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machine Learning in Healthcare: A Review of Applications, Opportunities and Challenges

Machine Learning in Healthcare: A Review of Applications, Op...

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2nd international conference on machine Learning and Autonomous Systems, ICMLAS 2025

作者： Thute, Trupti Saratkar, Saniya Chaudhari, Aarti Raut, Rohini Thakare, Gayatri Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

This article explores the potential of machine learning (ML) in transforming healthcare. It provides an overview of ML and its various categories, highlighting how supervised and unsupervised learning algorithms can be utilized for tasks like disease diagnosis, treatment personalization, and resource allocation. The article delves into specific applications of ML in healthcare, including image analysis for early disease detection, visualization of complex medical data, and personalized medicine approaches. It further explores the exciting opportunities presented by ML, such as improved diagnostics and treatment, increased efficiency and cost reduction, and advancements in research and development. However, the article also acknowledges the challenges associated with implementing ML in healthcare, including data privacy concerns, real-time data requirements, and the complexity of certain models. Overall, the article emphasizes the multifaceted landscape of ML in healthcare, showcasing its potential to revolutionize the industry while acknowledging the hurdles that need to be addressed for successful integration. © 2025 IEEE.

关键词： Theranostics

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Integrating Computer Vision and pattern recognition in Fraud Detection for Financial Accounts 2nd

Integrating Computer Vision and Pattern Recognition in Fraud...

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2nd international conference on Business Data Analytics, ICBDA 2023

作者： Ahmad, Vasim Goya, Lalit Arora, Jigyasha Uttaranchal Institute of Management Uttaranchal University Uttarakhand Dehradun India Graduate School of Business Tula’s Institute Dhoolkot Road Uttarakhand Dehradun India Sudhowala Uttarakhand Dehradun India

ISBN: (纸本)9783031807770

Financial fraud detection plays a crucial role in safeguarding the integrity of financial accounts and maintaining the trust of stakeholders. Traditional fraud detection methods often rely on structured data and rule-based approaches, which may struggle to detect complex and evolving fraudulent activities. In recent years, combining computer vision and pattern recognition methods has become an interesting way to improve the identification of fraud in bank accounts. So, these are the basics of computer vision and pattern recognition. They show how they can be used to look at visual data and find complex patterns that might be lost in structured data alone. The integration of these techniques offers a comprehensive approach to detecting fraud by incorporating visual cues present in financial documents and transactions. Data acquisition and preprocessing can be addressed as critical steps to ensure the quality and reliability of visual financial data. There is a need for future potential of advanced computer vision technologies in fraud detection, emphasizing the need for ongoing research and innovation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： machine vision

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Classification of Potatoes Using Artificial intelligence Techniques in High Andean Areas of Peru 2

Classification of Potatoes Using Artificial Intelligence Tec...

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2nd international conference on machine Learning and Autonomous Systems, ICMLAS 2025

作者： Sifuentes-Lopez, Kety Auccahuasi, Wilver Universidad Cientifica del Sur Lima Peru

ISBN: (纸本)9798331505745

Agriculture is one of the means that provide food to the population, hence the need to improve its performance levels. Thanks to the artificial intelligence and image processing tools that are currently available, it is possible to improve the planting processes of various agricultural products. In Peru, one of the most produced products, both for domestic consumption and for export, are potatoes, in its various classes, so it is necessary to have a tool that can help farmers in their selection processes. In this research we configured a low-pass and power consumption device, such as model 5 of the Raspberry pi embedded card, where the YoloV3 model is configured, which has the ability to recognize different types of potatoes. For the training of the model, a database of 1791 images was configured, training with 80% of the images and validation of the trained model was performed with the remaining 20%. As a result, the sensitivity of the model was 82%, which means that the model can be used as an aid in classification by farmers. In conclusion, it is indicated that the level of sensitivity can be increased by training with a greater number of images and with the increase of potato types, additionally the classification can be configured in recorded video as in the online recording from a camera, for which performance levels can be selected in terms of the probability of success in the classification. © 2025 IEEE.

关键词： Seed

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STAFNet: Spatiotemporal-Aware Fusion Network for Air Quality Prediction 27th

STAFNet: Spatiotemporal-Aware Fusion Network for Air Qualit...

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27th international conference on pattern recognition, ICPR 2024

作者： Yin, Hang Zhang, Yan-Ming Tan, Ji-Hua Liu, Cheng-Lin School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Institution of Automation Chinese Academy of Sciences Beijing100190 China College of Resources and Environment University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9783031781858

Due to its important role in public health and environmental protection, Air Quality (AQ) prediction has drawn increasing attention from data mining and machine learning communities. Despite the great progress that has been made, AQ prediction still faces several challenges: (1) Implicitly modeling spatial dependencies disregards location relationships between sparse monitoring stations. (2) The misalignment of the stations of the different types hinders the fusion of air quality and meteorological features. (3) The intricate multi-periodicity temporal patterns in AQ data are ignored. To tackle these challenges, in this paper, we propose Spatiotemporal-Aware Fusion Networks (STAFNet) for accurate AQ prediction. Specifically, we first design the Spatial-Aware Graph Neural Network to explicitly incorporate spatial information into representation learning. Then, we develop a Cross-Graph Fusion Attention to fuse multimodal features distributed on unaligned graphs. Moreover, we capture the multi-periodicity features and model temporal dependencies with TimesNet. Extensive experiments on three real-world datasets show that STAFNet achieves the best performance compared with ten baselines (3.3% ∼ 5.0% on 25–48 hours future predictions). Our source code is available at https://***/dylan-yin/STAFNet_Paddle. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Graph neural networks

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Artificial intelligence-Based Student Computational Thinking Analysis Model (AI-SCTAM) for Curriculum Development 2

Artificial Intelligence-Based Student Computational Thinking...

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2nd international conference on machine Learning and Autonomous Systems, ICMLAS 2025

作者： Sowmiya, S. Umar Farook, R. Giridharan, B. Shalini, A. Airtifical Intellegence and Data Science Karpagam Academy Higher Education Coimbatore India Airtifical Intellegence and Data Science Karpagam Academy Higher Education India Computer Science and Engineering Karpagam Academy Higher Education Coimbatore India

ISBN: (纸本)9798331505745

Computational thinking (CT) has been widely used to improve students' comprehension of mathematical and scientific subjects. However, there is a scarcity of information available regarding the challenges faced by students and the development of their conceptual knowledge in these learning environments, particularly in relation to the integrated curriculum. There is a need to help with course-level curriculum development to improve teaching and learning. Hence, this paper proposes an artificial intelligence-based student computational thinking analysis model (AI-SCTAM) to predict student engagement in learning and improve curriculum development. This research attempted to determine the association between student engagement measures using the machine learning (ML) method to determine the degrees of student involvement. The ML divides students into two groups: highly engaged and poorly engaged, according to the criteria used to measure their degree of involvement. The findings of the correlation study showed a positive association between engagement measures and performance. Analyzing the relationship between student engagement and performance will guide the development of course materials and activities. STEM education metrics based on engagement levels will be proposed to personalize students' learning experiences. © 2025 IEEE.

关键词： Curricula

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Automated Crack Detection in Pipelines Using Advanced Image Processing

Automated Crack Detection in Pipelines Using Advanced Image ...

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2nd international conference on machine Learning and Autonomous Systems, ICMLAS 2025

作者： Subramani, Nandhagopal Jithesh, M.D. Harish, R. Rithish Hari, G. KPR Institute of Engineering and Technology Department of Artificial Intelligence and Machine Learning Coimbatore India KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India KPR Institute of Engineering and Technology Department of Mechanical Engineering Coimbatore India

ISBN: (纸本)9798331505745

The automated detection of pipeline cracks is vital for maintaining the safety and operational lifespan of this crucial infrastructure. Early crack detection prevents costly repairs and minimizes the risk of catastrophic failures and environmental damage. Existing methods primarily rely on manual inspections and traditional image processing techniques, which often lead to inaccuracies and inefficiencies. These methods suffer from high false-negative rates and poor adaptability to different environmental conditions. This study proposes a deep learning framework utilizing YOLOv10 to automate the detection of pipeline cracks with enhanced accuracy. Studies using deep learning report detection accuracies between 80% and 92%, although performance varies across different studies. The range reflects the influence of factors such as dataset and model specifics. © 2025 IEEE.

关键词： Deep learning

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A Hybrid Deep Learning Algorithm for Forecasting of Global Currency Market 2nd

A Hybrid Deep Learning Algorithm for Forecasting of Global C...

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2nd international conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： San, Tan Jun Ajibade, Samuel-Soma M. Yong, Yoke Leng Ajibade, Temiloluwa Iyanuoluwa Ayodele, Olamide Emmanuel Oyewopo, Abdulgafar Olorunleke Majeed, Anwar P. P. Abdul Luo, Yang Department of Smart Computing and Cyber Resilience Faculty of Engineering and Technology Sunway University Selangor Malaysia Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan47500 Malaysia Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan47500 Malaysia Faculty of Computing and Digital Technology Help University Bukit Damansara Kuala Lumpur Malaysia Department of Business Management Scholar School System Birmingham United Kingdom Department of Banking and Finance Ekiti State University Ekiti State Ado Ekiti Nigeria Department of Business Administration Kwara State University Malete Nigeria Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

The global currency market, which has millions of active participants and is the largest financial market in the world, has been greatly affected by the emergence of deep learning techniques. There is widespread recognition of deep learning’s effectiveness for price movement predictions, particularly in financial markets where its effectiveness has been shown. When compared to other machine learning methods, hybrid models like CNN-LSTM have shown to be the most effective and efficient. Nevertheless, there is a lack of thorough comparative research across deep learning hybrids, which makes it difficult to validate these claims empirically, especially in the diversified and extremely unpredictable FX market. To fill that void, this research builds a generic CNN-LSTM model to predict key currency pairs and compares it to another hybrid deep learning model. The objective is to evaluate and strengthen the reliability of deep learning techniques for FOREX forecasting. In order to maximize the generalizability of the model, this study mimics a categorization scenario using stringent cross-validation procedures. Important measures, including accuracy, precision, recall, and F1-score, were used to assess the models’ performance. The results show that there is a performance hierarchy, with CNN-LSTM, CNN-RNN. These findings highlight the need to evaluate deep learning models in different real-world contexts. In conclusion, this study highlights the difficulties of FOREX forecasting and the necessity for further evaluations of the predictive power of machine learning models in an ever-changing market. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Application of intelligence System: ANN and ANFIS for Enhanced Slope Stability Analysis 2nd

Application of Intelligence System: ANN and ANFIS for Enhanc...

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2nd international conference on Geotechnical Issues in Energy, Infrastructure and Disaster Management, ICGEID 2024

作者： Kumari, Pratima Sabri, Md Shayan Samui, Pijush Verma, Amit Kumar Department of Civil Engineering National Institute of Technology Patna India Department of Civil and Environmental Engineering Indian Institute of Technology Patna Patna India

ISBN: (纸本)9789819717569

To comprehensively address the mitigation and prevention of disasters in railroad embankments, landslides, and dam slope failures, a thorough slope stability analysis is essential. Various conventional approaches exist to investigate slope instability. Currently, geotechnical engineers have developed numerous soft computing techniques for conducting slope stability assessments. The current study investigates the machine learning based method including artificial neural network and Adaptive Neuro-Fuzzy Inference System (ANFIS) for slope stability assessment. To conduct this investigation, 700 databases were collected, incorporating input variables such as slope height, soil cohesiveness, internal angle of friction, slope angle, unit weight, and peak ground acceleration, with the aim of predicting the Factor of Safety (FoS) of soil slopes. The entire database was partitioned, allocating 70% for training the models and reserving the remaining 30% for model validation. To assess model accuracy, five statistical metrics, namely TIC, R2, RRSE, RAE, and LMI, were employed in both the training and testing phases. The statistical analysis reveals that the Adaptive Neuro-Fuzzy Inference System (ANFIS) model attains higher accuracy with values of (TIC = 0.0010;R2 = 0.9999;RRSE = 0.0105;RAE = 0.0087;LMI = 0.9913) during the training phase and (TIC = 0.0044;R2 = 0.9999;RRSE = 0.0308;RAE = 0.0204;LMI = 0.9796) during the testing phase. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Soil testing

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Prototype-Guided Contrastive Knowledge Graph Representation Learning for Diagnosis Prediction 4th

Prototype-Guided Contrastive Knowledge Graph Representation ...

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4th international conference on pattern recognition and Artificial intelligence, ICPRAI 2024

作者： Park, Wooyeol Mulyadi, Ahmad Wisnu Kang, Eunsong Suk, Heung-Il Department of Artificial Intelligence Korea University Seoul Korea Republic of Department of Brain and Cognitive Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9789819787012

The medical knowledge graph (KG) constructed from electronic health records (EHR) offers a comprehensive understanding of patients as it facilitates interconnections among medical codes. While KG has been proven to improve diagnostic accuracy, it inevitably contains noisy and incomplete attributes that could compromise the verdicts. Hence, direct utilization of medical KG for downstream clinical tasks may entail inadequacy, even by resorting to proper machine learning methods. Several prior studies have initiated noise filtering methods upon medical KG to relieve such issues by masking its task-irrelevant nodes and relations. However, they focused solely on a patient-wise perspective that feasibly overlooked essential shared traits from a (sub-)population viewpoint. This study proposes a novel prototype-guided predictive model called ProtoCare to predict diagnoses in EHR by unifying prototype and contrastive learning. ProtoCare leverages a set of prototypes to discover latent shared characteristics among groups of patients, while contrastive learning plays the role of noise filtering and KG refinement accounting for both learned patient and selected prototype features. Experimental results on the real-world EHR cohort exhibited that our proposed ProtoCare outperformed several baselines on the diagnosis prediction task. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Enhancing 3D Referential Grounding by Learning Coarse Spatial Relationships 27th

Enhancing 3D Referential Grounding by Learning Coarse Spati...

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27th international conference on pattern recognition, ICPR 2024

作者： Joshi, Soham Tripathi, Aditay Gopalakrishnan, Viswanath Chakraborty, Anirban International Institute of Information Technology Bangalore India Indian Institute of Science Bengaluru India

ISBN: (纸本)9783031781124

Large-scale pre-training is commonly used in 2D referential grounding tasks owing to the easy availability of a large number of image-text pairs with corresponding bounding box annotations. However, for 3D referential grounding, the unavailability of high-quality 3D scene-text pairs with annotations poses a significant challenge. To address this issue, we leverage the large corpus of 3D scenes with bounding box annotations of object instances and design an automated strategy to synthesize scene-text data for pre-training by utilising the coarse spatial relationships between the objects in the scene without any human supervision. The proposed strategy first clusters the 3D bounding boxes and then uses these clusters to create pairwise and triplet relations between the objects in the 3D scene. We achieved improved results consistently across various top-performing methods in 3D referential grounding, when the proposed pre-training strategy is deployed. In addition to pre-training with the samples containing coarse spatial relations, we also encode semantic relationships between the bounding boxes conditioned on the language utterance, using a compatibility measure between the box features and the language utterance. To evaluate the performance of our proposed techniques, we conduct experiments on large-scale publicly available datasets, namely ScanRefer and ReferIt3D (SR3D and NR3D). Our proposed techniques can be seamlessly integrated with any off-the-shelf 3D referential grounding method. Specifically, when integrated with BUTD-DETR, we observed notable improvements of 2.2% and 1% in performance on the SR3D and NR3D datasets, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantics

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