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Twin attention based multi-task convolutional bidirectional long short term memory for facial expression recognition

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Multimedia Tools and Applications 2025年第10期84卷 8037-8070页

作者： Sreenivas, Velagapudi Sivaneasan, B. Vani, K. Suvarna Chakrabarti, Prasun Singapore Institute of Technology Singapore138683 Singapore Engineering Cluster Singapore Institute of Technology Singapore138683 Singapore Department of Computer Science and Engineering AI/ML Research Group Head VR Siddhartha Engineering College Andhra Pradesh Vijayawada520007 India Department of Computer Science and Engineering Sir Padampat Singhania University Rajasthan Udaipur313601 India

Facial Expression Recognition (FER) aims to detect the emotional state of facial images. It is playing an increasingly important role in several application areas, including human–computer interaction (HCI), video transcriptions, and social communications. This article provides an adequate attention-based multi-task deep learning method for facial expression recognition. First, the input videos are collected from the RAVDESS and MELD datasets. Then, the input videos are converted using a threshold-based keyframe extraction algorithm. Next, the input data is pre-processed using the Adaptive Pixel Density Median Filtering (A-PDMF) method. Key features such as shape, color and texture are extracted from the pre-processed images. Finally, the facial expressions are recognized by proposing a novel twin attention-based multi-task convolutional bidirectional long-short-term memory method (TA-MC-BiLSTM). In addition, the classification parameters are optimally tuned using the EX-AHA method (Extended Artificial Hummingbird Algorithm). The proposed model reduces the size of facial features while accurately identifying the wide range of facial expressions. For simulation, the proposed method prefers python tool and results are analyzed using RAVDESS and MELD datasets. The simulation results show that the proposed model provides better results than other existing models in terms of accuracy of 99.6% for RAVEDESS and 99.4% for MELD. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Pixels

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Next-Gen Human-computer Interaction: A Hybrid LSTM-CNN Model for Superior Adaptive User Experience 3

Next-Gen Human-Computer Interaction: A Hybrid LSTM-CNN Model...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Changala, Ravindra Manoranjini, J. Misba, M. Kakad, Shital Deepthi, S Sai Ponni Valavan, M. Guru Nanak Institutions Technical Campus Department of Computer Science and Engineering Hyderabad India Rajalakshmi Engineering College Department Of Ai&ml Chennai India Tamil Nadu Chennai India Vishwakarma Institute of Technology Department of Ai & Ds Maharashtra Pune India Department It Hyderabad Narsapur India K. Ramakrishnan College of Engineering Department of Ai & Ds Tamil Nadu Trichy India

ISBN: (纸本)9798350369083

In the rapidly evolving landscape of human-computer interaction (HCI), the demand for personalized and adaptive user experiences has grown exponentially. To meet this demand, Research propose a groundbreaking approach leveraging the fusion of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) architectures. This hybrid LSTM-CNN model is designed to enhance adaptability, responsiveness, and user engagement across various interactive platforms. Traditional HCI models often struggle to effectively capture the dynamic nature of user behavior and preferences. By integrating LSTM and CNN, This model achieves a synergistic blend of temporal and spatial feature extraction capabilities. The LSTM component excels in capturing sequential dependencies and long-term patterns, enabling the system to learn from past interactions and anticipate future user actions. Meanwhile, the CNN component efficiently processes spatial information, extracting meaningful features from multimedia inputs such as images, videos, and text. One of the key strengths of proposed model is its adaptability to diverse user contexts and preferences. Through continuous learning and adaptation, it dynamically adjusts its interface, content, and interaction patterns to match the evolving needs and preferences of individual users. Moreover, the hybrid architecture enables real-time processing of multimodal inputs, facilitating seamless interaction across a wide range of devices and platforms. In experimental evaluations, Hybrid LSTM-CNN model demonstrated superior performance compared to baseline methods in terms of user satisfaction, engagement, and task completion rates. Furthermore, it exhibited robustness and scalability, making it suitable for deployment in real-world applications across domains such as e-commerce, entertainment, education, and healthcare. In summary, The proposed approach represents a significant advancement in HCI research, paving the way for next-generation interactiv

关键词： Convolutional neural networks

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Efficient Liver and Tumor identification from CT Images and Clinical Data Using a Hybrid Deep Learning and Machine Learning Model

Efficient Liver and Tumor identification from CT Images and ...

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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： Vinish Kumar Virat Raj Saxena Mahima Tayal Department of Computer Science Engineering (AI&ML) Raj Kumar Goel Institute of Technology Ghaziabad India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

The present research investigates the integration of machine learning and artificial intelligence models for precise liver and tumour segmentation from CT images, with the goal of increasing diagnostic accuracy in liver cancer. A full dataset size of 2000 patients’ records were used, with standardised dosage information, treatment, as well as CT scans. The comprehensive preprocessing of the data, including normalisation, noise reduction, contrast enhancement, and segmentation, provided an opportunity not only to improve the image quality but also to select the desired features. The tracking of the tumor responses to the treatment was completed with the help of such machine learning models as Support Vector Machines, deep learning models such as VGG 16, VGG 19, Inception V3. The SVM + Inception V3 hybrid model was chosen as the most appropriate one, showing 98.76% of accuracy in detecting the correct responses to liver cancer. The SVM ensured the proper temporal data handling, and the Inception V3 made use of multi-scale feature extraction capabilities to localise and classify cancerous regions within CT scans accurately. The VGG 19 achieved an accuracy of 94.5%, and VGG 16 achieved an accuracy of 91.2%. Both VGG 16 and VGG19 performed commendably because of their image analysis capabilities. SAR values further emphasise that SVM + Inception V3 achieves the max precision and recall rates, while VGG displays slight limitations in this regard. The implications for this study are that hybrid models can provide a practical implementation of machine learning/deep learning artificial intelligence applications for medical imaging.

关键词： Support vector machines Deep learning Liver cancer Accuracy Computed tomography Liver Predictive models Feature extraction Data models Tumors

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A Novel Approach to Skin Cancer Detection using CNN, ResNet, and Hybrid Feature Extraction 2

A Novel Approach to Skin Cancer Detection using CNN, ResNet,...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Kiran, T.Srinivasa Ravi Madhuri, Y.Sudha Venkata Bala Annapurna, P. Lakshmanarao, A. Murthy, B.S.N. Parvathaneni Brahmayya Siddhartha College of Arts & Science Department of Computer Science AP Vijayawada India India Aditya University Department of Ai&ml Surampalem India Aditya University Department of It Surampalem India Bonam Venkata Chalamayya Engineering College Andhra Pradesh Odalarevu533210 India

ISBN: (纸本)9798350368413

Skin cancer is a prevalent and potentially life-threatening condition, making early and accurate detection crucial for effective treatment. This work proposes a novel hybrid approach to skin cancer detection, integrating both deep learning and traditional ml techniques. Utilizing a dataset from Kaggle, the process began with initial classification using a Convolutional Neural Network (CNN) and achieved an accuracy of 90%. Features were then extracted using Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), and ResNet, adding multiple layers of feature richness. These features were combined into a comprehensive hybrid feature vector and analyzed using ml models, including Random Forest, XGBoost, and AdaBoost. The Random Forest model achieved an accuracy of 96%, while XGBoost obtained an accuracy of 95. 60%. AdaBoost reached an accuracy of 95%. The proposed method demonstrated significant improvements in detection accuracy and robustness, highlighting its effectiveness in enhancing skin cancer detection. © 2024 IEEE.

关键词： Lung cancer

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Clustering based Ensemble Machine Learning for Integrated Prediction and Classification of Ecological Zones

Clustering based Ensemble Machine Learning for Integrated Pr...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Alabdeli, Haider Reddy, Francis Vijaykumar Anna Chanti, Yerrolla Aruna, T.M. Das, Abhijeet The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Malla Reddy Engineering College for Women Dept of Computer Science and Engineering Secunderabad India Sr University School of Computer Science & Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of Ai&ml Bengaluru India Department of Civil Engineering Bhubaneswar India

ISBN: (纸本)9798350360660

Accurate classification of ecological zones helps identifying the location of specific ecological features, and analyzing changing conditions. However, existing methods had limitations such as forming different image dimensions and analysing the areas within the images due to high noisy data. To overcome this, the proposed research integrated Density Based Spatial Clustering of Applications with Noise (DBSCAN) with Ensemble Machine Learning (Eml) algorithms using Multilayer Perceptron (mlP), Naïve Bayes (NB), Classification and Regression Tree (CART), and Logistic Regression (LR). To train the model, the Landsat series data which is obtained from Geographical Information System (GIS) is used and then pre-processed using Normalized Difference Vegetation Index (NDVI) to characterize a variety of vegetation properties. The DBSCAN is then used for noisy data to distinguish the densities of data points and to segment data in different fields. The integration of DBSCAN with ml classifiers improved the classification accuracy. The overall experimental results show that the proposed DBSCAN-Eml achieved 98.43% of accuracy and 97.89% of precision when compared with the existing methods Eml based spatial prediction method and Artificial Neural Network Cellular Automata (ANN-CA). © 2024 IEEE.

关键词： Adversarial machine learning

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A Gaussian Multivariate Mixture Model and VGG19 Model for Classification and Detection of Motor Vehicle 2

A Gaussian Multivariate Mixture Model and VGG19 Model for Cl...

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

作者： Deepa, N. Kumar, Bura Vijay Sowmya, M. Al-Jawahry, Hassan M. Bhushan, Yannam Bharath Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Sr University School of Computer Science and Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of Ai & Ds Bengaluru India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Gokaraju Rangaraju Institute of Engineering and Technology Department of Ai&ml Bachupally Hyderabad India

ISBN: (纸本)9798350372892

In daily life, the moving vehicle plays a significant part in effective system of transportation and traffic visual surveillance systems. Most critical task is to detect the vehicle image and most of the existing method, concentrated on maximizing the prediction percentage only. Also it lacks in classifying the imbalance data. To overcome this kind of problem, the Gaussian Multivariate Mixture Model (G-MMM) and VGG19 model is implemented to detect and classify the vehicle types. The proposed method contained four process namely, preprocessing performed by Flexible Histogram Equalization method (FHE), vehicle detection using G-MMM, feature extraction using Steerable Pyramid Transform (SPT) and Weber Local Descriptor (WLD) and classification using VGG19 ensemble deep learning model. G-MMM significantly improves detection sensitivity and it transformed the correlation between attributes through the training process. VGG19 is pretrained model, so it allows the fast learning recognition on new images of vehicle. The proposed method tested on Beijing Institute of Technology (BIT) dataset and MIO vision traffic camera dataset. When compared to the others methods like modified AlexNet, Deep Convolution Neural Network (DCNN), this proposed G-MMM-VGG19 gives 99.47 % of output rate in classification phase. © 2024 IEEE.

关键词： Equalizers

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A Review and Comprehensive Analysis of Recent Research in Crop Yield Prediction using Machine Learning Algorithms

A Review and Comprehensive Analysis of Recent Research in Cr...

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IEEE Recent Advances in Intelligent Computational Systems (RaiCS)

作者： Selvakumar R. K. Salehundam Sai Tanoj Tigulla Hrushitha Goud Department of Computer Science and Engineering CVR College Of Engineering Hyderabad India Department of Computer Science and Engineering (AI&ML) CVR College Of Engineering Hyderabad India

ISBN: (数字)9798350381689

ISBN: (纸本)9798350381696

Traditional farming based on conventional methods has been the pillar of agricultural practices for a long time. But, due to the escalating global population, unstable climatic patterns and limited resources, the efficiency of traditional methods is facing challenges. The introduction of data-driven and artificial intelligence based approaches involving machine learning and deep learning have gained significant popularity capturing the interest of numerous researchers. This review examines various algorithms, including Random Forest, Decision Trees, K-Nearest Neighbours, Lasso Regression, Stacked Regression, and XGBoost, along with deep learning techniques like LSTM, RNN, and SVM. The study highlights their performance across multiple metrics such as accuracy, RMSE, MAE, R 2 , and MSE. Notably, Random Forest emerges as a highly effective model due to its excellent performance in accuracy, RMSE, MAE, and R2. Lasso Regression and XGBoost also show remarkable results in RMSE and MSE, respectively. Deep learning models like LSTM, RNN, and SVM are recognized for their high accuracy, underlining their potential in handling complex agricultural data. Through an examination of diverse approaches and data sources, this review seeks to explain and define the capabilities of these innovative technologies for sustainable agrarian applications.

关键词： Deep learning Support vector machines Machine learning algorithms Accuracy Reviews Prediction algorithms Crop yield

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A Beetle Antennae Search-Based Sparrow Search Algorithm for Network Intrusion Detection

A Beetle Antennae Search-Based Sparrow Search Algorithm for ...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Baig, Ayub Reddy, R. Archana Muhsin Hassan, Muntather Subramanya, S.G. Manu, Hajari Department of Infornation Technology Hyderabad India Sr University Department of Computer Science and Engineering Warangal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Cse Bengaluru India Gokaraju Rangaraju Institute of Engineering and Technology Department of Ai&ml Hyderabad India

ISBN: (纸本)9798350375442

Nowadays, the network Intrusion Detection System (IDS) is a crucial cybersecurity technique that predicts unauthorized access, and exploits malfunction of computer networks. Traditional IDS struggle with classifying multiple, varied attack patterns which often lead to poor performance in detecting critical threats. In order to improve detection accuracy, identify numerous attacks, developing an efficient system becomes crucial which handles imbalanced data. Thus, this paper presents an Adaptive network IDS that employs a hybrid approach by combining the Improved Beetle Antenna Search (BAS) Algorithm and the Sparrow Search Algorithm (SSA) to enhance detection accuracy and adaptability in identifying cyber threats on NSL-KDD and CSE-CIC-IDS2018 datasets. Additionally, a Bi-directional Long Short-Term Memory (Bi-LSTM) network is utilized to reduce the False Positive Rate (FPR) and increase the entire efficiency of the system by effectively overcoming the challenges such as imbalanced data and low detection rate. Experimental evaluations on benchmark intrusion datasets such as NSL-KDD and CSE-CIC-IDS2018 using proposed IBAS-SSA + Bi-LSTM demonstrate higher detection results with accuracy of 98.12% and 98.64% respectively, when compared to other traditional IDS methods. © 2024 IEEE.

关键词： Network intrusion

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Crop Disease Detection with Autonomous Robotic Systems 1

Crop Disease Detection with Autonomous Robotic Systems

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Rithani, M. Shabarirajan, K.J. Shivanesh, B. Syamdev, R.S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India New Horizon College of Engineering Department of AI & ML Bangalore India

ISBN: (纸本)9798331518943

Crop disease detection is an important maintenance aspect for the upkeep of agricultural productivity. This paper presents a novel autonomous robot system designed to navigate and monitor crop health. The system was developed within a ROS framework using a camera to capture real-time video feeds of crops and a YOLO v8 model for detecting the presence of three specific plant diseases. Such a configuration resembles a farm in the presence of crops and trees and hence can be termed as a dirty farm. Automated navigation will cover all the area. The detection model processes each frame to detect plant disease. Such an approach has the potential to make improvements in early detection of diseases, thus leading to proper management and informed decisions about precision agriculture. © 2024 IEEE.

关键词： Microrobots

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Detection and Quantification of TB Bacilli in Sputum Smear Images 1

Detection and Quantification of TB Bacilli in Sputum Smear I...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Rithani, M. Jeevanantham Sona, B. Syamdev, R.S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India New Horizon College of Engineering Department of AI \& ML Bangalore India

ISBN: (纸本)9798331518943

Early and precise detection is essential to stop the spread of tuberculosis (TB) and enhance patient outcomes. This research aims to create an automated system that employs cutting-edge image processing and machine-learning approaches to identify tuberculosis (TB) bacilli in sputum smear microscopy pictures. An automated solution is required to increase diagnostic accuracy and efficiency because traditional diagnostic methods are labor-intensive, time-consuming, and prone to human mistakes. In the end, we optimize the best-performing model to reduce processing time while preserving diagnostic accuracy. Our goal is to assess the performance of several models in terms of training time, testing time, and accuracy. By enabling the use of sophisticated diagnostic technologies in environments with limited resources, the suggested method supports international efforts to combat tuberculosis. © 2024 IEEE.

关键词： Convolutional neural networks

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