检索结果-内蒙古大学图书馆

9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Swathisree, M. Gayathri, E. Manivannan, R. KoneruLakshmaiah Education Foundation University Department of Computer Science And Engineering Telangana Hyderabad522502 India Stanley College of Engineering And Technology For Women Department of Computer Science And Engineering Telangana Hyderabad500001 India

ISBN: (纸本)9798350365092

A critical health condition that continues to be encyclopedically significant is heart disease, which poses a challenge to reliable prophetic models for early opinion and intervention. The purpose of this investigation is to determine whether or not machine learning algorithms are effective in predicting cardiac-related complaints. A comprehensive dataset that included a variety of clinical characteristics, such as age, cholesterol levels, blood pressure, and life circumstances, was implemented. The development of prophetic models was accomplished through the utilization of a number of machine literacy techniques, such as logistic retrogression, decision trees, and arbitrary timbers. It was necessary to implement point selection and cross-validation methods in order to improve the generalizability and delicateness of the model. There is a promising performance in heart complaint vaticination, with certain algorithms displaying higher prophetic potential, as demonstrated by the results. The findings highlight the potential for machine literacy to assist healthcare interpreters in relating individuals who are at risk of experiencing heart complaints. This would allow for the development of innovative intervention measures and improved patient concerns. Additional research is necessary to validate these models in a variety of clinical contexts and populations. © 2024 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Lung Cancer Segmentation with Three-Parameter Logistic Type Distribution Model

引用

computers, Materials & Continua 2023年第4期75卷 1447-1465页

作者： Debnath Bhattacharyya EaliStephen Neal Joshua N.Thirupathi Rao Yung-cheol Byun Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramGuntur522302Andhra PradeshIndia Department of Computer Science&Engineering Vignan’s Institute of Information Technology(A)Visakhapatnam530049Andhra PradeshIndia Department of Computer Engineering Jeju National University102 Jejudaehak-roJeju-siJeju-do690-756Korea

Lung cancer is the leading cause of mortality in the world affectingboth men and women *** a radiologist just focuses on the patient’sbody, it increases the amount of strain on the radiologist and the likelihoodof missing pathological information such as abnormalities are *** of the primary objectives of this research work is to develop computerassisteddiagnosis and detection of lung cancer. It also intends to make iteasier for radiologists to identify and diagnose lung cancer accurately. Theproposed strategy which was based on a unique image feature, took intoconsideration the spatial interaction of voxels that were next to one *** the U-NET+Three parameter logistic distribution-based technique, wewere able to replicate the situation. The proposed technique had an averageDice co-efficient (DSC) of 97.3%, a sensitivity of 96.5% and a specificity of94.1% when tested on the Luna-16 dataset. This research investigates howdiverse lung segmentation, juxta pleural nodule inclusion, and pulmonarynodule segmentation approaches may be applied to create computer AidedDiagnosis (CAD) systems. When we compared our approach to four otherlung segmentation methods, we discovered that ours was the most *** employed 40 patients from Luna-16 datasets to evaluate this. In termsof DSC performance, the findings demonstrate that the suggested techniqueoutperforms the other strategies by a significant margin.

关键词： Magnetic resonance imaging(MRI) lung cancer Luna-16 logistic distribution segmentation deep learning juxta plural pulmonary nodules

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Survey of Deep Learning Techniques in Malaria Diagnosi 6

A Comprehensive Survey of Deep Learning Techniques in Malari...

引用

6th International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2024

作者： Saha, Mainak Bhaumik, Rabin Ranganath, Abadhan Basu, Miriyala Trinath Pagadala, Pavan Kumar Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Telangana Hyderabad500075 India Techno College of Engineering Agartala Department of Computer Science and Engineering West Tripura Agartala799004 India

ISBN: (纸本)9798331505790

Malaria is a malignant disease, and it has a high mortality rate as compared to other disease. The survey of death cases in the past century reported the maximum due to malaria. Many times, it seems like a normal fever and body pain. So, many patients avoid proper medication and treatment. Many times, it is seen that the patients are unable to get cured from malaria due to inaccurate diagnosis and inproper medication. The clinical diagnosis and trials were followed in the early days, which took much time. The advancement of diagnostic techniques and processes such as rapid diagnosis technique (RDT) blood testing kit, Internet of Things (IoT) wearables with deep learning techniques, reverse transcriptase-Polymer chain reaction (RT-PCR) etc. Rapid and accurate identification of the protozoan Plasmodium parasites that cause human malaria is essential for improving the treatment and managing a lifethreatening disease. The most commonly used method for detecting Plasmodium in blood films is still microscopic analysis with Blood film staining. This article discusses the history of Plasmodium detection and evaluates the benefits and drawbacks of the current diagnostic techniques. © 2024 IEEE.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Wrist joint synovial hypertrophy and effusion detection in musculoskeletal ultrasound images using self-attention U-Net

引用

Multimedia Tools and Applications 2024年第41期83卷 89317-89334页

作者： Chang, Chuan-Wang Chang, Chuan-Yu Zhu, Yu-Xian Wang, Sz-Tsan Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Douliu Taiwan Division of Allergy Immunology and Rheumatology Dalin Tzu Chi Hospital Buddhist Tzu Chi Medical Foundation Dalin Chiayi Taiwan

Skeletal muscle ultrasound has emerged as a pivotal imaging modality in rheumatology clinics, offering unparalleled advantages such as radiation-free imaging, safety, and dynamic examination capabilities. However, its reliance on operator expertise often leads to inconsistent interpretations and diagnostic variability. In this study, we present a novel diagnostic system aimed at detecting rheumatoid arthritis (RA) in the wrist joint, with a focus on identifying synovial hypertrophy and effusion using musculoskeletal ultrasound images. Leveraging deep learning techniques, specifically semantic segmentation models, we introduce SEAT-UNet, which combines the U-Net architecture with a self-attention mechanism to enhance the accuracy of lesion classification and localization. SEAT-UNet addresses the challenge of discontinuous dispersion encountered in conventional segmentation models, particularly when delineating lesion areas. Our experimental results demonstrate exceptional performance, achieving a sensitivity and Dice coefficient of 100% and 84%, respectively, in synovial hypertrophy detection, and 86% sensitivity with an 84% Dice coefficient in effusion detection. These findings underscore the potential of SEAT-UNet as a valuable tool for early RA diagnosis, offering improved patient outcomes and facilitating more effective disease management strategies. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Enhancing Data Aggregation Efficiency: Dynamic Energy-Aware Strategies in Wireless Sensor Networks

Enhancing Data Aggregation Efficiency: Dynamic Energy-Aware ...

引用

2024 International Conference on Smart Devices, ICSD 2024

作者： Agrawal, Navin Kumar Priya, Naincy Sinha, Priyanka Singh, Pranita Jain, Arpit Kumar, Munish GLA University Department of Computer Engineering & Applications U.P. Mathura India Muzaffarpur Institute of Technology Department of Computer Science & Engineering Bihar Muzaffarpur India Government Engineering College Khagaria Department of Computer Science & Engineering India Roorkee Institute of Technology Department of Computer Science and Engineering Roorkee India Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering Vaddeswaram AP India

ISBN: (纸本)9798350394412

In today's surplus world, wi-fi sensor networks are essential for many systems. This network encapsulates sensor nodes powered by irreplaceable batteries, preserving a fixed topology for displaying specific locations. Their efficiency makes it crucial for large data packages. Wireless sensor networks continuously analyze physical factors such as temperature, pressure, ambient soft, sunlight and humidity, dominating the process Energy consumption monitoring is a key task in this area. To address this framework, this paper proposes a different approach: a modified controlled, privacy-controlled approach to record collection. The method aims to reduce redundant sensitivity, thus increasing system lifetime by reducing the frequency of data transfer between nodes and large sinks A comparative study with existing methods shows how the proposed method is effective. Performance findings show that the proposed bundling method (MEBPP) provides high packet transmission values while consuming less power and extending network lifetime. © 2024 IEEE.

关键词： Data aggregation

来源：评论

学校读者我要写书评

暂无评论

An Efficient Unsupervised Learning Approach for Detecting Anomaly in Cloud

引用

computer Systems science & engineering 2023年第4期45卷 149-166页

作者： P.Sherubha S.P.Sasirekha A.Dinesh Kumar Anguraj J.Vakula Rani Raju Anitha S.Phani Praveen R.Hariharan Krishnan Department of Information Technology Karpagam College of EngineeringCoimbatoreTamilnaduIndia Department of Computer Science and Engineering Karpagam Academy of Higher EducationCoimbatoreTamilnaduIndia Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationAndhra PradeshIndia Department of MCA CMR Institute of TechnologyBengaluruKarnatakaIndia Department of Computer Science and Engineering Prasad V.Potluri Siddhartha Institute of TechnologyAndhra PradeshIndia Department of Computer Science and Engineering Residency CollegeChennaiIndia

The Cloud system shows its growing functionalities in various industrial *** safety towards data transfer seems to be a threat where Network Intrusion Detection System(NIDS)is measured as an essential element to fulfill ***,Machine Learning(ML)approaches have been used for the construction of intellectual *** IDS are based on ML techniques either as unsupervised or *** supervised learning,NIDS is based on labeled data where it reduces the efficiency of the reduced model to identify attack ***,the unsupervised model fails to provide a satisfactory ***,to boost the functionality of unsupervised learning,an effectual auto-encoder is applied for feature selection to select good ***,the Naïve Bayes classifier is used for classification *** approach exposes the finest generalization ability to train the *** unlabelled data is also used for adoption towards data ***,redundant and noisy samples over the dataset are *** validate the robustness and efficiency of NIDS,the anticipated model is tested over the NSL-KDD *** experimental outcomes demonstrate that the anticipated approach attains superior accuracy with 93%,which is higher compared to J48,AB tree,Random Forest(RF),Regression Tree(RT),Multi-Layer Perceptrons(MLP),Support Vector Machine(SVM),and ***,False Alarm Rate(FAR)and True Positive Rate(TPR)of Naive Bayes(NB)is 0.3 and 0.99,*** compared to prevailing techniques,the anticipated approach also delivers promising outcomes.

关键词： Network intrusion detection system feature selection auto-encoder support vector machine(SVM) anomaly

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Different Approaches for Cyber Forensics 4

Comparative Analysis of Different Approaches for Cyber Foren...

引用

4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Raj, Dharm Gupta, Ajay Kumar Krishna, Kunchanapaalli Rama Sharda School of Engineering & Technology Sharda University Department of Computer Science & Engineering India Iimt College of Engineering Department of Computer Science & Engineering Greater Noida India Koneru Lakshmaiah Education Foundation Vaddeswaram India

ISBN: (纸本)9798350387490

The Cyberspace has been enhanced rapidly during the recent years, where people are facing major crisis in that situation cybercrimes all around world are at peak. Cybercriminals always try to exploit system and gain potential information. Cyber Forensics plays major preventive role from cybercrimes, many researchers have proposed their different method to block such type of malwares links with help of Machine Learning, they created advance frameworks. Law enforcement agencies use different types of advance tools for the detection and prevention. In this paper authors propose the comparative review about the work done by other people in this field and analyzed them how they are working what are the limitation have currently also we studied about the different hardware and software tools that is currently being used by cyber experts in forensics. Also we tried to examine current scenario of cybercrimes in India. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

BAHGRF^(3):Human gait recognition in the indoor environment using deep learning features fusion assisted framework and posterior probability moth flame optimisation

引用

CAAI Transactions on Intelligence Technology 2025年第2期10卷 387-401页

作者： Muhammad Abrar Ahmad Khan Muhammad Attique Khan Ateeq Ur Rehman Ahmed Ibrahim Alzahrani Nasser Alalwan Deepak Gupta Saima Ahmed Rahin Yudong Zhang Foundation University Islamabad IslamabadPakistan Department of Artificial Intelligence College of Computer Engineering and SciencePrince Mohammad Bin Fahd UniversityAl KhobarSaudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadhSaudi Arabia Department of Computer Science Maharaja Agrasen Institute of TechnologyDelhiIndia United International University DhakaBangladesh School of Informatics University of LeicesterLeicesterUK

Biometric characteristics are playing a vital role in security for the last few *** gait classification in video sequences is an important biometrics attribute and is used for security purposes.A new framework for human gait classification in video sequences using deep learning(DL)fusion assisted and posterior probability-based moth flames optimization(MFO)is *** the first step,the video frames are resized and finetuned by two pre-trained lightweight DL models,EfficientNetB0 and *** models are selected based on the top-5 accuracy and less number of ***,both models are trained through deep transfer learning and extracted deep features fused using a voting *** the last step,the authors develop a posterior probabilitybased MFO feature selection algorithm to select the best *** selected features are classified using several supervised learning *** CASIA-B publicly available dataset has been employed for the experimental *** this dataset,the authors selected six angles such as 0°,18°,90°,108°,162°,and 180°and obtained an average accuracy of 96.9%,95.7%,86.8%,90.0%,95.1%,and 99.7%.Results demonstrate comparable improvement in accuracy and significantly minimize the computational time with recent state-of-the-art techniques.

关键词： deep learning feature fusion feature optimization gait classification indoor environment machine learning

来源：评论

学校读者我要写书评

暂无评论

Leveraging Deep Learning for Accurate Detection and Precise Localization of Vertebral Fractures in Medical Imaging 4

Leveraging Deep Learning for Accurate Detection and Precise ...

引用

4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Kumar, M. Lakshmana Sampath, Patchigolla Srinivas, P.V.V.S. Dommeti, Dhiren Nallapati, Siva Ramakrishna Koneru Lakshmaiah Education Foundation Department of Electronics and Communication Engineering India Koneru Lakshmaiah Education Foundation Department of Computer Science and Information Technology India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering India

ISBN: (纸本)9798350300888

Cervical spine fractures present a serious risk to the patient's health and necessitate an early and precise diagnosis for the right course of action. Deep learning techniques have recently demonstrated promising results in the analysis of medical imaging, offering a potential remedy for automated fracture detection. An innovative method for identifying cervical spine fractures using deep learning algorithms is presented in this abstract. Our suggested approach makes use of a Convolutional Neural Network (CNN) architecture that was trained on a sizable dataset of CT scans of the cervical spine (neck) at both the level of a single vertebrae and the entire patient. To ensure that the model can distinguish between fracture patterns and healthy anatomy, the dataset includes both normal and fractured cases. To increase the model's robustness and generalizability, preprocessing methods like image normalization and augmentation are used. To maximize its performance in fracture detection, the CNN is trained using a combination of supervised learning and transfer learning strategies. The experiments are performed on a separate test set made up of a wide variety of cervical spine fracture cases in order to assess the model's performance. The outcomes show how effective our deep learning methodology is at detecting fractures, with a high accuracy rate and a low false-negative rate. Additionally, the model performs better than conventional manual interpretation by radiologists, demonstrating its potential to support clinical judgement. Rapid and automated analysis, reduced human error, and potentially improved patient outcomes are just a few benefits of the proposed deep learning-based cervical spine fracture detection system. Its incorporation into current radiology workflows may increase productivity and permit prompt interventions, which would improve patient care. © 2023 IEEE.

关键词： Medical imaging

来源：评论

学校读者我要写书评

暂无评论

RTPD-YOLO: Reconciling YoLo-V8 Model for Real-Time Potholes Detection

RTPD-YOLO: Reconciling YoLo-V8 Model for Real-Time Potholes ...

引用

2024 IEEE International Conference on Machine Learning and Applied Network Technologies, ICMLANT 2024

作者： Balakrishna, Sivadi Solanki, Vijender Kumar Department of Advanced Computer Science & Engineering Vignan’s Foundation for Science Technology & Research Guntur A.P Vadlamudi India Department of Computer Science & Engineering Stanley College of Engineering and Technology for Women Telangana Hyderabad India

ISBN: (纸本)9798350389180

Indian road privilege systems employ low-quality building materials, making pothole detection and avoidance difficult. Preventing accidents requires quickly identifying and fixing potholes. Roadside potholes may damage cars and endanger drivers. This article introduces a new method for realtime pothole identification called RTPD-YOLOv8. It can identify several forms of potholes, including cracks, oil stains, patches, and stones. To begin processing, pothole videos are first transformed into picture frames. The Ultratics are applied to these frames before processing to lessen distortions. The next step is to use the pre-processed photos to detect the pothole and identify its region using the Sobel edge detector and YOLOv8. Thereafter. The proposed RTPD-YOLO will detect the potholes efficiently. The Simulation results show that Accuracy, Precision, Recall, and F1-score were used to measure the performance of the RTPD-YOLO approaches. A total Accuracy of 99.72% is attained by the RTPD-YOLO. Also, the RTPD-YOLO model gets a 98.86% precision, 94.64 percent recall, and 91.35% F1-score. When compared to other models, like Faster RCNN, SSD, and mask R CNN, the RTPD-YOLO network achieves a higher Accuracy range. © 2024 IEEE.

关键词： Edge detection

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：