extended periods of treatment in hospitals, are subjected to multiple hardships apart from the physical implications, for example, experienced isolation and disrupted social and academic development. This has negative effects long after the child's recovery from the illness. The purpose of this paper is to examine the non-medical needs of children suffering from a long-term illness, as well as research the field of artificial intelligence (AI) – more specifically, the use of socially intelligent agents (SIAs) – in order to study how technology can enhance children's interaction, participation and quality of life.Design/methodology/approachInterviews were performed with experts in three fields: housing manager for hospitalized children, a professor in computing science and researcher in AI, and an engineer and developer at a tech company.FindingsIt is important for children to be able to take control of the narrative by using an SIA to support the documentation of their period of illness, for example. This could serve as a way of processing emotions, documenting educational development or keeping a reference for later in life. The findings also show that the societal benefits of AI include automating mundane tasks and recognizing patterns.Originality/valueThe originality of this study concerns the holistic approach of increasing the knowledge and understanding of these children's specific needs and challenges, particularly regarding their participation and interaction with teachers and friends at school, using an SIA.

THE RESEARCH ON THE QUALITY OF WORK LIFE OF WORKING POPULATION (USING THE EXAMPLE OF THE VOLOGDA REGION)

2021 ◽  
pp. 36-45
Author(s):  
G. V. Leonidova ◽  
◽  
E. A. Basova ◽  
G. V. Belekhova ◽  
◽  
...  
Keyword(s):  
Federal District ◽  
Work Life ◽  
Quality Of Work Life ◽  
Regional Differentiation ◽  
Similar Data ◽  
Working Population ◽  
Demographic Groups ◽  
Quality Of Work ◽  
Vologda Region

To increase labor efficiency, the level of quality of work-life (QWL) of the working population is of particular importance. The limited number of studies dealing with the research of the quality of work at the regional level has confirmed the research interest. The paper presents the results of the assessment of QWL of the working population of the Vologda Region obtained using the subjective approach. The authors studied special aspects of regional differentiation of the assessment of the labor activity quality by the population. The paper draws attention to the fact that in the Vologda Region, the assessments of the working population concerning satisfaction with various aspects of labor activity have a lower level compared to similar data in the subjects of the Northwestern Federal District and Russia. The paper shows the results of the QWL assessment of the Vologda Region population in the context of various socio-demographic groups based on the index system of indicators. The study identified that more than half of the economically active population of the region is satisfied with the quality of labor activity. The authors found that the degree of satisfaction with the QWL varies for different socio-demographic groups of the population. The highest level of assessments is demonstrated by the groups with the increased level of wages, as well as those with higher education, officially registered marriage, and carrying out labor activities following their inclinations, abilities, and avocation. The authors conclude that the quality of labor potential and the level of implementation in labor activity of the qualitative characteristics of the Vologda Region residents significantly correlate with the QWL satisfaction. The paper presents a list of measures promoting the improvement of QWL satisfaction.

scholarly journals Setting up an assertive community treatment service

1996 ◽  
Vol 2 (4) ◽  
pp. 143-150 ◽  
Author(s):  
Andrew Kent ◽  
Tom Burns
Keyword(s):  
Assertive Community Treatment ◽  
Mentally Ill ◽  
Revolving Door ◽  
Community Treatment ◽  
Treatment Service ◽  
Flexible Support ◽  
Severely Mentally Ill ◽  
Functional Areas ◽  
Key Worker

The last 20 years have witnessed a surge of interest in assertive community treatment (ACT) for the severely mentally ill (Drake & Burns, 1995). ACT aims to help people who would otherwise be in and out of hospital on a ‘revolving door’ basis live in the community and enjoy the best possible quality of life. Services based on the ACT model seek to replace the total support of the hospital with comprehensive, intensive and flexible support in the community, delivered by an individual key worker or core services team. They are organised in a way that optimises continuity of care across different functional areas and across time.

Ground-truth uncertainty-aware metrics for machine learning applications on seismic image interpretation: Application to faults and horizon extraction

2020 ◽  
Vol 39 (10) ◽  
pp. 734-741
Author(s):  
Sébastien Guillon ◽  
Frédéric Joncour ◽  
Pierre-Emmanuel Barrallon ◽  
Laurent Castanié
Keyword(s):  
Deep Learning ◽  
Image Interpretation ◽  
Ground Truth ◽  
Learning Model ◽  
Seismic Interpretation ◽  
Data Set ◽  
Seismic Image ◽  
Machine Learning Applications ◽  
Deep Learning Model

We propose new metrics to measure the performance of a deep learning model applied to seismic interpretation tasks such as fault and horizon extraction. Faults and horizons are thin geologic boundaries (1 pixel thick on the image) for which a small prediction error could lead to inappropriately large variations in common metrics (precision, recall, and intersection over union). Through two examples, we show how classical metrics could fail to indicate the true quality of fault or horizon extraction. Measuring the accuracy of reconstruction of thin objects or boundaries requires introducing a tolerance distance between ground truth and prediction images to manage the uncertainties inherent in their delineation. We therefore adapt our metrics by introducing a tolerance function and illustrate their ability to manage uncertainties in seismic interpretation. We compare classical and new metrics through different examples and demonstrate the robustness of our metrics. Finally, we show on a 3D West African data set how our metrics are used to tune an optimal deep learning model.

scholarly journals Analysis of COVID-19 Infections on a CT Image Using DeepSense Model

2020 ◽  
Vol 8 ◽  
Author(s):  
Adil Khadidos ◽  
Alaa O. Khadidos ◽  
Srihari Kannan ◽  
Yuvaraj Natarajan ◽  
Sachi Nandan Mohanty ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Input Data ◽  
Training Data ◽  
Learning Framework ◽  
Learning Classifiers ◽  
Tomography Image ◽  
Mining Model ◽  
Deep Learning Model

In this paper, a data mining model on a hybrid deep learning framework is designed to diagnose the medical conditions of patients infected with the coronavirus disease 2019 (COVID-19) virus. The hybrid deep learning model is designed as a combination of convolutional neural network (CNN) and recurrent neural network (RNN) and named as DeepSense method. It is designed as a series of layers to extract and classify the related features of COVID-19 infections from the lungs. The computerized tomography image is used as an input data, and hence, the classifier is designed to ease the process of classification on learning the multidimensional input data using the Expert Hidden layers. The validation of the model is conducted against the medical image datasets to predict the infections using deep learning classifiers. The results show that the DeepSense classifier offers accuracy in an improved manner than the conventional deep and machine learning classifiers. The proposed method is validated against three different datasets, where the training data are compared with 70%, 80%, and 90% training data. It specifically provides the quality of the diagnostic method adopted for the prediction of COVID-19 infections in a patient.

APPLYING INTELLIGENT SOFTWARE AGENTS IN A DISTRIBUTED CHANNEL ALLOCATION SCHEME FOR CELLULAR NETWORKS

2002 ◽  
Vol 16 (08) ◽  
pp. 1021-1039
Author(s):  
ELIANE L. BODANESE ◽  
LAURIE G. CUTHBERT
Keyword(s):  
Mobile Communications ◽  
Intelligent Agents ◽  
Channel Allocation ◽  
Base Stations ◽  
Agent Negotiation ◽  
Intelligent Software ◽  
Intelligent Software Agents ◽  
Distributed Channel Allocation ◽  
Limited Spectrum

As the demand for mobile services has increased, the need for an efficient allocation of channels is essential to ensure good performance, given the limited spectrum available. Techniques for increasing flexibility in radio resource acquisition are needed to handle the heterogeneity of services and bit rates to be supported in the forthcoming generations of mobile communications. To improve the performance and efficiency of the channel allocation, we propose the use of a particular agent architecture that allows base stations to be more flexible and intelligent, including planning to attempt to balance the load in advance of reactive requests. The simulation results prove that the use of intelligent agents controlling the allocation of channels is feasible and the agent negotiation is an important feature of the system in order to improve perceived quality of service and to improve the load balancing of the traffic.

scholarly journals FAKTOR-FAKTOR YANG MEMENGARUHI KESETIAAN TENAGA KERJA GENERASI Y

2018 ◽  
Author(s):  
Billy Chandra ◽  
Edi Purwanto
Keyword(s):  
Job Performance ◽  
Quantitative Research ◽  
Secondary Data ◽  
Work Life ◽  
Quality Of Work Life ◽  
Primary Data ◽  
Gen Y ◽  
Quality Of Work ◽  
Performance Factors

<p align="center"><strong><em>ABSTRACT</em></strong><strong><em>:</em></strong><em></em></p><h5><em>This research was conducted to find out what factors influenced the loyalty of Gen Y labor who worked as permanent employees and worked in Jabodetabek area. Many of the Gen Y workforce seen haven’t loyal like the previous generation that can work very long in a company. Researcher are interested to explore what factors can trigger the emergence of loyalty to appears in Gen Y. At a glance researcher consider the quality of work and job performance factors are affect the loyalty of the Gen Y labor. In this study the model used is a quantitative research model that serves to see whether there is a positive and significant relationship between the variables that affect the loyalty of Gen Y labor, such as the quality of work life and job performance. The data used in this study are primary data obtained directly through distributed questionnaires, as well as library search through journals as secondary data, to compare and examine what variables are positively affected and can affect the quality of work life and job performance so that can create loyalty.During the research, the researcher found that some variables were allegedly able to increase the loyalty of the Gen Y labor in addition to quality of work life and performance, including compensation, career development and social support that simultaneously affected this research.</em><em></em></h5><h5><em> </em></h5><p><strong><em>Keywords</em></strong><strong><em>: </em></strong><em>Loyalty, Employee, Gen Y</em></p>

scholarly journals GLE-Net: A Global and Local Ensemble Network for Aerial Object Detection

2022 ◽  
Vol 15 (1) ◽  
Author(s):  
Jiajia Liao ◽  
Yujun Liu ◽  
Yingchao Piao ◽  
Jinhe Su ◽  
Guorong Cai ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Aerial Images ◽  
Visual Object ◽  
Detection Algorithms ◽  
Integration Strategy ◽  
Bounding Boxes ◽  
Global And Local ◽  
Deep Learning Model

AbstractRecent advances in camera-equipped drone applications increased the demand for visual object detection algorithms with deep learning for aerial images. There are several limitations in accuracy for a single deep learning model. Inspired by ensemble learning can significantly improve the generalization ability of the model in the machine learning field, we introduce a novel integration strategy to combine the inference results of two different methods without non-maximum suppression. In this paper, a global and local ensemble network (GLE-Net) was proposed to increase the quality of predictions by considering the global weights for different models and adjusting the local weights for bounding boxes. Specifically, the global module assigns different weights to models. In the local module, we group the bounding boxes that corresponding to the same object as a cluster. Each cluster generates a final predict box and assigns the highest score in the cluster as the score of the final predict box. Experiments on benchmarks VisDrone2019 show promising performance of GLE-Net compared with the baseline network.

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