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.
Special Issue on AI and FinTech: The Challenge Ahead
موضوع ویژه هوش مصنوعی و FinTech : چالش پیش رو-2020
IT IS OUR pleasure to share with you this special issue on AI and Fintech, which includes 17 articles published in the March/April (eight articles) and May/June (nine articles) issues of IEEE Intelligent Systems (IS). After our announcement in early September 2019 for this special issue on AI and Fintech for IS, we received a larger volume of manuscripts than anticipated by the January 2020 deadline. These worldwide submissions included both academic researchers and practitioners in IT and financial industries. After a long round of revision from independent, anonymous referees that ultimately led to the current, official versions of the articles published in both issues, we would like to first and foremost thank all the contributors and the anonymous referees for their hard work on this project. Second, we give our sincere gratitude to the supportive effort of the IS team led by Professor Venkatramanan Subrahmanian (VS) to ensure the timely publication of both issues
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
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.
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
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 , 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
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.
Harnessing AI to Transform Agriculture and Inform Agricultural Research
استفاده از هوش مصنوعی برای تبدیل کشاورزی و اطلاع از تحقیقات کشاورزی-2020
We provide an overview of the Special Issue on current advances, challenges, and opportunities for AI technologies in agriculture. We illustrate the potential of AI using four major components of the food system: production, distribution, consumption, and uncertainty. We recognize that the transformation of agriculture will require new tools to more precisely manage fields to increase production while minimizing the environmental risk to water and air quality. Combining AI with other technologies will be needed to provide effective production management strategies for a given combination of soil, climate, pest complexes, and vegetation. New methods will be needed to determine production limitations, and effective management options. The agricultural enterprise is prime for the use of AI and other technologies if they can be adapted for the unique characteristics of agroecosystems, including variability and directional changes in climate and other global change drivers as well as novel management and policy decisions, and economic market volatility.
Looking in the Right Place for Anomalies: Explainable Ai Through Automatic Location Learning
جستجوی مکان مناسب برای ناهنجاری ها: هوش مصنوعی قابل توضیح از طریق یادگیری خودکار مکان-2020
Deep learning has now become the de facto approach to the recognition of anomalies in medical imaging. Their ’black box’ way of classifying medical images into anomaly labels poses problems for their acceptance, particularly with clinicians. Current explainable AI methods offer justifications through visualizations such as heat maps but cannot guarantee that the network is focusing on the relevant image region fully containing the anomaly. In this paper we develop an approach to explainable AI in which the anomaly is assured to be overlapping the expected location when present. This is made possible by automatically extracting location-specific labels from textual reports and learning the association of expected locations to labels using a hybrid combination of Bi-Directional Long Short-Term Memory Recurrent Neural Networks (Bi- LSTM) and DenseNet-121. Use of this expected location to bias the subsequent attention-guided inference network based on ResNet101 results in the isolation of the anomaly at the expected location when present. The method is evaluated on a large chest X-ray dataset.
Smart Technologies for Visually Impaired: Assisting and conquering infirmity of blind people using AI Technologies
فناوری های هوشمند برای افراد دارای اختلال بینایی : کمک و تسخیر ناتوانی افراد نابینا با استفاده از فناوری های هوشمند مصنوعی-2020
Physical disability has affected many people’s lives across the world. One of these disabilities that strongly affected some large category of people is visual lose. Blind people often face difficulties in moving around freely such as: in crossing the street, in reading, driving or socializing. They often rely on using certain aid devices to reach certain places or perform any other daily activities such as walking sticks. There are ongoing scientific researches in the area of rectifying blindness, but it has to go long way to achieve the solution. Also, there are research unleashes the ideas of assisting the blind people deficiency but lacks in technological aspects of implementation. This research project aims at helping blind people of all categories to achieve their day to day tasks easier through the use of a smart device. By using artificial intelligent and image processing, this smart device is able to detect faces, colors and deferent objects. The detection process is manifested by notifying the visually impaired person through either a sound alert or vibration. Additionally, this study presents a palpable survey that entails visually impaired people from the local community. Subsequently, the project uses both Open CV and Python for programming and implementation. The exertion of this project prototype investigates the algorithms which are used for detecting the objects. Also, it demonstrates how this smart device could detects certain physical object and how it could send a warning signal when faced by any obstacles. Overall, this research will be a positive addition in the world of health care sector by supporting blind people with the use of smart technology.
Keywords: Artificial Intelligent | Open CV | Python | Face Recognition | Object Detection | Health Care Introduction