检索结果-内蒙古大学图书馆

2025 International Conference on Ambient Intelligence in Health Care, ICAIHC 2025

作者： Tripathy, Nrusingha Hota, Sarbeswara Satapathy, Pranati Nayak, Subrat Kumar Nayak, Debasish Swapnesh Kumar Nayak, Purnya Prava Siksha 'O' Anusandhan Deemed to be University Department of Computer Science and Engineering Odisha Bhubaneswar India Siksha 'O' Anusandhan Deemed to be University Department of Computer Application Odisha Bhubaneswar India Utkal University Department of Imca Odisha Bhubaneswar India Centurion University of Technology and Management Department of Computer Science and Engineering Odisha Bhubaneswar India Giet Department of Computer Science and Engineering Baniatangi Odisha Bhubaneswar India

ISBN: (纸本)9798331530259

Diabetes mellitus is among the most dangerous conditions that many people suffer from. Age, obesity, poor food, heredity, high blood pressure, and inactivity are some of the variables that might lead to diabetes mellitus. Though there are additional forms of diabetes, such as gestational diabetes, which appears during pregnancy, the two most common types are type 1 and type 2. In data science, machine learning is a relatively new field of study that looks at how machines pick up knowledge via experience. The goal of this work is to build a system that can more accurately forecast a patient's risk of developing diabetes in their early years by utilizing a combination of research data and machine learning techniques. Interpretable findings using logistic regression (LR) show how various risk variables affect the chance of acquiring diabetes. The most crucial characteristics for diabetes prediction may be automatically discovered by neural networks (NN) from unprocessed input data, eliminating the requirement for human feature selection. While compared to previous models, both the logistic regression and the neural network in this study provide a respectable accuracy score following a 10-fold classification. © 2025 IEEE.

关键词： Nutrition

来源：评论

学校读者我要写书评

暂无评论

Review of Smart Agricultural Technologies for Precision Pest control and Sustainable Water in Cotton Farming

Review of Smart Agricultural Technologies for Precision Pest...

引用

8th International Conference on Inventive Computation Technologies, ICICT 2025

作者： Prince Charles, M. Pandiyarajan, Pandiselvam Department of Computer Application Kalasalingam Academy of Research and Education Tamil Nadu Krishnankoil India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamil Nadu Krishnankoil India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Cotton production is extremely susceptible to pests, disease, and irrigation mismanagement, resulting in considerable yield loss and environmental degradation. Conventional practices like manual monitoring of pests and blanket spraying of pesticides are time-consuming, ecologically detrimental, and frequently inadequate in averting timely infestations. The intersection of Artificial Intelligence (AI), the Internet of Things (IoT), and smart water management offers a revolutionary chance towards sustainable agriculture. This paper discusses the cooperative function of IoT and AI in cotton cultivation, particularly real-time pest identification, disease classification, and dynamic irrigation management. Deep learning techniques precisely detect pests at all stages of development, whereas IoT sensors track environmental data like soil moisture, temperature, and humidity. The sensor data are used as input to machine learning algorithms that adjust irrigation schedules and predict outbreaks of disease. Furthermore, adaptive learning methods such as federated and incremental learning optimize system performance through time, allowing for ongoing improvement without violating data privacy. Major findings indicate that the integration of these technologies decreases pesticide use, saves water, and enhances crop resistance. Yet, the absence of complete integrated systems prevents the full potential from being realized. This research highlights the necessity for integrated frameworks that address pest management and water management simultaneously in real time. Closing these gaps will open the door to innovations that increase yield, enhance sustainable agriculture practices, and make cotton farming sustainable in the long term. © 2025 IEEE.

关键词： Agricultural science Artificial Intelligence in Agriculture crop production Integrated Pest Management IoT in Agriculture

来源：评论

学校读者我要写书评

暂无评论

Innovative anti-phishing framework using machine learning approach with evolutionary optimization to secure multimedia applications

引用

Multimedia Tools and applications 2025年 1-28页

作者： Sharma, Bhawna Khurana, Savita Sharma, Gaurav Rani, Sunita Nandal, Sudesh Kumari Chandi, Pragya Rastogi, Rashi Castilla, Ángel Kuc Goyal, Nitin Department of Computer Science & Engineering JMIT Radaur India Department of Information Technology JMIT Radaur India Department of Computer Science & Engineering and Information Technology BPS Mahila Vishwavidalaya Sonipat India Department of Electronics and Communication Engineering BPS Mahila Vishwavidalaya Sonipat India Department of Computer Science & Engineering University Institute of Engineering and Technology Kurukshetra University Haryana India Department of Computer Application Sir Chhotu Ram Institute of Engineering and Technology CCS University Meerut India Engineering Research & Innovation Group Universidad Europea del Atlántico Santander Spain Department of Project Management Universidad Internacional Iberoamericana Campeche México Department of Project Management Universidad Internacional Iberoamericana Arecibo USA Department of Computer Science & Engineering School of Engineering and Technology Central University of Haryana Mahendergarh India

The transmission of users’ personal data via networks poses an increased risk of being exposed to phishing threats. Phishing URLs hiding malicious web addresses have become a widespread means of compromising user privacy. This paper is to improve the accuracy and efficiency of detecting and defending against phishing attacks in multimedia applications, aiming to safeguard users'personal data from exposure via phishing URLs. The proposed approach can be summarized in three phases. In the first phase, cosine and Jaccard similarities between training and test datasets are calculated to obtain a comprehensive understanding of the similarities of data points. A hybrid similarity score is built by combining these two measures in the second phase of the experiment to effectively blend content-related and quantity-related similarities. The third and final phase involves training a model on XGBoost that uses a hybrid similarity score as an input and then tunning up its hyperparameters using a genetic algorithm with the optimization of classification performance. Finally, the created powerful classifiers are merged to form a stacked ensemble model, increasing the overall accuracy of the classification. The strenuous test evaluation of 11,429 rows, 88 columns, through key metrics, such as Precision, Recall, F-Score, and Accuracy show that the proposed anti-phishing model has outperformance parameters in other models. With this lean approach, accuracy in classification can be improved by the incorporation of similarity-based techniques and machine learning and evolutionary optimization, in which defense can be performed against phishing threats in multimedia environments. The new innovation involved in the proposed work is based on stacking an ensemble of similarity-based techniques with a learning and optimization framework of machines, ensuring robustness toward phishing threat defense.

关键词：

来源：评论

学校读者我要写书评

暂无评论

HiCoCS: High Concurrency Cross-Sharding on Permissioned Blockchains

引用

IEEE Transactions on computers 2025年第7期74卷 2168-2182页

作者： Yang, Lingxiao Dong, Xuewen Wan, Zhiguo Lu, Di Zhang, Yushu Shen, Yulong Xidian University School of Computer Science and Technology China Ministry of Education Engineering Research Center of Blockchain Technology Application and Evaluation China Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China Zhejiang Lab Zhejiang Hangzhou311121 China Shaanxi Key Laboratory of Network and System Security Xi’an710071 China Jiangxi University of Finance and Economics School of Computing Nanchang330013 China Jiangxi University of Finance and Economics Nanchang330013 China

As the foundation of the Web3 trust system, blockchain technology faces increasing demands for scalability. Sharding emerges as a promising solution, but it struggles to handle highly concurrent cross-shard transactions (CSTxs), primarily due to simultaneous ledger operations on the same account. Hyperledger Fabric, a permissioned blockchain, employs multi-version concurrency control for parallel processing. Existing solutions use channels and intermediaries to achieve cross-sharding in Hyperledger Fabric. However, the conflict problem caused by highly concurrent CSTxs has not been adequately resolved. To fill this gap, we propose HiCoCS, a high concurrency cross-shard scheme for permissioned blockchains. HiCoCS creates a unique virtual sub-broker for each CSTx by introducing a composite key structure, enabling conflict-free concurrent transaction processing while reducing resource overhead. The challenge lies in managing large numbers of composite keys and mitigating intermediary privacy risks. HiCoCS utilizes virtual sub-brokers to receive and process CSTxs concurrently while maintaining a transaction pool. Batch processing is employed to merge multiple CSTxs in the pool, improving efficiency. We explore composite key reuse to reduce the number of virtual sub-brokers and lower system overhead. Privacy preservation is enhanced using homomorphic encryption. Evaluations show that HiCoCS improves cross-shard transaction throughput by 3.5-20.2 times compared to the baselines. © 1968-2012 IEEE.

关键词： Scalability

来源：评论

学校读者我要写书评

暂无评论

MEMS sensitive structure sand and dust pollution test and its degradation characteristics 14

MEMS sensitive structure sand and dust pollution test and it...

引用

14th International Conference on Reliability, Maintainability and Safety, ICRMS 2023

作者： Ruan, Bin Zhu, Chunlong Liu, Tingting He, Chunhua Liu, Ruiwen Huang, Qinwen School of Information Engineering Southwest University of Science and Technology Mianyang China Science and Technology on Reliability Physics and Application Technology of Electronic Component Laboratory China Electronic Product Reliability Environmental Testing Research Institute Guangzhou China School of Computer Guangdong University of Technology Guangzhou China Institute of Microelectronics Chinese Academy of Science Beijing China

ISBN: (纸本)9798350329988

The sensitive structures of certain devices containing micro-electromechanical system (MEMS) must be directly exposed to the working environment, making them vulnerable to contamination by dust and other pollutants in that environment. Consequently, this contamination degrades the performance of the device, impacting both its working life and stability. Existing test methods, such as GB/T 4208-2017 and GB/T 2423.37-2006, are not entirely suitable for evaluating the sand and dust contamination test of these MEMS devices. To address these issues, a thermistor-based test structure was designed and processed to develop an evaluation method suitable for testing the sand and dust contamination sensitive MEMS structures. The test results indicate that, unlike other hermetically sealed electronic devices, the imposition of electrical stress and cycling of the operating voltage "on-off" during the test does not increase dust accumulation. Furthermore, due to sand and dust contamination, the thermistor value of the device drifts during operation. This drift primarily arises from increased thermal resistance between the heating element of the device and the surrounding environment, resulting from the accumulation of sand and dust, which results in the increase of temperature of the heating element and ultimately results in an elevated thermistor value. This study can provide support for the structural design and package design of MEMS sensitive structures, and provide a basis for the identification and inspection of related products and the development of detailed specifications. © 2023 IEEE.

关键词： Thermistors

来源：评论

学校读者我要写书评

暂无评论

Diabetes Prediction in Healthcare Utilizing Machine Learning Algorithms

Diabetes Prediction in Healthcare Utilizing Machine Learning...

引用

Ambient Intelligence in Health Care (ICAIHC), International Conference on

作者： Nrusingha Tripathy Sarbeswara Hota Pranati Satapathy Subrat Kumar Nayak Debasish Swapnesh Kumar Nayak Purnya Prava Nayak Department of Computer Science and Engineering Siksha ‘O' Anusandhan Deemed to be University Bhubaneswar Odisha India Department of Computer Application Siksha ‘O' Anusandhan Deemed to be University Bhubaneswar Odisha India Department of IMCA Utkal University Bhubaneswar Odisha India Department of Computer Science and Engineering Centurion University of Technology and Management Bhubaneswar Odisha India Department of Computer Science and Engineering GIET Baniatangi Bhubaneswar Odisha India

ISBN: (数字)9798331530259

ISBN: (纸本)9798331530266

关键词： Pregnancy Logistic regression Accuracy Machine learning algorithms Machine learning Medical services Artificial neural networks Prediction algorithms Diabetes Biomedical monitoring

来源：评论

学校读者我要写书评

暂无评论

Research and application of Machine Learning in Automatic Program Generation

引用

Chinese Journal of Electronics 2020年第6期29卷 1001-1015页

作者： ZHANG Xiaojiang JIANG Ying Yunnan Key Laboratory of Computer Technology Application Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming University of Science and Technology

With the development of artificial intelligence,machine learning has been applied in more and more *** order to improve the quality and efficiency of software,automatic program generation is becoming a research *** recent years,machine learning has also been gradually applied in automatic program *** trees,language models,and cyclic neural networks have been applied in code generation,code completion and code knowledge *** efficiency of software development has been improved to a certain extent using machine *** at the automatic program generation,this paper analyzes and summarizes the models of machine learning,the modifications involved in the models and the application *** research direction is discussed from the aspects of programmer behavior and automatic program generation of machine learning.

关键词： code generation Combinatorial mathematics neural nets decision trees machine learning code completion automatic programming language models Data handling techniques data mining cyclic neural networks Programming support software engineering automatic program generation Software engineering techniques programmer behavior code knowledge mining Neural computing techniques learning (artificial intelligence) Knowledge engineering techniques

来源：评论

学校读者我要写书评

暂无评论

Utilizing Deep Convolutional Neural Network and Local Binary Pattern Fusion for Plant Leaf Disease Identification: An Integrated Approach 3

Utilizing Deep Convolutional Neural Network and Local Binary...

引用

3rd IEEE International Conference on Industrial Electronics: Developments and applications, ICIDeA 2025

作者： Rahamat Basha, S. Kaladeep Yalagi, Pratibha C. Puli, Balaram Sundaramoorthy, Pandian Daruvuri, Rajesh Vinayagapriya, S. Malla reddy college of engineering and technology Department of computer science and engineering India Walchand Institute of Technology Solapur Department of Computer Science and Engineering Solapur India Engineering and Data Science Everest Computers Inc India Application Developer EL CIC-1W-AMI IBM India Google India St.josephs college of engineering Dept of ece India

ISBN: (纸本)9798331533205

Precise detection of plant diseases is essential in agriculture to maximize crop management and yield. The present study presents an innovative approach that integrates convolutional neural networks (CNNs) with local binary patterns (LBPs) in order to enhance the categorization of plant leaf diseases. The intricacy of plant disease images complicates the evaluation of their many features by conventional approaches. To solve this, the suggested technique utilizes the strengths of both CNNs and LBPs. The CNN component is highly effective in extracting discriminative features from raw pictures using hierarchical representations, which are crucial for differentiating between different illness categories. Simultaneously, the LBP descriptor enhances its usefulness by acquiring intricate texture information from specific areas of the images, so providing more effectiveness in distinguishing small differences resulting from various diseases. Utilizing a feature fusion approach, the merging of CNN and LBP features merges spatial and textural data, resulting in a more thorough representation of the input images. The fusion described herein significantly improves the precision of plant disease classification. The analysis of benchmark datasets of plant leaf diseases showed that this integrated method outperforms the use of CNNs or LBPs individually in terms of classification accuracy. The research findings indicate that the suggested approach, which combines deep Convolutional Neural Networks (CNNs) with Linear Bayesian Processes (LBPs), provides a more efficient approach for detecting plant leaf illnesses. This, in turn, makes a substantial contribution to crop management and agricultural productivity. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Understanding Social Networks Using Machine Learning Sentimental Analysis Algorithms

Understanding Social Networks Using Machine Learning Sentime...

引用

Optimization Computing and Wireless Communication (ICOCWC), International Conference on

作者： Garima Goswami M N Varun Somanna Inzimam College of Engineering Teetthanker Mahaveer University Moradabad Uttar Pradesh India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Department of Computer Science & Application Vivekananda Global University Jaipur India

Social networks are dynamic and complex structures that depend closely on human interplay. Machine learning and nostalgic evaluation algorithms can be used to gain insights into the shape and dynamics of these networks. Through those techniques, patterns of connections between people may be exposed, in addition to sentiment associated with subjects or entities. This know-how can help understand higher social networks over more than a few subjects, including fitness, politics, and the stock market. Moreover, such techniques may be implemented to leverage community effects to optimize the overall performance of advertising and marketing techniques.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Enhance Version of YouTube Video Downloader Using Python

An Enhance Version of YouTube Video Downloader Using Python

引用

computer science and Emerging Technologies (CSET), International Conference on

作者： Ayush Rawat Vandana Rawat Neelam Singh Navya Kuchhal Jigyasa Barmola Harendra Singh Negi Department Of Computer Application Graphic Era Deemed To Be University Dehradun India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India

YouTube has become the dominant online video platform, fueling demand for downloading content. In response, numerous third-party tools have emerged to enable downloading YouTube videos, though this violates their terms of service. This research presents the development and evaluation of a custom YouTube video downloader application in Python. The motivation is building an easy-to-use yet customizable solution for saving YouTube videos locally. The tool combines a graphical user interface using PySimpleGUI with the yt-dlp media extraction library. Key innovations include supporting flexible customization of download settings, bulk playlist/channel downloads, a modular architecture, cross-platform packaging, and open source release. Quantitative performance evaluations reveal efficient resource usage, throughput, and scalability when downloading high resolution videos in bulk. User studies highlight satisfaction with the extensive features compared to alternative download tools. However, increased access poses ethical considerations around copyright compliance and fair use versus abuse. While technical and usability goals are largely achieved, fostering responsible use relies on user discretion. Ongoing interdisciplinary research can help guide ethical innovation in this controversial domain. The main contributions are the functional open source tool itself, performance bench marking, user experience evaluations, analysis of legal/ethical impacts, and promotion of community enhancement.

关键词：

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：