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Facial Expression Recognition Using Multi-Block Deep CNN 5

Facial Expression Recognition Using Multi-Block Deep CNN

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5th International Conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Priyadarshini, V. Srinivasulu Reddy, U. Reddy Chirra, Venkata Rami Mrudula, M. Suneetha, M. National Institute of Technology Dept. of Computer Science & Engineering Andhra Pradesh India Machine Learning & Data Analytics Lab National Institute of Technology Dept. of Computer Applications Tiruchirappalli India School of Computer Science & Engineering VIT-AP University Amaravathi India Malla Reddy College of Engineering Department of CSE Telangana India Independent Researcher Amaravati India

ISBN: (纸本)9798331528539

Emotions play a crucial role in decision-making, moral judgments, and other cognitive processes. The goal of this work is to identify people's facial emotions from face images. Facial expression recognition (FER), a highly diverse field with many commonalities between classes, is a challenging problem. The capabilities of current FER systems are limited in this regard. In this work, a multi-block Deep Convolutional Neural Networks (DCNN) model was proposed to identify facial expressions. Five blocks with different computational components were defined in a multi-block DCNN to extract the unique characteristics from input images. The second model (DCNN-SVM) is developed with an ensemble of SVM classifier to improve predictability and stability. The third model (DCNN-MV) is based on the majority voting with 4 classifiers SVM, KNN, Random Forest, and Decision Tree. The CK+ dataset was utilized and expanded via image data augmentation to enhance model performance and generalization. By adjusting hyperparameters, the suggested DCNN model's accuracy was investigated. The DCNN model reports an accuracy score of 97.71%. Both DCNN-SVM and DCNN-MV models achieved 97.79% accuracy. On the CK+ dataset, the proposed models demonstrated good accuracy in identifying facial expressions. When compared to existing models, the proposed model outperformed existing models in terms of accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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Boosting-Based Machine Learning Approaches for Diabetes Prediction using Indian Demographic and Health Survey Data 3

Boosting-Based Machine Learning Approaches for Diabetes Pred...

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Kallur, Shanta Yaligar, Suman Thotad, Puneeth N. Kle Institute of Technology Affiliated to Visvesvaraya Technological University Dept. of Computer Science & Engineering Hubballi580027 India Kle Institute of Technology Affiliated to Visvesvaraya Technological University Dept. of Master of Computer Applications Hubballi580027 India

ISBN: (纸本)9798350364569

Traditional diagnostic techniques are time-consuming and prone to error. Performance is added however time and cost are reduced by using computer-aided diagnostic methods. This study aims to calculate how healthy boosting-based machine learning classifiers perform in automatically identifying diabetes in the Indian context. For this experiment, Indian demographic and health survey 2021 data on women aged 19 to 79 are considered. This data includes clinical, anthropometric, and biochemical components. 12,103 of the 706,504 responders who tested positive for diabetic testing were considered. The predictive exploration-based boosting machine learning models are adopted, such as adaptive boosting, categorical boosting, extreme gradient boosting, gradient boosting, and light gradient boosting models. Kernel Principal Component Analysis is employed for feature extraction. The Extreme Gradient Boosting model demonstrated strong performance on the diabetes dataset both before and after applying Kernel PCA. The metrics including accuracy, F1-score, precision, and recall for the XGBoost model showed improvements: accuracy increased from 81% to 83%, F1-score from 81% to 82%, precision from 81% to 88%, and recall from 81% to 76%.In contrast, Adaptive Boosting performed poorly, while Categorical Boosting, Gradient Boosting, and Light Gradient Boosting models achieved moderate performance levels. Overall, the model attained optimal performance across metrics such as accuracy, precision, Area Under the Curve (AUC), and recall. © 2024 IEEE.

关键词： Adaptive boosting

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Shortest Route ∗ Bidirectional Search Algorithm for Predicting Shortest Routes in Traffic 4

Shortest Route ∗ Bidirectional Search Algorithm for Predict...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Lakshna, A. Gokila, S. Ramesh, K. Hindustan Institute of Technology and Science Department of Computer Applications Chennai India Sri Krishna College of Engineering and Technology Department of Cse Coimbatore India

ISBN: (纸本)9798350300888

Road traffic congestion is a pressing issue that plagues urban areas, causing delays, inefficiency, and environmental problems. Smart traffic management systems aim to tackle this problem by utilizing advanced Shortest route ∗ Bidirectional Search algorithms. The Shortest route ∗ bidirectional search algorithm(SRSBSA) in smart traffic systems is used to efficiently avoid road traffic congestion and find the shortest route to reach the destination. Simultaneously exploring the search space from both the starting point and the destination, the algorithm significantly reduces the computational effort required to find the optimal path. distance, driver behavior, estimated travel considers road, type of holiday, peak hours, tr flow, and road capacities, vehicle characteristics. It considers the characteristic road type, holiday typing one-way streets, turn road capacities, and d limits, to generate accurate and feasible route suggestions to determine the best shortest route. The algorithm leverages heuristic estimates and evaluates various route options to guide drivers toward the shortest route. Implementing the Shortest route ∗ bidirectional Search algorithm in smart traffic produces the result in high accuracy level of 95%. This leads to improved travel time, increased transportation efficiency, and a more sustainable urban environment. © 2023 IEEE.

关键词： Traffic congestion

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Detection of Mango Leaves Disease using CSUBW based Residual Attention with EfficientNet 2

Detection of Mango Leaves Disease using CSUBW based Residual...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Kumar V, Sunil Jyothi, A.P. Nagamani, H.S. Sumanth, S. Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Ramaiah University of Applied Sciences Faculty of Engineering and Technology Department of Cse Bengaluru India Department of Computer Science Bengaluru India Department of Computer Science and Computer Applications Kolar India

ISBN: (纸本)9798350382952

Mangoes are one of the most popular fruits worldwide due to their taste and nutrition. Plant diseases may reduce plant quantity and quality. Disease management to improve crop quality and productivity requires early plant illness detection. Automation of plant disease detection reduces large farm surveillance. Since plants feed us, leaf problems must be diagnosed quickly and accurately. Our advanced learning method automatically detects leaf disease in several mango plant varieties. This study creates the RAM-EfficientNet, a novel illness classification network. The RAM block in EfficientNet extracts additional features and handles slight leaf lesion changes. Transfer learning was used to train EfficientNet on ImageNet to overcome the mango leaf disease sample size issue. The Cat Swarm Updated Black Widow Model (CSUBW), a hybrid optimization model, changes RAM-EfficientNet's weights to improve classification accuracy. Black Widow Optimization Algorithm (BWO) and Cat Swarm Optimization Algorithm (CSO) are combined to create the hybrid optimization model. The model's performance is then assessed to confirm its efficacy. © 2024 IEEE.

关键词： Fruits

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State of the Art Review: Revolution in Farming with Assistance from Smart Internet of Things Based System 5

State of the Art Review: Revolution in Farming with Assistan...

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5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Dubey, Nilesh Jayswal, Hardik Patel, Atul Goyal, Dinesh Department of Computer Science and Engineering Devang Patel Institute of Advance Technology and Research FTE CHARUSAT University India IT Department Devang Patel Institute of Advance Technology and Research FTE CHARUSAT University India Faculty of Computer Science and Applications Charusat University Changa Gujarat Anand India Computer Science and Engineering Poornima Institute of Engineering and Technology Rajasthan Jaipur India

ISBN: (纸本)9798400709418

Nowadays, IoT is impacting day-to-day lifestyles by making everything smart. Agriculture is the backbone of the economy of every country. In the past, farmers used to examine soil, weather, and crops using traditional methods, but with the smart innovative sensors and the help of IoT, this type of work can be easily done without any human efforts. The IoT-based smart agriculture system can be operated from anywhere by using networking technologies. This paper presents a survey of IoT technologies and applications in the agriculture sector. The survey article presents different research works in this domain, including applications, tools, and technologies, as well as approaches implemented in the field of agriculture using IoT. Furthermore, it discusses the major obstacles and challenges faced in the field of agriculture. Lastly, open challenges have been presented to assist researchers with possible solutions. © 2023 ACM.

关键词： Internet of things

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Automated Diagnosis of Breast Cancer using Combined Features and Random Forest Classifier 6

Automated Diagnosis of Breast Cancer using Combined Features...

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6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Muduli, Debendra Priyadarshini, Rojalina Barik, Ram Chandra Nanda, Surendra Kumar Barik, Rabindra K. Roy, Diptendu Sinha C.V.Raman Global University Bidya Nagar Mahura Janla Department of Computer Science and Engineering Bhubaneswar752054 India Kalinga Institute of Industrial Technology School of Computer Applications Bhubaneswar India National Institute of Technology Department of Computer Science and Engineering Meghalaya India

ISBN: (纸本)9798350346961

In recent years, several computer-aided diagnosis (CAD) models have been proposed for the early detection of breast cancer to minimize the mortality rate among women. However, the CAD model needs better accuracy with fewer features to improvise the model's efficiency. In this work, we propose an effective automated CAD model for mammogram image classification using the least number of features. The model presented here employs a two-dimensional discrete wavelet transform for feature extraction from mammogram images. The combined technique called PCA and LDA utilize feature reduction and select significant features. The combined reduced feature matrices are then fed into a random forest classifier, which is used to make the classification between normal and abnormal mammography images. Conventional datasets including INbreast, MIAS, and DDSM were used to test and evaluate the proposed CAD model with minimal features.. © 2023 IEEE.

关键词： Principal component analysis

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Crop Classification on Hyperspectral Images Using Residual Network and Multi-Head Self-Attention Feature Extraction 2

Crop Classification on Hyperspectral Images Using Residual N...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Saranya, N. Naga Vivekanandhan, V. Al-Farouni, Mohammed Shreyas, A.V. Kumar, Gotte Ranjith Saveetha College of Liberal Arts & Science Simats Department of Computer Applications Chennai India Malla Reddy College of Engineering Department of Computer Science and Engineering Hyderabad Secunderabad India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Civil Engineering Bengaluru India School of Computer Science & Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798350373110

Nowadays, hyperspectral imaging has empowered classification and prediction in many fields such as agriculture, land usage, tourism, etc. due to advancements in deep learning. This research focuses on obtaining efficient crop classification using Residual Network, Multi-Head Self- Attention, Convolutional Neural Network (ResNet-MHSA- CNN) approach. The Adaptive Wiener Filtering (AWF) is applied in preprocessing to eliminate noise, handles blurred parts of images effectively and decreases error rate in inverse filtering. The Residual Network (ResNet) method is employed by implement residual learning to eliminate degradation issues of deep neural networks. The high-resolution depth features of hyperspectral images are extracted by residual learning ResNet network. The Multi-Head Self-Attention (MHSA) utilized to capture long-range dependencies and global context information and helps the model in learning diverse patterns. Finally, the extracted features are given to Convolutional Neural Network (CNN) with complex pattern learning ability that excels in image classification with multiple layers of filters. The proposed ResNet-MHSA-CNN approach is evaluated with Indian Pines dataset and achieved 99.74%, 99% accuracy and kappa value respectively. The proposed method outperformed state-of-art methods such as Cross-Mixing Residual Network of Convolutional Neural Network (CMR-CNN), Deep Stacked Denoising Autoencoder (DSDA). © 2024 IEEE.

关键词： Convolutional neural networks

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Contrastive Loss Function Siamese Network for Change Detection on Remote Sensing Images 2

Contrastive Loss Function Siamese Network for Change Detecti...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Saranya, N. Naga Alkhayyat, Ahmad Shreyas, A.V. Raviprakash, M.L. Jayachandran, T. Saveetha College of Liberal Arts & Science Simats Department of Computer Applications Chennai India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Civil Engineering Bengaluru India Kalpataru Institute of Technology Department of Computer Science and Engineering Tiptur India Nandha Engineering College Department of Ece Erode India

ISBN: (纸本)9798350373110

To observe the changes in the earth surface is increasing due to development of navigation software applications and also mainly prediction purposes for agriculture purpose and environmental hazards. To overcome the problem one of the most crucial technological choices for effectively identifying changes in observations in remote sensing photos is Change Detection (CD). applications for CD are numerous and include mapping disasters, environmental monitoring, urban planning and land use study. On the other hand, the CD applications face significant hurdles due to the often-occurring class imbalance issue. In order to tackle this problem, a new type of Contrastive loss function Siamese network (CLFS-Net) that integrates the links between scenes and foregrounds to yield global-aware features for effective change detection. The CLFS-Net improve symbiotic connection learning between scene and foreground while simultaneously learning contextual relationships. The proposed CLFS-Network gained in terms of accuracy as 90%, precision as 95% and recall as 91% and f1 score as 95% respectively. © 2024 IEEE.

关键词： Environmental monitoring

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Generative Adversarial Networks (GANs) in the Wild: applications, Challenges, and Future Prospects 2

Generative Adversarial Networks (GANs) in the Wild: Applicat...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Kumar, Rakesh Chaudhary, Pooja Gupta, Shilpi Singhal, Manoj Gla University Department of Computer Engineering & Applications UP Mathura India UP Ghaziabad India Meerut Institute of Technology Department of Computer Science & Engineering UP Meerut India Gl Bajaj Institute of Technology & Management Department of Information Technology UP Greater Noida India

ISBN: (纸本)9798350371055

Generative adverse Networks (GANs) have become increasingly famous for a variety of tasks in picture and audio processing, especially unsupervised getting to know. GANs encompass components, a 'generator' and a 'discriminator,' that are skilled adversarial. The generator is tasked with producing a pattern of statistics, and the discriminator is tasked with figuring out whether the information is 'actual' or 'fake.' The generator 'learns' through trial and mistakes as it attempts to create samples that appear practical to the discriminator. Through the antagonistic schooling manner, the generator can create more accurate 'fake' records while the discriminator can emerge as better at distinguishing between 'actual' and 'faux' records. GANs have been applied to various duties, which include picture era, textual content to photosynthesis, photo-to-image translation, and automatic music composition. Recent tries have been made to apply GANs to extra complicated obligations in the wild, including self-reliant use and drug discovery. GANs have inspired an ever-developing quantity of industries because of their potential to generate sensible facts with minimal human input. © 2024 IEEE.

关键词： antagonistic complicated discriminator photosynthesis potential

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Recognition of Human Activity using Signal Processing & Deep Neural Networks 8

Recognition of Human Activity using Signal Processing & Deep...

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8th IEEE International Conference for Convergence in Technology, I2CT 2023

作者： Moharana, Bhimasen Biswal, Bhramara Bar Samant, Tapaswini Behera, Trupti Mayee Mishra, Subhashree Banerjee, Shobhan Lovely Professional University Department of Computer Science & Engineering Punjab India College of Engineering P.G. Department of Computer Science & Applications Bhubaneswar India Kalinga Institute of Industrial Technology School of Electronics Engineering Bhubaneswar India Birla Institute of Technology and Science - Pilani Department of Engineering Technology Rajasthan India

ISBN: (纸本)9798350334012

The task of tracking the activities being performed by humans is of immense importance as it finds application in healthcare, marketing, surveillance, etc. To keep track of this, they need to be recognized and classified into various activities. The data can be collected through smart devices such as smartwatches or smartphones which internally use various sensors to measure the physical movements of the person using it. We have used Human Activity Recognition Dataset from the UCI ML Repository, which consists of the data being collected from the smartphones of 30 volunteers. After choosing the appropriate features, we trained an Artificial Neural Network and a Long Short Term Memory Neural Network to classify the activities into various categories and performed a comparative analysis of these neural networks among themselves & also with respect to the previously leveraged techniques & algorithms on the dataset. © 2023 IEEE.

关键词： Long short-term memory

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