Toward intelligent food drying: Integrating artificial intelligence into drying systems DOI
Seyed-Hassan Miraei Ashtiani, Alex Martynenko

Drying Technology, Journal Year: 2024, Volume and Issue: 42(8), P. 1240 - 1269

Published: May 24, 2024

Artificial intelligence (AI) and its data-driven counterpart, machine learning (ML), are rapidly evolving disciplines with increasing applications in modeling, simulation, control, optimization within the drying industry. This paper presents a comprehensive overview of progress made ML from shallow to deep implications for food drying. Theoretical foundations, advantages, limitations various approaches employed this domain explored. Additionally, advancements models, particularly those enhanced by algorithms, reviewed. The review underscores role intelligent configuration which affects their accuracy ability solve problems high energy consumption, nutrient degradation, uneven Drawing upon research achievements, integrating AI models real-time measuring methods is discussed, enabling dynamic determination optimal conditions parameter adjustments. integration facilitates automated decision-making, reducing human errors enhancing operational efficiency Moreover, demonstrate proficiency predicting times analyzing usage patterns, thereby minimize resource consumption while preserving product quality. Finally, identifies current obstacles technology development proposes novel avenues sustainable technologies.

Language: Английский

Optimizing the thermal performance of solar energy devices using meta-heuristic algorithms: A critical review DOI
Asif Afzal, Abdulrajak Buradi, Ravindra Jilte

et al.

Renewable and Sustainable Energy Reviews, Journal Year: 2022, Volume and Issue: 173, P. 112903 - 112903

Published: Dec. 6, 2022

Language: Английский

Citations

71
Deep learning in food authenticity: Recent advances and future trends DOI

Zhuowen Deng,

Tao Wang,

Yun Zheng

et al.

Trends in Food Science & Technology, Journal Year: 2024, Volume and Issue: 144, P. 104344 - 104344

Published: Jan. 20, 2024

Language: Английский

Citations

51
Mathematical Models for the Design of GRID Systems to Solve Resource-Intensive Problems DOI Creative Commons
Valeriya Valerievna Tynchenko, В С Тынченко,

Vladimir A. Nelyub

et al.

Mathematics, Journal Year: 2024, Volume and Issue: 12(2), P. 276 - 276

Published: Jan. 15, 2024

Artificial neural networks are successfully used to solve a wide variety of scientific and technical problems. The purpose the study is increase efficiency distributed solutions for problems involving structural-parametric synthesis network models complex systems based on GRID (geographically disperse computing resources) technology through integrated application apparatus evolutionary optimization queuing theory. During course research, following was obtained: (i) New mathematical assessing performance reliability systems; (ii) A new multi-criteria model designing high-resource problems; (iii) decision support system design using genetic algorithm. Fonseca Fleming’s algorithm with dynamic penalty function as method solving stated multi-constrained problem. developed program problem choosing an effective structure centralized that configured models. To test proposed approach, Pareto-optimal configuration built characteristics: average performance–103.483 GFLOPS, cost–500 rubles per day, availability rate–99.92%, minimum performance–51 GFLOPS.

Language: Английский

Citations

47
A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering DOI Creative Commons
Elaheh Yaghoubi, Elnaz Yaghoubi, Ahmed A. Khamees

et al.

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: 36(21), P. 12655 - 12699

Published: May 13, 2024

Abstract Artificial neural networks (ANN), machine learning (ML), deep (DL), and ensemble (EL) are four outstanding approaches that enable algorithms to extract information from data make predictions or decisions autonomously without the need for direct instructions. ANN, ML, DL, EL models have found extensive application in predicting geotechnical geoenvironmental parameters. This research aims provide a comprehensive assessment of applications addressing forecasting within field related engineering, including soil mechanics, foundation rock environmental geotechnics, transportation geotechnics. Previous studies not collectively examined all algorithms—ANN, EL—and explored their advantages disadvantages engineering. categorize address this gap existing literature systematically. An dataset relevant was gathered Web Science subjected an analysis based on approach, primary focus objectives, year publication, geographical distribution, results. Additionally, study included co-occurrence keyword covered techniques, systematic reviews, review articles data, sourced Scopus database through Elsevier Journal, were then visualized using VOS Viewer further examination. The results demonstrated ANN is widely utilized despite proven potential methods engineering due real-world laboratory civil engineers often encounter. However, when it comes behavior scenarios, techniques outperform three other methods. discussed here assist understanding benefits geo area. enables practitioners select most suitable creating certainty resilient ecosystem.

Language: Английский

Citations

20
Implementation of high step-up power converter for fuel cell application with hybrid MPPT controller DOI Creative Commons

V. Prashanth,

Shaik Rafikiran,

CH Hussaian Basha

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Feb. 9, 2024

Abstract As of now, there are multiple types renewable energy sources available in nature which hydro, wind, tidal, and solar. Among all that the solar source is used many applications because its features low maitainence cost, less human power for handling, a clean source, more availability nature, reduced carbon emissions. However, disadvantages networks continuously depending on weather conditions, high complexity storage, lots installation place required. So, this work, Proton Exchange Membrane Fuel Stack (PEMFS) utilized supplying to local consumers. The merits fuel stack density, ability work at very temperature values, efficient heat maintenance, water management. Also, gives quick startup response. only demerit PEMFS excessive current production, plus output voltage. To optimize supply stack, Wide Input Operation Single Switch Boost Converter (WIOSSBC) circuit placed across improve load voltage profile. advantages WIOSSBC ripples, uniform supply, good conversion ratio. Another issue nonlinear production. linearize Grey Wolf Algorithm Dependent Fuzzy Logic Methodology (GWADFLM) introduced article maintaining operating point cell near Maximum Power Point (MPP) place. entire system investigated by utilizing MATLAB software.

Language: Английский

Citations

19
Predictive models for flexible pavement fatigue cracking based on machine learning DOI Creative Commons
Ali Alnaqbi, Waleed Zeiada, Ghazi G. Al-Khateeb

et al.

Transportation Engineering, Journal Year: 2024, Volume and Issue: 16, P. 100243 - 100243

Published: March 11, 2024

Pavement performance prediction is crucial for ensuring the longevity and safety of road networks. In our extensive study, we employ a diverse array techniques to enhance fatigue models in flexible pavements. The methodology begins with Random Forest feature selection, identifying top 15 critical variables that significantly impact pavement performance. These form basis subsequent model development. Our investigation into indicates superiority advanced machine learning methods such as Regression Trees (RT), Gaussian Process (GPR), Support Vector Machines (SVM), Ensemble (ET), Artificial Neural Networks (ANN) over traditional linear regression methods. This consistent outperformance underscores their potential reshape forecasting accuracy. Through optimization, reveal robust across both complete selected sets, emphasizing importance meticulous selection enhancing forecast accuracy best optimized highlighted by its Performance Measurement metrics: RMSE 22.416, MSE 502.46, R-squared 0.80848, MAE 8.9958. Additionally, comparative analysis previous empirical demonstrates outperforms existing models. work significance curation prediction, highlighting sophisticated modeling methodologies. Embracing cutting-edge technologies facilitates data-driven decisions, ultimately contributing development more networks, safety, prolonging lifespan.

Language: Английский

Citations

19
Enhanced Random Vector Functional Link Based on Artificial Protozoa Optimizer to Predict Wear Characteristics of Cu-ZrO2 Nanocomposites DOI Creative Commons
Mamdouh I. Elamy, Mohamed Abd Elaziz, Mohammed Azmi Al‐Betar

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: 24, P. 103007 - 103007

Published: Sept. 25, 2024

Language: Английский

Citations

19
Obstacle Avoidance and Path Planning Methods for Autonomous Navigation of Mobile Robot DOI Creative Commons
Kornél Katona, Husam A. Neamah, Péter Köröndi

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(11), P. 3573 - 3573

Published: June 1, 2024

Path planning creates the shortest path from source to destination based on sensory information obtained environment. Within planning, obstacle avoidance is a crucial task in robotics, as autonomous operation of robots needs reach their without collisions. Obstacle algorithms play key role robotics and vehicles. These enable navigate environment efficiently, minimizing risk collisions safely avoiding obstacles. This article provides an overview algorithms, including classic techniques such Bug algorithm Dijkstra’s algorithm, newer developments like genetic approaches neural networks. It analyzes detail advantages, limitations, application areas these highlights current research directions robotics. aims provide comprehensive insight into state prospects applications. also mentions use predictive methods deep learning strategies.

Language: Английский

Citations

18
Machine learning applications for electrospun nanofibers: a review DOI Creative Commons

Balakrishnan Subeshan,

Asonganyi Atayo,

Eylem Asmatulu

et al.

Journal of Materials Science, Journal Year: 2024, Volume and Issue: 59(31), P. 14095 - 14140

Published: July 30, 2024

Abstract Electrospun nanofibers have gained prominence as a versatile material, with applications spanning tissue engineering, drug delivery, energy storage, filtration, sensors, and textiles. Their unique properties, including high surface area, permeability, tunable porosity, low basic weight, mechanical flexibility, alongside adjustable fiber diameter distribution modifiable wettability, make them highly desirable across diverse fields. However, optimizing the properties of electrospun to meet specific requirements has proven be challenging endeavor. The electrospinning process is inherently complex influenced by numerous variables, applied voltage, polymer concentration, solution flow rate, molecular weight polymer, needle-to-collector distance. This complexity often results in variations nanofibers, making it difficult achieve desired characteristics consistently. Traditional trial-and-error approaches parameter optimization been time-consuming costly, they lack precision necessary address these challenges effectively. In recent years, convergence materials science machine learning (ML) offered transformative approach electrospinning. By harnessing power ML algorithms, scientists researchers can navigate intricate space more efficiently, bypassing need for extensive experimentation. holds potential significantly reduce time resources invested producing wide range applications. Herein, we provide an in-depth analysis current work that leverages obtain target nanofibers. examining work, explore intersection ML, shedding light on advancements, challenges, future directions. comprehensive not only highlights processes but also provides valuable insights into evolving landscape, paving way innovative precisely engineered various Graphical abstract

Language: Английский

Citations

18
Exploring Types of Photonic Neural Networks for Imaging and Computing—A Review DOI Creative Commons
Svetlana N. Khonina, Nikolay L. Kazanskiy, Р. В. Скиданов

et al.

Nanomaterials, Journal Year: 2024, Volume and Issue: 14(8), P. 697 - 697

Published: April 17, 2024

Photonic neural networks (PNNs), utilizing light-based technologies, show immense potential in artificial intelligence (AI) and computing. Compared to traditional electronic networks, they offer faster processing speeds, lower energy usage, improved parallelism. Leveraging light’s properties for information could revolutionize diverse applications, including complex calculations advanced machine learning (ML). Furthermore, these address scalability efficiency challenges large-scale AI systems, potentially reshaping the future of computing research. In this comprehensive review, we provide current, cutting-edge insights into types PNNs crafted both imaging purposes. Additionally, delve intricate encounter during implementation, while also illuminating promising perspectives introduce field.

Language: Английский

Citations

16