检索结果-内蒙古大学图书馆

2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Simonthomas, S. Rohith, K. Shalini, K.Shantha Aarupadai Veedu Institute of Technology Department of Computer Science and Engineering Chennai India Aarupadai Veedu Institute of Technology Department of Intelligence and Data Science Chennai India

ISBN: (纸本)9798331539948

The increasing incidence of vehicle-animal collisions poses significant risks to both human and wildlife safety. To address this challenge, the implementation of IoT (Internet of Things) sensor networks for wild-animal detection near roads offers a promising solution. This paper explores the design and deployment of an IoT-based system aimed at detecting the presence of wild animals near roadways, thereby enhancing road safety and wildlife conservation. The proposed system utilizes a network of infrared (IR) sensors, motion detectors, and cameras strategically placed along road sections prone to wildlife crossings. These sensors continuously monitor the area, and upon detecting an animal, they transmit real-time data to a central processing unit via wireless communication protocols. The system then processes this data to generate immediate alerts for drivers through connected road signs or mobile applications, warning them of potential hazards ahead. Additionally, the collected data is stored and analyzed to understand animal movement patterns, which aids in making informed decisions about further preventive measures and the placement of wildlife crossings. The incorporation of machine learning algorithms improves the system's precision in distinguishing between different types of animals and minimizing false alarms. This IoT sensor network not only mitigates the risk of accidents but also contributes to the conservation of wildlife by preventing unnecessary fatalities. This project explores the use of Genetic Algorithms (GA) to optimize a detection system that can identify wild animals near roadsides. The paper summarize with a discussion on the effectiveness of the system, potential challenges in implementation, and outlines future pathways for research and innovation in this domain. © 2024 IEEE.

关键词： Traffic signs

来源：评论

学校读者我要写书评

暂无评论

SAND: semi-automated adaptive network defense via programmable rule generation and deployment

引用

science China(Information sciences) 2022年第7期65卷 121-138页

作者： Haoyu CHEN Deqing ZOU Hai JIN Shouhuai XU Bin YUAN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabHubei Engineering Research Center on Big Data Security School of Cyber Science and EngineeringHuazhong University of Science and Technology Department of Computer Science University of Colorado at Colorado Springs

Cyber security is dynamic as defenders often need to adapt their defense postures. The state-ofthe-art is that the adaptation of network defense is done manually(i.e., tedious and error-prone). The ideal solution is to automate adaptive network defense, which is however a difficult problem. As a first step towards automation, we propose investigating how to attain semi-automated adaptive network defense(SAND). We propose an approach extending the architecture of software-defined networking, which is centered on providing defenders with the capability to program the generation and deployment of dynamic defense rules enforced by network defense tools. We present the design and implementation of SAND, as well as the evaluation of the prototype implementation. Experimental results show that SAND can achieve agile and effective dynamic adaptations of defense rules(less than 15 ms on average for each operation), while only incurring a small performance overhead.

关键词： network defense adaptive defense automated defense programmable defense security services software-defined networking security management

来源：评论

学校读者我要写书评

暂无评论

Application of Machine Learning for Ensuring Water Quality in Aquaculture Ponds

Application of Machine Learning for Ensuring Water Quality i...

引用

2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Bharathi, S.V.Shri Sai, Paidipati Lakshman Ayyappa, Paidipati Charan Ram Reddy, Kakuru Jaya Praveen Reddy, Gangireddy Sai Preetham Rajesh, Katuri Madhava SRMIST- Ktr Department Of Data Science And Business Systems Tamil Nadu Chennai India Vit Department Of Computer Science And Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350352689

Aquaculture plays a pivotal role in meeting the growing global demand for seafood. However, ensuring optimal water quality within aquaculture ponds is a pressing challenge. This project proposes a paradigm shift by integrating machine learning to enhance water quality assurance. The system leverages real-time monitoring, predictive analytics, and adaptable algorithms, contributing to sustainability and responsible aquaculture practices. © 2024 IEEE.

关键词： Water quality

来源：评论

学校读者我要写书评

暂无评论

Hate Speech Detection Using Enhanced Recurrent Neural Network Based on Elephant Herding Optimization on Social Media 3

Hate Speech Detection Using Enhanced Recurrent Neural Networ...

引用

3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Senthilkumar, K. Sathiyabama, S. Ayyamuthukumar, D. Thiruvalluvar Government Arts College Department of Computer Science Namakkal Rasipuram India Department of Artificial Intelligence and Data Science Muthayammal College of Engineering Rasipuram India

ISBN: (纸本)9798350369083

In the past ten years, there has been a rise in nasty behaviors on social media due to the increased use of these platforms. One of the most offensive of these behaviors is hate speech, so users must safeguard themselves against it on social media. This research presents an approach to detect hate speech from social media using an optimized Elman recurrent neural network (ERNN) based on the hybrid local search algorithm (LSA) and elephant herding optimization (EHO) algorithm called LSAEHO. The LSA-EHO algorithm is used to LSA to obtain the optimal solution of EHO, enhance the convergence rate, and avoid the local optima problem of EHO. The LSA-EHO is used to find appropriate hyperparameters for ERNN to enhance the convergence rate and accuracy. The experimental results confirmed that the developed hate speech prediction-based optimized ERNN produced higher prediction accuracy and a faster convergence rate. © 2024 IEEE.

关键词： Elman neural networks

来源：评论

学校读者我要写书评

暂无评论

A Corpus-Based Auto-encoder-and-Decoder Machine Translation Using Deep Neural Network for Translation from English to Telugu Language

引用

SN computer science 2023年第4期4卷 354页

作者： Mahanty, Mohan Vamsi, Bandi Madhavi, Dasari Department of Computer Science and Engineering Vignan’s Institute of Information Technology (A) Visakhapatnam 530046 India Department of Artificial Intelligence and Data Science Madanapalle Institute of Technology and Science Madanapalle 517326 India Department of Computer Science and Engineering Sridevi Women’s Engineering College Hyderabad 500075 India

There is a huge demand for machine translation to design automated auto-encoders that can convert English to the Telugu language. Neural machine translation (NMT) is a new and effective technology that has significantly benefited traditional machine translation strategies. As a result, the primary goal of this work is to propose an auto-encoder–decoder-based deep neural network for translating English to the Telugu language. Deep neural networks are used for learning, where input language is supplied to the neural network following data processing and analysis. The neural network then does auto-tuning, which helps to improve the translation quality. This proposed work produces target sentences in the Telugu language, resulting in a higher Bilingual Evaluation Understudy (BLEU) Score and a lower Word Error Rate (WER). The BLEU finds the similarity of the translated text by Machine Translation models with high-quality references and WER finds better accuracy in identifying the target sentence. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Deep neural network Encoder-and-decoder English-to-Telugu Neural machine translation

来源：评论

学校读者我要写书评

暂无评论

Phasmatodea Population Evolution Algorithm Based on Spiral Mechanism and Its Application to data Clustering

引用

computers, Materials & Continua 2025年第4期83卷 475-496页

作者： Jeng-Shyang Pan Mengfei Zhang Shu-Chuan Chu Xingsi Xue Václav Snášel College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China Department of Information Management Chaoyang University of TechnologyTaichung41349TaiwanChina Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou350118China Faculty of Electrical Engineering and Computer Science VŠB-Technical University of OstravaOstrava70833Czech Republic

data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data *** clustering algorithms,such as K-means,are widely used due to their simplicity and *** paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm(SPPE)to improve clustering *** SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution(PPE)***,a Variable Neighborhood Search(VNS)factor is incorporated to strengthen the local search capability and foster population ***,a position update model,incorporating a spiral mechanism,is designed to improve the algorithm’s global exploration and convergence ***,a dynamic balancing factor,guided by fitness values,adjusts the search process to balance exploration and exploitation *** performance of SPPE is first validated on CEC2013 benchmark functions,where it demonstrates excellent convergence speed and superior optimization results compared to several state-of-the-art metaheuristic *** further verify its practical applicability,SPPE is combined with the K-means algorithm for data clustering and tested on seven *** results show that SPPE-K-means improves clustering accuracy,reduces dependency on initialization,and outperforms other clustering *** study highlights SPPE’s robustness and efficiency in solving both optimization and clustering challenges,making it a promising tool for complex data analysis tasks.

关键词： Phasmatodea population evolution algorithm data clustering meta-heuristic algorithm

来源：评论

学校读者我要写书评

暂无评论

AI for Smart Agriculture - A Deep Learning Based Turmeric Leaf Disease Detection 9

AI for Smart Agriculture - A Deep Learning Based Turmeric Le...

引用

9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Selvaraj, R. Devasena, M. S. Geetha Satheesh, T. Sathishkumar, C. Dr.N.G.P. Institute of Technology Department of Computer Science and Engineering Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Dr.N.G.P. Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Department of Computer Science and Engineering RVS Technical Campus-Coimbatore Coimbatore India

ISBN: (纸本)9798350377972

Plants is one of the major food sources for world population. In addition to that, some plants and their outcomes are used in food preparation. One of the plants which is widely used in foods and having huge medicinal value is turmeric. Curcuma long the botanically named spice turmeric has large anti-inflammatory properties, antioxidants, etc., the tropical perennial plant is cultivated worldwide. Diseases in plant leaves is one of the serious factors that affects the turmeric cultivation. Leaf disease reduces the production and it requires continuous monitoring to avoid total loss. Manual monitoring is challenging when the crop is planted in large fields. Due to industrial revolution and smart agriculture concepts, artificial intelligence- based disease detection procedure is presented in recent times. Thus, in this research work a deep learning-based turmeric leaf disease detection model is presented using AlexNet. The leaf disease classification model is experimented with our own dataset which includes diseases like Leaf Spot Disease, Leaf Blight, Leaf Rot, and Leaf Curl. The experimentation results provide the maximum accuracy of proposed leaf disease classification model as 95.5% which is better than traditional machine learning models. © 2024 IEEE.

关键词： AlexNet classification Deep learning Turmeric Leaf disease

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

Metaheuristic Based Clustering with Deep Learning Model for Big data Classification

引用

computer Systems science & engineering 2023年第1期44卷 391-406页

作者： R.Krishnaswamy Kamalraj Subramaniam V.Nandini K.Vijayalakshmi Seifedine Kadry Yunyoung Nam Department of Electronics and Communication Engineering University College of Engineering AriyalurAriyalur621704India Department of Biomedical Engineering Faculty of EngineeringKarpagam Academy of Higher EducationCoimbatore641021India Department of Computer Science and Engineering Sona College of TechnologySalem636005India Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai600077India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Recently,a massive quantity of data is being produced from a distinct number of sources and the size of the daily created on the Internet has crossed two *** the same time,clustering is one of the efficient techniques for mining big data to extract the useful and hidden patterns that exist in ***-based clustering techniques have gained significant attention owing to the fact that it helps to effectively recognize complex patterns in spatial *** data clustering is a trivial process owing to the increasing quantity of data which can be solved by the use of Map Reduce *** this motivation,this paper presents an efficient Map Reduce based hybrid density based clustering and classification algorithm for big data analytics(MR-HDBCC).The proposed MR-HDBCC technique is executed on Map Reduce tool for handling the big *** addition,the MR-HDBCC technique involves three distinct processes namely pre-processing,clustering,and *** proposed model utilizes the Density-Based Spatial Clustering of Applications with Noise(DBSCAN)techni-que which is capable of detecting random shapes and diverse clusters with noisy *** improving the performance of the DBSCAN technique,a hybrid model using cockroach swarm optimization(CSO)algorithm is developed for the exploration of the search space and determine the optimal parameters for density based ***,bidirectional gated recurrent neural network(BGRNN)is employed for the classification of big *** experimental validation of the proposed MR-HDBCC technique takes place using the benchmark dataset and the simulation outcomes demonstrate the promising performance of the proposed model interms of different measures.

关键词： Big data data classification clustering mapreduce dbscan algorithm

来源：评论

学校读者我要写书评

暂无评论

Classification of Pigmented Skin Lesions using Early Fusion of Multi-Deep Features and Support Vector Machine

Classification of Pigmented Skin Lesions using Early Fusion ...

引用

2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Chowdam, Jayanth Panigrahi, Sibarama National Institute of Technology Intelligent Data Analytics Laboratory Department of Computer Science and Engineering Odisha Rourkela India

ISBN: (纸本)9798350369175

One of the primary organ is the skin. It protects and separates humans from various dangers. Nevertheless, the skin is susceptible to damage and can develop pigmented lesions. Manual classification of Pigmented Skin Lesions(PSL) is a major challenge due to availability and bias of physician, time consuming, invasive methods are painful and with elevated expenses. To overcome this, in this paper, two automated computer vision based approaches (using and without using dimensionality reduction) are employed by modelling multi-deep features using Support Vector Machine(SVM) for classification of PSL. The suggested system comprises of two operational stages. Initially, the performance of six Convolutional Neural Network models (VGG16, VGG19, AlexNet, Xception, ResNet50, and EfficientB3) in deep feature extraction and three types of hand-crafted features extraction methods (HOG, LBP, and SIFT) employing SVM are evaluated. The top three CNN models (AlexNet, ResNet50, and EfficientB3) based on accuracy are used to form binary and ternary early fusion hybrid models to classify the skin lesions. In the second stage, combining multiple CNN models resulted in increased dimensionality and number of features. These features are then classified using SVM once in the higher dimension and again by reducing the dimensionality by applying Principal Component Analysis(PCA). The accuracies of the combined models are then compared. It is observed that the ternary early fusion model with SVM (Alexnet+Resnet50+EfficientB3-SVM) produces the highest accuracy of 82.22% with higher number of features whereas the same model while using PCA produces the accuracy of 81.57%. Consequently, the findings indicate the suggested techniques are useful for classification of PSL. © 2024 IEEE.

关键词： Dimensionality reduction

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：