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5th International Conference on Smart Technologies in Data Science and Communication, SMART-DSC 2022

作者： Joshua, Eali Stephen Neal Vardhan, K. Asish Rao, N. Thirupathi Bhattacharyya, Debnath Department of Computer Science and Engineering Vignan’s Institute of Information Technology AP Visakhapatnam530016 India Department of Computer Science and Engineering Bullayya College of Engineering for Women AP Visakhapatnam India Department of Computer Science and Engineering Koneru Laksmaiah Education Foundation AP Vaddeswaram Guntur522302 India

ISBN: (纸本)9789811968792

The researchers find it difficult to extract information from a large data set through a standard function. It is found insufficient of standard functions to extract needed information. It has been considered that the k-means algorithm in the situation where the data is too enormous to be stored in main memory and must be retrieved sequentially, such as from a disk, and where it must be used as slight memory as possible. The k-means clustering also converges very quickly when it is employed to obtain data from huge data collections. It is also on other hand, k-means has some disadvantages too, and it includes affluent computation by getting cluster centers which are randomly selected at initial. It influences the two factors, such as performance of the algorithm and number of clusters initialization. In this paper, an improved k-means algorithm in terms of data clash strainer mechanism is given. The data clash strainer mechanism is implemented through a function regional centroid component (RCC) mechanism which is added to the standard k-means algorithm. This density-based recognition mechanism is built on the properties of clash data. The clustering result is effectively enhanced by ignoring the clash data prior to the process of data clustering. Hence, the improved algorithm offers a great accuracy when compared to other existing cluster algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cluster Data clash strainer K-means RCC

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Analysis for Text Detection on Scenic Images Using Deep Learning 3

Analysis for Text Detection on Scenic Images Using Deep Lear...

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3rd International Conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Vardhan, Harsh Joshi, Kamal Chhabra, Prachi Shrivastava, Aparna Kumar, Sunil Chhabra, Gunjan Jss Academy of Technical Education Department of Information Technology UP Noida India Csjm University Deportment of Information Technology UP Kanpur India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350342338

Text detection in scenic images is the process of precisely localizing instances of text in them. This detection is required for a numerous computer vision applications including text recognition, autonomous vehicles, script narrators, translation apps etc. Text detection and localization refer to the process of identifying and locating text within an image or document. Scene text detection was mostly limited to putting together handcrafted low-level attributes of the texts contained in images until the emergence of deep learning in this area. A combination of methods or exploring domainspecific approaches help in enhancing the efficacy of detecting text in scenic images. In this paper, we have implemented the performance of three text detection models EAST, CRAFT and Text Fuse Net on two benchmark public datasets used for detection and localization. Experiments on two benchmark datasets show that the Text Fuse Net achieves state-of-the-art performance. Specifically, we have achieved an F measure of 94.6% and 92.2% on IC-2013 and IC-2015. respectively, followed by CRAFT [89.8%, 84.8%] and EAST [85.3%, 80.7%1. © 2023 IEEE.

关键词： CRAFT EAST IC-13 IC-15 Text Fuse Net

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Robust graph‐based localization for industrial Internet of things in the presence of flipping ambiguities

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CAAI Transactions on Intelligence technology 2023年第4期8卷 1140-1149页

作者： Mian Imtiaz ul Haq Ruhul Amin Khalil Muhannad Almutiry Ahmad Sawalmeh Tanveer Ahmad Nasir Saeed Faculty of Electrical Engineering Swedish College of Engineering and TechnologyWah CanttPakistan Department of Electrical Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and TechnologyPeshawarPakistan Department of Electrical Engineering Remote Sensing UnitNorthern Border UniversityArarSaudi Arabia Data Science and Artificial Intelligence Department‐College of Science and Information Technology Irbid National UniversityIrbadJordan Innovation Education and Research Center for On‐Device AI Software(Bk21) Department of Computer Science and EngineeringChungnam National UniversityDaejeonRepublic of Korea Department of Electrical and Communication Engineering United Arab Emirates University(UAEU)Al AinUnited Arab Emirates

Localisation of machines in harsh Industrial Internet of Things(IIoT)environment is necessary for various ***,a novel localisation algorithm is proposed for noisy range measurements in IIoT *** position of an unknown machine device in the network is estimated using the relative distances between blind machines(BMs)and anchor machines(AMs).Moreover,a more practical and challenging scenario with the erroneous position of AM is considered,which brings additional uncertainty to the final position ***,the AMs selection algorithm for the localisation of BMs in the IIoT network is *** those AMs will participate in the localisation process,which increases the accuracy of the final location ***,the closed‐form expression of the proposed greedy successive anchorization process is derived,which prevents possible local convergence,reduces computation,and achieves Cramér‐Rao lower bound accuracy for white Gaussian measurement *** results are compared with the state‐of‐the‐art and verified through numerous simulations.

关键词： Cramér‐Rao lower bound greedy successive anchorization industrial internet of things localization

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Cross-Domain Sentiment Classification with Mere Contrastive Learning and Improved Method 3

Cross-Domain Sentiment Classification with Mere Contrastive ...

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3rd International Conference on Artificial Intelligence and computer information technology, AICIT 2024

作者： Zhang, Li Wei, Xing Yang, Fan Zhao, Chong Wen, Bin Lu, Yang School of Computer and Information Hefei University of Technology Hefei China Anhui Mine IOT and Security Monitoring Technology Key Laboratory Anhui China Hefei University of Technology Hefei China Engineering Quality Education Center of Undergraduate School Hefei University of Technology Hefei China

ISBN: (纸本)9798350365443

Cross-domain sentiment classification refers to training a classification model using labeled data in the source domain, and migrating to the target domain to perform sentiment classification on unlabeled data and meet the performance requirements, so as to deal with the scarcity of labeled data and the time-consuming manual labeling work. However, most previous studies use cross-entropy-based methods, and the cross-entropy loss will bring problems such as poor stability and weak generalization ability. In this paper, instead of using cross-entropy, we propose a model that uses only contrastive learning on BERT and applies mutual information maximization. Past research has devoted to exploring algorithms for manually selecting common features, and traditional supervised methods cannot meet the needs of deep learning. In this regard, we use the Wasserstein distance to estimate the feature representations of the source domain and the target domain to share domain-invariant information between the both domains, and learn domain-invariant features with an unsupervised adaptive method. Due to the scarcity of labels on the target domain, we apply mutual information maximization to balance the model's predictions. Experiments on datasets show that our model has an average accuracy of 92.40% on cross-domain sentiment classification and 93.03% on multi-domain sentiment classification, both higher than CLIM and COBE on cross-domain and multi-domain tasks. The visualization results more intuitively demonstrate the advanced performance of our model, and the ablation experiments clearly reflect the effectiveness of our method. © 2024 IEEE.

关键词： Contrastive Learning

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Improving Food Safety by IoT-based Climate Monitoring and Control Systems for Food Processing Plants

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Remote Sensing in Earth Systems Sciences 2025年第2期8卷 387-399页

作者： Kolikipogu, Ramakrishna Shivaputra Muniyandy, Elangovan Maroor, Jnaneshwar Pai Lakshmi, Gundala Venkata Rama Konduri, Bhagavan Naveenkumar, R. Department of Information Technology Chaitanya Bharathi Institute of Technology Hyderabad India Department of Electronics and Communication Engineering Dr. Ambedkar Institute of Technology Bengaluru India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Business Administration Nitte Institute of Professional Education Nitte (Deemed to Be University) Karnataka Mangaluru India Department of Computer Science and Engineering CVR College of Engineering Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Computer Science and Engineering Chandigarh Colleges of Engineering Chandigarh Group of Colleges Jhanjeri Punjab Mohali 140307 India

Web different IoT apps offer lot more options for every player in the meat supply chain to monitor, control, and optimize their manufacturing method. Beginning within late 2017, industrial IoF2020 program will concentrate toward massive deployments of IoT via three use cases in the meat supply chain: pig farm management, poultry chain tracking, and for livestock visibility with openness. This initiative will include eleven collaborators spanning 5 nations. Installing and integrating IoT sensors for environmental and livestock management would alleviate producers’ difficulty getting knowledge that data needed regarding continuously tracking how well they are doing. Alert detection is additionally being created, integrating multiple sources of data in order to give farmers helpful input along with communicating news with other partners. By carrying out this, preventative or remedial measures can be done for infections, swine taint, bird mortality, feed waste, the natural world, et cetera. Additionally, EPCIS-based traceability from farm to fork will be allowed to ensure customers may get precise data about the source and caliber of their livestock. Each of these three use scenarios’ existing stage in work along with future plans are expected to be discussed. IoF2020 seeks to support the digital revolution in smart farming by tackling various educational and commercial problems while also promoting its use throughout Europe. It maintains mechanical necessary degree and security against physical along with toxic contaminants during industrial butchering of chicken, a lot measures have been taken. Buyers’ worries about food safety and health are the result of eating chicken increasingly. Our authorities have begun many kinds of initiatives aimed at improving safeguards for food. Our goal is to create an apparatus that alerts the factory farms affecting potential threats for product hygiene of food. With such setup, the detector can be mounted on top of the poultry slaugh

关键词： Food safety Internet of Things Meat processing Monitoring Supply chain

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A Dynamic Interest-Aware Message-Passing GCN for Recommendation 24

A Dynamic Interest-Aware Message-Passing GCN for Recommendat...

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24th IEEE International Conference on Software Quality, Reliability and Security Companion, QRS-C 2024

作者： He, Weichao Zhu, Yi Song, Mei Wu, Zixin Hao, Guosheng School of Computer Science and Technology Jiangsu Normal University Jiangsu Xuzhou China Jiangsu Engineering Technology Research Center of Ict in Education Jiangsu Xuzhou China Ministry of Industry and Information Technology Key Laboratory of Safety-Critical Software Jiangsu Nanjing China

ISBN: (纸本)9798350365658

Over the past few years, Graph Convolutional Networks (GCN) have emerged as a promising tool in the field of recommendation. They have the ability to effectively learn user and item embeddings by leveraging collaborative signals from higher-order neighbors. Nevertheless, the recommendation models based on GCN also face a common issue. As more layers are added, the node embeddings start to become increasingly similar and eventually become indistin-guishable, leading to a decline in performance. To address the issues at hand, we present a recommendation model called DIMP-GCN. This model utilizes a Dynamic Interest-aware Message-Passing approach, where users and interacted items are organized into distinct subgraphs based on their interests. Within each subgraph, single-layer graph convolution operations are performed. As a result, the users who have been updated are divided based on their interests once again, leading to dynamic changes in subgraph partitioning as the network layers stack. Nodes are able to acquire more accurate representations through this process. We conduct extensive experiments on three established datasets, showcasing the superior performance of our DIMP-GCN compared to other state-of-the-art GCN-based recommendation models. © 2024 IEEE.

关键词： Message passing

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Leveraging Internet of Things (IoT) Sensors and Deep Learning Techniques for Precision Agriculture 1

Leveraging Internet of Things (IoT) Sensors and Deep Learnin...

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1st International Conference on Innovative engineering Sciences and Technological Research, ICIESTR 2024

作者： Wilfred Blessing, N.R. Pyingkodi, M. Wasim Haidar, S.K. Palarimath, Suresh Paravathavarthini, S. Balakumar, C. Sheeja Kumari, V. Sutherlin Subitha, G. Seldev Christopher, S. University of Technology and Applied Sciences-Ibri College of Computing and Information Sciences Ibri Oman Kongu Engineering College Department of Computer Applications Erode India University of Technology and Applied Sciences-Salalah College of Computing and Information Sciences Salalah Oman Kongu Engineering College Department of Computer Technology Erode India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Saveetha Institute of Medical and Technical Sciences Department of Computational Intelligence Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Aruthenganvilai India St. Xavier's Catholic College of Engineering Department of Computer Science and Engineering Nagercoil India

ISBN: (纸本)9798350348637

Precision agriculture, driven by advancements in technology, aims to optimize farming practices by utilizing data and technology to enhance efficiency, productivity, and sustainability This research introduces an innovative pilot investigation centered on the convergence of two revolutionary technologies are the Internet of Things (IoT) and Deep Learning. With a specific focus on advancing precision agriculture, the primary objective of this study is to evaluate the combined effects of data collection facilitated by IoT and analytical capabilities of deep learning in enhancing and optimizing various agricultural processes. The integration of IoT in agriculture has revolutionized data acquisition, employing an array of sensors to monitor critical parameters such as soil moisture, temperature, and crop health. Concurrently, Deep Learning, a subset of artificial intelligence, exhibits the potential to glean actionable insights from voluminous datasets, offering advanced analytics and predictive capabilities. This study investigates the practical implementation and efficacy of this integration in a controlled agricultural setting. Sensors strategically positioned in the pilot study capture real-time data, while deep learning algorithms process and analyze this information. The primary objectives include evaluating the effectiveness of this combined technology in optimizing irrigation schedules, predicting crop yields, and identifying anomalies in crop health. Preliminary findings underscore the transformative potential of IoT and Deep Learning, empowering farmers with real-time data for informed decision-making. Key considerations encompassed in the study include IoT Sensors, Deep learning algorithms, and user adoption. The research not only sheds light on the technical intricacies of the integration but also delves into the challenges and opportunities inherent in merging these technologies within the agricultural landscape. As agriculture transitions towards the next

关键词： Smart agriculture

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Visual Modeling of Rice Root Growth Based on B-Spline Curve

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Wuhan University Journal of Natural Sciences 2022年第1期27卷 85-92页

作者： YANG Le TANG Zizong WU Panpan PENG Jun SHI Junlin LONG Lan School of Computer and Information Engineering Jiangxi Agricultural UniversityNanchang 330045JiangxiChina Key Laboratory of Agricultural Information Technology in Jiangxi Higher Education Institutions Nanchang 330045JiangxiChina

As a major food production crop in China,the growth and development of rice is an extremely complex systemic process,and the root system is the main organ for rice to obtain ***,3D modeling and visualization of the rice root system can help to further understand its morphology,structure and function,and provide an aid for scientific cultivation of rice and improving rice yield for decision *** this paper,a mathematical model of the rice root system is established based on the B spline curve combined with the L-system approach,using mathematical knowledge based on the 3D morphological characteristics of the real rice root *** B-Spline Curve is chosen to simulate this,and the recursive definition of B-Spline Curve and its formula are used to realize the modeling of the rice root system *** on the mathematical method of rice root system integration,the bending effect of rice root system at different periods and different growth positions is ***,the L-system combined with B-Spline Curve is used to construct a rice root system model and realize the rice root system visualization *** simulated image is closer to the real rice root system image in terms of morphological structure and has a strong sense of realism.

关键词： rice root system visual modeling L-system B-Spline Curve

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Implementation of A Deep Learning Framework for Intelligent Intrusion Detection in Internet of Things Networks 5

Implementation of A Deep Learning Framework for Intelligent ...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Jeyapriyanga, S. Ravi, C.N. Rathiya, R. Kalaivani, K. Rama Devi, C. Kumar, Kallakunta Ravi St.Joseph's Institute of Technology Department of Information Technology Chennai India Cmr Engineering College Computer Science and Engineering Department Telangana Hyderabad India Information Technology Dr. Ngp Institute of Technology Coimbatore India School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India St.Joseph's College of Engineering Electrical and Electronics Engineering India Koneru Lakshmaiah Education Foundation Department of Ece AP Vaddeswaram India

ISBN: (纸本)9798350321425

Internet of Things (IoT) has ushered in a new age that benefits humanity greatly. From automated healthcare to energy and transportation, the Internet of Things covers many subject topics. IoT devices are vulnerable to several cyberattacks due to their limited resources. As data output has expanded exponentially from a zettabyte to a trillion per year, computer and IoT network expansion has surged. Network growth has created new security issues. In such large data sets, intrusions are hard to spot. Today's networks are used for 'smart' dwellings and cities, grids, devices, objects, online commerce, banking, government, etc. Due to internet data privacy and security concerns, Intrusion Detection Systems (IDS) have proliferated. Privacy, security, and resilience would suffer if IDS protection fails. Traditional defences can't handle modern attacks. Cutting-edge deep learning methods may automatically detect intrusions and network anomalies. This study's main goal is to evaluate intrusion detection using deep learning algorithms and compare the outcomes to other systems. This research work has proposed a novel deep-learning-based IoT invasion detection method. The cutting-edge Internet of Things dataset contains IoT traces and real-world attack traffic including DoS, DDoS, data harvesting, and theft attempts. Finally, all publicly available network-based IDS datasets are analyzed in this research study. The accuracy, false alarm rate, recall, precision, f-1 score, and detection rate of many deep learning IDS algorithms have been examined. Furthermore, this research study has investigated the network security and privacy issues and solutions. © 2023 IEEE.

关键词： Internet of things

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Computing Challenges of UAV Networks: A Comprehensive Survey

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computers, Materials & Continua 2024年第11期81卷 1999-2051页

作者： Altaf Hussain Shuaiyong Li Tariq Hussain Xianxuan Lin Farman Ali Ahmad Ali AlZubi School of Computer Science&Technology Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of EducationChongqing University of Posts and TelecommunicationsChongqing400065China School of Computer Science and Technology Zhejiang Gongshang UniversityHangzhou310018China School of Artificial Intelligence Nanjing University of Information Science&TechnologyNanjing210044China Department of Computer Science and Engineering School of ConvergenceCollege of Computing and InformaticsSungkyunkwan UniversitySeoul03063Republic of Korea Department of Computer Science Community CollegeKing Saud UniversityRiyadh11495Saudi Arabia

Devices and networks constantly upgrade,leading to rapid technological ***-dimensional(3D)point cloud transmission plays a crucial role in aerial computing terminology,facilitating information *** network types,including sensor networks and 5G mobile networks,support this ***,Flying Ad hoc Networks(FANETs)utilize Unmanned Aerial Vehicles(UAVs)as nodes,operating in a 3D environment with Six Degrees of Freedom(6DoF).This study comprehensively surveys UAV networks,focusing on models for Light Detection and Ranging(LiDAR)3D point cloud compression/*** topics covered include autonomous navigation,challenges in video streaming infrastructure,motivations for Quality of Experience(QoE)enhancement,and avenues for future ***,the paper conducts an extensive review of UAVs,encompassing current wireless technologies,applications across various sectors,routing protocols,design considerations,security measures,blockchain applications in UAVs,contributions to healthcare systems,and integration with the Internet of Things(IoT),Artificial Intelligence(AI),Machine Learning(ML),and Deep Learning(DL).Furthermore,the paper thoroughly discusses the core contributions of LiDAR 3D point clouds in UAV systems and their future prediction along with mobility *** also explores the prospects of UAV systems and presents state-of-the-art solutions.

关键词： Autonomous vehicles UAV systems UAV transmission UAV in healthcare future of UAV system security measures

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