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Topic Review
Computer-Aided Diagnosis Using Gastrointestinal Endoscopy
Computer-aided diagnosis (CAD) system based on deep learning to assist doctors in diagnosis is of great significance, because diagnosing lesions in the stomach, intestines, and esophagus is laborious for doctors. In addition, misdiagnoses can occur based on a subjective judgment.
  • 795
  • 22 Apr 2021
Topic Review
Path-Planning Approaches for Multiple Mobile Robots
Numerous path-planning studies have been conducted due to the challenges of obtaining optimal solutions. The multi-robot path-planning approaches have been classified as classical approaches, heuristic algorithms, bio-inspired techniques, and artificial intelligence approaches. Bio-inspired techniques are the most employed approaches, and artificial intelligence approaches have gained more attention. 
  • 793
  • 15 Sep 2022
Topic Review
Supply Chain Management in Pandemics
Pandemics cause chaotic situations in supply chains (SC) around the globe, which can lead towards survivability challenges. The ongoing COVID-19 pandemic is an unprecedented humanitarian crisis that has severely affected global business dynamics. Similar vulnerabilities have been caused by other outbreaks in the past. In these terms, prevention strategies against propagating disruptions require vigilant goal conceptualization and roadmaps. In this respect, there is a need to explore supply chain operation management strategies to overcome the challenges that emerge due to COVID-19-like situations. 
  • 793
  • 16 Mar 2021
Topic Review
Application of Machine Learning in Traumatic Brain Injury
One of the main challenges in traumatic brain injury (TBI) patients is to achieve an early and definite prognosis. Despite the recent development of algorithms based on artificial intelligence for the identification of these prognostic factors relevant for clinical practice, the literature lacks a rigorous comparison among classical regression and machine learning (ML) models. The utility of comparing traditional regression modeling to ML is highlighted here, particularly when using a small number of reliable predictor variables after TBI. The dataset of clinical data used to train ML algorithms will be publicly available to other researchers for future comparisons. 
  • 792
  • 21 Mar 2022
Topic Review
Machine Learning-Based Application Life-Cycle
The application of emerging technologies, such as Artificial Intelligence (AI), entails risks that need to be addressed to ensure secure and trustworthy socio-technical infrastructures. Machine Learning (ML), the most developed subfield of AI, allows for improved decision-making processes. ML models exhibit specific vulnerabilities that conventional IT systems are not subject to. As systems incorporating ML components become increasingly pervasive, the need to provide security practitioners with threat modeling tailored to the specific AI-ML pipeline is of paramount importance.
  • 792
  • 21 Sep 2022
Topic Review
Applications of Brain–Computer Interfaces to Control and Automation
Brain–computer interfacing (BCI) is a real-time communication system that connects the brain and external devices. A BCI system can directly convert the information sent by the brain into commands that can drive external devices and can replace human limbs or phonation organs to achieve communication with the outside world and to control the external environment. In other words, a BCI system can replace the normal peripheral nerve and muscle tissue to achieve communication between a human and a computer or between a human and the external environment. BCIs have been validated in various noisy structured environments such as homes, hospitals, and expositions, resulting in the direct application of BCIs gaining popularity with regular consumers.
  • 789
  • 12 Jun 2023
Topic Review
Population-Based Deep Reinforcement Learning
Many real-world applications can be described as large-scale games of imperfect information, which require extensive prior domain knowledge, especially in competitive or human–AI cooperation settings. Population-based training methods have become a popular solution to learn robust policies without any prior knowledge, which can generalize to policies of other players or humans. 
  • 785
  • 15 Jun 2023
Topic Review
Stock Index Prediction
The stock index is an important indicator to measure stock market fluctuation, with a guiding role for investors’ decision-making, thus being the object of much research. However, the stock market is affected by uncertainty and volatility, making accurate prediction a challenging task. 
  • 784
  • 07 Feb 2022
Topic Review
Smart Distribution Network Situation Awareness
Due to the rapid development of emerging information and communication technologies (ICT) and advanced metering infrastructure (AMI), distribution networks are in an evolvement from passive to active distribution networks (ADN), also called smart distribution networks (SDN). Operation and maintenance (O&M) cost is an economic factor that the SDN management must consider. Among multiple O&M technologies, situation awareness (SA) emerges and is gradually integrated into the SDN. Facing a high proportion of RES, adequate monitoring, analysis, and prediction of the SDN operating status are urgent. Therefore, comprehensive SA, which contains detection, comprehension, and projection, becomes a significant guarantee for the optimal operation of SDN.
  • 784
  • 24 Feb 2022
Topic Review
Swarm Intelligence Based Load Balancing Techniques
Swarm Intelligence aims to combine relatively high approximate techniques to guide local optimization strategies in order to explore a solution space successfully and efficiently.
  • 780
  • 28 Mar 2022
Topic Review
Machine Learning Applications in Surface Transportation Systems
Surface transportation has evolved through technology advancements using parallel knowledge areas such as machine learning (ML). ML is the most sophisticated state-of-the-art knowledge branch offering the potential to solve unsettled or difficult-to-solve problems as a data-driven approach.
  • 780
  • 23 Sep 2022
Topic Review
Reminiscence Therapy in Depression Treatment in the Elderly
Reminiscence therapy is a mechanism to help someone remember events from their life. It is often used as a therapy tool for reducing depression, calming behavioral and psychological symptoms of dementia, or affecting mood of the elderly. Although its most common use is for the elderly and people affected with dementia or depression, it has also been used with people of all ages, including children. The reminiscing process can take place in a group or individually or by using technological devices such as mobile devices or robots. It is marked by remembering notable events from the past.
  • 780
  • 03 Mar 2022
Topic Review
Vulnerabilities and Potential Threats of Cloud computing
Cloud computing has become a prominent technology due to its important utility service; this service concentrates on outsourcing data to organizations and individual consumers. Cloud computing has considerably changed the manner in which individuals or organizations store, retrieve, and organize their personal information. Despite the manifest development in cloud computing, there are still some concerns regarding the level of security and issues related to adopting cloud computing that prevent users from fully trusting this useful technology.
  • 775
  • 28 Mar 2022
Topic Review
Application of Triboelectric Nanogenerator in Fluid Dynamics Sensing
The triboelectric nanogenerator (TENG) developed by Z. L. Wang’s team to harvest random mechanical energy is a promising new energy source for distributed sensing systems in the new era of the internet of things (IoT) and artificial intelligence (AI) for a smart world. In industry and academia, fluid dynamics sensing for liquid and air is urgently needed but lacking. In particular, local fluid sensing is difficult and limited to traditional sensors. Fortunately, with advantages for ordinary TENGs and TENGs as fluid dynamics sensors, fluid dynamics sensing can be better realized.
  • 775
  • 30 Sep 2022
Topic Review
Lamport Timestamps
The algorithm of Lamport timestamps is a simple algorithm used to determine the order of events in a distributed computer system. As different nodes or processes will typically not be perfectly synchronized, this algorithm is used to provide a partial ordering of events with minimal overhead, and conceptually provide a starting point for the more advanced vector clock method. They are named after their creator, Leslie Lamport. Distributed algorithms such as resource synchronization often depend on some method of ordering events to function. For example, consider a system with two processes and a disk. The processes send messages to each other, and also send messages to the disk requesting access. The disk grants access in the order the messages were sent. For example process [math]\displaystyle{ A }[/math] sends a message to the disk requesting write access, and then sends a read instruction message to process [math]\displaystyle{ B }[/math]. Process [math]\displaystyle{ B }[/math] receives the message, and as a result sends its own read request message to the disk. If there is a timing delay causing the disk to receive both messages at the same time, it can determine which message happened-before the other: [math]\displaystyle{ A }[/math] happens-before [math]\displaystyle{ B }[/math] if one can get from [math]\displaystyle{ A }[/math] to [math]\displaystyle{ B }[/math] by a sequence of moves of two types: moving forward while remaining in the same process, and following a message from its sending to its reception. A logical clock algorithm provides a mechanism to determine facts about the order of such events. Lamport invented a simple mechanism by which the happened-before ordering can be captured numerically. A Lamport logical clock is a numerical software counter value maintained in each process. Conceptually, this logical clock can be thought of as a clock that only has meaning in relation to messages moving between processes. When a process receives a message, it re-synchronizes its logical clock with that sender. The above-mentioned vector clock is a generalization of the idea into the context of an arbitrary number of parallel, independent processes.
  • 774
  • 24 Oct 2022
Topic Review
Convolutional Neural Network in Histopathology
Deep learning (DL) and convolutional neural networks (CNNs) have achieved state-of-the-art performance in many medical image analysis tasks. Histopathological images contain valuable information that can be used to diagnose diseases and create treatment plans. Therefore, the application of DL for the classification of histological images is a rapidly expanding field of research.
  • 774
  • 04 May 2023
Topic Review
Generative Adversarial Network in Amodal Completion
The generative adversarial network (GAN) is a structured probabilistic model that consists of two networks, a generator that captures the data distributions and a discriminator that decides whether the produced data come from the actual data distribution or from the generator. The two networks train in a two-player minimax game fashion until the generator can generate samples that are similar to the true samples, and the discriminator can no longer distinguish between the real and the fake samples. Although current computer vision systems are closer to the human intelligence when it comes to comprehending the visible world than previously, their performance is hindered when objects are partially occluded. Since we live in a dynamic and complex environment, we encounter more occluded objects than fully visible ones. Therefore, instilling the capability of amodal perception into those vision systems is crucial. However, overcoming occlusion is difficult and comes with its own challenges. GAN, on the other hand, is renowned for its generative power in producing data from a random noise distribution that approaches the samples that come from real data distributions.
  • 773
  • 24 Apr 2023
Topic Review
Contextual Route Recommendation System
The traffic composition in developing countries comprises of variety of vehicles which include cars, buses, trucks, and motorcycles. Motorcycles dominate the road with 77.5% compared to other types. Meanwhile, route recommendation such as navigation and Advanced Driver Assistance Systems (ADAS) is limited to particular vehicles only. Traffic condition prediction aims to discuss the proper method to result a better prediction analysis. Route recommendation aims to explore the existing work on how to provide the best route for users. The two domains would be the parts of our framework to result contextual route recommendations in heterogeneous traffic flow.
  • 771
  • 28 Mar 2022
Topic Review
Rheumatoid Arthritis Diagnosis
Rheumatoid arthritis (RA) is a systemic autoimmune disease that preferably affects small joints. As the well-timed diagnosis of the disease is essential for the treatment of the patient, several works have been conducted in the field of deep learning to develop fast and accurate automatic methods for RA diagnosis.
  • 770
  • 29 Dec 2021
Topic Review
Artificial Intelligence in Alzheimer’s Disease
Alzheimer’s disease (AD) represents most of the dementia cases and stands as the most common neurodegenerative disease. A shift from a curative to a preventive approach is imminent, and we are moving towards the application of personalized medicine, whereas we can shape the best clinical intervention for each patient at a given point. This new step in medicine requires the most recent tools and the analysis of huge amounts of data where the application of artificial intelligence (AI) plays a critical part in the depiction of disease-patient dynamics, critical to reach early/optimal diagnosis, monitoring and intervention. Predictive models and algorithms are the key elements in this innovative field. 
  • 769
  • 02 Mar 2022
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