Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.8
3.9
Journals
Sustainability
Special Issues
Sophisticated Soft Computing Techniques for Sustainable Engineering and Sciences
sustainability-logo
Submit to Sustainability Review for Sustainability Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Sophisticated Soft Computing Techniques for Sustainable Engineering and Sciences

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 7853

Special Issue Editors

Dr. Mohammed H. Alsharif

E-Mail Website
Guest Editor
College of Electronics and Information Engineering, Sejong University, Seoul 05006, Republic of Korea
Interests: wireless communications; network information theory; internet of things; green communication; sustainable cellular networks
Special Issues, Collections and Topics in MDPI journals
Dr. Kannadasan Raju

E-Mail Website
Guest Editor
Department of Electrical and Electronics Engineering, Sri Venkateswara College of Engineering, Sripeumbudur, Chennai, India
Interests: renewable energy resources; micro-grid; electric vehicles; smart waste management; Internet of Things (IoT); metal oxide surge arrester; distributed generations; repowering of the wind farm
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Presently, sustainability engineering and science is a rapidly developing research area that seeks to comprehend the fundamental characteristics of the connection between society and nature. However, it faces intricate challenges due to its high degree of complexity; therefore, it is essential to introduce sophisticated approaches, tools, and techniques that facilitate stakeholders and decision makers to make decisions by considering the broad range of uncertainty with diminutive data sets. Sophisticated soft computing techniques have the potential to solve complex problems in various applications, irrespective of the domain. The main objective of this Special Issue is to consolidate the most advanced soft computational approaches to solve cumbersome problems experienced in various applications that also lead to sustainable development in engineering and sciences.

We are seeking sophisticated soft computing approaches based on well-defined work that considers the topics mentioned below but that is not necessarily limited to the following areas:           

  • Sustainable natural resources and environmental modelling;
  • Sustainable waste management;
  • Air quality modeling;
  • Crop yield prediction and drought prediction;
  • Green and 5G/6G communication systems;
  • Renewable energy resources for sustainable power generation;
  • Sustainable transportation;
  • Smart metering for sustainable power distribution;
  • Smart cities;
  • Green buildings;
  • Interaction of machine learning with IoT;
  • Forecasting problems;
  • Disaster management;
  • Health care applications.

We hope this Special Issue will reach precise, concrete, and concise conclusions that will make significant contributions to the establishment of new horizons for future research directions.

Dr. Mohammed H. Alsharif
Dr. Kannadasan Raju
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • soft computing
  • internet of things
  • communication systems
  • renewable energy resources
  • agronomy
  • smart cities
  • health care engineering
  • disaster management
  • smart transportation

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 2168 KiB  
Article
Traffic Pattern Classification in Smart Cities Using Deep Recurrent Neural Network
by Ayad Ghany Ismaeel, Krishnadas Janardhanan, Manishankar Sankar, Yuvaraj Natarajan, Sarmad Nozad Mahmood, Sameer Alani and Akram H. Shather
Sustainability 2023, 15(19), 14522; https://doi.org/10.3390/su151914522 - 06 Oct 2023
Cited by 9 | Viewed by 1346
Abstract
This paper examines the use of deep recurrent neural networks to classify traffic patterns in smart cities. We propose a novel approach to traffic pattern classification based on deep recurrent neural networks, which can effectively capture traffic patterns’ dynamic and sequential features. The [...] Read more.
This paper examines the use of deep recurrent neural networks to classify traffic patterns in smart cities. We propose a novel approach to traffic pattern classification based on deep recurrent neural networks, which can effectively capture traffic patterns’ dynamic and sequential features. The proposed model combines convolutional and recurrent layers to extract features from traffic pattern data and a SoftMax layer to classify traffic patterns. Experimental results show that the proposed model outperforms existing methods regarding accuracy, precision, recall, and F1 score. Furthermore, we provide an in-depth analysis of the results and discuss the implications of the proposed model for smart cities. The results show that the proposed model can accurately classify traffic patterns in smart cities with a precision of as high as 95%. The proposed model is evaluated on a real-world traffic pattern dataset and compared with existing classification methods. Full article
(This article belongs to the Special Issue Sophisticated Soft Computing Techniques for Sustainable Engineering and Sciences)
►▼ Show Figures

Figure 1

24 pages, 2178 KiB  
Article
Enhancing Traffic Intelligence in Smart Cities Using Sustainable Deep Radial Function
by Ayad Ghany Ismaeel, Jereesha Mary, Anitha Chelliah, Jaganathan Logeshwaran, Sarmad Nozad Mahmood, Sameer Alani and Akram H. Shather
Sustainability 2023, 15(19), 14441; https://doi.org/10.3390/su151914441 - 03 Oct 2023
Cited by 11 | Viewed by 1978
Abstract
Smart cities have revolutionized urban living by incorporating sophisticated technologies to optimize various aspects of urban infrastructure, such as transportation systems. Effective traffic management is a crucial component of smart cities, as it has a direct impact on the quality of life of [...] Read more.
Smart cities have revolutionized urban living by incorporating sophisticated technologies to optimize various aspects of urban infrastructure, such as transportation systems. Effective traffic management is a crucial component of smart cities, as it has a direct impact on the quality of life of residents and tourists. Utilizing deep radial basis function (RBF) networks, this paper describes a novel strategy for enhancing traffic intelligence in smart cities. Traditional methods of traffic analysis frequently rely on simplistic models that are incapable of capturing the intricate patterns and dynamics of urban traffic systems. Deep learning techniques, such as deep RBF networks, have the potential to extract valuable insights from traffic data and enable more precise predictions and decisions. In this paper, we propose an RBF-based method for enhancing smart city traffic intelligence. Deep RBF networks combine the adaptability and generalization capabilities of deep learning with the discriminative capability of radial basis functions. The proposed method can effectively learn intricate relationships and nonlinear patterns in traffic data by leveraging the hierarchical structure of deep neural networks. The deep RBF model can learn to predict traffic conditions, identify congestion patterns, and make informed recommendations for optimizing traffic management strategies by incorporating these rich and diverse data. To evaluate the efficacy of our proposed method, extensive experiments and comparisons with real-world traffic datasets from a smart city environment were conducted. In terms of prediction accuracy and efficiency, the results demonstrate that the deep RBF-based approach outperforms conventional traffic analysis methods. Smart city traffic intelligence is enhanced by the model capacity to capture nonlinear relationships and manage large-scale data sets. Full article
(This article belongs to the Special Issue Sophisticated Soft Computing Techniques for Sustainable Engineering and Sciences)
►▼ Show Figures

Figure 1

17 pages, 2741 KiB  
Article
Prediction of Cooling Load of Tropical Buildings with Machine Learning
by Gebrail Bekdaş, Yaren Aydın, Ümit Isıkdağ, Aidin Nobahar Sadeghifam, Sanghun Kim and Zong Woo Geem
Sustainability 2023, 15(11), 9061; https://doi.org/10.3390/su15119061 - 03 Jun 2023
Cited by 3 | Viewed by 1223
Abstract
Cooling load refers to the amount of energy to be removed from a space (or consumed) to bring that space to an acceptable temperature or to maintain the temperature of a space at an acceptable range. The study aimed to develop a series [...] Read more.
Cooling load refers to the amount of energy to be removed from a space (or consumed) to bring that space to an acceptable temperature or to maintain the temperature of a space at an acceptable range. The study aimed to develop a series of models and determine the most accurate ones in the prediction of the cooling load of low-rise tropical buildings based on their basic architectural and structural characteristics. In this context, a series of machine learning (regression) algorithms were tested during the research to determine the most accurate/efficient prediction model. In this regard, a data set consisting of ten features indicating the basic characteristics of the building (floor area, aspect ratio, ceiling height, window material, external wall material, roof material, window wall ratio north faced, window wall ratio south faced, horizontal shading, orientation) were used to predict the cooling load of a low-rise tropical building. The dataset was generated utilizing a set of generative and algorithmic design tools. Following the dataset generation, a series of regression models were tested to find the most accurate model to predict the cooling load. The results of the tests with different algorithms revealed that the relationship between the predictor variables and cooling load could be efficiently modeled through Histogram Gradient Boosting and Stacking models. Full article
(This article belongs to the Special Issue Sophisticated Soft Computing Techniques for Sustainable Engineering and Sciences)
►▼ Show Figures

Figure 1

29 pages, 7829 KiB  
Article
A Novel Hybrid MPPT Approach for Solar PV Systems Using Particle-Swarm-Optimization-Trained Machine Learning and Flying Squirrel Search Optimization
by Dilip Kumar, Yogesh Kumar Chauhan, Ajay Shekhar Pandey, Ankit Kumar Srivastava, Varun Kumar, Faisal Alsaif, Rajvikram Madurai Elavarasan, Md Rabiul Islam, Raju Kannadasan and Mohammed H. Alsharif
Sustainability 2023, 15(6), 5575; https://doi.org/10.3390/su15065575 - 22 Mar 2023
Cited by 10 | Viewed by 2466
Abstract
In this paper, a novel hybrid Maximum Power Point Tracking (MPPT) algorithm using Particle-Swarm-Optimization-trained machine learning and Flying Squirrel Search Optimization (PSO_ML-FSSO) has been proposed to obtain the optimal efficiency for solar PV systems. The proposed algorithm was compared with other well-known methods [...] Read more.
In this paper, a novel hybrid Maximum Power Point Tracking (MPPT) algorithm using Particle-Swarm-Optimization-trained machine learning and Flying Squirrel Search Optimization (PSO_ML-FSSO) has been proposed to obtain the optimal efficiency for solar PV systems. The proposed algorithm was compared with other well-known methods viz. Perturb & Observer (P&O), Incremental Conductance (INC), Particle Swarm Optimization (PSO), Cuckoo Search Optimization (CSO), Flower Pollen Algorithm (FPA), Gray Wolf Optimization (GWO), Neural-Network-trained Machine Learning (NN_ML), Genetic Algorithm (GA), and PSO-trained Machine Learning. The proposed algorithm was modelled in the MATLAB/Simulink environment under different operating conditions, for example, with step changes in temperature, solar irradiance, and partial shading. The proposed algorithm improved the efficiency up to 0.72% and reduced the settling time up to 76.4%. The findings of the research highlight that PSO_ML-FSSO is a potential approach that outperforms all other well-known algorithms tested herein for solar PV systems. Full article
(This article belongs to the Special Issue Sophisticated Soft Computing Techniques for Sustainable Engineering and Sciences)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop