Transfer learning enables the leveraging of knowledge acquired from other piezoelectric actuators (PEAs) to facilitate the positioning control of a target PEA. However, blind knowledge transfer from datasets irrelevant to the target PEA often leads t...
Transfer learning (TL) hopes to train a model for target domain tasks by using knowledge from different but related source domains. Most TL methods focus more on improving the predictive performance of the single model across domains. Since domain di...
Cervical cells classification is a crucial component of computer-aided cervical cancer detection. Fine-grained classification is of great clinical importance when guiding clinical decisions on the diagnoses and treatment, which remains very challengi...
The rate of technical innovation, system interconnection, and advanced communications undoubtedly boost distributed energy networks’ efficiency. However, when an additional attack surface is made available, the possibility of an increase in att...
Land use and land cover (LULC) classification plays a significant role in the analysis of climate change, evidence-based policies, and urban and regional planning. For example, updated and detailed information on land use in urban areas is highly nee...
Prediction of remaining useful life (RUL) is greatly significant for improving the safety and reliability of manufacturing equipment. However, in real industry, it is difficult for RUL prediction models trained on a small sample of faults to obtain s...
Background: Health disparities research increasingly relies on complex survey data to understand survival differences between population subgroups. While Peters–Belson decomposition provides a principled framework for distinguishing disparities...
Unmanned aerial vehicle (UAV) remote sensing has a wide area of applications and in this paper, we attempt to address one such problem—road extraction from UAV-captured RGB images. The key challenge here is to solve the road extraction problem...
Computer vision-based automation has become popular in detecting and monitoring plants’ nutrient deficiencies in recent times. The predictive model developed by various researchers were so designed that it can be used in an embedded system, kee...
Accurate workload prediction plays a key role in intelligent scheduling decisions on cloud platforms. There are massive amounts of short-workload sequences in the cloud platform, and the small amount of data and the presence of outliers make accurate...
31 March 2023
Background: Access to oral healthcare is not uniform globally, particularly in rural areas with limited resources, which limits the potential of automated diagnostics and advanced tele-dentistry applications. The use of digital caries detection and p...
Pollen identification is an important task for the botanical certification of honey. It is performed via thorough microscopic examination of the pollen present in honey; a process called melissopalynology. However, manual examination of the images is...
Malignant lymphoma, which impacts the lymphatic system, presents diverse challenges in accurate diagnosis due to its varied subtypes—chronic lymphocytic leukemia (CLL), follicular lymphoma (FL), and mantle cell lymphoma (MCL). Lymphoma is a for...
The COVID-19 pandemic has claimed the lives of millions of people and put a significant strain on healthcare facilities. To combat this disease, it is necessary to monitor affected patients in a timely and cost-effective manner. In this work, CXR ima...
Sugarcane is the primary crop in the global sugar industry, yet it remains highly susceptible to a wide range of diseases that significantly impact its yield and quality. An effective solution is required to address the issues caused by the manual id...
Creating an effective deep learning technique for accurately diagnosing leak signals across diverse environments is crucial for integrating artificial intelligence (AI) into the power plant industry. We propose an automatic weight redistribution ense...
With the continuous development of earth observation technology, space-based synthetic aperture radar (SAR) has become an important source of information for maritime surveillance, and ship classification in SAR images has also become a hot research...
Pneumonia is an acute respiratory infection caused by pathogens such as bacteria or viruses, and accurate early diagnosis is critical for reducing mortality. Chest X-ray (CXR) imaging serves as a conventional diagnostic tool. However, radiographic fe...
Tumor boundary identification during colorectal cancer surgery can be challenging, and incomplete tumor removal occurs in approximately 10% of the patients operated for advanced rectal cancer. In this paper, a deep learning framework for automatic tu...
Background/Objectives: Melanoma is an aggressive type of skin cancer that poses serious health risks if not detected in its early stages. Although early diagnosis enables effective treatment, delays can result in life-threatening consequences. Tradit...
Background: Psoriasis is a chronic, immune-mediated skin disease characterized by lifelong persistence and fluctuating symptoms. The clinical similarities among its subtypes and the diversity of symptoms present challenges in diagnosis. Early diagnos...
Gastric cancer is a significant health concern in East Asia, where early risk prediction is critical for prevention. However, the scarcity of single-hospital electronic health records (EHRs) data limits the applicability and generalizability of machi...
Most cross-domain intelligent diagnosis approaches presume that the health states in training datasets are consistent with those in testing. However, it is usually difficult and expensive to collect samples under all failure states during the trainin...
Effective building energy prediction is essential for optimizing energy management, but existing models struggle with data scarcity and sensor heterogeneity across different buildings. Conventional approaches, including centralized and transfer learn...
Ultrasound is one of the most commonly used imaging methodologies in obstetrics to monitor the growth of a fetus during the gestation period. Specifically, ultrasound images are routinely utilized to gather fetal information, including body measureme...
Background and motivation: Lung computed tomography (CT) techniques are high-resolution and are well adopted in the intensive care unit (ICU) for COVID-19 disease control classification. Most artificial intelligence (AI) systems do not undergo genera...
As internet traffic grows daily, so does the need to protect it. Network security protects data from unauthorized access and ensures their confidentiality and integrity. Steganography is the practice and study of concealing communications by insertin...
Ransomware attacks on cloud-encrypted data pose a significant risk to the security and privacy of cloud-based businesses and their consumers. We present RANSOMNET+, a state-of-the-art hybrid model that combines Convolutional Neural Networks (CNNs) wi...
Object detection in uncrewed aerial vehicle (UAV) images has been a longstanding challenge in the field of computer vision. Specifically, object detection in drone images is a complex task due to objects of various scales such as humans, buildings, w...
In this study, we propose a method for inspecting the condition of hull surfaces using underwater images acquired from the camera of a remotely controlled underwater vehicle (ROUV). To this end, a soft voting ensemble classifier comprising six well-k...
Tuberculosis (TB) is an infectious disease affecting humans’ lungs and is currently ranked the 13th leading cause of death globally. Due to advancements in technology and the availability of medical datasets, automatic analysis and classificati...
Alzheimer’s disease is a chronic neurodegenerative disease that causes brain cells to degenerate, resulting in decreased physical and mental abilities and, in severe cases, permanent memory loss. It is considered as the most common and fatal fo...
BCS (Body Condition Score) is a method to estimate body fat reserves and accumulated energy balance of cows, placing estimations (or BCS values) in a scale of 1 to 5. Periodically rating BCS of dairy cows is very important since BCS values are associ...
Breast cancer is the most prevalent cancer worldwide. Thus, it is necessary to improve the efficiency of the medical workflow of the disease. Therefore, this study aims to develop a supplementary diagnostic tool for radiologists using ensemble transf...
Waste disposal remains a challenge due to land availability, and environmental and health issues related to the main disposal method, landfilling. Combining computer vision (machine learning) and robotics to sort waste is a cost-effective solution fo...
Natural ventilation is a critical method for reducing energy consumption for heating, cooling, and ventilating buildings. Recent research has focused on utilizing environmental IoT data from both inside and outside buildings for NVR prediction based...
Process monitoring plays an important role in ensuring the safety and stable operation of equipment in a large-scale process. This paper proposes a novel data-driven process monitoring framework, termed the ensemble adaptive sparse Bayesian transfer...
Skin cancer is one of the most severe forms of the disease, and it can spread to other parts of the body if not detected early. Therefore, diagnosing and treating skin cancer patients at an early stage is crucial. Since a manual skin cancer diagnosis...
Skin lesion classification plays a crucial role in dermatology, aiding in the early detection, diagnosis, and management of life-threatening malignant lesions. However, standalone transfer learning (TL) models failed to deliver optimal performance. I...
Precision and efficacy are vital in the constantly advancing field of food image identification, particularly in the domains of medicine and healthcare. Transfer learning and deep ensemble learning techniques are employed to enhance the accuracy and...
Differential interferometric synthetic aperture radar (DInSAR), coherence, phase, and displacement are derived from processing SAR images to monitor geological phenomena and urban change. Previously, Sentinel-1 SAR data combined with Sentinel-2 optic...
Brain tumor classification plays an important role in clinical diagnosis and effective treatment. In this work, we propose a method for brain tumor classification using an ensemble of deep features and machine learning classifiers. In our proposed fr...
Fused deposition modeling (FDM) is a form of additive manufacturing where three-dimensional (3D) models are created by depositing melted thermoplastic polymer filaments in layers. Although FDM is a mature process, defects can occur during printing. T...
The recognition performance of visual recognition systems is highly dependent on extracting and representing the discriminative characteristics of image data. Convolutional neural networks (CNNs) have shown unprecedented success in a variety of visua...
Melanoma is considered to be the most serious and aggressive type of skin cancer, and metastasis appears to be the most important factor in its prognosis. Herein, we developed a transfer learning-based biomarker discovery model that could aid in the...
Stock price prediction is a core challenge in quantitative finance. While machine learning has advanced the modeling of complex financial time series, existing methods often rely on single-target predictions, underutilize multidimensional market info...
Accurate classification of brain tumors in medical imaging is crucial to ensure reliable diagnoses and effective treatment planning. This study introduces a novel double ensemble framework that synergistically combines pre-trained Deep Learning (DL)...
For cases in which a machine learning model needs to be adapted to a new task, various approaches have been developed, including model-agnostic meta-learning (MAML) and transfer learning. In this paper, we investigate how the differences in the data...
Waste classification is a critical aspect of sustainable waste management systems. Traditional methods for waste classification are often inadequate to handle the complexity and diversity of materials encountered in real-world scenarios. This paper p...
Aiming at the problems of large intra-class differences, small inter-class differences, low contrast, and small and unbalanced datasets in dermoscopic images, this paper proposes a dermoscopic image classification method based on an ensemble of fine-...
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