Parameter Tuning Research Articles

Enhancing Malicious User Recognition Using Coot Optimization Algorithm with Bayesian Belief Network for Cognitive Radio Networks

As a dynamic paradigm, Cognitive radio networks (CRNs) in wireless transmission enable devices to intelligently adapt their communication parameter based on real-world spectrum availability. Spectrum sensing lies at the core of CRNs, where nodes continue to monitor the spectrum for underutilized or unused band detection. However, the presence of malicious users (MUs) has a significant impact reliability and performance of the network. MUs detection is indispensable to prevent interference or unauthorized access and ensure network integrity. Advanced techniques combining game theory, machine learning, and signal processing are used for effectively identifying and mitigating malicious activities. CRNs can ensure efficient spectrum utilization and enhance security in heterogeneous and dynamic environments by incorporating robust MU detection systems into spectrum sensing protocols. This article presents a Malicious User Recognition using the Coot Optimization Algorithm with Bayesian Belief Network (MUR-COABBN) technique for CRN. The MUR-COABBN technique exploits metaheuristics with a Bayesian machine-learning method for the classification of the MUs in the CRN. In the MUR-COABBN technique, the COA is initially used to choose better feature subsets. Moreover, the detection of MUs can be performed by the use of BBN. Finally, the parameter tuning of the BBN model is carried out using an improved seeker optimization algorithm (ISOA). The experimental evaluation of the MUR-COABBN technique takes place with respect to distinct aspects. The experimentation outcomes implied the improved performance of the MUR-COABBN methodology with other methods under distinct measures. Therefore, the MUR-COABBN model can effectually and accurately improve security in the CRN.

Efficient wind farm layout optimization with the FLOWERS AEP model and analytic gradients

Wind farm layout optimization (WFLO) studies often aim to maximize the annual energy production (AEP) of a wind farm by choosing an arrangement of turbines that minimizes wake interactions. One way to reduce the cost of WFLO studies is by using more computationally efficient AEP models. The cost of standard AEP modeling approaches, based on the numerical integration of low-fidelity engineering wake models, scales poorly with the number of simulated discrete wind conditions. A second way to reduce cost when using a gradient-based algorithm is to supply exact gradient information instead of finite-difference estimates. However, analytical functions for the derivatives of AEP with respect to turbine positions are not always available in the conventional modeling approach. FLOWERS is a computationally inexpensive, analytical model for wind farm AEP that is specifically developed for WFLO applications. In this paper, we analyze the performance of the FLOWERS AEP model with analytic gradients in a layout optimization study compared with a reference optimization framework across three wind farm case studies. We find that the FLOWERS-based approach reduces computation time by a factor of 50–4000 and improves optimal AEP by about 0.3% with less than half of the variability in AEP across instances with randomized initial conditions. We also find the optimal layouts to be insensitive to model parameter tuning, making FLOWERS-based layout optimization a streamlined, user-friendly approach.

Comprehensive Analysis of Stock Price Dynamics Using Ensemble Machine Learning Models for Enhanced Prediction Accuracy

Stock price prediction is an important component of the financial analysis because the results influence the increase in economic growth and investment. This work aims to develop an ensemble SL technique that consists of mainly PCA, PSO, and SVM to achieve better prediction. Hence, through PCA, large numbers of stocked data dimensions are compressed without compromising on the crucial feature of data set. The problem of parameter selection for non-linear datasets is handled by using a bio-inspired optimization technique known as PSO in order to optimize the SVM hyperparameters. As the core accurate predictor model, the SVM employs the Radial Basis Function to provide the substantial regression capacity for sophisticated financial data sets. The ensemble framework was used with actual stock price data and the information set into training and testing sets. The acknowledgement of probable manifold values indicated that the proposed approach is more accurate than conventional approaches, with an accuracy rate of 95.5 %, when benchmarked using RMSE or MAE. In particular, the forecasts of stock prices by integrating PCA for feature reduction and PSO for parameter tuning with SVM regression is a notable improvement. The proposed methodology can be easily applied to scale for financial analytics since it manages to solve for the issues of noisy and non-linear high dimensional data.

Artificial Intelligence based Automated Sign Gesture Recognition Solutions for Visually Challenged People

Gesture recognition is employed in human-machine communications, enhancing human life with impairments or who depend on non-verbal instructions. Hand gestures role an important role in the field of assistive technology for persons with visual impairments, whereas an optimum user communication design is of major importance. Many authors with substantial development for gesture recognition modeled several methods by using deep learning (DL) methods. This article introduces a Robust Gesture Sign Language Recognition Using Chicken Earthworm Optimization with Deep Learning (RSLR-CEWODL) approach. The projected RSLR-CEWODL algorithm majorly focuses on the recognition and classification of sign language. To accomplish this, the presented RSLR-CEWODL technique utilizes a residual network (ResNet-101) model for feature extraction. For optimal hyper parameter tuning process, the presented RSLR-CEWODL algorithm exploits the CEWO algorithm. Besides, the RSLR-CEWODL technique uses a whale optimization algorithm (WOA) with deep belief network (DBN) method for the sign language recognition method. The simulation result of the RSLR-CEWODL algorithm is tested using sign language datasets and the outcome was measured under various measures. The simulation values demonstrated the enhancements of the RSLR-CEWODL technique over other methodologies.

Adaptive Parameter Tuning and Virtual Impedance Injection Control for Coupled Harmonic Mitigation of Photovoltaic Converter

Adaptive Parameter Tuning and Virtual Impedance Injection Control for Coupled Harmonic Mitigation of Photovoltaic Converter

Development of MHz X-ray phase contrast imaging at the European XFEL.

We report on recent developments that enable megahertz hard X-ray phase contrast imaging (MHz XPCI) experiments at the Single Particles, Clusters, and Biomolecules and Serial Femtosecond Crystallography (SPB/SFX) instrument of the European XFEL facility (EuXFEL). We describe the technical implementation of the key components, including an MHz fast camera and a modular indirect X-ray microscope system based on fast scintillators coupled through a high-resolution optical microscope, which enable full-field X-ray microscopy with phase contrast of fast and irreversible phenomena. The image quality for MHz XPCI data showed significant improvement compared with a pilot demonstration of the technique using parallel beam illumination, which also allows access to up to 24 keV photon energies at the SPB/SFX instrument of the EuXFEL. With these developments, MHz XPCI was implemented as a new method offered for a broad user community (academic and industrial) and is accessible via standard user proposals. Furthermore, intra-train pulse diagnostics with a high few-micrometre spatial resolution and recording up to 128 images of consecutive pulses in a train at up to 1.1 MHz repetition rate is available upstream of the instrument. Together with the diagnostic camera upstream of the instrument and the MHz XPCI setup at the SPB/SFX instrument, simultaneous two-plane measurements for future beam studies and feedback for machine parameter tuning are now possible.

A critical approach to clustering precipitation series in the Dobrogea region, Romania

This study provides a detailed framework for applying clustering algorithms to analyze precipitation data from the Dobrogea region in Romania, covering 46 meteorological stations from 1965 to 2005. Three clustering methods—K-means, K-medoids, and DBSCAN—were employed to partition the stations based on their monthly precipitation patterns. The primary goal was to outline the implementation process, highlight the use of specific R packages, and demonstrate parameter tuning to optimize clustering performance. Validation measures, including internal and stability indices, were used to assess the quality of each clustering method. While initial results indicated that K-medoids offer stable clusters and DBSCAN effectively handles noise, further comparative analysis with additional methods is necessary to determine the most suitable clustering technique for precipitation data. This work serves as a practical guide for selecting, implementing, and validating clustering algorithms in environmental data analysis.

Integrating Neutrosophic Vague N-Soft Sets with Chimp Optimization Algorithm for Sentiment Analysis on Social Media

The swift development in social media through the internet produces vast data in a real-time scenario that has startling effects on large datasets. It generated the high-level use of sentiments and emotions in social networking media. Sentiment analysis (SA) using a neutrosophic set presents a new technique to handle the integral ambiguity and uncertainty in text datasets. Different from classical approaches, which categorize sentiment as positive, negative, or neutral, the neutrosophic set allows for the comparison analysis of truth-, indeterminacy-, and falsie-membership functions for all the sentiments. This allows a more flexible and nuanced representation of sentiments, which accommodates the contradictions and complexities commonly depicted in natural language. SA can accomplish high performance and depth in interpreting and understanding the emotions expressed in uncertain and diverse text datasets by leveraging a neutrosophic set. This manuscript presents a Neutrosophic Vague N-Soft set with a Chimp Optimization Algorithm for Sentiment Analysis (NVNSS-COASA) technique on Social Media. The NVNSS-COASA technique is initiated by the comprehensive preprocessing stage to normalize and clean the text dataset, which ensures superior input for the succeeding stage. Then, the Term Frequency-Inverse Document Frequency (TF-IDF) mechanism is employed to convert the preprocessed text into mathematical features, which capture the word importance in terms of datasets. Subsequently, a strong NVNSS classifier is employed for accurately categorizing the sentiment. We integrate COA for the parameter tuning to further improve the performance of the method. The simulation outcomes emphasized that the NVNSS-COASA method shows superior outcomes over other techniques. The outcomes indicated that the NVNSS-COASA can able to deliver reliable and precise insights from the text dataset.

A constrained optimisation framework for parameter identification of the SIRD model.

We consider a numerical framework tailored to identifying optimal parameters in the context of modelling disease propagation. Our focus is on understanding the behaviour of optimisation algorithms for such problems, where the dynamics are described by a system of ordinary differential equations associated with the epidemiological SIRD model. Applying an optimise-then-discretise approach, we examine properties of the solution operator and determine existence of optimal parameters for the problem considered. Further, first-order optimality conditions are derived, the solution of which provides a certificate of goodness of fit, which is not always guaranteed with parameter tuning techniques. We then propose strategies for the numerical solution of such problems, based on projected gradient descent, Fast Iterative Shrinkage-Thresholding Algorithm (FISTA), nonmonotone Accelerated Proximal Gradient (nmAPG), and limited memory BFGS trust region approaches. We carry out a thorough computational study for a range of problems of interest, determining the relative performance of these numerical methods. Our results provide insights into the effectiveness of these strategies, contributing to ongoing research into optimising parameters for accurate and reliable disease spread modelling. Moreover, our approach paves the way for calibration of more intricate compartmental models.

Leveraging Double-Valued Neutrosophic Set for Real-Time Chronic Kidney Disease Detection and Classification

Chronic kidney disease (CKD) is a non-communicable disease that has made a significant contribution to admission, morbidity, and mortality rates of patients globally. CKD is a common kidney disease that happens when both kidneys fail, and the CKD patient suffers from these conditions for a long time. Machine learning (ML) is becoming more crucial in medical diagnoses as it allows detailed examination, thus reducing human error and optimizing prediction accuracy. Now, ML classifiers and algorithms are highly dependable techniques for the diagnoses of diverse diseases such as diabetes, heart disease, liver disease, and tumor disease predictions. A neutrosophic set (NS) is especially suitable in applications where information is vague, incomplete, or inconsistent, which provides an effective means for analyzing and modeling intricate mechanisms. A NS is a mathematical approach to handle indeterminacy, uncertainty, and imprecision. It expands IF sets, classical sets, and fuzzy sets by introducing three degrees: truth (T), indeterminacy (I), and false (F). This manuscript offers a Double-Valued Neutrosophic Set for Chronic Kidney Disease Detection and Classification (DVNS-CKDDC) technique. In the DVNS-CKDDC technique, three major processes are involved. At the primary phase, the DVNS-CKDDC technique performs a linear scaling normalization (LSN) model. Next, the DVNS-CKDDC technique makes use of the DVNS model for the identification of CKD. Finally, the beluga whale optimization (BWO) algorithm is employed for the parameter tuning of the DVNS method. To ensure the supremacy of the DVNS-CKDDC technique, a widespread simulation analysis is involved. The experimental values stated that the DVNS-CKDDC approach attains improved performance over other models

Prognostic impact and correlation with disease activity of three‐dimensional oct biomarkers in vogt‐koyanagi‐harada patients

Aims/Purpose: Vogt‐Koyanagi‐Harada syndrome (VKH) is an autoimmune disease characterized by a chronic bilateral granulomatous panuveitis accompanied by extraocular manifestations, namely of the nervous, auditory, and integumentary systems. Retinal changes documented by optical coherence tomography (OCT) were found to have prognostic value, including detachments between retinal layers with fluid accumulation. The aim of this study was to estimate the total detachment volume (TDV) between retinal layers with fluid accumulation that characterizes the Vogt‐Koyanagi‐Harada syndrome.Methods: An OCT analysis framework was developed in Python which, after a parameter tuning process, automatically segments the 3D detachments between retinal layers and calculates the TDV. The pipeline processes each slice individually following a chain of image processing steps: image enhancement; binarization; size filter; and contour detection. To validate this strategy, this method was applied to one 3D OCT image of a patient with VKH, and the resulting masks were compared with the ground truth obtained after manual segmentation by an expert.Results: Our pipeline has revealed an Intersection of Union (IoU) score of 84%, demonstrating its ability to efficiently segment retinal detachments after a parameter tuning process. As a consequence of such capability, the relative deviation between the experimental and ground truth TDV was 8.3%.Conclusions: This semi‐automated approach offers a promising tool for clinicians to detect retinal changes in VKH patients. By enabling early and precise detection of fluid accumulation and retinal detachments, this method can enhance prognostic evaluations and potentially improve patient management and treatment outcomes. Future work should focus on validating this framework across a larger dataset and refining the processing pipeline to further improve segmentation accuracy and robustness.

Autonomous injection molding parameter tuning via enhanced TD3-based reinforcement learning with behavior cloning

Autonomous injection molding parameter tuning via enhanced TD3-based reinforcement learning with behavior cloning

Modelling of Neutrosophic Set-Based k-Nearest Neighbors Classifier for Virus Pneumonia and COVID-19 Recognition

COVID19 otherwise called Severe Acute Respiratory Syndrome Corona virus-2 is an infectious illness. Another transmittable infection called Pneumonia is mainly attributable to infection because of bacteria in the alveoli of the lungs. Once a diseased lung tissue has infection, it elevates excretion in it. Specialists conduct health examinations and identify the patient through ultrasound, biopsy, or Chest X-ray of lungs to identify whether the patient has these diseases. Incorrect treatment, misdiagnosis, and if the disease was disregarded will result in the fatality. The development of Deep Learning and neutrosophic set (NS) supports the decision-making procedure of professionals to identify patients with this disease. NS is a prolongation of the fuzzy set and classical theories. The NS determines three memberships such as T, I and F. T, I, and F display the degree of truth, the false, and the indeterminacy membership, correspondingly. This enables a more nuanced representation of contradiction, uncertainty, and ambiguity within the dataset, allowing superior handling of imprecise and complex data. This study develops a new Deep learning with Neutrosophic Set-Based k-Nearest Neighbors Classifier for disease detection (DLNSKNN-DD) technique. The major purpose of the DLNSKNN-DD method is to identify the existence of virus pneumonia and COVID-19. In the DLNSKNN-DD technique, the feature extraction from the medical images is carried out by residual network (ResNet50v2). Moreover, the parameter tuning of the ResNetv2 model is done using Adadelta optimizer. The DLNSKNN-DD technique exploits NSKNN model for classification purposes. The performance evaluation of the DLNSKNN-DD algorithm can be assessed on medicinal image dataset. The experimental outcomes underlined the effectual recognition results of the DLNSKNN-DD technique on the identification of diseases

Improving Urban Heat Island Predictions Based on Support Vector Regression and Multi-Sensor Remote Sensing: A Case Study in Malang City

The Urban Heat Island (UHI) phenomenon causes significant temperature increases in urban areas, adversely affecting the environment and public health. This research develops a prediction model of land surface temperature in Malang City using Support Vector Regression (SVR) with remote sensing data from Landsat-8, Sentinel-2, and SRTM. A cloud masking process is applied to improve image quality, while features such as NDVI, NDBI, NDWI, NDMI, elevation, and LST are calculated and normalized. The test results show that the Radial Basis Function (RBF) kernel with hyperparameters C = 10, Epsilon = 0.1, and gamma = 1 provides the best performance, with R² of 0.887, MSE of 1.625, and MAPE of 2.71%. This study shows that SVR with RBF kernel and appropriate tuning parameters can improve prediction accuracy. These results provide a strong basis for the development of more effective prediction models in managing UHI in big cities .

Interactive infill topology optimisation guided by user drawn patterns

ABSTRACT Widespread use of topology optimisation as a design tool for additive manufacturing faces major inhibiting obstacles, such as high computational costs and complexity, concern for other failure modes, and manufacturability. Interactive infill topology optimisation presents an alternative approach to circumvent some of these barriers. The novel contribution of the present work prompts the user to draw a tailored infill pattern, specify regions of interest to locate the infill, and control how strictly the pattern is replicated in the material layout of the design using appearance constraints. This approach improves engineering metrics not directly included in the optimisation formulation by incorporating the user’s engineering experience, thereby avoiding increased computational costs, parameter tuning, and numerical artifacts associated with complex objective functions and constraints. Two 2D benchmark examples increase the linear buckling resistance and energy absorption, respectively, and a 2.5D example minimises compliance while reducing the quantity of overhang supports for additive manufacturing.

Accurate recognition of human abnormal behaviours using adaptive 3D residual attention network with gated recurrent units (GRU) in the video sequences

ABSTRACT Abnormal or violent behaviour by individuals with mental disorders presents significant risks to public safety, necessitating advanced systems capable of detecting such behaviours in real time. Traditional single-sensing methods for human activity recognition often struggle with issues like signal noise, dropped data, and limited scalability, which hinder their ability to accurately detect abnormal behaviours in dynamic and complex environments. This paper introduces a novel solution that addresses these challenges by proposing an adaptive 3D residual attention network (A3D-RAN) combined with Gated Recurrent Units (GRUs). The A3D-RAN utilises an adaptive attention mechanism to focus on the most relevant regions in video sequences, while residual connections improve feature reuse and maintain gradient flow, enabling fine-grained detail capture. GRUs are integrated to efficiently model long-term temporal dependencies, ensuring a more comprehensive understanding of human behaviour across time. Through extensive experimentation on real-world datasets, our model achieved a remarkable accuracy of 97%, significantly surpassing the 78% accuracy of standalone A3D-RAN implementations. Moreover, the robustness of the model under challenging conditions – such as occlusions and lighting variations – demonstrates its potential for real-world surveillance applications. By employing the Improved War Strategy Optimization (IWSO) Algorithm for parameter tuning, we further enhanced performance, reaching an unprecedented accuracy of 99%. This breakthrough underscores the practical value of our approach in improving public safety and security through accurate and timely detection of abnormal behaviours in surveillance systems.

A new model for predicting IRI using TabNet with hunter-prey optimization

ABSTRACT To address the prediction problem of the international roughness index (IRI), which is influenced by multiple factors, we propose a hunter-prey optimization (H-PO) TabNet prediction model. First, we combined multiple data sources, including traffic flow, climate data, pavement basic, and pavement condition data, aligned with annual detection data for highways at a spatial resolution of 100 m. Second, we conducted feature importance analysis on the integrated dataset to identify the most significant factors influencing pavement surface smoothness, which serve as inputs for the prediction model. We employed multiple machine-learning prediction models to predict the IRI using the compiled multisource dataset. After comparing the predictive performance of different models, we selected the TabNet model as the base model and optimised its parameter tuning process using the H-PO algorithm. Finally, ablation experiments were conducted to validate the proposed model. The results demonstrate that the H-PO-TabNet model achieved the best performance, with an R 2 increase of 7.92% compared with a model with default parameter settings. The H-PO-TabNet model also attained the highest overall accuracy, with an R 2 value of 0.8763. This model can improve the accuracy of IRI prediction and provide certain data support for pavement maintenance activities.

Joint Modelling of Longitudinal Measurements and Time-to-Event Outcomes With a Cure Fraction Using Functional Principal Component Analysis.

In studying the association between clinical measurements and time-to-event outcomes within a cure model, utilizing repeated observations rather than solely baseline values may lead to more accurate estimation. However, there are two main challenges in this context. First, longitudinal measurements are usually observed at discrete time points and second, for diseases that respond well to treatment, a high censoring proportion may occur by the end of the trial. In this article, we propose a joint modelling approach to simultaneously study the longitudinal observations and time-to-event outcome with an assumed cure fraction. We employ the functional principal components analysis (FPCA) to model the longitudinal data, offering flexibility by not assuming a specific form for the longitudinal curve. We used a Cox's proportional hazards mixture cure model to study the survival outcome. To investigate the longitudinal binary observations, we adopt a quasi-likelihood method which builds pseudo normal distribution for the binary data and use the E-M algorithm to estimate the parameters. The tuning parameters are selected using the Akaike information criterion. Our proposed method is evaluated through extensive simulation studies and applied to a clinical trial data to study the relationship between the longitudinal prostate specific antigen (PSA) measurements and overall survival in men with metastatic prostate cancer.

Generalized Fused Lasso for Treatment Pooling in Network Meta-Analysis.

This work develops a generalized fused lasso (GFL) approach to fitting contrast-based network meta-analysis (NMA) models. The GFL method penalizes all pairwise differences between treatment effects, resulting in the pooling of treatments that are not sufficiently different. This approach offers an intriguing avenue for potentially mitigating biases in treatment rankings and reducing sparsity in networks. To fit contrast-based NMA models within the GFL framework, we formulate the models as generalized least squares problems, where the precision matrix depends on the standard error in the data, the estimated between-study heterogeneity and the correlation between contrasts in multi-arm studies. By utilizing a Cholesky decomposition of the precision matrix, we linearly transform the data vector and design matrix to frame NMA within the GFL framework. We demonstrate how to construct the GFL penalty such that every pairwise difference is penalized similarly. The model is straightforward to implement in R via the "genlasso" package, and runs instantaneously, contrary to other regularization approaches that are Bayesian. A two-step GFL-NMA approach is recommended to obtain measures of uncertainty associated with the (pooled) relative treatment effects. Two simulation studies confirm the GFL approach's ability to pool treatments that have the same (or similar) effects while also revealing when incorrect pooling may occur, and its potential benefits against alternative methods. The novel GFL-NMA method is successfully applied to a real-world dataset on diabetes where the standard NMA model was not favored compared to the best-fitting GFL-NMA model with AICc selection of the tuning parameter ( .

PHYSICS BASED ALGORITHMS FOR FUZZY TRANSPORTATION PROBLEM

Transportation problems are computationally challenging due to their nondeterministic polynomial-time hard (NP-hard) nature, especially when uncertainties in costs, supply, and demand are involved. The fuzzy transportation problem addresses these uncertainties by representing them as fuzzy numbers, requiring robust methods for ranking and solving optimization models. Traditional exact methods become impractical for such problems, leading to the adoption of metaheuristic approaches. This study introduces three novel physics-based algorithms: one single-point-based and two population-based methods. These algorithms leverage principles from gravitational attraction, electromagnetism, and water flow dynamics, incorporating innovative neighborhood structures tailored to the fuzzy nature of transportation problems. The proposed methods were tested on diverse problem sizes and compared against established optimization techniques, such as genetic algorithms and commercial software, using Python implementations. An experimental design methodology was employed to optimize parameter tuning, enhancing computational efficiency and reducing experimentation time. Results demonstrate that the physics-based algorithms achieve competitive performance in terms of solution quality, computational efficiency, and adaptability to uncertainty. These findings underscore the potential of physics-based methods to advance optimization in fuzzy transportation and other domains with inherent uncertainties, paving the way for further research in hybrid and multi-objective approaches.