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Applied Sciences
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Recent Applications of High-Performance Computing
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Recent Applications of High-Performance Computing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 5342

Special Issue Editors

Dr. Sanam Shahla Rizvi

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Guest Editor
Raptor Interactive (Pty) Ltd., Eco Boulevard, Witch Hazel Ave., Centurion 0157, South Africa
Interests: storage systems; wireless sensor networks; high performance computing and communication
Dr. Usman Tariq

E-Mail Website
Guest Editor
College of Business Administration, Prince Sattam bin Abdulaziz University, Alkharj 16278, Saudi Arabia
Interests: internet of things; network communication; cyber security; distributed systems; machine learning; pattern recognition; predictive analytics; smart infrastructures
Dr. Shafique Ahmad Chaudhry

E-Mail Website
Guest Editor
David D. Reh School of Business, Clarkson University, Potsdam, NY 13699, USA
Interests: data analytics; internet of things; service provisioning
Dr. Irfan Ahmed

E-Mail Website
Guest Editor
Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
Interests: digital forensics; malware; cyber-physical systems; cybersecurity education
Dr. Adnan Shahid Khan

E-Mail Website
Guest Editor
Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Kota Samarahan 94300, Malaysia
Interests: wireless communication; cloud computing; internet of things; software defined networking; cryptography; network and information security
Dr. Yasir Malik

E-Mail Website
Guest Editor
Computer Science Department, Bishop's University, 2600 College St., Sherbrooke, QC J1M 1Z7, Canada
Interests: discipline of software engineering; machine learning; security and vulnerability assessment; distributed and mobile computing system

Special Issue Information

Dear Colleagues,

The future generation of smart settings may be powered by the Internet of Things (IoT), the Industrial  Internet of Things (IIoT), and the Internet of Medical Things (IoMT). These devices can transfer the required data through the utilization of wireless technology as well as the cloud computing infrastructure. Effective process execution necessitates both low latency and fully autonomous operations. Intelligent computerization has allowed the perspectives of artificial intelligence to improve both online and offline business operations in a reliable, secure, and consistent manner.

This Special Issue aims to provide an opportunity for academic and industry experts to share innovative research and discuss how it relates to the broader context and limitations in the subject area. Submissions are sought after that are novel, previously unpublished, and display essential research impacts from either an operational perspective or an application viewpoint.

For this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Novel research on network connectivity and interoperability
  • Artificial intelligence (AI)-driven IoT system engineering
  • Urban computing
  • Autonomous, adaptive, and reconfigurable systems
  • Novel data management techniques
  • Novel data management techniques
  • Social community and service discovery method of IoT
  • Intelligent workflows for smart environments
  • Advances in blockchain
  • Prediction modeling using data, algorithms, and machine learning
  • Smart healthcare (reliable connectivity, cybersecurity, and platform scalability)
  • Green Industry 4.0
  • Pervasive analytics for intelligent systems
  • Mobile cloud computing intrusion detection systems
  • Evolutionary algorithms for mining smart-network environments for
  • decision support
  • Vehicular information systems, healthcare information systems, and other
  • such interactive applications
  • Trust, security, and privacy issues
  • Location-based services

We look forward to receiving your contributions.

Dr. Sanam Shahla Rizvi
Dr. Usman Tariq
Dr. Shafique Ahmad Chaudhry
Dr. Irfan Ahmed
Dr. Adnan Shahid Khan
Dr. Yasir Malik
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. Applied Sciences 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

  • novel research on network connectivity and interoperability
  • artificial intelligence (AI)-driven IoT system engineering
  • urban computing
  • autonomous, adaptive, and reconfigurable systems
  • novel data management techniques
  • novel data management techniques
  • social community and service discovery method of IoT
  • intelligent workflows for smart environments
  • advances in blockchain
  • prediction modeling using data, algorithms, and machine learning
  • smart healthcare (reliable connectivity, cybersecurity, and platform
  • scalability)
  • Green Industry 4.0
  • pervasive analytics for intelligent systems
  • mobile cloud computing intrusion detection systems
  • evolutionary algorithms for mining smart-network environments for
  • decision support
  • vehicular information systems, healthcare information systems, and other
  • such interactive applications
  • trust, security, and privacy issues
  • location-based services

Published Papers (3 papers)

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Research

18 pages, 3827 KiB  
Article
Renewable-Aware Frequency Scaling Approach for Energy-Efficient Deep Learning Clusters
by Hyuk-Gyu Park and Dong-Ki Kang
Appl. Sci. 2024, 14(2), 776; https://doi.org/10.3390/app14020776 - 16 Jan 2024
Viewed by 642
Abstract
Recently, renewable energy has emerged as an attractive means to reduce energy consumption costs for deep learning (DL) job processing in modern GPU-based clusters. In this paper, we propose a novel Renewable-Aware Frequency Scaling (RA-FS) approach for energy-efficient DL clusters. We have developed [...] Read more.
Recently, renewable energy has emerged as an attractive means to reduce energy consumption costs for deep learning (DL) job processing in modern GPU-based clusters. In this paper, we propose a novel Renewable-Aware Frequency Scaling (RA-FS) approach for energy-efficient DL clusters. We have developed a real-time GPU core and memory frequency scaling method that finely tunes the training performance of DL jobs while maximizing renewable energy utilization. We introduce quantitative metrics: Deep Learning Job Requirement (DJR) and Deep Learning Job Completion per Slot (DJCS) to accurately evaluate the service quality of DL job processing. Additionally, we present a log-transformation technique to convert our non-convex optimization problem into a solvable one, ensuring the rigorous optimality of the derived solution. Through experiments involving deep neural network (DNN) model training jobs such as SqueezeNet, PreActResNet, and SEResNet on NVIDIA GPU devices like RTX3060, RTX3090, and RTX4090, we validate the superiority of our RA-FS approach. The experimental results show that our approach significantly improves performance requirement satisfaction by about 71% and renewable energy utilization by about 31% on average, compared to recent competitors. Full article
(This article belongs to the Special Issue Recent Applications of High-Performance Computing)
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22 pages, 1959 KiB  
Article
On Predictive Modeling Using a New Three-Parameters Modification of Weibull Distribution and Application
by Yusra Tashkandy and Walid Emam
Appl. Sci. 2023, 13(6), 3909; https://doi.org/10.3390/app13063909 - 19 Mar 2023
Cited by 3 | Viewed by 992
Abstract
In this article, a new modification of the Weibull model with three parameters, the new exponential Weibull distribution (E-WD), is defined. The new model has many statistical advantages, the heavy-tailed behavior and the regular variation property were offered. Many of the important statistical [...] Read more.
In this article, a new modification of the Weibull model with three parameters, the new exponential Weibull distribution (E-WD), is defined. The new model has many statistical advantages, the heavy-tailed behavior and the regular variation property were offered. Many of the important statistical functions of the modified model are presented in closed forms. The flexibility of E-WD has been improved. The proposed model can be used to fit data with different shapes, it can be right-skewed, left-skewed, decreasing, curved and symmetric. Some distribution properties of the proposed model, including moment generating function, characteristic function, moment, quantile and identifiability property, have been derived. In addition to the information generating function, the Shannon entropy and information energy are also discussed. The maximum likelihood approach and Bayesian estimation are used to estimate the distribution parameters. In the Bayesian method, three different loss functions are used. The calculations show the biases and estimated risks to obtain the best estimator. The bootstrap confidence intervals, the asymptotic confidence intervals and the observed variance-covariance matrix are obtained. Metropolis Hastings’ MCMC procedure is used for the calculations. We apply the composite distribution to stock data for four variables. The goodness-of-fit results show that the model performs well compared to its competitors. The proposed model can be used for forecasting and decision making. Full article
(This article belongs to the Special Issue Recent Applications of High-Performance Computing)
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17 pages, 3115 KiB  
Article
Prediction and Classification of User Activities Using Machine Learning Models from Location-Based Social Network Data
by Naimat Ullah Khan, Wanggen Wan, Rabia Riaz, Shuitao Jiang and Xuzhi Wang
Appl. Sci. 2023, 13(6), 3517; https://doi.org/10.3390/app13063517 - 09 Mar 2023
Cited by 4 | Viewed by 3076
Abstract
The current research has aimed to investigate and develop machine-learning approaches by using the data in the dataset to be applied to classify location-based social network data and predict user activities based on the nature of various locations (such as entertainment). The analysis [...] Read more.
The current research has aimed to investigate and develop machine-learning approaches by using the data in the dataset to be applied to classify location-based social network data and predict user activities based on the nature of various locations (such as entertainment). The analysis of user activities and behavior from location-based social network data is often based on venue types, which require the input of data into various categories. This has previously been done through a tedious and time-consuming manual method. Therefore, we proposed a novel approach of using machine-learning models to extract these venue categories. In this study, we used a Weibo dataset as the main source of research and analyzed machine-learning methods for more efficient implementation. We proposed four models based on well-known machine-learning techniques, including the generalized linear model, logistic regression, deep learning, and gradient-boosted trees. We designed, tested, and evaluated these models. We then used various assessment metrics, such as the Receiver Operating Characteristic or Area Under the Curve, Accuracy, Recall, Precision, F-score, and Sensitivity, to show how well these methods performed. We discovered that the proposed machine-learning models are capable of accurately classifying the data, with deep learning outperforming the other models with 99% accuracy, followed by gradient-boosted tree with 98% and 93%, generalized linear model with 90% and 85%, and logistic regression with 86% and 91%, for multiclass distributions and single class predictions, respectively. We classified the data using our machine-learning models into the 10 classes we used in our previous study and predicted tourist destinations among the data to demonstrate the effectiveness of using machine learning for location-based social network data analysis, which is vital for the development of smart city environments in the current technological era. Full article
(This article belongs to the Special Issue Recent Applications of High-Performance Computing)
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