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A Deep Analysis on Security Aspects of Cloud Computing Ontologies 5

A Deep Analysis on Security Aspects of Cloud Computing Ontol...

引用

5th ieee international conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Bisht, Girish Singh Gobi, N. Kapse, Vinod Mansiram Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Science and Information Technology Bangalore India Noida Institute of Engineering and Technology Department of Electronics and Communication Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350383522

The primary objective of this research is to get a comprehensive understanding of Cloud Computing ontologies, their applications, and their specific areas of interest. The proposed research methodology for this systematic review comprises a well-defined research topic, criteria for including and excluding studies, and a comprehensive search strategy to identify relevant source papers for the review. The chosen research is categorized into four main areas: elucidation of cloud resources and services, elaboration of cloud security, clarity of cloud interoperability, and understanding of cloud services discovery and selection. Utilizing ontologies to improve the security and smooth integration of cloud computing services uncovers various challenges and identifies areas for more research. © 2024 ieee.

关键词： Cloud Computing Cloud service ontology systematic review

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Research on Kiwi Surface Defect Detection Algorithm Based on Improved YOLOv7-Tiny 6

Research on Kiwi Surface Defect Detection Algorithm Based on...

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6th international conference on Communications, Information System and computer engineering, CISCE 2024

作者： Xu, Chuan Su, Jianhuan School of Automation Guangxi University of Science and Technology Guangxi province Liuzhou China School of Artificial Intelligence and Control Hechi University Guangxi province Hechi China

ISBN: (纸本)9798350353174

In order to solve the problems of low precision and slow speed of kiwifruit defect detection in agricultural production line, an improved target detection algorithm GC-yolov7-tiny based on Yolov7-tiny is proposed in this paper, to detect the defect of kiwifruit on the production line in real time. Firstly, the data enhancement method is used to enrich the dataset by enhancing transformations and other operations on the collected dataset to improve the generalization ability of the model. Secondly, in the Backbone part of the model, the attention mechanism CBAM has been integrated into the MP module, resulting in the MP-CBAM structure. This innovation significantly enhances the model's feature extraction capabilities, leading to a notable improvement in detection efficiency. The optimized C3-Ghost lightweight network structure has been adopted to substitute certain ELAN modules within the backbone network. This modification effectively reduces model parameters while enhancing the detection rate. Finally, In the original network, the CIOU_Loss function has been substituted with SIOU_Loss in order to enhance both the convergence rate of the model and its detection efficiency. According to the experimental results, the defect detection mAP value of the proposed algorithm reaches 93%, and the model size is 8.5M. Compared with the original model, the mAP value increases by 0.9%, and the model size decreases by 3.8M. The proposed algorithm not only ensures a certain detection accuracy, but also reduces the size of the network model to meet the needs of real-time detection. © 2024 ieee.

关键词： Macroinvertebrates

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Underpinning of Resource-Constrained IoT Networks: Analysis and Countermeasures for Attacks and Vulnerabilities 14

Underpinning of Resource-Constrained IoT Networks: Analysis ...

引用

14th ieee international conference on Communication Systems and Network Technologies, CSNT 2025

作者： Singh, Umeshwer Sood, Manu Bhadoria, Robin Singh Department of Computer Science Himachal Pradesh University Shimla India Department of Computer Science and Engineering National Institute of Technology Hamirpur Himachal Pradesh India

ISBN: (纸本)9798331531935

The Internet of Things integrate various electronics devices on common platform as objects over the Internet. IoT devices have limited capabilities, such as low power, less memory, and low computational processing. Besides these constraints, IoT deals with a large amount of sensitive and personal data, making the IoT networks highly prone to attacks and various vulnerabilities. This paper summarizes the different possible attacks and vulnerabilities on resource-constrained IoT networks and the security requirements compromised. It also presents available countermeasures for these possible attacks. Furthermore, the paper discusses the simulation-based parameters for results on detection of such vulnerabilities and attacks in IoT networks. The paper presents protocols for resource-constrained IoT networks and maps different attacks with existing protocols. Finally, the paper summarizes the vulnerabilities in the IoT specially designed for resource-constrained environments and, discusses the potential impact of these vulnerabilities, and outlines countermeasures to address the same. © 2025 ieee.

关键词： HTTP

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Facial and Ocular-Based Cattle Biometric Recognition Using Convolutional Neural Networks

Facial and Ocular-Based Cattle Biometric Recognition Using C...

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2025 ieee international Students' conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Balaji, P. Mary, Martina Jose M. Devi, Shyamala R. Akash, S. Balachandar, S. Devaraj, M. Ifet College of Engineering Department of Cse Tamilnadu India

ISBN: (纸本)9798331529833

Biometric recognition of cattle will be one of the most effective ways of improving livestock management, controlling diseases, and breeding. Traditional methods, such as ear tagging, are invasive, prone to errors, and lost or even tampered with. This paper proposes a biometric recognition system for cattle using facial and ocular features as signatures to identify individual cattle using DNN. It utilizes state-of-the-art CNNs to extract and analyze features from cattle faces and eyes to deliver accurate identification. The use of biometric markers like muzzle patterns, the structure of the eye, and symmetry for meaningful characterization provides an alternative approach to traditional methods that are non-invasive and reliable. Deep learning-based model training on a diverse training dataset of cattle images enables the network to learn robust features that generalize well across different breeds and environmental conditions. © 2025 ieee.

关键词： Convolutional neural networks

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On-Tree Coconut Maturity Estimation and Age Prediction Using Machine Vision 1

On-Tree Coconut Maturity Estimation and Age Prediction Using...

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1st international conference on Trends in engineering Systems and Technologies, ICTEST 2024

作者： Syamraj, B.S. Sasidharan, Sikha Arya, M.H. Hari Priya, V. Pranav, R. Mathew, Milan Georgy Providence College of Engineering Computer Science and Engineering Department Chengannur India

ISBN: (纸本)9798350307795

This work proposes an on-tree coconut maturity estimation and age prediction application using machine vision. It is an Android application made using Flutter that can be used to estimate the maturity of the coconut image that will be uploaded to the system. The system uses CNN for image classification purposes. In the Android application, there are two options for uploading the image of the coconut either from camera or gallery. On uploading the image, its corresponding maturity and age will be displayed on the screen. This approach also improves post-harvest quality standards as farmers can identify coconut at its right maturity and not over-matured. © 2024 ieee.

关键词： computer vision

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AgroXAI: Explainable AI-Driven Crop Recommendation System for Agriculture 4.0

AgroXAI: Explainable AI-Driven Crop Recommendation System fo...

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2024 ieee international conference on Big Data, BigData 2024

作者： Turgut, Özlem Kök, Ibrahim Özdemir, Suat Pamukkale University Department of Computer Engineering Denizli Turkey Ankara University Department of Ai and Data Engineering Ankara Turkey Hacettepe University Department of Computer Engineering Ankara Turkey

ISBN: (纸本)9798350362480

Today, crop diversification in agriculture is a critical issue to meet the increasing demand for food and to improve food safety and quality. This issue is considered to be the most important challenge for the next generation of agriculture due to diminishing natural resources, limited arable land and unpredictable climatic conditions caused by climate change. In this paper, we employ emerging technologies such as the Internet of Things (IoT), machine learning (ML) and explainable artificial intelligence (XAI) to improve operational efficiency and productivity in the agricultural sector. Specifically, we propose an edge computing-based explainable crop recommendation system, AgroXAI, which suggests suitable crops for a region based on weather and soil conditions. In this system, we provide local and global explanations of ML model decisions with methods such as ELI5, LIME, SHAP, which we integrate into ML models. More importantly, we provide regional alternative crop recommendations with the Counterfactual explainability method. In this way, we envision that our proposed AgroXAI system will be a platform that provides regional crop diversity in the next generation agriculture. © 2024 ieee.

关键词： Smart agriculture

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Optimizing Smart Grid Energy and Load Prediction with Namib Beetle Algorithm and Self-Adaptive Physics-Informed Neural Networks 3

Optimizing Smart Grid Energy and Load Prediction with Namib ...

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3rd international conference on automation, Computing and Renewable Systems, ICACRS 2024

作者： Santhakumar, D. Mishra, Dilip Mallala, Balasubbareddy Sridhar, S. Dhanadhya, Trupti Department of Computer Science and Engineering Saveetha School of Engineering Thandalam Tamil Nadu Chennai India Department of Science and Technology ICFAI University Chhattisgarh Raipur India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Telangana Hyderabad India Department of Research Rajalakshmi Institute of Technology Tamil Nadu Chennai India Department of Electrical Engineering Dr. D Y Patil Institute of Technology Maharashtra Pune India

ISBN: (纸本)9798331532420

For smart grid (SG) operations to be effective and improve resource allocation and grid stability, it is essential to forecast electricity load and renewable energy generation. However, because renewable energy sources are very variable and load demand patterns in smart grid environments are complicated, it is still difficult to estimate renewable energy output and electrical load with any degree of accuracy. This paper proposes a hybrid approach for predicting wind energy generation. The proposed method is combined execution of Namib Beetle Optimization Algorithm (NBOA) and Self-Adaptive Physics-Informed Neural Networks (SAPINN). Hence, it is named as NBOA-SAPINN Approach. The input data is collected from the Germany Wind Energy and Weather Dataset. Then, the input data is fed to pre-processing using Generalized Multi-kernel Maximum Correntropy Kalman Filter (GMKMCKF). The GMKMCKF is used to normalize the data. The pre-processed data is then fed to SAPINN for wind energy generation forecast. To achieve this prediction, the SAPINN is fine-tuned using the NBOA. The proposed method is implemented in MATLAB. It is therefore feasible to use several performance criteria to measure the efficiency of the proposed NBOA-SAPINN model such as Accuracy, Root Mean Square Error (MSE), Mean Squared Error (MSE). The NBOA-SAPINN model achieves an accuracy of 98%, with MSE and RMSE values converging to 0.23 and 0.22 by epoch 100 compared with existing methods such as Gated Recurrent Unit-Recurrent Neural Network (GRU-RNN), Feed-Forward Neural Network (FFNN), Deep Reinforcement Learning (DRL), and Recurrent Trend Predictive Neural Network (RTNN). © 2024 ieee.

关键词： Mean square error

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Energy-Efficient Communication Protocols for IoT Devices 9

Energy-Efficient Communication Protocols for IoT Devices

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9th international conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Rahamathunnisa, U. Vivekanand, Chettiyar Vani Chithras, T. Sreedevi, E. Kiran, Venneti Rajendiran, M. School of Computer Science Engineering And Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India Rmk College of Engineering And Technology Department of Electronics And Communication Engineering Tamil Nadu Thiruvallur India Veltech Rangarajan Dr.Sagunthala R&d Institute of Science And Technology Department of Electrical And Electronics Engineering Chennai Tamil Nadu Avadi600062 India KoneruLakshmaiah Education Foundation Department of Computer Science And Engineering Andhra Pradesh Vaddeswaram India Andhra Pradesh Surampalem India Panimalar Engineering College Department of Computer Science And Engineering Tamil Nadu Chennai600123 India

ISBN: (纸本)9798350365092

The development and optimisation of energy-efficient communication protocols that are specifically adapted for Internet of Things (IoT) devices is the focus of this research. The proliferation of interoperable bias has given rise to the converse girding energy consumption, which has come a consummate concern in the contemporary period. The primary end of this bid is to probe innovative approaches and protocols with the capacity to reduce the energy consumption of Internet of Effects(IoT) connections. As a result, the bias will retain the capability to attain an extended functional lifetime while coincidently producing a lowered environmental footmark. The presented protocols aim to achieve a result that balances the utilisation of energy coffers with the conservation of dependable communication. A thorough examination of the presently employed protocols, coupled with the perpetration of sophisticated optimisation ways, will be the styles employed to achieve this thing. Theprovides a significant donation to the development and prosecution of communication protocols that effectively address the unique energy constraints encountered by Internet of effects(10T)- connected ecosystems. This contributes to the enhancement of effectiveness and sustainability in the world of networked bias, which is expanding at an accelerated rate. © 2024 ieee.

关键词： Energy efficiency

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ROSE-BOX: An Approach for Intrusion Detection in Industrial Internet of Things 22

ROSE-BOX: An Approach for Intrusion Detection in Industrial ...

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22nd ieee international Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Peng, Silin Han, Yu Liang, Xiaojun Yang, Chunhua Gui, Weihua Zhou, Nan School of Intelligent Systems Engineering Sun Yat-sen University Peng Cheng Laboratory Shenzhen China Sun Yat-sen University School of Intelligent Systems Engineering Shenzhen China Peng Cheng Laboratory Shenzhen China Central South University School of Automation Changsha China School of Automation Central South University Peng Cheng Laboratory Changsha China

ISBN: (纸本)9798331509712

With the rapid development of industrial network, Industrial Internet of Things (IIoT) has become an indispensable part of industrial network development. However, due to the vulnerability of Industrial Internet of Things to network intrusion attacks. Therefore, anomaly detection in IIoT is particularly important. In this paper, an effective intrusion detection approach ROSE-BOX (Random fOrest, SmotE, BO-Xgboost) is proposed to detect multi-class cyberattacks based on Random Forest, SMOTE and BO-XGBoost in IIoT. It is worth mentioning that BO-XGBoost is obtained by optimizing the parameters of XGBoost using Bayesian optimization. Finally, compared with other existing methods, the proposed approach has better detection performance, with an accuracy rate of over 99.85%. © 2024 ieee.

关键词： Network intrusion

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Formal Algebraic Model of a Generic BCube Data Center Network Architecture 5

Formal Algebraic Model of a Generic BCube Data Center Networ...

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5th international conference on Electrical, Communication and computer engineering, ICECCE 2024

作者： Roig, Pedro Juan Alcaraz, Salvador Gilly, Katja Bernad, Cristina Juiz, Carlos Miguel Hernández University Dept. of Computing Engineering Alicante Elche Spain University of the Balearic Islands Dept. of Mathematics and Computer Science Palma de Mallorca Spain

ISBN: (纸本)9798331529437

The optimization of data center performance need to consider different points of view, such as higher bandwidth, lower latency, lower costs, lower energy efficiency, or higher resource capacity. However, the selection of an appropriate data center network architecture is one of the key features as it can make a difference in some of those points. In this paper, a formal algebraic model of a generic BCube is presented ir order to remark the main characteristics of this server-centric approach. © 2024 ieee.

关键词： Network architecture

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