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Topic Review
Journalistic Knowledge Platform
A Journalistic Knowledge Platform (JKP) is an information system that employ artificial intelligence and big data techniques such as machine learning and knowledge graphs to manage and support the knowledge work needed in all stages of news production. JKPs automate the process of annotating metadata and support daily workflows like news production, archiving, monitoring, management and distribution. JKPs harvest and analyse news and social media information over the net in real time, leverage encyclopaedic sources, and provide journalists with both meaningful background knowledge and newsworthy information. JKPs can provide a digitalisation path towards reduced production costs and improved information quality while adapting the current workflows of newsrooms to new forms of journalism and readers’ demands.
  • 1.6K
  • 20 Jun 2022
Topic Review Video Peer Reviewed
Geometry-Based Deep Learning in the Natural Sciences
Nature is composed of elements at various spatial scales, ranging from the atomic to the astronomical level. In general, human sensory experience is limited to the mid-range of these spatial scales, in that the scales which represent the world of the very small or very large are generally apart from our sensory experiences. Furthermore, the complexities of Nature and its underlying elements are not tractable nor easily recognized by the traditional forms of human reasoning. Instead, the natural and mathematical sciences have emerged to model the complexities of Nature, leading to knowledge of the physical world. This level of predictiveness far exceeds any mere visual representations as naively formed in the Mind. In particular, geometry has served an outsized role in the mathematical representations of Nature, such as in the explanation of the movement of planets across the night sky. Geometry not only provides a framework for knowledge of the myriad of natural processes, but also as a mechanism for the theoretical understanding of those natural processes not yet observed, leading to visualization, abstraction, and models with insight and explanatory power. Without these tools, human experience would be limited to sensory feedback, which reflects a very small fraction of the properties of objects that exist in the natural world. As a consequence, as taught during the times of antiquity, geometry is essential for forming knowledge and differentiating opinion from true belief. It not only provides a framework for understanding astronomy, classical mechanics, and relativistic physics, but also the morphological evolution of living organisms, along with the complexities of the cognitive systems. Geometry also has a role in the information sciences, where it has explanatory power in visualizing the flow, structure, and organization of information in a system. This role further impacts the explanations of the internals of deep learning systems as developed in the fields of computer science and engineering.
  • 1.6K
  • 21 Jun 2023
Topic Review
Arabic Optical Character Recognition Challenges
When it is necessary to store or edit a text written by hand in Arabic, this can only be performed manually, which can take significant time. However, fortunately, optical character recognition can be applied for this particular case. Optical character recognition (OCR) is a technique that is used to read and recognize the text present in an image and then convert it into a textual format. Once the text is extracted and digitized, it can utilize the applications of storing, retrieving, searching, and editing. Here, researchers shed light on the challenges accompanying OCR in general, then narrow it down to the case of Arabic OCR.
  • 1.6K
  • 07 Jul 2023
Topic Review
Lane Marking Detection Using Deep Neural Networks
Lane marking recognition is one of the most crucial features for automotive vehicles as it is one of the most fundamental requirements of all the autonomy features of Advanced Driver Assistance Systems (ADAS).
  • 1.6K
  • 27 Oct 2022
Topic Review
Machine Learning in Gastroenterology/Endoscopy
Over time, machine learning (ML), a component of artificial intelligence (AI), has been implemented in a variety of medical specialties, such as radiology, pathology, gastroenterology, neurology, obstetrics and gynecology, ophthalmology, and orthopedics, with the goal of improving the quality of healthcare and medical diagnosis. In clinical gastroenterology practice, due to technological developments, estimates show that AI could have the ability to create a predictive model; for instance, it could develop an ML model that can stratify the risk in patients with upper gastrointestinal bleeding, establish the existence of a specific gastrointestinal disease, define the best treatment, and offer prognosis and prediction of the therapeutic response. In this context, by applying ML or deep learning (DL) (AI using neural networks), clinical management in gastroenterology can begin to focus on more personalized treatment centered on the patient and based on making the best individual decisions, instead of relying mostly on guidelines developed for a specific condition. Moreover, the goal of implementing these AI-based algorithms is to increase the possibility of diagnosing a gastrointestinal disease at early stage or the ability to predict the development of a particular condition in advance. Because both AI and gastroenterology encompass many subdomains, the interaction between them might take on various forms. In recent years, we have witnessed a large explosion of research in attempts to improve various fields of gastroenterology, such as endoscopy, hepatology, inflammatory bowel diseases, and many others, with the aid of ML. We also note that, because of the requirement to diagnose more patients with gastrointestinal cancers at an early stage of the disease, which is associated with curative treatment and better prognosis, many studies were developed to address improvement of the detection of these tumors with the aid of AI. The term ML, introduced for the first time in 1959 by Arthur Samuel from the IBM company, refers to an IT domain whereby a computer system can acquire the ability to “learn” by using data without specific programming and can therefore develop a predictive mathematical algorithm based on input data, using recognition of “features”. The ML “model” is subsequently able to adapt to new situations in which it becomes able to predict and make decisions.
  • 1.6K
  • 02 Feb 2021
Topic Review
Data-Driven Predictive Maintenance
Cyber-physical systems in Industry 4.0 are reforming conventional decision-making processes, mainly through integrating entities and functionalities via telecommunication systems and intelligent data processing approaches. This reformulation brings new challenges and increases complexity. Nevertheless, these advancements might provide new solutions for typical problems, such as system failures, and thus, for maintenance approaches. Predictive Maintenance (PdM) is a data-based approach that emerged as a prominent field of research among many existing maintenance approaches. We have three main categories in PdM: model-based prognosis, knowledge-based prognosis, and data-driven prognosis. Data-driven PdM strategies appeared with great prominence and importance both in industry and academia.
  • 1.5K
  • 28 Sep 2021
Topic Review
Machine Learning and Image Processing
Images constitute one of the most important forms of communication used by society and contain a large amount of important information. The human vision system is usually the first form of contact with media and has the ability to naturally extract important, and sometimes subtle, information, enabling the execution of different tasks, from the simplest, such as identifying objects, to the more complex, such as the creation and integration of knowledge. However, this system is limited to the visible range of the electromagnetic spectrum. On the contrary, computer systems have a more comprehensive coverage capacity, ranging from gamma to radio waves, which makes it possible to process a wide spectrum of images, covering a wide and varied field of applications. On the other hand, the exponential growth in the volume of images created and stored daily makes their analysis and processing a difficult task to implement outside the technological sphere. In this way, image processing through computational systems plays a fundamental role in extracting necessary and relevant information for carrying out different tasks in different contexts and application areas.
  • 1.5K
  • 17 Oct 2023
Topic Review
Simulators in Educational Robotics
Simulators are part of educational robotics, which easily and quickly enable the user to engage virtually with the development and programming of robots through GUIs. Using a simulator means that it is not necessary to deal exclusively with real robots that might have a significant cost. Hence, simulators are a useful tool that might save resources and assist the educational process.
  • 1.5K
  • 18 Mar 2021
Topic Review
Intelligent Power Management System
This is a cloud-based intelligent power management system that uses analytics as a control signal and processes balance achievement pointer, and describes operator acknowledgments that must be shared quickly, accurately, and safely. Current studies of it aims to introduce a conceptual and systematic structure with three main components: demand power (direct current (DC)-device), power mix between renewable energy (RE) and other power sources, and a cloud-based power optimization intelligent system.
  • 1.5K
  • 01 Nov 2021
Topic Review
Natural Language Processing for the COVID-19 Pandemic
The COVID-19 pandemic is the most devastating public health crisis and has affected the lives of billions of people worldwide in unprecedented ways. Compared to pandemics of this scale in the past, societies are now equipped with advanced technologies that can mitigate the impacts of pandemics if utilized appropriately. However, opportunities are not fully utilized, particularly at the intersection of data science and health. Health-related big data and technological advances have the potential to significantly aid the fight against such pandemics, including the pandemic’s ongoing and long-term impacts. Specifically, the field of natural language processing (NLP) has enormous potential at a time when vast amounts of text-based data are continuously generated from a multitude of sources, such as health/hospital systems, published medical literature, and social media. Effectively mitigating the impacts of the pandemic requires tackling challenges associated with the application and deployment of NLP systems.
  • 1.5K
  • 24 Nov 2022
Topic Review
Vision-Based Autonomous Vehicle Systems
Autonomous vehicle systems (AVS) have advanced at an exponential rate, particularly due to improvements in artificial intelligence, which have had a significant impact on social as well as road safety and the future of transportation systems. Deep learning is fast becoming a successful alternative approach for perception-based AVS as it reduces both cost and dependency on sensor fusion.
  • 1.5K
  • 12 Jul 2022
Topic Review
Skeleton-Based Sign Language Recognition
Skeleton-based sign language recognition (SLR) systems that mainly use specific skeleton points instead of the pixels of images and/or sensors. The main advantage of skeleton-based SLR systems is that they can increase the attention paid to signs and have strong adaptability to complicated backgrounds and dynamic circumstances. The use of hand skeleton information alone is sometimes insufficient to correctly represent the exact meaning of a sign due to a lack of emotion and bodily expression.
  • 1.5K
  • 04 Jul 2023
Topic Review
Network Threat Detection with ML/DL in SDN-Based Platforms
A revolution in network technology has been ushered in by software defined networking (SDN), which makes it possible to control the network from a central location and provides an overview of the network’s security. Despite this, SDN has a single point of failure that increases the risk of potential threats. Network intrusion detection systems (NIDS) prevent intrusions into a network and preserve the network’s integrity, availability, and confidentiality. Much work has been done on NIDS but there are still improvements needed in reducing false alarms and increasing threat detection accuracy. Advanced approaches such as deep learning (DL) and machine learning (ML) have been implemented in SDN-based NIDS to overcome the security issues within a network.
  • 1.5K
  • 14 Nov 2022
Topic Review
ANN in Intelligent Attendance System
Determining the rate of student attendance is an important task in determining the completion of the courses. Despite the success of the technology, it is unfortunate that in many academic institutions, the current systems used to detect student absences. Furthermore, one of the crucial problems in the attendance system does not count student background for continuing in the courses. In this paper, we propose an intelligent approach for calculating student attendance based on their Grade Point Average (GPA) and their activities, this approach uses Artificial Neural Network (ANN) for calculating the attendance rating accurately, meaning the system provide a new rating for each student based on their background. The aim of this research is developing an attendance system for motivation students taking attendance or taking high grade in the class. The result of this approach helps the instructor to allow students who have more activities with more absents to continue in the courses if not the students have low activity should taking high attendance. This system will more efficient for monitoring students for replacing absent to activity.
  • 1.5K
  • 28 Oct 2020
Topic Review
Community-Specific Overview of Knowledge Graph Research
Knowledge graphs (KGs) have rapidly emerged as an important area in AI over the last ten years. Building on a storied tradition of graphs in the AI community, a KG may be simply defined as a directed, labeled, multi-relational graph with some form of semantics. In part, this has been fueled by increased publication of structured datasets on the Web, and well-publicized successes of large-scale projects such as the Google Knowledge Graph and the Amazon Product Graph. However, another factor that is less discussed, but which has been equally instrumental in the success of KGs, is the cross-disciplinary nature of academic KG research. Arguably, because of the diversity of this research, a synthesis of how different KG research strands all tie together could serve a useful role in enabling more ‘moonshot’ research and large-scale collaborations.
  • 1.5K
  • 01 Apr 2022
Topic Review
Pothole Detection
Many datasets used to train artificial intelligence systems to recognize potholes, such as the challenging sequences for autonomous driving (CCSAD) and the Pacific Northwest road (PNW) datasets, do not produce satisfactory results. This is due to the fact that these datasets present complex but realistic scenarios of pothole detection tasks than popularly used datasets that achieve better results but do not effectively represents realistic pothole detection task. In an attempt to improve the detection accuracy of the pothole object detection problems, researchers have proposed varieties of object detection methods enhanced with super-resolution (SR) techniques that are employed to generate an enhanced image from a low-resolution image before performing object detection.
  • 1.5K
  • 24 Jun 2022
Topic Review
Applications of Internet of Things
IoT-dependent systems (IoTSs) cause heavy usage of energy. This is one of the biggest issues associated with IoTSs. Another issue is that the security of digital content is a big challenge and difficulty. Image processing has recently played an essential role in resolving these difficulties.
  • 1.5K
  • 02 Mar 2023
Topic Review
Discrimination, Bias, Fairness, and Trustworthy AI
It has been identified that there exists a set of specialized variables, such as security, privacy, responsibility, etc., that are used to operationalize the principles in the Principled AI International Framework.  Bias, discrimination, and fairness are mainly approached with an operational interest by the Principled AI International Framework.
  • 1.5K
  • 01 Jul 2022
Topic Review
Finger-Vein-Based Identity Recognition
Finger vein recognition is a relatively new method of biometric authentication. It matches the vascular pattern in an individual’s finger to previously obtained data.
  • 1.5K
  • 19 May 2021
Topic Review
Artificial Intelligence in Translational Medicine
Between preclinical and clinical research, translational research is benefitting from computer-based approaches like Artificial Intelligence, resulting in breakthroughs for advancing human health. 
  • 1.5K
  • 17 Dec 2021
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