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Journal articles on the topic 'Machine Translation Algorithm (ML)'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Shanshan Wang. "Cross-Language Translation and Comparative Study of English Literature Using Machine Translation Algorithms." Journal of Electrical Systems 20, no. 6s (2024): 2221–29. http://dx.doi.org/10.52783/jes.3136.

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Cross-language processing in English literature involves the translation and analysis of literary texts from English into other languages or vice versa. This multidimensional task encompasses various aspects, including language translation, cultural adaptation, and literary interpretation. Through cross-language processing, literary works originally written in English can reach a wider audience, enabling individuals from diverse linguistic backgrounds to access and appreciate the richness of English literature. This paper presents an innovative approach to language processing tasks through the

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Ruan, Xushan. "Design and Exploration of College English Reading, Writing, and Translation Teaching Classroom Based on Machine Learning." Wireless Communications and Mobile Computing 2022 (March 10, 2022): 1–8. http://dx.doi.org/10.1155/2022/4506025.

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The aims are to ameliorate the dull classroom atmosphere and unsatisfactory teacher-student interaction and cultivate students’ reading, writing, and translation (R-W-T) English proficiency level (EPL). Specifically, this paper designs an optimized polynomial kernel-based support vector machine (SVM) classification algorithm based on the machine learning (ML) theory. Firstly, this paper expounds on the correlation between ML and teaching classrooms to analyze the optimization direction in the application of ML in classroom teaching. Afterward, SVM and RF algorithms are selected for data normal

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Wongvibulsin, Shannon, Katherine C. Wu, and Scott L. Zeger. "Improving Clinical Translation of Machine Learning Approaches Through Clinician-Tailored Visual Displays of Black Box Algorithms: Development and Validation." JMIR Medical Informatics 8, no. 6 (2020): e15791. http://dx.doi.org/10.2196/15791.

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Background Despite the promise of machine learning (ML) to inform individualized medical care, the clinical utility of ML in medicine has been limited by the minimal interpretability and black box nature of these algorithms. Objective The study aimed to demonstrate a general and simple framework for generating clinically relevant and interpretable visualizations of black box predictions to aid in the clinical translation of ML. Methods To obtain improved transparency of ML, simplified models and visual displays can be generated using common methods from clinical practice such as decision trees

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Prof., Teena Varma, and Silveira Lenora. "Machine Learning in the Business Sector." Advancement of Computer Technology and its Applications 4, no. 1 (2021): 1–7. https://doi.org/10.5281/zenodo.4481928.

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<em>Machine learning is all algorithm-based models being primarily built using statistical techniques and theoretical computer science, models are ‘trained’ based on experience to become better at their assigned tasks through iterations of inputs and data and gain the desired accuracy. Inevitably machine learning is starting to influence our daily lives to an extent of which we cannot even predict.ML has enabled new applications and uses difficult cases, by traditional programming. Some applications of machine learning widely used today are language translation, image recognition.

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Narinder, Kaur, and Gupta Ganesh. "Refurbished and improvised model using convolution network for autism disorder detection in facial images." Refurbished and improvised model using convolution network for autism disorder detection in facial images 29, no. 2 (2023): . 883–889. https://doi.org/10.11591/ijeecs.v29.i2.pp883-889.

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The main quality of deep learning over conventional machine learning (ML) techniques empowers firsthand uses in processing of images, speech recognition, medical imaging, machine translation and robotics, computer vision, and numerous other fields. The purpose of this study is to assess algorithms of deep learning for person with the disorder of autism. This disorder is developing disorder that causes significant communicative, social and behavioral difficulties in those who have it. In this research paper, the enhanced version of convolution network is discussed. Visual geometry group (VGG),

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Elov, Botir Boltayevich, Hamroyeva Shahlo Mirjonovna ), and Zilola Yuldashevna Xusainova. "NLPNING ZAMONAVIY ALGORITMLARI VA KONSEPSIYALARI." International journal of word art 6, no. 1 (2023): 3. https://doi.org/10.5281/zenodo.7679911.

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Natural language processing (NLP) algorithms serve to process human language data, including unstructured text data. Today, NLP algorithms are developed based on language rule-based, statistical and artificial intelligence approaches. Based on the approach based on language rules, the formation of linguistic bases for NLP tasks and the operations of classification in language corpora are performed. Statistical algorithms allow machines to read, understand and derive meaning from human languages and are based on processing large volumes of (bigdata) texts. Statistical algorithms are used in man

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Kaur, Narinder, and Ganesh Gupta. "Refurbished and improvised model using convolution network for autism disorder detection in facial images." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 2 (2023): 883. http://dx.doi.org/10.11591/ijeecs.v29.i2.pp883-889.

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<span lang="EN-US">The main quality of deep learning over conventional machine learning (ML) techniques empowers firsthand uses in processing of images, speech recognition, medical imaging, machine translation and robotics, computer vision, and numerous other fields. The purpose of this study is to assess algorithms of deep Learning for person with the disorder of autism. This disorder is developing disorder that causes significant communicative, social and behavioral difficulties in those who have it. In this research paper, the Enhanced version of convolution network is discussed. Visu

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Arkabaev, Nurkasym, Elshan Rahimov, Alisher Abdullaev, Harish Padmanaban, and Vugar Salmanov. "MODELLING AND ANALYSIS OF OPTIMIZATION ALGORITHMS." Jurnal Ilmiah Ilmu Terapan Universitas Jambi 9, no. 1 (2025): 161–77. https://doi.org/10.22437/jiituj.v9i1.38410.

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The purpose of this study was to comprehensively analyze existing optimization algorithms for Machine Learning (ML) models and develop new approaches aimed at improving their performance and efficiency. The study compared traditional and novel machine learning optimization techniques to evaluate their impact on model performance. The main results include a detailed overview of the main optimization methods in ML, including gradient descent, stochastic gradient descent, metaheuristic-based methods, and non-zero methods. Specific cases of using optimization algorithms in ML tasks, such as image

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Verma, Aishwarya R. "Comparative Analysis of Language Translation and Detection System Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (2021): 1200–1211. http://dx.doi.org/10.22214/ijraset.2021.37577.

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Abstract: Words are the meaty component which can be expressed through speech, writing or signals. It is important that the actual message or meaning of the words sent must conveys the same meaning to the one receives. The evolution from manual language translator to the digital machine translation have helped us a lot for finding the exact meaning such that each word must give at least close to exact actual meaning. To make machine translator more human-friendly feeling, natural language processing (NLP) with machine learning (ML) can make the best combination. The main challenges in machine

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Wagner, Martin, André Schulze, Michael Haselbeck-Köbler, et al. "Artificial intelligence for decision support in surgical oncology - a systematic review." Artificial Intelligence Surgery 2, no. 3 (2022): 159–72. http://dx.doi.org/10.20517/ais.2022.21.

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Aim: We systematically review current clinical applications of artificial intelligence (AI) that use machine learning (ML) methods for decision support in surgical oncology with an emphasis on clinical translation. Methods: MEDLINE, Web of Science, and CENTRAL were searched on 19 January 2021 for a combination of AI and ML-related terms, decision support, and surgical procedures for abdominal malignancies. Data extraction included study characteristics, description of algorithms and their respective purpose, and description of key steps for scientific validation and clinical translation. Resul

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Cerasa, Antonio, Gennaro Tartarisco, Roberta Bruschetta, et al. "Predicting Outcome in Patients with Brain Injury: Differences between Machine Learning versus Conventional Statistics." Biomedicines 10, no. 9 (2022): 2267. http://dx.doi.org/10.3390/biomedicines10092267.

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Defining reliable tools for early prediction of outcome is the main target for physicians to guide care decisions in patients with brain injury. The application of machine learning (ML) is rapidly increasing in this field of study, but with a poor translation to clinical practice. This is basically dependent on the uncertainty about the advantages of this novel technique with respect to traditional approaches. In this review we address the main differences between ML techniques and traditional statistics (such as logistic regression, LR) applied for predicting outcome in patients with stroke a

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Filipow, Nicole, Eleanor Main, Neil J. Sebire, et al. "Implementation of prognostic machine learning algorithms in paediatric chronic respiratory conditions: a scoping review." BMJ Open Respiratory Research 9, no. 1 (2022): e001165. http://dx.doi.org/10.1136/bmjresp-2021-001165.

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Machine learning (ML) holds great potential for predicting clinical outcomes in heterogeneous chronic respiratory diseases (CRD) affecting children, where timely individualised treatments offer opportunities for health optimisation. This paper identifies rate-limiting steps in ML prediction model development that impair clinical translation and discusses regulatory, clinical and ethical considerations for ML implementation. A scoping review of ML prediction models in paediatric CRDs was undertaken using the PRISMA extension scoping review guidelines. From 1209 results, 25 articles published be

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de la O, Víctor, Edwin Fernández-Cruz, Pilar Matía Matin, et al. "Translational Algorithms for Technological Dietary Quality Assessment Integrating Nutrimetabolic Data with Machine Learning Methods." Nutrients 16, no. 22 (2024): 3817. http://dx.doi.org/10.3390/nu16223817.

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Recent advances in machine learning technologies and omics methodologies are revolutionizing dietary assessment by integrating phenotypical, clinical, and metabolic biomarkers, which are crucial for personalized precision nutrition. This investigation aims to evaluate the feasibility and efficacy of artificial intelligence tools, particularly machine learning (ML) methods, in analyzing these biomarkers to characterize food and nutrient intake and to predict dietary patterns. Methods: We analyzed data from 138 subjects from the European Dietary Deal project through comprehensive examinations, l

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Mohammed Ahtesham Farooqui. "Machine Learning in Brain Tumor Diagnosis: Assessing the Efficacy of Diverse Methods." Power System Technology 48, no. 4 (2024): 4315–23. https://doi.org/10.52783/pst.1272.

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Background: Brain tumors constitute a critical and potentially life-threatening condition that requires accurate and timely diagnosis. In recent years, machine learning (ML) approaches have emerged as powerful tools for assisting radiologists and clinicians in identifying and classifying tumors on medical imaging. While numerous ML methods, including conventional machine learning algorithms and deep learning-based models, have been explored, a comprehensive analysis of their efficacy in detecting, segmenting, and classifying brain tumors remains essential. Methods: We conducted a systematic ev

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Vlontzos, Athanasios, Daniel Rueckert, and Bernhard Kainz. "A Review of Causality for Learning Algorithms in Medical Image Analysis." Machine Learning for Biomedical Imaging 1, November 2022 (2022): 1–17. http://dx.doi.org/10.59275/j.melba.2022-4gf2.

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Medical image analysis is a vibrant research area that offers doctors and medical practitioners invaluable insight and the ability to accurately diagnose and monitor disease. Machine learning provides an additional boost for this area. However, machine learning for medical image analysis is particularly vulnerable to natural biases like domain shifts that affect algorithmic performance and robustness. In this paper we analyze machine learning for medical image analysis within the framework of Technology Readiness Levels and review how causal analysis methods can fill a gap when creating robust

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Jekel, Leon, Waverly R. Brim, Marc von Reppert, et al. "Machine Learning Applications for Differentiation of Glioma from Brain Metastasis—A Systematic Review." Cancers 14, no. 6 (2022): 1369. http://dx.doi.org/10.3390/cancers14061369.

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Glioma and brain metastasis can be difficult to distinguish on conventional magnetic resonance imaging (MRI) due to the similarity of imaging features in specific clinical circumstances. Multiple studies have investigated the use of machine learning (ML) models for non-invasive differentiation of glioma from brain metastasis. Many of the studies report promising classification results, however, to date, none have been implemented into clinical practice. After a screening of 12,470 studies, we included 29 eligible studies in our systematic review. From each study, we aggregated data on model de

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Trofimova, Ekaterina, Emil Sataev, and Andrey Ustyuzhanin. "Linguacodus: a synergistic framework for transformative code generation in machine learning pipelines." PeerJ Computer Science 10 (September 23, 2024): e2328. http://dx.doi.org/10.7717/peerj-cs.2328.

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In the ever-evolving landscape of machine learning, seamless translation of natural language descriptions into executable code remains a formidable challenge. This article introduces Linguacodus, an innovative framework designed to tackle this challenge by deploying a dynamic pipeline that iteratively transforms natural language task descriptions into code through high-level data-shaping instructions. The core of Linguacodus is a fine-tuned large language model, empowered to evaluate diverse solutions for various problems and select the most fitting one for a given task. This article details t

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Dhanatwal, M., S. R. Jyothi, and A. Kumar. "Optimization of Amyloidosis Detection Method for Right Ventricle and Its Deployment Using Machine Learning (ML) Approach." CARDIOMETRY, no. 26 (March 1, 2023): 348–54. http://dx.doi.org/10.18137/cardiometry.2023.26.348354.

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When an aberrant protein called amyloid accumulates in our organs and tissues, it causes amyloidosis. When it happens, both their form and functionality are impacted and amyloidosis is a critical medical condition that may result in organ failure that is serious and may be lethal. In this present study, the author discussed about the amyloidosis in the right ventricle of the heart and its detection using the Machine Learning (ML). The methodology used for this research includes an infrastructure of amyloidosis detection, created by using supervised and unsupervised learning technique. The resu

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Obeagu, Emmanuel Ifeanyi, Christiana Uchenna Ezeanya, Fabian Chukwudi Ogenyi, and Deborah Domini Ifu. "Big data analytics and machine learning in hematology: Transformative insights, applications and challenges." Medicine 104, no. 10 (2025): e41766. https://doi.org/10.1097/md.0000000000041766.

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The integration of big data analytics and machine learning (ML) into hematology has ushered in a new era of precision medicine, offering transformative insights into disease management. By leveraging vast and diverse datasets, including genomic profiles, clinical laboratory results, and imaging data, these technologies enhance diagnostic accuracy, enable robust prognostic modeling, and support personalized therapeutic interventions. Advanced ML algorithms, such as neural networks and ensemble learning, facilitate the discovery of novel biomarkers and refine risk stratification for hematologica

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Zhang, Da, Lihong Zhao, Bo Guo, et al. "Integrated Machine Learning Algorithms-Enhanced Predication for Cervical Cancer from Mass Spectrometry-Based Proteomics Data." Bioengineering 12, no. 3 (2025): 269. https://doi.org/10.3390/bioengineering12030269.

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Early diagnosis is critical for improving outcomes in cancer patients; however, the application of diagnostic markers derived from serum proteomic screening remains challenging. Artificial intelligence (AI), encompassing deep learning and machine learning (ML), has gained increasing prominence across various scientific disciplines. In this study, we utilized cervical cancer (CC) as a model to develop an AI-driven pipeline for the identification and validation of serum biomarkers for early cancer diagnosis, leveraging mass spectrometry-based proteomics data. By processing and normalizing serum

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Abu Owida, Hamza, Bashar Al-haj Moh'd, Nidal Turab, Jamal Al-Nabulsi, and Suhaila Abuowaida. "The Evolution and Reliability of Machine Learning Techniques for Oncology." International Journal of Online and Biomedical Engineering (iJOE) 19, no. 08 (2023): 110–29. http://dx.doi.org/10.3991/ijoe.v19i08.39433.

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It is no secret that the rise of the Internet and other digital technologies has sparked renewed interest in AI-based techniques, especially those that fall under the umbrella of the subset of algorithms known as "Machine Learning" (ML). These advancements in electronics have allowed us to comprehend the world beyond the bounds of human cognition. A high-dimensional dataset's complicated nature. Although these techniques have been regularly employed by the medical sciences, their adoption to enhance patient care has been a bit slow. The availability of curated diverse data sets for model

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Sanfelici, Rachele, Dominic Dwyer, Linda A. Antonucci, and Nikolaos Koutsouleris. "T107. INDIVIDUALIZED DIAGNOSTIC AND PROGNOSTIC MODELS FOR PATIENTS WITH PSYCHOSIS RISK SYNDROMES: A META-ANALYTIC VIEW ON THE STATE-OF-THE-ART." Schizophrenia Bulletin 46, Supplement_1 (2020): S271—S272. http://dx.doi.org/10.1093/schbul/sbaa029.667.

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Abstract Background The Clinical High Risk (CHR) paradigm has led research into the biological and clinical underpinnings of the risk for psychosis, aiming at predicting and possibly preventing transition to the disorder. Statistical methods like machine learning (ML) and Cox proportional hazard regression have enabled the construction of diagnostic and prognostic models based on different data modalities, e.g., clinical risk factors, neurocognitive performance, or neurobiological data. However, their translation to clinical practice is still hindered by the heterogeneity both of CHR populatio

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Researcher. "THE EVOLUTION AND IMPACT OF AI/ML CHATBOTS IN ENTERPRISE APPLICATIONS: A TECHNICAL ANALYSIS." International Journal of Research In Computer Applications and Information Technology (IJRCAIT) 7, no. 2 (2024): 1089–103. https://doi.org/10.5281/zenodo.14077389.

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This technical article examines the implementation and evolution of AI/ML-powered chatbots in enterprise telecommunications environments, focusing on deployments at Vodafone and Verizon. Through detailed analysis of system architectures, performance metrics, and business outcomes, we demonstrate how advanced NLP models and machine learning algorithms have transformed customer service operations and incident management. The article reveals significant improvements, including a 42% reduction in customer interaction costs, 78% first-contact resolution rate, and 99.99% system uptime.  Vodafon

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Idowu, Oluwatobi, Nicholas Aderinto, Gbolahan Olatunji, and Emmanuel Kokori. "Machine Learning in Schizophrenia: A Systematic Review and Meta-Analysis of Diagnostic and Predictive Models." BJPsych Open 11, S1 (2025): S44. https://doi.org/10.1192/bjo.2025.10148.

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Aims: Schizophrenia is a psychiatric disorder characterized by diverse clinical presentations, posing challenges in early diagnosis and prognosis. Machine learning (ML) has emerged as a promising tool to enhance diagnostic accuracy, predict disease progression, and personalize treatment strategies. This systematic review and meta-analysis synthesized current evidence on the application of ML in schizophrenia diagnosis, prognosis, and treatment response prediction.Methods: A search was conducted across databases including PubMed, Embase, Scopus, Web of Science, and IEEE Xplore, adhering to PRIS

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Aleena Ahmad and Dr. Fouzia Jabeen. "Application Of Machine And Deep Learning In Epileptic Seizure Detection a Systematic Review." Annual Methodological Archive Research Review 3, no. 3 (2025): 63–75. https://doi.org/10.63075/98ay0441.

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Epilepsy, the fourth most common neurological disorder, affects 1% of the global population. Manual EEG-based seizure detection is error-prone, impacting diagnostic and prognostic accuracy. To evaluate the performance of machine and deep learning methods in improving detection of epileptic seizures, define challenges and map out future opportunities for clinical translation. This review collected and analyzed studies published between 2015 and 2023 indexed in PubMed, IEEE Xplore, and ScienceDirect. This review selected studies with an approach to seizure detection based on ML and DL for electr

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Porras, Jose Luis, Roger Soberanis-Mukul, S. Swaroop Vedula, et al. "106 Neurosurgery Resident Feedback through Artificial-Intelligence." Journal of Clinical and Translational Science 7, s1 (2023): 31. http://dx.doi.org/10.1017/cts.2023.189.

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OBJECTIVES/GOALS: Surgical training is constrained by duty hour limits, bias, and a trial-and-error learning process. Surgeon skill variation is a healthcare system disparity that can impact patient outcomes. Incorporating validated, standardized assessment tools and machine learning (ML) algorithms may help to standardize and reduce bias in surgeon education. METHODS/STUDY POPULATION: To support assessment tool and ML algorithm development, we are curating an annotated video registry of neurosurgical procedures. Point-of-view video of resident and attending neurosurgeons performing craniotomi

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Lavagnino, L., F. Amianto, B. Mwangi, et al. "Identifying neuroanatomical signatures of anorexia nervosa: a multivariate machine learning approach." Psychological Medicine 45, no. 13 (2015): 2805–12. http://dx.doi.org/10.1017/s0033291715000768.

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BackgroundThere are currently no neuroanatomical biomarkers of anorexia nervosa (AN) available to make clinical inferences at an individual subject level. We present results of a multivariate machine learning (ML) approach utilizing structural neuroanatomical scan data to differentiate AN patients from matched healthy controls at an individual subject level.MethodStructural neuroimaging scans were acquired from 15 female patients with AN (age = 20, s.d. = 4 years) and 15 demographically matched female controls (age = 22, s.d. = 3 years). Neuroanatomical volumes were extracted using the FreeSur

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Awoeyo, Olayemi Michael. "Exploring the Significance of AI through Chi-Square Testing in Business Decisions." December 2023 5, no. 4 (2023): 385–403. http://dx.doi.org/10.36548/jitdw.2023.4.005.

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A computer system capable of performing operations like speech recognition, visual perception, decision-making, and language translation would typically need human intellect. Thanks to artificial intelligence (AI), this is now feasible. AI, the general term for any intelligent computer program, includes machine learning as a subset. To put it another way, not all AI is machine learning, but all machine learning is AI, and so on. The study of machine learning (ML) is a burgeoning discipline with many promising directions for future advancement in various techniques and uses. This study explores

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Kapsner, Lorenz A., Manuel Feißt, Ariawan Purbojo, et al. "Using Machine Learning and Feature Importance to Identify Risk Factors for Mortality in Pediatric Heart Surgery." Diagnostics 14, no. 22 (2024): 2587. http://dx.doi.org/10.3390/diagnostics14222587.

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Background: The objective of this IRB-approved retrospective monocentric study was to identify risk factors for mortality after surgery for congenital heart defects (CHDs) in pediatric patients using machine learning (ML). CHD belongs to the most common congenital malformations, and remains the leading mortality cause from birth defects. Methods: The most recent available hospital encounter for each patient with an age <18 years hospitalized for CHD-related cardiac surgery between the years 2011 and 2020 was included in this study. The cohort consisted of 1302 eligible patients (mean age [S

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Guntamukkala, Gopi Krishna. "Multilingual NLP." International Journal of Advanced Engineering and Nano Technology (IJAENT) 10, no. 6 (2023): 9–12. https://doi.org/10.35940/ijaent.E4119.0610623.

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<strong>Abstract: </strong>The subject area of multilingual natural language processing (NLP) is concerned with the processing of natural language data in several languages. NLP systems that can translate between languages are becoming more and more necessary as the globe gets more interconnected in order to promote understanding and communication among speakers of various languages. To be effective, communication must overcome a number of obstacles presented by multilingual NLP. Lack of language standardization, which results in major variations in the grammatical constructions, vocabulary, a

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Pelayes, David Eduardo, Jose A. Mendoza, and Anibal Martin Folgar. "Artificial intelligence use in diabetes." Latin American Journal of Ophthalmology 5 (December 10, 2022): 6. http://dx.doi.org/10.25259/lajo_4_2022.

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Diabetic retinopathy (DR) affects the small vessels of the eye and is the leading cause of blindness in people on reproductive age; however, less than half of patients are aware of their condition; therefore, early detection and treatment is essential to combat it. There are currently multiple technologies for DR detection, some of which are already commercially available. To understand how these technologies work, we must know first some basic concepts about artificial intelligence (AI) such as machine learning (ML) and deep learning (DL). ML is the basic process by which AI incorporates new

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Brogi, Simone, and Vincenzo Calderone. "Artificial Intelligence in Translational Medicine." International Journal of Translational Medicine 1, no. 3 (2021): 223–85. http://dx.doi.org/10.3390/ijtm1030016.

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The huge advancement in Internet web facilities as well as the progress in computing and algorithm development, along with current innovations regarding high-throughput techniques, enable the scientific community to gain access to biological datasets, clinical data and several databases containing billions of pieces of information concerning scientific knowledge. Consequently, during the last decade the system for managing, analyzing, processing and extrapolating information from scientific data has been considerably modified in several fields, including the medical one. As a consequence of th

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Gutiérrez Benítez, Rodrigo, Alejandra Segura Navarrete, Christian Vidal-Castro, and Claudia Martínez-Araneda. "Guide for the application of the data augmentation approach on sets of texts in Spanish for sentiment and emotion analysis." PLOS ONE 19, no. 9 (2024): e0310707. http://dx.doi.org/10.1371/journal.pone.0310707.

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Over the last ten years, social media has become a crucial data source for businesses and researchers, providing a space where people can express their opinions and emotions. To analyze this data and classify emotions and their polarity in texts, natural language processing (NLP) techniques such as emotion analysis (EA) and sentiment analysis (SA) are employed. However, the effectiveness of these tasks using machine learning (ML) and deep learning (DL) methods depends on large labeled datasets, which are scarce in languages like Spanish. To address this challenge, researchers use data augmenta

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Ortuño-Miró, Sergio, Sergio Molina-Rodríguez, Carlos Belmonte, and Joaquín Ibañez-Ballesteros. "Identifying ADHD boys by very-low frequency prefrontal fNIRS fluctuations during a rhythmic mental arithmetic task." Journal of Neural Engineering 20, no. 3 (2023): 036018. http://dx.doi.org/10.1088/1741-2552/acad2b.

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Abstract Objective. Computer-aided diagnosis of attention-deficit/hyperactivity disorder (ADHD) aims to provide useful adjunctive indicators to support more accurate and cost-effective clinical decisions. Deep- and machine-learning (ML) techniques are increasingly used to identify neuroimaging-based features for objective assessment of ADHD. Despite promising results in diagnostic prediction, substantial barriers still hamper the translation of the research into daily clinic. Few studies have focused on functional near-infrared spectroscopy (fNIRS) data to discriminate ADHD condition at the in

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Xu, Jie, Sankalp Talankar, Jinqian Pan, et al. "Combining Federated Machine Learning and Qualitative Methods to Investigate Novel Pediatric Asthma Subtypes: Protocol for a Mixed Methods Study." JMIR Research Protocols 13 (July 8, 2024): e57981. http://dx.doi.org/10.2196/57981.

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Background Pediatric asthma is a heterogeneous disease; however, current characterizations of its subtypes are limited. Machine learning (ML) methods are well-suited for identifying subtypes. In particular, deep neural networks can learn patient representations by leveraging longitudinal information captured in electronic health records (EHRs) while considering future outcomes. However, the traditional approach for subtype analysis requires large amounts of EHR data, which may contain protected health information causing potential concerns regarding patient privacy. Federated learning is the k

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Elsborg, Jonas, and Marco Salvatore. "Using LLMs and Explainable ML to Analyze Biomarkers at Single-Cell Level for Improved Understanding of Diseases." Biomolecules 13, no. 10 (2023): 1516. http://dx.doi.org/10.3390/biom13101516.

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Single-cell RNA sequencing (scRNA-seq) technology has significantly advanced our understanding of the diversity of cells and how this diversity is implicated in diseases. Yet, translating these findings across various scRNA-seq datasets poses challenges due to technical variability and dataset-specific biases. To overcome this, we present a novel approach that employs both an LLM-based framework and explainable machine learning to facilitate generalization across single-cell datasets and identify gene signatures to capture disease-driven transcriptional changes. Our approach uses scBERT, which

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Gallagher, Gareth, Ra’ed Malallah, Jonathan P. Epperlein, et al. "A novel flexible near-infrared endoscopic device that enables real-time artificial intelligence fluorescence tissue characterization." PLOS ONE 20, no. 3 (2025): e0317771. https://doi.org/10.1371/journal.pone.0317771.

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Real-time endoscopic rectal lesion characterization employing artificial intelligence (AI) and near-infrared (NIR) imaging of the fluorescence perfusion indicator agent Indocyanine Green (ICG) has demonstrated promise. However, commercially available fluorescence endoscopes do not possess the flexibility and anatomical reach capabilities of colonoscopy while commercial flexible scopes do not yet provide beyond visible spectral imaging. This limits the application of this AI-NIR classification technology. Here, to close this technical gap, we present our development of a colonoscope-compatible

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Paulson, Lawrence C. "Michael John Caldwell Gordon. 28 February 1948—22 August 2017." Biographical Memoirs of Fellows of the Royal Society 65 (September 12, 2018): 89–113. http://dx.doi.org/10.1098/rsbm.2018.0019.

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Michael Gordon was a pioneer in the field of interactive theorem proving and hardware verification. In the 1970s, he had the vision of formally verifying system designs, proving their correctness using mathematics and logic. He demonstrated his ideas on real-world computer designs. His students extended the work to such diverse areas as the verification of floating-point algorithms, the verification of probabilistic algorithms and the verified translation of source code to correct machine code. He was elected to the Royal Society in 1994, and he continued to produce outstanding research until

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Qu, Aili, Zhipeng Yan, Haiyan Wei, et al. "Research on Grape-Planting Structure Perception Method Based on Unmanned Aerial Vehicle Multispectral Images in the Field." Agriculture 12, no. 11 (2022): 1894. http://dx.doi.org/10.3390/agriculture12111894.

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In order to accurately obtain the distribution of large-field grape-planting sites and their planting information in complex environments, the unmanned aerial vehicle (UAV) multispectral image semantic segmentation model based on improved DeepLabV3+ is used to solve the problem that large-field grapes in complex environments are affected by factors such as scattered planting sites and complex background environment of planting sites, which makes the identification of planting areas less accurate and more difficult to manage. In this paper, firstly, the standard deviation (SD) and interband cor

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Di Stefano, Valeria, Martina D’Angelo, Francesco Monaco, et al. "Decoding Schizophrenia: How AI-Enhanced fMRI Unlocks New Pathways for Precision Psychiatry." Brain Sciences 14, no. 12 (2024): 1196. http://dx.doi.org/10.3390/brainsci14121196.

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Schizophrenia, a highly complex psychiatric disorder, presents significant challenges in diagnosis and treatment due to its multifaceted neurobiological underpinnings. Recent advancements in functional magnetic resonance imaging (fMRI) and artificial intelligence (AI) have revolutionized the understanding and management of this condition. This manuscript explores how the integration of these technologies has unveiled key insights into schizophrenia’s structural and functional neural anomalies. fMRI research highlights disruptions in crucial brain regions like the prefrontal cortex and hippocam

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Żelasko, Dariusz, Wojciech Książek, and Paweł Pławiak. "Transmission Quality Classification with Use of Fusion of Neural Network and Genetic Algorithm in Pay&Require Multi-Agent Managed Network." Sensors 21, no. 12 (2021): 4090. http://dx.doi.org/10.3390/s21124090.

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Modern computer systems practically cannot function without a computer network. New concepts of data transmission are emerging, e.g., programmable networks. However, the development of computer networks entails the need for development in one more aspect, i.e., the quality of the data transmission through the network. The data transmission quality can be described using parameters, i.e., delay, bandwidth, packet loss ratio and jitter. On the basis of the obtained values, specialists are able to state how measured parameters impact on the overall quality of the provided service. Unfortunately,

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Lotierzo, Manuela, Romain Bruno, Amanda Finan-Marchi, et al. "Could a Multi-Marker and Machine Learning Approach Help Stratify Patients with Heart Failure?" Medicina 57, no. 10 (2021): 996. http://dx.doi.org/10.3390/medicina57100996.

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Half of the patients with heart failure (HF) have preserved ejection fraction (HFpEF). To date, there are no specific markers to distinguish this subgroup. The main objective of this work was to stratify HF patients using current biochemical markers coupled with clinical data. The cohort study included HFpEF (n = 24) and heart failure with reduced ejection fraction (HFrEF) (n = 34) patients as usually considered in clinical practice based on cardiac imaging (EF ≥ 50% for HFpEF; EF < 50% for HFrEF). Routine blood tests consisted of measuring biomarkers of renal and heart functions, inflammat

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Bonny, Talal, Wafaa Al Nassan, Khaled Obaideen, Tamer Rabie, Maryam Nooman AlMallahi, and Swati Gupta. "Primary Methods and Algorithms in Artificial-Intelligence-Based Dental Image Analysis: A Systematic Review." Algorithms 17, no. 12 (2024): 567. https://doi.org/10.3390/a17120567.

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Artificial intelligence (AI) has garnered significant attention in recent years for its potential to revolutionize healthcare, including dentistry. However, despite the growing body of literature on AI-based dental image analysis, challenges such as the integration of AI into clinical workflows, variability in dataset quality, and the lack of standardized evaluation metrics remain largely underexplored. This systematic review aims to address these gaps by assessing the extent to which AI technologies have been integrated into dental specialties, with a specific focus on their applications in d

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Madhumita, Zbyslaw Sondka, and Jon Teague. "Abstract 4884: Evaluating the utility of in silico variant annotation tools for cancer driver detection." Cancer Research 84, no. 6_Supplement (2024): 4884. http://dx.doi.org/10.1158/1538-7445.am2024-4884.

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Abstract In cancer genomics, precise variant annotation is crucial for clinical decisions, drug development, and research. The burgeoning genomic data offers an opportunity to use data-driven approaches to generate knowledge that supports clinical decisions. Particularly machine learning (ML) and Deep learning (DL), are becoming essential, as their application is fast, scalable, simple to implement, and generates reproducible results. The methods ranging from simple sequence-based alignment scoring to advanced algorithms like Logistic regression, Support vector machine, and Recurrent neural ne

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-, Gummadi Anjali Sr Rajya Lakshmi, Dasam Uma -, and Dr M. Ravi Kumar -. "Language Sphere: Next-Gen Language Translation Using ML." International Journal on Science and Technology 16, no. 2 (2025). https://doi.org/10.71097/ijsat.v16.i2.3645.

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Language translation has become more important in today's technical world,effective communication is essential for advanced international collaboration. This study addresses the challenges of achieving accurate and efficient translations between these two widely spoken languages, focusing on linguistic and cultural differences. The proposed translation model employs state-of-the-art neural machine translation techniques, integrating advanced tokenization and context-aware algorithms to enhance precision and fluency. Special attention is given to maintain the language's original meaning, tone,

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"A Study on Machine Learning Algorithms and its Applications." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 06 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35784.

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Machine learning is one of the rapidly developing fields of technology . It is growing very rapidly day by day . Applications of machine learning are vast in our daily life. Recently machine learning techniques are used in Google Maps, Google assistant, Alexa, Cortana, Siri etc. Machine learning's face detection and recognition algorithm are used in facebook for automatic friend tagging suggestion. ML algorithms for speech recognition is used in search by voice in google maps which shows the correct shortest route and predicts the traffic conditions. ML algorithms are also used for product rec

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Yang, Wen, Xiaogang Huang, Jianfang Fei, Juli Ding, and Xiaoping Cheng. "Applying Weighted Salinity Stratification to Rapid Intensification Prediction of Tropical Cyclone With Machine Learning." Earth and Space Science 11, no. 7 (2024). http://dx.doi.org/10.1029/2023ea002932.

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AbstractTropical cyclone (TC) intensification is influenced by environmental conditions, inner‐core dynamics, and interactions with upper‐ocean layers. Rapid intensification (RI) is a significant threat that is difficult to predict, prompting multiple institutions to collaborate. However, the accuracy still needs further improvements. It is well‐known that a warm upper ocean is conducive to RI, but the role of salinity stratification in this process is not well understood, particularly under different TC translation speeds. This study reveals that rapidly intensifying TCs are related to large

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Abraham, Ajith, Bineet Kumar Gupta, Satya Bhushan Verma, et al. "Improvement of Translation Accuracy for the Word Sense Disambiguation System using Novel Classifier Approach." International Arab Journal of Information Technology 21, no. 6 (2024). http://dx.doi.org/10.34028/iajit/21/6/14.

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Machine Translation (MT) is a crucial application of Natural language Processing (NLP). This MT technique automatic and based on computers. One of the most modern techniques adopted in MT is Machine Learning (ML). Over the past few years, ML has grown in popularity during MT process among researchers. Ambiguity is a major challenge in MT. Word Sense Disambiguation (WSD) is a common technique for solving the ambiguity problem. ML approaches are commonly used for the WSD techniques and are used for training and testing purposes. The outcome prediction of the test data gives encouraging results.

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Mu, Nan, Mostafa Rezaeitaleshmahalleh, Zonghan Lyu, et al. "Can We Explain Machine Learning-based Prediction For Rupture Status Assessments of Intracranial Aneurysms?" Biomedical Physics & Engineering Express, January 10, 2023. http://dx.doi.org/10.1088/2057-1976/acb1b3.

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Abstract Although applying machine learning (ML) algorithms to rupture risk assessment of intracranial aneurysms (IA) has yielded promising results, the opaqueness of some ML methods has limited their clinical translation. We present the first explainability comparison of six commonly used ML algorithms: multivariate logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), multi-layer perceptron neural network (MLPNN), and Bayesian additive regression trees (BART). A total of 128 IAs with known rupture status were selected for this study.

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Shara, Dr Jollanda. "Solving A ML Problem Using The Grossone." International Journal of Current Science Research and Review 05, no. 04 (2022). http://dx.doi.org/10.47191/ijcsrr/v5-i4-45.

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Machine learning (ML) has grown at a remarkable rate, becoming one of the most popular research directions. It is widely applied in various fields, such as machine translation, speech recognition, image recognition, recommendation system, etc. Optimization problems lie at the heart of most machine learning approaches. So, the essence of most ML algorithms is to build an optimization model and learn the parameters in the objective function from the given data. A series of effective optimization methods were put forward, in order to promote the development of ML. They have improved the performan

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