Machine Learning Processing Research Articles

Integrating AI in testing automation: Enhancing test coverage and predictive analysis for improved software quality

This research explores the integration of Artificial Intelligence (AI) in testing automation, focusing on its ability to enhance test coverage and enable predictive analysis for improved software quality. As software systems become increasingly complex, traditional testing methods often struggle to meet quality demands. This study evaluates various AI techniques, including machine learning and natural language processing, and their applications in generating test cases, optimizing testing processes, and predicting defects. Through empirical case studies from diverse organizations, we demonstrate significant improvements in test coverage and defect detection rates following AI implementation. The findings highlight the efficiency gains and quality enhancements achieved through AI-driven testing, while also addressing challenges such as data dependency, complexity of implementation, and the need for skilled personnel. This research contributes to the understanding of AI's role in software testing and encourages organizations to adopt these technologies for better quality assurance and faster development cycles.

Advancing business performance through data-driven process automation: A case study of digital transformation in the banking sector

This review paper examines the impact of data-driven process automation on business performance within the banking sector, emphasizing its role in enhancing operational efficiency, customer experience, and profitability. The paper explores how automation technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Robotic Process Automation (RPA) are transforming traditional banking processes by streamlining operations and enabling more personalized services. A comparative analysis highlights the advantages of automated systems over conventional methods. It acknowledges potential challenges, such as job displacement and cybersecurity risks. The paper concludes with strategic recommendations for banks seeking to adopt or enhance automation technologies. It suggests areas for future research, including the long-term effects of automation on the workforce and the integration of emerging technologies like blockchain.

Leveraging openCV and image processing for crime identification through facial recognition

To maintain the safety and security of communities, law enforcement agencies provide vital functions such as crime prevention and identification. Identifying people with a criminal record efficiently and accurately is one of the biggest hurdles in this field. This study delves into a fresh strategy for taking on this problem by making use of state-of-the-art image processing methods and machine learning algorithms. The project's main goal is to identify convicted felons by utilizing OpenCV, a robust open-source computer vision toolkit, and different facial recognition algorithms. By giving them a tool that guarantees a faster reaction rate and improved accuracy in suspect identification, the system is aimed to boost the capacities of law enforcement organizations. This project is based on the tenet of criminal psychology, which states that criminals are prone to committing new crimes. The system's goal is to detect and track repeat criminals both before and after they commit crimes by making use of this information. The method works by taking pictures of people who are known to be criminals and putting them in a database. From there, they can be compared to either live video streams or static photographs taken by surveillance cameras. In order to analyze and identify facial characteristics effectively, algorithms like LBPH and the Haar cascade classifier are also used. The system's robustness and efficiency are guaranteed by integrating these approaches into the OpenCV framework. This research makes a valuable contribution to the field of crime prevention by offering law enforcement authorities a dependable and scalable alternative. This system provides a streamlined interface for storing, retrieving, and matching facial photos. It was built using Python 3.5 and uses SQLite to manage the database of offenders' details. Proactively monitoring persons with a criminal record is made easier and faster with the suggested method. Suspect identification is also made easier. This research showcases the possibility of integrating machine learning and image processing to enhance public safety and crime prevention initiatives. It is an early step towards more complete video surveillance systems.

Multimodal machine learning-based technical success prediction in patients undergoing transcatheter aortic valve implantation

Abstract Background Procedural success is fundamental to achieving optimal clinical outcome, and prediction of procedural success and failure is of key importance in the selection of the appropriate treatment strategy in patients with severe aortic stenosis requiring aortic valve replacement. Purpose We aimed to develop a prediction model for procedural success in patients undergoing transcatheter aortic valve implantation (TAVI) using multimodal information and employing machine learning algorithms. Method In a prospective TAVI registry, patient baseline demographics, clinical history, medication, laboratory, procedural characteristics, electrocardiogram, angiography, transthoracic and transesophagel echocardiography, and computed tomography information were collected prospectively and extracted as numeric data. Data preprocessing included the imputation of missing values and the elimination of features with high collinearity. We incorporated several feature selection (FS) and classification algorithms for model development. Datasets for each target were randomly split into train/validation sets (70/10%) and hold-out test sets (20%). All machine learning processes, including preprocessing, feature selection, parameter, and hyperparameter optimization, were carried out on training and validation sets in 10-fold cross-validation. Different metrics, including area under the curve (AUC) in receiver operating characteristic curve analyses, sensitivity, and specificity, were calculated for model evaluation on the holdout test set. Procedural success was defined as technical success in the Valve Academic Research Consortium-3 (VARC-3). Technical failure differentiated between vascular and cardiac complications. Results In a total of 2,576 patients undergoing TAVI with a contemporary device between March 2012 and June 2023, 242 (9.4%) had technical failure: 70 (2.7%) had cardiac and 178 (6.9%) had vascular technical failure. The sensitivity and specificity for overall technical failure were 0.71 and 0.77, respectively, with AUC of 0.76 (95% CI: 0.69-0.84) achieved by the recursive feature elimination and AdaBoost algorithm. For cardiac technical failure, the ANOVA FS and Random Forest (RF) model yielded sensitivity and specificity of 0.71 and 0.77, with an AUC of 0.76 (95% CI: 0.65 to 0.87). Vascular technical failure showed AUC of 0.76 (95% CI: 0.69 to 0.84), with sensitivity and specificity of 0.72 and 0.73, respectively, with achieved using the ANOVA FS and RF model. Conclusion The initial results of the novel prediction model for technical success are promising. Further improvements and external validation are warranted to support the use of the proposed model in clinical practice.Central Illustration

Predicting the Wear Amount of Tire Tread Using 1D−CNN

Since excessively worn tires pose a significant risk to vehicle safety, it is crucial to monitor tire wear regularly. This study aimed to verify the efficient tire wear prediction algorithm proposed in a previous modeling study, which minimizes the required input data, and use driving test data to validate the method. First, driving tests were conducted with tires at various wear levels to measure internal accelerations. The acceleration signals were then screened using empirical functions to exclude atypical data before proceeding with the machine learning process. Finally, a tire wear prediction algorithm based on a 1D−CNN with bottleneck features was developed and evaluated. The developed algorithm showed an RMSE of 5.2% (or 0.42 mm) using only the acceleration signals. When tire pressure and vertical load were included, the prediction error was reduced by 11.5%, resulting in an RMSE of 4.6%. These findings suggest that the 1D−CNN approach is an efficient method for predicting tire wear states, requiring minimal input data. Additionally, it supports the potential usefulness of the intelligent tire technology framework proposed in the modeling study.

A Multi-Modal Approach for A Healthier Weight and More Active Lifestyle

Abstract Background Obesity and overweight has been increasing globally and in the UK over the last few years, bringing along a wide array of challenges in health and wellbeing. Not only do they take a heavy toll on those affected, but obesity and overweight-related ill-health is costing the UK National Health Service £6.1 with projected costs to run up to £9.7 billion in 2050. This project aimed to apply artificial intelligence to identify the predictive indicators for obesity and co-create an intervention with public health practitioners and stakeholders. Methods Using health and wellbeing public health population data from the 2015, 2018 and 2022 from a city in the East Midlands, we applied artificial intelligence that uses machine learning techniques to discover insights, find hidden patterns and discover relationships in the data about engaging in physical activity and a healthy diet. The data on dietary choices and physical activity preferences were further analysed using advanced machine learning techniques. Results The outcomes from our machine learning process revealed patterns of physical activity engagement and diet from specific locations, along with a range of demographic variables that influence individuals’ patterns of physical ability and dietary practices. These findings, which will be presented in detail, have been integrated into a comprehensive city map. This map not only showcases opportunities for engaging in a healthy lifestyle but also aids in prioritising interventions in areas of greatest need. Conclusions The AI technology we employed allowed us to parameterise and generalise our findings with the possibility of scaling up the machine learning approach to other similar datasets. The findings facilitated the development of bespoke training for public health officers working with underserved communities, further demonstrating the impact of applying AI to inform the design of public health interventions. Key messages • Applying Artificial Intelligence to public health data has the potential to inform the development of effective interventions and increase precision. • Using AI in public health data can generate new insights into population health and well-being and inform public health policy.

TECRR: a benchmark dataset of radiological reports for BI-RADS classification with machine learning, deep learning, and large language model baselines

BackgroundRecently, machine learning (ML), deep learning (DL), and natural language processing (NLP) have provided promising results in the free-form radiological reports’ classification in the respective medical domain. In order to classify radiological reports properly, a high-quality annotated and curated dataset is required. Currently, no publicly available breast imaging-based radiological dataset exists for the classification of Breast Imaging Reporting and Data System (BI-RADS) categories and breast density scores, as characterized by the American College of Radiology (ACR). To tackle this problem, we construct and annotate a breast imaging-based radiological reports dataset and its benchmark results. The dataset was originally in Spanish. Board-certified radiologists collected and annotated it according to the BI-RADS lexicon and categories at the Breast Radiology department, TecSalud Hospitals Monterrey, Mexico. Initially, it was translated into English language using Google Translate. Afterwards, it was preprocessed by removing duplicates and missing values. After preprocessing, the final dataset consists of 5046 unique reports from 5046 patients with an average age of 53 years and 100% women. Furthermore, we used word-level NLP-based embedding techniques, term frequency-inverse document frequency (TF-IDF) and word2vec to extract semantic and syntactic information. We also compared the performance of ML, DL and large language models (LLMs) classifiers for BI-RADS category classification.ResultsThe final breast imaging-based radiological reports dataset contains 5046 unique reports. We compared K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient-Boosting (GB), Extreme Gradient Boosting (XGB), Long Short-Term Memory (LSTM), Bidirectional Encoder Representations from Transformers (BERT) and Biomedical Generative Pre-trained Transformer (BioGPT) classifiers. It is observed that the BioGPT classifier with preprocessed data performed 6% better with a mean sensitivity of 0.60 (95% confidence interval (CI), 0.391-0.812) compared to the second best performing classifier BERT, which achieved mean sensitivity of 0.54 (95% CI, 0.477-0.607).ConclusionIn this work, we propose a curated and annotated benchmark dataset that can be used for BI-RADS and breast density category classification. We also provide baseline results of most ML, DL and LLMs models for BI-RADS classification that can be used as a starting point for future investigation. The main objective of this investigation is to provide a repository for the investigators who wish to enter the field to push the boundaries further.

A Machine Learning Approach for Modeling the Spatial-temporal Propagation Pattern of COVID-19

Abstract. Predicting infections resulting from epidemiological contagions involves a complex therapeutic endeavor that necessitates the consideration of various factors within the available data. COVID-19, a globally recognized highly dangerous disease, presents an opportunity to effectively understand its patterns and spatial determinants to combat similar ailments. This study seeks to employ a deep neural network and several machine learning techniques to identify the crucial factors influencing COVID-19 infections and to forecast the spatial-temporal spread of the disease. Previous research has focused on predicting outcomes using a limited set of 8 patient variables. To contribute to this field, the current investigation analyzes a dataset comprising 47,029 records of infected individuals who sought medical attention, with 18,433 of them testing positive for the coronavirus and requiring hospitalization. The study explores the prediction of illness and hospitalization needs based on geographical locations by utilizing machine learning algorithms such as Random Forest, Naive Bayes, Decision Tree, and K-Nearest Neighbor. The machine learning process commences with input data (comprising the 47,029 records), enabling the machine to identify patterns within the dataset and subsequently make informed decisions based on these patterns and insights gained. The primary objective is to allow the machine to autonomously learn and refine its predictions without human intervention. Applying these algorithms to COVID-19 data for spatial-temporal prediction revealed that the Random Forest algorithm achieved the highest accuracy of 0.84, while the Simple Bayes algorithm exhibited the lowest accuracy at 0.50. Additionally, the study compared random forest, decision tree, simple bayes, and K-nearest neighbor algorithms to predict the severity of road accidents.

AI in Business and Finance: A Micro-to-Macro Perspective on Its Impact

This comprehensive survey examines the transformative impact of machine learning (ML), deep learning (DL), and natural language processing (NLP) across diverse business and finance sectors. From micro-level operational efficiencies to macro-level strategic decision-making, we delve into the multifaceted applications of AI and its influence on traditional practices. Our analysis highlights the increasing prominence of ML in enhancing decision-making and achieving greater accuracy and efficiency. We also identify key challenges such as data quality, modelinterpretability, and linguistic information processing. By addressing these challenges, we can further unlock the potential of ML in revolutionizing business and finance. We conducted a systematic literature review using the PRISMA technique to identify relevant research published within the last five years. Our findings reveal the growing adoption of ML techniques in various business and finance applications, such as fraud detection, risk assessment, customer relationship management, and algorithmic trading. We identified relevant publications through comprehensive searches in leading academic databases, including Scopus, Web of Science, and Google Scholar. We also discuss the challenges associated with implementing ML models in these domains, including data quality issues, model interpretability, and ethical considerations. Byunderstanding the applications, challenges, and opportunities associated with AI, businesses, policymakers, researchers, and investors can make informed decisions and leverage AI to drive innovation and growth in the business and finance sectors.

Enhancing Thoracic Surgery with AI: A Review of Current Practices and Emerging Trends.

Artificial intelligence (AI) is increasingly becoming integral to medical practice, potentially enhancing outcomes in thoracic surgery. AI-driven models have shown significant accuracy in diagnosing non-small-cell lung cancer (NSCLC), predicting lymph node metastasis, and aiding in the efficient extraction of electronic medical record (EMR) data. Moreover, AI applications in robotic-assisted thoracic surgery (RATS) and perioperative management reveal the potential to improve surgical precision, patient safety, and overall care efficiency. Despite these advancements, challenges such as data privacy, biases, and ethical concerns remain. This manuscript explores AI applications, particularly machine learning (ML) and natural language processing (NLP), in thoracic surgery, emphasizing their role in diagnosis and perioperative management. It also provides a comprehensive overview of the current state, benefits, and limitations of AI in thoracic surgery, highlighting future directions in the field.

An Improved Hybrid Model Based on Ensemble Features and Regularization Selection for Classification

Feature selection is a pivotal process in machine learning, essential for enhancing model performance by reducing dimensionality, improving generalization, and mitigating overfitting. By eliminating irrelevant or redundant features, simpler and more interpretable models are achieved, which generally perform better. In this study, we introduce an advanced hybrid method combining ensemble feature selection and regularization techniques, designed to optimize model accuracy while significantly reducing the number of features required. Applied to a customer satisfaction dataset, our method was first tested without feature selection, where the model achieved a ROC AUC value of 0.946 on the test set using all 369 features. However, after applying our proposed feature selection method, the model achieved a higher ROC AUC value of 0.954, utilizing only 12 key features and completing the task in approximately 43% less time. These findings demonstrate the effectiveness of our approach in producing a more efficient and superior-performing model.

Fundamentals and Applications Related to Data Science, Machine Learning, and Statistical Processing

Fundamentals and Applications Related to Data Science, Machine Learning, and Statistical Processing

Case Identification of Depression in Inpatient Electronic Medical Records: Scoping Review.

Electronic medical records (EMRs) contain large amounts of detailed clinical information. Using medical record review to identify conditions within large quantities of EMRs can be time-consuming and inefficient. EMR-based phenotyping using machine learning and natural language processing algorithms is a continually developing area of study that holds potential for numerous mental health disorders. This review evaluates the current state of EMR-based case identification for depression and provides guidance on using current algorithms and constructing new ones. A scoping review of EMR-based algorithms for phenotyping depression was completed. This research encompassed studies published from January 2000 to May 2023. The search involved 3 databases: Embase, MEDLINE, and APA PsycInfo. This was carried out using selected keywords that fell into 3 categories: terms connected with EMRs, terms connected to case identification, and terms pertaining to depression. This study adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. A total of 20 papers were assessed and summarized in the review. Most of these studies were undertaken in the United States, accounting for 75% (15/20). The United Kingdom and Spain followed this, accounting for 15% (3/20) and 10% (2/20) of the studies, respectively. Both data-driven and clinical rule-based methodologies were identified. The development of EMR-based phenotypes and algorithms indicates the data accessibility permitted by each health system, which led to varying performance levels among different algorithms. Better use of structured and unstructured EMR components through techniques such as machine learning and natural language processing has the potential to improve depression phenotyping. However, more validation must be carried out to have confidence in depression case identification algorithms in general.

Consecutive Image Acquisition without Anomalies.

An image is a visual representation that can be used to obtain information. A camera on a moving vector (e.g., on a rover, drone, quad, etc.) may acquire images along a controlled trajectory. The maximum visual information is captured during a fixed acquisition time when consecutive images do not overlap and have no space (or gap) between them. The images acquisition is said to be anomalous when two consecutive images overlap (overlap anomaly) or have a gap between them (gap anomaly). In this article, we report a new algorithm, named OVERGAP, that remove these two types of anomalies when consecutive images are obtained from an on-board camera on a moving vector. Anomaly detection and correction use here both the Dynamic Time Warping distance and Wasserstein distance. The proposed algorithm produces consecutive, anomaly-free images with the desired size that can conveniently be used in a machine learning process (mainly Deep Learning) to create a prediction model for a feature of interest.

EthoWatcher OS: improving the reproducibility and quality of categorical and morphologic/kinematic data from behavioral recordings in laboratory animals.

Behavioral recordings annotated by human observers (HOs) from video recordings are a fundamental component of preclinical animal behavioral models of neurobiological diseases. These models are often criticized for their vulnerability to reproducibility issues. Here, we present the EthoWatcher-Open Source (EW-OS), with tools and procedures for the use of blind-to-condition categorical transcriptions that are simultaneous with tracking, for the assessment of HOs intra- and interobserver reliability during training and data collection, for producing video clips of samples of behavioral categories that are useful for observer training. The use of these tools can inform and optimize the performance of observers, thus favoring the reproducibility of the data obtained. Categorical and machine vision-derived outputs are presented in an open data format for increased interoperability with other applications, where behavioral categories are associated frame-by-frame with tracking, morphological and kinematic attributes of an animal's image. The center of mass (X and Y pixel coordinates), the animal's area in square millimeters, the length and width in millimeters, and the angle in degrees were recorded. It also assesses the variation in each morphological descriptor to produce kinematic descriptors. While the initial measurements are in pixels, they are later converted to millimeters using the scale calibrated by the user via the graphical user interfaces. This process enables the creation of databases suitable for machine learning processing and behavioral pharmacology studies. EW-OS is constructed for continued collaborative development, available through an open-source platform, to support initiatives toward the adoption of good scientific practices in behavioral analysis, including tools for evaluating the quality of the data that can alleviate problems associated with low reproducibility in the behavioral sciences.

Digital Revolution: How AI is Transforming Content Marketing

Artificial Intelligence (AI) is revolutionising content marketing by automating key processes and enabling personalisation on a massive scale. Using technologies such as Machine Learning and Natural Language Processing, AI can generate content quickly, identify SEO-relevant keywords and improve campaign performance. It analyses user behaviour, anticipates their needs and optimises marketing strategies in real time. However, this automation poses ethical challenges, such as algorithmic bias and over-reliance on technology. To exploit these tools responsibly, it is crucial that companies adopt ethical and transparent practices, respecting data confidentiality rules. In the future, AI will continue to transform marketing, with innovations such as virtual agents and even more personalised strategies, while requiring a balance between technological efficiency and human authenticity.

The Impact of Artificial Intelligence in The Financial Sector: Opportunities and Challenges

This article explores the transformative impact of Artificial Intelligence (AI) in the financial sector, highlighting its various applications and associated challenges. Through a detailed analysis, key technologies such as machine learning, computer vision, and natural language processing are examined, which have revolutionized critical areas such as customer service, risk management, and financial advising. Although AI has significantly improved operational efficiency and service personalization, it also presents important risks and limitations, such as data privacy, lack of model interpretability, and cybersecurity. Additionally, the article addresses future forecasts for AI adoption in finance, suggesting that its integration will continue to expand, driven by technological advances and regulatory improvements. However, this expansion must be balanced with adequate oversight to mitigate potential risks and maximize benefits. Overall, this study offers a balanced view of the opportunities and challenges that AI presents for the future of global finance.

Leveraging SAP Analytics Cloud for Data-Driven Decision Making in Modern Enterprises

In today's fast-paced business environment, organizations are increasingly relying on data analytics to make informed decisions. SAP Analytics Cloud (SAC) has emerged as a leading solution for integrating data from various sources, visualizing trends, and driving data-driven decisions. This paper examines the functionalities of SAC, its impact on decision-making processes, and how enterprises use it to gain a competitive edge. Key features such as predictive analytics, machine learning, and real-time data processing are discussed. The paper also includes case studies that illustrate the practical application of SAC in enterprise environments.

Evaluating User Acceptance of Mobile Payment Systems in Uganda

Purpose: This research evaluates user acceptance of mobile payment systems. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Combining machine learning with DW systems provides significant advantages in different fields. This synergy boosts analytical aptitudes, allowing the organization to go a notch higher than descriptive analytics in predictive and prescriptive analytics. However, such a decision is not simple as it has implementation matters such as data quality problems, scalability, and real-time processing problems. Integration best practices include in-database machine learning processing, a feature store, and proper MLOps practices. Real-life examples from the healthcare industry, banking and financial services, retail, and manufacturing industries show that this integration brings operational enhancements for the business and positive effects on customers and overall organizational performance. Recommendations: Mobile payment providers should prioritize user-centric design principles, focusing on simplicity, usability, and intuitive interfaces. Policymakers should develop regulatory frameworks that establish clear standards for mobile payment systems, focusing on data protection, privacy, and security. Keywords: User Acceptance, Mobile Payment Systems

Generating Artistic Images Through Neural Style Transfer Using Machine Learning and Image Manipulation Techniques

Neural Style Transfer (NST) is a computational technique that merges the content of one image with the stylistic characteristics of another, creating visually appealing compositions. Leveraging deep learning and image processing, this method separates andcombines the content and style of input images using convolutional neural networks (CNNs). The content representation of an image is captured by the high-level features extracted from a pre-trained CNN, while its style is characterized by the statistical correlations of features at multiple network layers. It explores the fusion of machine learning and image processing techniques in NST. Initially introduced by Gatys et al., NST has gained popularity due to its ability to generate artistic renditions and novel visualizations. The process involves extracting content and style features from separate images and optimizing a generated image to minimize differences in content and style representations. In this work, explored into the underlying concepts and technical aspectsof NST. This proceedings discussed the utilization of pre-trained CNN models like VGG19 or ResNet for feature extraction and how optimization algorithms, such as gradient descent, iteratively refine the generated image to achieve a fusion of content and style. Additionally, This development has been examined the impact of hyper parameters and layer choices on the quality of stylized outputs.Mainly this technology focuses on reduction of total variation loss, which we incur during the style transfer.These proceedings has carried on work with VGG19, XCEPTION, EfficientnetB7. These findings show how Convolutional Neural Networks can learn deep image representations and show how they may be used for sophisticated image synthesis andmodification.