Special Issue "Artificial Intelligence and Machine Learning Algorithms in Real-World Applications and Theories"

Submission Deadline: 03 January 2022 (closed)
Guest Editors
Dr. Elsayed Elknawy, Delta Higher Institute of Engineering and Technology, Egypt.
Dr. Abdelhameed Ibrahim, Mansoura University, Egypt.
Dr. Marwa M. Eid, Delta Higher Institute for Engineering & amp; Technology, Egypt.
Dr. Ali Wagdy Mohamed, Cairo University& Nile University, Egypt.

Summary

Artificial intelligence and machine learning are the hot research topics in recent years, both in theory and applications. However, machine learning models are quick to slip into overfitting issues because machine learning systems have uncertainty or backbox problems; it is challenging to consider how a given algorithm makes a judgment, which is essential in certain fields, especially real-world applications and theories.

 

This special issue focuses on the latest development in the Artificial Intelligence and Machine Learning foundation of real-world applications and theories. We welcome the new research ideas and developments in mathematics and computing relevant to real-world applications and theories from a machine learning perspective, including foundation, systems, innovative application, and other research contributions.


Keywords
Modeling real-world problems
Medical diagnosis
Statistical arbitrage
Predictive analytics
Image recognition
Speech recognition
Artificial intelligence applications
Internet of Things (IoT), IoMT, AIoT & AIoMT
eBusiness, eCommerce, eHealth, eLearning
Deep learning
Computer-based algorithms
Time Series and Forecasting
Smart City
Smart Traffic
Swarm Intelligence
Evolutionary Algorithms

Published Papers


  • Robust Interactive Method for Hand Gestures Recognition Using Machine Learning
  • Abstract The Hand Gestures Recognition (HGR) System can be employed to facilitate communication between humans and computers instead of using special input and output devices. These devices may complicate communication with computers especially for people with disabilities. Hand gestures can be defined as a natural human-to-human communication method, which also can be used in human-computer interaction. Many researchers developed various techniques and methods that aimed to understand and recognize specific hand gestures by employing one or two machine learning algorithms with a reasonable accuracy. This work aims to develop a powerful hand gesture recognition model with a 100% recognition rate. We… More
  •   Views:563       Downloads:368        Download PDF

  • Optimized Ensemble Algorithm for Predicting Metamaterial Antenna Parameters
  • Abstract Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve performance. Metamaterial antennas can overcome the bandwidth constraint associated with tiny antennas. Machine learning is receiving a lot of interest in optimizing solutions in a variety of areas. Machine learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today's technology. The accuracy of the forecast is mostly determined by the model used. The purpose of this article is to provide an optimal ensemble model for predicting the bandwidth and gain of the Metamaterial Antenna. Support Vector… More
  •   Views:657       Downloads:494        Download PDF

  • Hybrid Ensemble-Learning Approach for Renewable Energy Resources Evaluation in Algeria
  • Abstract In order to achieve a highly accurate estimation of solar energy resource potential, a novel hybrid ensemble-learning approach, hybridizing Advanced Squirrel-Search Optimization Algorithm (ASSOA) and support vector regression, is utilized to estimate the hourly tilted solar irradiation for selected arid regions in Algeria. Long-term measured meteorological data, including mean-air temperature, relative humidity, wind speed, alongside global horizontal irradiation and extra-terrestrial horizontal irradiance, were obtained for the two cities of Tamanrasset-and-Adrar for two years. Five computational algorithms were considered and analyzed for the suitability of estimation. Further two new algorithms, namely Average Ensemble and Ensemble using support vector regression were developed… More
  •   Views:670       Downloads:425       Cited by:1        Download PDF

  • CDLSTM: A Novel Model for Climate Change Forecasting
  • Abstract Water received in rainfall is a crucial natural resource for agriculture, the hydrological cycle, and municipal purposes. The changing rainfall pattern is an essential aspect of assessing the impact of climate change on water resources planning and management. Climate change affected the entire world, specifically India’s fragile Himalayan mountain region, which has high significance due to being a climatic indicator. The water coming from Himalayan rivers is essential for 1.4 billion people living downstream. Earlier studies either modeled temperature or rainfall for the Himalayan area; however, the combined influence of both in a long-term analysis was not performed utilizing Deep… More
  •   Views:775       Downloads:628       Cited by:1        Download PDF

  • Gaining-Sharing Knowledge Based Algorithm for Solving Stochastic Programming Problems
  • Abstract This paper presents a novel application of metaheuristic algorithms for solving stochastic programming problems using a recently developed gaining sharing knowledge based optimization (GSK) algorithm. The algorithm is based on human behavior in which people gain and share their knowledge with others. Different types of stochastic fractional programming problems are considered in this study. The augmented Lagrangian method (ALM) is used to handle these constrained optimization problems by converting them into unconstrained optimization problems. Three examples from the literature are considered and transformed into their deterministic form using the chance-constrained technique. The transformed problems are solved using GSK algorithm and… More
  •   Views:622       Downloads:582        Download PDF

  • Numerical Analysis of Laterally Loaded Long Piles in Cohesionless Soil
  • Abstract The capability of piles to withstand horizontal loads is a major design issue. The current research work aims to investigate numerically the responses of laterally loaded piles at working load employing the concept of a beam-on-Winkler-foundation model. The governing differential equation for a laterally loaded pile on elastic subgrade is derived. Based on Legendre-Galerkin method and Runge-Kutta formulas of order four and five, the flexural equation of long piles embedded in homogeneous sandy soils with modulus of subgrade reaction linearly variable with depth is solved for both free- and fixed-headed piles. Mathematica, as one of the world's leading computational software,… More
  •   Views:742       Downloads:565        Download PDF

  • Forecasting of Appliances House in a Low-Energy Depend on Grey Wolf Optimizer
  • Abstract This paper gives and analyses data-driven prediction models for the energy usage of appliances. Data utilized include readings of temperature and humidity sensors from a wireless network. The building envelope is meant to minimize energy demand or the energy required to power the house independent of the appliance and mechanical system efficiency. Approximating a mapping function between the input variables and the continuous output variable is the work of regression. The paper discusses the forecasting framework FOPF (Feature Optimization Prediction Framework), which includes feature selection optimization: by removing non-predictive parameters to choose the best-selected feature hybrid optimization technique has been… More
  •   Views:624       Downloads:595        Download PDF

  • A Deep Two-State Gated Recurrent Unit for Particulate Matter (PM2.5) Concentration Forecasting
  • Abstract Air pollution is a significant problem in modern societies since it has a serious impact on human health and the environment. Particulate Matter (PM2.5) is a type of air pollution that contains of interrupted elements with a diameter less than or equal to 2.5 m. For risk assessment and epidemiological investigations, a better knowledge of the spatiotemporal variation of PM2.5 concentration in a constant space-time area is essential. Conventional spatiotemporal interpolation approaches commonly relying on robust presumption by limiting interpolation algorithms to those with explicit and basic mathematical expression, ignoring a plethora of hidden but crucial manipulating aspects. Many advanced… More
  •   Views:778       Downloads:536        Download PDF

  • An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna
  • Abstract Metamaterial Antenna is a special class of antennas that uses metamaterial to enhance their performance. Antenna size affects the quality factor and the radiation loss of the antenna. Metamaterial antennas can overcome the limitation of bandwidth for small antennas. Machine learning (ML) model is recently applied to predict antenna parameters. ML can be used as an alternative approach to the trial-and-error process of finding proper parameters of the simulated antenna. The accuracy of the prediction depends mainly on the selected model. Ensemble models combine two or more base models to produce a better-enhanced model. In this paper, a weighted average… More
  •   Views:812       Downloads:632        Download PDF

  • SMOTEDNN: A Novel Model for Air Pollution Forecasting and AQI Classification
  • Abstract Rapid industrialization and urbanization are rapidly deteriorating ambient air quality, especially in the developing nations. Air pollutants impose a high risk on human health and degrade the environment as well. Earlier studies have used machine learning (ML) and statistical modeling to classify and forecast air pollution. However, these methods suffer from the complexity of air pollution dataset resulting in a lack of efficient classification and forecasting of air pollution. ML-based models suffer from improper data pre-processing, class imbalance issues, data splitting, and hyperparameter tuning. There is a gap in the existing ML-based studies on air pollution due to improper data… More
  •   Views:683       Downloads:708        Download PDF


  • Graph Transformer for Communities Detection in Social Networks
  • Abstract Graphs are used in various disciplines such as telecommunication, biological networks, as well as social networks. In large-scale networks, it is challenging to detect the communities by learning the distinct properties of the graph. As deep learning has made contributions in a variety of domains, we try to use deep learning techniques to mine the knowledge from large-scale graph networks. In this paper, we aim to provide a strategy for detecting communities using deep autoencoders and obtain generic neural attention to graphs. The advantages of neural attention are widely seen in the field of NLP and computer vision, which has… More
  •   Views:752       Downloads:770        Download PDF