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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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Greeting Gesture Analysis Using Boosting Algorithms and SHAP 13

Greeting Gesture Analysis Using Boosting Algorithms and SHAP

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Joint 13th International Conference on Soft Computing and Intelligent systems and 25th International Symposium on Advanced Intelligent systems, SCIS and ISIS 2024

作者： Wibowo, Angga Wahyu Sato-Shimokawara, Eri Kurnianingsih Graduate School of Systems Design Tokyo Metropolitan University Department of Computer Science Tokyo Japan Politeknik Negeri Semarang Department of Electrical Engineering Semarang Indonesia

ISBN: (纸本)9798350373332

Recently, studies related to human activity recognition have developed which have been applied in various fields. In that field Machine learning and deep learning techniques have been widely used for several classification and prediction tasks. However, the first problem is that there is rarely any research on human activities that reflect the culture of a region, such as greeting gestures. Second, there are few studies that do not provide models resulting from the classification or prediction process using machine learning, which can result in a lack of understanding of how a model from machine learning is generated. This study aims to predict Japanese greeting gestures (eshaku, keirei, saikeirei, waving hand) and Indonesian (placing both hands in front of the chest) using Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), and Gradient Boosting. Then, represent the model of the learning machine using SHapley Additive exPlanations (SHAP) to find out how the learning machine obtain a model. Our findings show the efficacy of this method and show that the model not only predicts greeting gestures with high accuracy but also offers a transparent understanding of the factors influencing its predictions, thus enhancing the interpretability and applicability of machine learning in recognizing culturally significant human activities. In the future, we plan to use machine learning and other Explainable Artificial Intelligence (XAI) methods to study human activities with cultural concepts such as greeting gestures in different styles, create more greeting gesture-based human activity datasets, and examine the ethical implications or considerations of using machine learning in cultural contexts to respect cultural differences. © 2024 IEEE.

关键词： Contrastive Learning

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A Novel Knowledge Semantic Video game Prototype to treat patients with Absence Epilepsy 5

A Novel Knowledge Semantic Video game Prototype to treat pat...

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5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Kumar, Srikoti Rishi Reshmitha, Uppala Sarath, Tenneti Sai Challa, Tanmayi Akhil, Karnam Vnr Vignana Jyothi Institute Of Engineering And Technology Department Of Computer Science And Business Systems Hyderabad India Vnr Vignana Jyothi Institute Of Engineering And Technology Department Of Computer Sience And Engineering Hyderabad India

ISBN: (纸本)9798350330861

In recent times, children are increasingly experiencing anxiety and depression, nervousness, emotional distress, and a susceptibility to conditions such as Absence Epilepsy, Juvenile Myoclonic Epilepsy (JME), and Migraine. Absence Epilepsy is one of the major challenging issues of current neurological research. Absence Epilepsy is a common form of childhood epilepsy that can have significant impact on children, leading problems to memory loss, lack of cognitive ability, attention deficit hyperactivity disorder (ADHD). Recent studies of neurological research highlight that specific video games have positive effects on cognitive and motor functions in children. The proposed research presents a novel approach to create a video game prototype exclusively for children with Absence Epilepsy. Concepts of semantic web are used to build the various relations among depression, anxiety, Absence Epilepsy to understand its cause, impact on human brain and memory. Additionally, the prototype aims to assess the game's usability to ensure it is accessible and effective for its intended audience. The research methodology involves a multidisciplinary approach, drawing from neurology, pediatrics, Game designers, and Semantic Web technologies. The initial phase involves an in-depth analysis of the cognitive and motor challenges faced by children with Absence Epilepsy, enabling the integration of suitable therapeutic elements into the game mechanics. Subsequently, Semantic Web technologies are utilized to construct an ontology capturing crucial information about the player's condition, medical history, and game play preferences. In conclusion, the research bridges the gap between pediatric healthcare and therapeutic gaming. The innovative video game prototype promises in enhancing the lives of children with Absence Epilepsy, enhancing their cognitive abilities, memory, and cognitive attention, and paving the way for further exploration of video games as a valuable complement to traditio

关键词： Ontology

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An Architecture-based Self-Typing Service for Cloud Native Applications 4

An Architecture-based Self-Typing Service for Cloud Native A...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Mandalapu, Srinivasa Rao Sivamuni, Kalaimagal Chitra Devi, D. Aswal, Upendra Singh Sherly, S. Irin Balaji, N. Alangudi R.V.R. and J.C. College of Engineering Department of Computer Science and Business Systems Chowdavaram522019 India Mk College of Engineering and Technology Department of Computer Science and Engineering Puduvoyal601206 India S.A.Engineering College Department of Computer Science and Engineering Thiruverkadu600077 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun248002 India Panimalar Engineering College Department of Information Technology Poonamallee600123 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram522302 India

ISBN: (纸本)9798350300888

This research study analyzes Kubow, a service that automatically adjusts to new circumstances, built on a modular framework for cloud-native applications. To accommodate Kubernetes and Docker containers, the Rainbow self-adaptation framework was upgraded to become Kubow. This research provides an overview of Kubow's architecture, highlights its main design decisions, and illustrates it with a simple example of how to set it up and utilize it. It takes more than just installing software on virtual machines to get apps running in the cloud. Cloud applications need continuous management so that they can 1) adjust their resources according to the demand, and 2) respond to temporary challenges by duplicating and restarting the components to offer resilience on unstable infrastructure. To give programmed and responsive responses to disappointments (wellbeing the board) and changing natural conditions (auto-scaling), and framework boundaries. © 2023 IEEE.

关键词： computer architecture

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Deep neural network-based secure healthcare framework

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Neural Computing and Applications 2024年第28期36卷 17467-17482页

作者： Aldaej, Abdulaziz Ahanger, Tariq Ahamed Ullah, Imdad College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj11942 Saudi Arabia Management Information Systems Department CoBA Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia School of Computer Science Faculty of Engineering The University of Sydney SydneyNSW2006 Australia

Healthcare stands out as a critical domain profoundly impacted by Internet of Things (IoT) technology, generating vast data from sensing devices as IoT applications expand. Addressing security challenges is paramount for a successful IoT healthcare framework, with blockchain technology offering a decentralized structure for robust data protection and secure data exchange within multi-node IoT networks. The research introduces a secure IoT healthcare diagnostic model empowered by deep neural networks, emphasizing encryption, safe transactions, and healthcare diagnostics as key components. Notably, the model incorporates innovative techniques like the orthogonal particle swarm optimization algorithm for sharing medical images and a neighborhood indexing sequence method for hash value encryption. The development of an optimized deep neural network-based classification model for illnesses, validated through extensive trials, demonstrates superior performance metrics compared to existing decision-making techniques, with significant improvements in f-Measure (96.25%), sensitivity (93.26%), specificity (94.26%), and accuracy (93.26%). This study’s scientific contribution lies in its innovative approach to securing IoT-healthcare diagnosis models, validated performance enhancements using real-world datasets, and insightful recommendations for future research directions, fostering advancements in healthcare technology for enhanced patient care and system efficiency. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Internet of things

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Machine Learning for Climate Change Impact Assessment and Adaptation Planning 2

Machine Learning for Climate Change Impact Assessment and Ad...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Balakrishnan, T. Suresh Krishnan, Prabhakar Ebenezar, U. Samson Mohammed Nizarudeen, M. Kamal, N. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Amrita Vishwa Vidyapeetham Center for Cyber Security Systems and Networks Amritapuri-Campus India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Grt Institute of Engineering and Technology Dept. of Computer Science and Engineering Tamil Nadu Tiruttani India

ISBN: (纸本)9798350384277

The presented research introduces a new advanced data analytics methodology for climate change impact assessment and adaptive planning that utilizes machine learning and deep-learning techniques. Quantitative analysis was performed over several databases such as GHCN, ERA5, National Centers for Environmental Prediction (NCEP) and JRA-55 in addition to CRU for a thorough assessment of the proposed methodology. In climate modeling, the Convolutional Neural Networks (CNN) showed better accuracy than ERA5 and JRA-55 which outperformed Global Historical Climatology Network (GHCN) with lower Mean Absolute Error (MAE), Root Mean Squared Error values. The Early Warning System, which was built using the LSTM network algorithm, had a higher accuracy of 0.90 and recall rate of 0.88 especially with JRA-55 proving its effectiveness in early detection for extreme weather events before they occur. The RL-based adaptive planning component performed well across all datasets, with ERA5 reaching the best feedback loop performance score of 94%. The precision agriculture optimization, which was enabled by Random Forest revealed different accuracy and AUC-ROC values across various datasets showing flexibility. Against international climate agreements, the overall alignment score was 0.78 with ERA5 being slightly better at 0.80 using Support Vector Machines (SVM). These quantitative assessments accentuate the practicality, efficacy, and applicability of this methodology in various datasets making it an apt tool for evidence-based decisions on climate resilience strategies at the global level. © 2024 IEEE.

关键词： Adversarial machine learning

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Model for Recognition of Sign Language and Hand Gesture by Hard Hearing People

Model for Recognition of Sign Language and Hand Gesture by H...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Manjula, R. Jagadeesan, S. Ramalingam, Venkadeshan Vetriselvi, T. Johnpeter, T. Srm Institute of Science and Technology College of Engineering and Technology Department of Computing Technologies Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Tamilnadu Vellore India Information Technology Department University of Technology and Applied Sciences-Shinas Branch Sultanate of Oman Oman Vellore Institute of Technology School of Computer Science and Engineering Department of Iot Tamilnadu Vellore India K.Ramakrishnan College of Engineering Department of Computer Science and Engineering Trichy India

ISBN: (纸本)9798350313987

Hard hearing and deaf people are communicating with other people using the sign language. This is the only way of communication of such people to convey Since this is the only means by which these individuals can communicate, it is imperative that others be able to understand their language. Sign language recognition by computer begins with the acquisition of sign gestures and ends with recognition and conversion to text or voice. Static and dynamic are the two ways of sign gestures used. This study aims at focusing on the dynamic gesture recognition and the steps for recognizing sign language are discussed. The collected data are preprocessed and feature are extracted for classification. Finally the results are all investigated and developed for single user. © 2023 IEEE.

关键词： Gesture analysis Hand gesture recognition Hand tracking Sign language

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Fusion of speech and handwritten signatures biometrics for person identification

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International Journal of Speech Technology 2023年第4期26卷 833-850页

作者： Abushariah, Ahmad A. M. Abushariah, Mohammad A. M. Gunawan, Teddy Surya Chebil, J. Alqudah, Assal A. M. Ting, Hua-Nong Mustafa, Mumtaz Begum Peer Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia Department of Computer Information Systems King Abdullah II School of Information Technology The University of Jordan Amman Jordan ECE Department Faculty of Engineering International Islamic University Malaysia Kuala Lumpur53100 Malaysia Department of Computer Science Faculty of Science and Information Technology Al-Zaytoonah University of Jordan Amman Jordan Department of Biomedical Engineering Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia Department of Software Engineering Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia

Automatic person identification (API) using human biometrics is essential and highly demanded compared to traditional API methods, where a person is automatically identified using his/her distinct characteristics including speech, fingerprint, iris, handwritten signatures, and others. The fusion of more than one human biometric produces bimodal and multimodal API systems that normally outperform single modality systems. This paper presents our work towards fusing speech and handwritten signatures for developing a bimodal API system, where fusion was conducted at the decision level due to the differences in the type and format of the features extracted. A data set is created that contains recordings of usernames and handwritten signatures of 100 persons (50 males and 50 females), where each person recorded his/her username 30 times and provided his/her handwritten signature 30 times. Consequently, a total of 3000 utterances and 3000 handwritten signatures were collected. The speech API used Mel-Frequency Cepstral Coefficients (MFCC) technique for features extraction and Vector Quantization (VQ) for features training and classification. On the other hand, the handwritten signatures API used global features for reflecting the structure of the hand signature image such as image area, pure height, pure width and signature height and the Multi-Layer Perceptron (MLP) architecture of Artificial Neural Network for features training and classification. Once the best matches for both the speech and the handwritten signatures API are produced, the fusion process takes place at decision level. It computes the difference between the two best matches for each modality and selects the modality of the maximum difference. Based on our experimental results, the bimodal API obtained an average recognition rate of 96.40%, whereas the speech API and the handwritten signatures API obtained average recognition rates of 92.60% and 75.20%, respectively. Therefore, the bimodal API system is a

关键词： Biometrics

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Exploratory Data Analysis and Predicting Indian Crime Against Women Using Machine Learning Approaches 3

Exploratory Data Analysis and Predicting Indian Crime Agains...

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3rd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2025

作者： Gupta, N. Srinivasa Valarmathi, B. Santhi, K. Chellatamilan, T. Nanda, Sameeksha Vellore Institute of Technology School of Mechanical Engineering Department of Manufacturing Tamil Nadu Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Tamil Nadu Vellore India Vellore Institute of Technology School of Computer Science and Engineering Department of Analytics Tamil Nadu Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Information Technology Tamil Nadu Vellore India

ISBN: (纸本)9798331521691

Crime forecasting is a critical endeavor aimed at preventing future criminal actions, specifically the rising rate of violence against women in India. This study proposes the model to predict crimes committed against women using data from the National Crime Records Bureau's (NCRB) monthly crime report. The dataset encompasses district and state-wise crime activities from 2001 to 2014. The proposed work objectives are identifying factors influencing these crimes and developing predictive models using machine learning (ML). The study employs Exploratory Data Analysis (EDA), to enhance the predictive power of the models. The suggested study uses XGBoost, Random Forest (RF), and Neural Network (NN). The neural network algorithm outperforms the Random Forest and XGBoost techniques among these three. The Random Forest algorithm has the worst performance. After comparing the accuracies of various methods, such as XGBoost (92%), and Random Forest (89.84%) classifiers using imbalanced datasets (without employing Synthetic Minority Oversampling Technique (SMOTE) technique), it was found that the neural network algorithm (95.81%) delivers the highest accuracy. This study looks at model accuracies and finds that, after applying the SMOTE algorithm, neural network algorithms performed best, with an accuracy of 96.76%, outperforming the accuracies of the remaining models, which included the Random Forest (93.08%) and XG-Boost (95%). © 2025 IEEE.

关键词： Crime prediction Exploratory Data Analysis (EDA) Neural Networks Random Forest SMOTE XGBoost

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Performance Analysis of Machine Learning Algorithms for Prediction of Cerebral Attack (Stroke) 7th

Performance Analysis of Machine Learning Algorithms for Pred...

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7th International Conference on Emerging Applications of Information Technology, EAIT 2022

作者： Sengupta, Diganta Mondal, Subhash Singh, Yash Raj Pandey, Amartya Department of Computer Science and Engineering Meghnad Saha Institute of Technology Kolkata700150 India Department of Computer Science and Business Systems Meghnad Saha Institute of Technology Kolkata700150 India

ISBN: (纸本)9789811951909

In this study, we have analysed the behaviour and performance of twelve machine learning (ML) algorithms in prediction of cerebral attack (stroke). The ML algorithms used were light gradient boosting machine (LGBM), histogram-based gradient boost (HGB), random forest (RF), gradient boost classifier (GBC), XGBoost (XGB), AdaBoost (AB), decision tree (DT), support vector machine (SVM), K-nearest neighbours (KNN), logistic regression (LR), Bernoulli Naïve Bayes and Gaussian Naïve Bayes. The dataset comprised of 5110 entries, which were subjected to pre-processing prior to deployment in the ML algorithms. We observed an accuracy of 97.36% for RF classifier which was the highest among the twelve algorithms under consideration. Post hyper-parameter tuning, GBC reflected an accuracy of 97.73%. Stacking resulted in an accuracy of 98.85% having GBC as the meta model and XGB, RF, SVM, KNN, DT, AB and LR as the base models. Hence, form our observation, we conclude that GBC performs best in predicting stroke among all the ML algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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