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Topic Review
Malware Detection Method for with ViT Attention Mechanism
Artificial intelligence (AI) is increasingly being utilized in cybersecurity, particularly for detecting malicious applications. However, the black-box nature of AI models presents a significant challenge. This lack of transparency makes it difficult to understand and trust the results. In order to address this, it is necessary to incorporate explainability into the detection model. There is insufficient research to provide reasons why applications are detected as malicious or explain their behavior.
  • 789
  • 24 Jul 2023
Topic Review
Building Footprint Extraction in Very-High-Resolution Remote Sensing Images
With the rapid development of very-high-resolution (VHR) remote-sensing technology, automatic identification and extraction of building footprints are significant for tracking urban development and evolution. Nevertheless, while VHR can more accurately characterize the details of buildings, it also inevitably enhances the background interference and noise information, which degrades the fine-grained detection of building footprints. In order to tackle the above issues, the attention mechanism is intensively exploited to provide a feasible solution. The attention mechanism is a computational intelligence technique inspired by the biological vision system capable of rapidly and automatically catching critical information.
  • 788
  • 27 Nov 2023
Topic Review
Autonomous Navigation of Agricultural Robots
Regarding agricultural harvesting robots, they typically consist of mobile platforms carrying robotic arms. These robots require advanced vision systems, employing adaptive thresholding algorithms, as well as texture-based methods and color shape characteristic extraction, to identify target fruits.
  • 788
  • 23 Jan 2024
Topic Review
Speech Emotion Recognition
Speech is the most natural way of human communication. Affective computing systems based on speech play an important role in promoting human–computer interaction, and emotion recognition is the first step. Due to the lack of a precise definition of emotion and the inclusive and complex influence of emotion generation and expression, accurately recognizing speech emotions is still difficult. Speech emotion recognition (SER) is an important problem that is receiving increasing interest from researchers due to its numerous applications, such as e-learning, clinical trials, audio monitoring/surveillance, lie detection, entertainment, video games, and call centers.
  • 787
  • 25 Jan 2024
Topic Review
AI-Informed Decision Making
AI-assisted decision-making that impacts individuals raises critical questions about transparency and fairness in artificial intelligence (AI). Much research has highlighted the reciprocal relationships between the transparency/explanation and fairness in AI-assisted decision-making. Thus, considering their impact on user trust or perceived fairness simultaneously benefits responsible use of socio-technical AI systems, but currently receives little attention.
  • 786
  • 01 Jul 2022
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. 
  • 785
  • 23 Feb 2024
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.
  • 784
  • 07 Oct 2023
Topic Review
An Improved Modulation Recognition Algorithm
Modulation recognition is an important technology in wireless communication systems. Deep learning-based modulation recognition algorithms, which can autonomously learn deep features and achieve superior recognition performance compared with traditional algorithms.
  • 783
  • 19 May 2023
Topic Review
Indoor Hydroponic Greenhouses
Indoor hydroponic greenhouses are becoming increasingly popular for sustainable food production. On the other hand, precise control of the climate conditions inside these greenhouses is crucial for the success of the crops. Time series deep learning models are adequate for climate predictions in indoor hydroponic greenhouses, but a comparative analysis of these models at different time intervals is needed.
  • 781
  • 26 Jun 2023
Topic Review
Taxonomy of Machine Learning Methods for Urban Applications
Machine Learning (ML) as an intersection of informatics and statistics is a promising challenge for more evidence-based decisions to fill in the gap of existing technological tools and instruments for spatiotemporal requirements. As ML transcended the conventional techniques of modeling, a huge potential of big data management to address complex city problems is presented at the crossroads of modern urban planning challenges to make up their dynamics. Generally speaking, the ML methods are categorized based on the type of ‘learning’.
  • 780
  • 10 Jan 2023
Topic Review
Relevant Approaches of Zero Trust Network Model
Zero Trust Architecture research is now in its early stages, with a primary focus on the framework itself, access control, algorithms of trust evaluation, and identity authentication. These are the primary study domains within the Zero Trust field.
  • 777
  • 29 Feb 2024
Topic Review
Augmented Reality-Artificial Intelligence Tools in Manufacturing
An important research area in the field of Industry 4.0 is to find a user-interface that is as convenient and intuitive to use as possible to ensure optimal human–machine interaction. Augmented Reality (AR) together with Advanced Image Recognition, powered by Artificial Intelligence (AI) seem to be a set of technologies supportive in this topic. Apart from user friendly interface, AR-AI tools are proved to provide time savings in manufacturing tasks, while simplifying the job at the same time, enabling inexperienced, unskilled, or less skilled employees to perform the work in the selected manual production processes.
  • 776
  • 24 Jun 2022
Topic Review
Diffusion-Based Method for Pavement Crack Detection
Pavement crack detection is of significant importance in ensuring road safety and smooth traffic flow. However, pavement cracks come in various shapes and forms which exhibit spatial continuity, and algorithms need to adapt to different types of cracks while preserving their continuity. Some studies have already applied the feature learning capability of generative models to crack detection. 
  • 775
  • 01 Apr 2024
Topic Review
Algorithms for Facial Expression Recognition in the Wild
Facial expression recognition (FER) in the wild has attracted much attention due to its wide range of applications. Approaches use deep learning models trained on relatively large images, which significantly reduces their accuracy when they have to infer low-resolution images.
  • 773
  • 22 Sep 2023
Topic Review
AI-Supported Programming Tasks
AI-assisted programming or development is defined as the utilization of machine learning models trained on the vast amount of available source code. Its purpose is to support various aspects of programming and, more broadly, software engineering implementation tasks. 
  • 773
  • 19 Feb 2024
Topic Review
Anti-Aliasing Attention U-net Model for Skin Lesion Segmentation
The need for a lightweight and reliable segmentation algorithm is critical in various biomedical image-prediction applications. However, the limited quantity of data presents a significant challenge for image segmentation. Additionally, low image quality negatively impacts the efficiency of segmentation, and previous deep learning models for image segmentation require large parameters with hundreds of millions of computations, resulting in high costs and processing times.
  • 772
  • 31 Jul 2023
Topic Review
Raft (Computer Science)
Raft is a consensus algorithm designed as an alternative to the Paxos family of algorithms. It was meant to be more understandable than Paxos by means of separation of logic, but it is also formally proven safe and offers some additional features. Raft offers a generic way to distribute a state machine across a cluster of computing systems, ensuring that each node in the cluster agrees upon the same series of state transitions. It has a number of open-source reference implementations, with full-specification implementations in Go, C++, Java, and Scala. It is named after Reliable, Replicated, Redundant, And Fault-Tolerant. Raft is not a Byzantine fault tolerant algorithm: the nodes trust the elected leader.
  • 771
  • 31 Oct 2022
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.
  • 771
  • 27 Jun 2023
Topic Review
Automatic Genre Identification for Massive Text Collections
Automatic genre identification is a text classification task, as a method of providing insights into the content of large text collections. It evaluates various machine learning models for their generalization capabilities, including pre-Transformer approaches, BERT-like encoder models and instruction-tuned GPT large language models. As a result, it introduces the first publicly-available benchmark for this task. What is more, a high-performing genre classifier that can be applied to numerous languages is introduced.
  • 771
  • 30 Oct 2023
Topic Review
Mass Appraisal Models of Real Estate Tax Value
Artificial neural network (ANN)-based analysis can reveal differences in tax leakage loss rates in different geographical regions of countries. Experts can adjust a region’s valuation data based on property tax leakage loss rates. Appraisers can contribute to solving the problem by highlighting areas with high tax leakage loss rates and communicating their findings to valuation stakeholders, local administrators, and policymakers. This can lead to more fair and efficient tax policies that benefit the real estate sector and the economy.
  • 769
  • 23 Oct 2023
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