Selection of optimal threshold using cost and revenue matrix

Image segmentation is imperative for image processing applications. Thresholding technique is the easiest way of partitioning an image into different regions. Mostly, entropy-based threshold selection methods are used for multilevel thresholding. However, these methods suffer from their dependencies on spatial distribution of gray values. To solve this issue, a novel segmentation error minimization (SEM)-based method for multilevel optimal threshold selection using opposition equilibrium optimizer (OEO) is suggested. In this contribution, a new segmentation score (SS) (objective function) is derived while minimizing the segmentation error function. Our proposal is explicitly free from gray level spatial distribution of an image. Optimal threshold values are achieved by maximizing the SS (fitness value) using OEO. The key to success is the maximization of score among classes, ensuring the sharpening of the shred boundary between classes, leading to an improved threshold selection method. It is empirically demonstrated how the optimal threshold selection is made. Experimental results are presented using standard test images. Standard measures like PSNR, SSIM and FSIM are used for validation The results are compared with state-of-the-art entropy-based technique. Our method performs well both qualitatively and quantitatively. The suggested technique would be useful for biomedical image segmentation.

To obtain the load spectrum of the traction resistance of the three-point suspension device under tractor-plowing conditions, a load spectrum extrapolation method based on a genetic algorithm optimal threshold selection is proposed. This article first uses a pin force sensor to measure the plowing resistance of the tractor’s three-point suspension device under plowing conditions and preprocesses the collected load signal. Next, a genetic algorithm is introduced to select the threshold based on the Peak Over Threshold (POT) extremum extrapolation model. The Generalized Pareto Distribution (GPD) fits the extreme load distribution that exceeds the threshold range, generating new extreme points that follow the GPD distribution to replace the extreme points in the original data, achieving the extrapolation of the load spectrum. Finally, the loading spectrum that can be achieved on the test bench is obtained based on the miner fatigue theory and accelerated life theory. The results show that the upper threshold of the time-domain load data obtained by the genetic algorithm is 10.975 kN, and the grey correlation degree is 0.7249. The optimal lower threshold is 8.5455 kN, the grey correlation degree is 0.7722, and the fitting effect of the GPD distribution is good. The plowing operation was divided into five stages: plowing tool insertion, acceleration operation, constant speed operation, deceleration operation, and plowing tool extraction. A traction resistance loading spectrum that can be achieved on the test bench was developed. The load spectrum extrapolation method based on the genetic algorithm optimal threshold selection can improve the accuracy of threshold selection and achieve the extrapolation and reconstruction of the load spectrum. After processing the extrapolated load spectrum, it can be transformed into a load spectrum that can be recognized by the test bench.

This study aims to establish an adaptive attention regulation method by combining neurofeedback and classical feedback control theory. It combined open-loop control and closed-loop negative feedback to achieve a universal attention regulation model. The EEG data under attention stimulation was collected., and the baseline threshold range was selected using the optimal threshold selection method., and each individual was calibrated individually. The sliding window method is used to evaluate the individual's attention state and make the feedback training scene to follow its changes. The experiment found that through adaptive feedback training, the individual can adjust the attention state to the individual's optimal baseline threshold. This method can cope with the differences between different individuals and provide each individual with the same level of feedback regulation. Similarly, this study may provide a universal treatment method for other mental diseases.

Capital market players or investors will consider several elements that can indicate the state of the company, such as examining financial reports from cash flow to profit and loss statements. In addition, the company's share price will ultimately be related to its market capitalization value. The profits obtained by investors will increase along with the increase in stock market capitalization, and the greater the appreciation, the higher the level of profit from an alternative investment. This study aims to determine the effect of net income and net cash flow on market capitalization. Secondary data in the form of Sharia Banking quarterly financial reports that have been listed on the Indonesia Stock Exchange (IDX) for 2019-2022 is the source of the data. Three samples for this research sample were obtained, namely Bank Panin Dubai Syariah (PNBS), Sharia National Pension Savings Bank (BTPS), and Bank Syariah Indonesia (BSI). Panel data regression analysis with the Eviews 12 program was used in this study. The results show that partially net income has a negative and insignificant impact while net cash flow has a positive and significant impact on market capitalization. Meanwhile, market capitalization is simultaneously influenced by net profit and net cash flow variables.

Suppose asset pricing is governed by the CAPM or the ICAPM, and the expected one-period simple returns on the net cash flows of investment projects are constant through time. Then the net cash flows are priced by discounting their expected values with their expected one-period simple returns. But when net cash flows are priced by discounting their expected values with constant CAPM or ICAPM expected one-period simple returns, distributions of net cash flows more than one period ahead are likely to be skewed right. Expected payoffs are then larger than median pay-offs, and expected pay-offs are progressively more unusual outcomes for longer investment horizons. Copyright 1996 by University of Chicago Press.

New trends in maintenance techniques are oriented to digitization and prognosis. The new electronic devices based on IoT (Internet of Things) technology among others that support the industry 4.0 paradigm let enhance the traditional condition monitoring techniques to better understand and predict the state of a machine in service. Related to maintenance applications, one of the important steps in condition monitoring tasks for fault diagnosis is the selection of the optimal pattern to provide accurate results (avoiding fault positives/negatives) with adequate computation time. When implementing this, the selection of optimal parameters and thresholds for setting alarms are important to detect problems in the machine before the failure occurs. Vibratory signals have been proved to be a good variable to determine their mechanical behavior. Nevertheless, parameters obtained from time domain measurements are not computationally efficient nor good patterns to compare different machine conditions. In this sense, tools that represent the frequency domain or time–frequency domain have been useful to detect defects in rotating elements such as bearings. In this work, defects in ball bearings are studied using wavelet packet transform. For this, a methodology will be developed for the optimal selection of the mother wavelet, incorporating intelligent classification systems, and using a medium Gaussian support vector machine model. In this way, it will be verified that the correct selection of this function influences both the results and the ease and reliability of detection. The results using the selected mother wavelet will be compared to those using Daubechies 6, since it is the mother wavelet that has been used in previous works and which was selected based on experience. For it, vibratory signals are obtained from a testbench with different bearing conditions: healthy bearings and defective bearings (inner and outer race).

Image segmentation is the process of partitioning an image into multiple regions and is widely utilized in various domains to ease separating and searching particular objects within a given image. Multilevel thresholding is one of the most important techniques used for segmenting images. However, selection of optimum thresholds is still a challenging problem. Since evolutionary algorithms are being applied in literature over the decade for improving the accuracy and computational efficiency of multilevel thresholding methods, in many cases, such stochastic algorithms may prematurely converge into local optima and lack accuracy and stability. Structured Evolutionary Algorithms are proposed to overcome these limitations. Therefore the main aim of this research work is to propose a multilevel thresholding method based on Cellular Differential Evolution for color images segmentation. We present in this paper a comparative performance study to evaluate the efficiency and the accuracy of Standard DE and Cellular DE. We also present the results of this comparative study which shows that the structuring of the population within DE improves its performance in terms of accuracy, robustness and speed convergence. The proposed algorithm is also evaluated by comparing it with three other algorithms using a well-known benchmark images. Such comparison reflects the efficiency of our algorithm.

Summary We study firms' technical efficiency and associatedimplications for investment planning for Finnish greenhousefirms. A firm's technical efficiency is measured using intra-technology and inter-technologymeasures. A firm's investment strategies include investmentsin new energy-intensive assimilation light technology, new greenhouses to enlarge the scale of operation, or a combination of the two. Alternatively, a firm's strategy is not to invest. The central question is whether one technology outperforms another, controlling for firms' technical efficiency. The main result is that the new technology in and by itself does neitherprovide the highest technical performance nor maximisethe net cash flow for all types of firms. Small family firms reach highest technical efficiency and maximum net cash flow using a traditional energy-extensive technology with current greenhouse area. For inefficient large firms, partially adoption of new energy-intensive technology is required to fully exploit efficiency potentials. If the large firms are efficient, the area expansion with assimilation lights increase the net cash flow too. The results also indicate that the more inefficient firms the minor price reduction they tolerate when investing in energy-intensive technology.

This study examines into the effect of financial reporting disclosure on the performance of deposit money banks in Nigeria. The study employed ex-post facto research design and convenience sampling technique to select five deposit money banks in Nigeria. The secondary data was sourced from the audited financial statement of the banks from 2013 to 2022. The panel regression technique was employed to determine the relationship between financial reporting disclosures and financial performance of deposit money banks in Nigeria Findings from revealed that retained earnings, net cash flow and firm size have positive significant effect on Net Interest margin. Firm size has negative significant effect on Net Interest margin. It also revealed that retained earnings and net cash flow have negative significant effect on earnings per share while intangible asset and firm size has positive significant effect on earnings per share. It is therefore recommended banks are advised to prioritize increasing retained earnings and optimizing net cash flow to improve their net interest margin. It's important to manage intangible assets carefully to reduce their negative impact, possibly by closely examining their acquisition and amortization costs.

Aims: Irreversible electroporation is an ablation technique being adapted for the treatment of atrial fibrillation. Currently, there are many differences reported in the in vitro and pre-clinical literature for the effective voltage threshold for ablation. The aim of this study is a direct comparison of different cell types within the cardiovascular system and identification of optimal voltage thresholds for selective cell ablation. Methods: Monophasic voltage pulses were delivered in a cuvette suspension model. Cell viability and live–dead measurements of three different neuronal lines, cardiomyocytes, and cardiac fibroblasts were assessed under different voltage conditions. The immediate effects of voltage and the evolution of cell death was measured at three different time points post ablation. Results: All neuronal and atrial cardiomyocyte lines showed cell viability of less than 20% at an electric field of 1000 V/cm when at least 30 pulses were applied with no significant difference amongst them. In contrast, cardiac fibroblasts showed an optimal threshold at 1250 V/cm with a minimum of 50 pulses. Cell death overtime showed an immediate or delayed cell death with a proportion of cell membranes re-sealing after three hours but no significant difference was observed between treatments after 24 h. Conclusions: The present data suggest that understanding the optimal threshold of irreversible electroporation is vital for achieving a safe ablation modality without any side-effect in nearby cells. Moreover, the evolution of cell death post electroporation is key to obtaining a full understanding of the effects of IRE and selection of an optimal ablation threshold.

Neuromorphic cameras, also known as event cameras, are asynchronous brightness-change sensors that can capture extremely fast motion without suffering from motion blur, making them particularly promising for 3D reconstruction in extreme environments. However, existing research on 3D reconstruction using monocular neuromorphic cameras is limited, and most of the methods rely on estimating physical priors and employ complex multi-step pipelines. In this work, we propose an end-to-end method for dense voxel 3D reconstruction using neuromorphic cameras that eliminates the need to estimate physical priors. Our method incorporates a novel event representation to enhance edge features, enabling the proposed feature-enhancement model to learn more effectively. Additionally, we introduced Optimal Binarization Threshold Selection Principle as a guideline for future related work, using the optimal reconstruction results achieved with threshold optimization as the benchmark. Our method achieves a 54.6% improvement in reconstruction accuracy compared to the baseline method.

Objective: Analyze the Pacific hake (Merluccius productus) fishery in the northern Gulf of California from 2010 to 2021 to evaluate fleet performance, profitability, and implications for a quota system. Design/methodology/approach: Information on fleet composition, fishing effort, catch efficiency, and economic indicators was analyzed for small and large vessels. Net cash flow, cost-benefit ratio, and return on investment were calculated. A break-even analysis was conducted to estimate the minimum harvest required to sustain fleet operations. Results: Small vessels were more numerous and active, while large vessels showed 1.5 times higher efficiency. Average catch per vessel rose from 30 t in 2010 to 108 t in 2021, and CPUE in large vessels increased from 2 t/d to 6 t/d. In 2021, small vessels recorded negative returns (net cash flow: -MX$176,604; C/B=0.90; ROI=-10%), while large vessels achieved positive outcomes (MX$337,735; C/B=1.13; ROI=12%). Break-even analysis indicated that sustaining half the fleet required 7,798 t, equal to 75% of the proposed TAC. Limitations/Implications: Market price variability and environmental fluctuations may affect results beyond the study period. Findings/Conclusions: Quota systems must consider efficiency disparities. Implementing quotas, monitoring, and stakeholder participation is essential to prevent overfishing and ensure sustainability.

BackgroundManufacturer-defined AI thresholds for chest x-ray (CXR) often lack customization options. Threshold optimization strategies utilizing users’ clinical real-world data along with pathology-enriched validation data may better address subgroup-specific and user-specific needs.Materials and methodsA pathology-enriched dataset (study cohort, 563 (CXRs)) with pleural effusions, consolidations, pneumothoraces, nodules, and unremarkable findings was analysed by an AI system and six reference radiologists. The same AI model was applied to a routine dataset (clinical cohort, 15,786 consecutive routine CXRs). Iterative receiver operating characteristic analysis linked achievable sensitivities (study cohort) to resulting AI alert rates in clinical routine inpatient or outpatient subgroups. “Optimized” thresholds (OTs) were defined by a 1% sensitivity increase leading to more than a 1% rise in AI alert rates. Threshold comparisons (OTs versus AI vendor’s default thresholds (AIDT) versus Youden’s thresholds) were based on 400 clinical cohort cases with expert radiologists’ reference.ResultsAIDTs, OTs, and Youden’s thresholds varied across scenarios, with OTs differing based on tailoring for inpatient or outpatient CXRs. AIDT lowering most reasonably improved sensitivity for pleural effusion, with increases from 46.8% (AIDT) to 87.2% (OT) for outpatients and from 76.3% (AIDT) to 93.5% (OT) for inpatients; similar trends appeared for consolidations. Conversely, regarding inpatient nodule detection, increasing the threshold improved accuracy from 69.5% (AIDT) to 82.5% (OT) without compromising sensitivity. Graphical analysis supports threshold selection by illustrating estimated sensitivities and clinical routine AI alert rates.ConclusionAn innovative, subgroup-specific AI threshold optimization is proposed, automatically implemented and transferable to other AI algorithms and varying clinical subgroup settings.Relevance statementIndividually customizing thresholds tailored to specific medical experts’ needs and patient subgroup characteristics is promising and may enhance diagnostic accuracy and the clinical acceptance of diagnostic AI algorithms.Key PointsCustomizing AI thresholds individually addresses specific user/patient subgroup needs.The presented approach utilizes pathology-enriched and real-world subgroup data for optimization.Potential is shown by comparing individualized thresholds with vendor defaults.Distinct thresholds for in- and outpatient CXR AI analysis may improve perception.The automated pipeline methodology is transferable to other AI models or subgroups.Graphical

This study empirically analyzes the impact of the International Exchange Complex Zone development in Seoul's Gangnam on land and building unit prices, focusing on the significance of spatial autocorrelation. Utilizing 1,339 actual transaction cases from 2006 to 2024, OLS, Spatial Lag Model (SAR), and Spatial Error Model (SEM) were comparatively analyzed. OLS regression results indicated that development phases, property area, macroeconomic indicators, and location variables significantly influenced both dependent variables. However, OLS residual diagnostics revealed statistically significant spatial autocorrelation, underscoring the necessity of applying spatial econometric models and leading to a 1000m optimal spatial threshold selection. SAR and SEM models, applied with this threshold, demonstrated comparable or improved explanatory power (R²/Pseudo R²) compared to OLS, with SEM showing the best fit by information criteria (AIC, BIC). Moran's I tests on their residuals confirmed successful resolution of spatial autocorrelation. Significant spatial coefficients (in SAR and SEM) proved strong spatial spillover effects and the influence of unobserved spatial characteristics, highlighting OLS estimation bias. This research demonstrates that spatial autocorrelation significantly impacts real estate price determination and that spatial econometric models are essential for reliable analysis and spatially informed policymaking. However, it acknowledges limitations in estimating pure causal effects, suggesting future research incorporate causal inference methodologies

Recently, image thresholding methods based on various entropy functions have been found popularity. Nonetheless, entropic-based methods depend on the spatial distribution of the grey level values in an image. Hence, the accuracy of these methods is limited due to the non-uniform distribution of the grey values. Further, the analysis of the COVID-19 X-ray images is evolved as an important area of research. Therefore, it is needed to develop an efficient method for the segmentation of the COVID-19 X-ray images. To address these issues, an efficient non-entropy-based thresholding method is suggested. A novel fitness function in terms of the segmentation score (SS) is introduced, which is used to reduce the segmentation error. A soft computing approach is suggested. An efficient optimizer using the chance-based birds’ intelligence is introduced to maximize the fitness values. The new optimizer is validated utilizing the benchmark test functions. The statistical parameters reveal that the suggested optimizer is efficient. It shows a quite significant improvement over its counterparts—optimization based on seagull/cuckoo birds. Precisely, the paper includes three novel contributions—(i) fitness function, (ii) chance-based birds’ intelligence for optimization, (iii) multiclass segmentation. The COVID-19 X-ray images are taken from the Kaggle Radiography database, to the experiment. Its results are compared with three different state-of-the-art entropy-based techniques—Tsallis, Kapur’s, and Masi. For providing a statistical analysis, Friedman’s mean rank test is conducted and our method Ranked one. Its superiority is claimed in terms of Peak Signal to Noise Ratio (PSNR), Feature Similarity Index (FSIM) and Structure Similarity Index (SSIM). On the whole, an improvement of about 11% in PSNR values is achieved using the proposed method. This method would be helpful for medical image analysis.