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TPU-Accelerated Deep Learning for Accurate Satellite Land Classification 3

TPU-Accelerated Deep Learning for Accurate Satellite Land Cl...

引用

3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024

作者： Babu, Tina Nair, Rekha R. Kishore, S. Department of Computer Science and Engineering School of Advanced Computing Alliance University Bengaluru India Nitte Institute of Professional Education Karnataka Mangalore India

Tensor Processing Units (TPUs) offer significant computational advantages for deep learning tasks, particularly in processing high-dimensional satellite imagery. This study presents a systematic approach to land classification using Convolutional Neural Networks (ConvNets) accelerated by TPUs. The implemented ConvNet architecture achieved 94.5% classification accuracy across ten land cover categories, outperforming traditional machine learning methods by 6-9% and standard CNN implementations by 4%. The model demonstrated high precision (93.8%), recall (94.2%), and F1-score (94.0%) across diverse land cover types, with particularly strong performance in identifying water bodies (95.1% F1-score) and forest regions (94.9% F1-score). Through ex- tensive hyperparameter optimization enabled by TPU acceleration, training time was reduced by 60% compared to GPU-based implementations. The study utilized a dataset of 27,000 labeled satellite images across 10 land cover categories, with robust validation through 5-fold cross-validation. The improved performance and computational efficiency demonstrate the viability of TPU-accelerated deep learning for large-scale geospatial analysis applications. © 2025 The Author(s).

关键词： Convolutional neural networks

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Facial Expression Based User Concentration Alert System

Facial Expression Based User Concentration Alert System

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2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Arora, Ginni Adlakha, Garima Joshi, Rashmi Batra, Mohit Sarkar, Tanisha Luthra, Meghna Manav Rachna International Institute of Research and Studies Department of Computer Science and Engineering Faridabad India

ISBN: (纸本)9798350344387

Nowadays, due to more social platform usage, the user concentration on major activities has been declined. It can be seen in the education sector as the students are unable to pay attention in online sessions. Therefore, there is a need of an automatic alert system to make a student engaged. This paper focus on implementing face detection, face recognition and face emotion recognition in the education sector. With the concepts of Machine Learning and Neural Networks such as ResNet, AlexNet, VGG-16 as pretrained network helps in recognizing the human face. The emotions of the recognized face are then analysed through the machine learning model i.e. face emotion detector which detects facial emotions like happy, sad, confused, focused, angry, etc. This facial expression system help to send alert to the parents and students for evaluation and for preventing them from getting distracted during sessions. © 2023 IEEE.

关键词： Deep Neural Network Emotion detection Face Expression Machine learning

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Enhancing Credit Card Fraud Detection With Sbpmc-Oa Based Feature Optimization

Enhancing Credit Card Fraud Detection With Sbpmc-Oa Based Fe...

引用

International Conference on Inventive Computation Technologies (ICICT)

作者： V. Suganthi J. Jebathangam Department of Computer Science Vels Institute of Science Technology & Advanced Studies(VISTAS) Chennai India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Credit Card Fraud Detection (CCFD) protects cardholders from money loss during transactions. However, the prevailing works could not adapt to new attack techniques, leading to fraudulent transactions. Hence, the Bias Wrapper C1 Gated Recurrent Unit (BWC1-GRU) is proposed. Initially, to train the CCFD model, the credit card transaction details are collected and pre-processed. Next, the data augmentation is done using the Synthetic Heuristic Minority Over-Sampling Technique (S-HeMOTE) technique. After augmentation, the features are extracted and using the Secretary Bird Principle of Maximum Continuous Optimization Algorithm (SBPMC-OA), optimal features are selected. Then, the CCFD is done using the BWC1-GRU method. Thus, the proposed framework detected Credit Card Fraud (CCF) with an accuracy of 97.721 %, which outperformed the existing works.

关键词： Adaptation models Accuracy Feature extraction Credit cards Data augmentation Birds Data models Fraud Mobile applications Optimization

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MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulation 40

MergeSFL: Split Federated Learning with Feature Merging and ...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Liao, Yunming Xu, Yang Xu, Hongli Wang, Lun Yao, Zhiwei Qiao, Chunming University of Science and Technology of China School of Computer Science and Technology China Suzhou Institute for Advanced Research University of Science and Technology of China China University at Buffalo Department of Computer Science and Engineering The State University of New York United States

ISBN: (纸本)9798350317152

Recently, federated learning (FL) has emerged as a popular technique for edge AI to mine valuable knowledge in edge computing (EC) systems. To boost the performance of AI applications, large-scale models have received increasing attention due to their excellent generalized abilities. However, training and transmitting large-scale models will incur significant computing and communication burden on the resource-constrained workers, and the exchange of entire models may violate model privacy. To relax the burden of workers and protect model privacy, split federated learning (SFL) has been released by integrating both data and model parallelism. Despite resource limitations, SFL also faces two other critical challenges in EC systems, i.e., statistical heterogeneity and system heterogeneity. In order to address these challenges, we propose a novel SFL framework, termed MergeSFL, by incorporating feature merging and batch size regulation in SFL. Concretely, feature merging aims to merge the features from workers into a mixed feature sequence, which is approximately equivalent to the features derived from IID data and is employed to promote model accuracy. While batch size regulation aims to assign diverse and suitable batch sizes for heterogeneous workers to improve training efficiency. Moreover, MergeSFL explores to jointly optimize these two strategies upon their coupled relationship to better enhance the performance of SFL. Extensive experiments are conducted on a physical platform with 80 NVIDIA Jetson edge devices, and the experimental results show that MergeSFL can improve the final model accuracy by 5.82% to 26.22%, with a speedup by about 1.39× to 4.14×, compared to the baselines. © 2024 IEEE.

关键词： Merging

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Natural Language Processing for Prediction of Disaster Tweets using Ensemble Learning

Natural Language Processing for Prediction of Disaster Tweet...

引用

2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Chowdhary, Mohit Nandal, Poonam Manav Rachna International Institute of Research and Studies Department of Computer Science and Engineering Faridabad India

ISBN: (纸本)9798350344387

People who have access to the internet now often utilize social media as a result of the growing digitization. The widespread usage of social media has considerably hastened the propagation of social events while also accelerating human connection. It seemed obvious that the broad and quick use of information would have advantages for social scientists and computer scientists. People's sentences on social media may be classified based on the emotional emotions they include, making it simple to utilize them to describe and forecast events. NLP (Natural Language Processing) approaches may carry out this estimate procedure. In this paper, we used the ensemble learning approach to evaluate tweets that included terms associated with catastrophes using various pre-processing techniques and TF-IDF (Term Frequency-Inverse Document Frequency) feature extraction method. We compared these classification algorithms (Gaussian Navies Bayes, Logistic Regression, Random Forest,) according to their f1 scores, accuracy, precision and Recall. © 2023 IEEE.

关键词： Ensemble Learning Information Retrieval Natural Language Processing. Prediction

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Performance Analysis of Liver Disease Prediction Using Supervised Machine Learning Classification Models 1

Performance Analysis of Liver Disease Prediction Using Super...

引用

1st International Conference on advanced Computing and Emerging Technologies, ACET 2024

作者： Singh, Ramveer Sharma, Rahul Chaudhary, Shalbha Yadav, Deepika Jain, Jaishree Kumar, Pawan Raj Kumar Goel Institute of Technology Department of Computer Science & Engineering UP Ghaziabad India Raj Kumar Garg Institute of Technology Department of Computer Science & Engineering UP Ghaziabad India R.D. Engineering College Department of Management Studies UP Ghaziabad India Bharati Vidyapeeth's College of Engineering Department of Computer Science & Engineering New Delhi India Teerthanker Mahaveer University Department of Computer Science & Engineering UP Moradabad India Satyug Darshan College of Engineering Department of Computer Science Faridabad India

ISBN: (纸本)9798350367720

Liver is most fundamental part of human body. It is highly dependable to performing several body functions such as helping in food digestion, waste filtering, enzyme activation, detoxification, nutrition and glycogen storage, carbohydrate metabolism, vitamin and mineral storage etc. It has been found from various survey reports that across the world, 58 million people are suffering from liver disorders and nearly 01 million liver disorder patients are present in India. The growth rate of liver disorder patients is increasing rapidly worldwide due to a sharp rise in obesity, eating more junk foods, more alcohol consumption and unhealthy lifestyles. People who have liver disorders problem need medical treatment at the correct time and early disease prediction helps to reduce the number of deaths. The research focuses on designing an intelligent system that helps for prediction liver disorder by using various classification models of supervised machine learning and it also helps to prevent becoming India a global hub of liver diseases by 2030. The research emphasis on comparative performance analysis of different classification algorithms and ensemble methods. It also helps to select best accurate model for prediction of the liver diseases. © 2024 IEEE.

关键词： Self-supervised learning

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Blockchain-Enabled Smart Contract Design and Development with Hybrid Deep Learning Model

Blockchain-Enabled Smart Contract Design and Development wit...

引用

Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Etikala Aruna Arun Sahayadhas Department of Computer Science and Engineering VELS Institute of Science Technology & Advanced Studies Chennai India

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

Blockchain-enabled smart contracts have revolutionized secure, automated, and decentralized transaction handling across various industries. However, they face limitations in complex decision-making due to their rigid execution and predefined rules. This paper explores the integration of a hybrid deep learning model with blockchain-enabled smart contracts to enhance their functionality and decision-making capabilities. By embedding deep learning layers within the smart contract framework, this approach enables real-time data analysis, predictive analytics, and adaptive decision-making, fostering a more robust and dynamic contract execution. Through this integration, the hybrid model can analyse transaction data, external conditions, and contextual parameters, improving contract outcomes in applications like finance, supply chain management, and healthcare. Experimental evaluations demonstrated that the proposed model achieved 98% accuracy, with a precision of 97.65%, a recall of 97.4%, and an F1-score of 97.5%, significantly enhancing smart contract flexibility and resilience while maintaining security and transparency.

关键词： Deep learning Adaptation models Supply chain management Computational modeling Smart contracts Decision making Real-time systems Security Integrated circuit modeling Resilience

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Secured Mutual Authentication Framework using Hybrid Chaotic Encryption for IoT Edge Devices

Secured Mutual Authentication Framework using Hybrid Chaotic...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Somireddy Pavani Arun Sahayadhas Department of Computer Science and Engineering VELS Institute of Science Technology & Advanced Studies Chennai India

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

The accelerated growth of IoT edge devices requires better data transmission methods which need to be both secure and dependable because of the security risks from resource-constrained distributed environments. This article design and build a Hybrid Optimized Chaotic Encryption Scheme for Mutual Authentication in IoT Edge Environments. The proposed security system enhances Tent logistics chaotic algorithm with Seasoning Optimization as its main components. The Tent logistics algorithm demonstrates successful encryption capabilities because of its vulnerability to initial values and random behavior. The algorithm functions with Seasoning Optimization to achieve better security and lower its processing cost. The implementation of the proposed method leads to 30% less computational workload and superior speed and precision for encrypting data when compared against general encryption protocols. The strong encryption security of the algorithm has been validated through NIST (National institute of Standards and Technology) randomness tests which protect against attacks. The validated findings confirm both the high-security performance and efficient usage of the proposed solution for IoT edge environments that have limited resources.

关键词： Protocols Authentication NIST Encryption Security Internet of Things Optimization Standards Logistics Testing

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Utilizing Gradient Boosting Models to Identify Risk Factors for Stroke 2

Utilizing Gradient Boosting Models to Identify Risk Factors ...

引用

2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Tamilselvi, P. Harini, C. Rao, Guduri Padma Narasimha Raju, P.V. Subbulakshmi, R. Channabasava, Ugranada Department of Computer Science Vels Institute of Science Technology and Advanced Studies Chennai India Maisammaguda Secunderabad India SRKR Engineering College Andhra Pradesh Bhimavaram India Sagi Rama Krishnam Raju Engineering College Bhimavaram India Department of Computer Science and Engineering Karpagam Institute of Technology Coimbatore India Department of Information Science and Engineering Don Bosco Institute of Technology VTU Karnataka Belagavi India

ISBN: (纸本)9798350383867

This research looks at the usage of the models of gradient boosting to find out the ones that are relevant in the stroke incidence. With the help of a large dataset ranging from demographics to clinical and imaging characteristics, the three most effective gradient boosting algorithms (XGBoost with LightGBM and CatBoost) were evaluated in terms of their ability for stroke risk prediction. Model performance metrics, including accuracy, area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, precision, and F1 score, were estimated through re-scheduling validation proses. Through feature importance analysis, I discovered which of predictors, such as age, hypertension, diabetes and others contribute to the stroke risk prediction and where age and hypertension occupy the highest positions. The result of external validation in separate cohorts was the confirmation of the predictive properties of the data assortment as well as the generalizability of the presented models in variety of populations. Also, the role of these models has been examined from the perspective of their clinical usefulness and ability to inform the implementation of targeted preventive interventions that will result in improved outcomes regarding heart health. We evaluated various lifestyle adjustments, medications and precision monitoring plans as being the most likely to have a positive effect on stroke rates and patient outcomes. Overall the present study proves the problem-solving capability of the gradient boosting model in a tailored risk assessment of stroke and stroke prevention, enabling us to implement the necessary interventions to minimize the impact of the diseases on the healthcare systems and individuals. © 2024 IEEE.

关键词： Risk assessment

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Parallel Repetition of k-Player Projection Games 27

Parallel Repetition of k-Player Projection Games

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27th International Conference on Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2024 and the 28th International Conference on Randomization and Computation, RANDOM 2024

作者： Bhangale, Amey Braverman, Mark Khot, Subhash Liu, Yang P. Minzer, Dor Department of Computer Science and Engineering University of California RiversideCA United States Department of Computer Science Princeton University NJ United States Department of Computer Science Courant Institute of Mathematical Sciences New York University NY United States School of Mathematics Institute for Advanced Study PrincetonNJ United States Department of Mathematics Massachusetts Institute of Technology CambridgeMA United States

We study parallel repetition of k-player games where the constraints satisfy the projection property. We prove exponential decay in the value of a parallel repetition of projection games with a value less than 1. ... 详细信息

ISBN: (纸本)9783959773485

关键词： Multiplayer games Parallel Repetition Projection games

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