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Data Science and Big Data in Biology, Physical Science and...
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Data Science and Big Data in Biology, Physical Science and Engineering II

A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Information and Communication Technologies".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 8208

Special Issue Editor

Dr. Mohammed Mahmoud

E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, School of Applied Sciences, Dickinson State University, 291 Campus Drive, Dickinson, ND 58601, USA
Interests: data science; big data; machine learning; deep learning; artificial intelligence (AI); cybersecurity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Currently, big data analysis represents one of the most important contemporary areas of development and research. Tremendous amounts of data are generated every single day from digital technologies and modern information systems, such as cloud computing and Internet of Things (IoT) devices. The analysis of these enormous amounts of data has become of crucial significance and requires a great deal of effort in order to extract valuable knowledge for decision-making, which, in turn, will make important contributions in both academia and industry.

Big data and data science have emerged due to the significant need for generating, storing, organising, and processing immense amounts of data. Data scientists strive to use artificial intelligence (AI) and machine learning (ML) approaches and models to enable computers to detect and identify what the data represents and detect patterns more quickly, efficiently, and reliably than humans.

The goal behind this Special Issue is to explore and discuss various principles, tools, and models in the context of data science, aside from the diverse and varied concepts and techniques relating to big data in biology, chemistry, biomedical engineering, physics, mathematics, and other areas that work with big data.

Related SI “Data Science and Big Data in Biology, Physical Science and Engineering”

https://www.mdpi.com/journal/technologies/special_issues/Data_Science_Biology

Dr. Mohammed Mahmoud
Guest Editor

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. Technologies is an international peer-reviewed open access monthly 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 1600 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

  • data science
  • big data
  • machine learning
  • artificial intelligence

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Published Papers (4 papers)

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Research

16 pages, 4393 KiB  
Article
A Field-Programmable Gate Array-Based Quasi-Cyclic Low-Density Parity-Check Decoder with High Throughput and Excellent Decoding Performance for 5G New-Radio Standards
by Bilal Mejmaa, Ismail Akharraz and Abdelaziz Ahaitouf
Technologies 2024, 12(11), 215; https://doi.org/10.3390/technologies12110215 - 31 Oct 2024
Viewed by 889
Abstract
This work presents a novel fully parallel decoder architecture designed for high-throughput decoding of Quasi-Cyclic Low-Density Parity-Check (QC-LDPC) codes within the context of 5G New-Radio (NR) communication. The design uses the layered Min-Sum (MS) algorithm and focuses on increasing throughput to meet the [...] Read more.
This work presents a novel fully parallel decoder architecture designed for high-throughput decoding of Quasi-Cyclic Low-Density Parity-Check (QC-LDPC) codes within the context of 5G New-Radio (NR) communication. The design uses the layered Min-Sum (MS) algorithm and focuses on increasing throughput to meet the strict needs of enhanced Mobile BroadBand (eMBB) applications. We incorporated a Sub-Optimal Low-Latency (SOLL) technique to enhance the critical check node processing stage inherent to the MS algorithm. This technique efficiently computes the two minimum values, rendering the architecture well-suited for specific Ultra-Reliable Low-Latency Communication (URLLC) scenarios. We design the decoder to be reconfigurable, enabling efficient operation across all expansion factors. We rigorously validate the decoder’s effectiveness through meticulous bit-error-rate (BER) performance evaluations using Hardware Description Language (HDL) co-simulation. This co-simulation utilizes a well-established suite of tools encompassing MATLAB/Simulink for system modeling and Vivado, a prominent FPGA design suite, for hardware representation. With 380,737 Look-Up Tables (LUTs) and 32,898 registers, the decoder’s implementation on a Virtex-7 XC7VX980T FPGA platform by AMD/Xilinx shows good hardware utilization. The architecture attains a robust operating frequency of 304.5 MHz and a normalized throughput of 49.5 Gbps, marking a 36% enhancement compared to the state-of-the-art. This advancement propels decoding capabilities to meet the demands of high-speed data processing. Full article
(This article belongs to the Special Issue Data Science and Big Data in Biology, Physical Science and Engineering II)
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22 pages, 2370 KiB  
Article
A Hierarchical Machine Learning Method for Detection and Visualization of Network Intrusions from Big Data
by Jinrong Wu, Su Nguyen, Thimal Kempitiya and Damminda Alahakoon
Technologies 2024, 12(10), 204; https://doi.org/10.3390/technologies12100204 - 17 Oct 2024
Viewed by 1224
Abstract
Machine learning is regarded as an effective approach in network intrusion detection, and has gained significant attention in recent studies. However, few intrusion detection methods have been successfully applied to detect anomalies in large-scale network traffic data, and low explainability of the complex [...] Read more.
Machine learning is regarded as an effective approach in network intrusion detection, and has gained significant attention in recent studies. However, few intrusion detection methods have been successfully applied to detect anomalies in large-scale network traffic data, and low explainability of the complex algorithms has caused concerns about fairness and accountability. A further problem is that many intrusion detection systems need to work with distributed data sources in the cloud. In this paper, we propose an intrusion detection method based on distributed computing to learn the latent representations from large-scale network data with lower computation time while improving the intrusion detection accuracy. Our proposed classifier, based on a novel hierarchical algorithm combining adaptability and visualization ability from a self-structured unsupervised learning algorithm and achieving explainability from self-explainable supervised algorithms, is able to enhance the understanding of the model and data. The experimental results show that our proposed method is effective, efficient, and scalable in capturing the network traffic patterns and detecting detailed network intrusion information such as type of attack with high detection performance, and is an ideal method to be applied in cloud-computing environments. Full article
(This article belongs to the Special Issue Data Science and Big Data in Biology, Physical Science and Engineering II)
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18 pages, 2515 KiB  
Article
Discovering Data Domains and Products in Data Meshes Using Semantic Blueprints
by Michalis Pingos and Andreas S. Andreou
Technologies 2024, 12(7), 105; https://doi.org/10.3390/technologies12070105 - 7 Jul 2024
Viewed by 1473
Abstract
Nowadays, one of the greatest challenges in data meshes revolves around detecting and creating data domains and data products for providing the ability to adapt easily and quickly to changing business needs. This requires a disciplined approach to identify, differentiate and prioritize distinct [...] Read more.
Nowadays, one of the greatest challenges in data meshes revolves around detecting and creating data domains and data products for providing the ability to adapt easily and quickly to changing business needs. This requires a disciplined approach to identify, differentiate and prioritize distinct data sources according to their content and diversity. The current paper tackles this highly complicated issue and suggests a standardized approach that integrates the concept of data blueprints with data meshes. In essence, a novel standardization framework is proposed that creates data products using a metadata semantic enrichment mechanism, the latter also offering data domain readiness and alignment. The approach is demonstrated using real-world data produced by multiple sources in a poultry meat production factory. A set of functional attributes is used to qualitatively compare the proposed approach to existing data structures utilized in storage architectures, with quite promising results. Finally, experimentation with different scenarios varying in data product complexity and granularity suggests a successful performance. Full article
(This article belongs to the Special Issue Data Science and Big Data in Biology, Physical Science and Engineering II)
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14 pages, 5243 KiB  
Article
Neural Network-Based Body Weight Prediction in Pelibuey Sheep through Biometric Measurements
by Alfonso J. Chay-Canul, Enrique Camacho-Pérez, Fernando Casanova-Lugo, Omar Rodríguez-Abreo, Mayra Cruz-Fernández and Juvenal Rodríguez-Reséndiz
Technologies 2024, 12(5), 59; https://doi.org/10.3390/technologies12050059 - 30 Apr 2024
Viewed by 1915
Abstract
This paper presents an intelligent system for the dynamic estimation of sheep body weight (BW). The methodology used to estimate body weight is based on measuring seven biometric parameters: height at withers, rump height, body length, body diagonal length, total body length, semicircumference [...] Read more.
This paper presents an intelligent system for the dynamic estimation of sheep body weight (BW). The methodology used to estimate body weight is based on measuring seven biometric parameters: height at withers, rump height, body length, body diagonal length, total body length, semicircumference of the abdomen, and semicircumference of the girth. A biometric parameter acquisition system was developed using a Kinect as a sensor. The results were contrasted with measurements obtained manually with a flexometer. The comparison gives an average root mean square error (RMSE) of 9.91 and a mean R2 of 0.81. Subsequently, the parameters were used as input in a back-propagation artificial neural network. Performance tests were performed with different combinations to make the best choice of architecture. In this way, an intelligent body weight estimation system was obtained from biometric parameters, with a 5.8% RMSE in the weight estimations for the best architecture. This approach represents an innovative, feasible, and economical alternative to contribute to decision-making in livestock production systems. Full article
(This article belongs to the Special Issue Data Science and Big Data in Biology, Physical Science and Engineering II)
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