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ردیف | عنوان | نوع |
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11 |
AI-enabled emotion-aware robot: The fusion of smart clothing, edge clouds and robotics
ربات آگاه از احساسات مجهز به هوش مصنوعی: تلفیق لباس های هوشمند ، ابرهای لبه ای و رباتیک-2020 Mental health has become a severe problem that significantly influences people’s living quality. With
the rapid development of science and technology, a completely new direction for mental health
improving by using the interaction between robots and people has emerged. As an intelligent personal
agent, a robot can be easily accepted in people’s daily life, meeting users’ behavior and mental demands
to a certain extent. Nevertheless, the existing robot design is very limited, and a household personal
robot is too large to be carried anywhere . The usage of wearable devices is simple, but these devices
cannot offer diversified services. Therefore, this paper puts forward an emotion-aware system that
integrates a personal robot, smart clothing, and cloud terminal. A new ’people-centered’ emotioninteraction
mode is realized. Namely, personal robot and smart clothing supplement each other
seamlessly and interact jointly with users . Artificial intelligence technology and knowledge graph are
used to design emotion perception and interaction algorithms including intelligent recommendation,
relation recognition, emotional expression recognition. Also, different scenarios are analyzed . Finally,
a testbed is built to carry out relevant tests to verify the effectiveness of the proposed algorithms and
emotion-aware system. According to the obtained test results, the system can be widely used to serve
people and improve people’s mental health. Keywords: Artificial intelligence | Emotion-aware | Personal robot | Smart clothing |
مقاله انگلیسی |
12 |
A stochastic model of particulate matters with AI-enabled technique-based IoT gas detectors for air quality assessment
مدل تصادفی ذرات معلق با ردیاب های گاز IoT مبتنی بر تکنیک هوش مصنوعی برای ارزیابی کیفیت هوا-2020 Monitoring air quality in urban and industrial environments and estimating exposure to particulate matter (PM)
pollution concentrations are critical issues that affect human health. Because of aerosols (suspended particles),
PM is mostly observed near the surface and thus can be inhaled. To predict the modeling of micro-to-nano-sized
particle suspensions, this study presents a stochastic model in environmental dynamics with internet of things
(IoT) gas detectors based on an artificial intelligence (AI)-enabled technique; the model can determine floating
fine PM dispersion in a city to assess and monitor air quality. The factors that influence the prediction are
weather- and air pollution-related data, such as humidity, temperature, wind, PM2.5, and PM10. In this study,
these factors have been considered at 7 measuring stations across the urban region in Taipei City, Taiwan, from
2013 to 2018. A nonlinear autoregressive network with exogenous inputs model is constructed using estimated
states to investigate approaches for identifying PM; the model can be a state–space self-tuning stochastic model
for predicting unknown nonlinear sampled data. The results indicate that a satisfactory agreement was obtained
using a normalized root mean square deviation, with small values of 0.0504 and 0.0802 for PM2.5 and PM10,
respectively. Accordingly, this study presents that the time-domain causality between PM and the atmospheric
environment can be constructed using discrete-time models that can be satisfactorily implemented in developing
different air quality monitoring systems for the long-term prediction of air pollution. Keywords: Particulate matter | Micro-to-nano-sized particle suspensions | Modeling | Micropollutants | Artificial intelligence | Atmospheric environment |
مقاله انگلیسی |
13 |
Towards privacy preserving AI based composition framework in edge networks using fully homomorphic encryption
به سمت حفظ حریم خصوصی و حفظ چارچوب ترکیب مبتنی بر هوش مصنوعی در شبکه های لبه ای با استفاده از رمزنگاری کاملاً همگن-2020 We present a privacy-preserving framework for Artificial Intelligence (AI) enabled composition for the edge
networks. Edge computing is a very promising technology for provisioning realtime AI services due to low
response time and network bandwidth requirements. Due to the lack of computational capabilities, an edge
device alone cannot provide the complex AI services. Complex AI tasks should be divided into multiple subtasks
and distributed among multiple edge devices for efficient service provisioning in the edge network.
AI-enabled or automatic service composition is one of the essential AI tasks in the service provisioning.
In edge computing-based service provisioning, service composition related tasks need to be offloaded to
several edge nodes for efficient service. Edge nodes can be used for monitoring services, storing Qualityof-
Service (QoS) data, and composing services to find the best composite service. Existing service composition
methods use plaintext QoS data. Hence, attackers may compromise edge devices to reveal QoS data of
services and modify them for giving an advantage to particular edge service providers, and the AI-based
service composition becomes biased. From that point of view, a privacy-preserving framework for AI-based
service composition is required for the edge networks. In our proposed framework, we introduce an AI-based
composition model for edge services in the edge networks. Additionally, we present a privacy-preserving
AI service composition framework to perform composition on encrypted QoS data using fully homomorphic
encryption (FHE) algorithm. We conduct several experiments to evaluate the performance of our proposed
privacy-preserving service composition framework using a synthetic QoS dataset. Keywords: Edge-AI | Artificial Intelligence | Privacy in edge networks | Privacy-preserving AI | Privacy-preserving AI-based service | composition | Privacy-preserving service composition |
مقاله انگلیسی |
14 |
AI-enabled Microscopic Blood Analysis for Microfluidic COVID-19 Hematology
آنالیز خون میکروسکوپی مجهز به هوش مصنوعی برای هماتولوژی میکروسیال COVID-19-2020 Microscopic blood cell analysis is an important
methodology for medical diagnosis, and complete blood cell
counts (CBCs) are one of the routine tests operated in hospitals.
Results of the CBCs include amounts of red blood cells, white
blood cells and platelets in a unit blood sample. It is possible to
diagnose diseases such as anemia when the numbers or shapes
of red blood cells become abnormal. The percentage of white
blood cells is one of the important indicators of many severe
illnesses such as infection and cancer. The amounts of platelets
are decreased when the patient suffers hemophilia. Doctors
often use these as criteria to monitor the general health
conditions and recovery stages of the patients in the hospital.
However, many hospitals are relying on expensive hematology
analyzers to perform these tests, and these procedures are often
time consuming. There is a huge demand for an automated, fast
and easily used CBCs method in order to avoid redundant
procedures and minimize patients’ burden on costs of
healthcare. In this research, we investigate a new CBC detection
method by using deep neural networks, and discuss state of the
art machine learning methods in order to meet the medical
usage requirements. The approach we applied in this work is
based on YOLOv3 algorithm, and our experimental results
show the applied deep learning algorithms have a great
potential for CBCs tests, promising for deployment of deep
learning methods into microfluidic point-of-care medical
devices. As a case of study, we applied our blood cell detector to
the blood samples of COVID-19 patients, where blood cell clots
are a typical symptom of COVID-19. Keywords : microfluidic device | microscopic imaging | blood analysis haematology | COVID-19 | deep learning at edge |
مقاله انگلیسی |
15 |
A Novel AI-enabled Framework to Diagnose Coronavirus COVID-19 using Smartphone Embedded Sensors: Design Study
یک چارچوب جدید مبتنی بر هوش مصنوعی برای تشخیص Coronavirus COVID-19 با استفاده از حسگرهای داخلی تلفن های هوشمند: مطالعه طراحی-2020 Coronaviruses are a famous family of viruses that
cause illness in both humans and animals. The new type of
coronavirus COVID-19 was firstly discovered in Wuhan, China.
However, recently, the virus has widely spread in most of
the world and causing a pandemic according to the World
Health Organization (WHO). Further, nowadays, all the world
countries are striving to control the COVID-19. There are many
mechanisms to detect coronavirus including clinical analysis of
chest CT scan images and blood test results. The confirmed
COVID-19 patient manifests as fever, tiredness, and dry cough.
Particularly, several techniques can be used to detect the initial
results of the virus such as medical detection Kits. However,
such devices are incurring huge cost, taking time to install
them and use. Therefore, in this paper, a new framework is
proposed to detect COVID-19 using built-in smartphone sensors.
The proposal provides a low-cost solution, since most of
radiologists have already held smartphones for different dailypurposes.
Not only that but also ordinary people can use the
framework on their smartphones for the virus detection purposes.
Today’s smartphones are powerful with existing computationrich
processors, memory space, and large number of sensors
including cameras, microphone, temperature sensor, inertial
sensors, proximity, colour-sensor, humidity-sensor, and wireless
chipsets/sensors. The designed Artificial Intelligence (AI) enabled
framework reads the smartphone sensors’ signal measurements
to predict the grade of severity of the pneumonia as well as
predicting the result of the disease. Index Terms: COVID-19 | smartphone | coronavirus Detection | smartphone sensors |
مقاله انگلیسی |
16 |
AI-enabled biometrics in recruiting: Insights from marketers for managers
بیومتریک با استفاده از هوش مصنوعی در جذب نیرو: بینش بازاریابان برای مدیران-2020 Both researchers and practitioners are only in the early stages of examining and understanding the ap- plication of artificial intelligence (AI) in terms of marketing themselves as employers or the open jobs they have. AI has the potential to significantly affect how firms reach, identify, attract, and select human capital. We examine factors that can influence a job candidate’s intent to complete AI-enabled recruiting processes, especially the influence of a firm’s use of biometrics in that process. The results show that (1) social media can increase technology use motivation and AI-enabled recruiting with (2) trendiness as a first stage boundary condition and (3) biometrics as a second stage boundary condition. We contribute to marketing knowledge by identifying that for managers wanting to influence job seekers’ technology use motivation in order to increase their participation in AI-enabled recruiting; they must focus on the indirect effects of trendiness, biometrics, and their social media usage. Keywords: AI-enabled | Biometrics | Technology use motivation | Trendiness | Recruiting | Social media usage |
مقاله انگلیسی |
17 |
Artificial intelligence (AI) and value co-creation in B2B sales: Activities, actors and resources
هوش مصنوعی و ایجاد ارزش در فروش B2B: فعالیت ها ، بازیگران و منابع-2020 Continuous advances in information technologies, such as Artificial intelligence (AI), are opening up new and exciting opportunities for value co-creation between economic actors. However, little is known about the mechanisms and the process of value co-creation enabled by AI. While scholars agree that AI tech- nology significantly changes human activities and human resources, currently we do not have an ade- quate understanding of how humans and AI technology interact in value co-creation. This is the central phenomenon investigated in this article. Specifically, using Service-Dominant Logic (S-DL) as a lens, this study investigates the activities, roles and resources that are exchanged in AI-enabled value co-creation, using the creation of competitive intelligence as a research context. The analysis suggests that AI-enabled value co-creation processes are complex interactions between human and non-human actors who per- form any of six different roles either jointly or independently. This article contributes to SD-L and pro- vides a deeper understanding of the activities (the ‘how’), the actors (the ‘who’), and the resources (the ‘what’) in AI-enabled value co-creation, thus helping to close an identified gap in the literature. Keywords: Artificial intelligence | Value c o-creation | Service-dominant logic | Competitive intelligence | Machine learning | B2B marketing, B2B sales resources | Operant resources | Operand resources |
مقاله انگلیسی |
18 |
Artificial intelligence in retail: The AI-enabled value chain
هوش مصنوعی در خرده فروشی: زنجیره ارزش مجهز به هوش مصنوعی-2020 Traditional retailers’ business models are facing disruption by new entrants who can deliver greater value to customers more efficiently. In recent years, authors have argued that the tradi- tional value chain drives inefficiencies ( Begley et al., 2018 ) and that the value chain is shortening as manufacturers, third parties and customers are increasingly engaging with customers directly ( Reinartz et al., 2019 ). These inefficiencies combined with the in- ability to adapt to a changing competitive landscape leaves tradi- tional retailers vulnerable to disruption from market entrants. To remain competitive and survive in an ever-changing and diversified customer market, retailers need to become leaner ( Campbell et al., 2020 ), more agile ( Goworek, 2014 ), and innovate their value chain by adopting new technologies ( Lee et al., 2018 ). Of the new technologies that are impacting the retail industry, AI has been earmarked as the most transformative ( Kietzmann et al., 2018 ; Lee et al., 2018 ; Silva et al., 2019 ). Yet while there is great ex- citement about artificial intelligence (AI), it has yet to fully deliver on its promise ( Ransbotham et al., 2017 ) and academics and prac- titioners are in the early stages of understanding the application of AI (Van Esch et al., 2020). This article introduces a conceptual framework to understand the role that AI can play in the retail value chain by proposing an AI-enabled retail value chain. |
مقاله انگلیسی |
19 |
AI-enabled remote and objective quantification of stress at scale
کمی سازی از راه دور و استرس در مقیاس با استفاده از هوش مصنوعی-2020 Accurate measurement of human stress at scale is a major mHealth challenge. Here we explore the potential for deep neural networks (DNNs) to improve remote and objective quantification of stress from voluntary selfie videos captured through mobile device front-facing cameras.
Methods
Two DNNs were trained with heart rate (HR) and heart rate variability (HRV) data obtained through photophlethysmographic imaging (PPGI) of 11,823 mobile device selfie videos captured in tandem with self-assessments of stress, and compared to contemporary algorithms used to estimate stress from HR and HRV data.
Results
A classification DNN and predictive DNN determined self-reported stress with 86 % accuracy and a mean absolute error of 0.001, respectively. Both DNNs performed far better than other recently described approaches when applied to the identical dataset.
Conclusions
Well-trained DNNs can objectively and remotely quantify stress at scale. Future efforts may concentrate on the measurement of additional enigmatic cognitive states.. Keywords : Artificial intelligence | Digital therapeutics | Photophlethysmography | Heart rate variability | Stress |
مقاله انگلیسی |
20 |
An Overview of AI-Enabled Remote Smart-Home Monitoring System Using LoRa
مروری بر سیستم مانیتورینگ از راه دور هوشمند در خانه با هوش مصنوعی با استفاده از LoRa-2020 With the advancement of communication
technology, Internet of Things (IoT) enabled smart
home (SH) applications have engrossed substantial
attention nowadays. However, a few meter range
coverage and higher implementation cost are the main
limitations of existing SH systems based on other
cellular networks or short distance technologies. In this
paper, a long range (LoRa) based SH system is
proposed for remote monitoring and maintenance of
IoT sensors and devices using artificial intelligence (AI)
concept. A brief overview of what tasks LoRa can
perform in SH networking are conferred. An AI-based
data flow system for IoT server and cloud is also
presented in this paper. Keywords: Low-power | wide area network (LPWAN) | internet of things (IoT) | long range (LoRa) | smart home (SH) | artificial intelligence (AI) |
مقاله انگلیسی |