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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.
  • 437
  • 21 Nov 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.
  • 437
  • 29 Dec 2023
Topic Review
Video Super-Resolution
Super-resolution (SR) refers to yielding high-resolution (HR) images from corresponding low-resolution (LR) images. As a branch of this field, video super-resolution (VSR) mainly utilizes the spatial information of the frame and the temporal information between neighboring frames to reconstruct the HR frame. 
  • 436
  • 27 Oct 2023
Topic Review
Distributed Bayesian Inference for Large-Scale IoT Systems
The Internet of Things (IoT) has emerged as a transformative force in contemporary society, substantially impacting various facets of daily life. Nevertheless, the IoT ecosystem’s rapid expansion is accompanied by a significant increase in data generation, known as Big Data. This expansion presents a complex challenge, necessitating advanced, scalable, and efficient data processing techniques. Given the complex nature of large-scale data analysis in IoT systems, distributed Bayesian inference arises as a practical and efficient solution in this domain. Bayesian methods, which are influential in deriving informed conclusions and predictions from complex datasets, are widely recognized for their probabilistic underpinnings.
  • 436
  • 28 Dec 2023
Topic Review
Incremental Deep Learning for Defect Detection in Manufacturing
Deep learning based visual cognition has greatly improved the accuracy of defect detection, reducing processing times and increasing product throughput across a variety of manufacturing use cases. There is however a continuing need for rigorous procedures to dynamically update model-based detection methods that use sequential streaming during the training phase.
  • 436
  • 23 Feb 2024
Topic Review
Keystroke Dynamics as a Language Profiling Tool
Understanding the distinct characteristics of unidentified Internet users is helpful in various contexts, including digital forensics, targeted advertising, and user interaction with services and systems. Keystroke dynamics (KD) enables the analysis of data derived from a user’s typing behaviour on a keyboard as one approach to obtain such information. 
  • 435
  • 20 Oct 2023
Topic Review
Generative AI
Generative AI models harness the capabilities of neural networks to discern patterns and structures within existing datasets and create original content. These AI models draw inspiration from human neuronal processes, learning from data inputs to create new output that matches learned patterns.
  • 435
  • 22 Feb 2024
Topic Review
Learning Individualized Hyperparameter Settings
The performance of optimization algorithms, and consequently of AI/machine learning solutions, is strongly influenced by the setting of their hyperparameters. Over the last decades, a rich literature has developed proposing methods to automatically determine the parameter setting for a problem of interest, aiming at either robust or instance-specific settings. Robust setting optimization is already a mature area of research, while instance-level setting is still in its infancy, with contributions mainly dealing with algorithm selection.
  • 434
  • 03 Jul 2023
Topic Review
The Detection of Lanes and Lane Markings
Vision-based identification of lane area and lane marking on the road is an indispensable function for intelligent driving vehicles, especially for localization, mapping and planning tasks. However, due to the increasing complexity of traffic scenes, such as occlusion and discontinuity, detecting lanes and lane markings from an image captured by a monocular camera becomes persistently challenging. The lanes and lane markings have a strong position correlation and are constrained by a spatial geometry prior to the driving scene. Most existing studies only explore a single task, i.e., either lane marking or lane detection, and do not consider the inherent connection or exploit the modeling of this kind of relationship between both elements to improve the detection performance of both tasks.
  • 434
  • 08 Aug 2023
Topic Review
Adversarial Attacks in Camera-Based Vision Systems
Vision-based perception modules are increasingly deployed in many applications, especially autonomous vehicles and intelligent robots. These modules are being used to acquire information about the surroundings and identify obstacles. Hence, accurate detection and classification are essential to reach appropriate decisions and take appropriate and safe actions at all times. Adversarial attacks can be categorized into digital and physical attacks.
  • 434
  • 11 Dec 2023
Topic Review
Piano Performance in Unimodality and Multimodality
With the rise in piano teaching in recent years, many people have joined the ranks of piano learners. However, the high cost of traditional manual instruction and the exclusive one-on-one teaching model have made learning the piano an extravagant endeavor. Most existing approaches, based on the audio modality, aim to evaluate piano players' skills. These methods overlook the information contained in videos, resulting in a one-sided and simplistic evaluation of the piano player's skills.
  • 433
  • 09 Jul 2023
Topic Review
Recommendation Systems for e-Shopping
The interest in recommendation systems (RSs) has dramatically increased, as they have become main components of all online stores. The aims of an RS can be multifaceted, related not only to the increase in sales or the convenience of the customer, but may include the promotion of alternative environmentally friendly products or to strengthen policies and campaigns. In addition to accurate suggestions, important aspects of contemporary RSs are therefore to align with the particular marketing goals of the e-shop and with the stances of the targeted audience, ensuring user acceptance, satisfaction, high impact, and achieving sustained usage by customers.
  • 433
  • 21 Dec 2023
Topic Review
Text Emotions on Non-English Datasets
Machine learning approaches, in particular graph learning methods, have achieved great results in the field of natural language processing, in particular text classification tasks. However, many of such models have shown limited generalization on datasets in different languages. 
  • 432
  • 23 Oct 2023
Topic Review
Intrusion Detection and Datasets
With the significant increase in cyber-attacks and attempts to gain unauthorised access to systems and information, Network Intrusion-Detection Systems (NIDSs) have become essential detection tools. Anomaly-based systems use machine learning techniques to distinguish between normal and anomalous traffic. They do this by using training datasets that have been previously gathered and labelled, allowing them to learn to detect anomalies in future data. However, such datasets can be accidentally or deliberately contaminated, compromising the performance of NIDS.
  • 432
  • 30 Jan 2024
Topic Review
Blockchain and Machine Learning
Blockchain is the foundation of all cryptocurrencies, while machine learning (ML) is one of the most popular technologies with a wide range of possibilities. Blockchain may be improved and made more effective by using ML.
  • 431
  • 25 May 2023
Topic Review
Hybrid Evolutionary Approaches for Feature Selection
The efficiency and the effectiveness of a machine learning (ML) model are greatly influenced by feature selection (FS), a crucial preprocessing step in machine learning that seeks out the ideal set of characteristics with the maximum accuracy possible. Due to their dominance over traditional optimization techniques, researchers are concentrating on a variety of metaheuristic (or evolutionary) algorithms and trying to suggest cutting-edge hybrid techniques to handle FS issues. The use of hybrid metaheuristic approaches for FS has thus been the subject of numerous research works.
  • 431
  • 26 Jul 2023
Topic Review
Commonsense Causal Reasoning
Commonsense causal reasoning is the process of understanding the causal dependency between common events or actions. Traditionally, it was framed as a selection problem. However, it cannot obtain enough candidates and needs more flexible causes (or effects) in many scenarios, such as causal-based QA problems. Thus, the ability to generate causes (or effects) is an important problem.
  • 431
  • 13 Dec 2023
Topic Review
Ele-Monitoring Systems and Ontology-Based Models in Asthma Domain
Asthma is a chronic respiratory disease characterized by severe inflammation of the bronchial mucosa. Allergic asthma is the most common form of this health issue. Asthma is classified into allergic and non-allergic asthma, and it can be triggered by several factors such as indoor and outdoor allergens, air pollution, weather conditions, tobacco smoke, and food allergens, as well as other factors. Asthma symptoms differ in their frequency and severity since each patient reacts differently to these triggers. 
  • 430
  • 24 Jun 2022
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.
  • 430
  • 19 Sep 2023
Topic Review
Intelligent Source Code Completion Assistants
As artificial intelligence advances, source code completion assistants are becoming more advanced and powerful. Existing traditional assistants are no longer up to all the developers’ challenges. Traditional assistants usually present proposals in alphabetically sorted lists, which does not make a developer’s tasks any easier (i.e., they still have to search and filter an appropriate proposal manually). As a possible solution to the presented issue, intelligent assistants that can classify suggestions according to relevance in particular contexts have emerged. Artificial intelligence methods have proven to be successful in solving such problems. Advanced intelligent assistants not only take into account the context of a particular source code but also, more importantly, examine other available projects in detail to extract possible patterns related to particular source code intentions. This is how intelligent assistants try to provide developers with relevant suggestions. 
  • 429
  • 17 Jan 2024
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