检索结果-内蒙古大学图书馆

2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Manivasagam Singh, Shweta Bhagwan, Shri Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India

ISBN: (纸本)9798350383348

Deep getting to know-based automated Detection of Retinal diseases with Convolutional Neural Networks (CNNs) is a unique method for screening and analyzing retinal diseases. With the improvements in deep studying and computational strength, the use of CNNs has emerged as increasingly more popular for figuring out and classifying retinal photographs for the presence of diverse retinal illnesses inclusive of diabetic retinopathy, age-related macular degeneration, glaucoma, and retinal vein occlusions. CNNs can process big numbers of images and extract complex capabilities indicative of the presence or absence of retinal diseases. CNNs can leverage present functions in addition to studying new features from pics, making the procedure of computerized detection more correct and green. Furthermore, those deep studying networks are trained on large, deep-mastering scientific datasets, enabling them to stumble on even the smallest variations among healthful and diseased eyes. In addition to detecting retinal sicknesses, CNNs can also be used for different programs, monitoring disease development, predicting remedy effects, and studying scientific outcomes. CNNs provide an enormously efficient, cost-effective way to stumble on retinal diseases with an excessive degree of accuracy. © 2024 IEEE.

关键词： computational detection efficient features retinopathy

来源：评论

学校读者我要写书评

暂无评论

MAVEN-FACT: A Large-scale Event Factuality Detection Dataset

MAVEN-FACT: A Large-scale Event Factuality Detection Dataset

引用

2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Li, Chunyang Peng, Hao Wang, Xiaozhi Qi, Yunjia Hou, Lei Xu, Bin Li, Juanzi Department of Computer Science and Engineering HKUST Hong Kong Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9798891761681

Event Factuality Detection (EFD) task determines the factuality of textual events, i.e., classifying whether an event is a fact, possibility, or impossibility, which is essential for faithfully understanding and utilizing event knowledge. However, due to the lack of high-quality large-scale data, event factuality detection is under-explored in event understanding research, which limits the development of EFD community. To address these issues and provide faithful event understanding, we introduce MAVEN-FACT, a large-scale and high-quality EFD dataset based on the MAVEN dataset. MAVEN-FACT includes factuality annotations of 112, 276 events, making it the largest EFD dataset. Extensive experiments demonstrate that MAVEN-FACT is challenging for both conventional fine-tuned models and large language models (LLMs). Thanks to the comprehensive annotations of event arguments and relations in MAVEN, MAVEN-FACT also supports some further analyses and we find that adopting event arguments and relations helps in event factuality detection for fine-tuned models but does not benefit LLMs. Furthermore, we preliminarily study an application case of event factuality detection and find it helps in mitigating event-related hallucination in LLMs. Our dataset and codes can be obtained from https://***/THU-KEG/MAVEN-FACT. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Understanding Time Series Analysis for Improved Forecasting Techniques

Understanding Time Series Analysis for Improved Forecasting ...

引用

2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Singh, Umesh Kumar Kannagi, A. Shukla, Meenu College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

Time collection evaluation is a vital device used in forecasting strategies. It involves looking at the temporal behavior of records set in which observations are recorded in sequence over time. Using diverse statistical and mathematical techniques, traits, discrepancies, and styles inside the records may be identified and used to make predictions or forecasts approximately what may manifest in the future. For example, time collection evaluation may be used to forecast sales, customer spending, inventory costs, or every other variable being monitored. It is changing through the years. Stepped-forward forecasting techniques can be acquired by as it should be figuring out and considering seasonality, fashion, and different temporal functions within the information set. Better predictions may be made for destiny by applying state-of-the-art fashions and algorithms, including the ARIMA (autoregressive integrated shifting average) version. ultimately, the goal of time series analysis is to broaden predictions that accurately replicate the natural order of device conduct. © 2024 IEEE.

关键词： accurately collection evaluation considering manifest mathematical

来源：评论

学校读者我要写书评

暂无评论

An Extended Deep Learning and Feature Pyramid Based Network for Screening CT-Scans Images 6

An Extended Deep Learning and Feature Pyramid Based Network ...

引用

6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Pandey, Dhiraj Pandey, Kavita JSS Academy of Technical Education Department of Information Technology Noida India Jaypee Institute of Information Technology Department of Computer Science Engineering & IT Noida India

ISBN: (纸本)9798350304480

Deep convolution neural Network is basically using a methodology to teach computer systems in the same way as humans learn by using examples and through experiences. It is a method of solving exhaustive image processing problems by using a heuristic-based approach. The objective of this research is to develop an extended deep learning-based hybrid method to detect abnormal symptoms using CT-scan Images. The detection of abnormal in any infected people is the most challenging issue due to less medical facility and time required to confirm its correctness based on the test conducted. Many AI-based approaches are suggested by researchers but most of the work is not integrated as a working model because of its correctness and accuracy level. The proposed work is implemented using Python and TensorFlow library. For experimentation, the CT-scan dataset is used with two different classes like normal or abnormal in nature. The proposed hybrid approach takes an input of CT-SCAN images from the selected data sets, converts it into grayscale, and then into. tiff format to get a more specific feature suitable for preprocessing step of the deep convolution network. Feature pyramid network has also been used on the selected images which provides better results in terms of low-level features in the deeper convolution networks. This hybrid methodology is helping us to get better results in comparison to state of art available. The implemented extended deep CNN network can accurately recognize varieties of real test cases CT-scans images. The learning accuracy of the hybrid method of RESNET101V2 with Feature pyramid network (FPN) and preprocessed dataset is up to 98.68 percent and it is maximizing the standard available state of art technique by a good margin in terms of result accuracy. © 2023 IEEE.

关键词： Abnormal computer Vision Deep Convolution Neural Network Feature pyramid network Image classification Residual Network

来源：评论

学校读者我要写书评

暂无评论

Performance Analysis of Machine Learning Based On Optimized Feature Selection for Type II Diabetes Mellitus

引用

Multimedia Tools and Applications 2025年第8期84卷 4945-4964页

作者： Bhat, Salliah Shafi Ansari, Gufran Ahmad Ansari, Mohd Dilshad B. S. Abdur Rahman Crescent Institute of Science and Technology Chennai48 India Department of Computer Science and Software Engineering University of Hail Hail55424 Saudi Arabia Department of Computer Science & Engineering SRM University Delhi-NCR Harayana Sonepat131029 India

Type 2 Diabetes Mellitus (T2DM) is a common chronic illness caused by variations in the secretion of insulin. T2DM will be treated early with the help of an early diagnosis reducing the risk of early death and controlling is course of illness. In this Paper Machine Learning Algorithms (MLA) for T2DM diagnosis are proposed. The major objective of this research is to assess use of MLA and its methods to forecast different illnesses at an initial stage. In this research, we examine most recent proposed strategies with some limitations and potential improvements for future work. The data is collected from Kaggle (University of California) *. Researchers highlight how important is to identify important features that enhance outcomes suggested by recent methods. Six MLA were used in this paper such as Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF) and K-Nearest Neighbour (KNN) were applied in Jupiter Notebook. Feature sets are selected using three feature selection methodologies such as information gain-based selection, correlation-based selection and sequential feature selection. On these feature subsets a variety of MLA are applied and optimal feature subset is chosen based on their performance. Finally a DT is suggested as the top model among the top six performances. The suggested method provides an improved performance label with an average accuracy of 96.10%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

A Semi-supervised Protein Complex Identification Algorithm Based on Sparseness Constraint

A Semi-supervised Protein Complex Identification Algorithm B...

引用

2024 International Conference on Applied Mathematics, Modelling and Statistics Application, AMMSA 2024

作者： Liu, Guangming Dang, Fengqing Wang, Bin Peng, Yonghong Wang, Xiaofan Tan, Li School of Computer Science and Engineering Shaanxi Key Laboratory for Network Computing and Security Technology Xi'An University of Technology Shaanxi Xi'an China Faculty of Science and Engineering Computing and Information Science Cambridge United Kingdom School of Computer and Information Engineering Beijing Technology and Business University Beijing China

Recognising protein complexes in protein interaction networks is crucial, but poses a major challenge due to the frequency of noisy interactions. These networks typically involve numerous protein complexes, with each protein generally only participating in a few complexes. Current recognition models often ignore this aspect. To address this problem, we present a semi-supervised protein complex identification algorithm that extends non-negative matrix factorization (NMF) with sparsity constraints. In contrast to conventional approaches that apply a global sparsity constraint to the entire matrix, our method imposes individual sparsity constraints on protein membership indicator vectors. This targeted strategy controls the algorithm more effectively. Our experimental results with yeast and human protein interaction networks show that our algorithm achieves higher accuracy in identifying protein complexes than leading contemporary methods. © Published under licence by IOP Publishing Ltd.

关键词： Non-negative matrix factorization

来源：评论

学校读者我要写书评

暂无评论

INTELLIGENT AUTONOMOUS PARKING SYSTEM INTEGRATING RFID AND IoT FOR SMART CITIES

引用

Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2024年第2期83卷 65-77页

作者： Pushgara Rani, D. Prasanna, R. Surenderanath, S. Kalaiselvi, V.K.G. Vaishnavi, V. Rithika, P. Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Ramapuram Campus Chennai India Department of Information Technology Sri Sai Ram Engineering College Chennai India

An autonomous parking system is the essential innovation for stress free car parking in smart cities. The efficient use of resources is a key goal of smart cities, and their integration with technology has improved productivity and dependability for urban infrastructure. In this paper, an efficient automated parking system integrating Internet of Things (IoT) and radio frequency identification (RFID) technologies is proposed. The proposed system is integrated with IoT in which the consumers can book the parking spots before arriving to the destination—drastically reducing the time taken by the consumers in searching for the parking slot. The proposed system effectively saves time for the consumers and also significantly provides solutions for all the parking-related issues faced by the management of flats, theaters, malls, hospitals, etc. In management perspective, this system will provide better use of their resources and reduce the amount of labor needed to maintain these parking lots. The automated parking system overcomes the important issue of jamming near the gates of parking spots by improving the check-in and check-out process of vehicles. The RFID tags given to the drivers reduce the time for vehicle identification as it is done automatically. The proposed system avoids long queues at the gates, unnecessary traffic jams near the gates, reduces the waiting time, and ensures security by allowing only the registered users identified through RFID tags. Thus, the proposed system will be a sophisticated system for the consumers, drivers, and a boon to mall owners and management. © 2024 by Begell House, Inc.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Discovering Patterns with Neural Networks in Agricultural Crop Monitoring

Discovering Patterns with Neural Networks in Agricultural Cr...

引用

2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Singh, Akhilendra Pratap Kaushik, Neeraj Pawar, Rahul Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

neural networks have emerged as practical tools for discovering styles in agricultural crop monitoring. The motive of this take a look at was to research whether or not neural network strategies will be implemented to seize and perceive patterns in various parameters related to crop monitoring. Data was accrued from four corn fields inside the Midwest US from October 2007 to October 2009. Linear and non-linear neural networks were used to analyze the information, to identify enormous styles associated with crop manufacturing. Effects showed that the neural networks have been able to as they should be perceived styles inside the records, with the non-linear network generating satisfactory results. The results confirmed that the most important parameters related to crop production were the width of the kernel, duration of the cob, and precise leaf location. These findings advocate that neural networks may be a promising technique for understanding complicated crop monitoring relationships. © 2024 IEEE.

关键词： emerged enormous implemented important parameters

来源：评论

学校读者我要写书评

暂无评论

A Wavelet Transformation Technique and Cryptographic Encryption for Image Steganography 3rd

A Wavelet Transformation Technique and Cryptographic Encrypt...

引用

3rd International Conference on Smart Computing and Cyber Security—Strategic Foresight, Security Challenges and Innovation, SMARTCYBER 2023

作者： Anilkumar, Chunduru Venkatesh, B. Velisi, Nirmalapriya Department of Information Technology GMR Institute of Technology AP Rajam532127 India Department of Computer Science and Engineering BVRIT Hyderabad College of Engineering for Women Telangana Hyderabad500090 India

ISBN: (纸本)9789819705726

Confidentiality, Integrity, and Availability are the three fundamental principles of information security. Every user must ensure that the above principles are followed in all operations performed from server to client and vice versa. Sensitive data can be protected in the domain of information security by various methods such as data encryption, data hiding, and the implementation of some information security policies. Even though there are many data protection methods, imposters attempt to retrieve sensitive information and change the entire data or modify it to some extent or steal it. Communication in these days has become very simple as technology has advanced. It became a boon for intruders to steal information from users. "Steganography," which hides data in any media file so that any intruder or a third party cannot read the hidden data, which makes it as one of the best techniques for preventing problems. When discussing steganographic techniques, image steganography takes precedence because it is very useful for users and requires very little bandwidth. The application of cryptography in steganography would ensure good and efficient results. We used both Advanced Encryption Standard (AES) and Blowfish in this proposed model, which are used for highly secure mechanisms that help to protect data. The Discrete Wavelet Transform (DWT) technique ensures encrypted data is hidden within the given image file and results in a stego image with a good Peak Signal Noise Ratio (PSNR). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Discrete wavelet transforms

来源：评论

学校读者我要写书评

暂无评论

The Threat of Quantum Computing to the Cryptographic Security of Blockchain 2

The Threat of Quantum Computing to the Cryptographic Securit...

引用

2nd International Conference on Emerging Trends in information technology and engineering, ic-ETITE 2024

作者： Modi, Pranit Asgola, Abhishek Mishra, Hiya Charanya, R. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Vellore India

ISBN: (纸本)9798350328202

Blockchain technology, born as the backbone of cryptocurrencies like Bitcoin, has rapidly expanded into a versatile platform spanning various industries. This review paper delves into the critical aspects of security and anonymity within blockchain applications. It simplifies complex technical concepts while exploring the foundation of blockchain security, including cryptography, decentralization, consensus mechanisms, and the challenges posed by smart contracts. Additionally, it examines the pseudonymous nature of blockchain addresses, privacy-enhancing cryptocurrencies, and the delicate balance between transparency and anonymity. As we navigate these intricacies, we address challenges such as scalability, regulatory concerns, and potential threats from quantum computing. This paper aims to provide a comprehensive yet easily digestible understanding of the security and anonymity dimensions in blockchain applications. © 2024 IEEE.

关键词： Blockchain

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：