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Topic Review
Transformers in Natural Language Processing Applications
The field of Natural Language Processing (NLP) has undergone a significant transformation with the introduction of Transformers. From the first introduction of this technology in 2017, the use of transformers has become widespread and has had a profound impact on the field of NLP.
  • 1.1K
  • 24 May 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.
  • 1.1K
  • 06 Nov 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.
  • 1.1K
  • 29 Sep 2021
Topic Review
AI-Enabled Models for Parkinson’s Disease Diagnosis
Parkinson’s disease (PD) is a devastating neurological disease that cannot be identified with traditional plasma experiments, necessitating the development of a faster, less expensive diagnostic instrument. Due to the difficulty of quantifying PD in the past, doctors have tended to focus on some signs while ignoring others, primarily relying on an intuitive assessment scale because of the disease’s characteristics, which include loss of motor control and speech that can be utilized to detect and diagnose this disease. It is an illness that impacts both motion and non-motion functions. It takes years to develop and has a wide range of clinical symptoms and prognoses. 
  • 1.1K
  • 15 May 2023
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. 
  • 1.1K
  • 09 Jun 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. 
  • 1.1K
  • 03 Feb 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
Swarm Intelligence Based Load Balancing Techniques
Swarm Intelligence aims to combine relatively high approximate techniques to guide local optimization strategies in order to explore a solution space successfully and efficiently.
  • 1.1K
  • 28 Mar 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
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
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.
  • 1.1K
  • 05 Jan 2023
Topic Review
Opium Poppy Detection in Unmanned Aerial Vehicle Imagery
Opium poppy is a medicinal plant, and its cultivation is illegal without legal approval in China. Unmanned aerial vehicle (UAV) is an effective tool for monitoring illegal poppy cultivation. Unmanned aerial vehicle (UAV) is more flexible and mobile than remote sensing satellite, and their high-resolution images can help to detect poppies in areas that are hard to see. 
  • 1.1K
  • 18 Feb 2024
Topic Review
Balanced Learning for Road Crack Segmentation
Road crack segmentation based on high-resolution images is an important task in road service maintenance. The undamaged road surface area is much larger than the damaged area on a highway. This imbalanced situation yields poor road crack segmentation performance for convolutional neural networks.
  • 1.1K
  • 22 Jul 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.
  • 1.1K
  • 22 Mar 2023
Topic Review
Background of Machine/Deep Learning Approaches on Mental Health
Mental health can be seen as a person’s emotional, psychological, and social well-being. It can be harmed by various mental health conditions, which negatively influence a person’s intellectual capacity, emotions, and social relationships. Machine learning (ML) is a subfield of artificial intelligence (AI) that deals with three problems: classification, regression, and clustering. It utilizes data and algorithms to mimic how people learn while progressively improving accuracy in various tasks.
  • 1.1K
  • 08 Feb 2023
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.
  • 1.1K
  • 25 Aug 2023
Topic Review
Using Colored Petri Net for Accounting System
Many learners who are not familiar with the accounting terms find blended learning very complex to understand with respect to the computerized accounting system, the journal entries process, and tracing the accounting transaction flows of accounting system. A simulation-based model is a viable option to help instructors and learners make understanding the accounting system components and monitoring the accounting transactions easier. This entry briefly introduce a colored Petri net (CPN)-based model.
  • 1.1K
  • 28 Mar 2022
Topic Review
Artificial Intelligence-Assisted Programming Tasks
Artificial intelligence (AI)-assisted programming can enable software engineers to work more efficiently and effectively with the existing software tools such as OpenAI ChatGPT, Github Copilot, DeepMind AlphaCode, Amazon Codewhisperer, Replit Ghostwriter, Microsoft IntelliCode and Codedium, especially in situations where complex algorithms are being used that involve large amounts of code (i.e., Big Code regime). It also strikes a balance between productivity and ensuring safety, security, and reliability within the programming development environment. There are two main categories of AI-assisted programming tasks related to software naturalness: generation and understanding. The former includes code generation, code completion, code translation, code refinement, and code summarization. The latter is concerned with understanding code and includes defect detection and clone detection.
  • 1.1K
  • 03 Jul 2023
Topic Review
Sustainable Food Production
Fault diagnosis and prognosis methods are the most useful tools for risk and reliability analysis in food processing systems. Proactive diagnosis techniques such as failure mode and effect analysis (FMEA) are important for detecting all probable failures and facilitating the risk analysis process. However, significant uncertainties exist in the classical-FMEA when it comes to ranking the risk priority numbers (RPNs) of failure modes. Such uncertainties may have an impact on the food sector’s operational safety and maintenance decisions.
  • 1.1K
  • 28 Mar 2022
Topic Review
Artificial Intelligence in Alzheimer’s Disease
Alzheimer’s disease (AD) represents most of the dementia cases and stands as the most common neurodegenerative disease. A shift from a curative to a preventive approach is imminent, and we are moving towards the application of personalized medicine, whereas we can shape the best clinical intervention for each patient at a given point. This new step in medicine requires the most recent tools and the analysis of huge amounts of data where the application of artificial intelligence (AI) plays a critical part in the depiction of disease-patient dynamics, critical to reach early/optimal diagnosis, monitoring and intervention. Predictive models and algorithms are the key elements in this innovative field. 
  • 1.1K
  • 02 Mar 2022
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