检索结果-内蒙古大学图书馆

12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Limkar, Suresh Nale, Soham Darade, Shreyash Chaudhari, Ashwin Taras, Atharva Department of Computer Science and Engineering Central University of Jammu Jammu and Kashmir Jammu India Department of Artificial Intelligence and Data Science AISSMS Institute of Information Technology Pune India

ISBN: (纸本)9789819601387

A new, improved computer program made specifically for banking helps in solving problems like inadequate guidance and makes it convenient for people who cannot visit banks for some reason. This helps people answer questions faster and makes it easier for bank workers. Making the program better involved picking the right model and making a special set of data. After making these improvements, the llama2_sharded model is really good at answering banking questions, doing better than others by 2.67%. This shows how important it is to give programs lots of practice with different information, proven by reaching a low error rate after many tries. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Banking

来源：评论

学校读者我要写书评

暂无评论

CLERC: An Intelligent Sign Language Translator for Improved Accessibility using Machine Learning 2

CLERC: An Intelligent Sign Language Translator for Improved ...

引用

2nd IEEE International Conference on Communication, Security and artificial intelligence, ICCSAI 2023

作者： Vijayaganth, V. Santhosh Kumar, S. Davis, L. Pranava Darisan, S.V. Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kpr Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350369960

Clerc's system is a modern smart language interpreter using advanced methods like artificial intelligence, imagery recognition, and natural languages translation. The Clergs System includes these are just some of tools used. The system is able to do this by turning gestures in sign language into verbal and written outputs smoothly thus helping people with hearing impairment to communicate easily in real time. In this research, an object recognition SSD MobileNets and FDN feature extraction models are used for accurate gesture detection. A machine-learning model can also be used to predict accurate translations. The efficacy of the Clerc system has been tested and validated to the utmost extent in different settings such as public places, schools, hospitals amongst others. This research seeks to explore possibilities of improving the system's precision, efficacy, scalability, and use with regards to the practical application. © 2023 IEEE.

关键词： Clerc system Computer vision FPN-lite feature extractor scalability SSD Mobile Net Object recognition model

来源：评论

学校读者我要写书评

暂无评论

Enhancing Ovarian Tumor Diagnosis with Advanced Computed Tomography Image Segmentation Techniques 1

Enhancing Ovarian Tumor Diagnosis with Advanced Computed Tom...

引用

1st International Conference on Computational intelligence for Security, Communication and Sustainable Development, CISCSD 2024

作者： Vinay, P. Sairaj, K. Samson, K. Ushasree, A. Kodipalli, Ashwini Kumar, N. Udaya Engineering Gokaraju Rangaraju Institute of Engineering and Technology Electronics and Communication Hyderabad India Global Academy of Technology Artificial Intelligence and Data Science Bangalore India Vivekananda Institute of Technology Hyderabad India

ISBN: (纸本)9798350365405

This work describes a unique technology that uses an intelligent segmentation algorithm to precisely delineate ovarian cancers from CT scans, revolutionizing diagnostic accuracy and clinical decision-making. Our system uses powerful machine learning and image processing techniques to segregate tumors with outstanding precision and efficiency, allowing for quantitative study of critical morphological aspects. These characteristics make it possible to distinguish between benign and malignant tumors, allowing for more accurate diagnosis and prognosis. Our approach standardizes CT image analysis, resulting in objective and consistent results that reduce variability across observers and improve diagnostic accuracy. The incorporation of this intelligent segmentation algorithm is a watershed moment in radiological assessment, providing doctors with an automated tool to supplement their expertise, ultimately enhancing patient care and results in ovarian cancer therapy. Furthermore, the proposed methodology includes two distinct approaches to image classification: a sequential model architecture optimized for binary classification with binary cross-entropy loss, and a modified LeNet-based convolutional neural network emphasizing categorical classification with softmax activation and categorical cross-entropy loss. Furthermore, the proposed model contains batch normalization for increased training stability and convergence, and the training and validation datasets are generated using Image data Generator with particular augmentation settings, allowing for robust model training and evaluation. By combining these methodologies, our method improves the accuracy and efficiency of ovarian tumor classification, resulting in better clinical decision-making and patient care. © 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

Augmented Reality and artificial intelligence in Sign Language Expression 3

Augmented Reality and Artificial Intelligence in Sign Langua...

引用

3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Kaviyaraj, R. Sathya, P. Nandhini, P. Vinoth Kumar, P. Chelliah, Sureshkumar Ezhilmathi, S. Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India Sri Shanmuga Collage of Engineering and Technology Department of Computer Science and Engineering Sankari India St. Joseph's Institute of Technology Omr Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350361186

Sign language, the vibrant tapestry of hand gestures and facial expressions, is the lifeblood of Deaf and hardof-hearing communities. For millions of signers, American Sign Language (ASL) runs deeper than communication, fundamental to identity, expression, and belonging. And yet an unshakeable communication gap leaves users of ASL frequently marooned away from the hearing world, kept from education, healthcare, or employment, or from basic, everyday transactions. By posing this new unsolved challenge to the power and promise of artificial intelligence (AI), this work leads the way towards closing that chasm by real-time recognition and translation of full ASL. Our approach employs a novel variant of Random Forest and utilizes cutting-edge video processing techniques to identify and understand the nuanced, often exquisitely delicate detail of ASL signing, at unprecedented levels of accuracy, and at speed. Another layer of innovation that characterizes our work is our integration of augmented reality (AR). By embedding AR along with our translator of artificial intelligence tech, we intend to completely change the way American Sign Language (ASL) is conveyed by directly engraining our already robust Random Forest model and advanced video processing techniques to project the ASL translation directly into your visual field in real time. The goal to demystify this complex and vivid language and, in doing so, to remove the communication barriers that persist between the Deaf community and the rest of the world, thus, fostering inclusion. © 2024 IEEE.

关键词： Augmented reality

来源：评论

学校读者我要写书评

暂无评论

Deep Learning based Brain Tumor Classification for MR Images using ResNet50 12

Deep Learning based Brain Tumor Classification for MR Images...

引用

12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Kokcam, Omer Mirac Boyaci, Aytug Colak, Muhammed Emre Firat University Faculty of Technology Department of Software Engineering Elaziǧ Turkey National Defence University Department of Computer Engineering Air Force Academy İstanbul Turkey Firat University Faculty of Engineering Department of Artificial Intelligence and Data Science Engineering Elaziǧ Turkey

ISBN: (纸本)9798350330366

Brain tumors are abnormal cell growths that occur in various parts of the brain, and the accurate classification of these tumors plays a critical role in determining treatment methods. Classification and diagnosis of brain tumors based on artificial intelligence and deep learning models have been made possible due to advances in medical image processing technologies. For instance, such technologies facilitate quick detection of tumors by health experts thereby improving early diagnosis rate and hence enhancing treatment outcomes. Importantly, however, one considers improvement or breakthroughs in artificial intelligence-based classification systems as a crucial step forward towards brain tumor diagnosis as well as its treatment strategy. In this study we use Google Research's ImageNet-21k pre-trained ResNet50 model for classifying brain tumor cases. This model is a deep learning-based image classification tool and is capable of learning from large data sets. The model is specifically trained for high-resolution image recognition and is designed to achieve high accuracy rates. The results showed a classification accuracy of 99.9 percent, which is an exceptional accuracy rate for this model. This accuracy rate indicates that the model is highly effective in detecting and classifying brain tumors. High accuracy rates allow for early diagnosis of patients' diseases and, as it is often said in medicine, 'Early Diagnosis Saves Lives'. This result is one of the many proofs of how effective artificial intelligence and deep learning are in medical image analysis. © 2024 IEEE.

关键词： Medical imaging

来源：评论

学校读者我要写书评

暂无评论

Analysis of Conjoint Response in Post-stroke Patients Using the Attention-coupled Weighting Method

引用

Machine intelligence Research 2025年第2期22卷 352-367页

作者： Jingyao Chen Chen Wang Ningcun Xu Zeng-Guang Hou Liang Peng Pu Zhang Faculty of Innovation Engineering CASIA-MUST Joint Laboratory of Intelligence Science and TechnologyMacao University of Science and TechnologyMacao 999078China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Analysis and Testing Beijing Academy of Science and TechnologyBeijing 100089China Department of Rehabilitation Evaluation China Rehabilitation Research CenterBeijing 100068China

Rehabilitation assessment plays a vital role in the recovery process of post-stroke ***,many studies have focused on the implementation of automated assessment ***,the impact of stroke on the synergistic pattern between different human joints is not well *** this study,we developed an attention-coupled weighting based quantitative analysis model to identify the pathological alteration in the conjoint response structure between stroke patients and healthy participants,thus providing more intrinsic suggestions for the post-stroke rehabilitation *** collaboration with the China Rehabilitation Research Center and the Macao University of science and technology,we recruited 15 post-stroke patients and 15 healthy participants to complete several actions selected from the Fugl-Meyer assessment(FMA)scale,which is a commonly used post-stroke rehabilitation assessment scale in clinical *** study proposed an attentional coupling weight(ACW)extraction *** filtering the weights in the attentional coupling network using a priori matrix,we can analyse the effect of stroke on the alteration of synergistic structures and the coordination patterns of the upper and lower ***,we used a commonly accepted synergy analysis method,the nonnegative matrix decomposition(NMF)method,for comparison and *** experimental results demonstrated the effectiveness of our method in quantifying the synergistic structure pattern alterations in post-stroke patients,which provides a new targeted option for the rehabilitation process.

关键词： Intelligent assessment conjoint response attentional coupling network spatio-temporal weight extraction.

来源：评论

学校读者我要写书评

暂无评论

Comparison of IDS in IoT, Mobile and Cloud based Systems 12

Comparison of IDS in IoT, Mobile and Cloud based Systems

引用

12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Gok, Gorkem Boyaci, Aytug Ulas, Mustafa Firat University Faculty of Technology Department of Software Engineering Elaziǧ Turkey Air Force Academy National Defence University Department of Computer Engineering İstanbul Turkey Firat University Faculty of Engineering Department of Artificial Intelligence and Data Science Engineering Elaziǧ Turkey

ISBN: (纸本)9798350330366

This study is meant to research the evolution of intrusion detection and network monitoring within computer, cloud-based systems, IIoT, and mobile environments. The source has outlined the novel technologies in IDS, from 'Fast Learning Network' to hybrid classifiers aiming at protecting cloud computing. The findings highlight innovation IDS technologies that provide flexible and peculiar solutions to ward off potential cyber threats. In addition, it presented with the use of Nagios, NConf, the state of the art in network traffic monitoring, and deep learning as tools for advanced management and network protection. Besides, the research also reviewed concepts of Big data Analytics and Federated Learning in bettering direct integration and anomaly detection in IIoT. A good comparison of the two would show that recent technological advancements are much needed for network efficiency and its cybersecurity. Based on the findings, the scope of networked system security develops dynamically amid current challenges. © 2024 IEEE.

关键词： Cybersecurity

来源：评论

学校读者我要写书评

暂无评论

Enhancing Detection of Breast Cancer Through a Novel Framework: A Machine Learning Approach with Explainable AI Interpretation 1

Enhancing Detection of Breast Cancer Through a Novel Framewo...

引用

1st International Conference for Women in Computing, InCoWoCo 2024

作者： Vidyashree, R. Kodipalli, Ashwini Rao, Trupthi Global Academy of Technology Dept. of Computer Science & Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bangalore India

ISBN: (纸本)9798331518943

Breast cancer, the most prevalent cancer among women, significantly contributes to increased mortality rates and ranks as the second leading cause of cancer-related deaths in women. Early detection is crucial for effective treatment, yet traditional diagnostic approaches often involve multiple laboratory tests supervised by medical experts. To mitigate human error and expedite breast cancer detection, there is a pressing need for automated systems capable of accurate and timely diagnosis. This study aims to investigate the application of machine learning (ML) techniques in classifying breast cancer using features from digitized images of fine-needle aspiration (FNA) breast mass samples. Machine learning methods are instrumental in addressing this imperative. The study conducts a comparative analysis of various ML classification methods, including Logistic Regression, Support Vector Machine (SVM), K Nearest Neighbor (KNN), Decision Trees, Bagging-Boosting, and ensemble models, using a breast cancer dataset to establish a novel framework. The performance of each algorithm is assessed based on metrics such as accuracy, precision, recall, and F1 score. The findings underscore the effectiveness of ensemble classifier models, achieving a notable accuracy rate of 98.87%. Additionally, the integration of explainable artificial intelligence enhances the interpretability of the models by providing valuable insights into their decision-making processes. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Enhancing Hospital Mortality Prediction for Heart Failure Patients through Hyperparameter Optimization and Interpretability Analysis with LIME and SHAP 1

Enhancing Hospital Mortality Prediction for Heart Failure Pa...

引用

1st International Conference for Women in Computing, InCoWoCo 2024

作者： Meena, S. Murthy, Monisha Kodipalli, Ashwini Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bengaluru India

ISBN: (纸本)9798331518943

Heart failure is one of the primary causes for deaths caused in the hospital. Predicting mortality rate of such patients is extremely important for the efficient use of health care resources. This research aims to estimate the hospital mortality rate of Heart Failure(HF) patients using machine learning(ML). Using computational technique, the obtained model has given the most efficient accuracy. This paper uses the classification model to detect two outcomes (0 and 1, i.e., if the patient will survive or not). The classification models used for the hospital mortality prediction are, single classifiers such as Logistic regression, SVM, KNN, Decision tree (Ginni and Entropy);with Logistic regression obtaining highest accuracy of 88.9%, Bagging and boosting models such as Randomforest, Adaptive boosting, Gradient boosting, XG boosting, Categorical boosting;with CatBoost obtaining highest accuracy of 90.2%. To achieve the best accuracy, ensemble models were used out of which ensemble classifier ensemble gave 91.1% accuracy. To understand the features playing important role in decision making, explainable AI(XAI) is used. LIME and SHAP are considered. LIME generated local explanations, it explained the decision of a complex model for a specific instance or observation. In contrast, SHAP provided global explanations, it explained the overall behavior of the model across all instances. © 2024 IEEE.

关键词： Adaptive boosting

来源：评论

学校读者我要写书评

暂无评论

Enhanced U-Net with Transformer-Driven Encoder for Medical Image Segmentation

Enhanced U-Net with Transformer-Driven Encoder for Medical I...

引用

2024 International Conference on Brain Computer Interface and Healthcare Technologies, iCon-BCIHT 2024

作者： Palaniappan, Ramya Surendran, Suseendran Balakrishnan, Saranya Madanapalle Institute of Technology & Science Department of Computer Science & Engineering Madanapalle India Bannari Amman Institute of Technology Department of Computer Science and Engineering Erode India KGiSL Institute of Technology Department of Artificial Intelligence and Data science Coimbatore India

ISBN: (纸本)9798331540067

The automatic segmentation of tumours or lesions from magnetic resonance imaging (MRI) pictures is a critical but difficult task in clinical settings, sometimes necessitating laborious and time-consuming techniques. Deep learning has made a significant advancement in the medical imaging field in recent years. U-Net has emerged as the predominant model used for segmenting medical images. Although the standard U-Net framework has shown excellent performance in segmenting multimodal medical pictures, it has been noticed that it lags in certain aspects when dealing with tough datasets. We propose specific improvements to the state-of-the-art U-Net model. We have introduced attention block in the skipped connections to highlight relevant features. Additionally, we have incorporated transformers in the encoding path to capture temporal dependencies in medical images. In this research work, we have introduced an innovative architecture that has the potential to replace the U-Net model for segmenting regions of interest (ROI) in medical images. This study utilizes a total of 1760 samples obtained from the ISBI 2015 and PH2 medical repositories. The experimental findings have demonstrated that our proposed model has obtained highly encouraging results of 78.02 Jaccard coefficient and IoU of 65.14. In addition, we conducted tests and compared the performance of the proposed model with the classical U-Net model using the same dataset. As a result, we observed a relative improvement in the performance of both 2.36 and 1.85, respectively. The proposed technology will function as a sophisticated toolkit for radiologists to precisely detect and diagnose anomalous tumour cells. © 2024 IEEE.

关键词： Semantic Segmentation

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：