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Topic Review
Artificial Intelligence in Cancer Research
Integration of artificial intelligence (AI) into cancer research is currently addressing many of the challenges where medical experts fail to bring cancer to control and cure, and the outcomes are quite encouraging. AI offers many tools and platforms to facilitate more understanding and tackling of this life-threatening disease. AI-based systems can help pathologists in diagnosing cancer more accurately and consistently, reducing the case error rates. Predictive-AI models can estimate the likelihood for a person to get cancer by identifying the risk factors. Big data, together with AI, can enable medical experts to develop customized treatments for cancer patients. The side effects from this kind of customized therapy will be less severe in comparison with the generalized therapies.
  • 806
  • 09 Dec 2022
Topic Review
Deep Learning in Causality Mining
Deep learning models for causality mining (CM) can enhance the performance of learning algorithms, improve the processing time, and increase the range of mining applications.
  • 805
  • 08 Nov 2021
Topic Review
Avalanche (Protocol)
Avalanche is a protocol for solving consensus in a network of unreliable machines, where failures may be crash-fault or Byzantine. The protocol was anonymously introduced on IPFS on May 16, 2018 and was formalized in more detail by Cornell University researchers in 2019. Protocol currently provides system operation of the Avalanche (platform) and his platform. The protocol has four basic interrelated mechanisms that compose structural support of the consensus tool. These four mechanisms are Slush, Snowflake, Snowball, and Avalanche. By using randomized sampling and metastability to ascertain and persist transactions, It represents a new protocol family. Although the original paper focused on a single protocol, namely Avalanche, it implicitly introduced a broad spectrum of voting-based, or quorum-based consensus protocols, called the Snow family. While Avalanche is a single instantiation, the Snow family seems to be able to generalize all quorum-based voting protocols for replica control. Unlike prior quorum-based work, the Snow family enables arbitrarily parametrizable failure probability at the quorum intersection level. Standard quorum-based protocols define this failure probability to be precisely zero, but by introducing errors in the quorum intersection, a larger set of consensus protocol design is available.
  • 804
  • 02 Dec 2022
Topic Review
Fashion Recommendation System Using Deep Learning
Recommender systems are one of the great improvements in Internet technology and e-commerce, and the origins of modern recommender systems date back to the early 1990s when they were mainly applied experimentally to personal email and information filtering. Later, recommender systems went through numerous improvements to facilitate users’ navigation through fashion, videos, books, papers, and especially e-commerce.
  • 804
  • 22 Aug 2023
Topic Review
Stereo Matching Algorithm
With the advancement of artificial intelligence technology and computer hardware, the stereo matching algorithm has been widely researched and applied in the field of image processing. In scenarios such as robot navigation and autonomous driving, stereo matching algorithms are used to assist robots in acquiring depth information about the surrounding environment, thereby improving the robot’s ability for autonomous navigation during self-driving.
  • 804
  • 18 Dec 2023
Topic Review
Wireless Sensor Networks with Mobile Sink
With the advances in sensing technologies, sensor networks became the core of several different networks, including the Internet of Things (IoT) and drone networks. This led to the use of sensor networks in many critical applications including military, health care, and commercial applications.
  • 800
  • 05 Jan 2023
Topic Review
Role of Blockchain Technology in COVID-19 Crisis
To obtain adequate performance in resolving issues that are associated with the COVID-19 pandemic, blockchain can be combined with other available technologies to establish a robust healthcare architecture.
  • 799
  • 29 Jan 2022
Topic Review
Driver Drowsiness Detection
Drowsy driving is a widespread cause of traffic accidents, especially on highways. It has become an essential task to seek an understanding of the situation in order to be able to take immediate remedial actions to detect driver drowsiness and enhance road safety.
  • 799
  • 25 Sep 2023
Topic Review
A New Container Throughput Forecasting Paradigm under COVID-19
COVID-19 has imposed tremendously complex impacts on the container throughput of ports, which poses big challenges for traditional forecasting methods. Combining this with change-point analysis and empirical mode decomposition (EMD), this uses the decomposition–ensemble methodology to build a throughput forecasting model. Firstly, EMD is used to decompose the sample data of port container throughput into multiple components. Secondly, fluctuation scale analysis is carried out to accurately capture the characteristics of the components. Subsequently, here tailor the forecasting model for every component based on the mode analysis. Finally, the forecasting results of all the components are combined into one aggregated output. 
  • 798
  • 24 Mar 2022
Topic Review
Update on Cyber Health Psychology
In recent years, there has been more and more talk of cyber health psychology and the implication that new technologies can have in the diagnosis, treatment, and rehabilitation of psychopathological issues in the field of mental health, ranging from post-traumatic stress disorder (PTSD) to addiction to substances of abuse.
  • 794
  • 25 Mar 2022
Topic Review
Surrogate-Based Optimisation
Surrogate-based optimisation (SBO) algorithms are a powerful technique that combine machine learning and optimisation to solve expensive optimisation problems. This type of problem appears when dealing with computationally expensive simulators or algorithms. By approximating the expensive part of the optimisation problem with a surrogate, the number of expensive function evaluations can be reduced.
  • 793
  • 31 Mar 2022
Topic Review
Novel Pooling Methods for Convolutional Neural Networks
Neural network computational methods have evolved over the past half-century. In 1943, McCulloch and Pitts designed the first model, recognized as the linear threshold gate. Hebbian developed the Hebbian learning rule approach for training the neural network. However, would the Hebbian rule remain productive when all the input patterns became orthogonal? The existence of orthogonality in input vectors is a crucial component for this rule to execute effectively. To meet this requirement, a much more productive learning rule, known as the Delta rule, was established. Whereas the delta rule poses issues with the learning principles outlined above, backpropagation has developed as a more complicated learning approach. Backpropagation could learn an infinite layered structure and estimate any commutative function. A feed-forward neural network is most often trained using backpropagation (FFNN).
  • 793
  • 08 Sep 2022
Topic Review
Surface Defect Detection of Strip-Steel
Surface-defect detection is crucial for assuring the quality of strip-steel manufacturing. Strip-steel surface-defect detection requires defect classification and precision localization, which is a challenge in real-world applications.
  • 792
  • 14 Sep 2022
Topic Review
3D Object Detection with Differential Point Clouds
3D object detection based on point clouds has many applications in natural scenes, especially in autonomous driving. Point cloud data provide reliable geometric and depth information. 
  • 790
  • 24 Dec 2022
Topic Review
Wavelet Threshold Denoising Algorithm
The denoising performance is affected by several factors, including wavelet basis function, decomposition level, thresholding method, and the threshold selection criteria. Traditional threshold selection rules rely on statistical and empirical variables, which influence their performance in noise reduction under various conditions. 
  • 787
  • 06 Jul 2022
Topic Review
Algorithms for Spam Detection
Spam emails have become a pervasive issue, as internet users receive increasing amounts of unwanted or fake emails. To combat this issue, automatic spam detection methods have been proposed, which aim to classify emails into spam and non-spam categories. Machine learning techniques have been utilized for this task with considerable success. 
  • 787
  • 16 Oct 2023
Topic Review
Three-Dimensional Point Cloud Semantic Segmentation for Cultural Heritage
In the cultural heritage field, point clouds, as important raw data of geomatics, are not only three-dimensional (3D) spatial presentations of 3D objects but they also have the potential to gradually advance towards an intelligent data structure with scene understanding, autonomous cognition, and a decision-making ability. The approach of point cloud semantic segmentation as a preliminary stage can help to realize this advancement.
  • 786
  • 09 Mar 2023
Topic Review
The Urban Transit Routing Problem
The Urban Transit Routing Problem (UTRP) is a challenging problem in transportation planning that involves designing and optimizing transit route networks for urban areas. The objective is to find the most efficient routes for public transportation vehicles, considering factors such as travel time, passenger demand, transfer connections, vehicle capacities, operating costs, and environmental impacts. 
  • 786
  • 21 Aug 2023
Topic Review
Light Field Image Super-Resolution
Light fields play important roles in industry, including in 3D mapping, virtual reality and other fields. However, as a kind of high-latitude data, light field images are difficult to acquire and store. Compared with traditional 2D planar images, 4D light field images contain information from different angles in the scene, and thus the super-resolution of light field images needs to be performed not only in the spatial domain but also in the angular domain. In the early days of light field super-resolution research, many solutions for 2D image super-resolution, such as Gaussian models and sparse representations, were also used in light field super-resolution. With the development of deep learning, light field image super-resolution solutions based on deep-learning techniques are becoming increasingly common and are gradually replacing traditional methods.
  • 785
  • 27 Jul 2022
Topic Review
Deepfake Identification and Traceability
Researchers and companies have released multiple datasets of face deepfakes labeled to indicate different methods of forgery. Naming these labels is often arbitrary and inconsistent. However, researchers must use multiple datasets in practical applications to conduct traceability research. The researchers utilize the K-means clustering method to identify datasets with similar feature values and analyze the feature values using the Calinski Harabasz Index method. Datasets with the same or similar labels in different deepfake datasets exhibit different forgery features. The KCE system can solve this problem, which combines multiple deepfake datasets according to feature similarity. In the model trained based on KCE combined data, the Calinski Harabasz scored 42.3% higher than the combined data by the same forgery name. It shows that this method improves the generalization ability of the model.
  • 783
  • 08 Jun 2023
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