machine learning

Diabetes poses significant challenges due to its prevalence and the potential consequences of inaccurate or delayed diagnosis. This study focuses on enhancing prediction reliability to mitigate such risks. Initially, it identifies diabetes-related factors through correlation analysis with the target variable and implements models to address missing data. Subsequently, various imputation methods including CART, GMM, and RFR are employed to evaluate these factors. Results from each imputation scenario inform the selection of the most effective method. The study then employs ensemble algorithms like AdaBoost, Bagging, Gradient Boosting, and RF to enhance classification model accuracy. Further refinement is achieved by optimizing hyper-parameters through grid search. Evaluation involves comparing model predictions with those of medical professionals to assess accuracy. The findings reveal superior performance of optimized machine learning models over human predictions, indicating potential for improved diagnosis accuracy and reduced medical errors. This research contributes to advancing predictive modeling in diabetes diagnosis, offering prospects for enhanced community health and reduced socioeconomic burdens.

This study addresses the Hub Location-Routing Problem (HLRP) in transportation networks, considering the inherent uncertainty in travel times between nodes. We employed a method centered on data-driven robust optimization, utilizing Support Vector Clustering (SVC) to form an uncertainty set grounded in empirical data. The proposed methodology is compared against traditional uncertainty sets, showcasing its superior performance in providing robust solutions. A comprehensive case study on a retail store's transportation network in Tehran is presented, demonstrating significant differences in hub locations, allocations, and vehicle routes between deterministic and robust models. The SVC-based model proves to be particularly effective, yielding substantially improved objective function values compared to polyhedral and box uncertainty sets. The study concludes by highlighting the practical significance of this research and suggesting future directions for advancing transportation network optimization under uncertainty.

One of the constant problems that people with mental health conditions are faced with now is that they cannot establish a good relationship with their therapist, or the client's disease type is not in the therapist's specialty. These clients may not receive adequate treatment and stop the therapy before feeling well. Therefore, the classification of mental patients based on their disorder types and allocating a therapist with the same expertise to them could lead to better treatment and improve the quality of the therapy sessions. This paper will compare several machine learning (ML) algorithms to classify patients with mental conditions. Moreover, benefiting from the best ML algorithm, patients will be categorized into different classes based on their disorder types. Finally, a mathematical model will be developed to determine the allocation policy of therapists to each group of patients to maximize the summation of the utilization between therapists and patients. To explore the implementation of the proposed method, we have conducted a real-life case study to assess the validation of the model.

Today, data mining and machine learning are recognized as tools for extracting knowledge from large datasets with diverse characteristics. With the increasing volume and complexity of information in various fields, decision-making has become more challenging for managers and decision-making units. Data Envelopment Analysis (DEA) is a tool that aids managers in measuring the efficiency of the units under their supervision. Another challenge for managers involves selecting and ranking options based on specific criteria. Choosing an appropriate multi-criteria decision-making (MCDM) technique is crucial in such cases. With the spread of COVID-19 and the significant financial, economic, and human losses it caused, data mining has once again played a role in improving outcomes, predicting trends, and reducing these losses by identifying patterns in the data. This paper aims to assess and predict the efficiency of countries in preventing and treating COVID-19 by combining DEA and MCDM models with machine learning models. By evaluating decision-making units and utilizing available data, decision-makers are better equipped to make effective decisions in this area. Computational results are presented in detail and discussed in depth.

The accelerated digital transformation in the contemporary business landscape, propelled by the Fourth Industrial Revolution, has fundamentally reshaped marketing research practices. This study leverages machine learning techniques and big data analytics to extract critical customer value propositions from extensive online reviews, aligning with predictive marketing strategies. Using a hybrid approach that combines qualitative and quantitative analyses, the research examines 8,290 customer reviews sourced from an online platform within the tourism industry. Two advanced analytical techniques were applied: clustering analysis to identify 20 distinct value components prioritized by tourists and associative rule mining to uncover seven essential patterns embedded in customer feedback. The results highlight the potential of big data and machine learning in accelerating marketing research processes, improving precision, and lowering operational costs. The findings emphasize the transformative role of digital tools in modern marketing practices, enabling businesses to enhance customer satisfaction, optimize services, and maintain competitive advantages in a data-driven economy.

This study aims to provide a comprehensive evaluation of current machine learning (ML) algorithms employed in targeted and personalized advertising. It reveals key findings and conclusions from a wide range of sources, offering readers a concise summary. The study addresses the gap by identifying and analyzing the most significant machine learning-based targeting methods utilized in the recent studies. This helps readers understand the strengths and weaknesses of different approaches and keeps them up-to-date with the most recent advancements and best practices. Employing the PRISMA methodology, the review systematically examines existing literature on ML-driven targeted advertising. It identifies effective ML methods and strategies, presenting real-world examples to illustrate their practical implementation. Reviewing key findings from existing literature, the analysis identifies the most effective ML methods for targeted advertising. It also examines three research questions across three key dimensions: targeting, personalizing, and predicting customer preferences. This study proposes a novel theoretical framework that elucidates the application of ML in targeted advertising. Specifically, the study explores ML algorithms that enhance precision in each dimension. Key models include Long Short-Term Memory (LSTM) networks for analyzing historical customer data, Convolutional Neural Networks (CNN) for image recognition tasks, and Factorization Machines for capturing feature interactions in click-through rate (CTR) predictions. Additionally, traditional models such as logistic regression, decision trees, random forests, and support vector machines (SVM) are utilized for classification tasks, while unsupervised learning techniques like k-means clustering and hierarchical clustering facilitate user segmentation based on behavioral and demographic similarities. These models collectively enable marketers to derive actionable insights, optimize advertising content, and improve overall campaign performance. By consolidating key findings from existing literature on ML-driven targeted advertising, this study offers a valuable resource for understanding current trends and gaps. It also proposes future research directions, highlighting potential areas for further exploration, which can inspire new studies and innovations in the field.

Emotional computing synergizes the understanding and quantification of emotions, drawing on diverse data sources such as text, audio, and visual indicators. A challenge arises when attempting to discern authentic emotions from those concealed deliberately via facial cues, vocal nuances, and other communicative behaviors. Integrating multiple physiological and behavioural signals can give more profound insights into an individual's emotional state. Historically, research has predominantly concentrated on a singular facet of emotional computing. In contrast, our study offers an in-depth exploration of its pivotal domains, encompassing emotional models, Databases (DBs), and contemporary developments. We begin by elucidating two prevalent emotional models and then examine a renowned sentiment analysis DB. Subsequently, we delve into cutting-edge emotion detection and analysis methodologies across varied sensory channels, elaborating on their design and operational principles. In conclusion, the fundamental principles of emotional computing and its real-world implications are discussed. This review endeavors to provide researchers from academia and industry with a holistic understanding of the latest progress in this domain.

Customer churn is one of the challenges of business management in today's complex competitive environment. For this purpose, the organization must have an efficient system to detect and analyze the factors influencing customer churn. To conduct this research, an attempt has been made to build a hybrid model based on data mining approaches from information related to 5830 customers of a chain store (demographic information and information based on customer purchase records) with 17 qualitative and quantitative characteristics. The features of higher importance were identified to build the model in the first stage using the logistic regression algorithm. In the second stage, the support vector machine algorithm, a critical supervised learning algorithm, was used to classify the customers and rank the essential features. Finally, the proposed model has been implemented as a case study in the chain store industry. The results indicate the optimal efficiency of the proposed analysis method. This research has been done to identify the influential factors in customer churn and focus on providing new solutions to reduce churn in the retail industry. Also, the results show that age, marital status, and average monthly income from the set of demographic features and how to get to know the store, the share of online shopping, and special sales from the set of features related to customer transaction records are among the most important factors affecting customer churn. In addition, practical suggestions have been presented that can be used for tactical and strategic planning of chain stores to attract and retain customers.

This study presents a novel predictive model for engineering graduates' placement outcomes using Machine Learning (ML) techniques. The model is built on a comprehensive dataset that includes students' performance in various skill areas and their subsequent placement status. By employing a range of ML algorithms, the study evaluates their performance in terms of accuracy. The findings reveal the Customized Random Forest Model (CRFM) algorithm as the most accurate, with a prediction rate of 89%. Furthermore, the study also evaluates the target job domain or field in which students aim to secure placements as well as their target salary packages using the Customized Principal Component Analysis (CPCA) model. The research highlights the importance of various skills, such as programming, aptitude, and domain knowledge, in determining the employability of engineering graduates. The study underscores the importance of various skills, such as programming, aptitude, and domain knowledge, in determining the employability of engineering graduates. The proposed model has directed and practical implications for educational institutions, policymakers, and employers, enabling them to identify the key factors that influence the employability of engineering graduates and develop strategies to enhance their employability.

The aim of this research is to determine how the implementation of machine learning has generated advantages in the field of engineering. Through a systematic review of the literature, it seeks to understand the importance of machine learning and its various applications in engineering, such as equipment maintenance, business demand forecasting, production chain optimization, customer service, and quality control. In this article, we conduct a systematic review and bibliometric analysis to explore the current state of research on machine learning and Industry 4.0 applications in the field of industrial engineering. Our goal is to identify established and emerging fields of research to guide future research. To carry out this study, we initially identified 639 scientific journal publications indexed by publishers such as Ebsco essentials, ScienceDirect, IEEEXplore, and MDPI, collected from 1 January of 2015 to May 2023. Subsequently, a group of specialists evaluated these publications, carefully selecting 65 of them that were placed in the literature review section and that were considered relevant to our analysis. In a second stage, we applied a detailed analysis using MAXQDA v.2020 software on our collected data, focusing on citation and keyword evaluation. This approach allowed us to gain a deeper understanding of trends and connections in existing research in this field.

Optimizing production in the plastic extrusion industry is a pivotal task for small scale industries. To enhance the efficiency in today’s competitive market being a small-scale manufacturer over their peers is challenging. With the limited resources, having constraints on manpower, capital, space, often facing fluctuations in demand and production, simultaneously maintaining high quality became very important for the success. Among the plethora of KPIS used in manufacturing, Overall Equipment Effectiveness (OEE) stands out as corner stone. In this study, we collected real-world data from a plastic extrusion company. i.e., an HDPE Pipe manufacturing company. It serves as the backdrop for our study, this is based on the plastic extrusion sector and set out a goal of enhancing OEE through a comparative investigation of various ML models.  To forecast and estimate OEE values, we used various Machine Learning models and examine each algorithm’s performance using metrics like Mean Squared Error (MSE) and model comparisons using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), creating a comprehensive picture of each algorithm’s strength which enables the small businesses to make informed decisions and empowers them to stay agile and adapt to the changes in the manufacturing environment.

We consider a continuous model of the optimal control of the customer dynamics based on marketing policies as a non-autonomous system of ODEs. The model tracks the history of the simultaneous changes from the beginning to the current time for the evolution of the company's regular, referral, and potential customers. We then present a new supervised machine-learning algorithm for the numerical simulation of the problem. The proposed learning algorithm implements a polynomial kernel to simplify the formulation of the method. To avoid computational complexity, the Bernstein kernels are used to get a simple optimization marketing strategy by using the Support Vector Regression (SVR) in a least-squares framework. Some numerical experiments are carried out to support the proposed model and the method. The method provides approximate numerical results with high accuracy by kernels of polynomials of low degree. The running time of the technique is also illustrated versus the increasing number of training points to see the polynomial behavior of the running time.

This paper considers an accurate and efficient diabetes detection scheme via machine learning. It uses the science of data mining and pattern matching in its diabetes diagnosis process. It implements and evaluates 4 machine learning classification algorithms, namely Decision tree, Random Forest, XGBoost and LGBM. Then selects and introduces the one that performs the best towards its objective using multi-criteria decision-making methods. Its results reveal that Random Forest algorithm outperformed other algorithms with higher accuracy. It also examines the details of features that have a greater effect on diabetes detection. Considering that diabetes is one of the most deadly, disabling, and costly diseases observed today, its alarmingly increasing rates, and difficulty of its diagnosis because of many vague signs and symptoms, utilization of such approach can help doctors increase accuracy of their diagnosis and treatment schemes. Hence, this paper uses the science of data mining as a tool to gather and analyze existing data on diabetes and help doctors with its diagnosis and treatment process. The main contribution of this paper can therefore be its applied nature to an essential field and accuracy of its pattern recognition via several analytical approaches.