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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.
  • 772
  • 08 Jun 2023
Topic Review
Smart Agriculture
Smart agriculture, or precision agriculture, is a crucial way to achieve greater yields by utilizing the natural deposits in a diverse environment. The yield of a crop may vary from year to year depending on the variations in climate, soil parameters and fertilizers used. Automation in the agricultural industry moderates the usage of resources and can increase the quality of food in the post-pandemic world. Agricultural robots have been developed for crop seeding, monitoring, weed control, pest management and harvesting. Physical counting of fruitlets, flowers or fruits at various phases of growth is labour intensive as well as an expensive procedure for crop yield estimation. Remote sensing technologies offer accuracy and reliability in crop yield prediction and estimation. The automation in image analysis with computer vision and deep learning models provides precise field and yield maps. In this review, it has been observed that the application of deep learning techniques has provided a better accuracy for smart farming. The crops taken for the study are fruits such as grapes, apples, citrus, tomatoes and vegetables such as sugarcane, corn, soybean, cucumber, maize, wheat. The research works which are carried out in this research paper are available as products for applications such as robot harvesting, weed detection and pest infestation. The methods which made use of conventional deep learning techniques have provided an average accuracy of 92.51%.
  • 771
  • 28 Apr 2021
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.
  • 768
  • 09 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.
  • 767
  • 19 May 2022
Topic Review
Methods for Supervised Learning in Diagnosis of COVID-19
The methods for supervised learning in diagnosis of COVID-19 refer to the samples used for model training being labeled. The label information is fully utilized to guide network model training. The advantage is that the model accuracy can be effectively improved by learning a large amount of label information and the model is easy to evaluate. The current state of deep learning for COVID-19 classification and segmentation tasks from aspects of supervised learning is summarized, including summarizing the application of VGG, ResNet, DenseNet and lightweight networks to the classification task of COVID-19, and summarizing the application of the attention mechanism, multiscale mechanism, residual connectivity mechanism, and dense connectivity mechanism to the segmentation task of COVID-19.
  • 767
  • 22 Mar 2023
Topic Review
Deep Learning-Based Diagnosis of Alzheimer’s Disease
Alzheimer’s disease (AD), the most familiar type of dementia, is a severe concern in modern healthcare. Around 5.5 million people aged 65 and above have AD, and it is the sixth leading cause of mortality in the US. AD is an irreversible, degenerative brain disorder characterized by a loss of cognitive function and has no proven cure. Deep learning techniques have gained popularity in recent years, particularly in the domains of natural language processing and computer vision. Since 2014, these techniques have begun to achieve substantial consideration in AD diagnosis research, and the number of papers published in this arena is rising drastically. Deep learning techniques have been reported to be more accurate for AD diagnosis in comparison to conventional machine learning models. 
  • 765
  • 01 Jun 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.
  • 765
  • 04 Dec 2023
Topic Review
Automatic Identification of Addresses
Address matching continues to play a central role at various levels, through geocoding and data integration from different sources, with a view to promote activities such as urban planning, location-based services, and the construction of databases like those used in census operations. Closely associated to address matching is the task of address parsing or address segmentation, which consists of decomposing an address into its different components, such as a street name or a postal code. However, these tasks continue to face several challenges, such as non-standard or incomplete address records or addresses written in more complex languages.
  • 764
  • 10 Jan 2022
Topic Review
Hesitant Fuzzy Graph Neural Network-Based Prototypical Network
Few-shot text classification aims to recognize new classes with only a few labeled text instances. Previous studies mainly utilized text semantic features to model the instance-level relation among partial samples. However, the single relation information makes it difficult for many models to address complicated natural language tasks. A novel hesitant fuzzy graph neural network (HFGNN) model that explores the multi-attribute relations between samples is proposed. HFGNN is combined with the Prototypical Network (HFGNN-Proto) to achieve few-shot text classification.
  • 762
  • 20 Dec 2022
Topic Review
AI on Smart City Technologies
As the global population grows, and urbanization becomes more prevalent, cities often struggle to provide convenient, secure, and sustainable lifestyles due to the lack of necessary smart technologies. Fortunately, the Internet of Things (IoT) has emerged as a solution to this challenge by connecting physical objects using electronics, sensors, software, and communication networks. This has transformed smart city infrastructures, introducing various technologies that enhance sustainability, productivity, and comfort for urban dwellers. By leveraging Artificial Intelligence (AI) to analyze the vast amount of IoT data available, new opportunities are emerging to design and manage futuristic smart cities. 
  • 758
  • 09 Jun 2023
Topic Review
Transformer-Based Model for Predicting Customers’ NPD in e-Commerce
Transformers offer advantages in capturing long-term dependencies within time series data through self-attention mechanisms. This adaptability to various time series patterns, including trends, seasonality, and irregularities, makes them a promising choice for next purchase day (NPD) prediction. The transformer model demonstrates improvements in prediction accuracy compared to the baselines. 
  • 758
  • 02 Nov 2023
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.
  • 758
  • 17 Feb 2024
Topic Review
Non-Iterative Cluster Routing
In conventional routing, a capsule network employs routing algorithms for bidirectional information flow between layers through iterative processes.
  • 758
  • 19 Mar 2024
Topic Review
Digital Face Manipulation Creation and Detection
Deepfake refers to the sophisticated manipulation of audiovisual content using deep learning techniques, particularly generative adversarial networks (GANs). It enables the creation of hyper-realistic fake videos or images by seamlessly superimposing one person's face or voice onto another's. These manipulated media raise significant concerns about misinformation, privacy invasion, and the potential to deceive audiences. Deepfakes have sparked discussions about the ethical implications of digital media manipulation and the challenges of distinguishing between genuine and fabricated content in the digital age. Efforts to counter deepfake technology involve developing advanced detection methods and raising awareness about the prevalence of manipulated media.
  • 757
  • 25 Aug 2023
Topic Review
Deep Learning-Based Methods for Crop Disease Estimation
Deep learning methods such as U-Net, SegNet, YOLO, Faster R-CNN, VGG and ResNet have been used extensively for crop disease estimation using Unmanned Aerial Vehicle (UAV)  imagery. The basic building block of the deep learning architecture is basically the success of convolutional neural networks (CNN). The deep learning models implemented for crop disease estimation using UAV imagery can be categorized into classification-based, segmentation-based and detection-based approaches. Segmentation-based models attempt to classify each pixel in an image into different categories such as healthy vs. diseased pixels, whereas classification-based models look into overall images and classify the image into pre-defined disease classes.
  • 756
  • 16 May 2023
Topic Review
Open-Domain Conversational AI
There are different opinions as to the definition of AI, but according to, it is any computerised system exhibiting behaviour commonly regarded as requiring intelligence. Conversational AI, therefore, is any system with the ability to mimick human–human intelligent conversations by communicating in natural language with users. Conversational AI, sometimes called chatbots, may be designed for different purposes. Open-domain conversational AI models are known to have several challenges, including bland, repetitive responses and performance degradation when prompted with figurative language, among others. 
  • 754
  • 24 Jun 2022
Topic Review
Deep Learning Approaches for Detecting Fake News
The unregulated proliferation of counterfeit news creation and dissemination poses a constant threat to democracy. Fake news articles have the power to persuade individuals, leaving them perplexed. State of the art in deep learning techniques for fake news detection are described herein.
  • 753
  • 10 Mar 2023
Topic Review
Continuous Pain Intensity Monitoring
The continuous pain intensity recognition system analyzes the Electrodermal Activity (EDA) sensor modality modality, compares the results obtained from both EDA and facial expressions modalities, and late fuses EDA and facial expressions modalities.
  • 753
  • 20 Oct 2023
Topic Review
Electrocardiogram Signal Denoising
The electrocardiogram (ECG) is widely used in medicine because it can provide basic information about different types of heart disease. 
  • 749
  • 29 Nov 2023
Topic Review
Dynamic Fault Tree analysis method
The Entry briefly introduces the Dynamic Fault Tree analysis method proposed by P. Gao et al on the 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC).
  • 746
  • 04 Apr 2022
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