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A Comparative Study of Hyperparameter Tuning in Deep Learning Models using Bayesian Optimization and XAI 15

A Comparative Study of Hyperparameter Tuning in Deep Learnin...

引用

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

作者： Vardhan, N. Jai Chandana, Daggupati Dheepak Raaj, R. Shanmukhi, Sudireddy Radhakrishnan, Anisha Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

Plant disease detection is a crucial step in improving the quantity and quality of farm products since many plant diseases that arise in rice crops reduce the production of agriculture and cause financial loss. The manual evaluation of plant health is tiring and time consuming. Combining optimization algorithms with deep learning architectures can lead to more effective and efficient plant disease detection, with improved accuracy and generalization capabilities. This paper presents a comparison study on hyperparameter tuning of deep learning architectures for early detection of rice plant diseases. The hyperparameter tuning was performed by incorporating Bayesian Optimization algorithm on CNN, ResNet, MobileNet, InceptionNet and RegNet. The experimental study is conducted on dataset containing 1007 images of rice seed crop that contains 501 images of healthy samples and 506 unhealthy samples. Performance evaluation metrics such as accuracy, precision and inference time are employed to compare models. Additionally, a comparative analysis using explainable Artificial Intelligence (XAI) is conducted to visualize the interpretability of hyperparameter combinations and their impact on disease detection. © 2024 IEEE.

关键词： Contrastive Learning

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A Strategic Analysis of Food Demand Using Machine Learning and Explainable AI 9

A Strategic Analysis of Food Demand Using Machine Learning a...

引用

9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Adith, M. Katti, Panduranga Gupta, Deepa Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

The growing importance of meal demand forecasting is increasingly recognized as vital for business growth, aiding in efficient procurement planning and minimizing waste. This paper employs the 'Meal demand forecasting' dataset from Kaggle to analyze customer order patterns at the top ten profit-making fulfillment centers, aiming to predict future food item demand. This focus enhances the precision of our analysis and underlines the need for optimized inventory management in key areas. Various regression models, including Random Forest, Xgboost, Linear, Ridge, and Lasso Regressors etc. were tested. The models were assessed through the examination of metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R-squared to provide a comprehensive evaluation. Furthermore, this study also addresses model interpretability, a significant gap in current research, by explaining how different features impact demand predictions. This effort aims to provide more understandable and actionable insights for strategic decision-making in meal demand forecasting. © 2024 IEEE.

关键词： Machine learning

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Federated learning on low-power Arduino Nano33 BLE Sense to predict the length of stay using a linear regression model 2

Federated learning on low-power Arduino Nano33 BLE Sense to ...

引用

2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Sriram, S. Rk, Hariharathmajan Barathi Babu, M. Pradeep, Amal Karthi, R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

Federated learning (FL) is a collaborative learning paradigm where multiple clients are used to build the model without sharing data and preserving privacy. An FL-based linear regression model is designed to predict the length of stay for patients at hospitals using the low-power Arduino Nano 33 BLE Sense microcontroller unit (MCU). FL uses a distributed learning technique that allows model building from decentralized data sources. The Arduino Nano 33 BLE Sense is a compact and energy-efficient MCU providing an ideal platform for implementing FL in resource-constrained environments. FL algorithms aggregate model parameters from multiple Arduino clients and collectively train and build a predictive model to estimate the length of stay at the hospital by patients. Experiments were conducted to understand the performance of FL on clients with data of equal and varying sizes and heterogeneous data from multiple sources. The performance of the algorithm is evaluated based on Mean Absolute Error (MAE), Percentage Decrease in Training error (PDTE), and Percentage Difference with Optimal Testing (PDOT) value. Experimental results show that the number of local epochs and FL rounds affects the convergence of clients to the optimal value. The experimental results demonstrate the applicability of FL on low-power MCUs, preserving privacy which is a core requirement for healthcare solutions. © 2024 Elsevier B.V.. All rights reserved.

关键词： Forecasting

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Enhancing Hate Speech Detection in Tamil Code-Mix Content: A Deep Learning Approach with Multilingual Embeddings 5

Enhancing Hate Speech Detection in Tamil Code-Mix Content: A...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Vishak Anand, S. Prathap, Ishwar Gupta, Deepa Nair, Aarathi Rajagopalan Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

The increasing amount of hate speech and language which are inappropriate on platforms of social media has emerged as a serious issue, prompting measures to counter it. This study examines hate speech and languages that are objectionable in a Tamil Code-Mix dataset, where Tamil content is provided in English. It assesses the performance of various deep learning classifiers that use different multilingual transformer embedding models, which includes many models such as DistilBERT, IndicBERT, XLM-RoBERTa, LASER, MuRIL, and BERT, in this domain. Among the models tested, an ensemble stacked model integrating Long Short-Term Memory and Gated Recurrent Units with XLM-RoBERTa embeddings had the greatest accuracy (0.76) and weighted F1-score (0.72). This exceptional performance underscores the potential of advanced ensemble models for distinguishing hate speech in code-mixed languages. The study's findings are notable for several reasons. They enhance understanding of hate speech in Tamil, particularly in its code-mixed variant, and offer insights into the application of advanced deep learning methods and multilingual embeddings for detecting harmful content. By improving accuracy and reliability in hate speech detection, this research contributes to fostering safer and more inclusive online environments for Tamil-speaking communities. In summary, the research advances the domain of hate speech identification and emphasizes the importance of technological solutions in creating more safer and very inclusive online spaces. © 2024 IEEE.

关键词： Contrastive Learning

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Addressing the Cold Start Challenge through the Implementation of Content-based Filtering and Hybrid Filtering for Movie Recommendation 15

Addressing the Cold Start Challenge through the Implementati...

引用

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

作者： Kolli, Mahi Mukil, L.D. Kartthikeyan, N. Pranavkrishnan, M. Sampath, Nalini Nair, Priyanka C. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350370249

Recommender systems are of great significance for the difficulty in increasing speed as well as the amount of online information of the users to sort out the relevant content as they offer personalized suggestions. Content-based filtering and collaborative filtering are the two very common types of recommender algorithms, but they are still facing some issues, e.g. sparsity and cold-start problems. To cope with these limitations and increase recommendation accuracy, this study proposes a new form of recommendation system that combines collaborative filtering and content-based filtering to form a hybrid recommender system. The system proposed integrates the advantages of both methods and, therefore, offers more precise and individualized recommendations. As an illustration, the hybrid method shows superiority compared to the traditional methods, which proves that deep learning might be the key to advancing recommendation systems. © 2024 IEEE.

关键词： Collaborative filtering

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Road Accident Detection and Alert System at Real-Time Using Deep Learning Techniques 5

Road Accident Detection and Alert System at Real-Time Using ...

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5th IEEE International Conference on Communication, computing and Industry 6.0, C2I6 2024

作者： Mithul, C. Abdulla, D Mohammad Hari Virinchi, M. Sathvik, M. Palaniswamy, Suja Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350379884

This paper presents a real-time road accident detection and alert system that leverages a custom sequential CNN model optimized for rapid accident identification. Unlike existing approaches, this method integrates an automated alert system through Telegram's API, ensuring immediate notification to emergency responders. This enhancement significantly reduces response time, allowing for faster intervention and potentially saving lives. A convolutional neural network (CNN) model has been developed using frames extracted from CCTV video footage of accidents and non-accidents. The frames are processed and classified into an accident and non-accident categories for training the model to assess the incoming frames of a video in real-time. The data preprocessing, model selection, training, model validation, and performance assessment against benchmarks resulted in an efficient and accurate CNN-based accident detection system. It analyses live or recorded videos of the scene and determines whether an accident is present in the frame. In case of high confidence in an accident across multiple frames in succession, the system takes a picture in that frame, logs the time and location details, and then sends an alert message containing the time, location coordinates, and details of the accident to a specific Telegram channel through the Telegram Bot API. This monitoring method makes it easier to analyze the occurrence of an accident and report it within a short period. © 2024 IEEE.

关键词： Convolutional neural networks

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Efficient Underwater Object Detection Using Deep Neural Networks 5

Efficient Underwater Object Detection Using Deep Neural Netw...

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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Rasool, Shaik Atheeq Annamalai, R. Natarajan, B. Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amrita School of Computing Chennai India

ISBN: (纸本)9798350383522

The increasing amount of underwater visual data and deep sea research have led to the rise of marine animal identification as a major field for data processing and analysis. The need to protect the ecosystem emphasizes how important this endeavor is, yet it's still difficult because of things like the complexities of underwater backdrops, poor picture and video quality, and the variety of movements that marine life exhibits. This research used deep learning techniques to identify certain marine species and mark their presence with bounding boxes in uploaded images and videos in order to overcome these difficulties. The main goal is to provide workable methods for detecting marine life, so providing crucial backing for further studies in this area. By using the core deep learning frameworks, YOLOv8 and Faster-RCNN, the research was able to improve the efficiency and accuracy of marine life identification by making significant progress in detection time and mean average precision (mAP). This research work not only solves the current problems with finding marine animals but also establishes the groundwork for future research into comprehending and protecting the fragile undersea ecology. © 2024 IEEE.

关键词： Convolutional Neural Net- work Faster R-CNN Object Detection

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Precision Agriculture: Improving Water Stress Forecasting in Tomato Plants Using Bioristor Data and Advanced ML Techniques 1

Precision Agriculture: Improving Water Stress Forecasting in...

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1st International Conference for Women in computing, InCoWoCo 2024

作者： Archanaa, N. Rithani, M. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518943

The water stress in crops, especially tomato plants, is highly challenging to yields and qualities. This study involves improving the forecasting of water stress by using Bioristor sensor information combined with advanced machine learning models. Bioristor sensors capture ion concentration changes in plant saps;hence very crucial real-time data. We developed models such as RNN, LSTM, Bi-GRU, and ARIMA, for predictions and time series forecasting. We applied the models on classification with Decision Trees as well as Random Forest. The performance of the model was measured using applications in accuracy, precision, recall, and confusion matrix analysis metrics, when predicting 1 and 24 hours in advance, using classifications of 2-state (stressed vs. not stressed) and 4-state (healthy, stressed, uncertain, recovery). Our results suggest promising accuracy for the prediction of water-stress conditions, offering a framework which may be applied in real-world precision agriculture to optimize strategies for water management. © 2024 IEEE.

关键词： Fruits

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Smart Steps to Sporting Success: IoT-Driven Footwear Innovations 15

Smart Steps to Sporting Success: IoT-Driven Footwear Innovat...

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

作者： Thangam, S. Joshith, Gadi Santhosh, S. Harish, V. Jesy Janet Kumari, J. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Using the cloud platform Blynk IoT, smart sneakers with built-in step and calorie monitoring were created with athletes in mind. The primary characteristic of the smart shoe is its ability to detect early signals of cramping in the muscles during athletic exercises. This allows for prompt response to protect the athlete's foot and performance. It also acknowledges the vulnerabilities that athletes may encounter during training;this feature guarantees that athletes can notify the user by sending them messages in the event of any potential hazard. Beyond features for health and safety, the smart shoe calculates steps and tracks activity, giving athletes useful information for their training and recuperation regimens. With a focus on safety first, this project tackles important issues like sensor accuracy, device longevity, user comfort, and data security to produce a solution that improves athletic performance. By providing athletes with a comprehensive tool for health monitoring and personal safety, the smart shoe seeks to redefine wearable technology in sports. © 2024 IEEE.

关键词： Wearable sensors

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Matrix Factorization for Augmented Deep Learning Model Generation 2

Matrix Factorization for Augmented Deep Learning Model Gener...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Nived, P.A. Harikumar, Sandhya Anil, Aditya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

This paper aims to address two significant challenges of Deep Learning(DL) model generation, high computational cost involved during the training phase and data interpretability from high dimensional data. The computational cost is curbed using centralized as well as distributed matrix factorization technique known as CUR decomposition. CUR decomposition has the advantage of reducing the dimensions of the dataset without data transformation and minimal information loss. Though Singular Value De-composition(SVD) is the best matrix decomposition technique with least reconstruction error, it transforms the data and hence the original data cannot be interpreted from the decomposed matrices while applying DL technique. So CUR decomposition is leveraged to reduce the number of features in high-dimensional data while preserving the essential information. Extensive experimental analysis is conducted to evaluate and compare the performance of CUR and SVD techniques based on reconstruction error and time complexity in centralized as well as distributed setting. The results exhibit that CUR outperforms SVD in terms of computational efficiency, especially in a distributed setting where the decomposition time for CUR was a fraction compared to SVD. At the same time DL models generated from this reduced dataset exhibit comparable accuracy with the original high dimensional dataset as well as reduced dataset obtained through SVD. This work also enhances the accessibility of the CUR algorithm to users, for which an application is developed that enables users to execute the algorithm on any dataset. The user-friendly interface of the application facilitates the upload of datasets, configuration of desired decomposition rank, and execution of the algorithm with a single click. © 2024 Elsevier B.V.. All rights reserved.

关键词： Deep learning

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