检索结果-内蒙古大学图书馆

1st International Conference on artificial intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Burle, Rushikesh Gaurkhede, Sanskruti Dewangan, Leelkanth Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Computer Science and Design Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Wardha442001 India

ISBN: (纸本)9798350330915

In the event that glaucoma is not treated promptly, blindness may become permanent. It ranks second among the factors that might lead to blindness in the eyes. One example of a recent imaging technology that looks within the eye is the fundus camera. Glaucoma is diagnosed using a variety of techniques, such as the retinal nerve fibre layer analyzer, The Topcon image net technique and optical coherence tomography. Despite its expensive cost and the dearth of available research in this field, the optic cup to disc ratio is used to diagnose glaucoma. The degree of glaucomatous damage is mostly determined by the appearance of the optic cup. The beaker slowly gets better and inhabits most of the judgement as the glaucoma becomes worse. If glaucoma is not pickled trendy a timely manner, blindness may become irreversible. It is the second most common cause of ocular blindness. A contemporary imaging tool called the fundus camera is utilised to view within the eye. Among the methods used to diagnose glaucoma include the retinal nerve fibre layer analyzer, optical coherence tomography, and the Topcon image net approach. Despite its high cost and paucity of research, glaucoma is diagnosed using the optic cup to discus relation. The degree of glaucomatous damage is mostly determined by the appearance of the optic cup. The cup expands as glaucoma progresses until it finally occupies the majority of the eye. © 2023 IEEE.

关键词： CNN ConvNets Glaucoma machine learning MATLAB Rim-One

来源：评论

学校读者我要写书评

暂无评论

PE-DeepNet: A deep neural network model for pulmonary embolism detection

引用

International Journal of Intelligent Networks 2022年 3卷 176-180页

作者： Lynch, Damian M, Suriya School of Engineering University of Technology Kingston Jamaica Department of Artificial Intelligence & Data Science Sri Eshwar College of Engineering Coimbatore Tamil Nadu India

Machine learning in medical image processing has shown to be a useful way for discovering patterns in both poorly labelled and unlabeled datasets. Venous thromboembolism, which includes deep vein thrombosis and pulmonary embolism, is a major cause of death in patients and requires quick detection by specialists. Using an artificial neural network, the suggested study was carried out to aid doctors in identifying and forecasting the risk level of pulmonary embolism in patients. This research presents a hybrid deep learning convolutional neural network model called PE-DeepNet (Pulmonary Embolism detection using Deep neural Network) to perform quick and accurate pulmonary embolism detection. The experiment uses a case study from the standard RSNA STR Pulmonary Embolism Chest CT scan image dataset. The proposed work results in an accuracy of 94.2%, an improvement over existing CNN models with minor trainable parameters. © 2022 The Authors

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Development of an IoT-based Intelligent Energy Management System with Motion Notification on Telegram in a Smart Home 5

Development of an IoT-based Intelligent Energy Management Sy...

引用

5th International Conference on Information Management and Machine intelligence, ICIMMI 2023

作者： Kanwer, Budesh Mamodiya, Udit Department of Artificial Intelligence & Data Science Poornima Institute of Engineering and Technology Rajasthan Jaipur India Department of Electrical Engineering Poornima Institute of Engineering and Technology Rajasthan Jaipur India

ISBN: (纸本)9798400709418

This research paper focuses on the development of an intelligent energy management system based on the Internet of Things (IoT), emphasizing the imperative need to optimize energy consumption. Given the ubiquity of energy usage in various aspects of daily life and its essential role in our future sustainability, efficient energy management is of paramount importance. Our devised system utilizes IoT sensors to autonomously control the power supply to a room by detecting changes in user proximity and motion. Additionally, we have integrated a Telegram chatbot to provide real-time notifications about the status of lighting fixtures being turned on or off, ensuring homeowners are informed even when away from their residence. © 2023 ACM.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Exploring Novel CNN Architectures for Deep Learning-Based Liver Tumor Occurrence Prediction

Exploring Novel CNN Architectures for Deep Learning-Based Li...

引用

2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Kalaiselvi, P. Anusuya, S. Kumaravel, A. Esther, C. Gomathy, G. Aravind, Arun Sri Sairam Engineering College Department of Artificial Intelligence and Data Science India Simats Engineering Department of Computer Science and Engineering India Biher Department of Information Technology India

ISBN: (纸本)9798350394580

According to the authorized findings of,' Hepatocellular carcinoma (HCC)' a common in the human species, liver located, tumor,90 % of its occurrences demand any sort of support for predicting them in advance, if possible. For this purpose, Image Processing, Text Mining, and Deep learning techniques are handled with effective pre-processing methods in the recent research works to improve the results available so far. A majority of contribution by the researchers finds it challenging mostly in selecting suitable architectures for deep learning as most of the previous literature studies show the lacking of details on feasibility and justifiability in specifying the architectures. In this article, it is aimed at the primary goal of such studies as the first attempt on varying architectures in terms of profile and position of layers and observing the performance for recognizing the liver Tumor from the Image-data sets. The CNN architecture of neural network like AlexNet, VGGnet, ResNet, LeNet are standard architectures, based on this liver tumor dataset is implemented in customized architecture and its produced best performance with accuracy of 92.4 %. And It is compared with MeanderHandPD, Breast cancer and Liver Tumor datasets, the proposed architecture shows higher performance for Liver Tumor images. © 2023 IEEE.

关键词： AlexNet DeepLearning4j F-Measure Hepatocellular carcinoma Precision ROC SoftMax VGG

来源：评论

学校读者我要写书评

暂无评论

Heart Disease Prediction using Machine Learning 3

Heart Disease Prediction using Machine Learning

引用

3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Telsang, Supriya Pokale, Tanvi Posugade, Anagha Patwal, Pradyot Shirode, Prachi Popade, Om Vishwakarma Institute of Technology Department of Computer Engineering Pune India Vishwakarma Institute of Technology Department of Information Technology Pune India Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350364569

Heart attack and other heart diseases have acted as the major global cause of mortality. In accordance with WHO, about 32% death was due to cardiovascular disease, among which 85% was only due to heart disease There are various factors that lead to heart disease including high LDL, cholesterol, high blood pressure, Diabetes Smoking, Unhealthy diet, Obesity and Physical inactivity. If heart disease is not predicted earlier, it reduces patient's chances of survival. Therefore, it is essential to design a secure and precise system to predict heart disease early for proper treatment of patients. This research paper presents comprehensive analysis to identify heart disease using different predictive analytic methods. The research utilized a dataset of 1025 instances with 13 input parameters compromising age, sex, chest pain, blood pressure, blood sugar, electrocardiographic results, heart rate etc. The research tests and implements various predictive analytic methods such as Decision tree, Random Forest, Logistic regression and KN neighbor to predict the risk of heart disease on 60:40, 70:30 and 80:20 ratio data split. This research paper also highlights the superiority of Random Forest model for predicting heart disease with highest accuracy. © 2024 IEEE.

关键词： decision tree Heart disease KNN Logistic regression Randon Forest

来源：评论

学校读者我要写书评

暂无评论

Towards Safer Roads: A Comprehensive Review of Object Detection Techniques for Autonomous Vehicles

引用

SN Computer science 2025年第5期6卷 1-20页

作者： Parisapogu, Samson Anosh Babu Narla, Nitya Juryala, Aarthi Ramavath, Siddhu Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Amaravati Campus Andhra Pradesh Amaravati 522 503 India Department of Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology Hyderabad 500075 India

Nowadays, people spend a lot of time in travel, and road safety has become an important area of research. As a result, self-driving cars have been gaining popularity in recent years because they have a lot of benefits in terms of reducing traffic congestion and improving road safety, thereby reducing accidents. They also have an environmental benefit as they reduce travel times and hence reduce the usage of fuel. Though a lot of research has already been done in this field, there are always new problems being created and new solutions being found. Apart from self-driving cars, automated navigation also has a huge number of applications. Therefore, it is important to stay up to date about the latest developments in this field. This research is a comprehensive survey that explores and evaluates the different object-detecting algorithms that have been used throughout the years, their advantages, disadvantages, and trade-offs. It also analyses the datasets and benchmarks that have been used for evaluating the models. It aims to identify the gaps in research in this growing field so that it can act as a roadmap for researchers to focus their study on. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning Object detection Road safety Self-driving YOLO

来源：评论

学校读者我要写书评

暂无评论

Positron Emission Tomography Lung Image Respiratory Motion Correcting with Equivariant Transformer

引用

Computers, Materials & Continua 2024年第5期79卷 3355-3372页

作者： Jianfeng He Haowei Ye Jie Ning Hui Zhou Bo She Faculty of Information Engineering and Automation Kunming University of Science and TechnologyYunnan Key Laboratory of Artificial IntelligenceKunming650500China School of Physics and Electronic Engineering Yuxi Normal UniversityYuxi653100China PET/CT Center Affiliated Hospital of Kunming University of Science and TechnologyFirst People’s Hospital of Yunnan ProvinceKunming650031China

In addressing the challenge of motion artifacts in Positron Emission Tomography (PET) lung scans, our studyintroduces the Triple Equivariant Motion Transformer (TEMT), an innovative, unsupervised, deep-learningbasedframework for efficient respiratory motion correction in PET imaging. Unlike traditional techniques,which segment PET data into bins throughout a respiratory cycle and often face issues such as inefficiency andoveremphasis on certain artifacts, TEMT employs Convolutional Neural Networks (CNNs) for effective featureextraction and motion ***’s unique approach involves transforming motion sequences into Liegroup domains to highlight fundamental motion patterns, coupled with employing competitive weighting forprecise target deformation field generation. Our empirical evaluations confirm TEMT’s superior performancein handling diverse PET lung datasets compared to existing image registration networks. Experimental resultsdemonstrate that TEMT achieved Dice indices of 91.40%, 85.41%, 79.78%, and 72.16% on simulated geometricphantom data, lung voxel phantom data, cardiopulmonary voxel phantom data, and clinical data, respectively. Tofacilitate further research and practical application, the TEMT framework, along with its implementation detailsand part of the simulation data, is made publicly accessible at https://***/yehaowei/temt.

关键词： PET lung scans respiratory motion correction triple equivariant motion transformer lie group motion decomposition

来源：评论

学校读者我要写书评

暂无评论

EEG Information Transfer Changes in Different Daily Fatigue Levels during Drowsy Driving

IEEE Open Journal of Engineering in Medicine and Biology

引用

IEEE Open Journal of engineering in Medicine and Biology 2024年 5卷 180-190页

作者： Huang, Kuan-Chih Tseng, Chun-Ying Lin, Chin-Teng National Yang Ming Chiao Tung University Brain Science and Technology Center Hsinchu300 Taiwan University of Technology Sydney Australian Artificial Intelligence Institute Faculty of Engineering and IT SydneyNSW2007 Australia National Yang Ming Chiao Tung University Brain Science and Technology Center Department of Electrical and Computer Engineering Hsinchu300 Taiwan

A significant issue for traffic safety has been drowsy driving for decades. A number of studies have investigated the effects of acute fatigue on spectral power;and recent research has revealed that drowsy driving is associated with a variety of brain connections in a specific cortico-cortical pathway. In spite of this, it is still unclear how different brain regions are connected in drowsy driving at different levels of daily fatigue. This study identified the brain connectivity-behavior relationship among three different daily fatigue levels (low-, median-and high-fatigue) with the EEG data transfer entropy. According to the results, only low-and medium-fatigue groups demonstrated an inverted U-shaped change in connectivity from high performance to poor behavioral performance. In addition, from low-to high-fatigue groups, connectivity magnitude decreased in the frontal region and increased in the occipital region. These study results suggest that brain connectivity and driving behavior would be affected by different levels of daily fatigue. © 2020 IEEE.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm

引用

CAAI Transactions on intelligence technology 2024年第6期9卷 1347-1360页

作者： Jing Yang Touseef Sadiq Jiale Xiong Muhammad Awais Uzair Aslam Bhatti Roohallah Alizadehsani Juan Manuel Gorriz Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Centre for Artificial Intelligence Research(CAIR) Department of Information and Communication TechnologyUniversity of AgderGrimstadNorway Department of Creative Technologies Air UniversityIslamabadPakistan School of Information and Communication Engineering Hainan UniversityHaikouHainanChina Institute for Intelligent Systems Research and Innovation(IISRI) Deakin UniversityWaurn PondsVictoriaAustralia Data Science and Computational Intelligence Institute University of GranadaGranadaSpain

Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly *** is triggered by viral infections and presents symptoms such as chest pain and heart *** detection is crucial for successful treatment,and cardiac magnetic resonance imaging(CMR)is a valuable tool for identifying this ***,the detection of myocarditis using CMR images can be challenging due to low contrast,variable noise,and the presence of multiple high CMR slices per *** overcome these challenges,the approach proposed incorporates advanced techniques such as convolutional neural networks(CNNs),an improved differential evolution(DE)algorithm for pre-training,and a reinforcement learning(RL)-based model for *** this method presented a significant challenge due to the imbalanced classification of the Z-Alizadeh Sani myocarditis dataset from Omid Hospital in *** address this,the training process is framed as a sequential decision-making process,where the agent receives higher rewards/penalties for correctly/incorrectly classifying the minority/majority ***,the authors suggest an enhanced DE algorithm to initiate the backpropagation(BP)process,overcoming the initialisation sensitivity issue of gradient-based methods like back-propagation during the training *** effectiveness of the proposed model in diagnosing myocarditis is demonstrated through experimental results based on standard performance ***,this method shows promise in expediting the triage of CMR images for automatic screening,facilitating early detection and successful treatment of myocarditis.

关键词： classification differential evolution myocarditis reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Outsmarting Android Malware with Cutting-Edge Feature engineering and Machine Learning Techniques

引用

Computers, Materials & Continua 2024年第4期79卷 651-673页

作者： Ahsan Wajahat Jingsha He Nafei Zhu Tariq Mahmood Tanzila Saba Amjad Rehman Khan Faten S.A.lamri Faculty of Information Technology Beijing University of TechnologyBeijing100124China Artificial Intelligence and Data Analytics(AIDA)Lab CCIS Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Information Sciences University of EducationVehari CampusVehari61100Pakistan Department of Mathematical Sciences College of SciencePrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

The growing usage of Android smartphones has led to a significant rise in incidents of Android malware andprivacy *** escalating security concern necessitates the development of advanced technologies capableof automatically detecting andmitigatingmalicious activities in Android applications(apps).Such technologies arecrucial for safeguarding user data and maintaining the integrity of mobile devices in an increasingly digital *** methods employed to detect sensitive data leaks in Android apps are hampered by two major limitationsthey require substantial computational resources and are prone to a high frequency of false *** meansthat while attempting to identify security breaches,these methods often consume considerable processing powerand mistakenly flag benign activities as malicious,leading to inefficiencies and reduced reliability in *** proposed approach includes a data preprocessing step that removes duplicate samples,managesunbalanced datasets,corrects inconsistencies,and imputes missing values to ensure data *** Minimaxmethod is then used to normalize numerical data,followed by feature vector extraction using the Gain ratio andChi-squared test to identify and extract the most significant characteristics using an appropriate prediction *** study focuses on extracting a subset of attributes best suited for the task and recommending a predictivemodel based on domain expert *** proposed method is evaluated using Drebin and TUANDROMDdatasets containing 15,036 and 4,464 benign and malicious samples,*** empirical result shows thatthe RandomForest(RF)and Support VectorMachine(SVC)classifiers achieved impressive accuracy rates of 98.9%and 98.8%,respectively,in detecting unknown Androidmalware.A sensitivity analysis experiment was also carriedout on all three ML-based classifiers based on MAE,MSE,R2,and sensitivity parameters,resulting in a flawlessperformance for both *** app

关键词： Android malware detection machine learning SVC K-Nearest Neighbors(KNN) RF

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：