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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. 
  • 1.2K
  • 09 Jun 2023
Topic Review
Network Optimization in the Internet of Vehicles
The Internet of Things (IoT) has risen from ubiquitous computing to the Internet itself. Internet of vehicles (IoV) is the next emerging trend in IoT. People can build intelligent transportation systems (ITS) using IoV. However, overheads are imposed on IoV network due to a massive quantity of information being transferred from the devices connected in IoV. One such overhead is the network connection between the units of an IoV. To make an efficient ITS using IoV, optimization of network connectivity is required. 
  • 1.2K
  • 28 Jan 2023
Topic Review
Detecting Dementia from Face-Related Features
Alzheimer’s disease (AD) is a type of dementia that is more likely to occur as people age. It currently has no known cure. As the world’s population is aging quickly, early screening for AD has become increasingly important. Traditional screening methods such as brain scans or psychiatric tests are stressful and costly. The patients are likely to feel reluctant to such screenings and fail to receive timely intervention. While researchers have been exploring the use of language in dementia detection, less attention has been given to face-related features.
  • 1.2K
  • 08 Aug 2023
Topic Review
Federated Learning for Intrusion Detection Systems in IoV
The Internet of Vehicles (IoV) has garnered significant attention from researchers and automotive industry professionals due to its expanding range of applications and services aimed at enhancing road safety and driver/passenger comfort. However, the massive amount of data spread across this network makes securing it challenging. The IoV network generates, collects, and processes vast amounts of valuable and sensitive data that intruders can manipulate. An intrusion detection system (IDS) is the most typical method to protect such networks. An IDS monitors activity on the road to detect any sign of a security threat and generates an alert if a security anomaly is detected. Federated Learning (FL) is a decentralized machine learning technique, FL allows model training on client devices while maintaining user data privacy.
  • 1.2K
  • 22 Jan 2024
Topic Review
Machine Learning Applications in Surface Transportation Systems
Surface transportation has evolved through technology advancements using parallel knowledge areas such as machine learning (ML). ML is the most sophisticated state-of-the-art knowledge branch offering the potential to solve unsettled or difficult-to-solve problems as a data-driven approach.
  • 1.2K
  • 23 Sep 2022
Topic Review
Computer-Aided Breast Cancer Diagnosis
A computer-aided diagnosis (CAD) expert system is a powerful tool to efficiently assist a pathologist in achieving an early diagnosis of breast cancer. This process identifies the presence of cancer in breast tissue samples and the distinct type of cancer stages. In a standard CAD system, the main process involves image pre-processing, segmentation, feature extraction, feature selection, classification, and performance evaluation. Breast cancer can be distinguished as benign (non-cancerous) and malignant (cancerous/metastatic) tumours. Benign tissue refers to changes in normal tissue of breast parenchyma, which does not relate to the development of malignancy . Contrarily, malignant tissue can be categorised into two types: in-situ carcinoma and invasive carcinoma.  
  • 1.2K
  • 15 Jun 2021
Topic Review
DDoS Detection Using DL Models and Explanation Methods
With the rise of distributed denial of service (DDoS) attacks, several machine learning-based attack detection models have been used to mitigate malicious behavioral attacks. Understanding how machine learning models work is not trivial. This is particularly true for complex and nonlinear models, such as deep learning models that have high accuracy. The struggle to explain these models creates a tension between accuracy and explanation. Different methods have been used to explain deep learning models and address ambiguity issues.
  • 1.2K
  • 25 Aug 2023
Topic Review
Infrared and Visible Image Fusion Technologies
Infrared and visible image fusion is to combine the information of thermal radiation and detailed texture from the two images into one informative fused image. Deep learning methods have been widely applied in this task; however, those methods usually fuse multiple extracted features with the same fusion strategy, which ignores the differences in the representation of these features, resulting in the loss of information in the fusion process. Infrared and visible image fusion techniques can be divided into two categories: traditional methods and deep learning-based methods. Traditional methods have been proposed for the fusion of pixel-level or fixed features. 
  • 1.2K
  • 03 Feb 2023
Topic Review
Artificial Intelligence Marketing for Customer-Relationships
Artificial intelligence marketing (AIM), which is an interdisciplinary research topic, is a disruptive technology that enables machines to automate the process of collecting and processing a massive amount of data and information to create knowledge related to marketing mix. This capability is essential to manifest personalization at scale, which has been impossible through human effort alone. This paper synthesizes the literature and develops an AIM framework to create a quantum leap in customer relationship enhancement, including customer trust, satisfaction, commitment, engagement, and loyalty.
  • 1.2K
  • 29 Sep 2021
Topic Review
Applications of Machine Learning in Fluid Mechanics
The significant growth of artificial intelligence (AI) methods in machine learning (ML) and deep learning (DL) has opened opportunities for fluid dynamics and its applications in science, engineering and medicine. Developing AI methods for fluid dynamics encompass different challenges than applications with massive data, such as the Internet of Things.
  • 1.2K
  • 24 Aug 2023
Topic Review
Deep Learning for Motor Imagery Brain–Computer Interface
The field of brain–computer interface (BCI) enables us to establish a pathway between the human brain and computers, with applications in the medical and nonmedical field. Brain computer interfaces can have a significant impact on the way humans interact with machines. In recent years, the surge in computational power has enabled deep learning algorithms to act as a robust avenue for leveraging BCIs. 
  • 1.2K
  • 17 Oct 2023
Topic Review
Approaches of Automated Heart Disease Prediction
Cardiovascular diseases (CVDs) are the leading cause of death globally. Detecting this kind of disease represents the principal concern of many scientists, and techniques belonging to various fields have been developed to attain accurate predictions. 
  • 1.2K
  • 26 Oct 2023
Topic Review
Filmmaking Education and Artificial Intelligence
Artificial intelligence (AI) has witnessed remarkable advancements, revolutionizing various industries and domains, including education. From intelligent algorithms in the financial sector to diagnostic tools in healthcare and autonomous vehicles in transportation, artificial intelligence (AI) has demonstrated immense potential across various domains. However, despite its remarkable strides in many fields, the application of AI in the realm of education has lagged behind. Its full potential in the field of filmmaking education remains largely untapped. 
  • 1.2K
  • 26 Dec 2023
Topic Review
Artificial Intelligence in the Diagnosis of Dairy Cows
With the rapid growth of computational power and data transfer capabilities, machine learning (ML) and artificial intelligence (AI) are also making inroads into animal husbandry and veterinarian research. In particular, Infrared thermography (IRT) is being increasingly used for health monitoring and the diagnosis of dairy cows, especially in studies related to heat stress, which causes severe losses, helping us analyze its effects on nutrition, milk production, reproduction, etc. There is plenty of evidence for the potential benefits of using IRT for monitoring udder health status in dairy cows and for the early detection of mastitis. Its role in detecting hoof lesions and lameness has also been reported. The growth of the population and the increase of quality standards has set a requirement for the production of more and better quality food. The capabilities and potential benefits of IRT make systems for the automatic collection and processing of thermographic information and decision-making particularly important.
  • 1.2K
  • 06 Nov 2023
Topic Review
Mobile-Hypertensive Retinopathy
Hypertensive retinopathy (HR) is a serious eye disease that causes the retinal arteries to change. This change is mainly due to the fact of high blood pressure. Cotton wool patches, bleeding in the retina, and retinal artery constriction are affected lesions of HR symptoms.
  • 1.1K
  • 29 May 2023
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.
  • 1.1K
  • 22 Mar 2023
Topic Review
Remote Sensing Object Detection
Remote sensing image object detection holds signifificant research value in resources and the environment. Nevertheless, complex background information and considerable size differences between objects in remote sensing images make it challenging.
  • 1.1K
  • 04 Sep 2023
Topic Review
Vulgar Word Extraction in Chittagonian Dialect of Bangla
The proliferation of the internet, especially on social media platforms, has amplified the prevalence of cyberbullying and harassment. Addressing this issue involves harnessing natural language processing (NLP) and machine learning (ML) techniques for the automatic detection of harmful content. However, these methods encounter challenges when applied to low-resource languages like the Chittagonian dialect of Bangla. This entry compares two approaches for identifying offensive language containing vulgar remarks in Chittagonian. The first relies on basic keyword matching, while the second employs machine learning and deep learning techniques. The keyword- matching approach involves scanning the text for vulgar words using a predefined lexicon. Despite its simplicity, this method establishes a strong foundation for more sophisticated ML and deep learning approaches. An issue with this approach is the need for constant updates to the lexicon.
  • 1.1K
  • 17 Nov 2023
Topic Review
Data-Driven Learning Methods for Network Intrusion Detection Systems
An effective anomaly-based intelligent IDS (AN-Intel-IDS) must detect both known and unknown attacks. Hence, there is a need to train AN-Intel-IDS using dynamically generated, real-time data in an adversarial setting. Furthermore, the lack of real-time data produces potentially biased models that are less effective in predicting unknown attacks. Therefore, training AN-Intel-IDS using imbalanced and adversarial learning is instrumental to their efficacy and high performance. Unfortunately, the public datasets available to train AN-Intel-IDS are ineluctably static, unrealistic, and prone to obsolescence. Furthermore, the lack of real-time data produces potentially biased models that are less effective in predicting unknown attacks. Therefore, training AN-Intel-IDS using imbalanced and adversarial learning is instrumental to their efficacy and high performance. 
  • 1.1K
  • 28 Feb 2022
Topic Review
Deep Learning Techniques in Fault Diagnosis and Prognosis
The fault diagnosis and prognosis (FDP) technique based on data-driven machine learning (ML) methods recognizes or learns the health features of the system from historical data, and tries to discover and mine the information hidden in the data, so that it can accurately analyze and predict future system behavior without precisely knowing the forward physical model.
  • 1.1K
  • 06 Feb 2023
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