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Topic Review
Methods for Melanoma and Nonmelanoma Skin Cancers Classification
Skin cancer is considered a dangerous type of cancer with a high global mortality rate. Manual skin cancer diagnosis is a challenging and time-consuming method due to the complexity of the disease. In the field of transfer learning and federated learning, there are several new algorithms and techniques for classifying melanoma and nonmelanoma skin cancer.
  • 703
  • 19 Oct 2023
Topic Review
Emission Quantification via Passive Infrared Optical Gas Imaging
Passive infrared optical gas imaging (IOGI) is sensitive to toxic or greenhouse gases of interest, offers non-invasive remote sensing, and provides the capability for spatially resolved measurements. It has been broadly applied to emission detection, localization, and visualization.
  • 701
  • 08 Jul 2022
Topic Review
A Promising Downsampling Alternative in a Neural Network
Downsampling, which aims to improve computational efficiency by reducing the spatial resolution of feature maps, is a critical operation in neural networks. Upsampling also plays an important role in neural networks. It is often used for image super-resolution, segmentation, and generation tasks via the reconstruction of high-resolution feature maps during the decoding stage in the neural network.
  • 700
  • 04 Dec 2023
Topic Review
Remote Keyless Using Pre-Trained Deep Neural Network
Keyless systems have replaced the old-fashioned methods of inserting physical keys into keyholes to unlock the door, which are inconvenient and easily exploited by threat actors. Keyless systems use the technology of radio frequency (RF) as an interface to transmit signals from the key fob to the vehicle.
  • 699
  • 10 Nov 2022
Topic Review
Data-Driven Learning Methods for Network Intrusion Detection Systems
An effective anomaly-based intelligent IDS (AN-Intel-IDS) must detect both known and unknown attacks. Hence, there is a need to train AN-Intel-IDS using dynamically generated, real-time data in an adversarial setting. Furthermore, the lack of real-time data produces potentially biased models that are less effective in predicting unknown attacks. Therefore, training AN-Intel-IDS using imbalanced and adversarial learning is instrumental to their efficacy and high performance. Unfortunately, the public datasets available to train AN-Intel-IDS are ineluctably static, unrealistic, and prone to obsolescence. Furthermore, the lack of real-time data produces potentially biased models that are less effective in predicting unknown attacks. Therefore, training AN-Intel-IDS using imbalanced and adversarial learning is instrumental to their efficacy and high performance. 
  • 696
  • 28 Feb 2022
Topic Review
State-of-the-Art on Recommender Systems for E-Learning
Recommender systems (RSs) are increasingly recognized as intelligent software for predicting users’ opinions on specific items. Various RSs have been developed in different domains, such as e-commerce, e-government, e-resource services, e-business, e-library, e-tourism, and e-learning, to make excellent user recommendations. In e-learning technology, RSs are designed to support and improve the learning practices of a student or an organization.
  • 695
  • 06 Dec 2022
Topic Review
Activation-Based Pruning of Neural Networks
A novel technique is presented for pruning called activation-based pruning to effectively prune fully connected feedforward neural networks for multi-object classification. The technique is based on the number of times each neuron is activated during model training. Further analysis demonstrated that activation-based pruning can be considered a dimensionality reduction technique, as it leads to a sparse low-rank matrix approximation for each hidden layer of the neural network. The rank-reduced neural network generated using activation-based pruning has better accuracy than a rank-reduced network using principal component analysis. After each successive pruning, the amount of reduction in the magnitude of singular values of each matrix representing the hidden layers of the network is equivalent to introducing the sum of singular values of the hidden layers as a regularization parameter to the objective function.
  • 695
  • 17 Feb 2024
Topic Review
Optical Medieval Music Recognition
Optical Music Recognition (OMR) is one of the key technologies to accelerate and simplify the transcription task in an automatic way. Typically, an OMR system takes an image or manuscript of a musical composition and transforms its content encoded in some digital format such as MEI or MusicXML. 
  • 694
  • 11 Jul 2022
Topic Review
Machine Learning-Based Text Classification Comparison
The growth in textual data associated with the increased usage of online services and the simplicity of having access to these data has resulted in a rise in the number of text classification research papers. Text classification has a significant influence on several domains such as news categorization, the detection of spam content, and sentiment analysis. The classification of Turkish text is the research focus since only a few studies have been conducted in this context. Researchers utilize data obtained from customers’ inquiries that come to an institution to evaluate the proposed techniques. Classes are assigned to such inquiries specified in the institution’s internal procedures. The Support Vector Machine, Naïve Bayes, Long Term-Short Memory, Random Forest, and Logistic Regression algorithms were used to classify the data. The performance of the various techniques was then analyzed after and before data preparation, and the results were compared. The Long Term-Short Memory technique demonstrated superior effectiveness in terms of accuracy, achieving an 84% accuracy rate, surpassing the best accuracy record of traditional techniques, which was 78% accuracy for the Support Vector Machine technique. The techniques performed better once the number of categories in the dataset was reduced. Moreover, the findings show that data preparation and coherence between the classes’ number and the number of training sets are significant variables influencing the techniques’ performance.
  • 694
  • 01 Sep 2023
Topic Review
SAA-UNet
The disaster of the COVID-19 pandemic has claimed numerous lives and wreaked havoc on the entire world due to its transmissible nature. One of the complications of COVID-19 is pneumonia. Different radiography methods, particularly computed tomography (CT), have shown outstanding performance in effectively diagnosing pneumonia.
  • 694
  • 05 Jun 2023
Topic Review
Techniques Related to Chinese Speech Emotion Recognition
The use of Artificial Intelligence for emotion recognition has attracted much attention. The industrial applicability of emotion recognition is quite comprehensive and has good development potential. 
  • 692
  • 12 Jul 2022
Topic Review
Applications of Machine Learning in Fluid Mechanics
The significant growth of artificial intelligence (AI) methods in machine learning (ML) and deep learning (DL) has opened opportunities for fluid dynamics and its applications in science, engineering and medicine. Developing AI methods for fluid dynamics encompass different challenges than applications with massive data, such as the Internet of Things.
  • 692
  • 24 Aug 2023
Topic Review
Handwritten Chinese Text Recognition
Offline handwritten Chinese recognition is an important research area of pattern recognition, including offline handwritten Chinese character recognition (offline HCCR) and offline handwritten Chinese text recognition (offline HCTR), which are closely related to daily life. HCTR is more complex and relatively less accurate due to the unconstrained nature of text lines and the adhesion between characters. It can be further divided into line-level HCTR and page-level HCTR depending on whether the recognition object is a cropped image of a text line or an entire page.
  • 692
  • 24 Mar 2023
Topic Review
Traditional Computer-Vision Methods Implemented in Sports
Automatic analysis of video in sports is a possible solution to the demands of fans and professionals for various kinds of information. Analyzing videos in sports has provided a wide range of applications, which include player positions, extraction of the ball’s trajectory, content extraction, and indexing, summarization, detection of highlights, on-demand 3D reconstruction, animations, generation of virtual view, editorial content creation, virtual content insertion, visualization and enhancement of content, gameplay analysis and evaluations, identifying player’s actions, referee decisions and other fundamental elements required for the analysis of a game. Recent developments in video analysis of sports have a focus on the features of computer vision techniques, which are used to perform certain operations for which these are assigned, such as detailed complex analysis such as detection and classification of each player based on their team in every frame or by recognizing the jersey number to classify players based on their team will help to classify various events where the player is involved. In higher-level analysis, such as tracking the player or ball, many more such evaluations are to be considered for the evaluation of a player’s skills, detecting the team’s strategies, events and the formation of tactical positions such as midfield analysis in various sports such as soccer, basketball, and also various sports vision applications such as smart assistants, virtual umpires, assistance coaches. A higher-level semantic interpretation is an effective substitute, especially in situations when reduced human intervention and real-time analysis are desired for the exploitation of the delivered system outputs.
  • 692
  • 19 May 2022
Topic Review
Oriented Crossover in Genetic Algorithms
A genetic algorithm is a formula for resolving optimization issues that incorporate a constraint and natural selection similar to the biological process that propels evolution.
  • 691
  • 11 May 2023
Topic Review
Integrated IoT-Fog-Cloud Systems
Integrated IoT-fog-cloud system (iIFC) offers the opportunity to create suitable platforms to develop and operate important smart city applications. These applications can utilize services provided by IoT devices, fog nodes, and cloud services.
  • 690
  • 07 Dec 2021
Topic Review
Enhanced Perception for Autonomous Driving Using Semantic-Geometric Fusion
Environment perception remains one of the key tasks in autonomous driving for which solutions have yet to reach maturity. Multi-modal approaches benefit from the complementary physical properties specific to each sensor technology used, boosting overall performance. The presented 360∘ enhanced perception component is based on low-level fusion between geometry provided by the LiDAR-based 3D point clouds and semantic scene information obtained from multiple RGB cameras, of multiple types. This multi-modal, multi-sensor scheme enables better range coverage, improved detection and classification quality with increased robustness. Semantic, instance and panoptic segmentations of 2D data are computed using efficient deep-learning-based algorithms, while 3D point clouds are segmented using a fast, traditional voxel-based solution. 
  • 689
  • 01 Aug 2022
Topic Review
Detection-Based Vision-Language Understanding
Given a query language, a Detection-based Vision-Language Understanding (DVLU) system needs to respond based on the detected regions (i.e.,bounding boxes). With the significant advancement in object detection, DVLU has witnessed great improvements in recent years, such as Visual Question Answering (VQA) and Visual Grounding (VG).
  • 688
  • 09 Sep 2022
Topic Review
Enhancing Social Media Platforms with Machine Learning
Network analysis aids management in reducing overall expenditures and maintenance workload. Social media platforms frequently use neural networks to suggest material that corresponds with user preferences. Machine learning is one of many methods for social network analysis. Machine learning algorithms operate on a collection of observable features that are taken from user data. Machine learning and neural network-based systems represent a topic of study that spans several fields. Computers can now recognize the emotions behind particular content uploaded by users to social media networks thanks to machine learning.
  • 688
  • 19 Jun 2023
Topic Review
Multiscale-Deep-Learning Applications
In general, most of the existing convolutional neural network (CNN)-based deep-learning models suffer from spatial-information loss and inadequate feature-representation issues. This is due to their inability to capture multiscale-context information and the exclusion of semantic information throughout the pooling operations. In the early layers of a CNN, the network encodes simple semantic representations, such as edges and corners, while, in the latter part of the CNN, the network encodes more complex semantic features, such as complex geometric shapes. Theoretically, it is better for a CNN to extract features from different levels of semantic representation because tasks such as classification and segmentation work better when both simple and complex feature maps are utilized. Hence, it is also crucial to embed multiscale capability throughout the network so that the various scales of the features can be optimally captured to represent the intended task.
  • 687
  • 26 Oct 2022
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