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Topic Review
Detection-Based Vision-Language Understanding
Given a query language, a Detection-based Vision-Language Understanding (DVLU) system needs to respond based on the detected regions (i.e.,bounding boxes). With the significant advancement in object detection, DVLU has witnessed great improvements in recent years, such as Visual Question Answering (VQA) and Visual Grounding (VG).
  • 611
  • 09 Sep 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.
  • 611
  • 08 Jun 2023
Topic Review
AI Advancements
Artificial intelligence (AI) has seen remarkable advancements, stretching the limits of what is possible and opening up new frontiers. Beginning with the fundamentals of AI, including traditional machine learning and the transition to data-driven approaches, the narrative progresses through core AI techniques such as reinforcement learning, generative adversarial networks, transfer learning, and neuroevolution.
  • 611
  • 28 Dec 2023
Topic Review
Multiscale-Deep-Learning Applications
In general, most of the existing convolutional neural network (CNN)-based deep-learning models suffer from spatial-information loss and inadequate feature-representation issues. This is due to their inability to capture multiscale-context information and the exclusion of semantic information throughout the pooling operations. In the early layers of a CNN, the network encodes simple semantic representations, such as edges and corners, while, in the latter part of the CNN, the network encodes more complex semantic features, such as complex geometric shapes. Theoretically, it is better for a CNN to extract features from different levels of semantic representation because tasks such as classification and segmentation work better when both simple and complex feature maps are utilized. Hence, it is also crucial to embed multiscale capability throughout the network so that the various scales of the features can be optimally captured to represent the intended task.
  • 610
  • 26 Oct 2022
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.
  • 609
  • 05 Jan 2023
Topic Review
Machine Learning in Healthcare Industry
Machine learning is a mechanism that enables machines to learn automatically without explicit programming. The main area of machine learning is to use advanced algorithms and statistical techniques to access the data and predict accuracy instead of a rule-based system. The dataset is a primary component of machine learning accuracy prediction. As a result, the data are more relevant and the prediction is more accurate. Machine learning has been used in different fields, such as finance, retail, and the healthcare industry. The rising use of machine learning in healthcare provides more opportunities for disease diagnosis and treatment. Machine learning has a great feature of continuous improvement for data accurate prediction and classification purposes for disease analysis.
  • 608
  • 05 May 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.
  • 607
  • 18 Dec 2023
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.
  • 606
  • 20 Dec 2022
Topic Review
Electroencephalogram-Based Emotion Classification
Rapid advancements in the medical field have drawn much attention to automatic emotion classification from EEG data. People’s emotional states are crucial factors in how they behave and interact physiologically. The diagnosis of patients’ mental disorders is one potential medical use. When feeling well, people work and communicate more effectively. Negative emotions can be detrimental to both physical and mental health. Many earlier studies that investigated the use of the electroencephalogram (EEG) for emotion classification have focused on collecting data from the whole brain because of the rapidly developing science of machine learning.
  • 606
  • 09 Jan 2023
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.
  • 604
  • 02 Dec 2022
Topic Review
Brain Tumor Detection Using Federated Learning
Brain tumor segmentation in medical imaging is a critical task for diagnosis and treatment while preserving patient data privacy and security. Traditional centralized approaches often encounter obstacles in data sharing due to privacy regulations and security concerns, hindering the development of advanced AI-based medical imaging applications.
  • 603
  • 08 Nov 2023
Topic Review Peer Reviewed
Optimisation of Small-Scale Aquaponics Systems Using Artificial Intelligence and the IoT: Current Status, Challenges, and Opportunities
Environment changes, water scarcity, soil depletion, and urbanisation are making it harder to produce food using traditional methods in various regions and countries. Aquaponics is emerging as a sustainable food production system that produces fish and plants in a closed-loop system. Aquaponics is not dependent on soil or external environmental factors. It uses fish waste to fertilise plants and can save up to 90–95% water. Aquaponics is an innovative system for growing food and is expected to be very promising, but it has its challenges. It is a complex ecosystem that requires multidisciplinary knowledge, proper monitoring of all crucial parameters, and high maintenance and initial investment costs to build the system. Artificial intelligence (AI) and the Internet of Things (IoT) are key technologies that can overcome these challenges. Numerous recent studies focus on the use of AI and the IoT to automate the process, improve efficiency and reliability, provide better management, and reduce operating costs. However, these studies often focus on limited aspects of the system, each considering different domains and parameters of the aquaponics system. This paper aims to consolidate the existing work, identify the state-of-the-art use of the IoT and AI, explore the key parameters affecting growth, analyse the sensing and communication technologies employed, highlight the research gaps in this field, and suggest future research directions. Based on the reviewed research, energy efficiency and economic viability were found to be a major bottleneck of current systems. Moreover, inconsistencies in sensor selection, lack of publicly available data, and the reproducibility of existing work were common issues among the studies.
  • 603
  • 19 Feb 2024
Topic Review
Surface Defect Detection and Identification Methods on Leather
Genuine leather manufacturing is a multibillion-dollar industry that processes animal hides from varying types of animals such as sheep, alligator, goat, ostrich, crocodile, and cow. Due to the industry’s immense scale, there may be numerous unavoidable causes of damages, leading to surface defects that occur during both the manufacturing process and the bovine’s own lifespan. Owing to the heterogenous and manifold nature of leather surface characteristics, great difficulties can arise during the visual inspection of raw materials by human inspectors. To mitigate the industry’s challenges in the quality control process, there is a growing interest in leveraging artificial intelligence (AI) and computer vision techniques for automated and accurate leather surface defect detection.
  • 603
  • 21 Aug 2023
Topic Review
Techniques Related to Chinese Speech Emotion Recognition
The use of Artificial Intelligence for emotion recognition has attracted much attention. The industrial applicability of emotion recognition is quite comprehensive and has good development potential. 
  • 602
  • 12 Jul 2022
Topic Review
Data-Driven Methods in Power Grids
Applications of data-driven methods in power grids are motivated by the need to predict and mitigate intermittency in a (future) grid that is expected to lean heavily on renewables.
  • 601
  • 22 Jun 2022
Topic Review
Artificial Intelligence-Driven Digital Technologies on SDG
Artificial Intelligence-Driven (AI-Driven) digital technologies (DT) are intrinsically connected to interact, perceive, and understand people, businesses, economies, and lives in general. The term Artificial Intelligence (AI) can be understood as a general combination and integration of applications with other “DTs” to create machines capable of thinking like humans. AI-Driven DT economic and societal impacts increase on a continuous basis and more recently they are assuming an important role in the Sustainable Development Goals (SDG) Agenda 2030, and their implementations are a considerable decision for developed and developing countries. In turn, Brazil and Portugal have been elected in this research to display their view on AI-driven DT on SDG achievements, contradicting their perspectives in this field.
  • 599
  • 28 Mar 2022
Topic Review
Detection/Classification of Knee Injuries from MR Images
Magnetic resonance imaging (MRI) is a technique for mapping the interior structure of the body as well as specific aspects of functioning. 
  • 598
  • 16 Dec 2021
Topic Review
Artificial Intelligence and Radiomics Techniques
Hepatocellular carcinoma (HCC) is the most common primary hepatic neoplasm. Thanks to recent advances in computed tomography (CT) and magnetic resonance imaging (MRI), there is potential to improve detection, segmentation, discrimination from HCC mimics, and monitoring of therapeutic response. Radiomics, artificial intelligence (AI), and derived tools have already been applied in other areas of diagnostic imaging with promising results. 
  • 598
  • 30 Dec 2022
Topic Review
Handwritten Chinese Text Recognition
Offline handwritten Chinese recognition is an important research area of pattern recognition, including offline handwritten Chinese character recognition (offline HCCR) and offline handwritten Chinese text recognition (offline HCTR), which are closely related to daily life. HCTR is more complex and relatively less accurate due to the unconstrained nature of text lines and the adhesion between characters. It can be further divided into line-level HCTR and page-level HCTR depending on whether the recognition object is a cropped image of a text line or an entire page.
  • 597
  • 24 Mar 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).
  • 591
  • 04 Apr 2022
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