检索结果-内蒙古大学图书馆

Performance Analysis of the Machine Learning Algorithms for the Early Detection of Breast Carcinoma 2

Performance Analysis of the Machine Learning Algorithms for ...

2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Vanitha, K. Karpagavalli, S.M. Mahesh, T.R. Ali, A. Althaf Sridhar, T. Anitha, K. Faculty of Engineering Department of Computer Science and Engineering Coimbatore India Al Ameen Engineering College Department of Computer Science and Engg Tamilnadu Erode India Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India Madanapalle Institute of Technology & Science Department of Computer Applications Andhra Pradesh India Alliance University Alliance College of Engineering and Design Bangalore India Csi College of Engineering Department of Information Technology Tamilnadu Ooty India

ISBN: (纸本)9798350360523

Breast Carcinoma, generally known as breast cancer, primarily affects women, though men can develop it as well. Because of the existence of breast tissue and exposure to female hormones, notably oestrogen, women are at a higher risk It's critical to diagnose breast tumors early. Several techniques based on machine learning (ML were used in this study to classify breast cancer using a dataset that was made available to the public. F-score, recall, precision, preciseness, and other performance metrics were used to evaluate these ML algorithms. Previous research and experimental findings indicate that Random Forest achieved the highest accuracy, with a remarkable accuracy rate of 99.12%. © 2024 IEEE.

关键词： Hormones

来源：评论

学校读者我要写书评

暂无评论

A Novel Heart Disease Monitoring and Prediction Using Machine Learning Algorithm 8th

A Novel Heart Disease Monitoring and Prediction Using Machin...

引用

8th International Conference on Internet of Things and Connected Technologies, ICIoTCT 2023

作者： Senbagavalli, M. Karpagalakshmi, R.C. Sumathi, D. Lenin, J. Prasad, G.R.K. Manikandan, A. Department of Computer Science and Information Technology Alliance College of Engineering and Design Alliance University Bangalore India Department of ECE Koneru Lakshmaiah Education Foundation Vaddeswaram India Kattankulathur Chennai603203 India

ISBN: (纸本)9789819757855

Background: Continuous monitoring of patient health statistics becomes a difficult task in hospitals. Manually, it is difficult to monitor the health of the patients in the hospital continuously. Older and unconscious older people in particular need to be monitored regularly, and their relatives want to be informed about their health at every time. We, therefore, propose a revolutionary system that easily computerizes this task. Our device provides an intelligent device for monitoring patient status, which uses sensors to track hospital patients’ health status and informs their relatives in the event of a problem via the Internet. Our system uses temperature, glucose levels and heart rate detection to track health. This task has been proposed to improve the monitoring system by using the Internet of Things (IoT) for hospital applications. Result: The suggested system was developed by MAX30100, LM35, ultrasonic sensor and nodeMCU connected to the Internet. BLYNK IoT Android app has already been used to send the notification via Android Application. The remote healthcare monitoring system is also proposed with cloud service and data analytics as to the aiding features. The readings are captured by mobile phone, which acts as the graphical user interface to get the status of the patient’s health. The implemented hardware results illustrate that the suggested model can continuously observe the physiologic parameters and save lives promptly. Conclusion: ML classification algorithms like Regression tree, SVM, and RF analysis for prediction accuracy of patient health data. The proposed RF algorithm’s simulation results provide minimum prediction error and high accuracy compared with the existing Regression tree and SVM algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ultrasonic sensors

来源：评论

学校读者我要写书评

暂无评论

A Review of Advanced Technologies for Intelligent Sensing, Diagnostics, and Personalized Healthcare Solutions 4

A Review of Advanced Technologies for Intelligent Sensing, D...

引用

4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Ghantasala, G. S. Pradeep Rajesh Sharma, R. Sungheetha, Akey Anitha, P. Selvalakshmi, B. Pradeepa, K. Alliance College of Engineering and Design Alliance University Department of Computer Science and Engineering Karnataka Bengaluru India Sri Sai Renganathan Engineering College Department of Ece Tamil Nadu Coimbatore India Tagore Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India St. Michael College of Engineering & Technology Department of Computer Science and Engineering Tamil Nadu Sivaganga India

ISBN: (纸本)9798331529635

Intelligent Health is a revolutionary approach to modern healthcare that uses the Internet of Things (IoT), artificial intelligence (AI), and sensor technologies to transform patient-centric solutions. This study examines how these technologies are combining to change patient monitoring, illness management, personalized treatment, and healthcare delivery. Wearable sensors, medical equipment, and IoT-enabled infrastructure allow for real-time, continuous monitoring of patient health data, enabling the early identification of anomalies and the implementation of personalized interventions. IoT platforms enable remote connectivity and data-driven insights by easily integrating sensor data into healthcare systems. AI algorithms analyses large amounts of sensor data to maximize clinical decision-making, forecast health outcomes, and extract insightful information. Intelligent health solutions offer many advantages, such as better patient outcomes and a better patient experience. However, to fully utilize these technologies, issues like data privacy, interoperability, and regulatory compliance need to be resolved. Future directions in intelligent health include the establishment of standards and regulatory frameworks, patient-centric design, integration with electronic health records, and advances in AI and machine learning. With individualized, data-driven solutions, intelligent health holds great promise for revolutionizing the delivery of healthcare and enabling people to take charge of their own health and well-being © 2024 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Wireless Health Monitoring for Calculating Vitals Device by Using IoT 2

Wireless Health Monitoring for Calculating Vitals Device by ...

引用

2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Pradeep Ghantasala, G.S. Mahaveer, S. Ghosh, Debopriya Ananthakrishnan, P.S. Masali, Abhishek Basavaraj Singh, Susheel Kumar Alliance University Alliance College of Engineering and Design Department of Computer Science and Engineering Bengaluru India Alliance University Department of Electrical and Electronics Engineering Bengaluru India Heera Lal Yadav Balika Degree College UP Lucknow India

ISBN: (纸本)9798350364699

The fast evolution of the Internet of Things (IoT) has paved the way for innovative healthcare solutions. This paper affords the format and implementation of a wireless health monitoring device the use of IoT technology. The tool continuously tracks critical signs and signs along with coronary heart rate, blood pressure, and oxygen saturation, transmitting real-time facts to healthcare providers. The proposed solution enhances affected man or woman care with the useful resource of the usage of allowing early detection of anomalies and properly timed medical intervention. The paper discusses the architecture, hardware components, software program software integration, and functionality challenges, offering insights into future trends in IoT-enabled healthcare. © 2024 IEEE.

关键词： Blood pressure monitoring Healthcare Heart rate monitoring IoT Patient care Wireless Health Monitoring

来源：评论

学校读者我要写书评

暂无评论

Addressing Challenges and Opportunities in Precision Medicine Using Deep Learning and Personalized Cancer Treatment 3

Addressing Challenges and Opportunities in Precision Medicin...

引用

3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Ghantasala, G. S. Pradeep Kumari, Nalli Vinaya Rajesh Sharma, R. Vidyullatha, Pellakuri Sungheetha, Akey Bathool, Saliha Alliance College of Engineering and Design Alliance University Department of Computer Science and Engineering Bengaluru India Malla Reddy Engineering College for Women Jntuh Department of Computer Science and Engineering Hyderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram Andhra Pradesh Guntur India

ISBN: (纸本)9798331518592

The primary aim of personalized cancer treatment is to increase effectiveness and reduce side effects by tailoring therapies based on a patient's unique genetic and molecular characteristics. This research investigates how deep learning is transforming personalized cancer care through its ability to process and analyze complex, high-dimensional data. The study explores the use of deep learning techniques in predicting tumor classifications, genetic mutations, and treatment responses by integrating clinical data, genomic profiles, and medical imaging. It reviews recent advancements in deep learning applications for cancer, such as targeted therapy through genetic profiling, tumor identification from histopathology images, and optimization of treatment protocols. Additionally, the paper discusses challenges including model transparency, clinical integration, and data security. The study demonstrates how deep learning can enhance patient outcomes and outlines future opportunities for this evolving area of cancer care. © 2024 IEEE.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

Digit Recognition and Separation of Overlapped Digits using Deep CapsNet 2

Digit Recognition and Separation of Overlapped Digits using ...

引用

2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Moudgil, Aditi Sareen, Bhavna Ghantasala, G S Pradeep Rajesh Sharma, R. Sungheetha, Akey Soubache, I.D. Vidyullatha, Pellakuri Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Alliance University Department of Computer Science and Engineering Bengaluru India Alliance University Alliance College of Engineering and Design Department of Computer Science and Engineering Bengaluru India Pondicherry University Department of Eee Pondicherry India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India

ISBN: (纸本)9798350364699

Separation of overlapping digits is observed as a very difficult task when done with human eye, but subsequently it becomes easy when computer vision is used for the same task. The authors have constructed a capsule network which recognizes and separates the overlapped digits. Small sized convolutional kernels were used for the first step of feature extraction which provides grained features this reducing the training parameters. The same process could also possibly be applied for overlapping alphabets and characters also. The process was further followed by insertion of capsule layer into the model which resulted in 93.08% accuracy. Finally the overlapped digits were separated and the image of each digit was reconstructed. © 2024 IEEE.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Optimizing Deep Learning for Brain Pathology Detection Using SqueezeNet with CT Imaging

Optimizing Deep Learning for Brain Pathology Detection Using...

引用

2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Natarajan, Suresh Kumar Ghantasala, G S Pradeep Anthoniraj, S. Vidyullatha, Pellakuri Rajesh Sharma, R. Ananthanagu, U. School of Computer Science and Engineering Ramanagara India Alliance University Alliance College of Engineering and Design Department of Computer Science and Engineering Bengaluru India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram Guntur India

ISBN: (纸本)9798350385199

In this research, a explore the realm of medical imaging, specifically focusing on early brain pathology detection through the lens of deep learning. The primary objective is to optimize SqueezeNet, lightweight convolutional neural network architecture, for brain pathology detection using CT imaging. The motivation stems from the critical need for accurate and swift identification of brain abnormalities, with potential implications for patient outcomes. The meticulously preprocess a dataset of CT images of the human brain to enhance the model's ability to discern subtle pathology patterns. SqueezeNet, known for its efficiency, is employed, and optimization techniques during training enhance its performance. These findings suggest SqueezeNet's potential as a valuable tool in the medical field, offering a balance between efficiency and diagnostic accuracy. Throughout the discussion, A critically analyzes the implications of results, drawing comparisons with existing literature and addressing any encountered limitations. In this research contributes to the discourse on leveraging deep learning for brain pathology detection and the experimental results showcase promising outcomes, with a test accuracy of 78%. Precision, recall, and F1-score metrics indicate the model's proficiency in distinguishing between different pathologies: 87% for Hemorrhagic, 70% for Ischemic, and 79% for Normal cases. Confusion matrices and ROC curves provide comprehensive insights into the model's classification capabilities. The optimized SqueezeNet application demonstrates promise in enhancing diagnostic capabilities, with the obtained results providing valuable insights for future advancements in the intersection of deep learning and medical imaging, potentially impacting patient care and outcomes positively. © 2024 IEEE.

关键词： Medical imaging

来源：评论

学校读者我要写书评

暂无评论

Smart Measuring Device for Power with Digital Debiting System Using Mobile Application Tool 4

Smart Measuring Device for Power with Digital Debiting Syste...

引用

4th International Conference on Innovative Practices in technology and Management, ICIPTM 2024

作者： Rajesh Sharma, R. Gandhi, Rajiv Sungheetha, Akey Chinnaiyan, R. Deepaletchumi, N. Alliance University Alliance College of Engineering and Design Department of Cse Bangalore India Alagappa University Model Constituent College India Alagappa University Department of Computer Science Karaikudi India

ISBN: (纸本)9798350307757

Recently, it has been discovered that the electricity department's significant income loss is a result of the metering and billing system. At every level of the billing process, errors can occur due to malfunctioning meters, processing faults when processing bills, or human error when recording meter readings. According to a few study estimates, 40almost one-fourth of the electricity produced is either stolen or lost during transmission. A smart system for measuring electricity can solve these issues. designing a smart electricity meter with an Android-based payment system is the goal. We suggested a wireless IOT- based system for smart electricity meters and billing (Internet of Things). In line with the IOT idea, we also used relays to cut off an unpaid user's power supply, which was controlled wirelessly. Messages will be sent to users via cloud notification once reading has automatically occurred © 2024 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Detection and Classification of Diabetic Foot Ulcers via Cloud-Based Deep Learning Approach 3

Detection and Classification of Diabetic Foot Ulcers via Clo...

引用

3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Sengottaiyan, N. Eswaran, Malathi Sungheetha, Akey Rajesh Sharma, R. Pravin, A.L. Ghantasala, G.S. Pradeep Alliance University Department of Computer Sciences and Engineering Bangalore India Kongu Engineering College Department of Computer Sciences and Engineering Erode India Kongu Engineering College Department of Computer Technology - Pg Erode India

ISBN: (纸本)9798331518592

The study 'Mobile and Cloud-Based Detection of Diabetic Foot Ulcers and Their Classification Using Deep Learning Frameworks' addresses the demanding situations of Diabetic Foot Ulcers (DFUs), which could result in extreme headaches for people with diabetes. The present machine employs a cloud-primarily based, totally deep convolutional neural network (CNN) to investigate character foot snap shots dispatched through a cellular app for ulcer detection and next classification. The proposed framework complements this with the aid of permitting a couple of foot snap shots according to examination, facilitating the targeted categorization and segmentation of DFU types, infections, and ischemia Advanced deep studying strategies, including CNN s and Siamese Neural Networks (SNN s), are applied for analysis and similarity comparison. The intention is to streamline DFU detection and classification, facilitating early intervention and personalized management. By integrating cellular generation and cloud-primarily based totally deep studying, the studies pursuits enhance results for diabetic sufferers at the same time as doubtlessly lowering healthcare burdens. © 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

Efficient Bit-Plane Based Medical Image Cryptosystem Using Novel and Robust Sine-Cosine Chaotic Map

引用

computers, Materials & Continua 2025年第4期83卷 917-933页

作者： Zeric Tabekoueng Njitacke Louai A.Maghrabi Musheer Ahmad Turki Althaqafi Department of Electrical and Electronic Engineering College of Technology(COT)University of BueaBueaP.O.Box 63Cameroon Department of Software Engineering College of EngineeringUniversity of Business and TechnologyJeddah22246Saudi Arabia Department of Computer Engineering Jamia Millia IslamiaNew Delhi110025India Department of Computer Science School of EngineeringComputing and DesignDar Al-Hekma UniversityJeddah22246Saudi Arabia

This paper presents a high-security medical image encryption method that leverages a novel and robust sine-cosine *** map demonstrates remarkable chaotic dynamics over a wide range of *** employ nonlinear analytical tools to thoroughly investigate the dynamics of the chaotic map,which allows us to select optimal parameter configurations for the encryption *** findings indicate that the proposed sine-cosine map is capable of generating a rich variety of chaotic attractors,an essential characteristic for effective *** encryption technique is based on bit-plane decomposition,wherein a plain image is divided into distinct bit *** planes are organized into two matrices:one containing the most significant bit planes and the other housing the least significant *** subsequent phases of chaotic confusion and diffusion utilize these matrices to enhance *** auxiliary matrix is then generated,comprising the combined bit planes that yield the final encrypted *** results demonstrate that our proposed technique achieves a commendable level of security for safeguarding sensitive patient information in medical *** a result,image quality is evaluated using the Structural Similarity Index(SSIM),yielding values close to zero for encrypted images and approaching one for decrypted ***,the entropy values of the encrypted images are near 8,with a Number of Pixel Change Rate(NPCR)and Unified Average Change Intensity(UACI)exceeding 99.50%and 33%,***,quantitative assessments of occlusion attacks,along with comparisons to leading algorithms,validate the integrity and efficacy of our medical image encryption approach.

关键词： Image cryptosystem robust chaos sine-cosine map nonlinear analysis tools medical images

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：