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Topic Review
Blockchain and Machine Learning-Based Hybrid IDS
The cyberspace is a convenient platform for creative, intellectual, and accessible works that provide a medium for expression and communication. Malware, phishing, ransomware, and distributed denial-of-service attacks pose a threat to individuals and organisations. To detect and predict cyber threats effectively and accurately, an intelligent system must be developed. Cybercriminals can exploit Internet of Things devices and endpoints because they are not intelligent and have limited resources.
  • 303
  • 19 Sep 2023
Topic Review
Agricultural Image Segmentation
The Segment Anything Model (SAM) is a versatile image segmentation model that enables zero-shot segmentation of various objects in any image using prompts, including bounding boxes, points, texts, and more. However, studies have shown that the SAM performs poorly in agricultural tasks like crop disease segmentation and pest segmentation. To address this issue, the agricultural SAM adapter (ASA) is proposed, which incorporates agricultural domain expertise into the segmentation model through a simple but effective adapter technique.
  • 302
  • 27 Sep 2023
Topic Review
Enhanced Traffic Sign Recognition with Ensemble Learning
Traffic sign recognition plays a crucial role in the functioning of autonomous vehicles. The ability to accurately identify and interpret traffic signs is necessary for autonomous vehicles to navigate roads safely and efficiently. Machine learning techniques are used to train and test models on traffic sign data, including prohibitory, danger, mandatory, and other signs.
  • 302
  • 21 Nov 2023
Topic Review
Prediction of Cellular Network Traffic
Cellular communication systems have continued to develop in the direction of intelligence. The demand for cellular networks is increasing as they meet the public’s pursuit of a better life. Accurate prediction of cellular network traffic can help operators avoid wasting resources and improve management efficiency. Traditional prediction methods can no longer perfectly cope with the highly complex spatiotemporal relationships of the current cellular networks, and prediction methods based on deep learning are constantly growing.
  • 300
  • 14 Aug 2023
Topic Review
Predictability of Thalassemia Using AI
Thalassemia represents one of the most common genetic disorders worldwide, characterized by defects in hemoglobin synthesis. The affected individuals suffer from malfunctioning of one or more of the four globin genes, leading to chronic hemolytic anemia, an imbalance in the hemoglobin chain ratio, iron overload, and ineffective erythropoiesis. Despite the challenges posed by this condition, recent years have witnessed significant advancements in diagnosis, therapy, and transfusion support, significantly improving the prognosis for thalassemia patients.
  • 300
  • 23 Nov 2023
Topic Review
Artificial Intelligence Course Design Planning Framework
The use of Artificial Intelligence (AI) has become key in numerous domains, emphasizing the need for education in this field. The interdisciplinary nature of AI and its relevance across various sectors call for an integration of AI topics into university curricula. This article introduces the "AI Course Design Planning Framework", a comprehensive tool designed to structure the development of domain-specific AI courses at the university level. The AI Course Design Planning Framework forms a visual and practical tool for instructors and course developers in the higher education or professional education context with a special focus on non-computer science (non-CS) students. It can be used as a means to gather ideas, innovate, plan and communicate ideas for domain-specific AI courses. The framework can be used as a self-contained instrument for individuals, in tandem with AI and domain experts or in a workshop setting with multiple people. Scholars suggest filling it from left to right, first considering the questions on AI in the domain, the learning environment of the course and last, the course implementation.
  • 300
  • 06 Dec 2023
Topic Review
Perceptual Encryption-Based Image Communication System for Tuberculosis Diagnosis
Block-based perceptual encryption (PE) algorithms are becoming popular for multimedia data protection because of their low computational demands and format-compliancy with the JPEG standard. In conventional methods, a colored image as an input is a prerequisite to enable smaller block size for better security. However, in domains such as medical image processing, unavailability of color images makes PE methods inadequate for their secure transmission and storage. A PE method that is applicable for both color and grayscale images is proposed. The EfficientNetV2-based model is implemented for automatic tuberculosis (TB) diagnosis in chest X-ray images.
  • 299
  • 21 Sep 2022
Topic Review
Sequential Tracking Models, Physics-Based Models and Hybrid Models
Spatio-temporal, geo-referenced datasets are rapidly expanding and will continue to do so in the near future due to technological advancements as well as social and commercial factors. The introduction of the automatic identification system (AIS), which allows neighboring ships to communicate frequently with their location and navigation status via a radio signal, has enabled researchers to get their hands on datasets rich in spatio-temporal information. AIS data are collected from satellites and ground stations located all over the world. AIS data facilitates the mapping and characterization of maritime human and vessel activities, thus allowing for the real-time geo-tracking and identification of vessels equipped with AIS. Hence, in addition to its initial application in collision avoidance, AIS is now also a massive data source of unparalleled quality for diverse tracking tasks. The AIS dataset contains the location and motion features of the vessels. Each data point or row in the AIS data file is represented by a time-sequenced node that contains the vessel’s coordinates, speed, and traveling direction. Each node also has an associated time stamp indicating the data collection time. AIS dataset is suitable for  the track association problem solving approach for its spatio-temporal characteristics.
  • 299
  • 07 Aug 2023
Topic Review
Federated Learning-Based IoT Big Data Management Approach
Federated Learning (FL) is poised to play an essential role in extending the Internet of Things (IoT) and Big Data ecosystems by enabling entities to harness the computational power of private devices, thus safeguarding user data privacy. Despite its benefits, FL is vulnerable to multiple types of assaults, including label-flipping and covert attacks. The label-flipping attack specifically targets the central model by manipulating its decisions for a specific class, which can result in biased or incorrect results.
  • 299
  • 29 Dec 2023
Topic Review
Knowledge Graph Entity Alignment
The objective of the entity alignment (EA) task is to identify entities with identical semantics across distinct knowledge graphs (KGs) situated in the real world, which has garnered extensive recognition in both academic and industrial circles.
  • 298
  • 31 May 2023
Topic Review
Promoting AI Literacy for Children
The advancement of generative AI technologies underscores the need for AI literacy, particularly in elementary Science, Technology, Engineering, Art, and Mathematics (STEAM) education.
  • 298
  • 06 Dec 2023
Topic Review
Federated Learning Models Based on DAG Blockchain
With the development of the power internet of things, the traditional centralized computing pattern has been difficult to apply to many power business scenarios, including power load forecasting, substation defect detection, and demand-side response. How to perform efficient and reliable machine learning tasks while ensuring that user data privacy is not violated has attracted the attention of the industry. Blockchain-based federated learning (FL), proposed as a new decentralized and distributed learning framework for building privacy-enhanced IoT systems, is receiving more and more attention from scholars.
  • 298
  • 22 Nov 2023
Topic Review
Sign-Language Detection
Sign language is the most commonly used form of communication for persons with disabilities who have hearing or speech difficulties. However, persons without hearing impairment cannot understand these signs in many cases. As a consequence, persons with disabilities experience difficulties while expressing their emotions or needs. Thus, a sign character detection and text generation system is necessary to mitigate this issue.
  • 297
  • 25 Oct 2023
Topic Review
Anomaly Detection in Electrocardiogram Sensor Data
Monitoring heart electrical activity is an effective way of detecting existing and developing conditions. This is usually performed as a non-invasive test using a network of up to 12 sensors (electrodes) on the chest and limbs to create an electrocardiogram (ECG). By visually observing these readings, experienced professionals can make accurate diagnoses and, if needed, request further testing. However, the training and experience needed to make accurate diagnoses are significant. 
  • 296
  • 01 Mar 2024
Topic Review
Deep Learning Frameworks and Tools
Deep learning (DL) has been applied successfully in medical imaging such as reconstruction, classification, segmentation, and detection.
  • 296
  • 07 Feb 2024
Topic Review
Deep-Learning-Powered Zero-Watermarking Scheme for Images
In order to safeguard image copyrights, zero-watermarking technology extracts robust features and generates watermarks without altering the original image. Traditional zero-watermarking methods rely on handcrafted feature descriptors to enhance their performance.
  • 295
  • 24 Jan 2024
Topic Review
Improving Visual Defect Detection
Reliable functionality in anomaly detection in thermal image datasets is crucial for defect detection of industrial products. Nevertheless, achieving reliable functionality is challenging, especially when datasets are image sequences captured during equipment runtime with a smooth transition from healthy to defective images. This causes contamination of healthy training data with defective samples. Anomaly detection methods based on autoencoders are susceptible to a slight violation of a clean training dataset and lead to challenging threshold determination for sample classification. 
  • 294
  • 06 Sep 2023
Topic Review
Smoke and Fire Detection Approaches
Forest fires rank among the costliest and deadliest natural disasters globally. Identifying the smoke generated by forest fires is pivotal in facilitating the prompt suppression of developing fires. Nevertheless, succeeding techniques for detecting forest fire smoke encounter persistent issues, including a slow identification rate, suboptimal accuracy in detection, and challenges in distinguishing smoke originating from small sources.
  • 294
  • 17 Nov 2023
Topic Review
Commonsense-Guided Inductive Relation Prediction with Dual Attention Mechanism
Inductive relationship prediction for knowledge graphs, as an important research topic, aims to predict missing relationships between unknown entities and many practical applications. Most of the existing approaches to this problem use closed subgraphs to extract features of target nodes for prediction; however, there is a tendency to ignore neighboring relationships outside the closed subgraphs, which leads to inaccurate predictions. In addition, they ignore the rich commonsense information that can help filter out less compelling results.
  • 294
  • 07 Mar 2024
Topic Review
Connecting the Elderly Using VR
An innovative approach for creating a social virtual reality (VR) platform that seamlessly blends art, technology, artificial intelligence (AI), and VR. Developed as part of a European project, the methodology is designed to safeguard and improve neurological, cognitive, and emotional functions, with a particular emphasis on promoting mental health.
  • 294
  • 19 Mar 2024
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