Output Decisions Research Articles

The downstream firm's decision over R&D for energy-saving

Although energy saving for a firm is very important, the R&D literature has rarely discussed this issue. This paper provides a vertically-related model to analyze how conducting energy-saving R&D affects the upstream firm's pricing decision and downstream firm's energy-saving and output decisions. We show that an increase in energy price may induce the downstream firm to purchase more energy, but less efficient R&D may not induce the downstream firm to purchase more energy. Moreover, more efficient R&D may not increase the energy price under the upstream monopoly case. Finally, social welfare may not improve when the upstream market becomes more competitive.

Analysis of Physiological Signals to Detect the Symptoms of Paralysis Disorder using Neural Network Approach: a Cohort Study

As per World Medical Associations, Paralysis attack is one of the leading causes of disorder in the human subjects after other chronic disorders such as Cancer and Heart Attack. Due to the attack of Paralysis, the body parts of human body become un responsive due to which the person may be physically disabled from hands or legs-based locomotion. There is scarcity in the methodologies who can early indicate the symptoms and alert the medical practitioners or human subject under surveillance. In this study, the health-related signals were studied and as per consultation of medical experts, total eight key parameters were identified and then based on that the output decision that weather person has symptoms of paralysis or not. The observatory data base has been prepared from more than 40,000 human subjects for the duration January 2018 to December 2023. As per categorical analysis based on training, validation and testing of three-layer Recurrent Neural Network, it has been observed that the ECG, Blood pressure, Heart Rate and PPG signals are key indicators to judge the susceptance of Paralysis disorder in the human subjects. Based on the evaluation parameters of machine learning models, the conjunction of response in association with selected input parameters gives 94% accuracy than other models. Our study helps research community to develop such methodologies by which the early indication of such disorders can be helpful to human health.

Cleaning method for abnormal energy big data based on sparse self-coding

In order to reduce the interference of abnormal energy big data and improve the accuracy of anomaly cleaning and detection, this paper proposes a method for cleaning abnormal energy big database based on sparse self-coding. Firstly, the abnormal data detection method based on multi-criteria evaluation is used to analyze the spectral feature distribution of abnormal energy big data. By using chaotic time series reconstruction model, robust local weighted regression analysis and sparse self-coding method, the feature decomposition of time series of energy data is realized. Then, according to the periodicity of the original sequence, the original sequence is segmitated adaptively to represent the morphological characteristics of the abnormal energy data sub-sequences driven by dynamic carbon emissions, and the anomaly index of each sub-sequence is obtained by AFCM algorithm. Secondly, an energy anomaly evaluation model based on LOF value is established. Finally, the output decision function of data cleaning is constructed to realize the abnormal energy big data cleaning. The test results show that the error detection rate of this method is 0.24%, the missing detection rate is 0.27%, the cleaning rate can reach 99.49%, and the cleaning time is less than 2s.

Paranoid About Androids: A Review of Robotics in Radiology.

In tandem with the ever-increasing global population, the demand for diagnostic radiology service provision is on the rise and at a disproportionate rate compared to the number of radiologists available to practice. The current "revolution in robotics" promises to alleviate personnel shortages in many sectors of industry, including medicine. Despite negative depictions of robots in popular culture, their multiple potential benefits cannot be overlooked, in particular when it comes to health service provision. The type of robots used for interventional procedures are largely robotic-assistance devices, such as the Da Vinci surgical robot. Advances have also been made with regards to robots for image-guided percutaneous needle placement, which have demonstrated superior accuracy compared to manual methods. It is likely that artificial intelligence will come to play a key role in the field of robotics and will result in an increase in the levels of robotic autonomy attainable. However, this concept is not without ethical and legal considerations, most notably who is responsible should an error occur; the physician, the robot manufacturer, software engineers, or the robot itself? Efforts have been made to legislate in order to protect against the potentially harmful effects of unexplainable "black-box" decision outputs of artificial intelligence systems. In order to be accepted by patients, studies have shown that the perceived level of trustworthiness and predictability of robots is crucial. Ultimately, effective, widespread implementation of medical robotic systems will be contingent on developers remaining cognizant of factors that increase human acceptance, as well as ensuring compliance with regulations.

Enhancing Groundwater Recharge Prediction: A Feature Selection‐Based Deep Forest Model With Bayesian Optimisation

ABSTRACTAccurate prediction of groundwater recharge is crucial for the sustainable management of water resources. Existing models, while effective, still have potential for improved accuracy. This study proposed a novel deep forest model—the Feature Selection‐based Deep Forest model (FSDF)—for enhanced groundwater recharge prediction. This model consists of three key essential components: a feature selection layer, a cascade enhancement layer and a decision output layer, all designed to enhance the prediction accuracy of groundwater recharge rates. The feature selection layer effectively filtered out redundant features, ensuring that only relevant features are fed into the subsequent cascade enhancement layer. The cascaded enhancement layer was jointly constructed by random forests and completely random forests, processing the data layer‐by‐layer. Finally, the predictions of groundwater recharge rates were produced through an averaging strategy in the decision output layer. To further enhance the FSDF model's predictive capabilities, Bayesian optimization was applied for fine‐tuning model hyperparameters. The model's performance was evaluated and compared with existing models using a dataset comprising of groundwater recharge rates from 1549 wells in New South Wales, Australia. The FSDF model exhibited exceptional performance, achieving a training accuracy of 95.91% and a testing accuracy of 89.65%. It outperformed the adaptive boosting, categorical boosting, extreme gradient boosting, multiple linear regression and random forests by 2.02%, 6.98%, 9.05%, 17.02% and 2.74% in prediction performance, respectively. This study contributes to both hydrological processes and groundwater management by identifying key factors such as rainfall, surface geology and PET, and refining hydrological models for greater predictive accuracy. The FSDF model offers a powerful tool for accurately forecasting groundwater recharge, outperforming traditional models. The model's adaptability makes it applicable to different geographical regions for managing water resources in the face of challenges such as water scarcity and climate change.

Non-invasive, fast, and high-performance EGFR gene mutation prediction method based on deep transfer learning and model stacking for patients with Non-Small Cell Lung Cancer

PurposeTo propose an intelligent, non-invasive, highly precise, and rapid method to predict the mutation status of the Epidermal Growth Factor Receptor (EGFR) to accelerate treatment with Tyrosine Kinase Inhibitor (TKI) for patients with untreated adenocarcinoma Non-Small Cell Lung Cancer. Materials and methodsReal-world data from 521 patients with adenocarcinoma NSCLC who performed a CT scan and underwent surgery or pathological biopsy to determine EGFR gene mutation between January 2021 and July 2022, is collected. Solutions to the problems that prevent the model from achieving very high precision, namely: human errors made during the annotation of the database and the low precision of the output decision of the model, are proposed. Thus, among the 521 analyzed cases, only 40 were selected as patients with EGFR gene mutation and 98 cases with wild-type EGFR. ResultsThe proposed model is trained, validated, and tested on 12,040 2D images extracted from the 138 CT scans images where patients were randomly partitioned into training (80 %) and test (20 %) sets. The performance obtained for EGFR gene mutation prediction was 95.22 % for accuracy, 960.2 for F1_score, 95.89 % for precision, 96.92 % for sensitivity, 94.01 % for Cohen kappa, and 98 % for AUC. ConclusionAn EGFR gene mutation status prediction method, with high-performance thanks to an intelligent prediction model entrained by highly accurate annotated data is proposed. The outcome of this project will facilitate rapid decision-making when applying a TKI as an initial treatment.

Combined Physics- and Machine-Learning-Based Method to Identify Druggable Binding Sites Using SILCS-Hotspots.

Identifying druggable binding sites on proteins is an important and challenging problem, particularly for cryptic, allosteric binding sites that may not be obvious from X-ray, cryo-EM, or predicted structures. The Site-Identification by Ligand Competitive Saturation (SILCS) method accounts for the flexibility of the target protein using all-atom molecular simulations that include various small molecule solutes in aqueous solution. During the simulations, the combination of protein flexibility and comprehensive sampling of the water and solute spatial distributions can identify buried binding pockets absent in experimentally determined structures. Previously, we reported a method for leveraging the information in the SILCS sampling to identify binding sites (termed Hotspots) of small mono- or bicyclic compounds, a subset of which coincide with known binding sites of drug-like molecules. Here, we build on that physics-based approach and present a ML model for ranking the Hotspots according to the likelihood they can accommodate drug-like molecules (e.g., molecular weight >200 Da). In the independent validation set, which includes various enzymes and receptors, our model recalls 67% and 89% of experimentally validated ligand binding sites in the top 10 and 20 ranked Hotspots, respectively. Furthermore, we show that the model's output Decision Function is a useful metric to predict binding sites and their potential druggability in new targets. Given the utility the SILCS method for ligand discovery and optimization, the tools presented represent an important advancement in the identification of orthosteric and allosteric binding sites and the discovery of drug-like molecules targeting those sites.

Intuitionistic fuzzy three-way decision method based on data envelopment analysis

As a powerful mathematical tool for tackling uncertain decision problems, three-way decision has garnered substantial attention since its inception. However, real-world decision problems are inherently complex, and decision-makers often exhibit characteristics of incomplete rationality. Traditional three-way decision models, which rely on functions or relationships, face challenges when confronted with multi-output problems. In response to this challenge, this paper introduces an intuitionistic fuzzy three-way decision model grounded in data envelopment analysis (DEA). Initially, we propose an input–output correlation degree that integrates hesitancy information and serves as a procedural indicator for benefit scores. Subsequently, the traditional DEA is extended to accommodate the intuitionistic fuzzy environment and utilized to construct a comprehensive loss function. Furthermore, a novel intuitionistic fuzzy three-way decision model is developed, incorporating three dimensions: optimism, neutrality, and pessimism, and corresponding decision rules and algorithms are provided. Finally, the effectiveness of the proposed model is rigorously validated through a series of experiments and comparative analyses. The model offers a pioneering approach to address uncertain multi-input–output decision problems, effectively integrating decision-maker’s risk preferences within an intuitionistic fuzzy environment.

R&D capitalization vs. R&D expensing: A theoretical model with policy implications

The aim of this study is to establish a theoretical model to investigate the incentives of firms for choosing R&D capitalization. We focus on a monopoly firm managed by a typically myopic manager. In a two-stage setting, the manager determines the level of R&D prior to its output decision. Our model indicates that capitalizing some R&D costs yields higher profitability than fully expensing such investments. Moreover, we show that a longer useful life of capitalized assets is favorable for both consumer surplus and social welfare; thus, policy-makers should encourage policies that support extended lifespans of these assets.

An improved container-based deep forest model for predicting groundwater recharge

Abstract This paper proposes ICDF, an improved container-based deep forest model, for effectively modeling and predicting groundwater recharge. The model consists of four points: the construction and expansion of the container module, the assignment of weights to the base model, the growth of the cascade layer, and the decision output. First, container modules are created and a maximization objective function is assigned to each container to control its growth. Next, different weights are assigned to each base model based on its contribution to container prediction. Cascade layers are built using container modules until the model prediction decreases. Finally, the average prediction vectors of the last cascade layer are output. The model’s performance is evaluated and compared with DF and its base models (random forest, adaptive boosting, and extreme gradient boosting) using a case study of 1549 bores in New South Wales, Australia. Remarkably, compared to DF, ICDF has improved prediction accuracy by 6.66%. Moreover, it outperformed the RF, AdaBoost, and XGBoost by 2.94%, 5.85%, and 5.3% in prediction performance, respectively. The ICDF exhibited superior capabilities in predicting groundwater recharge, offering significant improvement over existing models. Practitioners are encouraged to consider adopting ICDF for groundwater management.

Explainable Histopathology Image Classification with Self-organizing Maps: A Granular Computing Perspective

The automatic analysis of histology images is an open research field where machine learning techniques and neural networks, especially deep architectures, are considered successful tools due to their abilities in image classification. This paper proposes a granular computing methodology for histopathological image classification. It is based on embedding tiles of histopathology images using deep metric learning, where a self-organizing map is adopted to generate the granular structure in this learned embedding space. The SOM enables the implementation of an explainable mechanism by visualizing a knowledge space that the experts can use to analyze and classify the new images. Additionally, it provides confidence in the classification results while highlighting each important image fragment, with the benefit of reducing the number of false negatives. An exemplary case is when an image detail is indicated, with small confidence, as malignant in an image globally classified as benign. Another implemented feature is the proposal of additional labelled image tiles sharing the same characteristics to specify the context of the output decision. The proposed system was tested using three histopathology image datasets, obtaining the accuracy of the state-of-the-art black-box methods based on deep learning neural networks. Differently from the methodologies proposed so far for the same purpose, this paper introduces a novel explainable method for medical image analysis where the advantages of the deep learning neural networks used to build the embedding space for the image tiles are combined with the intrinsic explainability of the granular process obtained using the clustering property of a self-organizing map.

Improved environment rehabilitation measures for combatting desertification using water and soil conservation assessment models

Environmental rehabilitation is initiated through water and soil conservation measures to reduce desertification. The rehabilitation process provides insights for people to construct living environments and eco-friendly habitats. However, the reverse desertification process requires analytical and stable rehabilitation processes that achieve green ecosystems. This article introduces a Rehabilitation Measure Longevity Assessment Model (RM-LAM) for predicting the measure's sustainability. In this assessment, the significance of water and soil and their conservation methods are analyzed for vegetation and living abilities through the ages. The positive feedback and the rehabilitation measure outputs are independently validated using intelligent fuzzy decisions. The objective is described as the living friendly environment with cultivation cultivation-facilitated ambiance. Toward this objective, the above independent features are analyzed using relationship constraints. Based on the decision outputs between distinct factor assessments, the desertification reverse process is analyzed. Such analysis is used for recommending ambient living through the conservation methods pursued.

Bearings remaining useful life prediction across equipment-operating conditions based on multisource-multitarget domain adaptation

Transfer learning has been extensively used to build bearings remaining useful life prediction models under multi-operating conditions. However, most models only consider single-target domain, the generalization of the model under across equipment-operating conditions cannot reach the development of modern industry. Therefore, this study proposes a multisource-multitarget domain adaption transfer learning network (MSMTDATLN) hoping to solve across equipment-operating conditions prediction. Constructing a one-total and multi-branch network in MSMTDATLN to extract domain-invariant and domain-specific features from multisource-multitarget domains, adaptively eliminating the differences between domain-specific features, enhancing adaptive regression capabilities by aligning output decision boundaries. Improving model generalization in multitarget domains by the above methods, while enabling end-to-end training and inference. Experimental results on across equipment-operating conditions datasets validate the proposed method reduces RMSE index by 7% compared with other advanced algorithms while providing better model generalization, which can provide theoretical foundation and technical support for the bearing prediction tasks across equipment-operating conditions.

Fault diagnosis of axial piston pump based on multi-source subdomain adaptation and sensor data fusion

The axial piston pump is the core component in hydraulic systems. Its condition monitoring and fault diagnosis are crucial to ensure the safe and reliable operation of hydraulic systems. However, most of the existing fault diagnosis methods for axial piston pumps use the same working condition data. In actual operation, axial piston pump often experience varying loads, and the collected data is typically correlated but has different distributions. Therefore, a transfer learning method of multi-source subdomain adaptation and sensor fusion (MSASF) is proposed for fault diagnosis of axial piston pump. The proposed MSASF has three modules, a shared feature extraction module, a domain-specific feature extraction module and an output decision module. Firstly, the adaptive weighted fusion of multi-sensor data features is realized by the shared feature extraction module and the common features of multi-source heterogeneous data are extracted. Secondly, in the domain-specific feature extraction module, multi-branch network is used to extract features of each pair of source and target domains and the local maximum mean difference is utilized to align the sub-domain distribution of each pair of source and target domains. Finally, in the output decision module, the distribution distance between each pair of source domain and target domain is calculated using maximum mean discrepancy to obtain its weighted score. Combined with the classification output of each source domain, the final diagnosis decision is made. A dataset was constructed for the axial piston pump design fault experiment, and four sets of transfer tasks were designed to compare with those of seven classic methods. The experimental results showed that the proposed MSASF method exhibits a superior domain adaptation effect and fault diagnosis performance.

Siting and operating incentives in electrical networks: A study of mispricing in zonal markets

The incentives electricity generators face in investment and output decisions hinge on market design. Under some zonal market designs, where profit-maximizing participants face a uniform regional price, achieving lowest-cost system-wide production can be impossible. Further, zonal designs can incentivize siting of intermittent renewables in inefficient locations behind network constraints, of concern for jurisdictions undergoing a clean energy transition. We develop measures of mispricing that compare the zonal prices generators receive to locational marginal prices that value congestion externalities from generator output. We apply these measures to show wind and solar generators are increasingly siting in constrained areas of the Australian network, and highlight sources of potential efficiency gains from adopting a nodal market design with locational marginal pricing.

Stock portfolio optimization for risk-averse investors: A novel hybrid possibilistic and flexible robust approach

This study proposes stock portfolio optimization models for risk-averse investors under uncertain conditions. To accomplish this, the study measures risk based on the conditional drawdown-at-risk (CDaR) measure, which can prevent major declines in investment as a conservative investment strategy. Given the uncertain character of the input parameters of the problem, the study first proposes a hybrid possibilistic and flexible model by considering the CDaR measure (CDaR-HPFM) to handle uncertainty. Furthermore, to offer more robust outcomes, the study constructs a hybrid possibilistic and flexible robust model by considering the CDaR measure (CDaR-HPFRM), in the forms of a non-linear and a linear mathematicalprogramming problem. The CDaR-HPFRM can process the robustness of output decisions while dealing with uncertain parameters. The real stock exchange data of 100 companies registered on the Tehran Stock Exchange are investigated to validate the functionality of the proposed models. The results reveal that, when various penalty costs are factored in, the CDaR-HPFRM yields more efficient and more robust results than the CDaR-HPFM. Comparative results also indicate that the proposed models almost always outperform existing methods, in terms of both CDaR and rate of return measures. Proposed models can be used for all types of stock market investments (including micro-investing and investment funds) and can handle portfolio optimization and selection processes in project-based organizations.

Semi-factual Explanations in AI

Most of the recent works on post-hoc example-based eXplainable AI (XAI) methods revolves around employing counterfactual explanations to provide justification of the predictions made by AI systems. Counterfactuals show what changes to the input-features change the output decision. However, a lesser-known, special-case of the counterfacual is the semi-factual, which provide explanations about what changes to the input-features do not change the output decision. Semi-factuals are potentially as useful as counterfactuals but have received little attention in the XAI literature. My doctoral research aims to establish a comprehensive framework for the use of semi-factuals in XAI by developing novel methods for their computation, supported by user tests.

Application of artificial intelligence based on the fuzzy control algorithm in enterprise innovation

Artificial Intelligence (AI) has gained immense popularity in recent years as many enterprises have realized their potential to change the way of conducting business innovatively. The new concepts, items, or procedures are developed and implemented within a business or organization to enhance productivity, effectiveness, and competitiveness, and this is called Enterprise Innovation (EI). AI techniques are required to make decisions more effectively in challenging and dynamic situations, like EI, as result of competitive marketplace. Hence, an intelligent, innovative strategy with Q-learning and Takagi Sugeno Fuzzy Control (Q-TSFC) algorithm has been proposed as it combines adaptive learning and Fuzzy Logic (FL) that humans understand to improve decision-making in enterprise innovation. Q-learning seeks to maximize the enterprise's profit by utilizing the newly acquired knowledge, exploring activities, and adaptive learning based on the optimal ε greedy policy that results with rewards and the experiences. To develop a framework for making decisions and connections between input from the learned Q values and output decisions using enterprise expertise and linguistic conventions. The objective is to handle language uncertainty and imprecision in market trend. So, it leads to right decisions even without accurate numerical facts. The proposed approach is validated by evaluating metrics like cost savings, customer satisfaction, and innovation performance efficiency in the competitive edge in the market. With this proposed Q-TSFC algorithm, the obtained results are 96.5% customer satisfaction ratio, 96% enterprise performance efficiency, cost savings of about 48% profitable value, and the coefficient of determination R2 is 0.83, respectively.

Deep learning approach to improve the recognition of hand gesture with multi force variation using electromyography signal from amputees

Variations in muscular contraction are known to significantly impact the quality of the generated EMG signal and the output decision of a proposed classifier. This is an issue when the classifier is further implemented in prosthetic hand design. Therefore, this study aims to develop a deep learning classifier to improve the classification of hand motion gestures and investigate the effect of force variations on their accuracy on amputees. The contribution of this study showed that the resulting deep learning architecture based on DNN (deep neural network) could recognize the six gestures and robust against different force levels (18 combinations). Additionally, this study recommended several channels that most contribute to the classifier's accuracy. Also, the selected time domain features were used for a classifier to recognize 18 combinations of EMG signal patterns (6 gestures and three forces). The average accuracy of the proposed method (DNN) was also observed at 92.0 ± 6.1 %. Moreover, several other classifiers were used as comparisons, such as support vector machine (SVM), decision tree (DT), K-nearest neighbors, and Linear Discriminant Analysis (LDA). The increase in the mean accuracy of the proposed method compared to other conventional classifiers (SVM, DT, KNN, and LDA), was 17.86 %. Also, the study's implication stated that the proposed method should be applied to developing prosthetic hands for amputees that recognize multi-force gestures.

Visualization and analysis of electrical parameter design based on digital sensors

Abstract This paper provides an in-depth study on the application of digital sensor technology in electrical fire detection. It firstly focuses on the design of electrical parameters to improve the accuracy of fire detection. Based on the research results of recurrent neural networks, the article combines the Long Short-Term Memory (LSTM) network and the Gated Recurrent Unit (GRU) neural network. It innovatively proposes an electrical fire feature recognition method based on the LSTMGRU network. The study also incorporates fuzzy inference techniques to optimize the fire alarm decision-making process, which achieves a hierarchical output for electrical fire detection recognition and alarm decision-making. The experimental results show that the LSTM-GRU network achieves an accuracy of 98.83% in electrical fire classification and recognition, significantly better than the results of using LSTM network or GRU network alone. Regarding electrical fire distance recognition, the relative error of the method only ranges from 0.32% to 2.10%, and its output of fire alarm decision is entirely correct. The study not only verifies the high accuracy and reasonableness of the electrical fire feature recognition method based on digital sensing technology, but also provides a brand new idea for recognizing electrical fire detection and alarm decision-making.