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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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Air Quality Management in Smart Cities by leveraging machine learning Techniques 3

Air Quality Management in Smart Cities by leveraging Machine...

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3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Shree, A.N. Ramya Shankaramma Reddy, Charulatha Ramaiah Institute of Technology Bengaluru560054 India Ramaiah Institute of Technology Bengaluru560054 India Dept. of CSE RNS Institute of Technology Bengaluru560098 India

ISBN: (纸本)9798331532420

In today’s world machine learning (ML) algorithms are actively employed to solve several real-world problems. It is used further to forecast the Air Quality Index (AQI) in smart cities, utilizing data from sensors to predict pollution levels and provide real-time information to the public. ML models are trained on well-processed datasets, and their effectiveness is assessed using standard evaluation metrics. The predictions generated by these ML models play a crucial role in air quality management strategies, such as issuing early warnings for days with poor air quality, informing policy decisions to mitigate environmental pollution, and guiding urban planning initiatives. This research underscores the importance and application of ML in predicting air quality within smart cities. © 2024 IEEE.

关键词： Sulfur dioxide

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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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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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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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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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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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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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A Novel machine learning Approach for Fast and Efficient Detection of Mango Leaf Diseases 3

A Novel Machine Learning Approach for Fast and Efficient Det...

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3rd international conference on Computing and machine Intelligence (ICMI)

作者： Swapno, S. M. Masfequier Rahman Nobel, Sm Nuruzzaman Islam, Md Babul Haque, R. Meena, V. P. Benedetto, F. Bangladesh Univ Business & Technol Dept Comp Sci & Engn Dhaka Bangladesh UNICAL Dept Comp Modeling Elect & Syst Engn Arcavacata Di Rende Italy Appl Bahari Big Data Res House Dhaka Bangladesh Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept EEE Bengaluru India Univ ROMA TRE Dept Econ Lab SP4TE Via Silvio Amico 77 Rome Italy

ISBN: (纸本)9798350372977;9798350372984

Monitoring plantation diseases is a fundamental issue for the agricultural industry. Early diagnosis of plant illnesses dramatically affects food quantity and quality. Here we focus on the case of Mangoes, whose demand is very high. To maximize earnings, mango plant diseases must be managed well. Manual mango leaf disease diagnosis is impractical in today's computerized world due to its high cost, shortage of mango experts, and variable symptoms. In this work, we provide a novel machine learning-based mango leaf disease classification method. We build a reliable categorization system using 4000 photographs from the Kaggle dataset grouped into 8 sickness categories. Our model reaches a remarkable accuracy of 96.87% throughout the training phase, and an accuracy rate of 93.12% during the validation phase. Finally, using previously unknown data, our model demonstrates an exceptional testing accuracy rate of 94.99%. Our solution outperforms other existing machine learning detection methods, thus proving to be very effective in enhancing the categorization of Mango Leaf disease. and, more generally, offering improved and enduring food security and safety in the future.

关键词： Food Safety EfficientNetv2L Leaf disease Classification dataset Mango Leaf

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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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