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Terrain Classification through Satellite Images using Deep learning 3

Terrain Classification through Satellite Images using Deep L...

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3rd international conference for Advancement in Technology, ICONAT 2024

作者： Khuman, Virendrasinh Arora, Shubh Siddhant Gupta, Rajeev Kumar Patel, Samir Pandit Deendayal Energy University Raisan Computer Science & Engineering Gujarat Gandhinagar382007 India

ISBN: (纸本)9798350354171

With the exponential growth of remote sensing data, particularly multispectral imagery, the integration of deep learning techniques such as Convolutional Neural Networks (CNNs) and Artificial Neural Networks (ANNs) has become relevant for efficient classification tasks. Satellite imagery is procured in the manner of multispectral imagery, which is a collection of a few image layers enlist of the same scene, each of them acquired at a particular wavelength band. Challenges in multispectral images arise owing to the large volume of image data and the data variety due to high dimensionality and number of channels. Land Cover and Terrain classification is the process of identifying, mapping and delineating the surface of the earth into different units. It remains vital for its applications in a variety of fields. It enables environmental monitoring allowing for adaptation to ecosystem variations. Fields like Urban Planning, Disaster Management and Infrastructure development find essential advantages in their planning phases owing to such classification. Deep CNNs, or Convolutional Neural Networks, are advanced machine learning models renowned for their ability to extract intricate patterns from visual data, crucial in tasks like image recognition and classification. The reason behind the application of deep CNNs arises from their unparalleled computing efficiency. Through our work we try to explore the utilization of above mentioned deep learning and machine learning techniques in the classification of land cover, focusing on both multispectral imagery and provide an overview of the existing literature, examining the methodologies employed and the datasets utilized and the processing and handling of these large datasets. Additionally, we introduce various data sources and datasets commonly utilized in these studies, shedding light on the advancements, challenges, and future directions in the field of remote sensing-based land use and land cover classification. © 2024 IE

关键词： Convolutional neural networks

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Innovations in Media C: Federated learning and BiLSTM Integration for Image and Video Analysis 3

Innovations in Media C: Federated Learning and BiLSTM Integr...

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3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Kumar, A Suresh Balavivekanandhan, A. Al Ansari, Mohammed Saleh Syamala Rao, M.P.N.V. Peera, D Gouse Muthuperumal, S. Rathinam Technical Campus Department of Cse Coimbatore India Prof. Dhanapalan College of Science and Management Department of Computer Science Chennai India University of Bahrain Department of Chemical Engineering Bahrain Srm University Department of Cse Andhra Pradesh India Annamacharya University Department of Civil Engineering India K.Ramakrishnan College of Engineering Department of Mathematics Trichy India

ISBN: (纸本)9798350369083

In the ever-evolving landscape of media, the demand for efficient and robust analysis of images and videos has intensified. Traditional methods often struggle to keep pace with the scale and complexity of media data. In response, this study introduces a novel approach that integrates Federated learning (FL) and Bidirectional Long Short-Term Memory (BiLSTM) networks to enhance the analysis of images and videos in media applications. Federated learning, a decentralized machine learning technique, enables collaborative model training across multiple edge devices without centralized data aggregation, thus addressing privacy concerns and data silo issues inherent in traditional approaches. By leveraging FL, The proposed framework facilitates the aggregation of insights from diverse sources while preserving data privacy. Furthermore, the integration of BiLSTM networks offers enhanced temporal modeling capabilities, allowing for the extraction of contextual information from sequential data such as video *** experimentation on diverse media datasets, including images and videos, demonstrate the effectiveness of approach in tasks such as object recognition, scene understanding, and action recognition. The results showcase significant improvements in accuracy and efficiency compared to baseline methods, highlighting the potential of Federated learning and BiLSTM integration for advancing image and video analysis in media ***, This study contributes to the ongoing efforts to innovate media analysis techniques by harnessing the power of decentralized learning and advanced sequential modeling, paving the way for more intelligent and privacy-preserving media analysis systems. This method achieves an accuracy of 97.5% and has been implemented in Python. © 2024 IEEE.

关键词： Federated learning

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Enhancing Healthcare data Security through Blockchain-Enhanced IDPS and Multi-Layered Firewall System 3

Enhancing Healthcare Data Security through Blockchain-Enhanc...

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3rd international conference for Advancement in Technology, ICONAT 2024

作者： Kurnala, Vishnu India

ISBN: (纸本)9798350354171

This paper introduces a novel security framework to fortify healthcare data against cyber threats, incorporating blockchain technology and ledger tokenization within an integrated Intrusion Detection and Protection System (IDPS). Faced with escalating cybersecurity challenges, our approach merges traditional protection mechanisms with cutting-edge technologies to offer unparalleled data integrity and security. By leveraging blockchain for immutable data trails and enhancing IDPS with advanced machine learning techniques, our solution provides a robust defense against a range of cyber threats, including ransomware and data breaches. Extensive testing in simulated environments confirms the effectiveness of our system, suggesting it as a viable blueprint for future developments in healthcare cybersecurity. This study not only demonstrates the application of blockchain in enhancing data security but also sets the stage for further innovative practices in protecting sensitive healthcare information. © 2024 IEEE.

关键词： data integrity

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Real-Time Object Detection using Mobile Robot Captured Images: A Deep learning Approach 3

Real-Time Object Detection using Mobile Robot Captured Image...

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3rd international conference for Innovation in Technology, INOCON 2024

作者： Hema Srivarshini, Ch. Preeti, N. Marri, Sreekar Praneeth Palaniswamy, Suja Pati, Peeta Basa Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350381931

Robots are becoming more and more prevalent in a variety of industries, such as transportation, agriculture, healthcare, and so forth. Deep learning is an area of study that is rapidly expanding and is applied in many fields including robots. For a robot to understand its environment and carry out tasks, object recognition is essential. This paper proposes a deep learning method that leverages both pre-existing data and real-time data for robotic object recognition which will enable robots to operate in challenging conditions. Using real-time data, CNN with decision tree is tested after being trained on both pre-existing and real-time data which yields 96% accuracy. A model has been created for reliable and accurate object recognition for robots. © 2024 IEEE.

关键词： Object detection

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2024 3rd international conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

2024 3rd International Conference on Smart Technologies and ...

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3rd international conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

ISBN: (纸本)9798350391565

The proceedings contain 99 papers. The topics discussed include: parsed region feature extraction method for hepatocellular carcinoma detection using medical images;predicting cyber-attacks and identifying perpetrators using machine learning techniques;enhanced real-time abnormal event detection in video surveillance for safety and security;an analysis of frequency response in integrated power system using power electronics controller tuned with whale optimization technique;classification of Alzheimer’s disease with generated MRI images using randomized CNN;data mining framework for discovering and clustering phenotypes of atypical diabetic lung;analyzing depression in social media: a study of basic deep learning algorithms and transformer model with a comparative approach;and gamifying language learning: applying augmented reality and gamification strategies for enhanced English language acquisition.

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Predictive Modeling of Environmental Parameters Using Ensemble machine learning Techniques 3

Predictive Modeling of Environmental Parameters Using Ensemb...

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3rd international conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Ponselvakumar, A.P. Mirthula, R.E. BarathKumar, M. Chindrella, S. Kongu Engineering College Dept. of Information Technology Erode India

ISBN: (纸本)9798331528201

Air quality monitoring plays a major role in safeguarding public health as well as environmental sanity. Present air quality monitoring approaches usually depend on apparatus and their high costs may hinder their availability and use. In this research, we propose a new machine learning based approach to estimate an air quality index (AQI). Specifically, we explore the application of random forests (RF), gradient boosting (GB) and majority voting proof (VC) for AQI estimation from data comprising several different environmental variables. We begin by preparing data for cleaning and normalization. There are specific features that define corresponding AQI predictors. Then we trained several learning machines including RF, GB and VC in order to assess the performance of AQI prediction. The model's performance is evaluated on a competition table basis. This enables hyperparameters adjustment via the competition table hence helps identify the best setup model. AQI prediction *** primary findings were that random forest models as well as gradient boosting have good predictive power while this system gains more strength when combining all three models together;vote-based one. These findings suggest that machine learning has great potential for improving air pollution control measures throughout broader sectors across the globe which will also increase its efficiency in several other goods/services areas including energy conservation programs. © 2024 IEEE.

关键词： Random forests

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Duplicate Detection in Quora Question Pairs Using machine learning 3

Duplicate Detection in Quora Question Pairs Using Machine Le...

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3rd international conference for Advancement in Technology, ICONAT 2024

作者： Shetty, Keerthi Prabhu, Tushar Kamath, Vinayaka Department of MCA Udupi India

ISBN: (纸本)9798350354171

The detection of question pair similarity on Quora presents a significant challenge in the realm of natural language processing and binary classification. This problem holds vital implications for user experience enhancement, redundancy reduction, and content quality improvement on the platform. In this paper, we explore the formulation of this problem, where each question pair is treated as a binary classification task, with the input comprising text strings and the output indicating whether the questions are duplicates. Log-loss serves as the primary performance metric, measuring the alignment between predicted probabilities and actual labels. The complexity of the task lies in assessing semantic similarity, which entails gauging the degree to which two questions convey similar information despite variations in wording, syntax, and structure. For instance, questions like 'How can I lose weight fast?' and 'What is the best way to slim down quickly?' exemplify semantic similarity. However, distinguishing between semantically similar and dissimilar questions, such as 'How do I get a date?' and 'How do I get a date fruit?' poses a unique challenge. Furthermore, natural language processing techniques play a crucial role in transforming textual data into numerical features for machine learning models. These techniques encompass various processes, including text preprocessing, feature extraction, engineering, and selection, each addressing the complexities, ambiguities, and noise inherent in natural language. © 2024 IEEE.

关键词： Semantics

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A Deep learning-Dominated Stacked machine learning and Deep learning Method for Electricity Theft Detection 3

A Deep Learning-Dominated Stacked Machine Learning and Deep ...

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3rd Asia Power and Electrical Technology conference, APET 2024

作者： Wang, Wanhao Huang, Qinyu Ling, Lang Ma, Tiejun Wang, Zheng Jin, Tao College of Electrical Engineering and Automation Fuzhou University Fuzhou China Conservancy and Hydropower Engineering Co. Ltd Fujian Huashun Water Fuzhou China Fujian Fuqing Nuclear Power Co. Ltd Fuqing China Fujian Xiexing Construction Co. Ltd Fuzhou China Fujian Province University College of Electrical Engineering and Automation Fuzhou University Fuzhou China

ISBN: (纸本)9798350367690

The swift advancement of smart grids has consistently highlighted the significance of managing operational costs, particularly in power enterprises. A key aspect of cost management is the precision of electricity theft detection (ETD), which is vital to minimize avoidable financial drains. This paper proposes a stacked machine learning and deep learning electricity theft detection method dominated by deep learning. The method first cleans the data and performs imbalance processing on it to solve the problem of class imbalance. Then, deep learning is used to extract features from the preprocessed data set, support vector machine (SVM), random forest (RF) and gradient boosting decision tree (GBDT) are used to classify the preprocessed data to obtain the pre-classification results of each classifier. Ultimately, the obtained features and pre-classification results are input into the final classifier eXtreme Gradient Boosting (XGBoost) for classification. To assess the efficacy of the proposed approach, comparative experiments have been conducted using various other models to detect electricity theft. The findings from the experiments indicate that the method for ETD introduced in this document possesses a high level of accuracy and robust generalizability, thereby substantiating the trustworthiness of the proposed method. © 2024 IEEE.

关键词： Adversarial machine learning

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Automated Detection of Lyme Disease using Transfer learning Techniques 3

Automated Detection of Lyme Disease using Transfer Learning ...

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3rd international conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Dipakkumar, Shah Vaidehi Shah, Vandita Dipak Shah, Dhir Deshmukh, Simran Swaminarayan University Kalol Gujarat India Shreyarth University Dept. of Comp. Appl. Gujarat Ahmedabad India Parul University Gujarat Vadodara India

ISBN: (纸本)9798350375190

This research study explores the use of transfer learning techniques for automated detection of Lyme illness. Lyme disease, a vector-borne infection transmitted by tick bites, may be challenging to diagnose because of its diverse clinical signs and potential similarity to other diseases. Transfer learning, a subtype of machine learning, involves using pre-trained models on large datasets to enhance a model's performance in a specific domain with less labeled data. This study utilizes transfer learning on medical imaging datasets to develop a dependable and precise automated method for diagnosing Lyme disease. The proposed approach demonstrates how advanced technology might improve the precision and reliability of diagnosing Lyme disease, perhaps resulting in quicker treatment and improved patient results. © 2024 IEEE.

关键词： Diagnosis

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Balanced Content Consumption Using machine learning Techniques 3

Balanced Content Consumption Using Machine Learning Techniqu...

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3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Ilangovan, Joshua Saraswathi, P. Vidhya Kalasalingam Academy of Research and Education Department of Computer Science and Information Technology Krishnankoil India

ISBN: (纸本)9798350369083

The rapid growth of Digital Media and communication technologies over the past two decades has led to various emerging platforms that have become an integral part of an individual's life. People share and connect through social media and other digital platforms, leading to a growth of closed communities categorized by similarities in their interests and beliefs. Recommendation systems and Personalization Algorithms have become extremely sophisticated in their ability to make inferences and provide users with content based on data collected by their engagement on these platforms. This has led to the dominance of effects like filter bubbles and echo chambers which narrows content consumption and leads to intellectual isolation, misinformation, and uninformed decision-making. To address these effects, we propose an approach that leverages deep learning and statistical techniques to provide legitimate means of measuring a user's content consumption against a "balanced content consumption"standard which would develop a sense of awareness as well as control over content consumption for a user. © 2024 IEEE.

关键词： Adversarial machine learning

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