检索结果-内蒙古大学图书馆

Sbb-Chi²-A²: stacking of bagging-boosting with the blend Chi-square for effective prediction of aortic aneurysm using biomarker profiling

引用

International Journal of Information technology (Singapore) 2025年 1-8页

作者： Jena, Sanjuktarani brahma, biswajit Khan, Zabiha Jyothi, G. Arunachalam, P. Aggarwal, Saurabh Department of Computer Science and Engineering Sardar Patel Institute of Technology (SPIT) Andheri West Mumbai India McKesson Corporation 32559 Lake Bridgeport St Fremont 94555 CA United States Nitte Meenakshi Institute of Technology Bengaluru India Department of Pharmaceutical Engineering B V Raju Institute Of Technology Telangana Narsapur India Department of Biomedical Engineering Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology Tamil Nadu Chennai India San Jose State University San Jose United States

Aortic Aneurysm (A2) remains a leading cause of morbidity and mortality worldwide, necessitating innovative approaches for accurate prediction and early intervention. This study proposes a novel ensemble learning fram... 详细信息

Aortic Aneurysm (A²) remains a leading cause of morbidity and mortality worldwide, necessitating innovative approaches for accurate prediction and early intervention. This study proposes a novel ensemble learning framework that integrates bagging and boosting techniques through stacking, combined with Chi-square feature selection, to enhance the prediction of A² condition using biomarker profiling. The stacking method leverages the strengths of individual models, including Decision Trees (DT), Random Forests (RF), and Adaboost, Gboost, and XGboost, to create a robust meta-model. The experimental evaluations are then conducted on Aorta vessel Tree (AVT) dataset taken from public repository. Chi-square (Chi²) feature selection is utilized to identify the most significant biomarkers, reducing dimensionality and ensuring that only the most relevant features contribute to the model. This preprocessing step enhances the model’s interpretability and performance by focusing on the critical factors associated with Aortic Aneurysm. The proposed framework is evaluated using key performance metrics, demonstrating superior predictive performance compared to conventional individual-model approaches. © bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Adaboost Aortic Aneurysm (A2) disease bagging biomarker profiling boosting Chi-square (Chi2)

来源：评论

学校读者我要写书评

暂无评论

AI-Assist tool to enhance the reliability and accuracy of automated speech and writing support for students with disabilities in learning languages 11

AI-Assist tool to enhance the reliability and accuracy of au...

引用

11th IEEE International Conference on Cloud Computing in Emerging Markets, CCEM 2023

作者： Surana, D. Amruta Devika, Mrs K. Prema, S. Dhannaseth, Nilesh H. Gopinath, N. Tamilarasan, P. Department of Computer Engineering Sinhgad Institute of Technology Lonavala410401 India Computer Scienceand Business Systems Sri Manakula Vinayagar Engineering College Pudhucherrry India Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India St. Vincent Pallotti College of Engineering and Technology Maharashtra Nagpur441108 India Department of CSE B. S. Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu India Department of English and Foreign Languages SRM College of engineering and Technology SRMIST Kattankulathur Chennai India

ISBN: (纸本)9798350360059

Innovative educational technology have made it possible to communicate with children who have special needs (SEN) in new ways. Artificial intelligence (A.I.)-based approaches have been among the most effective during the past ten years. The efficient application of artificial intelligence tools is seen as a means of improving SEN students' quality of life. As a result, the establishment of systems for both diagnosis and intervention calls for the adoption of A.I. techniques. Today, people from all socioeconomic backgrounds and walks of life concur on the value of education. Everyone needs to have a top-notch education in order to advance and succeed in today's society of information. It is now feasible to communicate with children with special needs (SEN) in new ways thanks to innovative educational technology. In the past ten years, artificial intelligence (A.I.)-based strategies have been among the most successful. The effective use of artificial intelligence tools is viewed as a way to enhance the quality of life for SEN students. As a result, the use of A.I. techniques is necessary for the creation of systems for both diagnosis and intervention. Today, people from all socioeconomic levels and walks of life agree that education is important. To advance and be successful in today's information culture, everyone needs to have a top-notch education. Then, it will display the statement that goes with that specific term. The learner will then get it in both text and audio versions after that. This makes it simple to assist any student with a disability in having good communication in a foreign language. ©2023 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Hybrid Competitive Swarm Optimization Algorithm based Scheduling in the Cloud Computing Environment 5

Hybrid Competitive Swarm Optimization Algorithm Based Schedu...

引用

5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Shrichandran, G.V. Narayana Tinnaluri, V.S. Senthil Murugan, J. Meeradevi, T. Dwivedi, Vijay Kumar Suma Christal Mary, S. B.Sc -CS Programme Srm Institute of Science and Technology Department of Computer Science & Applications Ramapuram Chennai89 India Koneru Lakshmaiah Education Foundation Department of Computer Science Vaddeswaram522025 India Vel Tech High Tech Dr. Rangarajan Dr.Sakunthala Engineering College Avadi Chennai India Kongu Engineering College Department of Ece Perundurai Erode India V.E.C. Ambikapur Dist Surguja497001 India Panimalar Engineering College Poonamalle Department of Information Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350321425

Cloud computing (CC) platform not only offers resource sharing for users and also provide many on-demand services. business procedures can be managed to utilize the workflow technology on the cloud which signifies most of the problems in employing the resources in an effectual approach because of the dependencies between the tasks. Task scheduling (TS) in a CC platform applies to the procedure of effectively allocating resources computing to tasks or jobs submitted to the CC environment. In a cloud platform, tasks are separated as smaller subtasks that are applied in parallel on distinct machines. There are many features to consider if the TS is in CC platform, comprising the kind of tasks like resources cost, resources required, and resources availability. Therefore, this study presents a Hybrid Competitive Swarm Optimization Algorithm based Task Scheduling (HCSOA-TS) technique in the CC platform. The presented HCSOA-TS technique schedules the tasks proficiently in such a way that maximum resource utilization and performance gets accomplished. In the design of HCSOA, Cauchy mutation operator is included in the CSO algorithm and thereby improves the overall performance. In addition, the derivation of the fitness function by the HCSOA-TS technique undergoes optimal scheduling process and decreases energy usage. The experimental result analysis of the HCSOA-TS technique is tested using a series of measures. The comprehensive comparison study highlighted the improvements of the HCSOA-TS technique over recent approaches. © 2023 IEEE.

关键词： Cloud computing

来源：评论

学校读者我要写书评

暂无评论

A comprehensive evaluation of polygon-based hologram algorithms 14

A comprehensive evaluation of polygon-based hologram algorit...

引用

Holography, Diffractive Optics, and Applications XIV 2024

作者： Wang, Fan Wang, Pin Zhang, Yaping blinder, David Gupta, Anuj Poon, Ting-Chung Ito, Tomoyoshi Shimobaba, Tomoyoshi Graduate School of Engineering Chiba University 1-33 Yayoi-cho Inage-ku Chiba Japan Yunnan Provincial Key Laboratory of Modern Information Optics Kunming University of Science and Technology Kunming Yunnan650500 China Pleinlaan 2 BrusselB-1050 Belgium IMEC Kapeldreef 75 LeuvenB-3001 Belgium CSIR-Central Scientific Instruments Organisation Chandigarh160030 India Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24061 United States

ISBN: (纸本)9781510682085

Polygon-based computer-generated holography algorithms can achieve high realism at low computational cost. Many recent contributions improve the hologram generation efficiency and solve many issues of 3D realistic rendering. This manuscript provides a comprehensive evaluation of their performance and potential through a detailed classification and comparison. Although the numerical-based algorithms using spectral interpolation offer greater flexibility in achieving realistic 3D displays, further phase-optimization algorithms are needed to render 3D reconstructions with higher definition, reduced noise, and faster updates. © 2024 SPIE. All rights reserved.

关键词： computer generated holography

来源：评论

学校读者我要写书评

暂无评论

Enhanced IoT System for Real-Time Detection and Monitoring of Cardiac Events Using Machine Learning Algorithms

Enhanced IoT System for Real-Time Detection and Monitoring o...

引用

Delhi Section Conference (DELCON), IEEE

作者： Safia Naveed.S Nabeena Ameen Nulin Jeriba J. Department of Computer Science and Engineering KCG College of Technology Chennai Department of Information Technology B. S. Abdur Rahman Crescent Institute of Science and Tech

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

Heart attacks remain a primary cause of global mortality, highlighting the urgent need for systems that enable early detection and intervention. This study proposes a Smart IoT-based system designed to detect and monitor heart attack symptoms in real-time, thus facilitating timely medical response. The system integrates wearable sensors to collect continuous data on heart rate, stress levels, and positional changes, which is then processed through Arduino IDE and transmitted to an IoT server. Machine learning algorithms, including Support Vector Regression (SVR) and Artificial Neural Networks (ANN), are employed to analyze these signals for abnormal patterns indicative of a potential heart attack. Major findings indicate that this system accurately detects heart rate irregularities, and stress fluctuations and can alert healthcare providers, significantly improving remote monitoring capabilities. The implications of this study suggest that IoT-enabled monitoring systems can substantially enhance patient outcomes by allowing for early diagnosis and proactive care in remote settings, thereby reducing the risk of fatal heart events.

关键词： Wireless sensor networks Machine learning algorithms Cardiac arrest Artificial neural networks Medical services Real-time systems biomedical monitoring Heart rate variability Monitoring Stress

来源：评论

学校读者我要写书评

暂无评论

Optimized Deep Learning Mechanism for Intrusion Detection: Leveraging RFE-based Feature Selection and PCA for Improved Accuracy

Optimized Deep Learning Mechanism for Intrusion Detection: L...

引用

Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： M. Rajkumar Lakshi VS Karthik R Pavithra S Department of Smart Computing School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu India B. Tech-Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

With the growing demand for wireless networks, network traffic has surged, particularly involving large-scale data transmission. This results in an increased number of network security threats and the emergence of various cyber-attacks. To overcome these threats, the development of an efficient intrusion detection system (IDS) has become crucial. by leveraging the recent advancements in Artificial intelligence (AI), this project introduces an optimized deep learning mechanism utilizing a prevalent deep learning model, the Deep Neural Network (DNN) algorithm. This proposed method uses feature engineering techniques like feature selection and feature reduction. Given the high dimensionality of the selected datasets, these techniques are employed to prevent over-fitting. Recursive feature elimination performs feature selection followed by Principal Component Analysis for the feature reduction process, thus enhancing the model's efficiency. The system is assessed using two benchmark datasets: KDD and UNSW-Nb15. The experimental results indicate that the presented DNN model on the KDD dataset has achieved 92.45% accuracy and 91.35% accuracy on the UNSW-Nb15 dataset for intrusion classification.

关键词： Deep learning Accuracy Wireless networks Computational modeling Intrusion detection Artificial neural networks Feature extraction Data models Tuning Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

DeepMind Trainer: Harnessing Cognitive Neural Networks on Raspberry Pi for Advanced Cognitive Enhancement Games 2

DeepMind Trainer: Harnessing Cognitive Neural Networks on Ra...

引用

2nd International Conference on Advancement in Computation and computer technologies, InCACCT 2024

作者： Abirami, P. Antony Joseph Rajan, D. Vivekanandan, T. Saillaja, V. Mohankumar, N. Rajmohan, M. Velammal Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sakunthala Engineering College Vel Tech Multi Tech Dr Rangarajan Dr Department of Mathematics Tamil Nadu Chennai India SRM Institute of Science and Technology Faculty of Management Studies Tamil Nadu Kattankulathur Chennai India B.V. Raju Institute of Technology Department of Electronics and Communication Engineering Telangana India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350371314

The presentation summarizes a groundbreaking cognitive training program designed specifically for those who suffer from cognitive impairments or neurodevelopmental abnormalities. The work creates a dynamic platform for immersive and adaptable cognitive training games by combining cutting-edge Convolutional Neural Networks (CNNs) with Raspberry Pi technology. Memory, focus, and ability to think creatively are all qualities directly targeted by these games. The CNN model, trained on a broad dataset, interprets real-time user inputs, delivering quick feedback and altering game complexity depending on an individual's performance. The incorporation of Raspberry Pi guarantees a solution that is both affordable and easily accessible. The capacity of the system to redefine cognitive rehabilitation by providing a solution that is individualizable, interactive, and broadly deployable is the potential source of the system's influence. The goal of the work is to make a substantial contribution to the field, which will eventually be to the advantage of users, caregivers, and healthcare professionals and ultimately improve the cognitive well-being of those coping with cognitive issues. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Combination kernel support vector machine based digital twin model for prediction of dyslexia in distributed environment 2nd

Combination kernel support vector machine based digital twin...

引用

2nd International Conference on Next-Generation Computing and Information Systems, ICNGCIS 2023

作者： Kareemullah, H. Quraishi, Aadam Prashant, G.C. Shabaz, Mohammad Kollu, Archana Sandhu, Mukta B.S.A Crescent Institute of Science and Technology Tamil Nadu Chennai India Interventional Treatment Institute Houston Texas USA McAllenTX United States USA University Texas Tech University Lubbock StateTX United States Model Institute of Engineering and Technology J&K Jammu India Pimpri Chinchwad College of Engineering and Research Ravet Pune India Skill Department of Computer Science and Engineering Shri Vishwakarma Skill University Haryana Palwal India

ISBN: (纸本)9781032738659

Dyslexia is a learning disability that is widespread and marked with a persistent problem with word identification and spelling. It has an impact on a person’s capacity to decipher letters and words accurately and fluently. Dyslexia has emerged as one of the most widespread learning disabilities, though the medical experts have not yet come out explaining its core causes. In this research, the prediction and classification of dyslexia is effectively carried out with the help of brain images which acts as input to machine learning techniques towards the prediction of dyslexia. The proposed dyslexic prediction model uses a Combination digital twin Kernel-based Support Vector Machine (KSVM) optimized by Whales algorithm. The combination kernel SVM shows better accuracy and less computational time compared to single kernel of SVM in a distributed environment. The proposed combinational kernel is less complex than deep learning models. © 2025 The Author(s).

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Deep Learning for Glioblastoma Subtyping: Leveraging DenseNet-201 in brain Tumor Radiogenomic Classification

Deep Learning for Glioblastoma Subtyping: Leveraging DenseNe...

引用

Cognitive Robotics and Intelligent Systems (ICC - RObINS), International Conference on

作者： S. Alvin Jesuraj S. V. Evangelin Sonia C. P. Shirley B.Tech Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372755

This research investigates the application of DenseNet-201, a deep convolutional neural network architecture, in the RSNA-MICCAI brain Tumor Radiogenomic Classification aimed at predicting the genetic subtype of glioblastoma using MRI imaging data. This study demonstrates the effectiveness of DenseNet-201 in accurately classifying glioblastoma cases based on MGMT promoter methylation status, a critical biomarker influencing treatment outcomes. Through comprehensive experimental evaluations, including training, validation, and testing phases, DenseNet-201 exhibits robust performance metrics such as high accuracy, precision, recall, F1-score, and AUC-ROC values. These results highlight the model's ability to effectively distinguish between MGMT promoter methylation-positive and negative glioblastoma cases, offering valuable support for clinical decision-making in treatment planning and prognosis assessment. Leveraging deep learning techniques and MRI imaging data, DenseNet-201 holds promise as a powerful tool for enhancing the understanding of glioblastoma genetics and guiding personalized therapeutic interventions, ultimately contributing to improved patient outcomes in brain cancer management.

关键词： Deep learning Training Magnetic resonance imaging Medical services Genetics Planning Prognostics and health management

来源：评论

学校读者我要写书评

暂无评论

Jellyfish Search Optimizer with Deep Learning Driven Cervical Cancer Recognition and Classification Model on biomedical Images 2

Jellyfish Search Optimizer with Deep Learning Driven Cervica...

引用

2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Anand, M. Suresh Venkatramulu, S. Vidhya, Sesha S. Chembian, W.T. Yashaswini, b.M. Kumar, Magesh b. Department of Computing Technologies School of Computing SRM Institute of Science & Technology Kattankulathur603203 India Department of CSE Kakatiya Institute of Technology and Science Warangal India Department of ECE RMK College of Engineering and Technology India Department of CSE Vel Tech High Tech Dr Rangarajan Dr Sakuntala Engineering College Avadi Morai India Electronics & Communication Engineering JSS Academy of Technical Education JSSATE-B Campus Dr. Vishnuvardhan Rd Uttarahalli-Kengeri Main Road JSS Campus Rd Srinivaspura Karnataka Bengaluru560060 India Department of Computer Science and Engineering Bannari Amman Institute of Technology Erode Sathyamangalam India

ISBN: (纸本)9798350383867

The emergence of Cervical Cancer (CC) Recognition and Classification Model for biomedical Images is a main advancement in fields of medical imaging and diagnostics. Leveraging state-of-the-art image processing and machine learning algorithms, this model can accurately detect and classify CC-related abnormalities in medical images, namely colposcopy or Pap smear images. by providing automated and precise assessments, the model empowers medical experts to make timely and informed decisions, which leads to earlier interference and potential life-saving treatment. With its potential to improve diagnostic accuracy and reduce human error, the model holds great promise in the fight against CC, especially in resource-constrained healthcare settings where medical practitioners may be limited. This study presents a Jellyfish Search Optimizer with Deep Learning Driven CC Recognition and Classification (JSODL-CCRC) model on biomedical Images. The JSODL-CCRC technique is mainly developed to investigate the medical images for detection and classification of CC. The JSODL-CCRC approach comprises median filtering (MF) for noise reduction, followed by feature extraction utilizing Inceptionv3 to derive complex image patterns. besides, the JSO based hyperparameter selection process is performed for the Inception v3 model. At last, Extreme Learning Machine (ELM) algorithm is applied to categorize the images which validates efficacy in handling complex biomedical image data. The experimental results stated that the JSODL-CCRC model enables effective detection results over other models. © 2024 IEEE.

关键词： Median filters

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：