scholarly journals The impact of artificial intelligence on commercial management

2020 ◽  
Vol 17 (4) ◽  
pp. 441-452
Author(s):  
Renato Costa ◽  
Álvaro Dias ◽  
Leandro Pereira ◽  
José Santos ◽  
André Capelo
Keyword(s):  
Artificial Intelligence ◽  
Data Collection ◽  
Real Time ◽  
Training And Development ◽  
Analytical Tool ◽  
Commercial Activity ◽  
Business Functions ◽  
The Impact ◽  
Depth Interviews ◽  
Shed Light

The essence of this research is to shed light on use and importance of artificial intelligence (AI) in commercial activity. As such, the objective of the present study is to understand the impact of AI tools on the development of business functions and if they can be affirmed as a means of help or as a substitute for these functions. In-depth interviews were conducted with 15 commercial managers from technological SMEs. The results indicate that all the participants use AI systems frequently, that these tools assist in developing of their functions, allowing having more time and better preparing to solve the commercial problems. The findings also indicate that the tools used by commercials are still somewhat limited, and companies should focus on their training and development in AI, as well as the training of their commercials. Furthermore, the results show that firms intend to use the data collection and the analytical tool that enable real-time response and customization according to customer needs.

scholarly journals Artificial Intelligence-Enabled Traffic Monitoring System

2020 ◽  
Vol 12 (21) ◽  
pp. 9177
Author(s):  
Vishal Mandal ◽  
Abdul Rashid Mussah ◽  
Peng Jin ◽  
Yaw Adu-Gyamfi
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Real Time ◽  
Monitoring System ◽  
Traffic Management ◽  
Traffic Monitoring ◽  
Deep Convolutional Neural Networks ◽  
Stage Of Development ◽  
Daunting Task ◽  
The Impact

Manual traffic surveillance can be a daunting task as Traffic Management Centers operate a myriad of cameras installed over a network. Injecting some level of automation could help lighten the workload of human operators performing manual surveillance and facilitate making proactive decisions which would reduce the impact of incidents and recurring congestion on roadways. This article presents a novel approach to automatically monitor real time traffic footage using deep convolutional neural networks and a stand-alone graphical user interface. The authors describe the results of research received in the process of developing models that serve as an integrated framework for an artificial intelligence enabled traffic monitoring system. The proposed system deploys several state-of-the-art deep learning algorithms to automate different traffic monitoring needs. Taking advantage of a large database of annotated video surveillance data, deep learning-based models are trained to detect queues, track stationary vehicles, and tabulate vehicle counts. A pixel-level segmentation approach is applied to detect traffic queues and predict severity. Real-time object detection algorithms coupled with different tracking systems are deployed to automatically detect stranded vehicles as well as perform vehicular counts. At each stage of development, interesting experimental results are presented to demonstrate the effectiveness of the proposed system. Overall, the results demonstrate that the proposed framework performs satisfactorily under varied conditions without being immensely impacted by environmental hazards such as blurry camera views, low illumination, rain, or snow.

scholarly journals Layanan Prima menuju ”Quality Tourism” Bali

1970 ◽  
Vol 1 (1) ◽  
pp. 53-66
Author(s):  
I Wayan Nurjaya ◽  
Solihin Solihin ◽  
I Nyoman Kanca
Keyword(s):  
Qualitative Research ◽  
Quality Of Service ◽  
Data Collection ◽  
Hotel Management ◽  
Positive Image ◽  
Special Services ◽  
Tourist Accommodation ◽  
The Impact ◽  
Depth Interviews

Increasingly competitive competition among tourist accommodation providers, encourage the star hotel management and villas in Kuta to provide special services to their guests. In fact, such kind of services will lead to a concept of tourism called ”quality tourism”. This paper is a qualitative research. Its data collection was done through document study, observation, and in-depth interviews with 11 informants, consisting of five villas’s star hotel liners, three hotel and villa’s guests, and three observers of Bali tourism business. The result of the study shows that the excellent services to the guests provided by villa and hotel management in Kuta is the implementation of the concept of sapta pesona, done at the stage of pre-arrival service, reception service, housekeeping service, and food & bevarage. The impact of this excellent services gives tourist loyalty and revisit, reinforces the positive image of Bali tourism, and supports the sustainability of accommodation services and tourism business. In general, the excellent services for hotel and villa guests in Kuta has supported the efforts to realize the quality tourism in Bali. Innovations to improve the quality of service for hotel and villa guests are needed according to the development and demands of the tourism market.

Changing the Quality of Education in a University Using Digital Technologies and Artificial Intelligence in the Context of the COVID-19 Pandemic

2020 ◽  
Vol 19 (4) ◽  
pp. 137-144
Author(s):  
M.V. Vinichenko ◽  
◽  
S.A. Makushkin ◽  
N.V. Lyapunova ◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Distance Learning ◽  
Personal Space ◽  
Quality Of Education ◽  
Quality Of Teaching ◽  
General Scientific ◽  
The University ◽  
The Impact ◽  
Depth Interviews

the purpose of the article was to identify the nature of the impact of the pandemic on the quality of education at a university using distance learning and artificial intelligence. The research methodology was based on a complex of general scientific and special methods. The data obtained during the survey and in-depth interviews were summarized and analyzed in a focus group. Stable connections and tendencies in the change in the quality of teaching at the university are revealed. Traps for students are attributed to stable connections: lack of a valid system of control over the authorship of completed works; the possibility of unauthorized use of various electronic sources when responding; coronavirus quarantine leads to the erasure of students’ boundaries between study and life, personal space and social environment; an increase in students’ desire to have high grades in subjects with a decrease in interest in learning. Trends: increased workload on teachers and supporting (technical) personnel; growing dissatisfaction with distance learning; reduction of responsibility on the part of students for mastering knowledge in the course of distance learning.

Traversing Anthropocentric Horizons With Her: Trans-Corporeal Surrogacy, Enchantment, and Disenchantment in Human-Machine Assemblage

2021 ◽  
pp. 019685992110411
Author(s):  
Mariam Betlemidze
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Romantic Relationship ◽  
Visual Analysis ◽  
Human Capabilities ◽  
In Trans ◽  
Audio Communication ◽  
Autonomous Agency ◽  
Machine Communication ◽  
Shed Light

This article aims to shed light on the intricacies that overturn McLuhan's vision of technologies as extensions or prosthetics of human capabilities when applied to human-machine communication (HMC). Human and nonhuman entities co-evolve on an equal agential footing, immersed in mediatized assemblages. Building on the concepts of Deleuze and Guattari, Bennett, and others, it theorizes HMC as a cycle of sonic enchantment, culminating in trans-corporeal surrogacy, disrupted by disenchantment, and started again through re-enchantment. A new materialist framework helps explain the process of posthuman HMC. It provides a close-textual and visual analysis of Spike Jonze's film Her (2013), in which a human develops a romantic relationship with his AI assistant. The aspects of vulnerability, neediness, authenticity, trust, and intimacy surpass the lure of real-time personalized audio communication. The paper argues that artificial intelligence acquires autonomous agency through the processes of enchantment and mutual surrogacy that decenter humans in mediatized assemblages.

scholarly journals Artificial intelligence prediction of adverse outcomes in emergency department patients with hyperglycemic crises in real-time

2020 ◽  
Author(s):  
Chin-Chuan Hsu ◽  
Yuan Kao ◽  
Chien-Chin Hsu ◽  
Chia-Jung Chen ◽  
Shu-Lien Hsu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Septic Shock ◽  
Logistic Regression ◽  
Prediction Model ◽  
Real Time ◽  
Adverse Outcomes ◽  
Support Vector ◽  
Icu Admission ◽  
All Cause Mortality ◽  
The Impact

Abstract Background Hyperglycemic crises are associated with high morbidity and mortality. Previous studies proposed methods for predicting adverse outcome in hyperglycemic crises, artificial intelligence (AI) has however never been tried. We implemented an AI prediction model integrated with hospital information system (HIS) to clarify this issue. Methods We included 3,715 patients with hyperglycemic crises from emergency departments (ED) between 2009 and 2018. Patients were randomized into a 70%/30% split for AI model training and testing. Twenty-two feature variables from their electronic medical records were collected, and multilayer perceptron (MLP) was used to construct an AI prediction model to predict sepsis or septic shock, intensive care unit (ICU) admission, and all-cause mortality within 1 month. Comparisons of the performance among random forest, logistic regression, support vector machine (SVM), K-nearest neighbor (KNN), Light Gradient Boosting Machine (LightGBM), and MLP algorithms were also done. Results Using the MLP model, the areas under the curves (AUCs) were 0.808 for sepsis or septic shock, 0.688 for ICU admission, and 0.770 for all-cause mortality. MLP had the best performance in predicting sepsis or septic shock and all-cause mortality, compared with logistic regression, SVM, KNN, and LightGBM. Furthermore, we integrated the AI prediction model with the HIS to assist physicians for decision making in real-time. Conclusions A real-time AI prediction model is a promising method to assist physicians in predicting adverse outcomes in ED patients with hyperglycemic crises. Further studies on the impact on clinical practice and patient outcome are warranted.

scholarly journals Artificial Intelligence Based Body Sensor Network Framework—Narrative Review: Proposing an End-to-End Framework using Wearable Sensors, Real-Time Location Systems and Artificial Intelligence/Machine Learning Algorithms for Data Collection, Data Mining and Knowledge Discovery in Sports and Healthcare

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Ashwin A. Phatak ◽  
Franz-Georg Wieland ◽  
Kartik Vempala ◽  
Frederik Volkmar ◽  
Daniel Memmert
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Data Collection ◽  
Real Time ◽  
Sensor Network ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Body Sensor Network

AbstractWith the rising amount of data in the sports and health sectors, a plethora of applications using big data mining have become possible. Multiple frameworks have been proposed to mine, store, preprocess, and analyze physiological vitals data using artificial intelligence and machine learning algorithms. Comparatively, less research has been done to collect potentially high volume, high-quality ‘big data’ in an organized, time-synchronized, and holistic manner to solve similar problems in multiple fields. Although a large number of data collection devices exist in the form of sensors. They are either highly specialized, univariate and fragmented in nature or exist in a lab setting. The current study aims to propose artificial intelligence-based body sensor network framework (AIBSNF), a framework for strategic use of body sensor networks (BSN), which combines with real-time location system (RTLS) and wearable biosensors to collect multivariate, low noise, and high-fidelity data. This facilitates gathering of time-synchronized location and physiological vitals data, which allows artificial intelligence and machine learning (AI/ML)-based time series analysis. The study gives a brief overview of wearable sensor technology, RTLS, and provides use cases of AI/ML algorithms in the field of sensor fusion. The study also elaborates sample scenarios using a specific sensor network consisting of pressure sensors (insoles), accelerometers, gyroscopes, ECG, EMG, and RTLS position detectors for particular applications in the field of health care and sports. The AIBSNF may provide a solid blueprint for conducting research and development, forming a smooth end-to-end pipeline from data collection using BSN, RTLS and final stage analytics based on AI/ML algorithms.

scholarly journals Development of intelligent agents through collaborative innovation

2019 ◽  
Vol 11 (3) ◽  
pp. 29-37
Author(s):  
Mateusz Kot ◽  
Grzegorz Leszczyński
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Agents ◽  
Machine Learning Algorithms ◽  
User Engagement ◽  
Collaborative Development ◽  
New Approach ◽  
Collaborative Innovation ◽  
The Impact ◽  
Depth Interviews ◽  
Agent Development

Abstract This study focuses on the development of a specific type of Intelligent Agents — Business Virtual Assistants (BVA). The paper aims to identify the scope of collaboration between users and providers in the process of agent development and to define the impact that user interpretations of a BVA agent have on this collaboration. This study conceptualises the collaboration between providers and users in the process of the BVA development. It uses the concept of the collaborative development of innovation and sensemaking. The empirical part presents preliminary exploratory in-depth interviews conducted with CEOs of BVA providers and analyses the use of the scheme offered by Miles and Hubermann (1994). The main results show the scope of the collaboration between BVA users and providers in the process of the BVA development. User engagement is crucial in the development of BVA agents since they are using machine learning algorithms. The user interpretation through sensemaking influences the process as their attitudes guide their behaviour. Apart from that, users have to adjust to this new kind of entity in the market and learn how to use it in line with savoir-vivre rules. This paper suggests the need to develop a new approach to the collaborative development of innovation when Artificial Intelligence is involved.

scholarly journals Artificial Intelligence Enabled Traffic Monitoring System

2020 ◽  
Author(s):  
Vishal Mandal ◽  
Abdul Rashid Mussah ◽  
Peng Jin ◽  
Yaw Adu-Gyamfi
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Real Time ◽  
Monitoring System ◽  
Traffic Management ◽  
Traffic Monitoring ◽  
Deep Convolutional Neural Networks ◽  
Real Time Traffic ◽  
Daunting Task ◽  
The Impact

Manual traffic surveillance can be a daunting task as Traffic Management Centers operate a myriad of cameras installed over a network. Injecting some level of automation could help lighten the workload of human operators performing manual surveillance and facilitate making proactive decisions which would reduce the impact of incidents and recurring congestion on roadways. This article presents a novel approach to automatically monitor real time traffic footage using deep convolutional neural networks and a stand-alone graphical user interface. The authors describe the results of research received in the process of developing models that serve as an integrated framework for an artificial intelligence enabled traffic monitoring system. The proposed system deploys several state-of-the-art deep learning algorithms to automate different traffic monitoring needs. Taking advantage of a large database of annotated video surveillance data, deep learning-based models are trained to detect queues, track stationary vehicles, and tabulate vehicle counts. A pixel-level segmentation approach is applied to detect traffic queues and predict severity. Real-time object detection algorithms coupled with different tracking systems are deployed to automatically detect stranded vehicles as well as perform vehicular counts. At each stages of development, interesting experimental results are presented to demonstrate the effectiveness of the proposed system. Overall, the results demonstrate that the proposed framework performs satisfactorily under varied conditions without being immensely impacted by environmental hazards such as blurry camera views, low illumination, rain, or snow.

scholarly journals Strategy to Determine the Priority of Teachers' Quality Problem Using USG (Urgency, Seriousness, Growth) Matrix

2020 ◽  
Vol 2 (2) ◽  
pp. 54
Author(s):  
Nova Syafira Ariyanti ◽  
Maulana Amirul Adha ◽  
Raden Bambang Sumarsono ◽  
Sultoni Sultoni
Keyword(s):  
Data Collection ◽  
Matrix Theory ◽  
Educational Background ◽  
Teacher Qualifications ◽  
Educational Personnel ◽  
Library Studies ◽  
The Matrix ◽  
Qualitative Descriptive ◽  
The Impact ◽  
Depth Interviews

The purpose of this study is to know, (1) identify the problem of teachers’ and educational personnel, (2) the application of USG matrix method, and (3) the impact of the problems of the educator components and education in learning at integrated islamic primary school. The method used in this study is qualitative descriptive. The research performs analysis using the matrix theory Urgency, Seriousness and Growth (USG). The site of this research is at SD Islam Terpadu Robbani Malang Regency, Indonesia. Data collection techniques with in-depth interviews, documentation, and library studies. The analysis of the data in the get resulted in the findings that correspond to the problems faced by the school. The results of the study are (1) problems that cause low PTK (Teachers and Educational Personnel) values i.e. teacher qualifications are not appropriate, teachers do not have a certificate of teachers’, and teachers do not teach in accordance with educational background, (2) problems that must be resolved in advance that (a) the teachers do not teach in accordance and (b) learners are lacking in receiving learning.

scholarly journals Efforts to Overcome Procrastination through Individual Construction Using Self-Introspection Methods in Achieving Santri Scholarship Program Students (PBSB)

2019 ◽  
Vol 2 (1) ◽  
pp. 1-6
Author(s):  
Alfiyah Laila Afiyatin
Keyword(s):  
Data Collection ◽  
General Picture ◽  
Scholarship Program ◽  
The Impact ◽  
Depth Interviews

The purpose of this article is to find out the description of student procrastination and theimplications of counseling individuals with self-introspection techniques to overcomeprocrastination. This research was conducted on seventh semester Santri Achievement ScholarshipProgram (PBSB) students, data collection techniques were carried out by interview and observation.Subjects were 7 students and special subjects for in-depth interviews of 3 students, with the aim ofdeep meaning. The results of the study and discussion are that the general picture of studentprocrastination can be seen from postponement, laziness, and delay. While the impact of theapplication of self-introspection (muhasabah) by students in overcoming procrastination is to reduceprocrastination, which is evident from the following indicators: first consideration before takingaction, careful on activities that are priority, create better change.

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