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Topic Review
A Systematic Approach to Healthcare Knowledge Management Systems
Big data in healthcare contain a huge amount of tacit knowledge that brings great value to healthcare activities such as diagnosis, decision support, and treatment. However, effectively exploring and exploiting knowledge on such big data sources exposes many challenges for both managers and technologists. A healthcare knowledge management system that ensures the systematic knowledge development process on various data in hospitals was proposed. It leverages big data technologies to capture, organize, transfer, and manage large volumes of medical knowledge, which cannot be handled with traditional data-processing technologies. In addition, machine-learning algorithms are used to derive knowledge at a higher level in supporting diagnosis and treatment.
  • 1.4K
  • 13 May 2022
Topic Review
Adversarial Attacks on Medical Imaging
One of the most important challenges in the computer vision (CV) area is Medical Image Analysis in which DL models process medical images—such as magnetic resonance imaging (MRI), X-ray, computed tomography (CT), etc.—using convolutional neural networks (CNN) for diagnosis or detection of several diseases. The proper function of these models can significantly upgrade the health systems. However, recent studies have shown that CNN models are vulnerable under adversarial attacks with imperceptible perturbations. 
  • 1.4K
  • 26 Oct 2021
Topic Review
Social Distancing
Social Distancing is a new terminology that became a popular term since mid-2020, after a global hit and pandemic by the new generation of the coronavirus (COVID-19). Social distancing is the act of maintaining a safe distance (equal to 6 feet or 2 meters) between individuals as a recommended solution by the World Health Organisation (WHO) to minimise the spread of COVID-19 in public places.  By the end of 2020, the majority of governments and national health authorities have set the 2-meter physical distancing as a mandatory measure in shopping centres, schools, pubs, restaurants, and public places.   
  • 1.4K
  • 06 Mar 2021
Topic Review
Proxy Modeling Highlighting Applications for Reservoir Engineering
Numerical models can be used for many purposes in oil and gas engineering, such as production optimization and forecasting, uncertainty analysis, history matching, and risk assessment. However, subsurface problems are complex and non-linear, and making reliable decisions in reservoir management requires substantial computational effort. Proxy models have gained much attention in recent years. They are advanced non-linear interpolation tables that can approximate complex models and alleviate computational effort. Proxy models are constructed by running high-fidelity models to gather the necessary data to create the proxy model. Once constructed, they can be a great choice for different tasks such as uncertainty analysis, optimization, forecasting, etc. The application of proxy modeling in oil and gas has had an increasing trend in recent years, and there is no consensus rule on the correct choice of proxy model. As a result, it is crucial to better understand the advantages and disadvantages of various proxy models.
  • 1.4K
  • 05 Sep 2022
Topic Review
Deep Learning and Lung Disease
The recent developments of deep learning support the identification and classification of lung diseases in medical images.
  • 1.4K
  • 26 Jan 2021
Topic Review
Coverage Path Planning Methods Focusing on Energy Efficient
The coverage path planning (CPP) algorithms aim to cover the total area of interest with minimum overlapping. The goal of the CPP algorithms is to minimize the total covering path and execution time. Significant research has been done in robotics, particularly for multi-unmanned unmanned aerial vehicles (UAVs) cooperation and energy efficiency in CPP problems.
  • 1.4K
  • 10 Feb 2022
Topic Review
Agri-Food Traceability System User Intention
Scientists believed the outbreak of COVID-19 could be linked to the consumption of wild animals, so food safety and hygiene have become the top concerns of the public. An agri-food traceability system becomes very important in this context because it can help the government to trace back the entire production and delivery process in case of food safety concerns. The traceability system is a complicated digitalized system because it integrates information and logistics systems. Previous studies used the technology acceptance model (TAM), information systems (IS) success model, expectation confirmation model (ECM), or extended model to explain the continuance intention of traceability system users.
  • 1.3K
  • 16 Mar 2022
Topic Review
Security Challenges and Malware Attacks in the IoT
Malware is a major security threat to the IoT, and detecting unknown malware is one of the key challenges for two reasons. First, the limitations of IoT devices, such as their low power retention capability and low computational processing capability, represent a significant challenge when aiming to apply security solutions. Second, introducing new ways to connect networks, such as cloud services, opens the door to many security attacks, such as malware attacks. Furthermore, connecting new devices that were not part of traditional networks via these new connection methods, such as smart sensors, makes applying security measures more complex. For these reasons, traditional malware detection mechanisms are not suitable for the IoT environment.
  • 1.3K
  • 10 Nov 2021
Topic Review
Cryptographically-Secure Pseudorandom Number Generator
A cryptographically secure pseudorandom number generator (CSPRNG) or cryptographic pseudorandom number generator (CPRNG) is a pseudorandom number generator (PRNG) with properties that make it suitable for use in cryptography. It is also loosely known as a cryptographic random number generator (CRNG) (see Random number generation § "True" vs. pseudo-random numbers). Most cryptographic applications require random numbers, for example: key generation nonces salts in certain signature schemes, including ECDSA, RSASSA-PSS The "quality" of the randomness required for these applications varies. For example, creating a nonce in some protocols needs only uniqueness. On the other hand, the generation of a master key requires a higher quality, such as more entropy. And in the case of one-time pads, the information-theoretic guarantee of perfect secrecy only holds if the key material comes from a true random source with high entropy, and thus any kind of pseudorandom number generator is insufficient. Ideally, the generation of random numbers in CSPRNGs uses entropy obtained from a high-quality source, generally the operating system's randomness API. However, unexpected correlations have been found in several such ostensibly independent processes. From an information-theoretic point of view, the amount of randomness, the entropy that can be generated, is equal to the entropy provided by the system. But sometimes, in practical situations, more random numbers are needed than there is entropy available. Also, the processes to extract randomness from a running system are slow in actual practice. In such instances, a CSPRNG can sometimes be used. A CSPRNG can "stretch" the available entropy over more bits.
  • 1.3K
  • 20 Oct 2022
Topic Review
Machine Learning and Artificial Intelligence
Machine learning (ML) is a type of artificial intelligence (AI) consisting of algorithmic approaches that enable machines to solve problems deprived of explicit computer programming. 
  • 1.3K
  • 28 Apr 2023
Topic Review
Interpretable Machine Learning in Healthcare
Recently, machine Learning (ML) has been highly used in many areas, such as speech recognition and image processing. The revolution in industrial technology using ML proves the great success of ML and its applications in analyzing complex patterns, which are presented in a variety of applications in a wide range of sectors, including healthcare.
  • 1.3K
  • 29 Dec 2021
Topic Review
Near-Infrared Spectroscopy Coupled to Hyperspectral Imaging
Near-infrared (800–2500 nm; NIR) spectroscopy coupled to hyperspectral imaging (NIR-HSI) has greatly enhanced its capability and thus widened its application and use across various industries. This non-destructive technique that is sensitive to both physical and chemical attributes of virtually any material can be used for both qualitative and quantitative analyses.
  • 1.3K
  • 18 Mar 2022
Topic Review
Machine Learning for Temperature Estimation
The modern and very effective methods of estimating the temperature of electric motors include machine learning and deep learning. Their unquestionable advantage is that on the basis of the collected measurement data, a function mapping the relationship between the values of the input features and the output is determined. This means that predictive modeling does not require knowledge of the material properties of a given device or having expertise knowledge about its construction.
  • 1.3K
  • 19 Jul 2021
Topic Review
Aquaculture System Using Cloud-Based Autonomous Drones
Aquaculture System Using Cloud-Based Autonomous Drones incorporated artificial intelligence (AI) services using computer vision and combined various deep learning recognition models to achieve scalability and added functionality, in order to perform aquaculture surveillance tasks. The recognition model is embedded in the aquaculture cloud, to analyze images and videos captured by the autonomous drone. The recognition models detect people, cages, and ship vessels at the aquaculture site. The inclusion of AI functions for face recognition, fish counting, fish length estimation and fish feeding intensity provides intelligent decision making. For the fish feeding intensity assessment, the large amount of data in the aquaculture cloud can be an input for analysis using the AI feeding system to optimize farmer production and income. The autonomous drone and aquaculture cloud services are cost-effective and an alternative to expensive surveillance systems and multiple fixed-camera installations. The aquaculture cloud enables the drone to execute its surveillance task more efficiently with an increased navigation time. The mobile drone navigation app is capable of sending surveillance alerts and reports to users. Our multifeatured surveillance system, with the integration of deep-learning models, yielded high-accuracy results. 
  • 1.3K
  • 03 Nov 2021
Topic Review
Human Activity Recognition Methods
Human activity recognition (HAR) can effectively improve the safety of the elderly at home. Many researchers have studied HAR from different aspects, such as sensors and algorithms. HAR methods can be divided into three categories based on the types of sensors: wearable devices, cameras, and millimeter-wave radars.
  • 1.3K
  • 04 Aug 2022
Topic Review
Deep Learning for Accurate Real-Time Weed Detection
This article discusses the possibility of accurately detecting the position of weeds in real-time in real conditions. Presented detailed recommendations for solving the problem with scene density, considered ways for increasing accuracy, and FPS.
  • 1.3K
  • 09 Jan 2022
Topic Review
Computational Diagnostic Techniques in Electrocardiogram
Cardiovascular diseases (CVDs), including asymptomatic myocardial ischemia, angina, myocardial infarction, and ischemic heart failure, are the leading cause of death globally. Early detection and treatment of CVDs significantly contribute to the prevention or delay of cardiovascular death. Electrocardiogram (ECG) records the electrical impulses generated by heart muscles, which reflect regular or irregular beating activity. Computer-aided techniques provide fast and accurate tools to identify CVDs using a patient’s ECG signal, which have achieved great success in recent years. Latest computational diagnostic techniques based on ECG signals for estimating CVDs conditions are summarized here. The procedure of ECG signals analysis is discussed in several subsections, including data preprocessing, feature engineering, classification, and application. In particular, the End-to-End models integrate feature extraction and classification into learning algorithms, which not only greatly simplifies the process of data analysis, but also shows excellent accuracy and robustness. Portable devices enable users to monitor their cardiovascular status at any time, bringing new scenarios as well as challenges to the application of ECG algorithms. Computational diagnostic techniques for ECG signal analysis show great potential for helping health care professionals, and their application in daily life benefits both patients and sub-healthy people.
  • 1.3K
  • 18 Feb 2021
Topic Review
Vehicle-to-Everything and Machine Learning Applications
The fifth-generation (5G) network is the current emerging technology that meets the increasing need for higher throughputs and greater system capacities. It is expected that 5G technology will enable many new applications and services. Vehicle-to-everything (V2X) communication is an example of an application that is supported by 5G technology and beyond. A V2X communication system allows a vehicle to be connected to an entity, such as a pedestrian, another vehicle, infrastructure, and a network, to provide a robust transportation solution.
  • 1.3K
  • 07 May 2022
Topic Review
Virtual Synchrony
Virtual synchrony is an interprocess message passing (sometimes called ordered, reliable multicast) technology. Virtual synchrony systems allow programs running in a network to organize themselves into process groups, and to send messages to groups (as opposed to sending them to specific processes). Each message is delivered to all the group members, in the identical order, and this is true even when two messages are transmitted simultaneously by different senders. Application design and implementation is greatly simplified by this property: every group member sees the same events (group membership changes and incoming messages) in the same order. A virtually synchronous service is typically implemented using a style of programming called state machine replication, in which a service is first implemented using a single program that receives inputs from clients through some form of remote message passing infrastructure, then enters a new state and responds in a deterministic manner. The initial implementation is then transformed so that multiple instances of the program can be launched on different machines, using a virtually synchronous message passing system to replicate the incoming messages over the members. The replicas will see the same events in the same order, and are in the same states, hence they will make the same state transitions and remain in a consistent state. The replication of the service provides a form of fault-tolerance: if a replica fails (by crashing), the others remain and can continue to provide responses. Different members of the replica group can also be programmed to subdivide the workload, typically by using the group membership to determine their respective roles. This permits a group of N members to run as much as N times faster than a single member, or to handle N times as many requests, while continuing to offer fault-tolerance in the event of a crash. Virtual synchrony is distinguished from classical state machine replication because the model includes features whereby a programmer can request early (optimistic) delivery of messages, or relaxed forms of ordering. When used appropriately, these features can enable substantial speedup. However, the programmer needs to be sure that the relaxation of guarantees will not compromise correctness. For example, in a service that uses locking to protect concurrently updated data, the messaging system can be instructed to use an inexpensive form of message ordering, in which the messaging system respects the ordering in which individual senders send messages (FIFO guarantee) but does not attempt to impose an agreed order if messages are sent concurrently by different senders. Provided that the sender indeed held locks on the data, it can be shown that FIFO ordering suffices for correctness. The benefit is that FIFO ordering is much less costly to implement than total ordering for concurrent messages. To give another example, by delivering messages optimistically, virtual synchrony systems can outperform the Paxos that is normally required for implementation of state machine replication: Paxos normally requires a 2-phase protocol, whereas optimistic virtual synchrony protocols can deliver messages immediately upon their arrival. However, this could result in a violation of the safety property of the state machine replication model. To prevent such problems, the programmer who uses this feature is required to invoke a primitive called flush, which delays the caller until any optimistically delivered messages have reached all of the group members. Provided that the programmer understands this behavior and is careful to call flush before interacting with external clients or persistent storage, higher performance can be achieved without loss of safety. The flexibility associated with these limited forms of event reordering and optimistic early delivery permit virtual synchrony platforms to achieve extremely high data rates while still preserving very strong fault-tolerance and consistency guarantees.
  • 1.3K
  • 01 Dec 2022
Topic Review
Machine Learning for Crop Disease
Crop diseases constitute a serious issue in agriculture, affecting both quality and quantity of agriculture production. Disease control has been a research object in many scientific and technologic domains. Technological advances in sensors, data storage, computing resources and artificial intelligence have shown enormous potential to control diseases effectively. A growing body of literature recognizes the importance of using data from different types of sensors and machine learning approaches to build models for detection, prediction, analysis, assessment, etc. However, the increasing number and diversity of research studies requires a literature review for further developments and contributions in this area. 
  • 1.2K
  • 24 Nov 2021
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