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Polar Region Climate Dynamics: Deep Learning and Remote Sensing Integration for Monitoring Arctic and Antarctic Changes

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 582-595页

作者： Maniraj, Subramanian Pitchiah Rose, Jesuharan Dafni Arunachalam, Rohini Rangasamy, Kotteeswaran Patil, Vishal Ratansing Kathirvelu, Selvakumarasamy Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Chennai India Department of Computer Science and Engineering Anil Neerukonda Institute of Technology and Sciences Andhra Pradesh Visakhapatnam India Joseph’s College of Engineering Chennai 600119 India Department of CSE(AIML) Vishwakarma Institute of Technology Pune India Department of Electronics and Communication Engineering Saveetha School of Engineering (SIMATS) Chennai India

The polar regions of the Earth, specifically the Arctic and Antarctic, play a pivotal role in regulating the planet’s climate systems. These regions are highly sensitive to climate change, with observable shifts such as diminishing sea ice, retreating glaciers, and rising temperatures. Understanding these dynamics is essential for predicting broader global climate impacts. This paper presents a novel approach for monitoring and predicting these changes by integrating an attention-based transformer model with a 3D convolutional neural network (3D-CNN). The proposed model is designed to capture both the spatial features of satellite imagery and the temporal dynamics of climate data, offering enhanced predictive capabilities for key climate variables such as sea ice extent, glacier movement, and temperature variations. The dataset was split into 80% training and 20% testing with tenfold cross-validation, and the model was trained using the Adam optimizer. The results show that the proposed model achieved a remarkable accuracy of 97.43%, outperforming traditional CNN and RNN models. The model also demonstrated a precision of 98.12% for sea ice extent prediction, a 96.45% precision for glacier movement detection, and a mean squared error (MSE) of 0.35 °C for temperature predictions. These findings suggest that deep learning models, specifically the integration of transformer and 3D-CNN architectures, can significantly contribute to polar region climate monitoring, offering improved tools for scientific research and policymaking. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. corrected publication 2024.

关键词： 3D convolutional neural network Attention-based transformer Climate dynamics Deep learning Glacier movement Polar region Remote sensing Sea ice extent Spatial–temporal analysis Temperature prediction

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Dynamic Network Intrusion Detection System for Virtual Machine Environment

Dynamic Network Intrusion Detection System for Virtual Machi...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Kadiravan, G. Sathish, P. Preethi, D. Sathishkumar, R. Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh India Cmr University Department of Computer Science and Engineering Bangalore India Jain University Department of Computer Science and Engineering Bangalore India Manakula Vinayagar Institute of Technology Department of Computer Science and Engineering Puducherry India

ISBN: (纸本)9798350315127

The biggest issue right now is cloud security, which is why many experts have invested a lot of time and energy into research and development over the past few years. Hackers could use a virtual machine's vulnerability to the cloud in the cloud to launch a massive DDoS attack. DDoS assaults often start with multiple steps of exploitation, infrequent vulnerability scanning, and compromise of vulnerable application virtual machines (VMs) to create botnets, followed by Distributed Denial of Service attacks using the compromised botnets. We provide the NICE system, a distributed vulnerability measuring, monitoring, and mitigation selection solution with multi-stage. To stop susceptible virtual machines from being used where VM security has been compromised. In a virtual networking context, NICE is a multi-stage fully distributed detection and mitigation architecture that detects and investigates suspicious cloud load without interfering with user applications or cloud services. © 2023 IEEE.

关键词： Network security

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An Ensemble-based Deep Learning Model for Multi-class Flower Recognition

An Ensemble-based Deep Learning Model for Multi-class Flower...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Rabbi, Md Fozle Dola, Sraboni Das Taha, Noorjahan Binte Kader Al Mahmud, Tamim Islam, Muhammad Nazrul Razzaque, Md. Abdur Al Faisal, Faiz Green University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Military Institute of Science and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh University of Dhaka Dept. of Computer Science and Engineering Dhaka1000 Bangladesh

ISBN: (纸本)9798350316001

Many species around the globe heavily depend on flowers, and even the use of flowers for medical purposes is enormous, which has been in practice since ancient times. In modern botany, agronomy, and species research, the identification and classification of flowers mainly depend on human intervention, which requires a lot of human resources, and the recognition accuracy rate is low. On the other hand, due to the vast number of flowers with different types, it is quite a challenge and increases the complexity of the accurate detection and classification process. Moreover, the traditional CNN model and other pre-trained models like- VGG16, VGG19, Xception, and MobileNet-V2 failed to ensure high validation accuracy. In this research, we have designed an ensemble-based deep learning model consisting of two pre-trained models DenseNet-201 and EfficientNet-B7, which we renamed 'FlowerConvNet'. This paper considers classifying five categories of flowers consisting of 4242 images, collected from the Kaggle flower recognition dataset. In this research study, the proposed deep learning model FlowerConvNet using transfer learning can obtain a competitive result with 95% validation accuracy. © 2023 IEEE.

关键词： Learning systems

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Sorted Range Selection and Range Minima Queries 2nd

Sorted Range Selection and Range Minima Queries

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2nd International Conference on Applied Algorithms, ICAA 2025

作者： Akram, Waseem Saxena, Sanjeev Department of Computer Science and Engineering Indian Institute of Technology Kanpur208016 India

ISBN: (纸本)9783031845420

Given an array A[1 : n] of n elements drawn from an ordered set, the sorted range selection problem is to build a data structure that can be used to answer following queries: Given a pair of indices i, j(1≤i≤j≤n), and a positive integer k, report the k smallest elements from the sub-array A[i : j] in sorted order. Brodal et al. (Brodal, G. S., Fagerberg, R., Greve, M., and López-Ortiz, A., Online sorted range reporting. Algorithms and Computation (2009) pp. 173–182) introduced the problem and gave a solution that can answer a query in O(k) time. The preprocessing takes O(nlogn) time and O(n) space. In this paper, we propose the only other possible optimal trade-off for the problem. After preprocessing using O(n) time and space, we can answer a range selection query in O(klogk) time. Moreover, the proposed algorithm reports the output elements individually in non-decreasing order. Our solution is simple and practical. We also study the problem in a dynamic setting. We also describe an extremely simple method for the range minima queries (most of whose parts are known), which takes almost (but not exactly) linear time. We believe that this method may be, in practice, faster and easier to implement in most cases. Moreover, we also give a simple dynamic solution to the range minimum problem. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Optimization

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Meta Heuristic Image Optimisation in LiDAR Sensor Based on Cloud IoT Network and Deep Learning Algorithms

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 566-575页

作者： Meenakshi, K. Manoj, Gunna Diwakar, Meenakshi Prabakaran, Manjula Sravan, Padala Babu, Gaddam Ravindra Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Cyber Security Marri Laxman Reddy Institute of Technology and Management Telangana Hyderabad India Department of IT Panimalar Engineering College Chennai India Dept of CSE (Data Science) Madanapalle Institute of Technology & Science Andhra Pradesh Angallu India Computer Science and Engineering NIIT with learning partner Hainan Normal University Haikou China Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Telangana Hyderabad India

LiDAR technology has become a vital tool for researching these atmospheric components because of its capacity to measure backscattered signals and investigate the atmosphere at extremely high spatial and temporal resolution. However, machine learning has become a potent technique for sophisticated data processing and analysis because of the complexity and diversity of lidar technology. The aim of this research is to propose a novel technique in cloud IoT network-based LiDAR sensor imaging with image optimization using a metaheuristic deep learning model. Input has been collected LiDAR image using a cloud IoT module and processed for noise removal and normalization. Then this processed image is classified and optimized utilizing a quantile transfer graph convolutional model with binary firefly whale colony optimization. The experimental analysis is carried out in terms of detection accuracy, random precision, recall, RMSE, and AUC for various LiDAR sensor-based datasets. Comparing the results of the various approaches reveals that the suggested approach outperforms them all with a recall of 94%, a random precision of 95%, and a detection accuracy of 98%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Cloud IoT network Convolutional model Image optimization LiDAR sensor Metaheuristic deep learning

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Provably Secure Efficient Key-Exchange Protocol for Intelligent Supply Line Surveillance in Smart Grids

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IEEE Internet of Things Journal 2025年第12期12卷 21784-21792页

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Alenazi, Mohammed J.F. Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan 250353 China

Intelligent supply line surveillance is critical for modern smart grids. Smart sensors and gateway nodes are strategically deployed along supply lines to achieve intelligent surveillance. They collect data continuously and transmit it to the control centre in real-time. It enables real-time monitoring, fault detection, and efficient energy management across distribution networks. This advanced surveillance system ensures continuous monitoring of supply lines, detecting anomalies, and optimizing operations to maintain the stability and reliability of the smart grid. However, the reliance of all participating nodes on public communication channels to transmit supply line surveillance data exposes these systems to critical cyber attacks. These cyber attacks include impersonation, physical tampering, ephemeral secret leakage, and desynchronization attacks. To address these issues, existing key-exchange protocols often fail to ensure robust security while imposing high computation and communication overheads, limiting their practicality for resource-constrained environments. Therefore, we propose a secure and efficient key exchange and tamper-resistant authentication protocol using Elliptic Curve Cryptography (ECC) and Physical Unclonable Functions (PUFs). The PUF mechanism provides robust resistance against physical tampering and cloning attacks, ensuring enhanced physical security for supply line devices. We validate the security robustness of the devised protocol using the Random or Real (ROR) model. Furthermore, informal security analysis demonstrates the protocol's robustness in rigorously resisting various attacks, including physical tampering, impersonation, ephemeral secret leakage and desynchronization. Moreover, we determine the performance evaluation that reveals the protocol's superior efficiency compared to competing protocols, achieving significant reductions of 28.64% in computation overhead and 9.96% in communication overhead. © 2014 IEEE.

关键词： Authentication Protocol

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Collision Avoidance System using YOLO-based Object Detection and Distance Estimation 3

Collision Avoidance System using YOLO-based Object Detection...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Raj, Shiv Akash Dhinakar Singh, Ashita Shekhawat, Tarakshraj Singh Manav Muthanna, M. Sivasankaran, K. School of Electronics Engineering Vellore Institute of Technology Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331527549

Road accidents are a significant global issue, with human error causing a substantial number of collisions. This emphasizes the need for advanced collision avoidance systems to improve road safety. In response, this study integrates object detection and distance estimation techniques to enhance vehicle safety. Utilizing recent iterations of the YOLO architecture—YOLOv6n, YOLOv8n, YOLOv9t, and YOLOv10n—the models were trained and evaluated on the KITTI dataset, a benchmark suite for driving applications. This research study provides a comparative analysis of these models, focusing on performance metrics such as precision, mean average precision, recall, and F1 score. A novel single-camera distance estimation method was also implemented to assess object proximity accurately. Among the evaluated models, YOLOv8n achieved the best results, with a F1 score of 0.74 and a mAP@0.5 of 0.782, demonstrating superior detection accuracy and reliability. Its swift convergence and low training losses make it well-suited for real-world use cases. While YOLOv9t and YOLOv10n also performed well, they were slightly less effective. The system enhances its capabilities by accurately estimating distances, which improves collision avoidance and road safety applications. Overall, YOLOv8n is the most efficient and reliable option for developing advanced safety features in vehicle systems. © 2025 IEEE.

关键词： Vehicle safety

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Machine Learning & Internet of Things in Plant Disease Detection: A comprehensive Review 7

Machine Learning & Internet of Things in Plant Disease Detec...

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Pareek, Shalinee Kumar, Awanit Degadwala, Sheshang Computer Science and Engineering Sangam University Bhilwara India HOD Computer Engineering Sigma Institute of Engineering Gujarat Vadodara India

ISBN: (纸本)9781665464086

Agriculture is crucial to the growth of every country's economy. Due to population growth, unpredictable weather, and a lack of available resources, meeting the world's food needs has become an increasingly challenging undertaking in recent years. There are a number of issues that threaten agriculture's long-term viability, but new technologies are providing farmers with a way to combat them. To effectively control agricultural productivity, it is essential to accurately diagnose plant diseases. The use of machine learning (ML) and the Internet of Things (IoT) is a relatively new development in the agricultural industry with the potential to improve crop management. Improved agricultural yields are possible with the integration of machine learning and other Internet of Thing's technologies. This study provides a comprehensive analysis of the ways in which ML and IoT have been used in the agricultural sector. Here, this study reviews IoT and ML-powered smart farming and agricultural strategies that have been tried and tested. This study suggests a new strategy for integrating ML and IoT in real-time environment. © 2023 IEEE.

关键词： Support vector machines

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A WSN and IoT-Based Proposed Intrusion Recognition System at Farmland

A WSN and IoT-Based Proposed Intrusion Recognition System at...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Mishra, Rashmi Painuly, Vaibhavi Karthikeyan, M.P. Noida Institute Of Engineering And Technology Department Of Biotechnology Uttar Pradesh Greater Noida India Tula's Institute Department Of Computer Science And Engineering Dehradun India Department Of Computer Science And Information Technology Bangalore India

ISBN: (纸本)9798350366846

Various techniques, combining conventional and cutting-edge technology, have been developed throughout time to identify human and animal invaders on agricultural property. Traditional methods of protecting crops include erecting block barriers, growing plants that repel pests, and deploying electric fences or offensive-smelling plants. Deep learning algorithms, the internet of things, and portable monitoring systems are examples of cutting-edge technology employed in farm security. Several agricultural invasion detection systems proposed by several authors are thoroughly examined in this research. It involves the use of wireless sensors Web, Internet of Things (IoT), and electricity Obstacles to propose intrusion recognition system at farmland. © 2024 IEEE.

关键词： Internet of things

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Performance Analysis of Hybrid Cryptographic Algorithms in Serverless Platforms 1

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3rd International Conference on Advanced Computing and Intelligent Technologies, ICACIT 2023

作者： Raj, Vinay Suresh, Satharla Phridviraj, M.S.B. Department of Computer Applications National Institute of Technology Tiruchirappalli Tamil Nadu Tiruchirappalli India Department of Computer Science and Engineering Kakatiya Institute of Technology & Science Warangal India

ISBN: (数字)9789819719617

ISBN: (纸本)9789819719600

Serverless computing has become the buzzword in IT and it has been widely accepted because of its diverse cloud computing benefits. One of the major drawbacks of serverless computing is the security of the data which is handed over to the third parties. To ensure the security of the data and secure private communication between the services, hybrid cryptographic algorithms play a predominant role by combining the best security features of symmetric and asymmetric encryption techniques. In this work, two hybrid cryptographic algorithms are proposed namely 2-tier and 3-tier models and the performance of the two algorithms are analyzed in terms of encryption and decryption time along with the file size. The obtained results are also compared with an existing hybrid cryptographic model and it is observed from the results that the proposed hybrid model outperforms the existing model on both the 2-tier and 3-tier models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cryptography

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