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Topic Review
LogNNet Neural Network
LogNNet - neural network which uses filters based on logistic mapping. LogNNet has a feedforward network structure, but possesses the properties of reservoir neural networks. The input weight matrix, set by a recurrent logistic mapping, forms the kernels that transform the input space to the higher-dimensional feature space. The most effective recognition of a handwritten digit from MNIST-10 occurs under chaotic behavior of the logistic map. The correlation of classification accuracy with the value of the Lyapunov exponent was obtained. An advantage of LogNNet implementation on IoT devices is the significant savings in memory used. At the same time, LogNNet has a simple algorithm and performance indicators comparable to those of the best resource-efficient algorithms available at the moment. The presented network architecture uses an array of weights with a total memory size from 1 to 29 kB and achieves a classification accuracy of 80.3–96.3%. Memory is saved due to the processor, which sequentially calculates the required weight coefficients during the network operation using the analytical equation of the logistic mapping. The proposed neural network can be used in implementations of artificial intelligence based on constrained devices with limited memory, which are integral blocks for creating ambient intelligence in modern IoT environments. From a research perspective, LogNNet can contribute to the understanding of the fundamental issues of the influence of chaos on the behavior of reservoir-type neural networks.
  • 1.7K
  • 11 Nov 2020
Topic Review
Palm Oil Background
Palm oil plantations cover millions of hectares worldwide, which encompass a significant portion of global trade. Palm oil trees, or Arecaceae, are a genus of stemless, tree-like monocot plants that thrive in the tropics and are extremely valuable to humans and the ecosystem. The African oil palm, or Elaeis guineensis, is the most prominent palm species native to West Africa, cultivated for its oil-rich fruit as a semiwild food source for over 7000 years. The tree produces a profusion of fruit bunches yearly with each containing between 1000 and 3000 fruits.
  • 1.7K
  • 14 Jul 2022
Topic Review
Remote Sensing and Deep Learning
The advances in remote sensing technologies, hence the fast-growing volume of timely data available at the global scale, offer new opportunities for a variety of applications. Deep learning being significantly successful in dealing with Big Data, is a great candidate for exploiting the potentials of such complex massive data. However, with remote sensing, there are some challenges related to the ground-truth, resolution, and the nature of data that require further efforts and adaptions of deep learning techniques.
  • 1.7K
  • 26 Jan 2021
Topic Review
Intelligent Connected Vehicle Cooperative Driving Development
Intelligent connected vehicle formation is mainly for more intelligent snatched vehicles in a complex traffic environment. By adjusting their driving speed and steering, it makes itself and nearby intelligent connected vehicles keep relatively stable geometric posture and the same movement, and meets the task requirements and constraints (such as obstacle avoidance), so as to realize more intelligent connected vehicles between wireless communication collaborative driving behavior. The main technologies involved in the autonomous vehicle formation include: vehicle combination positioning and multi-sensor and multi-source information fusion technology, collaborative formation control technology, and cooperative perception and communication technology.
  • 1.7K
  • 23 Nov 2022
Topic Review
Shunting-Yard Algorithm
In computer science, the shunting-yard algorithm is a method for parsing arithmetical or logical expressions, or a combination of both, specified in infix notation. It can produce either a postfix notation string, also known as Reverse Polish notation (RPN), or an abstract syntax tree (AST). The algorithm was invented by Edsger Dijkstra and named the "shunting yard" algorithm because its operation resembles that of a railroad shunting yard. Dijkstra first described the Shunting Yard Algorithm in the Mathematisch Centrum report MR 34/61. Like the evaluation of RPN, the shunting yard algorithm is stack-based. Infix expressions are the form of mathematical notation most people are used to, for instance "3 + 4" or "3 + 4 × (2 − 1)". For the conversion there are two text variables (strings), the input and the output. There is also a stack that holds operators not yet added to the output queue. To convert, the program reads each symbol in order and does something based on that symbol. The result for the above examples would be (in Reverse Polish notation) "3 4 +" and "3 4 2 1 − × +", respectively. The shunting yard algorithm will correctly parse all valid infix expressions, but does not reject all invalid expressions. For example, "1 2 +" is not a valid infix expression, but would be parsed as "1 + 2". The algorithm can however reject expressions with mismatched parentheses. The shunting-yard algorithm was later generalized into operator-precedence parsing.
  • 1.7K
  • 28 Nov 2022
Topic Review
On Predictive Maintenance in Industry 4.0
In the era of the fourth industrial revolution, several concepts have arisen in parallel with this new revolution, such as predictive maintenance, which today plays a key role in sustainable manufacturing and production systems by introducing a digital version of machine maintenance. The data extracted from production processes have increased exponentially due to the proliferation of sensing technologies. Even if Maintenance 4.0 faces organizational, financial, or even data source and machine repair challenges, it remains a strong point for the companies that use it. Indeed, it allows for minimizing machine downtime and associated costs, maximizing the life cycle of the machine, and improving the quality and cadence of production.
  • 1.7K
  • 24 Aug 2022
Topic Review
Computer Vision and Convolutional Neural Networks
Computer vision (CV) combined with a deep convolutional neural network (CNN) has emerged as a reliable analytical method to effectively characterize and quantify high-throughput phenotyping of different grain crops, including rice, wheat, corn, and soybean. In addition to the ability to rapidly obtain information on plant organs and abiotic stresses, and the ability to segment crops from weeds, such techniques have been used to detect pests and plant diseases and to identify grain varieties. The development of corresponding imaging systems to assess the phenotypic parameters, yield, and quality of crop plants will increase the confidence of stakeholders in grain crop cultivation, thereby bringing technical and economic benefits to advanced agriculture.
  • 1.7K
  • 14 Nov 2022
Topic Review
Deep Learning-Based Crack Detection Approaches
The application of deep architectures inspired by the fields of artificial intelligence and computer vision has made a significant impact on the task of crack detection. As the number of studies being published in this field is growing fast, it is important to categorize the studies at deeper levels.
  • 1.7K
  • 24 Feb 2022
Topic Review
Deep Learning towards Digital Additive Manufacturing
Machine learning is a type of deep learning. First in the machine learning (ML) process is the manual extraction of relevant image characteristics. These characteristics are also used to classify the image according to its particular characteristics. Researchers focused primarily on digital additive manufacturing, one of the most significant emerging topics in Industry 4.0.
  • 1.6K
  • 19 Dec 2022
Topic Review
Methods for Crowd Counting
The crowd counting task has become a pillar for crowd control as it provides information concerning the number of people in a scene. It is helpful in many scenarios such as video surveillance, public safety, and future event planning. To solve such tasks, researchers have proposed different solutions. In the beginning, researchers went with more traditional solutions, then the focus is on deep learning methods and, more specifically, on Convolutional Neural Networks (CNNs), because of their efficiency. 
  • 1.6K
  • 21 Jul 2022
Topic Review
Expert System and Decision Support System
Electrocardiography (ECG) is one of the most widely used recordings in clinical medicine. ECG deals with the recording of electrical activity that is generated by the heart through the surface of the body. The electrical activity generated by the heart is measured using electrodes that are attached to the body surface. The use of ECG in the diagnosis and management of cardiovascular disease (CVD) has been in existence for over a decade, and research in this domain has recently attracted large attention. Along this line, an expert system (ES) and decision support system (DSS) have been developed for ECG interpretation and diagnosis.
  • 1.6K
  • 13 Dec 2022
Topic Review
Monocular 3D Object Detection Methods
Owing to recent advancements in deep learning methods and relevant databases, it is becoming increasingly easier to recognize 3D objects using only RGB images from single viewpoints. 
  • 1.6K
  • 07 Apr 2021
Topic Review
Short Text Clustering Algorithms
Short text clustering (STC) has become a critical task for automatically grouping various unlabelled texts into meaningful clusters. STC is a necessary step in many applications, including Twitter personalization, sentiment analysis, spam filtering, customer reviews and many other social network-related applications.
  • 1.6K
  • 18 Jan 2023
Topic Review
Wearable Sensors and Computer-Vision-Based Methods
Real-time sensing and modeling of the human body, especially the hands, is an important research endeavor for various applicative purposes such as in natural human computer interactions. Hand pose estimation is a big academic and technical challenge due to the complex structure and dexterous movement of human hands. Boosted by advancements from both hardware and artificial intelligence, various prototypes of data gloves and computer-vision-based methods have been proposed for accurate and rapid hand pose estimation in recent years. However, existing reviews either focused on data gloves or on vision methods or were even based on a particular type of camera, such as the depth camera. The purpose of this survey is to conduct a comprehensive and timely review of recent research advances in sensor-based hand pose estimation, including wearable and vision-based solutions. Hand kinematic models are firstly discussed. An in-depth review is conducted on data gloves and vision-based sensor systems with corresponding modeling methods. Particularly, this review also discusses deep-learning-based methods, which are very promising in hand pose estimation. Moreover, the advantages and drawbacks of the current hand gesture estimation methods, the applicative scope, and related challenges are also discussed.
  • 1.6K
  • 22 Feb 2021
Topic Review
Expert System for Earthquake Prediction
Earthquake is one of the most hazardous natural calamity. Many algorithms have been proposed for earthquake prediction using expert systems (ES). We aim to identify and compare methods, models, frameworks, and tools used to forecast earthquakes using different parameters. The analysis shows that most of the proposed models have attempted long term predictions about time, intensity, and location of future earthquakes. An investigation on different variants of rule-based, fuzzy, and machine learning based expert systems for earthquake prediction has been presented. Moreover, the discussion covers regional and global seismic data sets used, tools employed, to predict earth quake for different geographical regions. Bibliometric and meta-information based analysis has been performed by classifying the articles according to research type, empirical type, approach, target area, and system specific parameters.
  • 1.6K
  • 03 Feb 2021
Topic Review
Communication Architectures
Communication architecture plays an important role in the intelligent control and autonomous collaboration of UAV (Unmanned Air Vehicle) swarms. And we know that UAV swarm communication architecture technology has already made great progress. When faced with different mission scenarios, there are different communication architectures to choose from. Centralized communication architecture is suitable for scenarios where the UAV swarm is small, and the task is relatively simple. Each individual UAV requires a long-range communication link with the infrastructure. The decentralized communication architecture expands communication coverage through a multi-hop network. The dedicated gateway UAV is responsible for U-T-I (UAV to Infrastructure) communication. The “single-group swarm Ad hoc network” architecture is appropriate for a swarm of the same type UAVs, while “multi-group swarm Ad hoc network” and “multi-layer swarm Ad hoc network” architectures can be deployed using different types of UAVs. In a “multi-group swarm Ad hoc network”, communication between two different groups can also suffer from delays. In addition, in terms of robustness, "multi-layer swarm Ad hoc network" architecture is a relatively reliable system because it overcomes SPOF (Single Point of Failure).
  • 1.6K
  • 12 Apr 2021
Topic Review
Human Detection in Heavy Smoke Scenarios
The most dangerous factor in a fire scene is smoke and heat, especially smoke. How to locate people and guide them out of a heavy smoke environment will be the key to surviving an evacuation process. A variety of instruments have been studied that can be used in fire and smoky situations, including visible camera, kinetic depth sensor, LIDAR, night vision, IR camera, radar, and sonar.
  • 1.6K
  • 04 Aug 2022
Topic Review
Predicting Students’ Performance by ML
Predicting students' performance is one of the most important topics for learning contexts such as schools and universities, since it helps to design effective mechanisms that improve academic results and avoid dropout, among other things. These are benefited by the automation of many processes involved in usual students' activities which handle massive volumes of data collected from software tools for technology-enhanced learning. Thus, analyzing and processing these data carefully can give us useful information about the students' knowledge and the relationship between them and the academic tasks. This information is the source that feeds promising algorithms and methods able to predict students' performance.
  • 1.6K
  • 02 Feb 2021
Topic Review
Machine Learning Algorithms for Depression
Over the years, stress, anxiety, and modern-day fast-paced lifestyles have had immense psychological effects on people’s minds worldwide. The global technological development in healthcare digitizes the scopious data, enabling the map of the various forms of human biology more accurately than traditional measuring techniques. Machine learning (ML) has been accredited as an efficient approach for analyzing the massive amount of data in the healthcare domain. ML methodologies are being utilized in mental health to predict the probabilities of mental disorders and, therefore, execute potential treatment outcomes. The ML-based depression detection algorithms are categorized into three classes, classification, deep learning, and ensemble. A general model for depression diagnosis involving data extraction, pre-processing, training ML classifier, detection classification, and performance evaluation is presented.
  • 1.6K
  • 02 Apr 2022
Topic Review
Datasets in the Field of Click Fraud Detection
Researchers go over the most relevant datasets to detect and prevent ad click fraud using AI techniques. Private/non-open-source datasets, as well as those related to other fraud types, such as ad or impression fraud, are excluded. 
  • 1.6K
  • 13 Feb 2023
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