检索结果-内蒙古大学图书馆

2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Jayakarthik, R. Kumar, Santosh Goswami, Sohan Mahaveerakannan, R. Hafeez, Shaik Abdul Tamilnadu Chennai India Department of Computer Science ERA University Uttar Pradesh Lucknow India Department of Computer Science and Technology Technique Polytechnic Institute West Bengal Panchrakhi India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

ISBN: (纸本)9789819776023

Crop production is a very complicated attribute that depends on a number of variables, including genotype, environment, and their interactions. The functional relationship between yield and these interaction elements must be fundamentally understood for accurate yield prediction to be possible, and both large datasets and potent algorithms are needed to show this relationship. In order to accurately estimate crops, we applied cutting-edge machine learning techniques. Our ability to gather data for huge agricultural areas spread throughout far-flung regions of the globe is made possible by the Internet of Things’ architectural principles. So that we may make forecasts about crops, our machine learning system can use this data. Nitrogen, phosphorus, potassium, temperature, humidity, and rainfall all have a role in determining the best crop to grow. Recommendations are made in light of these factors. There are 2200 total occurrences in the data collection, and there are 8 associated attributes for each one. For each of the 8 possible characteristic combinations, around twenty-two distinct plant species are available as potential options. The most effective model can be produced by utilizing the supervised learning approach and few of the machine learning methods available in WEKA. As prospective possibilities for the methods of machine learning that would be used in the classification process, the decision table classifier and the multilayer perceptron rules-based classifier JRip were selected. The system’s design took into account both the growing Internet of Things and the essential measurements required for farming. The average weighted value of the Receiving Operator Attributes has been found to be 1, the performance measured by the classifiers that were chosen has a value of 98.2272%, and the maximum time needed to construct the model is 8.03 s. Employing machine learning in agriculture aims to increase the production and nutritional value of the plants generated

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Complexity of Monomial Prediction in Cryptography and Machine Learning 26

Complexity of Monomial Prediction in Cryptography and Machin...

引用

26th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2024

作者： Dutta, Pranjal Rajasree, Mahesh Sreekumar Sarkar, Santanu Singapore Indian Institute of Technology Delhi Department of Computer Science and Engineering Delhi India Indian Institute of Technology Madras Department of Mathematics Tamil Nadu India

ISBN: (纸本)9798331532833

In this paper, we focus on the monomial prediction problem in two settings: (1) Decide whether a particular mono-mial m is present in a composite function f:=fr° fr-1° ldots fo, where fi are quadratic boolean functions, (2) Decide whether a particular monomial m is present in a composite function f:= fr° fr-1° ldots f0, where polynomials fi are efficiently computable by Probabilistic Generating circuits over rationals. Probabilistic generating circuits (PGCs) are economical representations of multivariate probability generating polynomials (PGPs), which capture many tractable probabilistic models in machine learning. The first problem has a strong connection with the security of symmetric-key primitives. Dinur and Shamir proposed the cube attack for distinguishing a cryptographic primitive from a random function, which can be thought of as an efficient monomial prediction. In a general setting, over any large finite field or integers, monomial prediction is known to be NP-hard. Here, we show that in the quadratic setting, the problem is oplus P complete. oplus P is an interesting complexity class that is not known to contain NP, however, it is believed to contain computationally hard problems. On the other hand, we also present several new zero-sum distinguishers for 5-round Ascon, which is one of the ten finalists for NIST light weight cryptography standardization competition. We show that the second problem is #P-complete. It is known that PGCs have efficient inference, i.e. given a monomial, one can efficiently output (which signifies the probability) its coefficient in the polynomial computed by the circuit. However, a composition of such functions makes the inference hard. Composition of prob-abilistic models and their efficient inference play a crucial role in the semantic contextualization and framework of uncertainty theories in graphical modelling. © 2024 IEEE.

关键词： Boolean functions

来源：评论

学校读者我要写书评

暂无评论

AI-Driven Digital Mirror technology for Securing IoT-Enabled Smart Infrastructures 2

AI-Driven Digital Mirror Technology for Securing IoT-Enabled...

引用

2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Chaudhary, Jyoti Jain, Upma Sudarshan, N. Pai, Vasudeva Sutaria, Kamal Natrayan, L. Department of Computer Engineering The Technological Institute of Textile and Sciences Bhiwani India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Mechanical Engineering The National Institute of Engineering Mysuru India Department of Information Science and Engineering NMAM Institute of Technology NITTE Deemed to be University Karkala India Faculty of Engineering and Technology Parul institute of Engineering and Technology Vadodara India Department of Mechanical Engineering Saveetha School of Engineering Chennai India

ISBN: (纸本)9798331504960

The expansion of the Internet of Things (IoT) is driven by the proliferation of interconnected wireless gadgets, made possible by ever-improving computer and Internet infrastructure. The IoT is an extensive system of interconnected sensors and gateways. In the course of their constant environmental interaction, IoT devices produce a wealth of data resources, including images, audio, video, digital signals, and more. Connectivity to the Internet allows all Internet of Things (IoT) equipment and apps to exchange data and resources more effectively. The proposed AI-Driven Digital Mirror technology effectively enhances the security of IoT-enabled smart infrastructures by integrating real-time monitoring, anomaly detection, and adaptive response mechanisms. By employing advanced AI algorithms such as Graph Neural Networks (GNNs) and Reinforcement Learning (RL), the technology mirrors IoT environments digitally, enabling dynamic modeling and threat assessment. The system demonstrated a significant improvement in threat detection accuracy, achieving a precision rate of 95.6% and a recall rate of 93.2% during simulations. Additionally, the adaptive response mechanism reduced the average response time to security breaches by 40%, ensuring minimal disruption to infrastructure operations. Comparative analysis with traditional security solutions highlighted the system's superiority in mitigating complex, multi-layered attacks, and its scalability makes it suitable for various smart infrastructure applications, including smart cities, industrial automation, and intelligent transportation systems. The results underscore the potential of AI-Driven Digital Mirror technology in securing IoT ecosystems against evolving cyber threats while maintaining operational efficiency and resilience. © 2024 IEEE.

关键词： Gateways (computer networks)

来源：评论

学校读者我要写书评

暂无评论

Dielectric Response Analysis Characteristic of Service-Aged XLPE Cables 14

Dielectric Response Analysis Characteristic of Service-Aged ...

引用

14th International Conference on the Properties and Applications of Dielectric Materials, ICPADM 2024

作者： Thongkong, Patchara Promphanich, Wiboon Udomluksananon, Patt Jeenmuang, Siwakorn Wiangtong, Theerayod Pattanadech, Norasage School of Engineering King Mongkut's Institute of Technology Ladkrabang Department of Electrical Engineering Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Department of Computer Engineering Bangkok Thailand

ISBN: (纸本)9798350374605

The main insulation used in plastic underground cables is cross-linked polyethylene (XLPE). The XLPE underground cable which has been utilized in service for a long time, has experienced various stresses, or so-called TEAM stresses. Consequently, the XLPE insulation will be degraded over time and lead to insulation properties degradation. This paper represents the dielectric response analysis technique for characterizing the insulation condition of the service-aged XLPE medium voltage underground cable. The dielectric response analysis was performed on the 22 kV XLPE underground cables that have been in service for more than 10 years and were directly buried. The dielectric response analysis measurement consists of a combination of polarization and depolarization current (PDC) measurement in the time-domain and frequency-domain spectroscopy (FDS) measurement. After that, test results were analyzed to evaluate the insulation condition and compare it to the new 22 kV underground cable. The analyzed dielectric response analysis test results will be reported in this paper. © 2024 IEEE.

关键词： Cable sheathing

来源：评论

学校读者我要写书评

暂无评论

Feature-enhanced deep learning technique with soft attention for MRI-based brain tumor classification

引用

International Journal of Information technology (Singapore) 2024年第3期16卷 1617-1626页

作者： Mohanty, Bipin Ch. Subudhi, P.K. Dash, Ratnakar Mohanty, Bidyadhar Department of Electronics and Communication Engineering BPUT Odisha Rourkela 769015 India Department of Electronics and Communication Engineering Nalanda Institute of Technology Odisha Bhubaneshwar 754005 India Department of Computer Science and Engineering National Institute of Technology Odisha Rourkela 769008 India

Brain tumor classification using Magnetic Resonance Imaging (MRI) is a pivotal area in medical diagnostics, with the potential to influence early detection and subsequent treatment strategies. Over the years, various machine learning and deep learning models have been proposed to enhance the accuracy of MRI-based tumor detection. In this evolving landscape, this paper introduces a distinctive deep-learning model that harnesses the power of a soft attention mechanism. We employ a meticulously designed Convolutional Neural Network (CNN) comprising four convolution layers. One of the significant innovations in our approach is the method of feature extraction. Instead of extracting features solely from the final layer, as is common in many models, our approach aggregates and combines features from all layers. This ensures that the vital characteristics intrinsic to each layer are not lost but rather amalgamated into a robust and comprehensive feature vector. The incorporation of a soft attention mechanism at the terminal stages ensures that the most salient and clinically relevant features are emphasized, enhancing classification accuracy. To validate the efficacy of our proposed model, we employed standard datasets for training and testing. A comparative analysis with existing state-of-the-art models affirms the superiority and potential of our approach in the realm of MRI-based brain tumor classification. © 2024, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： Brain tumor CNN Deep learning MRI Soft-attention

来源：评论

学校读者我要写书评

暂无评论

Elite-guided equilibrium optimiser based on information enhancement:Algorithm and mobile edge computing applications

引用

CAAI Transactions on Intelligence technology 2024年第5期9卷 1126-1171页

作者： Zong-Shan Wang Shi-Jin Li Hong-Wei Ding Gaurav Dhiman Peng Hou Ai-Shan Li Peng Hu Zhi-Jun Yang Jie Wang School of Information Science and Engineering Yunnan UniversityKunmingChina Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhenChina Academic Affairs Office Yunnan University of Finance and EconomicsKunmingChina Faculty of Management and Economics Kunming University of TechnologyKunmingChina Department of Electrical and Computer Engineering Lebanese American UniversityBeirutLebanon Department of Computer Science and Engineering University Centre for Research and DevelopmentChandigarh UniversityChennaiIndia Department of Computer Science and Engineering Graphic Era Deemed to Be UniversityDehradunIndia School of Computer Science Fudan UniversityShanghaiChina Rackham Graduate School University of MichiganAnn ArborMIUSA Research and Development Department Youbei Technology Co.LTDKunmingChina School of Mechanical and Power Engineering Zhengzhou UniversityZhengzhouChina

The Equilibrium Optimiser(EO)has been demonstrated to be one of the metaheuristic algorithms that can effectively solve global optimisation *** the paradox between exploration and exploitation operations while enhancing the ability to jump out of the local optimum are two key points to be addressed in EO *** alleviate these limitations,an EO variant named adaptive elite-guided Equilibrium Optimiser(AEEO)is ***,the adaptive elite-guided search mechanism enhances the balance between exploration and *** modified mutualism phase reinforces the information interaction among particles and local optima *** cooperation of these two mechanisms boosts the overall performance of the basic *** AEEO is subjected to competitive experiments with state-of-the-art algorithms and modified algorithms on 23 classical benchmark functions and IEE CEC 2017 function test *** results demonstrate that AEEO outperforms several well-performing EO variants,DE variants,PSO variants,SSA variants,and GWO variants in terms of convergence speed and *** addition,the AEEO algorithm is used for the edge server(ES)placement problem in mobile edge computing(MEC)*** experimental results show that the author’s approach outperforms the representative approaches compared in terms of access latency and deployment cost.

关键词： ANT COLONY optimization CLOUD COMPUTING GENETIC ALGORITHMS SWARM intelligence

来源：评论

学校读者我要写书评

暂无评论

A Systematic Literature Review of Machine Learning and Deep Learning Approaches for Spectral Image Classification in Agricultural Applications Using Aerial Photography

引用

computers, Materials & Continua 2024年第3期78卷 2967-3000页

作者： Usman Khan Muhammad Khalid Khan Muhammad Ayub Latif Muhammad Naveed Muhammad Mansoor Alam Salman A.Khan Mazliham Mohd Su’ud College of Computing and Information Sciences Karachi Institute of Economics and TechnologyKarachi75190Pakistan Faculty of Computing and Informatics(FCI) Multimedia UniversityCyberjaya63100Malaysia Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyAustralia

Recently,there has been a notable surge of interest in scientific research regarding spectral *** potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Aerial Vehicles(UAVs),has captured considerable *** encouraging aspect is their combination with machine learning and deep learning algorithms,which have demonstrated remarkable outcomes in image *** a result of this powerful amalgamation,the adoption of spectral images has experienced exponential growth across various domains,with agriculture being one of the prominent *** paper presents an extensive survey encompassing multispectral and hyperspectral images,focusing on their applications for classification challenges in diverse agricultural areas,including plants,grains,fruits,and *** meticulously examining primary studies,we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical ***,our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural *** findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in ***,we also shed light on the various issues and limitations of working with spectral *** comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.

关键词： Machine learning deep learning unmanned aerial vehicles multi-spectral images image recognition object detection hyperspectral images aerial photography

来源：评论

学校读者我要写书评

暂无评论

Real-Time Object Detection on Edge Devices Using Mobile Neural Networks 2

Real-Time Object Detection on Edge Devices Using Mobile Neur...

引用

2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Jose Reena, K. Nigam, Charul Kirubasri, G. Jayachitra, S. Aeron, Anurag Suganthi, D. Vistas Department of Computer Science Tamilnadu Chennai India Institute of Information Technology and Management Department of It New Delhi India Sona College of Technology Department of Computer Science and Engineering Tamilnadu Salem India Psna College of Engineering and Technology Department of Ece Tamilnadu Dindigul India Dit University Dehradun School of Computing Department of Computer Science and Engineering Dehradun India Saveetha College of Liberal Arts and Sciences Simats Department of Computer Science and Engineering Thandalam Chennai India

ISBN: (纸本)9798350306415

In an era when edge computing rapidly evolves, this study addresses the significant difficulty of real-time object identification on resource-constrained edge devices. We provide a unique neural network model for edge scenarios in this paper. Our model covers object recognition system accuracy, computational efficiency, and speed. Object detection methods that need processing are impractical for edge computing. This paper proposes a Mobile Neural Network tailored to the limitations. Pruning, which decreases model size by 30%, and quantization make the model efficient. On several edge devices, the model had an average accuracy of 92% on Dataset 1 and 89% on Dataset 2. The model's 40-millisecond inference time and 2.3-watt power consumption were impressive. It outperformed standard CNN models and edge-optimized algorithms like YOLOv3 and SSD MobileNet under identical conditions. This study shows that the proposed paradigm can revolutionise edge computing real-time object detection. The model is suitable for high-responsiveness, energy-efficient applications. © 2024 IEEE.

关键词： Edge Computing Mobile Neural Networks Optimization Techniques Real-Time Object Detection

来源：评论

学校读者我要写书评

暂无评论

An Empirical Literature Review on Multi Class Data Stream Learning with Class Imbalance Problem in Real World Data Sources 5

An Empirical Literature Review on Multi Class Data Stream Le...

引用

5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Muley, Rajesh Reddy Battula, Bhanu Prakash Sanaboina, Chandra Sekhar Jawaharlal Nehru Technological University Department of Computer Science and Engineering Andhra Pradesh Kakinada India Kkr & Ksr Institute of Technology & Sciences Department of Computer Science and Engineering Andhra Pradesh Guntur India University College of Engineering Kakinada Jawaharlal Nehru Technological University Department of Computer Science and Engineering Andhra Pradesh Kakinada India

ISBN: (纸本)9798331522100

Data mining and knowledge discovery is one of the cutting-edge fields in science and technology for identifying pressing issues in real world. The amount of data collected has increased due to the quick development of technology and the low cost of internet access. One of the difficult problems is analyzing the massive volume of data that comes in from various data streams. Machine learning research has primarily concentrated on binary classification data problems with high unbalanced data from the earliest literatures published on till recently. When it comes to processing data streams effectively, research on highly unbalanced multi-class data is still mostly underdeveloped. This study focuses on reviews of the models or methods for dealing with extremely unbalanced multi-class data, as well as their advantages and disadvantages and related fields. The objectives of this article are to: comprehend the trend of highly imbalanced multi-class data through examination of related literatures;to analyze the traditional and contemporary approaches to handling highly imbalanced multi-class data;and develop a framework for highly imbalanced multi-class data. After doing the chosen highly imbalanced multi-class dataset analysis and adapting it to the most recent machine learning algorithms or approaches, there will be talks on unresolved issues and the way highly imbalanced multi-class data should go in the future. © 2025 IEEE.

关键词： Labeled data

来源：评论

学校读者我要写书评

暂无评论

Breast Mammogram Analysis and Classification Using Deep Convolution Neural Network

引用

computer Systems Science & engineering 2022年第10期43卷 275-289页

作者： V.Ulagamuthalvi G.Kulanthaivel A.Balasundaram Arun Kumar Sivaraman Department of Computer Science and Engineering Sathyabama UniversityChennai600119India Department of Electrical Electronics and Communication EngineeringNITTTRChennai600113India Centre for Cyber Physical Systems School of Computer Science and EngineeringVellore Institute of Technology(VIT)Chennai600127India School of Computer Science and Engineering Vellore Institute of Technology(VIT)Chennai600127India

One of the fast-growing disease affecting women’s health seriously is breast *** is highly essential to identify and detect breast cancer in the earlier *** paper used a novel advanced methodology than machine learning algorithms such as Deep learning algorithms to classify breast cancer *** learning algorithms are fully automatic in learning,extracting,and classifying the features and are highly suitable for any image,from natural to medical *** methods focused on using various conventional and machine learning methods for processing natural and medical *** is inadequate for the image where the coarse structure matters *** of the input images are downscaled,where it is impossible to fetch all the hidden details to reach accuracy in *** deep learning algorithms are high efficiency,fully automatic,have more learning capability using more hidden layers,fetch as much as possible hidden information from the input images,and provide an accurate *** this paper uses AlexNet from a deep convolution neural network for classifying breast cancer in mammogram *** performance of the proposed convolution network structure is evaluated by comparing it with the existing algorithms.

关键词： Medical image processing deep learning convolution neural network breast cancer feature extraction classification

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：