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A prediction model to detect non-compliant taxpayers using a supervised machine learning approach: evidence from Tunisia

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JOURNAL OF BUSINESS ANALYTICS 2025年第2期8卷 116-133页

作者： Kamoun, Aicha Boujelbane, Rahma Boujelben, Saoussen Univ Sfax Fac Econ & Management Res Lab Econ & Financial Anal & Modeling LAMEF Sfax Tunisia Univ Sfax Fac Econ & Management Multimedia InfoRmat Syst & Adv Comp Lab Miracl ANLP Res Grp Sfax Tunisia Univ Sfax Sfax Business Sch ESCS Dept Accounting Res Lab Econ & Financial Anal & Modeling LAMEF Airport Rd Km 4-5 Sfax 3018 Tunisia

This study aims to develop a tax non-compliance prediction model in Tunisia using supervised machine learning algorithms. A data mining analysis was conducted following the Knowledge Discovery in Databases (KDD) process, utilizing a dataset of 20,930 labeled observations from 2013 to 2017, comprising 110 attributes. We employed supervised learning algorithms, including K-Nearest Neighbors, Decision Trees, Na & iuml;ve Bayes, Gradient Boosting, and Random Forest, to identify the most accurate model. Notably, Random Forest outperformed the other algorithms, achieving a prediction accuracy of 83%. Furthermore, through a combined interpretation of feature importance derived from Random Forest, SHAP value analysis, and ANOVA, our findings provide tax auditors with insights into the most influential attributes for predicting tax non-compliance. This study holds significant practical implications by enhancing the efficiency of tax audits and supporting tax authorities in their efforts to combat tax non-compliance.

关键词： Tax non-compliance Tunisian tax authorities data mining KDD supervised learning algorithms big data

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Development of Predictive Classification Models and Extraction of Signature Wavelengths for the Identification of Spoilage in Chicken Breast Fillets During Storage Using Near Infrared Spectroscopy

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FOOD AND BIOPROCESS TECHNOLOGY 2025年第1期18卷 933-941页

作者： Siddique, Aftab Herron, Charles B. Wu, Bet Melendrez, Katherine S. S. Sabillon, Luis J. G. Garner, Laura J. Durstock, Mary Sanz-Saez, Alvaro Morey, Amit Auburn Univ Dept Poultry Sci Auburn AL 36849 USA Auburn Univ Dept Crop Soils & Environm Sci Auburn AL USA

Technologies for rapid identification and prediction of food spoilage can be crucial in minimizing food waste and losses, although their efficiency requires further improvement. This study aimed to pinpoint specific near-infrared (NIR) wavelengths that could indicate spoilage in raw chicken breast fillets. In this study, commercial tray-packs of boneless, skinless chicken breast fillets stored in a walk-in cooler at 4 degrees C were periodically tested every other day until they reached the spoilage state (identified by > 7 log CFU/ml). A portable Hyper spectral spectroscopy device (Field Spec Hi-Res4), with a range of wavelengths of 350-2500 nm, was used to measure reflectance. In addition to hyper-spectral analysis, aerobic plate counts were conducted on the fillets. The data from these counts were then used to train a Back Propagation Neural Network (B.P.N.N.) with specific parameters (250,000 steps, a learning rate of 0.02, and 5 hidden layers) and Linear-Support Vector machines (SVM-Linear) with ten-fold cross-validation technique to categorize spoilage into three stages: baseline microbial count (up to 3 log CFU/ml) (Initiation), propagation (between 3 and 6.9 log CFU/ml), and spoiled (> 7 log CFU/ml). The feature extraction process successfully identified the most representative signature wavelengths of 385 nm, 400 nm, 432 nm, 1141 nm, 1321 nm, 1374 nm, 2241 nm, 2292 nm, 2311 nm, and 2412 nm from the whole hyper-spectral profile, which facilitated the classification of different phases of spoilage. The BPNN model demonstrated a high classification accuracy, with 93.7% for baseline counts, 95.2% for the propagation phase, and 98% for the spoiled category. These signature hyperspectral wavelengths hold the potential for developing cost-effective and rapid food spoilage detection systems, particularly for perishable items.

关键词： supervised learning algorithms Andrew's Curve Analysis Back Propagation Neural Network Near-Infrared spectroscopy

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Using machine learning methods to predict future churners: an analysis of repeat hotel customers

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INTERNATIONAL JOURNAL OF CONTEMPORARY HOSPITALITY MANAGEMENT 2025年第1期37卷 36-56页

作者： Dursun-Cengizci, Aslihan Caber, Meltem Antalya Bilim Univ Coll Tourism Antalya Turkiye Akdeniz Univ Fac Tourism Dept Tourism Guidance Antalya Turkiye

PurposeThis study aims to predict customer churn in resort hotels by calculating the churn probability of repeat customers for future stays in the same hotel ***/methodology/approachBased on the recency, frequency, monetary (RFM) paradigm, random forest and logistic regression supervised machine learning algorithms were used to predict churn behavior. The model with superior performance was used to detect potential churners and generate a priority *** random forest algorithm showed a higher prediction performance with an 80% accuracy rate. The most important variables were RFM-based, followed by hotel sector-specific variables such as market, season, accompaniers and booker. Some managerial strategies were proposed to retain future churners, clustered as "hesitant," "economy," "alternative seeker," and "opportunity chaser" customer *** limitations/implicationsThis study contributes to the theoretical understanding of customer behavior in the hospitality industry and provides valuable insight for hotel practitioners by demonstrating the methods that facilitate the identification of potential churners and their ***/valueMost customer retention studies in hospitality either concentrate on the antecedents of retention or customers' revisit intentions using traditional methods. Taking a unique place within the literature, this study conducts churn prediction analysis for repeat hotel customers by opening a new area for inquiry in hospitality studies.

关键词： Churn prediction Hotel customer supervised learning algorithms CRM RFM paradigm

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Comparison of supervised-learning approaches for designing a channelized observer for image quality assessment in CT

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MEDICAL PHYSICS 2023年第7期50卷 4282-4295页

作者： Pouget, Eleonore Dedieu, Veronique Jean Perrin Comprehens Canc Ctr Dept Med Phys Clermont Ferrand France Clermont Ferrand Univ UMR INSERM IMoST 1240 Clermont Ferrand France Jean Perrin Comprehens Canc Ctr 58 Rue Montalembert F-63011 Clermont Ferrand 1 France

BackgroundThe current paradigm for evaluating computed tomography (CT) system performance relies on a task-based approach. As the Hotelling observer (HO) provides an upper bound of observer performances in specific signal detection tasks, the literature advocates HO use for optimization purposes. However, computing the HO requires calculating the inverse of the image covariance matrix, which is often intractable in medical applications. As an alternative, dimensionality reduction has been extensively investigated to extract the task-relevant features from the raw images. This can be achieved by using channels, which yields the channelized-HO (CHO). The channels are only considered efficient when the channelized observer (CO) can approximate its unconstrained counterpart. Previous work has demonstrated that supervised learning-based methods can usually benefit CO design, either for generating efficient channels using partial least squares (PLS) or for replacing the Hotelling detector with machine-learning (ML) methods. PurposeHere we investigated the efficiency of a supervised ML-algorithm used to design a CO for predicting the performance of unconstrained HO. The ML-algorithm was applied either (1) in the estimator for dimensionality reduction, or (2) in the detector function. MethodsA channelized support vector machine (CSVM) was employed and compared against the CHO in terms of ability to predict HO performances. Both the CSVM and the CHO were estimated with channels derived from the singular value decomposition (SVD) of the system operator, principal component analysis (PCA), and PLS. The huge variety of regularization strategies proposed by CT system vendors for statistical image reconstruction (SIR) make the generalization capability of an observer a key point to consider upfront of implementation in clinical practice. To evaluate the generalization properties of the observers, we adopted a 2-step testing process: (1) achieved with the same regularization strat

关键词： computed tomography model observers supervised learning algorithms

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A supervised learning approach for the influence of comorbidities in the analysis of COVID-19 mortality in Tamil Nadu

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SOFT COMPUTING 2023年 1-15页

作者： Koteeswaran, S. Suganya, R. Surianarayanan, Chellammal Neeba, E. A. Suresh, A. Chelliah, Pethuru Raj Buhari, Seyed M. SA Engn Coll Dept CSE AI&ML Chennai 600077 Tamil Nadu India Vellore Inst Technol Sch Comp Sci & Engn Chennai Tamil Nadu India Bharathidasan Univ Ctr Distance & Online Educ Tiruchirappalli Tamil Nadu India Rajagiri Sch Engn & Technol Dept Informat Technol Kochi Kerala India SRM Inst Sci & Technol Fac Engn & Technol Sch Comp Dept Networking & Commun Chennai 603202 Tamil Nadu India Reliance Jio Platforms Ltd Edge AI Div Bangalore Karnataka India Univ Teknol Brunei Sch Business Jalan Tungku Link BE-1410 Mukim Gadong A Brunei

COVID-19 has created many complications in today's world. It has negatively impacted the lives of many people and emphasized the need for a better health system everywhere. COVID-19 is a life-threatening disease, and a high proportion of people have lost their lives due to this pandemic. This situation enables us to dig deeper into mortality records and find meaningful patterns to save many lives in future. Based on the article from the New Indian Express (published on January 19, 2021), a whopping 82% of people who died of COVID-19 in Tamil Nadu had comorbidities, while 63 percent of people who died of the disease were above the age of 60, as per data from the Health Department. The data, part of a presentation shown to Union Health Minister Harsh Vardhan, show that of the 12,200 deaths till January 7, as many as 10,118 patients had comorbidities, and 7613 were aged above 60. A total of 3924 people (32%) were aged between 41 and 60. Compared to the 1st wave of COVID-19, the 2nd wave had a high mortality rate. Therefore, it is important to find meaningful insights from the mortality records of COVID-19 patients to know the most vulnerable population and to decide on comprehensive treatment strategies.

关键词： COVID-19 Mortality Comorbidities Healthcare Exploratory data analysis supervised learning algorithms Fuzzy Death pattern Vulnerable population

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A Comparison Analysis of Heart Disease Prediction Using supervised Machine learning Techniques 29

A Comparison Analysis of Heart Disease Prediction Using Supe...

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29th IEEE Symposium on Computers and Communications (IEEE ISCC)

作者： Elhadjamor, Emna Ammar Harbaoui, Houda Manouba Univ Lab ENSI RIADI Tunis Tunisia Univ Sousse Comp Sci Dept Sousse Tunisia

ISBN: (纸本)9798350354249;9798350354232

Evaluating multiple machine learning models for predicting and detecting heart disease is crucial yet challenging within clinical practice. In regions with limited cardiovascular expertise, misdiagnoses are frequent, highlighting the need for precise early-stage prediction using comprehensive analysis of digital patient records. This study aimed to pinpoint the most accurate machine learning classifiers for this pivotal purpose, leveraging a heart disease dataset sourced from the official 2022 annual CDC survey. Thirteen supervised machine learning algorithms underwent rigorous deployment and evaluation to gauge their effectiveness in predicting heart disease. Comparative assessments scrutinized the performance and accuracy of these algorithms, along with estimating the significance of each feature in predicting heart disease. Exploration extended to various ensemble methods and individual classifiers, including AdaBoost, Random Forest, Extra Trees, HistGradientBoosting, Decision Tree, K-Nearest Neighbors (KNN), Multi-layer Perceptron (MLP), Stochastic Gradient Descent (SGD), Logistic Regression, Gaussian Naive Bayes, among others. Particularly noteworthy was the exceptional performance of HistGradient-Boosting, achieving an outstanding in all evaluation metrics. This outcome underscores the potential of a relatively straightforward supervised machine learning approach, hinting at its promising role in enhancing early-stage prediction and detection of heart disease.

关键词： Machine learning Heart Disease Prediction Clinical Decision-Making Support supervised learning algorithms Kaggle Dataset

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Predicting online gambling self-exclusion: an analysis of the performance of supervised machine learning models

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INTERNATIONAL GAMBLING STUDIES 2016年第2期16卷 193-210页

作者： Percy, Christian Franca, Manoel Dragicevic, Simo Garcez, Artur d'Avila BetBuddy London England City Univ London Dept Comp Sci London EC1V 0HB England

As gambling operators become increasingly sophisticated in their analysis of individual gambling behaviour, this study evaluates the potential for using machine learning techniques to identify individuals who used self-exclusion tools out of a sample of 845 online gamblers, based on analysing trends in their gambling behaviour. Being able to identify other gamblers whose behaviour is similar to those who decided to use self-exclusion tools could, for instance, be used to share responsible gaming messages or other information that aids self-aware gambling and reduces the risk of adverse outcomes. However, operators need to understand how accurate models can be and which techniques work well. The purpose of the article is to identify the most accurate technique out of four highly diverse techniques and to discuss how to deal analytically and practically with a rare event like self-exclusion, which was used by fewer than 1% of gamblers in our data-set. We conclude that balanced training data-sets are necessary for creating effective models and that, on our data-set, the most effective method is the random forest technique which achieves an accuracy improvement of 35 percentage points versus baseline estimates.

关键词： Responsible gambling gambling self-exclusion machine learning supervised learning algorithms problem gambling Internet gambling three-tier model

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A supervised learning Approach for Differential Entropy Feature-based Spectrum Sensing 6

A Supervised Learning Approach for Differential Entropy Feat...

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6th International Conference on Wireless Communications, Signal Processing and Networking (IEEE WiSPNET)

作者： Saravanan, Purushothaman Chandra, Shreeram Suresh Upadhye, Akshay Gurugopinath, Sanjeev PES Univ Dept Elect & Commun Engn Bengaluru 560085 India

ISBN: (纸本)9781665440868

In this work, we consider a supervised machine learning-based approach for spectrum sensing in cognitive radios. The noise process is assumed to follow a generalized Gaussian distribution, which is of practical relevance. For classification, we consider the differential entropy estimate in the received observations as a feature vector. For our comparative study, we consider the support vector machine, K-nearest neighbor, random forest and logistic regression techniques. Through experimental results based on real-world captured datasets, we show that the proposed differential entropy feature-based technique outperforms the energy-based approach in terms of probability of detection. The proposed technique is particularly useful under low signal-to-noise ratio conditions, and when the noise distribution has heavier tails.

关键词： Cognitive radios differential entropy generalized Gaussian noise spectrum sensing supervised learning algorithms

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Detection of arrhythmia using weightage-based supervised learning system for COVID-19

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INTELLIGENT SYSTEMS WITH APPLICATIONS 2022年 16卷

作者： Ketkar, Yashodhan Gawade, Sushopti Pillai Coll Engn Dept Informat Technol Engn Panvel 410206 Maharashtra India Pillai Coll Engn Dept Comp Engn Panvel 410206 Maharashtra India

COVID-19 disease has became a global pandemic in the last few years. This disease was highly contagious, and it quickly spread throughout several countries. Its infection can lead to severe implications for its victims, including cardiovascular issues. This complication develops in some people with a history of cardiovascular illness, whereas it emerges in others after COVID-19 infection. Cardiovascular problems are the primary cause of mortality in COVID-19 patients and are used to predict disease prognosis. Identifying arrhythmia from abnormalities in patient ECG signals is one approach to the detection of cardiovascular disorders. This is a laborious and time-consuming procedure that can be automated. The proposed method selects the most suitable model for this task. The selection is made through the weightage generated from the user's requirements. The proposed method uses supervised learning to identify abnormalities in ECG waves. The models provided by the selection system during tests were able to meet user requirements. The models achieved up to 97% accuracy and 97% precision in predictive tasks.

关键词： Arrhythmia detection Automated model generation Automated model training Machine learning in healthcare supervised learning algorithms Weightage based model selection

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Spoil characterisation using UAV-based optical remote sensing in coal mine dumps

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International Journal of Coal Science & Technology 2023年第5期10卷 72-86页

作者： Sureka Thiruchittampalam Sarvesh Kumar Singh Bikram Pratap Banerjee Nancy F.Glenn Simit Raval School of Minerals and Energy Resources Engineering University of New South WalesSydneyNSW 2052Australia Agriculture Victoria Grains Innovation Park110 Natimuk RoadHorshamVIC 3400Australia Department of Geosciences Boise State UniversityBoiseIDUSA

The structural integrity of mine dumps is crucial for mining operations to avoid adverse impacts on the triple *** temporal assessments of coal mine dumps are a compliant requirement to ensure design reconciliation as spoil off-loading continues over ***,the conventional in-situ coal spoil characterisation is inefficient,laborious,hazardous,and prone to experts'observation *** this end,this study explores a novel approach to develop automated coal spoil characterisation using unmanned aerial vehicle(UAV)based optical remote *** textural and spectral properties of the high-resolution UAV images were utilised to derive lithology and geotechnical parameters(i.e.,fabric structure and relative density/consistency)in the proposed *** raw images were converted to an orthomosaic using structure from motion aided ***,structural descriptors were computed per pixel to enhance feature modalities of the spoil ***,machine learning algorithms were employed with ground truth from experts as training and testing data to characterise spoil rapidly with minimal human *** characterisation accuracies achieved from the proposed approach manifest a digital solution to address the limitations in the conventional characterisation approach.

关键词： Lithology Fabric structure Consistency/relative density Dimensionality reduction supervised learning algorithms

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