Retention campaigns depend on identifying the customers most likely to discontinue service, yet churn classifiers are usually deployed with the conventional decision threshold of one half, a rule that ignores both the rarity of churn and the unequal...
During the process of disease diagnosis, overdiagnosis can lead to potential health loss and unnecessary anxiety for patients as well as increased medical costs, while underdiagnosis can result in patients not being treated on time. To deal with thes...
Rare event detection in large tabular data remains a computationally challenging problem due to class imbalance, heterogeneous feature distributions, and unstable thresholds. Traditional machine learning approaches based on individual models and fixe...
This study presents a fairness-aware framework for modeling the Probability of Default (PD) in individual credit scoring, explicitly addressing the trade-off between predictive accuracy and fairness. As machine learning (ML) models become increasingl...
Background/Objectives: Urinary tract infections (UTIs) are frequently diagnosed empirically, often leading to overtreatment and rising antimicrobial resistance. This study aimed to develop and evaluate machine learning (ML) models that predict urine...
Assessment of the spatio-temporal dynamics of shifting cultivation is important to understand the opportunities for land restoration. The past studies on shifting cultivation mapping of North-East (NE) India lack systematic assessment techniques. We...
Overloaded network devices are becoming an increasing problem especially in resource limited networks with the continuous and rapid increase of wireless devices and the huge volume of data generated. Admission and routing control policy at a network...
With the increasing number of end devices and the growing heterogeneity of application demands, task orchestration in dynamic cloud–edge environments faces challenges arising from network fluctuations, resource contention, and decision uncertai...
Lung cancer can be discovered at an early stage to enhance patient survival. However, existing screening tools are both resource-intensive and inaccessible in low-resource countries. This paper introduces a machine learning model that uses an ensembl...
This study proposes a reproducible hybrid computational model for the explainable classification of territorial vulnerability using heterogeneous tabular data. The approach integrates fuzzy logic and extreme gradient boosting in a two-stage architect...
Bird–aircraft strikes are rare but high-impact events that pose persistent safety and operational challenges in aviation. Existing approaches often focus on predictive accuracy, with limited integration of probability calibration, rare-event pr...
Customer churn prediction is a core predictive analytics function in telecommunications information systems, where model outputs feed retention campaigns and resource allocation decisions. Although supervised learning is widely used to rank subscribe...
Cloud computing, an emerging computing paradigm, has been widely considered due to its high scalability and availability. An essential stage of cloud computing is the cloud virtual machine migration technology. Nevertheless, the current trigger timin...
In this paper, we propose an optimal cooperative sensing technique for cognitive radio to maximize sensing performance based on energy detection. In most spectrum sensing research, many cooperation methods have been proposed to overcome the sensitivi...
In arid regions, the tritium concentration in groundwater is typically very low and often falls below the minimum detectable activity (MDA) of the conventional liquid scintillation counter (LSC). Therefore, to measure the tritium activity concentrati...
Machine Learning (ML) models are increasingly used in credit risk assessment, creating a growing need for decision mechanisms that balance predictive performance and fairness. Fairness-aware threshold optimization has emerged as an attractive post-pr...
We present an analogue of the classical Gittins index for a one-armed decision problem with a continuous spectrum of payoffs. The model assumes that the decision-maker observes independent realizations of a random variable and, at each step, decides...
This paper introduces a novel approach to anomaly-based failure forecasting that jointly optimizes both the ensemble function and the anomaly threshold used for decision making. Unlike conventional methods that apply fixed or classifier-defined thres...
This paper proposes a model of a legislature, formed by several parties, which has to vote for or against a certain bill in the presence of a lobbyist interested in a certain vote outcome. We show that the ease with which the lobbyist can manipulate...
Motivated by call center systems, we investigate the optimal admission control problem for a retrial queueing system with multiple customer classes. Arrivals follow independent Poisson processes, service times are exponentially distributed, and the d...
This paper studies a novel self-pilot tone based adaptive threshold return-to-zero on-off keying (RZ-OOK) decision for free-space optical (FSO) communications. RZ-OOK has the characteristics of impulse series in the spectrum. Therefore, these impulse...
Background: Early screening of cognitive impairment is essential for timely clinical intervention; however, conventional cognitive tests such as the Mini-Mental State Examination (MMSE) rely on fixed thresholds that may not be optimal in real-world s...
We propose a post-hoc explanation layer for binary Quantum Logic Decision Tree classifiers constructed with QLDT+. The layer does not modify the fitted tree, the decision threshold, or the predicted label. It translates the class-labelled QLDT+ paths...
This paper investigates the status updating policy for information freshness in Internet of things (IoT) systems, where the channel quality is fed back to the sensor at the beginning of each time slot. Based on the channel quality, we aim to strike a...
The proliferation of Internet of Things (IoT) devices has introduced significant security challenges. Resource-constrained devices face sophisticated threats but lack the computational capacity for advanced security analysis. This study investigates...
This paper deals with heterogeneous queues where servers differ not only in service rates but also in operating costs. The classical optimisation problem in queueing systems with heterogeneous servers consists in the optimal allocation of customers b...
The method of determining probability thresholds of three-way decisions (3WDs) has always been the key of research, especially in the current environment with a large number of data and uncertainties. Among these problems, there will be correlation a...
Water scarcity critically constrains sustainable cotton production in arid Xinjiang, China. Conventional irrigation management often relies on empirical decision-making, resulting in a dynamic mismatch between water supply and the growth-stage-specif...
We study the secure distributed detection problems under energy constraint for IoT-oriented sensor networks. The conventional channel-aware encryption (CAE) is an efficient physical-layer secure distributed detection scheme in light of its energy eff...
Driven by government subsidies and advertising revenue, air taxis present an innovative solution to alleviate traffic congestion and are poised for growth. However, at their current stage of development, air taxi companies primarily operate short-dis...
Total alkalinity (TAC) plays a pivotal role in buffering acid–base fluctuations and maintaining pH stability in aquatic ecosystems. This study presents a data-driven approach to model TAC using decision tree regression, applied to a comprehensi...
Decision-making medical systems (DMS) refer to the design of decision techniques in the healthcare sector. They involve a procedure of employing ideas and decisions related to certain processes such as data acquisition, processing, judgment, and conc...
Condition-based maintenance (CBM) is a promising technique for a wide variety of deteriorating systems. Condition-based maintenance’s effectiveness largely depends on the quality of condition monitoring. The majority of CBM mathematical models...
28 February 2023
Considering the effect of carbon emission factors on financing, a carbon credit policy was introduced to explore the capital-constrained manufacturer’s remanufacturing and carbon emission decisions. Meanwhile, this paper also explored the bank&...
The successive growth of the aviation industry has progressively heightened the importance of airport pavement management systems. Existing research has primarily focused on the technological advancements of optimization models, with limited applicab...
Background/Objectives: Rules for predicting blood–brain barrier (BBB) permeability, including the CNS multiparameter optimization (CNS MPO) score, Lipinski’s Rule of Five, and Veber’s rules, were developed using relatively limited d...
Large-scale promotions lead to a huge number of orders, and the quantity of deliveries grows sharply, which puts considerable strain on cities’ logistics and imposes high related shipping costs. To alleviate these consequences, in this paper we...
The rapid development of wireless communication technology has led to an increasing number of internet of thing (IoT) devices, and the demand for spectrum for these devices and their related applications is also increasing. However, spectrum scarcity...
Background/Objectives: Postpartum haemorrhage (PPH) remains a leading cause of maternal morbidity, and the relationship between labour-induction duration and haemorrhagic risk has not been translated into a practical intrapartum risk-stratification f...
The evolution of commodity hardware makes it possible to use this type of equipment to implement traffic monitoring systems. A preliminary empirical evaluation of a network traffic probe based on Linux indicates that the system performance has signif...
Model induction is one of the most popular methods to extract information to better understand AI’s decisions by estimating the contribution of input features for a class of interest. However, we found a potential issue: most model induction me...
The paper studies a controllable multi-server heterogeneous queueing system where servers operate at different service rates without preemption, i.e., the service times are uninterrupted. The optimal control policy allocates the customers between the...
Accurate identification of transformer insulation faults is essential for reliable power-system operation, and dissolved gas analysis (DGA) remains an important diagnostic tool. However, conventional Rogers’ Ratio interpretation relies on fixed...
Geothermal power generation offers a high availability factor and independence from weather conditions, yet steam production in geothermal wells often declines over time due to factors such as pressure depletion and scale deposition. To enable early...
Multi-criteria decision-making (MCDM) models are moving toward being data-oriented. Meanwhile, MCDM models’ totalitarian reliance on experts’ preferences may reduce the accuracy of results in real-world challenges. Therefore, there is a h...
As the technology scales down, two-dimensional (2D) NAND flash memory has reached its bottleneck. Three-dimensional (3D) NAND flash memory was proposed to further increase the storage capacity by vertically stacking multiple layers. However, the new...
Blockchain-based financial ecosystems generate unprecedented volumes of multi-temporal data streams requiring sophisticated analytical frameworks that leverage both on-chain transaction patterns and off-chain market microstructure dynamics. This stud...
In this paper, we consider a scenario where the base station (BS) collects time-sensitive data from multiple sensors through time-varying and error-prone channels. We characterize the data freshness at the terminal end through a class of monotone inc...
This paper proposes a deep learning (DL)-assisted high-pass-filter (HPF)-based fixed-threshold decision (FTD) for free-space optical (FSO) communication. HPF is applied to reduce the scintillation effect by filtering out the low-frequency components...
Color multi-threshold image segmentation is a non-convex, gradient-free global optimization problem. The number of decision variables increases with the number of thresholds, leading to a rapid expansion of the search space and increased computationa...
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