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Machine Learning Applications for Classification in Insurance: A Case Study 2nd

Machine Learning Applications for Classification in Insura...

引用

2nd Decision science Alliance international Summer conference, DSA ISC 2024

作者： Osorio, Celia Tsertsvadze, Veronika Fuster, Noelia Perez-Bernabeu, Elena Segura-Gisbert, Jorge Research Center on Production Management and Engineering Universitat Politècnica de València Ferrandiz-Carbonell Alcoy03801 Spain Unión Alcoyana S.A. de Seguros y Reaseguro Alcoy03801 Spain

ISBN: (纸本)9783031782404

Within the financial sector, insurance companies generate significant amounts of data on a daily basis, from policy transactions to customer interactions and risk evaluation. This accumulation of data presents an important opportunity for all of these companies to use it for their own strategic advantage. Therefore, through a detailed analysis, this paper presents a case study on how several supervised classification machine learning algorithms (Logistic Regression, Random Forest, Support Vector Machine, etc.) can help in identifying and classifying new clients whether they are potential or non-potential clients for a company in the insurance sector. Moreover, this paper also considers a hybrid ensemble mechanism with the best-performing models among all evaluated models, alongside a decision threshold variable to balance accuracy and minority class recall. The results demonstrate the ability to choose different models to enhance the company’s decision-making in different market scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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A Study on Gynecological Cancers Using Artificial Intelligence: A Revolutionary Approach

A Study on Gynecological Cancers Using Artificial Intelligen...

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2025 international conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Upreti, Kamal Hunekari, Sheela Upreti, Shitiz Rai, Ashish Kumar Alam, Mohammad Shabbir Nasir, Mohammad Shahnawaz Christ University Dept. of Computer Science Delhi- NCR Campus Ghaziabad India School of Engineering and Technology Pimpri Chinchwad University Maharashtra India Department of Management Mullana Haryana Ambala India Ramchandra Chandravansi University Faculty of Commerce & Management Jharkhand India Cmj University Dept. of Computer Science India

ISBN: (纸本)9798331527495

An examination of the role of artificial intelligence (AI) in ovarian, cervical, and breast cancer early detection and management is presented in this paper. Artificial intelligence (AI) can improve diagnostic accuracy, streamline treatment protocols, and facilitate personalized medicine approaches by leveraging advancements in machine learning (ML) and deep learning (DL). Various Artificial Intelligence models have demonstrated success in improving the outcomes of cancer diagnostics, including their ability to distinguish benign from malignant tumors. Technology challenges and ethical issues related to the integration of AI into clinical practice are also discussed in the review. Specifically, we want to illustrate how artificial intelligence can lead to better prognoses and reduced mortality rates for cancer patients by enhancing early detection capabilities. © 2025 IEEE.

关键词： Deep learning

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A Fuzzy System to Measure Additive Manufacturing Maturity: A Case Study in the Automotive Industry 6

A Fuzzy System to Measure Additive Manufacturing Maturity: A...

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6th international conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Espadinha-Cruz, Pedro Reboredo, Elisa UNIDEMI Department of Mechanical and Industrial Engineering NOVA School of Science and Technology FCT NOVA NOVA University of Lisbon Lisbon Portugal Laboratório Associado de Sistemas Inteligentes LASI Guimaraes4800-058 Portugal NOVA School of Science and Technology | FCT NOVA NOVA University of Lisbon Lisbon Portugal

To survive in an increasingly competitive environment, adopting innovative technologies is crucial, particularly in the 4th industrial revolution. Additive manufacturing (AM), a key technology of Industry 4.0, enables layer-by-layer production and fosters business model innovation. Organizations must integrate AM using maturity models to diagnose, monitor, and guide its implementation effectively. However, no existing AM Maturity Model addresses the imprecision inherent in human judgment or the uncertainty in assessing AM maturity. This article proposes a novel contribution: a fuzzy system designed to diagnose and evaluate AM maturity, incorporating fuzzy set theory to handle imprecision and ambiguity and using knowledge rules to model various maturity perspectives. The system was applied in the automotive industry, diagnosing an intermediate AM maturity level of 3, where AM complements traditional manufacturing. The findings also identified challenges, such as the need to improve employee AM competencies. This system is as a valuable tool for decision-makers, supporting strategic planning and resource alignment with with AM capabilities. © 2024 The Authors.

关键词： Smart manufacturing

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Multi-model Ensemble and Region Specific Normalization for Myocardial Pathology Segmentation with Partial Modalities 1st

Multi-model Ensemble and Region Specific Normalization for...

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1st MICCAI Challenge Comprehensive Analysis and Computing of Real-World Medical Images, CARE 2024 Held in Conjunction with 27th international conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Luo, Zihao Wang, Guotai School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Shanghai AI Lab Shanghai China

ISBN: (纸本)9783031870088

Myocardial pathology segmentation in cardiac magnetic resonance (CMR) images is crucial for diagnosing and managing cardiovascular diseases. However, the challenge of partial modality availability in multi-sequence datasets complicates this task. In this paper, we propose a simple but efficient approach that combines a two-stage prediction strategy with a multi-model ensemble and Region Specific Normalization (RSN) to improve segmentation accuracy. Our method leverages the strengths of models trained on different combinations of CMR sequences, resulting in significant improvements in the Dice scores for myocardial scars and edema. RSN enhances contrast within regions of interest, reducing the impact of artifacts and intensity variability, thereby refining segmentation outcomes. We evaluate our approach using the CARE 2024 MyoPS++ challenge dataset, which includes data from multiple centers with diverse modality availability. The results demonstrate that our method outperforms full-sequence models, both quantitatively and qualitatively, providing a robust and effective solution for myocardial pathology segmentation in clinical settings. Our method achieved second place in the CARE 2024 MyoPS++ challenge, highlighting its efficacy and competitiveness. Code and model are available: https://***/Zihaoluoh/CARE-2024_MyoPS-. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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An Explainable AI Techniques for Advancing Diabetes Prediction Using Machine Learning

An Explainable AI Techniques for Advancing Diabetes Predicti...

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2025 international conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Saxena, Surabhi Parveen, Nikhat Yarlagadda, Bhanu Naga Karthik Srujan, Appasani Siva Siva Kumar, Mannava Ashok Likhitha, Koka Christ University Department of Computer Science Bangalore India College of Computing and Information Technology University of Bisha Department of Artificial Intelligence Saudi Arabia Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vijayawada Guntur India

ISBN: (纸本)9798331527495

Researchers have developed an automated system to identify diabetes risk. This system combines data from two sources: a collection of female patients in Bangladesh and an expanded dataset from a local textile factory. The expanded dataset includes information from 203 additional patients. The system uses several techniques to improve its accuracy. It first identifies the most important factors for predicting diabetes, then employs a special model to estimate insulin levels. It also addresses challenges like imbalanced data (where one outcome is more common) and explains its predictions using artificial intelligence techniques. This system achieved the superlative results has an 81.0% accuracy rate, 0.812 F1 score, and 0.844 Area Under the Curve (AUC).. These metrics indicate strong performance in identifying diabetes risk. © 2025 IEEE.

关键词： Textile mills

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Enhanced Multimodal Depression Detection With Emotion Prompts

Enhanced Multimodal Depression Detection With Emotion Prompt...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shiyu Teng Jiaqing Liu Hao Sun Shurong Chai Tomoko Tateyama Lanfen Lin Yen-Wei Chen College of Information Science and Engineering Ritsumeikan University Japan College of Computer Science and Technology Zhejiang University Hangzhou China Department of Intelligent Information Engineering Fujita Health University Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Depression is a pervasive mental health disorder that remains frequently undiagnosed and untreated due to societal barriers and the subjective nature of its symptoms. Leveraging recent advances in large language models (LLMs), we propose a novel depression detection pipeline that generates emotion prompts tailored to individual data, enhancing detection accuracy. Our approach integrates cross-modality fusion via cross attention mechanisms to combine depressive and emotional features, creating a comprehensive representation of depression indicators. Evaluated on the E-DAIC and EATD datasets, our method outperforms state-of-the-art techniques, demonstrating its potential for more precise emotion-based depression detection.

关键词： Attention mechanisms Large language models Pipelines Mental health Signal processing Depression Feature extraction Robustness Multisensory integration Speech processing

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Energy-Efficient Resource Allocation in Wireless Networks Using Federated-Double-Deep Q-Network 3

Energy-Efficient Resource Allocation in Wireless Networks Us...

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3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Anuradha, P. Fallahhusein, Muhamed Subramaniam, Sakthivel Srinivas, T. Aditya Sai Buckshumiyan, A. Chaitanya Bharathi Institute of Technology Department of Electronics and Communication Engineering Hyderabad India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Sona College of Technology Department of Computer Science and Engineering Salem India Jayaprakash Narayan College of Engineering Department of Artificial Intelligence and Machine Learning Mahbubnagar India New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India

ISBN: (纸本)9798331508456

Now a days, the resource allocation manages the resource networks such as bandwidth, power usage for ensuring Quality of Service (QoS) without overloading. However, the optimization fails to adapt traffic loads due to its dependance on static resource allocation that cannot adjust to user demands and power control. To overcome this limitation in this research resource allocation method Federated-Double-Deep Q-Network (F-DDQN) is proposed for maximizing throughput and achieve minimum QoS requirements for all active Device-to-Device communication(D2D) pairs and Cellular Users (CUE). whereas Federated Learning enhances adaptability to dynamically communication environments. Initially, the data is collected from D2D-enabled heterogeneous cellular networks (HetNet) dataset and 3-tier HetNet system model are designed. Further, for resource management the F-DDQN enhances the energy-efficient by decentralizing action states and minimizes communication overhead and power consumption by sharing model updates instead of raw data, and DDQN effectively improves the energy efficiency in resource allocation by estimating Q-values and optimizes resource allocation through selecting the most energy-efficient transmitting power and channels using target estimation. From the results the proposed model achieved better results to evaluate its effectiveness in increasing system throughput with 58.90% compared with both CUE and D2D Users Equipment (DUE) with existing method a Deep Q-network-based Reinforcement Learning algorithm (DRL). © 2025 IEEE.

关键词： Network performance

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Technological Advancement of Big Data Analytics in Enhancing Financial Decision-Making and Trade Performance in Emerging Markets

Technological Advancement of Big Data Analytics in Enhancing...

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advances in computer science, Electrical, Electronics, and Communication Technologies (CE2CT), international conference on

作者： Sandeep N K Ramya Lakshmi Bolla Hrishikesh Satyakumar Kakde Casanova-Villalba César Iván Meera K L Krishna Murthy Inumula Vidya Vikas Institute of Engineering and Technology Mysore Karnataka India ERP Analysts Ohio USA Institute of Management and Research MGM University Chhatrapati Sambhajinagar Maharashtra India Sede Santo Domingo Universidad Técnica Luis Vargas Torres de Esmeraldas Santo Domingo Ecuador Dayananda Sagar Business Academy Karnataka India Symbiosis Institute of International Business (SIIB) Symbiosis International (Deemed University) Pune Maharashtra India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

The revolutionary potential of big data analytics (BDA) is causing a dramatic transformation in the field of financial risk management (FRM). Financial companies are now leveraging massive databases as more than just historical information to change risk management practices. The financial industry has seen a paradigm shift as a result of machine learning's (ML) convergence, which has enabled businesses to gain deeper insights and make well-informed decisions. This abstract examines the various connections between these developments and demonstrates their effects on risk management, financial operations, predictive analytics, and customer-focused services. Proactive risk assessment, accurate forecast models, and customised financial plans are made possible by the integration of large databases and complex technologies. It has problems with interpretability, data privacy, and ethical use even though it is revolutionising the sector. The transformational potential and related ramifications of ML in the financial arena are examined in this paper.

关键词： Ethics Reviews Databases Navigation Decision making Machine learning Big Data Predictive models Risk management Predictive analytics

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Anything-DT Modeling: A New Framework for Digital Twin Modeling of Wild Objects from a Single RGB Image

Anything-DT Modeling: A New Framework for Digital Twin Model...

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2025 international conference on Electrical Automation and Artificial Intelligence, ICEAAI 2025

作者： Geng, Jun Wang, Siqi Yi, Xiaoyun Xiong, Wanqing Yin, Hongchao Li, Xiaoyu The School of Software Xinjiang University Urumqi China The School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331506797

Given the significant limitations of current digital twin modeling methods in practical applications, their lack of sufficient generalizability, and the inherent diversity and complexity of real-world objects and environments, digital twin modeling from a single RGB image in unconstrained scenarios faces several maj or challenges. In this paper, we first improve the Segment Anything Model (SAM) and propose the CSAM model, which enables more accurate segmentation of any object. Next, we introduce the Anything-DT Modeling framework, which cleverly integrates the CSAM object segmentation model with a vision-language model to accomplish digital twin modeling tasks using only a single image. The CSAM model can maintain high accuracy and efficiency in complex scenarios, providing high-quality input data for subsequent digital twin modeling. The framework fully utilizes the latest advances in computer vision and natural language processing, providing strong technical support for digital twin modeling, thereby ensuring the feasibility of digital twin modeling from a single RGB image. Compared with existing state-of-the-art methods, the Anything-DT Modeling framework demonstrates its ability to produce detailed and accurate digital twin models for a wide range of objects, showcasing promising potential in overcoming the limitations of current approaches. © 2025 IEEE.

关键词： Digital Twin Modeling Image Segmentation Single-View 3D Modeling Thin Object Structures

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Design and Implementation of an E-textile Vivaldi Antenna for Wearable Applications

Design and Implementation of an E-textile Vivaldi Antenna fo...

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2025 United States National Committee of URSI National Radio science Meeting, USNC-URSI NRSM 2025

作者： Hollman, Riley Li, Yang Department of Electrical and Computer Engineering Baylor University WacoTX76798 United States

ISBN: (纸本)9781946815200

With the recent advancements in 5G and 6G wireless communication technologies, ultra-wideband (UWB) antennas have found extensive use in wearable applications due to their unique advantages, including high data rate transmission, low power consumption, and precise positioning. Various types of UWB antennas have been employed for on-body wireless communication, such as planar monopole and fractal antennas. The Vivaldi antenna is also of interest for its simple design, high directional gain, and excellent radiation efficiency (Tael Saedi et al., "Metamaterial-based Antipodal Vivaldi Wearable UWB Antenna for IoT and 5G Applications, " IEEE Trans. Antennas Propag., Aug. 2020). However, most wearable Vivaldi antenna designs are implemented at millimeter-wave frequencies on rigid substrates. There is a growing need to develop wearable Vivaldi antennas at lower ISM frequency bands using flexible e-textile technology, particularly for biomedical applications. © 2025 USNC-URSI.

关键词： Millimeter waves

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