Understanding the complexities of the fine structure of interest rates: a Wasserstein barycenter learning approach
A novel methodology to investigate the fine structure of interest rates based on Machine Learning techniques is discussed. The aim is to capture in an unsupervised way the common stochastic structure that drives the dynamics of interest rates of ...
Multi-objective cell configuration considering part quality and machine compatibility
We formulate the generalized group technology concept in cellular manufacturing under two new assumptions. Each process routing is indexed by a level of quality and each pair of machines is indexed by a level of compatibility. The quality of parts ...
Implementation of four machine learning algorithms for forecasting stock’s low and high prices
Today, several tools and statistical techniques can be used to find profitable trading opportunities, particularly when it comes to predicting stock closing prices. Yet, a few studies have been done on daily low- and high-price predictions which ...
Temporal prediction model with context-aware data augmentation for robust visual reinforcement learning
While reinforcement learning has shown promising abilities to solve continuous control tasks from visual inputs, it remains a challenge to learn robust representations from high-dimensional observations and generalize to unseen environments with ...
Approaches based on language models for aspect extraction for sentiment analysis in the Portuguese language
- José Carlos Ferreira Neto,
- Denilson Alves Pereira,
- Bruno Henrique Groenner Barbosa,
- Danton Diego Ferreira
This work addresses the gap in aspect extraction techniques for Portuguese by adapting methods originally designed for English. It focuses on TV devices and literary reviews in the TV and ReLi datasets. For this, models based on the BERT ...
Deep learning-based few-shot person re-identification from top-view RGB and depth images
Person re-identification (re-id) attempts to match a person from the images of different time steps. Existing deep learning approaches either use appearance or geometry features for re-id which does not provide the required robustness because of ...
Deep learning for prediction of cardiomegaly using chest X-rays
In the past decade, deep learning in biomedical imaging has exponentially increased the accuracy of disease detection and improved the health standards. This research paper introduces a novel approach for the early detection and diagnosis of ...
Implementation of artificial neural network using Levenberg Marquardt algorithm for Casson–Carreau nanofluid flow over exponentially stretching curved surface
A theoretical framework is constructed for the Casson–Carreau nanofluid flow over a curved surface that is stretched exponentially. Artificial intelligence and machine learning are in vogue as the technologies that involve them, have expanded ...
Detection of neurodegenerative diseases using hybrid MODWT and adaptive local binary pattern
Neurodegenerative diseases cause significant irregularities in walking patterns, impacting gait dynamics and rhythms analyzed through gait time series. Human gait analysis is a promising avenue for identifying unique walking patterns. Automated ...
An empirical study on prediction of seismic activity using stochastic configuration networks
Predicting seismic events has long been a huge challenge due to intricate nature of the underlying occurrence mechanisms. The traditional approach to estimating recurrence intervals is highly reliant on prior knowledge and expert experience, which ...
Recognizing salat activity using deep learning models via smartwatch sensors
In this study, we focus on human activity recognition, particularly aiming to distinguish the activity of praying (salat) from other daily activities. To achieve this goal, we have created a new dataset named HAR-P (Human activity recognition for ...
Blood supply chain location-inventory problem considering incentive programs: comparison and analysis of NSGA-II, NRGA and electromagnetic algorithms
Problem Blood is a rare perishable substance with limited life in the real world and blood supply chain management is a vital subject. Hence, it is trying to design an efficient supply chain network to create a balance between blood supply and ...
A new iterative fuzzy approach to the multi-objective fractional solid transportation problem with mixed constraints using a bisection algorithm
A multi-objective solid transportation problem that includes source, destination, and mode of transport parameters may have fractional objective functions in real-life applications to maximize the profitability ratio, which could be the profit/...
Smoke detection in foggy surveillance environment using parallel vision transformer network
Recent years have seen an unprecedented increase in fire incidents, resulting in severe damage to forest regions, loss of human and animal lives, and unwarranted displacement of people. Owing to these issues, artificial intelligence-based fire ...
Forecasting intraday power output by a set of PV systems using recurrent neural networks and physical covariates
Accurate intraday forecasts of the power output by photovoltaic (PV) systems are critical to improve the operation of energy distribution grids. We describe a neural autoregressive model that aims to perform such intraday forecasts. We build upon ...
A deep learning LSTM-based approach for AMD classification using OCT images
Age-related macular degeneration (AMD) is an age-related, persistent, painless eye disease that impairs central vision. The central area (macula) of the retina, located at the back of the eye, sustains damage that is the cause of loss of vision. ...
Solving dynamic optimization problems using parent–child multi-swarm clustered memory (PCSCM) algorithm
The Particle Swarm Optimization (PSO) algorithm faces several inherent challenges when applied to dynamic and large-scale optimization problems. These challenges encompass the issues of outdated particle memory, inadequate scalability in high-...
Hybrid convolutional neural network approach for optimizing automatic identification of natural isotopes in gamma ray environmental sample spectra
Radioisotope identification presents challenges that can be effectively addressed through pattern recognition and machine learning (ML) techniques. However, further investigation is necessary to assess the accuracy of these algorithms in ...
A learning-based nearly optimal control framework for trajectory tracking of a flexible-link manipulator system with actuator fault
In this paper, a learning-based nearly optimal control framework with fault-tolerant capability is designed to tackle the tracking control problem of a flexible-link manipulator in the presence of actuator fault and model uncertainties. Initially, ...
Development of a non-destructive fruit quality assessment utilizing odour sensing, expert vision and deep learning algorithm
The food and agriculture sector is one of the world's most critical and essential industries, as it provides the necessities of life for a growing global population. Food assessment, especially fruit, is an essential mechanism for producers and ...
Semi-supervised learning for on-street parking violation prediction using graph convolutional networks
Controlled parking systems in cities provide designated parking zones and allow citizens to easily find parking spaces increasing comfort and potentially reducing traffic and pollution. However, illegally occupied parking spaces can negatively ...
Optimization and comparison of machine learning algorithms for the prediction of the performance of football players
Athletes’ performance evaluation is a critical step for the assessment of the skills and the quality of training of the athletes. This is even more important in team sports such as football, in which different roles, and hence different skills, ...
Mean policy-based proximal policy optimization for maneuvering decision in multi-UAV air combat
Autonomous maneuvering decision-making is a crucial technology for Unmanned Aerial Vehicles (UAVs) to take the air domination in modern unmanned warfare. With the advantage of balancing exploration and exploitation, as well as the immediacy of end-...
Using machine learning techniques for the classification of ultra-low concentrations of cannabis in biological fluids
- Hoda Mozaffari,
- Greter Ortega,
- Herlys Viltres,
- Syed Rahin Ahmed,
- Amin Reza Rajabzadeh,
- Seshasai Srinivasan
In this work, the application of three different Machine Learning algorithms, random forest (RF), support vector machine (SVM), and artificial neural network (ANN), to accurately classify ultra-low concentrations of Δ9-tetrahydrocannabinol in ...
Resnet50 and logistic Gaussian map-based zero-watermarking algorithm for medical color images
Medical image copyright protection is becoming increasingly relevant as medical images are used more frequently in medical networks and institutions. The traditional embedded watermarking system is inappropriate for medical images since it ...
A novel type-2 decision mechanism for dynamic parameter adaptation: theory and application in mathematical and structural problems
Metaheuristic algorithms are stochastic-based search techniques widely used for solving different types of optimization problems. These methods mostly adjust their search behavior using pre-defined search pattern(s) regardless of the current ...
Deepfake detection using convolutional vision transformers and convolutional neural networks
- Ahmed Hatem Soudy,
- Omnia Sayed,
- Hala Tag-Elser,
- Rewaa Ragab,
- Sohaila Mohsen,
- Tarek Mostafa,
- Amr A. Abohany,
- Salwa O. Slim
Deepfake technology has rapidly advanced in recent years, creating highly realistic fake videos that can be difficult to distinguish from real ones. The rise of social media platforms and online forums has exacerbated the challenges of detecting ...
MV-DUO: multi-variate discrete unified optimization for psychological vital assessments
Psychological vital assessments are required for monitoring health conditions and observing body reactions toward diseases and medications. Wearable sensors play a vital role in sensing body vitals and presenting them as signals for computer-based ...
A lightweight deep learning architecture for malaria parasite-type classification and life cycle stage detection
Malaria is an endemic in various tropical countries. The gold standard for disease detection is to examine the blood smears of patients by an expert medical professional to detect malaria parasite called Plasmodium. In the rural areas of ...
GCN-SA: a hybrid recommendation model based on graph convolutional network with embedding splicing layer
- Yifei Sun,
- Ao Zhang,
- Shi Cheng,
- Yifei Cao,
- Jie Yang,
- Wenya Shi,
- Jiale Ju,
- Jihui Yin,
- Qiaosen Yan,
- Xinqi Yang,
- Ziang Wang
Graph convolutional networks are capable of handling non-Euclidean data with sparse features, and some research has begun to apply them to the field of recommendation systems. Graph convolutional network’s aggregation and propagation mechanism can ...