ردیف | عنوان | نوع |
---|---|---|
1 |
Updating Diagnoses for Speed and Accuracy: Using AI, Cameras, Assays, and More
به روزرسانی تشخیص ها برای سرعت و دقت: استفاده از هوش مصنوعی ، دوربین ها ، سنجش ها و موارد دیگر-2020 When it comes to their health, people want
answers right now. But clinicians cannot always
make snap judgments about ailments or injuries. One
way to help both general practitioners and patients
is to introduce technologies that deliver quick and
accurate diagnoses in a standard clinical setting.
Here, IEEE Pulse features three examples of recently
U.S. Food and Drug Administration (FDA)-approved diagnostic
approaches that give patients fast responses about
their conditions from a simple trip to their doctor, and
without the need to see a specialist first. They include:
• an autonomous artificial intelligence (AI) algorithm
to diagnose diabetic retinopathy (DR);
• an assay to spot infection with Mycoplasma genitalium,
which can cause a sexually transmitted
disease (STD)
• an eye-tracking strategy to identify concussion. |
مقاله انگلیسی |
2 |
Towards optimal control of air handling units using deep reinforcement learning and recurrent neural network
به سمت کنترل بهینه واحدهای مدیریت هوا با استفاده از یادگیری تقویتی عمیق و شبکه عصبی بازگشتی -2020 A new generation of smart stormwater systems promises to reduce the need for new construction by enhancing the performance of the existing infrastructure through real-time control. Smart stormwater systems dynamically adapt their response to individual storms by controlling distributed assets, such as valves, gates, and pumps. This paper introduces a real-time control approach based on Reinforcement Learning (RL), which has emerged as a state-of-the-art methodology for autonomous control in the artificial intelligence community. Using a Deep Neu- ral Network, a RL-based controller learns a control strategy by interacting with the system it controls - effectively trying various control strategies until converging on those that achieve a desired objective. This paper formulates and implements a RL algorithm for the real-time control of urban stormwater systems. This algorithm trains a RL agent to control valves in a distributed stormwater system across thousands of simulated storm scenarios, seeking to achieve water level and flow set-points in the system. The algorithm is first evaluated for the control of an individual stormwater basin, after which it is adapted to the control of multiple basins in a larger watershed (4 km 2 ). The results indicate that RL can very effectively control individual sites. Performance is highly sensitive to the reward formulation of the RL agent. Generally, more explicit guidance led to better control performance, and more rapid and stable convergence of the learning process. While the control of multiple distributed sites also shows promise in reducing flooding and peak flows, the complexity of controlling larger systems comes with a number of caveats. The RL controller’s performance is very sensitive to the formulation of the Deep Neural Network and requires a significant amount of computational resource to achieve a reasonable performance en- hancement. Overall, the controlled system significantly outperforms the uncontrolled system, especially across storms of high intensity and duration. A frank discussion is provided, which should allow the benefits and draw- backs of RL to be considered when implementing it for the real-time control of stormwater systems. An open source implementation of the full simulation environment and control algorithms is also provided. Keywords: Real-time control | Reinforcement learning | Smart stormwater systems |
مقاله انگلیسی |
3 |
Democratization of AI, Albeit Constrained IoT Devices & Tiny ML, for Creating a Sustainable Food Future
دموکراتیک سازی هوش مصنوعی ، دستگاه های محدود IoT و Tiny ML ، برای ایجاد آینده غذایی پایدار-2020 Abstract—Big Data surrounds us. Every minute, our
smartphone collects huge amount of data from geolocations to
next clickable item on the ecommerce site. Data has become
one of the most important commodities for the individuals and
companies. Nevertheless, this data revolution has not touched
every economic sector, especially rural economies, e.g., small
farmers have largely passed over the data revolution, in the
developing countries due to infrastructure and compute
constrained environments. Not only this is a huge missed
opportunity for the big data companies, it is one of the
significant obstacle in the path towards sustainable food and a
huge inhibitor closing economic disparities. The purpose of the
paper is to develop a framework to deploy artificial intelligence
models in constrained compute environments that enable
remote rural areas and small farmers to join the data
revolution and start contribution to the digital economy and
empowers the world through the data to create a sustainable
food for our collective future. Keywords: edge | IoT device | artificial intelligence | Kalman filter | dairy cloud | small scale farmers | hardware constrained model | tiny ML| Hanumayamma | cow necklace |
مقاله انگلیسی |
4 |
Multidisciplinary and Interdisciplinary Teaching in the Utrecht AI Program: Why and How?
آموزش چند رشته ای و میان رشته ای در برنامه هوش مصنوعی Utrecht : چرا و چگونه؟-2020 MULTIDISCIPLINARY AND INTERDISCIPLINARY
education can provide relevant insights into
ubiquitous computing and other fields.1 In this
article, we share our experience with multidisciplinary
and interdisciplinary teaching in the twoyear
Artificial Intelligence Research Master’s
program at Utrecht University, the Netherlands.
In particular, we zoom in on our motivation for,
and experience with, revising courses in which
nonengineering topics can be related to a more
engineering inclined audience, and vice-versa. |
مقاله انگلیسی |
5 |
AI-based Reference Ankle Joint Torque Trajectory Generation for Robotic Gait Assistance: First Steps
تولید مسیر حرکت گشتاور مفصل مچ پا مبتنی بر هوش مصنوعی برای کمک به راه رفتن رباتیک: اولین قدم ها-2020 Robotic-based gait rehabilitation and assistance
have been growing to augment and to recover motor function in
subjects with lower limb impairments. There is interest in
developing user-oriented control strategies to provide
personalized assistance. However, it is still needed to set the
healthy user-oriented reference joint trajectories, namely,
reference ankle joint torque, that would be desired under healthy
conditions. Considering the potential of Artificial Intelligence (AI)
algorithms to model nonlinear relationships of the walking
motion, this study implements and compares two offline AI-based
regression models (Multilayer Perceptron and Long-Short Term
Memory-LSTM) to generate healthy reference ankle joint torques
oriented to subjects with a body height ranging from 1.51 to 1.83
m, body mass from 52.0 to 83.7 kg and walking in a flat surface
with a walking speed from 1.0 to 4.0 km/h. The best results were
achieved for the LSTM, reaching a Goodness of Fit and a
Normalized Root Mean Square Error of 79.6 % and 4.31 %,
respectively. The findings showed that the implemented LSTM
has the potential to be integrated into control architectures of
robotic assistive devices to accurately estimate healthy useroriented
reference ankle joint torque trajectories, which are
needed in personalized and Assist-As-Needed conditions. Future
challenges involve the exploration of other regression models and
the reference torque prediction for remaining lower limb joints,
considering a wider range of body masses, heights, walking speeds,
and locomotion modes. Keywords: Ankle Joint Torque Prediction | Artificial Intelligence | Control Strategies | Regression Models | Robotic Gait Rehabilitation |
مقاله انگلیسی |
6 |
Drivers, barriers and social considerations for AI adoption in business and management: A tertiary study
رانندگان ، موانع و ملاحظات اجتماعی برای پذیرش هوش مصنوعی در مشاغل و مدیریت: یک مطالعه عالی-2020 The number of academic papers in the area of Artificial Intelligence (AI) and its applications across business
and management domains has risen significantly in the last decade, and that rise has been followed by an
increase in the number of systematic literature reviews.
The aim of this study is to provide an overview of existing systematic reviews in this growing area of research
and to synthesise their findings related to enablers, barriers and social implications of the AI adoption in
business and management.
The methodology used for this tertiary study is based on Kitchenham and Charter’s guidelines [14], resulting in
a selection of 30 reviews published between 2005 and 2019 which are reporting results of 2,021 primary
studies.
These reviews cover the AI adoption across various business sectors (healthcare, information technology,
energy, agriculture, apparel industry, engineering, smart cities, tourism and transport), management and
business functions (HR, customer services, supply chain, health and safety, project management, decisionsupport,
systems management and technology acceptance).
While the drivers for the AI adoption in these areas are mainly economic, the barriers are related to the
technical aspects (e.g. availability of data, reusability of models) as well as the social considerations such as,
increased dependence on non-humans, job security, lack of knowledge, safety, trust and lack of multiple
stakeholders’ perspectives.
Very few reviews outside of the healthcare management domain consider human, organisational and wider
societal factors and implications of the AI adoption.
Most of the selected reviews are recommending an increased focus on social aspects of AI, in addition to more
rigorous evaluation, use of hybrid approaches (AI and non-AI) and multidisciplinary approaches to AI design
and evaluation.
Furthermore, this study found that there is a lack of systematic reviews in some of the AI early adopter sectors
such as financial industry and retail and that the existing systematic reviews are not focusing enough on
human, organisational or societal implications of the AI adoption in their research objectives. Keywords: artificial intelligence | business | machine learning | management | systematic literature review | tertiary study |
مقاله انگلیسی |
7 |
AI Down on the Farm
هوش مصنوعی کوچک در مزرعه-2020 Agriculture has become an information-intensive industry. In the production of
crops and animals, precision agriculture approaches have resulted in the collection of
spatially and temporally dense datasets by farmers and agricultural researchers. These
big datasets, often characterized by extensive nonlinearities and interactions, are often
best analyzed using machine learning (ML) or other artificial intelligence (AI) approaches.
In this article, we review several case studies where ML has been used to model aspects
of agricultural production systems and provide information useful for farm-level
management decisions. These studies include modeling animal feeding behavior as a
predictor of stress or disease, providing information important for developing precise and
efficient irrigation systems, and enhancing tools used to recommend optimum levels of
nitrogen fertilization for corn. Taken together, these examples represent the current
abilities and future potential for AI applications in agricultural production systems. |
مقاله انگلیسی |
8 |
Deep reinforcement learning based AGVs real-time scheduling with mixed rule for flexible shop floor in industry 4.0
زمانبندی مبتنی بر یادگیری تقویتی عمیق مبتنی بر AGV با قاعده مختلط برای کف انعطاف پذیر در صنعت 4.0-2020 Driven by the recent advances in industry 4.0 and industrial artificial intelligence, Automated Guided Vehicles
(AGVs) has been widely used in flexible shop floor for material handling. However, great challenges aroused by
the high dynamics, complexity, and uncertainty of the shop floor environment still exists on AGVs real-time
scheduling. To address these challenges, an adaptive deep reinforcement learning (DRL) based AGVs real-time
scheduling approach with mixed rule is proposed to the flexible shop floor to minimize the makespan and
delay ratio. Firstly, the problem of AGVs real-time scheduling is formulated as a Markov Decision Process (MDP)
in which state representation, action representation, reward function, and optimal mixed rule policy, are
described in detail. Then a novel deep q-network (DQN) method is further developed to achieve the optimal
mixed rule policy with which the suitable dispatching rules and AGVs can be selected to execute the scheduling
towards various states. Finally, the case study based on a real-world flexible shop floor is illustrated and the
results validate the feasibility and effectiveness of the proposed approach. Keywords: Automated guided vehicles | Real-time scheduling | Deep reinforcement learning | Industry 4.0 |
مقاله انگلیسی |
9 |
پیش بینی قیمت پایانی سهام با استفاده از روشهای یادگیری ماشینی
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 12 پیش بینی دقیق بازگشت های بازار سهام به دلیل ماهیت فرّار و غیرخطی بازارهای مالی بورس، کاری بسیار چالش انگیز است. اثبات شده است که با معرفی هوش مصنوعی و قابلیت های محاسباتی افزون، روشهای برنامه ریزی شده برای پیش بینی بورس در پیش بینی قیمت سهام کارآمدتر هستند. در این کار تحقیقی، از روشهای شبکه عصبی مصنوعی و جنگل تصادفی برای پیش بینی قیمت نهایی روز بعدی برای شرکتهای متعلق به بخشهای کاری مختلف استفاده شده است. از داده های مالی مربوط به قیمت های باز، بالا، پایین و نهایی سهام برای خلق متغیرهای جدیدی استفاده می شود که این متغیرها به عنوان ورودی های مدل به کار می روند. مدلها با استفاده از شاخص های راهبردی استاندارد RMSE و MAPE ارزیابی می شوند. مقادیر پایین این دو شاخص نشان می دهد که این مدلها در پیش بینی قیمت نهایی سهام کارآمد هستند.
کلیدواژه ها: رگراسیون جنگل تصادفی | شبکه عصبی مصنوعی | پیش بینی بازار سهام |
مقاله ترجمه شده |
10 |
به سمت لبه هوشمند: ارتباطات بی سیم به یادگیری ماشین میرسد
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 14 - تعداد صفحات فایل doc فارسی: 31 احیای هوش مصنوعی در اواخر (AI) تقریباً در هر شاخهای از علم و فناوری، انقلابی ایجاد کرده است. با توجه به گجتهای تلفن همراه هوشمند و همه جا حاضر و دستگاههای اینترنت اشیا (IoT)، انتظار میرود که اکثر برنامههای هوشمند را بتوان در لبهی شبکههای بی سیم استقرار داد. این روند باعث شده است، تمایل قوی برای تحقق «لبه هوشمند» ایجاد شود تا از برنامههای کاربردی مجهز به AI در دستگاههای لبه مختلف استفاده شود. بر این اساس، یک حوزهی پژوهشی جدید به نام یادگیری لبه به ظهور رسیده است که از دو رشته عبور میکند و انقلابی در آنها ایجاد میکند: ارتباطات بی سیم و یادگیری ماشین. یک موضوع اصلی در یادگیری لبه غلبه بر قدرت محاسباتی محدود و همچنین دادههای محدود در هر دستگاه لبه است. این امر با استفاده از پلت فرم محاسبات لبه تلفن همراه (MEC) و استخراج دادههای عظیم توزیع شده در تعداد زیادی دستگاه لبه محقق شده است. در چنین سیستمهایی، یادگیری از داده توزیع شده و برقراری ارتباط بین سرور لبه و دستگاهها دو جنبهی حیاتی و مهم است و همجوشی آنها، چالشهای پژوهشی جدید و زیادی را به همراه دارد. این مقاله از یک مجموعه جدید از اصول طراحی برای ارتباطات بی سیم در یادگیری لبه پشتیبانی میکند که در مجموع ارتباطات یادگیری محور نامیده میشوند. مثالهای گویایی ارائه شدند تا اثربخشی این اصول طراحی مشخص شوند و برای این منظور فرصتهای تحقیقاتی منحصر به فردی شناسایی شدند.
کلمات کلیدی: سرورها | مدل سازی جوی | هوش مصنوعی | پایگاه های داده توزیع شده | ارتباطات بی سیم | یادگیری ماشین | مدل سازی محاسباتی |
مقاله ترجمه شده |