检索结果-内蒙古大学图书馆

1st International Conference on Recent Advances in Information Technology for Sustainable Development, ICRAIS 2023

作者： Ankitha, K. Venugopala, P.S. Kunder, Harish Shetty, Ankitha NMAMIT Nitte Dept. of Artificial Intelligence and Data Science Karkala India Alvas Institute of Engineering and Technology Dept. of Artificial Intelligence and Machine Learning Moodbidri India

ISBN: (纸本)9798350306637

The Earth serves as a shared habitat not just for humans but also for an array of animal species and organisms. Throughout history, animals have played a crucial role in preserving the natural ecosystem balance and meeting diverse human needs. They contribute significantly to various sectors, such as Agriculture, Dairy, Poultry, and animal husbandry, thus bolstering a country's economy. Human responsibility lies in ensuring the well-being of these animals, which is overseen by Veterinary Science. In India, a major milk and milk product producer, the dairy industry actively contributes to the nation's economy. Among the primary milk sources-cows, buffaloes, goats, and sheep-cows hold a revered place in different faiths. Particularly in the dairy industry, cows are vital for milk production. However, monitoring cow health poses challenges. Clinical Mastitis, an inflammation affecting cows' mammary glands, hampers milk production. Automatic milking machines are commonly used, but identifying and segregating mastitis-infected cows from healthy ones remains difficult. The contagious nature of the disease increases the risk of widespread infection among cows. To combat this issue, we propose a solution called the Internet of Animal Health Things (IoAHT), based on the Internet of Things (IoT). IoAHT employs IoT technology for animal health, aiming to accurately identify cases of clinical mastitis in cows. The implementation of IoAHT has the potential to revolutionize health monitoring in the dairy industry, safeguarding cow well-being and ensuring sustainable milk production. © 2023 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Survey on Classification of Skin Diseases Using machine learning Techniques 3

Survey on Classification of Skin Diseases Using Machine Lear...

引用

3rd International Conference on Power Electronics and IoT Applications in Renewable Energy and its Control, PARC 2024

作者： Nirupama Virupakshappa Sharnbasva University KLB Department of Artificial Intelligence and Machine Learning Kalaburagi India Sharnbasva University KLB Department of Computer Science and Engineering Kalaburagi India

ISBN: (纸本)9798350309409

Skin is our body's most important organ;skin disorders are currently a common and serious problem due to patients' sensitive skin, as well as physical and psychological consequences. Skin disease symptoms might differ greatly from person to person and may be permanent or temporary, severe or painless. Skin disease symptoms include clogging, inflammation, rashes, irritation, and other changes in skin appearance. Early detection of skin disorders is ideal because it reduces the patient's actual expenses while additionally preventing illnesses from developing and spreading. In recent years, artificial intelligence (AI) has been used to diagnose skin disorders using machine learning algorithms and a large amount of different skin data stored in database. Recent studies on skin disease identification and classification using predictive algorithms, mechanisms, and specific systems resulted in higher accuracy. machine learning will play an important and promising role in real-time diagnosis of dermatological diseases in the future. © 2024 IEEE.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

An Analysis and Detection of Depression from Textual Data using Deep learning Methods 2

An Analysis and Detection of Depression from Textual Data us...

引用

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

作者： Chaithra, I.V. Sunitha, M.R. Department of Computer Science Engineering Adichunchanagiri Institute of Technology Chikkamagaluru India Department of Artificial Intelligence and Machine Learning Adichunchanagiri Institute of Technology Chikkamagaluru India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383867

Depression is a prevalent sickness, spreading worldwide with potentially serious implications. Timely recognition of emotional responses plays a pivotal function at present. Mental ill health is highly risky, stirring more than 300 million population. Because of this reason more focus of research is on this area. A statement by American Psychiatric Association "6.715 adults (6.7%) is get affected in any given year. One-sixth of people(16.6%) will experience depression in their life." Studies said that Women are in greater proportion to likely experience depression when compared to men. One among three women will share a significant depressive symptoms in their lifetime. Written text reflects its impact on language usage. The count of afflicted individuals can be lessened, using an advanced machine learning techniques and the availability of test data related to depression. This paper proposes different preprocessing techniques. Feature extraction techniques such as BoW(Bag of Words), Word2Vec, Glove, N-Gram, LIWC and LDA. Classification techniques such as Naive Bayes, SVM, LR, Adaboost and RF. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

DROPEX: Disaster Rescue Operations and Probing using EXpert drones 8

DROPEX: Disaster Rescue Operations and Probing using EXpert ...

引用

8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Kannan, Kausthub Awati, Aditya N. Rao, Smruthi S. Malagi, Vindhya P. Dayananda Sagar College of Engineering Department of Computer Science And Design Bangalore India Dayananda Sagar College of Engineering Department of Artificial Intelligence and Machine Learning Bangalore India

ISBN: (纸本)9798331505462

Disasters, both natural and man-made, pose significant risks to human life and infrastructure, necessitating swift and efficient search and rescue (SAR) operations. Traditional SAR methods often struggle to access hazardous areas, resulting in delayed responses and increased risk to rescuers. These methods are unable to run automated simultaneous rescues which lead the operations to face challenges such as difficulty in quickly assessing damage, locating survivors, and delivering aid. This paper address the problem by proposing a autonomous swarm of drones framework which improves the response time as well as increases accessibility zone of the rescue operation. The proposed framework DROPEX, is an autonomous UAV (Unmanned Aerial Vehicle) which employs a dual-dome architecture with surveillance drones to detect individuals in distress and payload drones to deliver aid. This framework ensures rapid deployment, accurate navigation, and efficient data transmission in disaster-stricken areas while minimizing the need for manual intervention. The drones are able to recognise the victims in need using object detection models such as YOLO and Detection Transformer (DETR) with thermal vision. By using Long Range Wide Area Network (LoRaWAN) and object detection models, the drones are able to reduce the response time and increase accessibility. The main focus is on creating a robust, scalable, and economical system to enhance the speed, efficiency, and effectiveness of disaster rescue operations. © 2024 IEEE.

关键词： Drones

来源：评论

学校读者我要写书评

暂无评论

DETECTION OF PULMONARY EMBOLISM WITH IMPROVED XCEPTION ARCHITECTURE 10

DETECTION OF PULMONARY EMBOLISM WITH IMPROVED XCEPTION ARCHI...

引用

10th International Conference on AI and the Digital Economy, CADE 2024

作者： Paulraj, Getzi Jeba Leelipushpam Princess, P. Joyce Beryl Jebadurai, Immanuel Johnraja Celshia, S. Division of Computer Science and Engineering Karunya Institute of Technology and Sciences India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences India

ISBN: (纸本)9781837241880

The blocking of arteries in the lungs through blood clots is known as pulmonary embolism. This blood vessel block usually originates in the legs and travels to the lungs causing fatal damage. An imaging test called Computed Tomography (CT) pulmonary angiography scans the lungs for blood clots. The preferred method for identifying pulmonary embolism is CT pulmonary angiography. Manual examination by radiologists increases the workload, consumes more time and also requires experience for correct examination. The objective of this paper is to propose an improved Xception architecture based on Convolutional Neural Networks. The existing architecture is improvised by replacing the Relu activation function with the Swish activation function. This improves the feature extraction of the algorithm thereby improving efficiency in terms of loss and accuracy. The algorithm has been trained and tested using the standard RSNA-STR Pulmonary Embolism CT Dataset. It is evident from the experimental results that our model achieves an improvement of 3.4% and 1.4% in terms of accuracy and loss respectively. © The Institution of engineering & Technology 2024.

关键词： Blood vessels

来源：评论

学校读者我要写书评

暂无评论

From Clustering to Intelligent Decision Support System: An Application to 3D Printing 26

From Clustering to Intelligent Decision Support System: An A...

引用

26th International Conference of the Catalan Association for artificial intelligence, CCIA 2024

作者： Karna, Ashutosh Gibert, Karina Knowledge Engineering and Machine Learning Group Intelligent Data Science and Artificial Intelligence Research Centre Universitat Politècnica de Catalunya Barcelona Spain

ISBN: (纸本)9781643685434

This study focuses on developing an intelligent decision support system (IDSS) that helps a human operator make data-driven decisions. To put IDSS in production, it is necessary to develop two additional components: one oriented to recognize the cluster of new data and the other a knowledge-based resulting from the interpretation of clusters and further association of actions to each cluster, constituting a knowledge base with the alerts and recommendations associated to every profile. Bootstrap-CURE technique is used to handle the initial component, whereas a meta-clustering framework is suggested for interpreting the clusters and providing recommendations. A detailed strategy is presented for handling a print job, examining patterns, and executing actions through IDSS, thus improving predictive accuracy and operational efficiency. Two distinct machine learning models were developed, one to detect the operational mode and another to choose the best meta-cluster for the type of printing jobs and detained steps are provided for implementing the recommendations. © 2024 The Authors.

关键词： Printing presses

来源：评论

学校读者我要写书评

暂无评论

Innovative Solutions for Cervical Cancer: Deep learning Models for Accurate Stage Prediction 2

Innovative Solutions for Cervical Cancer: Deep Learning Mode...

引用

2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Yadav, Roshan Kumar Kumar, Divyam Kadam, Kalyani Devi, Seeta Gangarde, Rupali Department of Artificial Intelligence and Machine Learning Pune India Department of Obstetrics and Gynecological Nursing Pune India Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798350383652

Cervical cancer, caused by the skin infection of the Human papillomavirus (HPV), ranked as the fourth most prevalent cancer globally, has profound and sometimes fatal implications for women. With approximately 500,000 new cases reported annually, the disease significantly impacts women's overall well-being. Ongoing efforts by researchers focus on developing a more cost-effective solution than regular HPV testing to manage and control this widespread health concern. Our research included an innovative framework for identifying and classifying stages of cervical cancer by processing the images of the tumours. To optimize the performance of our model, we developed a unique approach for selecting the best image data points by calculating a score, which is defined as the product of focus, sharpness, and resolution. Additionally, our study distinguishes itself by integrating three diverse feature extraction techniques-Local Binary Pattern (LBP), ResNet, and VGGNet-an inclusive strategy not commonly observed in similar projects. We even added deep convolutional architectures and machine learning techniques, including Random Forest, SVM, Decision Tree, XGBoost, a basic Neural Network Classifier, and a Multi-layered Perceptron (MLP). This hybrid approach allowed for an intrinsic comparison, identifying the most effective strategy among these. In contrast to current state-of-the-art methods, our approach yielded around 75% accuracy and 81% AUC score, which is greater than the existing studies on such dataset. © 2024 IEEE.

关键词： Contrastive learning

来源：评论

学校读者我要写书评

暂无评论

Transformative Trends in Diabetes Care: An Analysis of AI-Enhanced Predictive Healthcare 2

Transformative Trends in Diabetes Care: An Analysis of AI-En...

引用

2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Milmile, Atharva Verma, Prateek Datta Meghe Institute of Higher Education and Research Du Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798331528713

This article examines the ever-changing environment of artificial intelligence (AI) in diabetes treatment. It investigates its existing uses, future developments, and the complex interaction of technology innovation, regulatory concerns, and ethical imperatives. AI is transforming diabetes management, from predictive analytics to tailored treatment regimens, by providing real-time information, increasing patient empowerment, and contributing to proactive, individualized care. The developing legal framework and emerging standards work to assure responsible AI technology development and deployment, addressing algorithmic biases and data privacy issues. The study forecasts future developments in the industry, imagining a collaborative future in which AI, seamlessly linked with other technology, augments healthcare professionals' competence and empowers people with diabetes. This investigation is guided by a synthesis of technological and ethical issues, highlighting the importance of inclusion, accessibility, and a harmonious human-AI relationship to advance the future of diabetes treatment toward better results and greater patient well-being © 2024 IEEE.

关键词： artificial intelligence Diabetes machine learning Predictive Healthcare

来源：评论

学校读者我要写书评

暂无评论

A Secure and Efficient Framework for Storing Medical Data using Blockchain and Cloud Servers 8

A Secure and Efficient Framework for Storing Medical Data us...

引用

8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Lakshmanan, M. Mala, G.S. Anandha Sivakumar, G. Divya Sundar, V.S. Kesavan, Rupa Ajoe Sweetlin Jeena, A. Dayananda Sagar University Department of Artificial Intelligence and Machine Learning Bangalore India Easwari Engineering College Anna Univeristy Department of Computer Science and Engineering Chennai India Gnanamani College of Technology Anna University Department of Artificial Intelligence and Machine Learning India Anna Univeristy Saveetha Engineering College Chennai India Sri Venkateswara College of Engineering Computer Science and Engineering Sriperumbudur India

ISBN: (纸本)9798350367904

The digitization of Electronic Health Records (EHRs) has brought a revolution in health care delivery and even practices in reporting key organizational HR processes. However, these advancements have their merits and demerits particularly in regard to data security, the speed of data retrieval, and how data transactions are performed. This paper presents a solution to this problem in the medical world. It presents a unique architecture that connects cloud servers with Blockchain technology for better and secure medical databases. Using Merkle trees to protect health information and advanced heuristic algorithms to optimize operations, the system deals with main issues of transaction time, retrieval rate, and data privacy. The method is tested on a realized medical database and shows that the performance indicators increased substantially. It would seem that Blockchain assures that data cannot be altered once uploaded, while cloud servers provide flexibility to users and allow faster data retrieval. Proposed system improves the transaction time by 30 percent and the retrieval rate by 40 percent compared with traditional methods and supports strategic management of EHR systems. © 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

Finding the transcription factor binding locations using novel algorithm segmentation to filtration (S2F)

引用

Journal of Ambient intelligence and Humanized Computing 2024年第9期15卷 3347-3358页

作者： Theepalakshmi, P. Srinivasulu Reddy, U. Department of Computer Science and Engineering Gandhi Institute of Technology and Management Karnataka Bengaluru India Machine Learning and Data Analytics Lab Center of Excellence in Artificial Intelligence Department of Computer Applications National Institute of Technology Tamilnadu Tiruchirappalli India

The primary aim of identifying the binding motifs in gene regulation is to understand the transcriptional regulation molecular mechanism systematically. In this study, the (, d) motif search issue was considered which entails finding the length motifs which differ by at most d substitutions. However, identifying the high-quality pattern (, d) is challenging. It is intended to address the above problem with motif discovery and handle it using the proposed algorithm S2F (Segmentation to Filtration) based on the qPMS (quorum Planted Motif Search) algorithm model. From the entire DNA sequences, five percent are chosen at random to be used in the motif discovery process. This random sub segment (subseg) portion is split up into base, sub k-mers, and its sizes (motif length ()) are determined by the iterative approach. Corresponding to the sizes of and d (mutations), the k-mers are chosen which participated in filtration techniques and the base k-mer count and frequency are updated. The highest frequency of k-mer is recognized as the motif. The algorithm’s performance was evaluated using the two real datasets Escherichia coli cyclic AMP receptor protein (CRP) and mouse Embryonic Stem Cell (mESC) ChIP-seq (Chromatin Immuno Precipitation) dataset. Results from the experiments show that S2F can identify the motifs and appear faster compared to previous state-of-the-art PMS (Planted Motif Search) and qPMS algorithms. Graphical Abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： DNA sequences

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：