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检索条件"任意字段=3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024"

共 6844 条记录，以下是821-830 订阅

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Enhancing Interpretability: The Role of Explainable AI in Healthcare Diagnostics 3

Enhancing Interpretability: The Role of Explainable AI in He...

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Zade, Nikita Langote, Meher Verma, Prateek Faculty of Engineering & Technology Department of Artificial Intelligence & Data Science Maharashtra Sawangi442001 India Faculty of Engineering & Technology Department of Artificial Intelligence & Machine Learning Maharashtra Sawangi442001 India

ISBN: (纸本)9798331509675

XAI is now transforming the use of AI in diagnosing diseases by overcoming some of the problems inherent in most black-box approaches. In time-sensitive speciality areas like computer-aided diagnosis, image analysis, and predictive modelling, SHAP, LIME, and attention-based XAI techniques explain how AI decides to help doctors and other healthcare professionals trust AI. Therefore, this paper aims to clarify why XAI is needed in healthcare, introduce techniques, and show their examples in cancer diagnosis and disease prognosis. XAI improves diagnostic performance and satisfies regulatory and ethical requirements for increased usage in clinical practice. Because of XAI, clinicians can use the best of artificial intelligence to complement their knowledge in enhancing the treatment of patients. That is why the outlook of XAI in healthcare includes the next steps in achieving higher scalability, managing bias, and working with diverse regulatory frameworks, which will define the future of AI solutions in healthcare. © 2025 IEEE.

关键词： Adversarial machine learning

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Rational Jacobi Kernel Functions: A novel massively parallelizable orthogonal kernel for support vector machines 3

Rational Jacobi Kernel Functions: A novel massively parallel...

引用

3rd international conference on Distributed Computing and High Performance Computing, DCHPC 2024

作者： Moghaddam, Mahdi Movahedian Aghaei, Alireza Afzal Parand, Kourosh Shahid Beheshti University Department of Computer and Data Science Tehran Iran Shahid Beheshti University Institute for Cognitive and Brain Sciences Tehran Iran

ISBN: (纸本)9798350381580

machine learning has recently seen a significant upsurge in its influence across diverse scientific domains. Among the array of machine learning techniques, the support vector machine (SVM) has emerged as a powerful supervised method. To enhance the SVM's precision, the adoption of kernel methods has become commonplace. Notably, the integration of orthogonal functions as the SVM's kernel has led to substantial improvements in accuracy. However, it is essential to note that the use of these functions also introduces heightened time complexity to the SVM along with imposing some limitations on data. In response to this challenge, a general formulation of orthogonal Jacobi kernel functions has been proposed. This formulation allows the user to choose classical Jacobi polynomial kernels, fractional or rational ones. Next, we develop a data-parallel technique implemented on heterogeneous computing systems to accelerate the computation of kernel matrix. The ensuing experimental results, conducted on the benchmark datasets, were founded on three distinctive rational mappings: algebraic, exponential, and logarithmic. Spanning data sizes from 700 to 3800, these mappings exhibited remarkable speedup factors of 3.967, 4.599, and 2.807, respectively. © 2024 IEEE.

关键词： Orthogonal functions

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The Impact of Blockchain and AI in the Healthcare 3

The Impact of Blockchain and AI in the Healthcare

引用

3rd IEEE international conference on Electrical Power and Energy Systems, ICEPES 2024

作者： Thatikonda, Ramya Kempanna, M. Tatikonda, Reshmi Bhuvanesh, A. University of the Cumberlands PhD in Information Technology Williamsburg United States Bangalore Institute of Technology Department of Artificial Intelligence and Machine Learning Karnataka Bengaluru India University of the Central Missouri Masters in Computer Information Systems Warrensburg United States PSN College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350390728

Integrating blockchain and artificial intelligence (AI) in healthcare presents a transformative opportunity. Therefore, this can revolutionize patient care by improving data management and medical research. Decentralization and invulnerability granted by blockchain technology foster improved data consistency and interoperability. AI enhances blockchain by streamlining data examination, automatic tasks, and decision-making processes, resulting in more precise medical diagnoses and tailored therapy programs. The intersection of blockchain and AI is explored, along with their potential benefits and practical applications in healthcare. The document examines the hurdles encountered when integrating innovations, including scalability, interoperability, and regulatory adherence issues. Additionally, the study investigates moral questions surrounding data ownership and patient consent, thereby protecting confidentiality and trust. The synergy between blockchain, AI, and healthcare will lead to transformative advancements. It pledges to fortify data safety and expedite information sharing. Consequently, this will result in an improved, price-effective, and patient-focused healthcare environment. Recommendations were made for additional research and practical implementation in healthcare technology. The study should concentrate on creating scalable and compatible blockchain systems. A vital factor for extensive use is tackling regulatory and moral issues. Hybrid blockchain designs and federated learning strategies will bolster information privacy and utility. © 2024 IEEE.

关键词： data consistency

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Enhancing Unmanned Aerial Vechicle Surveillance: Real Time Drone Detection with Yolov7 Deep learning 3

Enhancing Unmanned Aerial Vechicle Surveillance: Real Time D...

引用

3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Priyadharshini, M. Srinidhi, K. Murugesh, V. Department of Computer Science and Engineering Hyderabad501 203 India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering AP Vaddeswaram India

ISBN: (纸本)9798350369083

This is an advanced object detection system with YOLOv7 model, implemented for UAV surveillance as outlined in the article. By integrating the system with Roboflow serving as its database manager and data augmentation software, it benefits from a broad array of reputable datasets which are improving training and inferences. In the blog, we cover all steps from model cloning (on TensorFlow) with one/two script line conversion to preprocessing of datasets and work automation for training or evaluation. The optimizations in the implementation of YOLOv7 dog led to scalability, allow more object categories and deploy security for real time surveillance with autonomous navigation. Extensive testing demonstrates that our system exceeds state-of-the-art methods in terms of detection performance as well as efficiency, demonstrating the significant benefits for UAV hotspots gained from a deployment of advanced deep learning techniques. © 2024 IEEE.

关键词： Deep learning

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Detection of Heart Disease Using machine learning 1

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3rd international conference on Artificial-Business Analytics, Quantum and machine learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Raheja, Supriya Ray, Navya Amity University Noida India

ISBN: (数字)9789819725083

ISBN: (纸本)9789819725076

Heart disease is one of the key causes of death worldwide, and it is important to detect in its early stage which helps in halting its progression. Because of its superiority in pattern identification and categorization, machine learning (ML) supports decision making and risk assessment from the massive amount of data produced by the healthcare industry on cardiac disorders. This research paper examines the ML techniques which are being used to detect heart diseases. This study made use of a dataset from a cardiovascular study of residents of Framingham, Massachusetts. The classification goal is to predict if the patient will develop coronary heart disease in the next ten years. The goal of this paper is to present the comparison among six different machine learning models namely Logistic Regression, Support Vector machine, Decision Tree, AdaBoost, KNN and Random Forest to predict the 10-year risk of coronary heart disease. The paper presents the performance comparison evaluation on the basis of accuracy, precision, recall and F1-Score. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Logistic regression

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Improving Fake News Detection on Social Media Platforms Using Modified Metaheuristics and machine learning 3

Improving Fake News Detection on Social Media Platforms Usin...

引用

3rd IEEE international conference on data, Decision and Systems, ICDDS 2024

ISBN: (纸本)9798350363890

Social networking and emerging technologies have fundamentally reshaped personal connectivity online. Despite the increasing reliance on the internet for everyday tasks, entertainment, and community building by businesses, corporations, and individuals, online social interactions continue to pose challenges. Misuse of social platforms can lead to the spread of misinformation. This issue has brought the emergence of fake news to the forefront of societal concerns. The vast volumes of data and rapidly evolving language used on social media platforms render traditional approaches, like static filter lists, ineffective. This study proposes a data-driven approach leveraging applied AI to detect and mitigate online misinformation using advanced natural language processing (NLP) techniques. A modified particle swarm optimization (PSO) algorithm is introduced for hyperparameter optimization, demonstrating promising results in maximizing performance. A comparative analysis across several contemporary optimizers on a real-world dataset demonstrates the efficacy of the proposed approach, achieving an accuracy of 0.846620 with the best-optimized models. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Predictive Modelling of Diabetes Using Ensemble Classifiers: Findings from a Large and Integrated data Set 10

Predictive Modelling of Diabetes Using Ensemble Classifiers:...

引用

10th international conference on engineering and Emerging Technologies, ICEET 2024

作者： Azam, Muhamad Ali, Rashid Muqaddas, Ranya Ashraf, Muhammad Zahoor, Ammara The Superior Universty Department of Computer Science and Information Technology Lahore5400 Pakistan The Superior University Department of Computer Science and Information Technology Lahore5400 Pakistan The Superior Universty Information Engineering Technology Department Lahore5400 Pakistan Gulab Devi Teaching Hospital Information Technology Department Lahore5400 Pakistan

ISBN: (纸本)9798331532895

In this research paper, we examine which of a number of machine learning classifiers are able to predict diabetes in a dataset of 100,000 cases where features include gender, age, state, binary health indicators, smoking, body mass index (BMI), and glucose level, one of which was known to be diabetic. Extensive exploratory data analysis was undertaken to explore the distribution and the relationships between features of the data set and these predictions. (Prajapati, (2023, July).We then evaluated the model output using metrics like as the Accuracy, AUC-ROC, Precision-Recall curves. Support Vector machine (SVM), Voting Classifier (VC), and Stacking Classifier (SC). The output of these models was then evaluated with the help of measures using metrics like accuracy, AUC-ROC, and Precision-Recall curves. (Hasan, 2020). The highest accuracy of 98.75% was achieved by the voting classifier (VC), Random Forest (RF) was second with 97.18%, and XGBoost was 3rd with 97.15%. The results of this research can be applied in diabetes prediction, with an important conclusion on how ensemble methods could improve the accuracy indicators for healthcare applications. © 2024 IEEE.

关键词： Adversarial machine learning

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Research on Steam Meter Calibration in Cigarette Factory Based on machine learning 3

Research on Steam Meter Calibration in Cigarette Factory Bas...

引用

3rd international conference on Testing Technology and Automation engineering, TTAE 2023

作者： Qin, Yuanli Zhao, Ningning Feng, Yulong Zhao, Shaolin Hebei Baisha tobacco Co. LTD Hebei Province Shijiazhuang City China

ISBN: (纸本)9781510674769

In the current industrial environment, electronic instruments have been widely used, but at the same time, electronic instruments also face many problems, such as zero temperature drift and sensitivity temperature drift caused by temperature changes. This article proposes an algorithm based on software proofreading that combines anomaly detection algorithms with neural networks. It is concluded that this method can indeed proofread instruments and maintain errors within an acceptable range. This method can further improve the accuracy and reliability of the current industry, ensuring process optimization and efficiency improvement. © 2024 SPIE.

关键词： machine learning

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A Survey on Predicting Depression Among Teachers and Students at Metropolitan University (Sylhet, Bangladesh) Using machine learning 3

A Survey on Predicting Depression Among Teachers and Student...

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3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Tabassum, Anika Ema, Yasmin Firuza Rafee, Md. Mushtaq Shahriyar Limon, Md Shamihul Islam Khan Metropolitan University Department of Computer Science and Engineering Sylhet Bangladesh

ISBN: (纸本)9798331515911

Depression, a psychiatric condition marked by profound sadness and apathy, influences cognitive processes, emotions, and behaviors, causing psychological and physiological challenges. Everyday tasks become arduous, life loses vibrancy, and in extreme cases, may lead to suicidal tendencies. University students face unique stressors due to academic, social, and personal pressures, underscoring the need for mental health support. This thesis investigates depression diagnosis among Metropolitan University teachers and students using machine learning techniques. Survey data, comprising 516 anonymized responses covering mental well-being, mood, and behavior, form the basis. The researchers preprocess data, visualize it, and train models to detect depression indicators, evaluating techniques like Logistic Regression, Decision Trees, and Gradient Boosting for effectiveness. The research highlights that the reasons for depression differ between females and males. The aim is to enhance early detection and support mechanisms, fostering proactive intervention within the university community. © 2025 IEEE.

关键词： Students

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Green Runner: A tool for efficient deep learning component selection 24

Green Runner: A tool for efficient deep learning component s...

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IEEE/ACM 3rd international conference on AI engineering - Software engineering for AI (CAIN)

作者： Kannan, Jai Barnett, Scott Cruz, Luis Simmons, Anj Selvi, Taylan Appl Artificial Intelligence Inst Geelong Vic Australia Delft Univ Technol Delft Netherlands

ISBN: (纸本)9798400705915

For software that relies on machine-learned functionality, model selection is key to finding the right model for the task with desired performance characteristics. Evaluating a model requires developers to i) select from many models (e.g. the Hugging face model repository), ii) select evaluation metrics and training strategy, and iii) tailor trade-offs based on the problem domain. However, current evaluation approaches are either ad-hoc resulting in sub-optimal model selection or brute force leading to wasted compute. In this work, we present GreenRunner, a novel tool to automatically select and evaluate models based on the application scenario provided in natural language. We leverage the reasoning capabilities of large language models to propose a training strategy and extract desired trade-offs from a problem description. GreenRunner features a resource-efficient experimentation engine that integrates constraints and trade-offs based on the problem into the model selection process. Our preliminary evaluation demonstrates that GreenRunner is both efficient and accurate compared to ad-hoc evaluations and brute force. This work presents an important step toward energy-efficient tools to help reduce the environmental impact caused by the growing demand for software with machine-learned functionality. Our tool is available at Figshare GreenRunner.

关键词： Green-AI Large Language Model Component Selection

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