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32 pages, 7243 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Artificial Intelligence and Extraction of Bioactive Compounds: The Case of Rosemary and Pressurized Liquid Extraction

by Martha Mantiniotou, Vassilis Athanasiadis, Konstantinos G. Liakos, Eleni Bozinou and Stavros I. Lalas

Processes 2025, 13(6), 1879; https://doi.org/10.3390/pr13061879 - 13 Jun 2025

Cited by 1 | Viewed by 482

Abstract

Rosemary (Rosmarinus officinalis or Salvia rosmarinus) is an aromatic herb that possesses numerous health-promoting and antioxidant properties. Pressurized Liquid Extraction (PLE) is an efficient, environmentally friendly technique for obtaining valuable compounds from natural sources. The optimal PLE conditions were established as 25% v/v ethanol at 160 °C for 25 min, and a liquid-to-solid ratio of 10 mL/g. The optimal extract exhibited high polyphenol and antioxidant content through various assays. The recovered bioactive compounds possess potential applications in the food, pharmaceutical, and cosmetics sectors, in addition to serving as feed additives. This research compares two distinct optimization models: one statistical, derived from experimental data, and the other based on artificial intelligence (AI). The objective was to evaluate if AI could replicate experimental models and ultimately supplant the laborious experimental process, yielding the same results more rapidly and adaptably. To further enhance data interpretation and predictive capabilities, six machine learning models were implemented on the original dataset. Due to the limited sample size, synthetic data were generated using Random Forest (RF)-based resampling and Gaussian noise addition. The augmented dataset significantly improved the model performance. Among the models tested, the RF algorithm achieved the highest accuracy. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Industrial Process Modelling and Optimization)

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23 pages, 8091 KiB

Open AccessFeature PaperArticle

GAINESIS: Generative Artificial Intelligence NEtlists SynthesIS

by Konstantinos G. Liakos, Georgios K. Georgakilas, Fotis C. Plessas and Paris Kitsos

Electronics 2022, 11(2), 245; https://doi.org/10.3390/electronics11020245 - 13 Jan 2022

Cited by 14 | Viewed by 4088

Abstract

A significant problem in the field of hardware security consists of hardware trojan (HT) viruses. The insertion of HTs into a circuit can be applied for each phase of the circuit chain of production. HTs degrade the infected circuit, destroy it or leak encrypted data. Nowadays, efforts are being made to address HTs through machine learning (ML) techniques, mainly for the gate-level netlist (GLN) phase, but there are some restrictions. Specifically, the number and variety of normal and infected circuits that exist through the free public libraries, such as Trust-HUB, are based on the few samples of benchmarks that have been created from circuits large in size. Thus, it is difficult, based on these data, to develop robust ML-based models against HTs. In this paper, we propose a new deep learning (DL) tool named Generative Artificial Intelligence Netlists SynthesIS (GAINESIS). GAINESIS is based on the Wasserstein Conditional Generative Adversarial Network (WCGAN) algorithm and area–power analysis features from the GLN phase and synthesizes new normal and infected circuit samples for this phase. Based on our GAINESIS tool, we synthesized new data sets, different in size, and developed and compared seven ML classifiers. The results demonstrate that our new generated data sets significantly enhance the performance of ML classifiers compared with the initial data set of Trust-HUB. Full article

(This article belongs to the Special Issue Circuits and Systems of Security Applications)

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29 pages, 1430 KiB

Open AccessReview

Machine Learning in Agriculture: A Review

by Konstantinos G. Liakos, Patrizia Busato, Dimitrios Moshou, Simon Pearson and Dionysis Bochtis

Sensors 2018, 18(8), 2674; https://doi.org/10.3390/s18082674 - 14 Aug 2018

Cited by 2060 | Viewed by 119849

Abstract

Machine learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multi-disciplinary agri-technologies domain. In this paper, we present a comprehensive review of research dedicated to applications of machine learning in agricultural production systems. The works analyzed were categorized in (a) crop management, including applications on yield prediction, disease detection, weed detection crop quality, and species recognition; (b) livestock management, including applications on animal welfare and livestock production; (c) water management; and (d) soil management. The filtering and classification of the presented articles demonstrate how agriculture will benefit from machine learning technologies. By applying machine learning to sensor data, farm management systems are evolving into real time artificial intelligence enabled programs that provide rich recommendations and insights for farmer decision support and action. Full article

(This article belongs to the Special Issue Sensors in Agriculture 2018)

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