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Topic Review
Credit Card Fraud Detection
With the rapid developments in electronic commerce and digital payment technologies, credit card transactions have increased significantly. Machine learning (ML) has been vital in analyzing customer data to detect and prevent fraud.
  • 462
  • 29 Jun 2023
Topic Review
Detection of Suspicious Behaviors from Movement Trajectory Data
Early detection of people’s suspicious behaviors can aid in the prevention of crimes and make the community safer. Existing methods are mostly focused on identifying abnormal behaviors from video surveillance that are based on computer vision, which are more suitable for detecting ongoing behaviors.  Due to advances in positioning technology and the increasing number of cameras, smart mobile terminals, and WLAN networks, large amounts of fine-grained personal trajectory data are collected. Such a large number of trajectories provide people with an unprecedented opportunity to automatically discover helpful knowledge, such as identifying suspicious movements and unusual activities. Therefore, crimes can be prevented if people’s suspicious behaviors can be automatically detected by mining the semantic information that is hidden in the trajectory data.
  • 461
  • 23 Sep 2022
Topic Review
Object Detection of Remote Sensing Image
Remote sensing image object detection tasks play a pivotal role in the realm of airborne and satellite remote sensing imagery, representing invaluable applications. Remote sensing technology has witnessed remarkable progress, enabling the capture of copious details that inherently reflect the contours, hues, textures, and other distinctive attributes of terrestrial targets. It has emerged as an indispensable avenue for acquiring comprehensive knowledge about the Earth’s surface. The primary objective of remote sensing image object detection is to precisely identify and locate objects of interest within the vast expanse of remote sensing images. This task finds extensive implementation across significant domains, including military reconnaissance, urban planning, environmental monitoring, soil science, and maritime vessel surveillance. With the incessant advancement of observational techniques, the availability of high-quality remote sensing image datasets, encompassing richer and more intricate information, has unlocked immense developmental potential for the ongoing pursuit of remote sensing image object detection.
  • 458
  • 23 Oct 2023
Topic Review
Challenges in Agricultural Image Datasets and Filter Algorithms
Smart farming is facilitated by remote sensing because it allows for the inexpensive monitoring of crops, crop classification, stress detection yield forecasting using lightweight sensors over a wide area in a relatively short amount of time. Deep learning (DL)-based computer vision is one of the important aspects of the automatic detection and monitoring of plant stress. Challenges for DL algorithms in the agricultural dataset include size variation in objects, image resolution, background clutter, precise annotation with the expert, high object density or the demand for different spectral images.
  • 458
  • 07 Mar 2024
Topic Review
Accurate Measurement of Urban Environments
In the field of urban environment analysis research, image segmentation technology that groups important objects in the urban landscape image in pixel units has been the subject of increased attention.
  • 458
  • 10 Nov 2023
Topic Review
Sleep Spindle
Sleep spindles are bursts of neural oscillatory activity that are generated by interplay of the thalamic reticular nucleus (TRN) and other thalamic nuclei during stage 2 NREM sleep in a frequency of ~10 –12 Hz for at least 0.5 seconds. After generation in the TRN, spindles are sustained and relayed to the cortex by a thalamo-thalamic and thalamo-cortical feedback loops regulated by both GABAergic and NMDA-receptor mediated glutamatergic neurotransmission. Sleep spindles have been found in all tested mammalian species and in vitro cells. Research supports that spindles (sometimes referred to as "sigma bands" or "sigma waves") play an essential role in both sensory processing and long term memory consolidation. Until recently, it was believed that each sleep spindle oscillation peaked at the same time throughout the neocortex. It was determined that oscillations sweep across the neocortex in circular patterns around the neocortex, peaking in one area, and then a few milliseconds later in an adjacent area. It has been suggested that this spindle organization allows for neurons to communicate across cortices. The time scale at which the waves travel at is the same speed it takes for neurons to communicate with each other.
  • 457
  • 12 Oct 2022
Topic Review
Remote Sensing Object Detection
Remote sensing image object detection holds signifificant research value in resources and the environment. Nevertheless, complex background information and considerable size differences between objects in remote sensing images make it challenging.
  • 457
  • 04 Sep 2023
Topic Review
Compressive Sensing in Image/Video Compression
Compressive Sensing (CS) has emerged as a transformative technique in image compression, offering innovative solutions to challenges in efficient signal representation and acquisition. 
  • 457
  • 08 Feb 2024
Topic Review
Modern Greek on Social Web
Mining social web text has been at the heart of the Natural Language Processing and Data Mining research community in the last 15 years. Though most of the reported work is on widely spoken languages, such as English, the significance of approaches that deal with less commonly spoken languages, such as Greek, is evident for reasons of preserving and documenting minority languages, cultural and ethnic diversity, and identifying intercultural similarities and differences. 
  • 455
  • 01 Jun 2021
Topic Review
Input Enhancement (Computer Science)
In computer science, input enhancement is the principle that processing a given input to a problem and altering it in a specific way will increase runtime efficiency or space efficiency, or both. The altered input is usually stored and accessed to simplify the problem. By exploiting the structure and properties of the inputs, input enhancement creates various speed-ups in the efficiency of the algorithm.
  • 453
  • 02 Dec 2022
Topic Review
Federated Learning and Blockchain Applications in Vehicular Networks
The Internet of Things (IoT) revitalizes the world with tremendous capabilities and potential to be utilized in vehicular networks. The Smart Transport Infrastructure (STI) era depends mainly on the IoT. Advanced machine learning (ML) techniques are being used to strengthen the STI smartness further. However, some decisions are very challenging due to the vast number of STI components and big data generated from STIs. Computation cost, communication overheads, and privacy issues are significant concerns for wide-scale ML adoption within STI. These issues can be addressed using Federated Learning (FL) and blockchain. FL can be used to address the issues of privacy preservation and handling big data generated in STI management and control. Blockchain is a distributed ledger that can store data while providing trust and integrity assurance. Blockchain can be a solution to data integrity and can add more security to the STI.  While transmitting data, valuable information can be disclosed through the model parameters by reverse engineering. The disclosure of valuable data motivated researchers and developers to adopt known security and privacy defense methods, e.g., functional encryption and differential privacy, to FL.
  • 453
  • 17 Jun 2022
Topic Review
Child Handwritten Arabic Character Recognition
Handwritten Arabic character recognition has received increasing research interest. However, as of yet, the majority of the existing handwriting recognition systems have only focused on adult handwriting. In contrast, there have not been many studies conducted on child handwriting, nor has it been regarded as a major research issue yet. Compared to adults’ handwriting, children’s handwriting is more challenging since it often has lower quality, higher variation, and larger distortions.
  • 453
  • 22 Aug 2023
Topic Review
EfficientNetV2 and Transfer Learning Applied to Nursing Homes
In the context of population aging, to reduce the run on public medical resources, nursing homes need to predict the health risks of the elderly periodically. However, there is no professional medical testing equipment in nursing homes. In the current disease risk prediction research, many datasets are collected by professional medical equipment. In addition, the currently researched models cannot be run directly on mobile terminals.
  • 452
  • 27 Jun 2023
Topic Review
Security Threats for Medical Wearables
In the past few years, “smart” objects and products have given rise to significant progress in industry production and its security. Advances in digitization that have occurred in the industry, combined with internet technologies and future-oriented technologies in the field of so-called “smart” objects (machines and products), have led to a new and fundamental paradigm shift in industrial production and in their security.
  • 449
  • 14 Mar 2022
Topic Review
Intelligent Virtual Agents
The use of intelligent virtual agents (IVA) to support humans in social contexts will depend on their social acceptability. Acceptance will be related to the human’s perception of the IVAs as well as the IVAs’ ability to respond and adapt their conversation appropriately to the human. Adaptation implies computer-generated speech (synthetic speech), such as text-to-speech (TTS).
  • 449
  • 08 Jul 2022
Topic Review
Steel Plates Faults Prediction Methods
Fault prediction is a vital task to decrease the costs of equipment maintenance and repair, as well as to improve the quality level of products and production efficiency. Steel plates fault prediction is a significant materials science problem that contributes to avoiding the progress of abnormal events.
  • 448
  • 03 Jul 2023
Topic Review
Deep-Learning-Based Approach to Keystroke-Injection Payload Generation
USB-based keystroke-injection attacks involve manipulating USB devices to inject malicious keystrokes into the target system. These attacks exploit the trust of USB devices and can bypass traditional security measures if they are taken into account and adapted to the systems designed to prevent such attacks. By impersonating a keyboard or using programmable USB devices, attackers can execute unauthorized commands or gain unauthorized access to sensitive information mimicking legitimate user keystrokes. Different attack vectors, such as BadUSB and rogue device attacks, have drawn attention to the potential risks and ramifications involved in these types of attacks. However, the emergence of advanced attack methods necessitates the development of more sophisticated countermeasures. These attacks pose a significant security risk and highlight the importance of implementing strong defenses to mitigate the potential impact of such exploits, especially those that can bypass keystroke dynamics systems using rogue USB devices with implants.
  • 448
  • 17 Jul 2023
Topic Review
Deep Learning Approach for Lung Cancer Diagnosis
Deep learning has emerged as a powerful tool for medical image analysis and diagnosis, demonstrating high performance on tasks such as cancer detection. As deep learning techniques continue to revolutionize the field of medical imaging, researchers have increasingly turned to large-scale databases to train and validate their algorithms. Many studies have been done to diagnose lung cancer using different datasets, both public and private. Each dataset has its own unique characteristics and challenges.
  • 448
  • 18 Feb 2024
Topic Review
Semantic Segmentation of Medical Images
There have been major developments in deep learning in computer vision since the 2010s. Deep learning has contributed to a wealth of data in medical image processing, and semantic segmentation is a salient technique in this field. Lesion detection is one of the primary objectives of medical imaging, as the size and location of lesions are often directly associated with a patient’s diagnosis, treatment, and prognosis. Since the development of computer vision algorithms, however, researchers have begun to utilize these algorithms in the field of medical imaging.
  • 448
  • 02 Dec 2022
Topic Review
Bio-Inspired Anomaly Detection with Cursory Dendritic Cell Algorithm
The autonomous and adaptable identification of anomalies in industrial contexts, particularly in the physical processes of Cyber-Physical Production Systems (CPPS), requires using critical technologies to identify failures correctly. Most of the existing solutions in the anomaly detection research area do not consider such systems’ dynamics. Immune-based models, such as the Dendritic Cell Algorithm (DCA), may provide a rich source of inspiration for detecting anomalies. Cursory Dendritic Cell Algorithm (CDCA), is a novel variation of the Dendritic Cell Algorithm (DCA), developed to be flexible and monitor physical industrial processes continually while detecting anomalies in an online fashion. 
  • 447
  • 14 Feb 2022
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