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Topic Review
IDS Using Feature Extraction with ML in IoT
With the continuous increase in Internet of Things (IoT) device usage, more interest has been shown in internet security, specifically focusing on protecting these vulnerable devices from malicious traffic. Such threats are difficult to distinguish, so an advanced intrusion detection system (IDS) is becoming necessary. Machine learning (ML) is one of the promising techniques as a smart IDS in different areas, including IoT. However, the input to ML models should be extracted from the IoT environment by feature extraction models, which play a significant role in the detection rate and accuracy.
  • 563
  • 22 Nov 2023
Topic Review
A Machine Learning-Based Sustainable University Field Training Framework
The proposed sustainable University Field Training (SUNFIT) is an educational data mining framework based on the pedagogical strategies of preparing, conducting, and assessing computing students’ skills in courses involving practical industry engagement.
  • 562
  • 29 May 2023
Topic Review
Kidney Transplant Care through the Integration of Chatbot
Kidney transplantation is a critical treatment option for end-stage kidney disease patients, offering improved quality of life and increased survival rates. However, the complexities of kidney transplant care necessitate continuous advancements in decision making, patient communication, and operational efficiency. 
  • 561
  • 23 Feb 2024
Topic Review
Dementia Severity Scale Based on MRI
In the clinical treatment of Alzheimer’s disease, one of the most important tasks is evaluating its severity for diagnosis and therapy. However, traditional testing methods are deficient, such as their susceptibility to subjective factors, incomplete evaluation, low accuracy, or insufficient granularity, resulting in unreliable evaluation scores. 
  • 559
  • 11 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.
  • 557
  • 27 Jun 2023
Topic Review
Self-Supervised Transfer Learning for Fine-Grained Image Recognition
Fine-grained image recognition aims to classify fine subcategories belonging to the same parent category, such as vehicle model or bird species classification. This is an inherently challenging task because a classifier must capture subtle interclass differences under large intraclass variances. Most previous approaches are based on supervised learning, which requires a large-scale labeled dataset. However, such large-scale annotated datasets for fine-grained image recognition are difficult to collect because they generally require domain expertise during the labeling process. Researchers propose a self-supervised transfer learning method based on Vision Transformer (ViT) to learn finer representations without human annotations. Interestingly, it is observed that existing self-supervised learning methods using ViT (e.g., DINO) show poor patch-level semantic consistency, which may be detrimental to learning finer representations. Motivated by this observation, researchers propose a consistency loss function that encourages patch embeddings of the overlapping area between two augmented views to be similar to each other during self-supervised learning on fine-grained datasets.
  • 557
  • 07 Oct 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.
  • 556
  • 29 Aug 2023
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.
  • 555
  • 30 Jun 2022
Topic Review
Knowledge Reasoning
A knowledge graph (KG) organizes knowledge as a set of interlinked triples, and a triple ((head entity, relation, tail entity), simply represented as (h, r, t)) indicates the fact that two entities have a certain relation. The availability of formalized and rich structured knowledge has emerged as a valuable resource in facilitating various downstream tasks, such as question answering and recommender systems. Although KGs such as DBpedia, Freebase, and NELL contain large amounts of entities, relations, and triples, they are far from complete, which is an urgent issue for their broad application. To address this, researchers have introduced the concept of knowledge graph completion (KGC), which has garnered increasing interest. It utilizes knowledge reasoning techniques to automatically discover new facts based on existing ones in a KG.
  • 555
  • 12 Oct 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.
  • 555
  • 26 Dec 2022
Topic Review
A Sub-Second Method for SAR Image Registration
For Synthetic Aperture Radar (SAR) image registration, successive processes following feature extraction are required by both the traditional feature-based method and the deep learning method. Among these processes, the feature matching process—whose time and space complexity are related to the number of feature points extracted from sensed and reference images, as well as the dimension of feature descriptors—proves to be particularly time consuming. Additionally, the successive processes introduce data sharing and memory occupancy issues, requiring an elaborate design to prevent memory leaks.
  • 554
  • 24 Oct 2023
Topic Review
Machine Learning-Based Anomaly Detection in NFV
Network function virtualization (NFV) is a rapidly growing technology that enables the virtualization of traditional network hardware components, offering benefits such as cost reduction, increased flexibility, and efficient resource utilization. Moreover, NFV plays a crucial role in sensor and IoT networks by ensuring optimal resource usage and effective network management. 
  • 552
  • 15 Jun 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). 
  • 552
  • 16 Jun 2023
Topic Review
Automatic Visual Pollution Detection
Visual pollution, characterized by disorderly and displeasing urban environments, is inherently subjective and challenging to quantify precisely. In recent years, substantial research efforts have been initiated to identify and categorize various forms of visual pollution by applying artificial intelligence and computer vision techniques. The automated recognition of visual disturbances using advanced deep learning methods can aid governmental bodies and relevant authorities in taking proactive measures. 
  • 552
  • 19 Jan 2024
Topic Review
Salp Swarm Algorithm
The Salp Swarm Algorithm (SSA) is a bio-inspired metaheuristic optimization technique that mimics the collective behavior of Salp chains hunting for food in the ocean. While it demonstrates competitive performance on benchmark problems, the SSA faces challenges with slow convergence and getting trapped in local optima like many population-based algorithms. 
  • 548
  • 26 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.
  • 547
  • 01 Dec 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.
  • 546
  • 02 Jun 2023
Topic Review
Impacts of Surface Microchannels on Porous Fibrous Media
The microchannel increases the permeability of flow both in the directions parallel and vertical to the microchannel direction. The microchannel plays as the highway for the pass of reactants while the rest of the smaller pore size provides higher resistance for better catalyst support, and the propagation path in the network with microchannels is more even and predictable. 
  • 545
  • 21 Dec 2021
Topic Review
HVS and Contrast Sensitivity to Assess Image Quality
The human visual system (HVS) has many characteristics, such as the dual-pathway feature, in which visual information is transmitted through the ventral pathway and dorsal pathway in the visual cortex. The contrast sensitivity characteristic of the HVS reflects the different sensitivity of the human eye to different spatial frequencies. This characteristic is similar to the widely used spatial attention mechanism and image saliency.
  • 544
  • 05 Jun 2023
Topic Review
A Benchmark Dataset for Wearable Low-Light Pedestrian Detection
Detecting pedestrians in low-light conditions is challenging, especially in the context of wearable platforms. Infrared cameras have been employed to enhance detection capabilities, whereas low-light cameras capture the more intricate features of pedestrians. With this in mind, a low-light pedestrian detection (called HRBUST-LLPED) dataset by capturing pedestrian data on campus using wearable low-light cameras is introduced.
  • 544
  • 19 Dec 2023
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