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Topic Review
Multitask-based Shared Feature Learning
Speech emotion recognition (SER), a rapidly evolving task that aims to recognize the emotion of speakers, has become a key research area in affective computing. Various languages in multilingual natural scenarios extremely challenge the generalization ability of SER, causing the model performance to decrease quickly, and driving researchers to ask how to improve the performance of multilingual SER. To solve this problem, an explainable Multitask-based Shared Feature Learning (MSFL) model is proposed for multilingual SER. The introduction of multi-task learning (MTL) can provide related task information of language recognition for MSFL, improve its generalization in multilingual situations, and further lay the foundation for learning MSFs.
  • 742
  • 11 Jan 2023
Topic Review
Promoting AI Literacy for Children
The advancement of generative AI technologies underscores the need for AI literacy, particularly in elementary Science, Technology, Engineering, Art, and Mathematics (STEAM) education.
  • 742
  • 06 Dec 2023
Topic Review
Self-Supervised Representation Learning for Geographical Data
Self-supervised representation learning (SSRL) concerns the problem of learning a useful data representation without the requirement for labelled or annotated data. This representation can, in turn, be used to support solutions to downstream machine learning problems. SSRL has been demonstrated to be a useful tool in the field of geographical information science (GIS). 
  • 741
  • 16 Jun 2023
Topic Review
Zero-Shot Semantic Segmentation with No Supervision Leakage
Zero-shot semantic segmentation (ZS3), the process of classifying unseen classes without explicit training samples, poses a significant challenge. Despite notable progress made by pre-trained vision-language models, they have a problem of “supervision leakage” in the unseen classes due to their large-scale pre-trained data.
  • 741
  • 29 Aug 2023
Topic Review
High-Fidelity Synthetic Face Generation for Rosacea Skin Condition
Similarly to the majority of deep learning applications, diagnosing skin diseases using computer vision and deep learning often requires a large volume of data. However, obtaining sufficient data for particular types of facial skin conditions can be difficult, due to privacy concerns. As a result, conditions like rosacea are often understudied in computer-aided diagnosis. The limited availability of data for facial skin conditions has led to the investigation of alternative methods of computer-aided diagnosis. Generative adversarial networks (GANs), mainly variants of StyleGANs, have demonstrated promising results in generating synthetic facial images.
  • 741
  • 14 Feb 2024
Topic Review
Multi-Label Fundus Image Classification
Fundus images are used by ophthalmologists and computer-aided diagnostics to detect fundus disease such as diabetic retinopathy, glaucoma, age-related macular degeneration, cataracts, hypertension, and myopia.
  • 739
  • 30 Jun 2022
Topic Review
Alan Mathison Turing
Alan Mathison Turing was a British mathematician, logician, cryptanalyst, and theoretical biologist whose groundbreaking work laid the foundations of modern computer science and artificial intelligence. He is best known for conceptualizing the Turing machine, a formal model of computation, and for his crucial role in deciphering the German Enigma code during World War II. His legacy extends across the fields of mathematics, cognitive science, and the philosophy of mind, and he is widely regarded as one of the most influential thinkers of the twentieth century.
  • 739
  • 23 Jun 2025
Topic Review
Approaches of Landslide Detection
Landslide detection can generally be categorized into two approaches: traditional methods of landslide identification and automatic identification methods based on machine learning algorithms. Traditional methods of landslide detection often rely on field surveys conducted by experienced geologists, complemented by instrumental imaging techniques for analysis. The second category predominantly utilizes pre-existing datasets of landslides and facilitates automatic identification through the construction of algorithmic models.
  • 737
  • 15 Aug 2023
Topic Review
Convolutional Neural Network-Based Layer-Adaptive Ground Control Points Extraction
Ground Control Points (GCPs) are of great significance for applications involving the registration and fusion of heterologous remote sensing images (RSIs). However, utilizing low-level information rather than deep features, traditional methods based on intensity and local image features turn out to be unsuitable for heterologous RSIs because of the large nonlinear radiation difference (NRD), inconsistent resolutions, and geometric distortions. Additionally, the limitations of current heterologous datasets and existing deep-learning-based methods make it difficult to obtain enough precision GCPs from different kinds of heterologous RSIs, especially for thermal infrared (TIR) images that present low spatial resolution and poor contrast.
  • 735
  • 02 Jun 2023
Biography
Obinna Johnphill
Obinna Johnphill is a remarkable individual, a married man, and a devoted father of two. His journey in computer science has been one of dedication and continuous pursuit of knowledge. He laid the foundation for his academic career by earning a Bachelor of Science (Hons) in Software Development in 2014 from the University of Wolverhampton. Building on his passion for computer science, he further
  • 735
  • 26 Jul 2023
Topic Review
Diagnosis of Monkeypox Disease Using Deep Learning
The virus that causes monkeypox has been observed in Africa for several years, and it has been linked to the development of skin lesions. Public panic and anxiety have resulted from the deadly repercussions of virus infections following the COVID-19 pandemic. Rapid detection approaches are crucial since COVID-19 has reached a pandemic level. 
  • 735
  • 03 Aug 2023
Topic Review
Multimodal Biometric Identification System
In the past two decades, many physical and behavioral biometric modalities have been under extensive research, such as fingerprints, palm prints, palms/Finger Textures, faces, irises, voice, gait and signature. All these modalities are vulnerable to presentation spoof attacks; hence, the level of provided security is compromised. Fingerprint- and palm-print-based biometric systems may be deceived by using gelatin or clay-made artificial fingerprint surfaces or images. Biometric systems using faces as a biometric modality may be attacked by using photographs, 3-D face models and recorded short clips. Iris-based biometric systems may encounter spoof attacks by employing iris images taken from enrolled users. Voice- and gait-based biometric systems may be attacked by feeding prerecorded audio and video to the recognition system, respectively. 
  • 735
  • 21 Dec 2023
Topic Review
Early Detection of Intrauterine Fetal Demise
Intrauterine fetal demise in women during pregnancy is a major contributing factor in prenatal mortality and is a major global issue in developing and underdeveloped countries. When an unborn fetus passes away in the womb during the 20th week of pregnancy or later, early detection of the fetus can help reduce the chances of intrauterine fetal demise.
  • 734
  • 25 May 2023
Topic Review
Different AI-Based Algorithms for COVID-19 Vaccine Development
Millions of people have died because of the COVID-19 epidemic, and economies have been severely damaged. The creation of a secure and reliable vaccination is essential to stopping the virus’s spread and preserving human life. Artificial intelligence (AI) has shown great promise as a tool for streamlining the process and improving vaccination efficacy in the field of vaccine development. Artificial intelligence (AI) is the application of computer algorithms to tasks that normally require human intelligence, such as pattern recognition, learning, and decision making. AI can be used in vaccine development to evaluate large datasets, identify potential vaccine targets, and forecast the efficacy of vaccine candidates.
  • 734
  • 09 Jan 2024
Topic Review
Generating Paraphrase Using Simulated Annealing for Citation Sentences
The paraphrase generator for citation sentences is used to produce several sentence alternatives to avoid plagiarism. Furthermore, the generation results need to pay attention to semantic similarity and lexical divergence standards. The generation process is guided by an objective function using a simulated annealing algorithm to maintain the properties of semantic similarity and lexical divergence. The objective function is created by combining the two factors that maintain these properties.
  • 733
  • 01 Dec 2023
Topic Review
Crack Detection and Segmentation in Concrete Structures
To prevent potential instability, the early detection of cracks is imperative due to the prevalent use of concrete in critical infrastructure. Automated techniques leveraging artificial intelligence, machine learning, and deep learning as the traditional manual inspection methods are time-consuming. The existing automated concrete crack detection algorithms, despite recent advancements, face challenges in robustness, particularly in precise crack detection amidst complex backgrounds and visual distractions, while also maintaining low inference times.
  • 732
  • 24 Jan 2024
Topic Review
Multi-Method Diagnosis of CT Images
Intracranial hemorrhaging is considered a type of disease that affects the brain and is very dangerous, with high-mortality cases if there is no rapid diagnosis and prompt treatment. CT images are one of the most important methods of diagnosing intracranial hemorrhages. CT images contain huge amounts of information, requiring a lot of experience and taking a long time for proper analysis and diagnosis. Thus, artificial intelligence techniques provide an automatic mechanism for evaluating CT images to make a diagnosis with high accuracy and help radiologists make their diagnostic decisions.
  • 729
  • 30 Aug 2022
Topic Review
Efficient Detection of Forest Fire Smoke
Forest fires are a significant environmental threat, causing loss of biodiversity, alteration of ecosystems, and impacting human lives and properties. Early detection is critical for effective firefighting and minimizing damages. Smoke detection plays an indispensable role in the early monitoring of forest fires. Its rapid dispersion, visibility, and integration with contemporary sensor technologies render it not only an effective complement but also a potential substitute for flame monitoring. In this context, various forest fire smoke detection methods and systems have been developed. These methods include satellite-based smoke detection, ground-based sensors for smoke detection, and UAV-based detection, each with its unique approach, advantages, and limitations. Moreover, image processing technology occupies a crucial position in the detection of forest fire smoke.
  • 728
  • 27 Dec 2023
Topic Review
Federated Learning Algorithms in Healthcare
Federated Learning (FL), an emerging distributed collaborative artificial intelligence (AI) paradigm, is particularly suitable for smart healthcare by coordinating the training of numerous clients, that is, in healthcare institutes, without the exchange of private data.
  • 727
  • 26 Dec 2022
Topic Review
Anomaly Detection Algorithms for LAN Failure Prediction
Predicting Local Area Network (LAN) equipment failure is of utmost importance to ensure the uninterrupted operation of modern communication networks. The utilization of machine learning algorithms, specifically decision trees and support vector machines (SVMs), for predicting LAN failures represents a groundbreaking approach in network management.
  • 725
  • 13 Oct 2023
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