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Advancements in Distributed Deep Learning: Federated Learning, AutoML Integration, and Beyond

Advancements in Distributed Deep Learning: Federated Learnin...

引用

2024 IEEE International Conference on Innovation and Novelty in engineering and Technology, INNOVA 2024

作者： Kavitha, T. Manikandan, S.P. Patil, Bhimaraya Patil, Anita Dept. of Computer Science and Engineering New Horizon College of Engineering Bangalore India CMR University Bengaluru India Dept. of Computer Science Engineering New Horizon College of Engg. Bangalore India Dept. of Information Science and Engg. City Engineering College Bengaluru India

ISBN: (纸本)9798331505134

This paper present advancements in distributed deep learning, focusing on federated learning, AutoML integration, and beyond. Leveraging the latest developments in machine learning (ML) and artificial intelligence (AI), our research explores novel approaches to enhance the efficiency, scalability, and security of distributed deep learning systems. This paper introduces federated learning techniques to enable collaborative model training across decentralized edge devices, thereby minimizing data privacy concerns and reducing communication overhead. Additionally, Proposed work investigate the integration of automated machine learning (AutoML) capabilities into distributed training pipelines, streamlining the model selection and hyperparameter tuning processes. Furthermore, Paper also explores cutting-edge advancements beyond traditional deep learning paradigms, such as generative adversarial networks (GANs) and reinforcement learning (RL), to unlock new opportunities in distributed AI. Through experimental evaluations and case studies, Paper demonstrate the effectiveness and potential impact of our proposed advancements, paving the way for future breakthroughs in distributed deep learning research. © 2024 IEEE.

关键词： Federated learning

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Sea Erosion Prediction and Monitoring Using IOT and ML 2

Sea Erosion Prediction and Monitoring Using IOT and ML

引用

2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Usha Bala, V. Harshavardhan Sai, Y. Slipa Shramila, V. Asritha, M. Bharath, M. Chandu, A. Anil Neerukonda Institute of Technology & Sciences Dept. of Computer Science & Engineering Visakhapatnam India

ISBN: (纸本)9798350366570

One of the major environmental issues that the world is currently facing is sea erosion, which requires an innovative solution for early detection and mitigation. Our paper focuses on the integrated system that combines both IoT and Machine Learning. The four main important environmental factors that are responsible for sea erosion are temperature, wave height, wave speed, and soil moisture. IoT sensors deployed along the prototype design are used to monitor these environmental parameters. Another key component is the machine learning algorithm, which is used analyse the interactions, patterns, and relations among the environmental data. By analysing the patterns among the previous data, the model can predict the likelihood of sea erosion events. This allows for the early detection of erosion events and timely interventions that can minimize the damage or loss caused by sea erosion. The main aim of this project is to help the coastal management authority take smart decisions and proactive steps using both real-time and past data analysis. © 2024 IEEE.

关键词： Soil erosion

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Epileptic Seizure Detection Using Quantum Support Vector Classifier

Epileptic Seizure Detection Using Quantum Support Vector Cla...

引用

2024 International Conference on Computational Intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Meesala, Gopichand Soni, Barkha Pandey, Manish Khare, Nilay Maulana Azad National Institute of Technology Dept. of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798350381887

Epilepsy is the second most prevalent neurological disorder. It poses significant challenges to both diagnosis and treatment. The information obtained from electroencephalography serves as crucial for understanding the dynamics of epileptic brain activity. It offers a non-invasive means for monitoring and detecting epileptic seizures. This paper explores the use of quantum machine learning algorithms to improve the precision and effectiveness of utilizing EEG data for epileptic seizure detection. Quantum machine learning, leveraging principles from quantum mechanics, holds promise for addressing these challenges by harnessing quantum computational power and exploiting quantum entanglement to process information more efficiently. Quantum features and quantum kernels are employed to capture intricate patterns within the EEG signals, allowing for improved discrimination between normal brain activity and seizure events. Results indicate that the Quantum Support Vector Classifier exhibit a satisfactory classification accuracy compared to traditional techniques despite having constraints with resources. The findings suggest that quantum computing holds great promise in revolutionizing the field of epilepsy diagnosis by providing more robust and accurate tools for distinguishing between epileptic and non-epileptic EEG patterns. © 2024 IEEE.

关键词： Quantum entanglement

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Enhancing Cervical Cancer Diagnosis: Deep Learning and Explainable AI in Multiclass Image Classification 1

Enhancing Cervical Cancer Diagnosis: Deep Learning and Expla...

引用

1st International Conference for Women in Computing, InCoWoCo 2024

作者： Shreya, Kuthati Divakarla, Lasya Priya Kumar, Kanderi Johith Singh, Tripty Afnaan, K. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331518943

Cervical Cancer is a major health concern amongst many women around the world. Cervical Cancer is usually caused by the Human Papilloma Virus (HPV). This causes unusual growth in the cervix region. All though this is such a serious health concern, proper treatment and diagnosis are not that prominent and available to the patients. Regular checkups and tests are quite necessary which can help in early detection and diagnosis. It has been observed if these cancerous growths are detected at an early stage then the patient has a high recovery rate. If not detected early then there are chances that these cancer cells spread to the other organs which can be a fatal condition for the patient. A technique has been introduced involving the use of deep learning models to categorize cancer cells into four distinct classes, aiding in accurate identification and effective diagnosis. Explainable AI techniques like Local Interpretable Model Agnostic Explanations (LIME) have been used with these models in order to identify the regions in the medical images that contribute towards the image classification of the cancer cells which can further provide a better understanding for performing diagnosis. © 2024 IEEE.

关键词： Lung cancer

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Optimized Parameter Design of Binocular Structured Light System for Precision Reflective Surface CPU in Industrial Assembly Processes

Optimized Parameter Design of Binocular Structured Light Sys...

引用

2024 IEEE Region 10 Conference, TENCON 2024

作者： Yiu, Cheuk Tung Shadow Woo, Kam Tim The Hong Kong University of Science and Technology Dept. of Electronic and Computer Engineering Hong Kong

ISBN: (纸本)9798350350821

This paper constructs a simulated CPU assembly line robot system. The system incorporates binocular structured light vision technology for CPU recognition and localization. The reflective surface of the CPU poses a significant challenge for 3D reconstruction, mainly when dealing with tilted angle situations. This inherent characteristic of the CPU adds complexity to the vision system to capture and reconstruct the object's 3D information accurately. This research aims to optimize the vision system's key parameters, balancing cost-effectiveness and the required precision. By achieving the necessary level of accuracy in picking the CPUs to the desired location, our work aims to enhance robotic assembly operations, offering an optimized parameter configuration approach and supporting the needs of modern industrial processes. © 2024 IEEE.

关键词： Robotic assembly

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A Machine Learning Based Auto-Grading Model for Question-Based Algebra Problems using RoBERTa 4

A Machine Learning Based Auto-Grading Model for Question-Bas...

引用

4th International Conference on Intelligent Technologies, CONIT 2024

作者： Vidula, N.A. Reddy, T. Anisha Manogna, Sodum Balakrishnan, Roshni M Pati, Peeta Basa Amrita School of Computing Dept of Computer Science and Engineering Amrita Vishwa Vidhyapeetham Bengaluru India

ISBN: (纸本)9798350349900

The use of Machine learning models into educational assessment has revolutionized the grading process for problemsolving *** research introduces a machine learning auto-grading model tailored for question-based algebra problems, utilizing RoBERTa, a natural language processing *** primary objective is to investigate the adaptability of RoBERTa, originally designed for natural language understanding, for representing non-natural language mathematical expressions. The study also explores the impact of different embedding strategies on the performance of autograding systems for student responses to equation *** mathematical and textual code inputs were transformed into vectors, and the vectors were then fed into several models. Evaluation metrics such as RMSE, MSE, R2, and MAE are utilized to assess the effectiveness of each embedding approach in grading student responses accurately. The use of k-fold cross-validation will improve the dependability of the assessment measures, offering insights into the model's consistency across different subsets of the data. The comparative analysis of different embedding strategies offers guidance on selecting the most suitable approach for enhancing the autograding process. © 2024 IEEE.

关键词： Embeddings

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An Effective Way to Detect Phishing Website Using Machine Learning 6

An Effective Way to Detect Phishing Website Using Machine Le...

引用

6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Toyeer-E-Ferdoush Shorab, Mst. Sumaiya Ritu, Saima Akter International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350385779

In today's interconnected digital landscape, phishing represents a significant threat to both individuals and organizations. Phishers cleverly utilize deceptive techniques to extract sensitive information from unsuspecting users, posing a risk to critical account details. Despite various proposed methods to detect phishing websites, the adaptability of phishers continually challenges the efficacy of existing approaches. Recognizing the vital role of Machine Learning (ML) in combating these evolving threats, this research investigates the application of ML techniques. Common characteristics in phishing attacks can be identified through accurate ML analysis. This research employs a range of ML models and transformer models, including Catboost (Cat), XGBoost (XGB), Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), TabNet, K-nearest neighbor (KNN), and Logistic Regression (LR) with grid-search. Among these models, the Catboost classifier achieved a remarkable accuracy of 97%. This study shows dedication to utilizing diverse methodologies to advance the field of phishing website detection. © 2024 IEEE.

关键词： Decision trees

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Context Aware Recommendation System Using LLM 8

Context Aware Recommendation System Using LLM

引用

8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Madhav, Kagolanu Venkata Chandra Veeramaneni, Asritha Vivek, Vinnakota Sai Belwal, Meena Khare, Sangita Amrita School of computing Dept of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bangalore India

ISBN: (纸本)9798331505462

This paper proposes a unique approach to emotion recognition and employs Vision Transformer (ViT) model for the sake of giving personalised suggestions of music selections and environments. ViT is able to determine the user emotion based on his facial expression detection technology. Those emotions will provide the critical inputs that will allow recommendations to be modified to context-aware standards the mood you're in the time of year, the specifics of the month. Proprietary technology creates music playlists suitable to the mood of the moment as it suggests the best locations for experiences based on an emotional context and other external factors such as the season and time of month. As we move deeper into the future, this merging of emotion detection technology and customized recommendations can deliver an unparalleled user experience by offering a user exactly what they would like in their location of the area they are you engaged in. It just ensures that the location to suggest should not only be ideal to the user mood but must also be practical as well by taking into account the locations weather and local events. The whole concept of this is to create a more rounded and complete approach to curating a fitness routine tailored to the individual, taking into account their mental health as well as external issues. © 2024 IEEE.

关键词： Emotion Recognition

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A Multi-Routing Paths' Exploration Algorithm for Multi-paired Communicating Vehicles' Data Transmission Offloading using the Multi-access Edge Computing Architecture 4

A Multi-Routing Paths' Exploration Algorithm for Multi-paire...

引用

4th European Conference on Communication Systems, ECCS 2024

作者： Huang, Chung-Ming Tsai, Cheng-Han National Cheng Kung University Dept. of Computer Science and Information Engineering Tainan Taiwan

ISBN: (纸本)9798350377927

This work proposed a novel method for multiple communicating paired vehicles' data transmission offloading using the Multi-access Edge Computing (MEC) technique. When two paired communicating vehicles are communicating with each other through cellular network's Base Stations (BSs), the data transmission can be offloaded to the Cellular Vehicle-to-Vehicle (C-V2V) path that exists between these two vehicles using 5G NR's sidelink communication;these two paired communicating vehicles can switch back to the Cellular Vehicle-to-Infrastructure (C-V2I) communication to communicate with each other when the corresponding C-V2V path is broken. Using the proposed method, the MEC architecture serves as a centralized computing mechanism to determine the availability of k-hop C-V2V paths for paired communicating vehicles' data transmission offloading. Each vehicle periodically reports its context, such as position, speed, driving direction, and C-V2V sidelinks' Channel Busy Ratios (CBRs), to the MEC server to derive feasible k-hop C-V2V data transmission offloading paths. The simulation result demonstrates that the proposed method has (i) better throughput compared to the method of relying solely on C-V2I communication through BSs and (ii) better throughput, higher offloading fraction and lower average data loss rate comparing with the other k-hop-limited CV2V data transmission offloading method. © 2024 IEEE.

关键词： Edge computing

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Deepfake Image Forgery Detection using Local Feature Descriptors 15

Deepfake Image Forgery Detection using Local Feature Descrip...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Dienash, S. Swapna, T.R. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

Deepfake Image Forgery,generates fake images after manipulating the original source images. This increases the risk of losing trust in images or videos. This paper proposes an efficient methodology to detect Deepfake images from real images. The methodology involves extracting Local Feature De scriptors(Local Binary Pattern (LBP),Frequency Decoded Local Binary Pattern (FDLBP),Binary Gabor Pattern (BGP)). The fusion of these features are fed to the DenseNet to classify the image as fake or real. The model uses Convolutional Neural Network (CNN) architecture that utilizes all three Local Feature Descriptors that extract features of the images. The model has an accuracy of 93.65% which is performing better than Deep learning models. © 2024 IEEE.

关键词： Binary Gabor Pattern (BGP) DenseNet Frequency Decoded Local Binary Pattern (FDLBP) Local Binary Pattern (LBP) Local Feature Descriptors (LFD)

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