检索结果-内蒙古大学图书馆

1st International Conference on Computation of Artificial Intelligence and Machine Learning, ICCAIML 2024

作者： Kamath, Ritesh V. Kedari, Bhavana Girisha, S. Savitha, G. Shreesha, S. Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9783031714832

Glaucoma, a progressive visual disease, damages the optic nerve, potentially resulting in irreversible vision loss. Effective treatment plans and prompt diagnosis are essential for positive patient outcomes. The principal glaucoma-related indication typically revolves around an odd ratio between the diameters of the optic cup and disc. In this regard, fundus images are analyzed for diagnosis of glaucoma. However, image quality, similar characteristics across classes, class imbalance issues, uneven shapes of optic disc and curves, and other factors make correct segmentation of optic disc and curve difficult from fundus images. To address these issues, the work proposes an enhanced Residual U-Net architecture capable of semantically segmenting the optic disc and cup from retinal fundus images. The proposed model has an Attention Module that can effectively extract the necessary feature map for segmentation from the input images. This improves the model’s capacity to pick out important features and eliminate extraneous noise. Using a publicly available dataset, the study analyses the efficacy of the proposed model using both quantitative and qualitative measures. The effectiveness of the model is examined in relation to various loss functions. To emphasize the significance of the suggested model, a comparative analysis between the proposed and the traditional model is also carried out. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Patient treatment

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Driven - Pest Classification and Sustainable Agriculture 3

Deep Learning Driven - Pest Classification and Sustainable A...

引用

3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kumar, M. Sirish Usha, R. Reddy, B. Rama Kantha Ramachandra, S. Prakash, P. Jaya Swetha, B. Mohan Babu University School of Computing Dept. of Data Science India Madanapalle Institute of Technology and Science Madanapalle India Sri Venkateswara College of Engineering Tirupati India Mother Theresa Institute of Engineering and Technology Palamaner India Sri Venkateswara College of Engineering Department of It Tirupati India Mohan Babu University Dept. of Master of Computer Applications India

ISBN: (纸本)9798331509675

This study introduces a system designed to identify pests in crops and classify them as either beneficial or harmful. The project begins by providing a comprehensive overview of various pest identification methods, analyzing their strengths and limitations. To address the problem, a categorization approach is proposed that incorporates convolutional neural networks (CNN) to build the pest identification and classification model. The model was trained using a dataset containing images of different pest species. Extensive testing was conducted on a large dataset, and the system's performance was validated against multiple traditional classification models. The proposed framework achieved a classification accuracy of 90%, outperforming conventional methods. This research highlights agriculture's vital role in the economy and underscores the potential of advanced technologies in supporting sustainable farming practices. © 2025 IEEE.

关键词： Agriculture Sustainability Classification Accuracy Classification Model Convolutional Neural Network (CNN) Pest Classification

来源：评论

学校读者我要写书评

暂无评论

A Federated Learning-Based Crop Yield Prediction for Agricultural Production Risk Management

A Federated Learning-Based Crop Yield Prediction for Agricul...

引用

2022 IEEE Delhi Section Conference, DELCON 2022

作者： Manoj, T. Makkithaya, Krishnamoorthi Narendra, V.G. Manipal Institute of Technology Manipal Academy of Higher Education Dept. of Computer Science and Engg. Manipal India

ISBN: (纸本)9781665458832

A spiralling global population and changing dietary needs have scaled up the demand for food and raw materials supplied to the industry. The agricultural production is struggling to keep up the level of required crop yields due to numerous risks affecting the yield. The past two decades have witnessed the increased intensity of agricultural production risks due to challenges posed by climate changes. There is a dire need to address it with proper insights into the data attributes impacting the crop yield. Currently, many of the machine learning and deep learning methods focus on training the model using the data collected and stored in a centralized data repository. However, many attributes related to weather data, soil data and crop management data are scattered and siloed to particular organization servers or smart farming devices. In this study, we are proposing a federated learning method for training yield prediction models on a horizontally distributed dataset located on different client devices. In particular, federated averaging algorithm is used to train the deep residual network based regression models such as ResNet-16 and ResNet-28 for soybean yield prediction in a decentralized setting and compare its performance with deep learning and machine learning methods. The results from experimented learning models show that federated averaging using ResNet-16 regression model with Adam optimizer yielded optimal results compared to centralized learning models and can be easily deployed for yield prediction in a federated setting. © 2022 IEEE.

关键词： Risk management

来源：评论

学校读者我要写书评

暂无评论

Lateralizing Seizure Onset Zone Through Interictal Sleep Scalp EEG High-Frequency Oscillations

Lateralizing Seizure Onset Zone Through Interictal Sleep Sca...

引用

International Joint Conference on computer science and Software Engineering (JCSSE)

作者： Dhruva P Achar Karunakar A Kotegar Kurupath Radhakrishnan Dept. of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India Department of Neurosciences AVITIS Institute of Medical Sciences Nemmara Palakkad Kerala India

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

Epilepsy, a neurological disorder impacting an estimated 50 million people globally, sees about 30% of patients developing drug-resistant epilepsy (DRE). For these patients, surgery emerges as a more promising option for achieving seizure freedom. The precise delineation of the Epileptogenic Zone (EZ) is crucial for considering a patient as a surgical candidate, with the Seizure Onset Zone (SOZ) considered the most reliable estimate of the EZ. In this retrospective study, we analysed interictal sleep electroencephalography (EEG) data from 5 patients to evaluate the rate of High-Frequency Oscillations (HFOs) in the 80-250 Hz range. Our objective was to explore whether regions exhibiting a higher incidence of HFO rates correlated with those diagnosed as the SOZ based on patient reports. Additionally, we computed the asymmetry index to examine whether the heightened HFO rate lateralizes to the hemisphere where the seizure occurs. Our results revealed a precise correlation between elevated HFO rates in a region and the specified SOZ as per the report in 4 out of 5 patients. Furthermore, the asymmetry index consistently lateralized to the same hemisphere causing seizure as specified in the report a cross all 5 patients. This implies that the analysis of HFOs can provide valuable assistance to clinicians in accurately identifying the hemisphere responsible for seizures.

关键词： Correlation Sleep Scalp Epilepsy Surgery Hafnium oxide Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

ECC-based Authenticated Key-Agreement Algorithm using Time-stamps for IoD networks 6

ECC-based Authenticated Key-Agreement Algorithm using Time-s...

引用

6th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2022

作者： Samanth, Snehal Prema, V. Balachandra, Mamatha Manipal Institute of Technology Manipal Academy of Higher Education Dept. of Computer Science & Engg. Manipal India

ISBN: (数字)9781665487160

ISBN: (纸本)9781665487160

Internet of Things (IoT) networks have advanced and have made the lives of humans much easier, during the past few decades. Raspberry Pi (RP) is a type of IoT device with memory constraints. Both military and civilian applications have used drones or Unmanned Aerial Vehicles (UAVs) over the past several decades. Internet of Drones (IoD) networks are subsets of IoT networks. Drones are resource-constrained devices. Moreover, IoD networks are vulnerable to different security attacks. Hence, 2 RP 3B+ boards are used for the network model to be treated like a drone and a Ground Control Station (GCS) respectively. Moreover, an Authenticated Key-Agreement (AKA) algorithm is designed based on Elliptic Curve Cryptography (ECC). The proposed algorithm has been designed using Python programming language, and the performance metrics analysis is done using Jupyter-Notebook. An idea of integration of timestamps and trigonometric concepts has been introduced to improve the security of the designed ECC-based AKA algorithm. The designed AKA algorithm provides different properties in terms of security, and can also resist some known attacks, as shown by the algorithm's security analysis. The proposed AKA algorithm's analysis in terms of performance shows that it outperforms 3 recent related security mechanisms in terms of performance metrics like Total Computation Cost, Total Storage Cost, and Total Communication Cost. © 2022 IEEE.

关键词： Authentication

来源：评论

学校读者我要写书评

暂无评论

Role of IoT in Smart Cities: A Review, applications, Open Challenges and Solutions 3

Role of IoT in Smart Cities: A Review, Applications, Open Ch...

引用

3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Vivek, Kolla Swetha, Gurrala Kumar M, V. T. Ram Pavan Veni, Anusuri Krishna Praveen, Neti Kale, Masthan Rao Dept. of Cse Andhra Pradesh Ongole India Dept. of Cse Telangana India Kakaraparti Bhavanarayana College Dept. of Computer Science Andhra Pradesh Vijayawada India Madanapalle Institute of Technology & Science Dept. of Computer Science & Engineering - Data Science Andhra Pradesh Madanapalle517325 India Srkr Engineering College Dept. of Computer Science and Design Andhra Pradesh Bhimavaram India Dept. of Cse Andhra Pradesh Ongole India

ISBN: (纸本)9798331509675

Smart cities have emerged as a response to the fast expansion of metropolitan areas;these places strive to improve the lives of their residents while simultaneously promoting environmentally responsible growth. To maximize urban services, the Internet of Things (IoT) is an essential technology that allows for the seamless integration of sensors, devices, and systems. This allows smart cities to become a reality. With an emphasis on smart transportation, energy management, healthcare, environmental monitoring, and public safety, this paper explores the critical role of IoT in smart cities. data security, privacy, scalability, and interoperability are some of the problems that arise when smart cities use IoT, despite the revolutionary potential it has. These obstacles are thoroughly examined in this work, along with possible solutions to these problems. This study gives a thorough overview of the IoT and its impact on smart city development, innovation, and urban resilience by focusing on the present situation, applications, and unanswered questions. © 2025 IEEE.

关键词： Digital technology Drainage Monitoring Fire detection Smart City Waste Management

来源：评论

学校读者我要写书评

暂无评论

Machine Learning-Enhanced Self-Management for Energy-Effective and Secure Statistics Assortment in Unattended WSNs

引用

SN computer science 2025年第2期6卷 1-10页

作者： Biswas, Preesat Mishra, Aishwarya Dixit, Rinku Sharma Dwivedi, Abhishek Choudhary, Shailee Lohmor Tiwari, Shweta Chakravarthi, M. Ayyappa Electronics & Telecommunications Government Engineering College C. G Jagdalpur India Department of Computer Science & Engineering IES College of Technology M.P Bhopal India Department of Artificial Intelligence and Machine Learning New Delhi Institute of Management Delhi India Department of Data Science and Engineering School of AIML IoT & IS CCE DS and Computer Applications Manipal University Jaipur Jaipur Rajasthan India Department of Computer Science and Engineering Gyan Ganga Institute of Technology and Sciences M.P Jabalpur India Department of CSE-Data Science KKR and KSR Institute of Technology and Sciences Guntur India

Unattended Wireless Sensor Networks (UWSNs) operate without human supervision and have limited resources. In a conventional WSN with a fixed sink for data collection, nodes one hop away from the sink consume more energy, causing a hotspot issue. This leads to network fragmentation and reduced sensing efficiency. To address this, a mobile sink is proposed. This study details the development and implementation of Secure MRL, focusing on legitimate message communication to the mobile sink via convex nodes. By using these convex nodes as data collection points, the mobile sink avoids traversing the entire network. We employ an enhanced system to generate keys and authenticate messages, assessing its resilience against various attacks, including sleep deprivation, snooze, network substitution, and modification. Findings indicate that Secure MRL maintains consistent performance across different node counts and significantly reduces energy consumption by 84%, improving the speed of attack detection. This improvement is achieved by monitoring the digital signature and residual energy of nodes. The proposed SVM-based network training establishes a residual energy cut-off, while convex nodes mitigate synchronization issues among other sensor nodes in Secure MRL. Additionally, convex nodes manage the availability checks for the mobile sink. The proposed approach demonstrates a 92% improvement in packet delivery ratio (PDR) and an 89% reduction in end-to-end delay, highlighting its efficacy in enhancing network performance. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Duty-cycled UWSN MRL Sec-MRL SEC-TMP SVM UWSN

来源：评论

学校读者我要写书评

暂无评论

Enhanced Building Prediction for Smart City Surveillance 1

Enhanced Building Prediction for Smart City Surveillance

引用

1st IEEE International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Natarajan, Yuvaraj Wadhwa, Gitanjali Saravanan, Lekshmipriya Natarajan, Mathivathani KPR Institute of Engineering and Technology Dept. of Computer Science and Engineering Coimbatore641407 India KPR Institute of Engineering and Technology Dept. of Artificial Intelligence and Data Science Coimbatore641407 India

ISBN: (纸本)9798350348057

Nearly three-quarters of the total power used in the world is consumed by the built environment sector. Therefore, looking for viable methods to reduce building energy needs and decrease negative environmental repercussions is vital. By incorporating intelligence into applications, everyone in the current situation has become intelligent, which lessens the load of constant interruption or human control. By using Machine to Machine interactions, intelligence enables the ability to connect more real-time parameters and make moral judgments in perilous circumstances. The current study examined the framework, approach, and performance of currently available AI-based techniques. According to the research, choosing the best machine and deep learning model for a given task can be difficult. Among the primary areas of study in relation to the vast amounts of data collected is the viability in the practice of the used algorithms, both standard machine learning and the more recent branch known as deep learning. Present a quantitative analysis contrasting the effectiveness of conventional deep learning and machine learning strategies using the direction of human movement in detecting the applications based on the analogue pyroelectric infrared sensor (PIR) outputs. Here, Random Forest is used as the machine learning method because of its great prediction accuracy. Over deeper datasets, deeper CNNs outperform shallow models as a deep learning method. The study demonstrates that traditional Machine Learning performs with detecting values within the first few seconds, improving real-time detection. Additionally, it might be used on more smart buildings without upgrades. © 2023 IEEE.

关键词： Intelligent buildings

来源：评论

学校读者我要写书评

暂无评论

Image To Text Description Platform For Yakshagana Images By Detecting Crown Using CNN

Image To Text Description Platform For Yakshagana Images By ...

引用

Artificial Intelligence and data Engineering (AIDE), International Conference on

作者： Kiranraj M Anantha Murthy Manish Prathwini Prathwini Savitha G Harshitha G M Department of Master of Computer Applications NMAM Institute of Technology NITTE (Deemed to be University) Nitte Karnataka India Department of Master of Computer Applications NMAM Institute of Technology NITTE (Deemed to be University) Nitte Karkala India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

This research called "Yakshakatha – Image to Text Platform for Yakshagana Images." It employs deep learning to classify crowns in Yakshagana Kirita using CNN classification. Yakshagana is a traditional South Indian art form, well known for its intricate costumes and headgear, posing specific challenges in identifying and classifying crowns. The system aims to classify various Kirita styles and provide detailed descriptions of the respective Yakshagana characters, or Vesha, offering valuable cultural and symbolic insights. The model was trained on a self-labelled dataset of Yakshagana images and used convolutional and pooling layers to capture fine details in crown designs. It achieved an impressive accuracy of 99%, ensuring precise identification and classification. Real-time predictions for the input images are made, which have predetermined mappings and description content for recognized characters. The system employs Google Gemini AI, in order to enrich the text description of the image by integrating cultural background. The responsive interface is provided for users on the image upload, real-time result, and enriched visual feedback using annotated output. This project combines modern image recognition with rich cultural content to preserve and promote the traditional heritage of Yakshagana. It will help the users to understand and appreciate the vibrancy of this art form.

关键词： Deep learning Visualization Art Image recognition Accuracy Real-time systems Robustness Cultural differences Convolutional neural networks Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Sequential Deep Learning Model Development for Battery Remaining Useful Life Forecasting and Anomaly Detection

Sequential Deep Learning Model Development for Battery Remai...

引用

IEEE Conference on Industrial Electronics and applications (ICIEA)

作者： Manohara MM Pai Radhika M Pai Pragya Gupta Ruthvik Avadhanam Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (数字)9798350360868

ISBN: (纸本)9798350360875

Lithium-ion batteries have become indispensable in various energy storage applications, powering a wide array of devices. Anomaly detection and remaining useful life forecasting are critical tasks in battery management for predictive maintenance and reliability testing. An integrated approach that combines both remaining useful life forecasting and outlier detection is proposed, by monitoring the deviation between prediction and ground truth. This approach is validated using real-world CALCE data and augmented datasets generated from it. First, the capacity degradation of the battery is predicted, then an anomaly is detected if the error crosses a predefined threshold. The models employed achieve high accuracy, forecasting errors limited to 1%, with minimal false positives. This establishes their reliability for practical deployment and makes them comparable to state-of-the-art approaches.

关键词： Training Accuracy Protocols Predictive models Real-time systems Reliability Forecasting

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：