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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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Exploring the Impact of Herbal Remedies on Autism Spectrum Disorder Using AGLSTM Analysis

Exploring the Impact of Herbal Remedies on Autism Spectrum D...

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Innovative computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Bagiyalakshmi N T. Veeramakali Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur

Autism is a personality disorder that can cause significant emotional, intellectual, and behavioral deficits. Autism is recognized as an illness on the spectrum because, while certain kids might have mild symptoms, others may have extensive life-challenging abnormalities. However, autistic people have unique characteristics and health goals and may react differently to conventional or herbal therapy than others. Herbal medications might be a possible approach to helping autistic people's bodies and brains. However, further research is needed to determine whether herbal therapy is useful for autistic individuals. To gain insight into how people think regarding herbal medication for autism, data must be processed promptly as it is generated, which may be accomplished using sentiment assessment, which analyzes polarization in writings. This paper proposes the AGLSTM Model, a hybrid model that solves the sentiment evaluation issue by combining LSTM with agglomerative clustering. The suggested system signifies text data into numerical vectors that illustrate text features such as the frequency of words and embeddings of phrases using the TF-IDF technique and the word2vec model. Every word becomes assigned to a cluster using a method known as clustering based on how similar it is to the cluster nucleus or other texts in the region. The outcome of the experiment is a set of clusters composed of sentences with comparable emotional content. The LSTM framework can detect long-term correlations between word patterns and classify feedback as positive or negative.

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Peak Evaluation for Liquid Chromatography-Mass Spectrometry data Using Neural Network on Statistical Quality Metrics 18

Peak Evaluation for Liquid Chromatography-Mass Spectrometry ...

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18th IEEE International Conference on Anti-Counterfeiting, Security, and Identification, ASID 2024

作者： Dong, Liheng Dai, Juehong Guan, Teo Bee Guo, Lei Wu, Caisheng Xu, Jingjing School of Computing and Data Science Xiamen University Malaysia Sepang43600 Malaysia Xiamen University Department of Electronic Science Xiamen361005 China Interdisciplinary Institute for Medical Engineering Fuzhou University Fuzhou350108 China School of Pharmaceutical Sciences Xiamen University Xiamen361005 China

ISBN: (纸本)9798331505639

Peak picking is a crucial step in the data analysis workflow of Liquid Chromatography-Mass Spectrometry (LCMS) in untargeted metabolomics. The process is challenging due to interference from background signals such as noise and matrix effects. To address this challenge, we first collected 5,447 ion peaks and manually scored them after alignment. Next, we calculated 12 quality metrics for each ion peak. Finally, we developed a Multilayer Perceptron (MLP) model to perform regression analysis, as well as binary and ternary classification, making it adaptable to various application scenarios. The results demonstrate that the MLP-based peak evaluation method is effective in accurately predicting peak quality. Researchers can quickly initiate their work by setting just one or two score thresholds based on their specific needs. This approach retains the flexibility to handle different datasets without the need to adjust thresholds for each peak quality metric, representing a step towards more flexible and efficient peak picking. © 2024 IEEE.

关键词： Regression analysis

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Integrating Instruction-based Learning with Bidirectional LSTM for Autonomous Vehicle Pedestrian Detection

Integrating Instruction-based Learning with Bidirectional LS...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Shoba, B. Raghunathan, T. Peter, S.Sam Karthick, K. Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak Psn College of Engineering and Technology Department of Electronics and Communication Engineering Tirunelveli India Dr.NG.p.Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Sri Krishna College of Technology Department of Computer Science and Engineering Coimbatore India Sona College of Technology Department of Information Technology Salem India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331509828

Pedestrian detection is a crucial aspect of autonomous vehicle (A V) safety, as it enables accurate identification and tracking of individuals in an A V environment. Effective pedestrian detection supports essential A V functions like braking, steering, and speed control to prevent accidents in urban settings. Traditional computer vision methods, such as Support Vector Machines and Random Forests, rely on pre-designed features like the Histogram of Oriented Gradients. While the existing techniques offer some performance, it face significant challenges in dynamic environments, especially under varying lighting conditions, occlusions, and diverse pedestrian poses. Traditional models also require extensive manual feature engineering and tuning, which limits scalability and adaptability. Recent advances in deep learning, particularly with Convolutional Neural Networks (CNNs), have allowed for automatic feature learning, yielding practical improvements over classical methods. However, CNNs can struggle in low light and when facing adversarial attacks, as they mainly capture spatial features. To address these limitations, this research proposes a new methodology combining CNNs for spatial feature extraction with Bidirectional Long Short-Term Memory (BiLSTM) networks to capture temporal dependencies. This hybrid approach enhances detection capabilities by learning both static and dynamic patterns. Our proposed CNN-BiLSTM model achieves a 92 % detection accuracy and an F1 score of 88.5% across diverse conditions, outperforming existing models by up to 15%. By integrating temporal features, the model reduces missed detections and false positives by up to 20%, providing a more reliable and adaptable solution for pedestrian detection in autonomous vehicles. © 2025 IEEE.

关键词： Convolutional neural networks

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A Joint Communication and Learning Design for Secure Federated Learning with Differential Privacy 100

A Joint Communication and Learning Design for Secure Federat...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Lin, Licheng Yang, Zhaohui Yang, Qianqian Chen, Mingzhe Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China University of Miami Department of Electrical and Computer Engineering Coral GablesFL33146 United States University of Miami Frost Institute for Data Science and Computing Coral GablesFL33146 United States

ISBN: (纸本)9798331517786

In this paper, the problem of resource allocation for non-orthogonal multiple access (NOMA) enabled secure federated learning (FL) is investigated. In the considered model, a set of users participate in the FL training through transmitting their trained FL model parameters to the base stations (BSs) via NOMA techniques. To prevent data leakage, each user uses the differential privacy (DP) technique through adding Gaussian noise to its FL model parameters. The problem of minimizing overall privacy leakage of all FL participaring users is formulated as an optimization problem through jointly optimizing the connections between users and BSs, transmit power of the users, and the DP noise power. To solve the formulated non-convex optimization problem, a genetic algorithm is proposed to search for feasible solutions in which user connection matrix is taken as gene and the objective function value is taken as the fitness of solution. Simulation results show that the proposed genetic algorithm reduces privacy leakage by up to 73% compared to the conventional alternating optimization algorithm. © 2024 IEEE.

关键词： Federated learning

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Message Verification Protocol Based on Bilinear Pairings and Elliptic Curves for Enhanced Security in Vehicular Ad Hoc Networks

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Computers, Materials & Continua 2024年第10期81卷 1029-1057页

作者： Vincent Omollo Nyangaresi Arkan A.Ghaib Hend Muslim Jasim Zaid Ameen Abduljabbar Junchao Ma Mustafa A.Al Sibahee Abdulla J.Y.Aldarwish Ali Hasan Ali Husam A.Neamah Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and TechnologyBondo40601Kenya Department of Applied Electronics Saveetha School of EngineeringSIMATSChennaiTamilnadu602105India Information Technology Department Management Technical CollegeSouthern Technical UniversityBasrah61004Iraq Department of Computer Science College of Education for Pure SciencesUniversity of BasrahBasrah61004Iraq College of Big Data and Internet Shenzhen Technology UniversityShenzhen518118China Huazhong University of Science and Technology Shenzhen InstituteShenzhen518000China National Engineering Laboratory for Big Data System Computing Technology Shenzhen UniversityShenzhen518060China Computer Technology Engineering Department Iraq University CollegeBasrah61004Iraq Department of Mathematics College of Education for Pure SciencesUniversity of BasrahBasrah61004Iraq Institute of Mathematics University of DebrecenDebrecenH-4002Hungary Department of Electrical Engineering and Mechatronics Faculty of EngineeringUniversity of DebrecenDebrecen4028Hungary

Vehicular ad hoc networks(VANETs)provide intelligent navigation and efficient route management,resulting in time savings and cost reductions in the transportation ***,the exchange of beacons and messages over public channels among vehicles and roadside units renders these networks vulnerable to numerous attacks and privacy *** address these challenges,several privacy and security preservation protocols based on blockchain and public key cryptography have been proposed ***,most of these schemes are limited by a long execution time and massive communication costs,which make them inefficient for on-board units(OBUs).Additionally,some of them are still susceptible to many *** such,this study presents a novel protocol based on the fusion of elliptic curve cryptography(ECC)and bilinear pairing(BP)*** formal security analysis is accomplished using the Burrows–Abadi–Needham(BAN)logic,demonstrating that our scheme is verifiably *** proposed scheme’s informal security assessment also shows that it provides salient security features,such as non-repudiation,anonymity,and ***,the scheme is shown to be resilient against attacks,such as packet replays,forgeries,message falsifications,and *** the performance perspective,this protocol yields a 37.88%reduction in communication overheads and a 44.44%improvement in the supported security ***,the proposed scheme can be deployed in VANETs to provide robust security at low overheads.

关键词： Attacks bilinear elliptic curve cryptography(ECC) privacy security vehicular ad hoc network(VANET)

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Exploring Sentiment Detection in Healthcare: An Exhaustive Analysis

Exploring Sentiment Detection in Healthcare: An Exhaustive A...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE), International Conference on

作者： Bagiyalakshmi N T. Veeramakali Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur

In our daily lives, sentiment analysis is essential. People usually make decisions after seeking advice from others. We used to rely on our judgments before the internet on the advice of our friends and relatives. But now, we have high-performance source sentiment analysis, which collects and analyses opinions from a variety of channels, including social media. To correctly represent emotion in a phrase, we must concentrate on idioms, sarcasm, negations, and context terms. A range of deep learning and machine learning techniques can be used to overcome the aforementioned issues. The aforementioned parameter will be used by the algorithm to identify the polarity of a statement. This study proposal covers the problems with sentiment analysis as well as its numerous levels. The review talks about the challenges and particularities of sentiment analysis. These have to do with issues like textual information, peculiarities of language, emotional vocabulary relevant to particular domains, and security and confidentiality of data. The report also emphasizes how sentiment analysis might promote public healthcare initiatives, boost pharmaceutical methods for making choices, as well as improve patient care.

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Novel signal-consistency test for gravitational-wave searches of generic black hole binaries

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Physical Review D 2024年第2期110卷 023042-023042页

作者： Stefano Schmidt Sarah Caudill Science Park 105 1098 XG Amsterdam The Netherlands Institute for Gravitational and Subatomic Physics (GRASP) Department of Physics Center for Scientific Computing and Data Science Research

We propose a novel signal-consistency test applicable to a broad search for gravitational waves emitted by generic binary black hole (BBH) systems. The test generalizes the time domain ξ2 signal-consistency test currently utilized by the gstlal pipeline, which quantifies the discrepancy between the expected signal-to-noise ratio time series with the measured one. While the traditional test is restricted to aligned-spin circular orbits and does not account for higher-order modes (HMs), our test does not make any assumption on the nature of the signal. After addressing the mathematical details of the new test, we quantify its advantages in the context of searching for precessing BBHs and/or BBHs with HM content. Our results reveal that, for precessing signals, the new test is optimal and has the potential to reduce the values of the ξ2 statistics by up to 2 orders of magnitude when compared to the standard test. However, in the case of signals with HM content, only a modest enhancement is observed. Recognizing the computational burden associated with the new test, we also derive an approximated signal-consistency test. This approximation maintains the same computational cost as the standard test and can be easily implemented in any matched-filtering pipeline with minimal changes, sacrificing only a few percent of accuracy in the low-SNR regime. However, in the high-SNR regime, the approximated signal-consistency test does not bring any improvement as compared to the “standard” one. By introducing our new test and its approximation and understanding their validity and limitation, this work will benefit any matched-filtering pipeline aimed at searching for BBH signals with strong precession and/or HM content.

关键词： Gravitational waves data analysis

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IP SafeGuard-An AI-Driven Malicious IP Detection Framework

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IEEE Access 2025年 13卷 90249-90261页

作者： Siam, Abdullah Al Alazab, Moutaz Awajan, Albara Hasan, Md Rakibul Obeidat, Areej Faruqui, Nuruzzaman Daffodil International University Department of Software Engineering Dhaka Daffodil Smart City1216 Bangladesh Liverpool John Moores University School of Computing and Data Sciences Oryx Universal College Cybersecurity Department Doha Qatar Al-Balqa Applied University Faculty of Artificial Intelligence Department of Intelligent Systems As-Salt19385 Jordan Department of Computer Science and Engineering Dhaka1216 Bangladesh Independent Researcher As-Salt19385 Jordan

The rising frequency and sophistication of cyberattacks have made real-time malicious IP detection a critical challenge for modern Security Operations Center (SOC). Traditional solutions, such as static blacklists and manual IP reputation checks, are no longer sufficient in today's dynamic threat scenario. To overcome these constraints, we present IP SafeGuard, an AI-driven platform that incorporates multi-source threat intelligence, sophisticated feature engineering, and machine learning (ML)for real-time IP categorization. The framework collects data from AbuseIPDB, VirusTotal, and other sources to compute a Dynamic Threat Score (DTS) for each IP address. It leverages an XGBoost-based classification model to achieve high accuracy and low false-positive rates, even in skewed datasets. Experimental findings indicate the improved performance of IP SafeGuard, with an accuracy of 98.2%, a precision of 97.8%, and a recall of 98.5%. The average detection duration of 45 milliseconds makes it appropriate for real-time SOC integration, enabling automated incident response through Security Information and Event Management (SIEM) alerting and firewall blocking. The framework's modular design assures scalability and adaptability, making it a vital tool for high-volume situations. By overcoming the limits of old approaches and using the power of ML, IP SafeGuard considerably boosts the efficiency and efficacy of current cybersecurity systems. Future work involves expanding the system to enable new threat intelligence sources and studying federated learning for secure and privacy-preserving threat information exchange. © 2013 IEEE.

关键词： Federated learning

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Implementing data science and Big data Analytics to Their Full Potential: Protocols for Gaining Useful Understanding and Promoting data-Driven Innovation 5

Implementing Data Science and Big Data Analytics to Their Fu...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Arthy, M. Nivethitha, S.B. Karuppiah, S.P. Vijayanandh, T. Sumalatha, A. Ramesh, T. School of Computing College of Engineering and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur India Rajalakshmi Engineering College Mechatronics Engineering Tamil Nadu Chennai India St.Joseph's College of Engineering Department of Mba India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science & Engineering Tamil Nadu Chennai India Kristu Jayanti College Autonomous Department of Computer Science Bengaluru India R.M.K Engineering College Department of Computer Science & Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376685

The proper implementation of data science and big data analytics has become an essential component for gaining valuable insight and driving innovation across a variety of industries in this era, which is characterized by the constant presence of data. The strategies and protocols that are necessary to fully exploit the promise of these transformational technologies are investigated in depth by this research. A complete framework for enterprises to harness their data assets is the goal of the study, which will be accomplished by analyzing the fundamental principles of data science as well as the difficulties connected with big data. A number of advanced analytics methods, including machine learning, predictive modeling, and natural language processing, are being investigated as potential integrations into the decision-making process as part of this research. The relevance of data governance, quality, and the ethical considerations that surround data usage are also discussed in this document. It is demonstrated in this study that data-driven insights may lead to strategic advantages, operational efficiencies, and the development of creative goods and services. This is accomplished through a number of case studies and practical examples. In order for businesses to make the most of the potential that are given by data science and big data analytics, it is vital for them to have a culture of data literacy as well as a robust infrastructure, according to the results made. At the end of the study, a collection of best practices and recommendations for businesses and governments to implement in order to cultivate a data-driven environment that encourages innovation and usage of data in a responsible manner are presented. In light of the findings of this study, it is clear that data science and big data analytics require a more comprehensive approach, one that strikes a balance between technological capabilities, organizational readiness, and the influence on society. © 2024

关键词： Big data Analytics data Governance data Infrastructure data Literacy data Quality data science data-Driven Decision Making data-Driven Innovation Ethical Considerations Machine Learning Natural Language Processing Predictive Modeling Strategic Advantage

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