(1) With the development of artificial intelligence, people expect to use modern information technology to solve the critical problems encountered in agriculture. How to identify sunflower diseases as early and quickly as possible and take correspond...
Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by cognitive impairment and aberrant protein deposition in the brain. Therefore, the early detection of AD is crucial for the development of effective treatments and interve...
Human organoids are 3D cell culture models that precisely replicate the microenvironment of real organs. In organoid-based experiments, assessing whether the internal vasculature has formed normally is essential for ensuring the reliability of experi...
Human activity recognition in privacy-sensitive indoor environments requires sensing modalities that remain robust under illumination variation and background clutter while preserving user anonymity. To this end, this study proposes a lightweight rad...
Printed circuit board (PCB) manufacturing processes are becoming increasingly complex, where even minor defects can impair product performance and yield rates. Precisely identifying PCB defects is critical but remains challenging. Traditional PCB def...
Synthetic aperture radar (SAR) is an essential tool for monitoring and managing maritime traffic and ensuring safety. It is particularly valuable because it can provide surveillance in all weather conditions. Ship wake detection has attracted conside...
Printed circuit boards (PCBs) are ubiquitous and essential electronic components. Tiny targets and high precision are the focus of PCB defect detection. This paper proposes an improved model focusing on tiny defect detection and model compression to...
The recognition of fire at its early stages and stopping it from causing socioeconomic and environmental disasters remains a demanding task. Despite the availability of convincing networks, there is a need to develop a lightweight network for resourc...
This study proposes ToRLNet, a lightweight tomato ripeness detector designed for real-time deployment in resource-constrained agricultural settings. Built on YOLOv12n, ToRLNet integrates three self-constructed modules (WaveFusionNet for frequency&nda...
Deep learning models are applied in precision agriculture for site-specific weed management by identifying weeds in farmlands. Unfortunately, because deep learning models are usually large, they are rarely adopted in resource-constrained devices (lik...
In the field of medical imaging, deep learning has made considerable strides, particularly in the diagnosis of brain tumors. The Internet of Medical Things (IoMT) has made it possible to combine these deep learning models into advanced medical device...
Nutrient analysis through mobile health applications can improve dietary choices, particularly among vulnerable populations. Current mobile nutrient analysis applications face critical limitations: sophisticated deep learning models require substanti...
In colonoscopy, computer vision and artificial intelligence technology have enabled the automatic detection of the location of polyps and their visualization. These advancements have facilitated considerable research in the field. However, deep learn...
Tea is a globally important economic crop, and the ability to quickly and accurately identify tea leaf diseases can significantly improve both the yield and quality of tea production. With advances in deep learning, many recent studies have demonstra...
Diabetic retinopathy (DR) is a severe complication of diabetes. It affects a large portion of the population of the Kingdom of Saudi Arabia. Existing systems assist clinicians in treating DR patients. However, these systems entail significantly high...
The breast is a vital organ in the female body, both physically and socially. One of the most fatal diseases and the second leading cause of cancer-related deaths among women is breast cancer. Malignant, cancerous lumps that form from breast cells ar...
Tunnel fires pose a severe threat to human safety and infrastructure, necessitating the development of advanced and efficient fire detection systems. This paper presents a novel lightweight deep learning (DL) model specifically designed for real-time...
In deep learning-based side-channel analysis (DL-SCA), there may be a proliferation of model parameters as the number of trace power points increases, especially in the case of raw power traces. Determining how to design a lightweight deep learning m...
Timely and accurate identification of plant diseases is critical to mitigating crop losses and enhancing yield in precision agriculture. This paper proposes AgriFusionNet, a lightweight and efficient deep learning model designed to diagnose plant dis...
Identifying crop growth stages is essential for comprehensive assessment that facilitates timely and precise agronomic management. However, conventional field-based manual scouting is labor-intensive, time-consuming, and lacks scalability. Digital fi...
Background/Objectives: Tuberculosis remains a significant global health problem, particularly in resource-limited environments. Its mortality and spread can be considerably decreased by early and precise detection via chest X-ray imaging. This study...
14 October 2024
Traditional methods of agricultural disease detection rely primarily on manual observation, which is not only time-consuming and labor-intensive, but also prone to human error. The advent of deep learning has revolutionized plant disease detection by...
Efficient spectrum utilization is critical for meeting the growing data demands of modern wireless communication networks. Automatic Modulation Classification (AMC) plays a key role in enhancing spectrum efficiency by accurately identifying modulatio...
Traditional pattern recognition approaches have gained a lot of popularity. However, these are largely dependent upon manual feature extraction, which makes the generalized model obscure. The sequences of accelerometer data recorded can be classified...
Although lightweight deep learning models have shown promise for livestock monitoring, there is still limited evidence regarding their comparative performance and practical deployment under real broiler production conditions characterized by high sto...
Machines in factories are typically operated 24 h a day to support production, which may result in malfunctions. Such mechanical malfunctions may disrupt factory output, resulting in financial losses or human casualties. Therefore, we investigate a d...
The Raman spectroscopy analysis technique has found extensive applications across various disciplines due to its exceptional convenience and efficiency, facilitating the analysis and identification of diverse substances. In recent years, owing to the...
Track fastener defect detection is an essential component in ensuring railway safety operations. Traditional manual inspection methods no longer meet the requirements of modern railways. The use of deep learning image processing techniques for classi...
Minimizing human intervention in engines, such as traffic lights, through automatic applications and sensors has been the focus of many studies. Thus, Deep Learning (DL) algorithms have been studied for traffic signs and vehicle identification in an...
This paper presents a lightweight deep learning framework for classifying electric fire short-circuit traces to enhance safety and fault diagnosis in electrical energy systems. Accurate differentiation between primary (PSCT) and secondary short-circu...
Aircraft wing icing significantly threatens aviation safety, causing substantial losses to the aviation industry each year. High transparency and blurred edges of icing areas in wing images pose challenges to wing icing detection by machine vision. T...
Owing to the demand for smoke and flame detection in natural scenes, this paper proposes a lightweight deep learning model, SF-YOLO (Smoke and Fire-YOLO), for video smoke and flame detection in such environments. Firstly, YOLOv11 is employed as the b...
Detecting bearing defects accurately and efficiently is critical for industrial safety and efficiency. This paper introduces Bearing-DETR, a deep learning model optimised using the Real-Time Detection Transformer (RT-DETR) architecture. Enhanced with...
The purpleback flying squid (Sthenoteuthis oualaniensis) is an economically significant cephalopod species in the Northwest Indian Ocean. Predicting its fishing grounds can provide a crucial foundation for fishery management and production. In this r...
Many individuals worldwide pass away as a result of inadequate procedures for prompt illness identification and subsequent treatment. A valuable life can be saved or at least extended with the early identification of serious illnesses, such as variou...
Digital images are widely used for informal information sharing, but the rise of fake photos spreading misinformation has raised concerns. To address this challenge, image forensics is employed to verify the authenticity and trustworthiness of these...
Early detection of foliar diseases in potato crops remains challenging in real-world agricultural settings because symptoms can be similar and field images present challenges (weather, illumination, orientation, scale, blur, etc.). Although deep lear...
This paper presents a sequential learning method aimed at improving the performance of a lightweight deep learning model used for detecting fires at an early stage in surveillance video streams. The proposed approach involves a sequence of supervised...
Citrus fruit size is a core quality attribute that determines commercial value and market acceptance. In postharvest processing, rapid, accurate, and non-destructive diameter estimation is an essential prerequisite for automated grading, yet existing...
At present, the majority of deep learning-based ship object detection algorithms concentrate predominantly on enhancing recognition accuracy, often overlooking the complexity of the algorithm. These complex algorithms demand significant computational...
Most of the current malware detection methods running on Android are based on signature and cloud technologies leading to poor protection against new types of malware. Deep learning techniques take Android malware detection to a new level. Still, mos...
Effective air quality monitoring and forecasting are essential for safeguarding public health, protecting the environment, and promoting sustainable development in smart cities. Conventional systems are cloud-based, incur high costs, lack accurate De...
Colon cancer is the second most common cause of cancer death in women and the third most common cause of cancer death in men. Therefore, early detection of this cancer can lead to lower infection and death rates. In this research, we propose a new li...
The sugar apple (Annona squamosa) is valued for its taste, nutritional richness, and versatility, making it suitable for fresh consumption and medicinal use with significant commercial potential. Widely found in the tropical Americas and Asia’s...
Background: The CRISPR-Cas9 system has emerged as one of the most promising gene-editing technologies in biology. However, off-target effects remain a significant challenge. While recent advances in deep learning have led to the development of models...
Kidney abnormality is one of the major concerns in modern society, and it affects millions of people around the world. To diagnose different abnormalities in human kidneys, a narrow-beam x-ray imaging procedure, computed tomography, is used, which cr...
Improving agricultural productivity is essential due to rapid population growth, making early detection of crop diseases crucial. Although deep learning shows promise in smart agriculture, practical applications for identifying wheat diseases in comp...
The rapid growth of the Internet of Things (IoT) has exposed billions of interconnected, heterogeneous, and resource-constrained devices to increasingly sophisticated threats. To evaluate the readiness of current intrusion detection systems (IDSs), t...
Nowadays WiFi based human activity recognition (WiFi-HAR) has gained much attraction in an indoor environment due to its various benefits, including privacy and security, device free sensing, and cost-effectiveness. Recognition of human-human interac...
Accurate grading of peach ripeness is essential for determining optimal harvest timing, postharvest storage potential, and market value. However, traditional methods are often inefficient and highly subjective. Meanwhile, existing deep learning model...
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