Dual experience replay-based TD3 for single intersection signal control
Compared to traditional traffic signal control methods, the method driven by Deep Reinforcement Learning (DRL) has shown better performance. But the problem of low sample utilization in reinforcement learning also arises. To deal with the problem, ...
An edge detection algorithm based upon the adaptive multi-directional anisotropic gaussian filter and its applications
Edge detection is essential to comprehend and analyze high-resolution remote sensing images. The Gaussian filter is widely used for image preprocessing prior to edge detection. However, some weak edge points in the images with low gradient values ...
A lightweight deep learning model for acute myeloid leukemia-related blast cell identification
Leukemia is a severe blood disorder that poses a threat to the life and well-being of patients. Accurate diagnosis of leukemia typing is crucial for treatment and prognosis. However, manual diagnosis requires significant resources and is subject ...
Wind farm layout optimization using adaptive equilibrium optimizer
The layout optimization of wind turbines seeks to improve wind power conversion efficiency by minimizing the wake effect in wind farm. However, the existing optimization methods cannot provide a layout with high output power due to their inability ...
Mellin transform-based D2D power optimization in 5G-enabled social IoT network
One of the main sources of information dissemination is social networks. Changes in social and Internet of Things (IoT) relationships can affect the quality of service (QoS) of device-to-device (D2D) communication in fifth-generation (5G) ...
A migration strategy based on cluster collaboration predictions for mobile edge computing-enabled smart rail system
As an important part of modern transportation, smart rail system need to handle a large number of delay-sensitive and task-intensive tasks in a high-speed mobile state. However, high-speed mobility challenges the traditional information processing ...
Secure symbol-level precoding for reconfigurable intelligent surface-aided cell-free networks
With the improvement in communication network density, inter-cell interference has become severe. Due to the blurring of boundaries, cell-free networks are considered as a solution. However, it faces some challenges, such as high energy ...
Credit-based energy trading system using blockchain and machine learning
In peer-to-peer (P2P) energy trading, members locally trade energy. Blockchain-based systems are employed for the above trading. These systems are limited in speed because of the time required in the consensus process to audit and verify ...
CBGA: A deep learning method for power grid communication networks service activity prediction
The prediction of power equipment activity plays a vital role in optimizing power resource dispatch, ensuring supply and demand balance, and guiding network planning and management. However, due to the complex nonlinear, multi-scale, and ...
Particle swarm optimization and FM/FM/1/WV retrial queues with catastrophes: application to cloud storage
The cloud storage service, known for its flexible and expandable nature, often has difficulties managing operating costs while ensuring dependable service and quick response times. This investigation presents a novel approach to optimizing cost ...
Optimal UAV deployment with star topology in area coverage problems
UAVs in most real-world deployments communicate with a ground control station either in a one-hop manner, which has a limited working range, or relies on infrastructure support such as satellite communication, which is expensive. Although multi-...
Trish: an efficient activation function for CNN models and analysis of its effectiveness with optimizers in diagnosing glaucoma
Glaucoma is an eye disease that spreads over time without showing any symptoms at an early age and can result in vision loss in advanced ages. The most critical issue in this disease is to detect the symptoms of the disease at an early age. ...
Training with One2MultiSeq: CopyBART for social media keyphrase generation
Keyphrase generation, which can help people obtain key information from a long document (social media posts or scientific articles) in a short time, has made significant progress in recent years, especially for training by concatenating keyphrases ...
Brain hyperintensities: automatic segmentation of white matter hyperintensities in clinical brain MRI images using improved deep neural network
White matter hyperintensities (WMH) are commonly found in the brains of healthy elderly individuals and have been associated with various neurological and geriatric disorders. Automatic WMH segmentation is essential for evaluating the natural ...
Energy-aware application mapping methods for mesh-based hybrid wireless network-on-chips
The 2D mesh topology-based Network-on-Chip (NoC) is a prevalent structure in System-on-Chip (SoC) designs, offering implementation and fabrication benefits. However, increased NoC scale leads to longer communication paths, more hops, and higher ...
A sentiment analysis model based on dynamic pre-training and stacked involutions
Sentiment analysis is one of the core tasks in natural language processing, and its main goal is to identify and classify the sentiment tendencies contained in texts. Traditional sentiment analysis methods and shallow models often fail to capture ...
LMGU-NET: methodological intervention for prediction of bone health for clinical recommendations
- Gautam Amiya,
- Pallikonda Rajasekaran Murugan,
- Kottaimalai Ramaraj,
- Vishnuvarthanan Govindaraj,
- Muneeswaran Vasudevan,
- M. Thirumurugan,
- S. Sheik Abdullah,
- Arunprasath Thiyagarajan
Osteoporosis (OP) is a bone-related ailment that aggravates owing to the decline in bone mineral density (BMD) or during deviations in the structure or quality of bone that may surge to fractures. The low BMD can be recognized from computed ...
e-Diagnostic system for diabetes disease prediction on an IoMT environment-based hyper AdaBoost machine learning model
- Abdulrahman Ahmed Jasim,
- Layth Rafea Hazim,
- Hayder Mohammedqasim,
- Roa’a Mohammedqasem,
- Oguz Ata,
- Omar Hussein Salman
One of the most fatal and serious diseases that humans have encountered is diabetes, an illness affecting thousands of individuals yearly. In this era of digital systems, diabetes prediction based on machine learning (ML) is gaining high momentum. ...
ROVM integrated advanced machine learning-based malaria prediction strategy in Tripura
Malaria is a deadly disease that can take a person's life if not predicted or cured correctly. Numerous factors like temperature, humidity, precipitation, etc., impact India's increasing cases of malaria diseases. This research presents an ...
Robust enhanced collaborative filtering without explicit noise filtering
Graph convolutional neural networks have been successfully applied to collaborative filtering to capture high-quality user-item representations. Despite their remarkable performance, there are still limitations that hinder further improvement of ...
Analyzing FOSS license usage in publicly available software at scale via the SWH-analytics framework
- Alessia Antelmi,
- Massimo Torquati,
- Giacomo Corridori,
- Daniele Gregori,
- Francesco Polzella,
- Gianmarco Spinatelli,
- Marco Aldinucci
The Software Heritage (SWH) dataset represents an invaluable source of open-source code as it aims to collect, preserve, and share all publicly available software in source code form ever produced by humankind. Although designed to archive ...
CryptoHHO: a bio-inspired cryptosystem for data security in Fog–Cloud architecture
The exponential growth of Internet-of-Things (IoT) has raised several data security risks to the Fog–Cloud architecture. The performance and the computation cost of security algorithms hinder providing a secure real-time environment for IoT. This ...
Optimization of uncertain dependent task mapping on heterogeneous computing platforms
Dependent tasks are typically modeled using directed acyclic graphs (DAGs), and scheduling algorithms based on DAGs have been extensively researched. Most of the existing algorithms assume that the task or communication duration is deterministic. ...
Approximate bilateral filters for real-time and low-energy imaging applications on FPGAs
Bilateral filtering is an image processing technique commonly adopted as intermediate step of several computer vision tasks. Opposite to the conventional image filtering, which is based on convolving the input pixels with a static kernel, the ...
Fog-Marketing: auction-based multi-tier decentralized markets for fog resource provisioning
The emergence of a new generation of applications such as the Internet of Things, Artificial Intelligence, and 5G has brought fog computing as cloud development toward the edge network. It is done by adding an extra layer of resources to meet low ...
Construction and analysis of students’ physical health portrait based on principal component analysis improved Canopy-K-means algorithm
With the advancement of society and the improvement of living standards, the significance of students’ physical health has become increasingly prominent. However, currently, the assessment and analysis of students’ physical health heavily depend ...