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Mitigating Catastrophic Forgetting in Deep Learning Models for Sentiment Analysis

Mitigating Catastrophic Forgetting in Deep Learning Models f...

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International Conference on Advances in Information Technology (ICaiT)

作者： J. Ranjith Santhi Baskaran B. Adithya Department of Computer Science and Engineering Puducherry Technological University Puducherry India Department of Inofrmation Technology Puducherry Technological University Puducherry India Department of CSE(AI&ML) Geethanjali College of Engineering and Technology Hyderabad India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Deep learning models have been employed to evaluate sentiment analysis. However, the issue of catastrophic forgetting is frequently seen in these models. Cognitive decline occurs when the model's performance on previously learned tasks deteriorates as it acquires new ones. A study is conducted to investigate the impact of catastrophic forgetting on sentiment analysis algorithms and propose potential solutions to address this issue. This study evaluates the adaptability of Elastic Weight Consolidation (EWC) and Learning without Forgetting (LwF) in effectively handling novel sentiment analysis tasks while preserving the model's performance on previously acquired tasks. Experimental results on the Amazon Product Reviews dataset demonstrate that both EWC and LwF significantly reduce the impact of catastrophic forgetting, with LwF outperforming EWC in terms of sentiment classification accuracy. Our findings highlight the importance of addressing catastrophic forgetting in sentiment analysis models to ensure their robustness and reliability in real-world applications.

关键词： Deep learning Training Sentiment analysis Analytical models Adaptation models Accuracy Reviews Robustness Data models Real-time systems

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Deep Learning-based Prediction of Parkinson's Disease Progression using Voice Recordings 7

Deep Learning-based Prediction of Parkinson's Disease Progre...

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Alagdeve, Vilas D. Thatipudi, Jayendra Gopal Roy, Priyanka Nithya, R. Anil Kumar, N. Ramesh, B. Yeshwantrao Chavan College of Engineering Department of Electronics Engineering Maharashtra Nagpur441110 India University of North Texas Department of Computer Science DentonTX76203 United States Vellore Institute of Technology Department of Electrical and Electronics Engineering Tamilnadu Vellore India Vivekanandha College of Engineering for Women Department of Computer Science and Engineering Tamilnadu Tiruchengode637205 India Mohan Babu University School of Engineering and Technology Department of Electronics and Communications Engineering Tirupati India St. Martin's Engineering College Department of Ai&ml Telangana Secunderabad Dhulapally500100 India

ISBN: (纸本)9798350341485

The main underlying factor behind Parkinson's Disease (PD) is the degeneration of brain cells responsible for producing dopamine, a neurotransmitter crucial for interconnecting brain cells. These dopamine-producing cells play a vital role in controlling adaptability, regulating movement, and facilitating fluency. The concept of active aging has emerged to enhance various aspects of fitness as individuals age, ultimately improving their overall quality of life. However, most efforts have concentrated on promoting normal aging, with limited attention given to the elderly population afflicted with chronic conditions like PD. This research focuses on the application of deep learning techniques for early detection and diagnosis of Parkinson's disease (PD). In the first experiment, this study utilizes the voice samples from healthy individuals and PD patients as a minimum training set for the Enhanced Deep Neural Network Model (EDNN) classifier, resulting in an optimal prediction model capable of identifying early onset PD. Subsequently, this study also investigates the effectiveness of transfer learning using the Convolutional Neural Network (CNN) architecture Alex Net for diagnosing Parkinson's disease from MR images in a second experiment. The results demonstrate the successful implementation of the EDNN classifier, showcasing its potential for early detection of Parkinson's disease, allowing for timely intervention and treatment. Moreover, by employing transfer learning with AlexNet, this study presents a novel approach for accurately diagnosing Parkinson's disease from MR images, representing a significant advancement in medical image analysis. © 2023 IEEE.

关键词： Neural network models

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An Emotion Aware Music Recommendation System Using Flask and Convolutional Neural Network

An Emotion Aware Music Recommendation System Using Flask and...

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Shashank Gajula Tigulla Hruthika Goud Bhawani Sankar Panigrahi M.A. Jabbar Computer Science & Engineering (AI&ML) Vardhaman College of Engineering Hyderabad Hyderabad India Department of Information Technology Mahatma Gandhi Institute Of Technology Hyderabad India

Music has been instrumental in influencing the emotions of a person. Every person develops a choice of music depending on varied factors like- philosophy, situations and personal emotions. Many organizations have been providing different ways to recommend a good playlist of music to their users based on their previous choices and emotion of user but couldn't bridge the gap of personalization and emotion driven recommendation. This paper emphasizes on building this gap by providing a recommendation of music based on user's choices and their current emotions. The proposed model is an innovative approach by harnessing the power of Convolutional Neural Networks (CNN) and music therapy approaches to ensure to provide people with an apt recommendation that enhances the user's experience of music. This model has achieved an accuracy of 71%. It can easily be integrated with music platforms to enhance the user experience and provide better services.

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Emotion Recognition in Visual Art on Color and Texture Analysis using Improved Vision Transformer 1

Emotion Recognition in Visual Art on Color and Texture Analy...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Suma, K.G. Balasm, Zaed Sasirekha, N. Veera Manikandan, P. Sandhya, K. Vellore Institute of Technology School of Computer Science and Engineering Department of Ai & Ml Andhra Pradesh India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Sona College of Technology Department of Electronics and Communication Engineering Salem India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Electrical and Electronics Engineering Mamallapuram India Tirupati India

ISBN: (纸本)9798331529246

Nowadays, Emotion Recognition plays a key role in social communication interaction in which human emotions are interpreted from facial expressions and speech. Several challenges occur in emotion recognition such as capturing context of any emotion also facial attributes make the recognition process complex. To overcome these drawbacks Improved Vision Transformer (IViT) classifier is proposed for emotion recognition. Initially, the input data is collected from the Japanese Female Facial Expression (JAFFE) to collect color features and visual features. Then, the input data is processed using min max scaling method which normalizes pixel values. After that, colour and texture-based features are extracted by utilizing Rotation Invariant Local Binary Patterns (RI-LBP) with Hue Saturation and Value (HSV) and Red Green Blue (RGB). Finally, classification is performed by IViT, which processes images as sequences of patches. Hence, the proposed IViT model achieved better output when compared to existing Visual-Audio Attention Network (VAANet) in terms of accuracy by obtaining 96.6% respectively. © 2025 IEEE.

关键词： Local binary pattern

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A Comparative Analysis of Machine Learning Algorithms for Agricultural Drought Forecasting 3

A Comparative Analysis of Machine Learning Algorithms for Ag...

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3rd International Conference on Issues and Challenges in Intelligent Computing Techniques, ICICT 2022

作者： Vrindavanam, Jayavrinda Babu, Tina Gandiboina, Harika Jayadev, Gopika School of Engineering Dayananda Sagar Univeristy Department of Ai and Ml Bengaluru India School of Engineering Dayananda Sagar Univeristy Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665482684

The occurrence of drought is a climatic feature and is a phenomenon that happens over time. Depending on the severity, it can last for a short or long time. Farming households are trying to meet due to rising agricultural operating costs that hinder the country's development. This study aims to forecast the severity of the drought over time. Drought scores vary from 0 to 5, with 0 and 5 indicating the least and highest intensity drought conditions. This is done using weather and soil data of a region consisting of Precipitation, Surface Pressure, Humidity, Temperature, Wind Speed, and Soil data. The main reasons for the cause of drought are first identified. These features are used to train the multivariate time series models like Prophet, VAR (Vector Auto-Regression), LSTM (Long short-term memory), and Comparison of actual v/s predicted values. The results were promising. The study has done an analysis comparing different machine learning algorithms for agricultural drought forecasting and it was found that the LSTM model performed better than VAR and Prophet models. © 2022 IEEE.

关键词： Drought

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Deep Learning Based air Quality Forecasting Using Satellite Data

Deep Learning Based Air Quality Forecasting Using Satellite ...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： K Navya Sree Mancharla Rakesh Reddy Saraswathi. S Department of Computer science and engineering Specialization with AI & ML Sathyabama Institute of Science and Technology Chennai India Department Computer science and engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

air pollution is a significant threat to human health and the environment. Accurate air quality forecasting is essential for effective mitigation strategies, including public health advisories, emission control measures, and policy interventions. This research focuses on the development of an advanced air quality forecasting system using machine learning techniques. The system uses historical data on pollutants such as PM2.5, NO2, and O3, along with meteorological parameters, to predict future air quality levels. The model is enhanced with the inclusion of sophisticated machine learning algorithms to better capture the intricate spatiotemporal patterns and enhance the accuracy of forecasting. The system may potentially provide real-time air quality forecasts that enable proactive measures for protecting public health and inform decisions for environmental policy. This research contributes to sustainable and resilient urban environments by providing valuable insights into air quality dynamics and supporting effective air pollution mitigation strategies.

关键词： Accuracy Atmospheric measurements Prevention and mitigation Urban areas Weather forecasting air pollution Pollution measurement Forecasting Public healthcare Diseases

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An Improved Wind Driven Optimization and Factored Conditional Restricted Boltzmann Machine for Electric Load forecasting 2

An Improved Wind Driven Optimization and Factored Conditiona...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Deepa, N. Adnan, Myasar Mundher Naveen, I.G. Kumar, Gotte Ranjith Palakurthy, Deepthi Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India Sr University School of Computer Science & Artificial Intelligence Warangal India Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Department of Ai & Ml Hyderabad India

ISBN: (纸本)9798350372892

Now-a-days, because of the expanding effects of both human behavior and the environment, the consumption of electric power is increasing rapidly in our century. It is used in decision-making and power grid operation;electric load forecasting is significant. The complex signal that is electric load profile arises from the nonlinear and stochastic behavior of users. The suggested model is a comprehensive framework that includes modules for training, forecasting, feature selection, and data preprocessing and optimization. An enhanced version of the mutual information approach, the Distance-Based Synthetic Minority Oversampling Technique (D-SMOTE) used for data pre-processing and feature selection module. It is used to choose abstractive features from historical data. The Factored Conditional Restricted Boltzmann Machine (FCRBM), a deep learning model enhanced by learning to estimate the future electric load, is used for training and forecasting module. The optimization module used for fine tuning the adjustable parameters of model is based on Improved Wind Driven Optimization (IWDO) algorithm. The evaluation metric used in this research is Mean Absolute Percentage Error (MAPE). The average MAPE value of IWDO algorithm is 0.4125% better than that of existing methods like Temporal Inception Convolution Network using Multi-Head Attention (TICN-Att), Residual Convolutional Neural Network (RCNN) and Multilayered Long Short-Term Memory (ml-LSTM) and Feature Preprocessing, Multi-Layer Perceptron and Genetic Wind-Driven Optimization (FPP-mlP-GWDO) algorithms. © 2024 IEEE.

关键词： Convolutional neural networks

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An Alternative Deep Learning Approach for Early Diagnosis of Malaria

An Alternative Deep Learning Approach for Early Diagnosis of...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Jayavrinda Vrindavanam E Mary Jasmine Gaurav Kamath Anirudh Narayanan Department of Computer Science and Engineering(AI & ML) Dayananda Sagar University Bengaluru India Department of Computer Science and Engineering Christ University Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Considering the malaria disease-related moralities prevailing mainly in underdeveloped countries, early detection and treatment of malaria must be an essential strategy for lowering morbidity and fatality rates. Detection of Malaria using traditional investigation methods through blood samples and expert judgments was found to be time-consuming. In this paper, the authors introduced a Machine Learning automated system to eliminate the need for human intervention, which in turn enables early detection of malaria. The study has used various Deep Learning techniques such as traditional Convolutional Neural Network (CNN), VGG19, ConvNeXtXLarge, ConvNeXtBase, ConvNeXtSmall, ConvNeXtTiny, InceptionResnetv2, Xception, DenseNet169, EfficientNetB7, MobileNet, ResNet50, and NasNetLarge as base models. These models have been trained and tested with microscopic blood smear images dataset and observed that ConvNeXtXLarge detects malarial parasites with an accuracy of 96%. The proposed method outperforms the existing approaches in terms of both accuracy and speed. The findings of this work can contribute to the development of more accurate and efficient automated systems for early detection of Malaria.

关键词： Deep learning Accuracy Malaria Microscopy Convolutional neural networks Blood Residual neural networks

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A Trustworthy Product Rating System Based on the Blockchain with E-Commerce Business Model

A Trustworthy Product Rating System Based on the Blockchain ...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Kranthi, Alapati Smitha Ranjith Kumar, Gotte Tejasri, Nimmagari Raviprakash Siddhanti, Prateeksha Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India Sr University School of Computer Science & Artificial Intelligence Warangal India Malla Reddy Engineering College for Women Dhulapally It Department Hyderabad India Kalpataru Institute of Technology Department of Computer Science and Engineering Tiptur India Nitte Meenakshi Institute of Technology Yelahanka Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350313413

This article covers the difficulties that customers and retailers encounter while searching for and getting real items at reasonable costs in the internet marketplace. While the development of e-commerce has made it simpler than ever for people to purchase from the comfort of their own homes, it has also resulted in an explosion of phoney reviews and rating manipulation, causing shoppers to lose trust in both the reviews they read and the rating systems themselves. This article offers a method based on blockchain technology to provide e-commerce enterprises with a trustworthy and transparent product rating system. The recommended method for evaluating things in the paper is based on blockchain technology, which the study believes provides e-commerce enterprises with a trustworthy and transparent rating system. According to the outcome analysis, 90% of users thought the blockchain-based grading system was easier to use and more straightforward than prior ones. © 2023 IEEE.

关键词： Blockchain technology Decentralization E-commerce Legitimate products Market disruptions Product Grade System Product quality Smart contract

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Hybrid RF-DNN-LSTM Ensemble for Short-Term Solar Energy Forecasting

Hybrid RF-DNN-LSTM Ensemble for Short-Term Solar Energy Fore...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Indumathi V V T Ram Pavan Kumar M Srilakshmi Ramya S Ch Harika Divya R Sahith Vellaturi Gopikrishna Department of CSE (AI&ML) B V Raju Institute of Technology Narsapur Medak Telangana India Department of Computer Science Kakaraparti Bhavanarayana College Vijayawada Andhra Pradesh India Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of AI&ML Aditya University Surampalem Dept of CSE CVR College of Engineering Hyderabad Dept of IT MLR Institute of Technology Hyderabad

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Given the drastic shift of global sentiment towards renewable energy, it becomes incredibly important to match supply with demand. However the highly variable nature of weather makes it difficult to accurately predict the output of a solar power plant. In this paper, solved this problem by using an ensemble model consisting of both machine learning and neural networks (NN) as base models to forecast the amount of energy that needs to be produced by a solar plant over a short-term time horizon, which in our case will be 0 minute (immediate), 5 minute, 30 minute and 90 minute. Each base model is fine tuned to encourage high diversity and low correlation to improve prediction accuracy. The expected stability or generalization from RF-DNN combined with the memory retention capability of the LSTM network should provide an ideal predictor for time series forecasting of a stochastic process like weather.

关键词： Renewable energy sources Time series analysis Weather forecasting Stochastic processes Solar energy Predictive models Stability analysis Forecasting Long short term memory Random forests

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