In search of the best fitness function for optimum generation of trajectories for Automated Guided Vehicles

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 133, P. 108440 - 108440

Published: April 19, 2024

This paper presents an offline optimization method designed for use with industrial robots in environments static obstacles. It is particularly useful industry where stability and predictability are crucial to meeting expected timelines automated guided vehicle operations. The main methodological contribution of this work lies the integral process used define effective fitness function that guides search optimal solutions. cost plays a critical role effectiveness trajectory tracking algorithm by quantifying path quality allowing comparisons between design poses challenges including accuracy, suitability, minimization length, avoiding or reducing collisions. To achieve objectives address some issues such as sensitivity parameter scaling risk premature convergence, different approaches can be used. proposes incorporate constraints into function, adjust parameters reflect conditions problem, based on prior knowledge accurate representation goals. three relevant contributions planning routes following. Firstly, development mathematical model trajectories Frenet curves considers occupancy map environment. Second, strategy generate safe paths. Finally, towards solutions considering resolution parameters. study exhaustive analysis functions obtained, each one evaluated key metrics length trajectory, average minimum distance map, number collisions along path. results show obtained successfully avoids environment all scenarios consistently remains largest obstacles, at least 50% higher than other study.

Language: Английский

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Dual optimization approach in discrete Hopfield neural network

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 164, P. 111929 - 111929

Published: July 7, 2024

Language: Английский

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Combined analysis of thermofluids and electromagnetism using physics-informed neural networks

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 133, P. 108216 - 108216

Published: March 12, 2024

Language: Английский

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Learning characteristic parameters and dynamics of centrifugal pumps under multiphase flow using physics-informed neural networks

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 138, P. 109378 - 109378

Published: Oct. 2, 2024

Language: Английский

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Higher order Weighted Random <i>k</i> Satisfiability ($k = 1, 3$) in Discrete Hopfield Neural Network

AIMS Mathematics, Journal Year: 2025, Volume and Issue: 10(1), P. 159 - 194

Published: Jan. 1, 2025

<p>Researchers have explored various non-systematic satisfiability approaches to enhance the interpretability of Discrete Hopfield Neural Networks. A flexible framework for has been developed investigate diverse logical structures across dimensions and improved lack neuron variation. However, logic phase this approach tends overlook distribution characteristics literal states, ratio negative literals not mentioned with higher-order clauses. In paper, we propose a new named Weighted Random $k$ Satisfiability ($k = 1, 3$), which implements in The proposed logic, integrated into Network, established structure by incorporating during phase. This enhancement increased network's storage capacity, improving its ability handle complex, high-dimensional problems. advanced was evaluated learning metrics. When values were $r 0.2$, 0.4, 0.6, 0.8, demonstrated potential better performances smaller errors. Furthermore, performance positive impact on management synaptic weights. results indicated that optimal global minimum solutions are achieved when set 0.8$. Compared state-of-the-art structures, novel more significant achieving solutions, particularly terms literals.</p>

Language: Английский

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Logic mining method via hybrid discrete hopfield neural network

Computers & Industrial Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 111200 - 111200

Published: May 1, 2025

Language: Английский

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Deep learning driven interpretable and informed decision making model for brain tumour prediction using explainable AI

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: June 1, 2025

Brain Tumours are highly complex, particularly when it comes to their initial and accurate diagnosis, as this determines patient prognosis. Conventional methods rely on MRI CT scans employ generic machine learning techniques, which heavily dependent feature extraction require human intervention. These may fail in complex cases do not produce human-interpretable results, making difficult for clinicians trust the model's predictions. Such limitations prolong diagnostic process can negatively impact quality of treatment. The advent deep has made a powerful tool image analysis tasks, such detecting brain Tumours, by advanced patterns from images. However, models often considered "black box" systems, where reasoning behind predictions remains unclear. To address issue, present study applies Explainable AI (XAI) alongside interpretable Tumour prediction. XAI enhances model interpretability identifying key features size, location, texture, crucial clinicians. This helps build confidence enables them make better-informed decisions. In research, integrated with is proposed develop an framework trained extensive dataset comprising imaging clinical data demonstrates high AUC while leveraging explainability selection. findings indicate that approach improves predictive performance, achieving accuracy 92.98% miss rate 7.02%. Additionally, tools LIME Grad-CAM provide clearer understanding decision-making process, supporting diagnosis represents significant advancement prediction, potential enhance outcomes contribute field neuro-oncology.

Language: Английский

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Data mining algorithm in the identification of accounting fraud by smart city information technology

Heliyon, Journal Year: 2024, Volume and Issue: 10(9), P. e30048 - e30048

Published: April 27, 2024

The identification of accounting fraud is an important measure to safeguard the interests stakeholders and ensure long-term development company. current traditional methods for identifying rely on manual review judgment, lacking objectivity accuracy. In order improve accuracy identification, efficiency objectivity, this article combines smart city information technology conduct in-depth research data mining algorithms identification. This first provides a brief overview cities technology, then introduces basic theory finally implements through k-means clustering algorithm. divided into k clusters, abnormal clusters are identified by checking characteristics attributes each cluster. Compared with rule-based pattern based methods, approach can more flexibly adapt different types forms fraud, discover unknown patterns fraud. experiment, used electronic collection, analysis, retrieval systems websites Shanghai Stock Exchange Shenzhen collect 641 annual reports financial from 62 listed companies that engaged in statement 84 were not reported have 2012 2021 as test samples. results tested analyzed several aspects, including number misjudgments, misjudgment rate, ROC curve. final show compared comprehensive rate has decreased 3 %. conclusion indicates identify help audit effectiveness.

Language: Английский

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Application of artificial bee colony algorithm based on homogenization mapping and collaborative acquisition control in network communication security

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(7), P. e0306699 - e0306699

Published: July 10, 2024

In order to optimize the spectrum allocation strategy of existing wireless communication networks and improve information transmission efficiency data security, this study uses independent correlation characteristics chaotic time series simulate collection control bees, proposes an artificial bee colony algorithm based on uniform mapping collaborative control. Furthermore, it The method begins by establishing a composite system uniformly distributed Chebyshev maps. neighborhood intervals where nectar sources are firmly connected relatively independent, then conducts traversal search. research results demonstrated great performance suggested in each test function as well positive effects optimization network throughput rate was over 300 kbps, quantity security service eavesdropping below 0.1, utilization algorithm-based could be enhanced 0.8 at most. Overall, proposed outperformed comparison algorithm, with high accuracy significant amount optimization. This is favorable for efficient use resources secure data, encourages development technology networks.

Language: Английский

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Unsupervised logic mining with a binary clonal selection algorithm in multi-unit discrete Hopfield neural networks via weighted systematic 2 satisfiability

AIMS Mathematics, Journal Year: 2024, Volume and Issue: 9(8), P. 22321 - 22365

Published: Jan. 1, 2024

<p>Evaluating behavioral patterns through logic mining within a given dataset has become primary focus in current research. Unfortunately, there are several weaknesses the research regarding models, including an uncertainty of attribute selected model, random distribution negative literals logical structure, non-optimal computation best logic, and generation overfitting solutions. Motivated by these limitations, novel model incorporating mechanism to control literal systematic Satisfiability, namely Weighted Systematic 2 Satisfiability Discrete Hopfield Neural Network, is proposed as structure represent behavior dataset. For we used ratio <italic>r</italic> structures prevent solutions optimize synaptic weight values. A new computational approach considering both true false classification values learning system was applied this work preserve significant Additionally, unsupervised techniques such Topological Data Analysis were ensure reliability attributes model. The comparative experiments models utilizing 20 repository real-life datasets conducted from repositories assess their efficiency. Following results, dominated all metrics for average rank. ranks each metric Accuracy (7.95), Sensitivity (7.55), Specificity (7.93), Negative Predictive Value (7.50), Mathews Correlation Coefficient (7.85). Numerical results in-depth analysis demonstrated that consistently produced optimal induced represented performance study.</p>

Language: Английский

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