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Enhancing Human Behaviour Analysis through Multi-Embedded learning for Emotion Recognition in Images 7

Enhancing Human Behaviour Analysis through Multi-Embedded Le...

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7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Jenefa, A. Khan, Aanya Mathew, Esther Alice Dani, Junia Sam Olivia, Giftlin Shivani, T.S. Karunya Institute of Technology Department of CSE Coimbatore India

ISBN: (纸本)9798350397253

Human behaviour analysis has been an active area of research in computer vision and artificial intelligence. the recognition of emotions in images has been a difficult task, as emotions are subjective and can be expressed in many different ways. Multi-Embedded learning has been proposed as a promising approach to tackle this problem by combining the outputs of multiple models. In this study, we aimed to enhance human behaviour analysis through Multi-Embedded learning for emotion recognition in images using stacking. the objective of this study was to enhance human behaviour analysis by combining multiple models for emotion recognition in images. the study aimed to demonstrate the benefits of stacking, a specific ensemble learning algorithm, in improving the performance of emotion recognition. Multiple base models were trained using different architectures and techniques, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and traditional machine learning algorithms like Support Vector machines (SVMs) and k-Nearest Neighbors (k-NNs). the outputs of these base models were used as inputs to a meta-model, which was a Convolutional Neural Network. the dataset utilised in this study was the AffectNet dataset, a large collection of images depicting facial expressions. the dataset consists of over 400,000 images of faces labeled with one of the seven emotions mentioned above. To facilitate model training and evaluation, the dataset was partitioned into separate subsets for training, validation, and testing. the results showed that stacking improved the performance of emotion recognition in images compared to the individual base models. the accuracy of the stacked model was 85%, which was greater than the accuracy of any of the basic models. © 2023 IEEE.

关键词： Emotion Recognition

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Application of machine learning Techniques to Predict Teenage Obesity Using Earlier Childhood Measurements from Millennium Cohort Study 23

Application of Machine Learning Techniques to Predict Teenag...

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2023 7th international conference on Cloud and Big Data computing, ICCBDC 2023

作者： Singh, Balbir Gorbenko, Anatoliy Palczewska, Anna Tawfik, Hissam School of Built Environment Engineering and Computing Leeds Beckett University United Kingdom Department of Electrical Engineering University of Sharjah United Arab Emirates

ISBN: (纸本)9798400707339

Obesity is a major global concern with more than 2.1 billion people overweight or obese worldwide, which amounts to almost 30% of the global population. If the current trend continues, the overweight and obese population is likely to increase to 41% by 2030. Individuals developing signs of weight gain or obesity are also at the risk of developing serious illnesses such as type 2 diabetes, respiratory problems, heart disease, stroke, and even death. It is essential to detect childhood obesity as early as possible since children who are either overweight or obese in their younger age tend to stay obese in their adult lives. this research utilises the vast amount of data available via UK's millennium cohort study to construct machine learning driven framework to predict young people at the risk of becoming overweight or obese. the focus of this paper is to develop a framework to predict childhood obesity using earlier childhood data and other relevant features. the use of novel data balancing technique and inclusion of additional relevant features resulted in sensitivity, specificity, and F1-score of 77.32%, 76.81%, and 77.02% respectively. the proposed technique utilises easily obtainable features making it suitable to be used in a clinical and non-clinical environment. © 2023 Owner/Author.

关键词： machine learning

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Digitization of Physical Notes: A Comprehensive Approach Using OCR, CNN, RNN, and NMF 7

Digitization of Physical Notes: A Comprehensive Approach Usi...

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Kulkarni, Soham Madurwar, Rhushabh Narlawar, Rushikesh Pandya, Anuj Gawande, Namrata Pimpri Chinchwad College of Engineering Department of Computer Engineering Pune India

ISBN: (纸本)9798350304268

2.3 trillion gigabytes of data is generated every day but that's today's world for you. In this technological era where we can use technology in almost all sectors and regular and constant day-to-day activities, these huge digital copies and data are communicated and accessed. Hence even though handwritten texts have their purpose and application they are of no use in the digital era. Questioning the same problem, innovations and key-to-note digitization and topic identification of this huge digital database carry inestimable merits and value. Before fitting execution, we went on exploring different methods, algorithms, and systems with our own which lead us to many trials and errors, and mistakes. Ultimately we came up with a system integrating Topic Detection and Identification. Intending to integrate both processes into a single system, we examine various algorithms involving neural networks like RNN, CNN, and ANN, and methods such as Tesseract, KNN, and LSTM that are used for implementing OCR while techniques such as K means clustering, TF-IDF, LDA, and LINGO have been employed to perform topic detection and identification. Based on our study and results from various papers, we have decided to use CNN for OCR. © 2023 IEEE.

关键词： Convolutional Neural Network Deep learning Natural Language Processing Optical Character Recognition Support Vector machine

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7th international conference on Emerging Research in computing, Information, Communication and Applications, ERCICA 2022

7th International Conference on Emerging Research in Computi...

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7th international conference on Emerging Research in computing, Information, Communication and Applications, ERCICA 2022

ISBN: (纸本)9789811954818

the proceedings contain 85 papers. the special focus in this conference is on Emerging Research in computing, Information, Communication and Applications. the topics include: Gesture Detection Using Accelerometer and Gyroscope;preface;Teaching learning-Based Optimization with learning Enthusiasm Mechanism for Optimal Control of PV Inverters in Utility Grids for Techno-Economic Goals;to Monitor Yoga Posture Without Intervention of Human Expert Using 3D Kinematic Pose Estimation Model—A Bottom-Up Approach;Upshot and Disparity of AI Allied Approaches Over Customary Techniques of Assessment on Chess—An Observation;network Intrusion Detection Using Neural Network Techniques;performing Cryptanalysis on the Secure Way of Communication Using Purple Cipher machine;artificial Intelligence and machine learning for Foundry Industry—A Case Study of Belagavi Foundry Industry;conversion of Sign Language to Text Using machine learning;a Hexagonal Sierpinski Fractal Antenna for Multiband Wireless Applications;Multi-level Hierarchical Information-Driven Risk-Sensitive Routing Protocol for Mobile-WSN: MHIR-SRmW;Target Classification Using CNN-LSTM Network with Reduced Sample Size in Surveillance Radar;machine learning Framework for Prediction of Parkinson’s Disease in Cloud Environment;iron Oxide Nanoparticle Image Analysis Using machine learning Algorithms;bankruptcy Prediction Using Bi-Level Classification Technique;Infant Brain MRI Segmentation Using Deep Volumetric U-Net with Gamma Transformation;analysis of Deep learning Architecture-Based Classifier for the Cervical Cancer Classification;covid Vaccine Adverse Side-Effects Prediction with Sequence-to-Sequence Model;Comparison Between ResNet 16 and Inception V4 Network for COVID-19 Prediction;Computational Deep learning Models for Detection of COVID-19 Using Chest X-Ray Images;an Ensemble Approach for Detecting Malaria Using Classification Algorithms;ioT-Enabled Intelligent Home Using Google Assistant.

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An Effective Diagnosis of Alzheimer's Disease with the Use of Deep learning based CNN Model 7

An Effective Diagnosis of Alzheimer's Disease with the Use o...

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7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Sharmili, K.C. Suja, G.P. Pandian, E. Walid, Md. Abul Ala Arunachalam, Sripriya Babu, G.Charles Computer Science Stella Maris College 17 Cathedral Road Chennai India Computer Science Muslim Arts College Thiruvithancode629174 India Sri Muthukumaran Institute of Technology Mangadu Master of Computer Application Chennai69 India Department of Computer Science and Engineering Khulna9203 Bangladesh Computer Applications-PG School of Computing Sciences VELS Institute of Science Technology & Advanced Studies - VISTAS Velan Nagar P.V. Vaithiyalingam Road Pallavaram Chennai600 117 India GRIET Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350397253

In recent years, Alzheimer's disease has become a major health concern. Over 45 million individuals worldwide suffer from this illness. Alzheimer's disease is a neurodegenerative illness of unidentified etiology and process that causes cognitive deterioration, and it mostly affects the elderly. Dementia, the gradual and irreversible loss of brain cells, is the leading cause of Alzheimer's disease. those with this illness lost the ability to think, read, and do other basic tasks. A machine learning system may aid in this situation by making accurate predictions about the onset of illness. Dementia diagnosis in a broad patient population is the major objective. this research presents the findings and analysis of many machine learning models for identifying dementia. To alleviate this obstacle and help in the diagnosis of AD will create a deep-learning architecture that uses stacked auto-encoders and a softmax output layer. the proposed technique can analyze numerous classes in a single setting, unlike earlier procedures, and only needs a small number of labeled training samples and basic domain expertise to get started. In the present studies, a substantial improvement in performance when it came to classifying all diagnostic subgroups. Using the proposed technique can run these time series through a Convolutional Neural Network (CNN) and ResNet50 model to extract the distinguishing patterns across stages. With an F1-score of 0.99 and an accuracy of 99.91 percent, the CNN-based technique outperformed the standard feature-based classifiers by a significant margin. © 2023 IEEE.

关键词： Convolutional neural networks

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A Comparative Study of Accuracy for Novel Bankruptcy Prediction System Using Diverse Algorithms 1

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7th international conference on Intelligent computing and Optimization, ICO 2023

作者： Babu, S. Ramesh Krishnaiah, B. Mohan, N. Rao, P. Narayana Ramachandran, M. KLBusiness School Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Department of Business Administration SRK Institute of Technology Andhra Pradesh Vijayawada India REST Labs Tamil Nadu Krishnagiri Kaveripattinam India

ISBN: (数字)9783031733185

ISBN: (纸本)9783031733178

this study evaluates the efficacy of multiple algorithms in forecasting corporate bankruptcies, using a dataset of 6,819 instances and 96 variables. We tested the Random Forest (RF), Multilayer Perceptron (MLP), Gradient Boosting (GB), and Support Vector machine (SVM) algorithms for bankruptcy prediction. Classification accuracies were observed as 91.1% for RF, 95.8% for MLP, 98.2% for GB, and 94.2% for SVM. An independent sample t-test confirmed significant accuracy differences between the algorithms (p © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Federated learning Models for Intrusion Detection in Industrial IoT Networks 7

Federated Learning Models for Intrusion Detection in Industr...

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7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Prasad, Sanjana Sharma, Ishu Rajendraprasad, Deepashree HKBK College of Engineering Department of Electronics and Communication Engineering Bangalore India Lovely Professional University Department of Computer Science and Engineering Punjab Phagwara India Boston University Department of Health Informatics BostonMA United States

ISBN: (纸本)9798350372816

the Industrial Internet of things has emerged as an essential tool for building Industry 4.0 and Industry 5.0 where timely information can be retrieved from different scenarios. these devices are highly vulnerable to cyberattacks as heterogeneous types of devices can be used in the infrastructure that may or may not be equipped with standardized security protocols. Artificial intelligence-based methodologies can effectively identify these types of attacks on a prior basis for taking mitigation action. this method raises concerns about data privacy as building a machine learning-based method requires the sharing of network data that can reveal the actual information of industries. the proposed Federated learning based framework handles this concern by preserving each device's critical data by utilizing the benefits of the decentralized model aggregation. this research work presents the comparison of the proposed framework on the CICIoT2023 dataset with federated averaging and federated proximal techniques for achieving a global model. the performance evaluation of these two aggregation techniques is performed based on metrics of accuracy, loss, precision and recall. the results prove that the federated proximal method achieves higher accuracy in comparison to the federated averaging method. © 2024 IEEE.

关键词： Network intrusion

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Advancing Polycystic Ovary Syndrome Diagnosis: A Holistic Ensemble-Based machine learning Approach 7

Advancing Polycystic Ovary Syndrome Diagnosis: A Holistic En...

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7th IEEE international conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023

作者： Mohammed Usman, F. Ayub, Afrah thirumala Akash, K. Ahmed, Mohammed Riyaz Nitesh Kumar, T. School of Computing and Information Technology Reva University Bengaluru India Professional 1 Storage Administrator Dxc Technology Bengaluru India Hkbk College of Engineering Bengaluru India School of Multidisciplinary Studies Reva University Bengaluru India

ISBN: (纸本)9798350343144

Polycystic ovary syndrome (PCOS) is a complex hormonal disorder that presents a diagnostic challenge due to its varied manifestations. Early detection of PCOS is crucial for timely intervention and management. In this paper, we propose a machine-learning-based approach for the early diagnosis of PCOS, leveraging demographic data to train a model that achieves high accuracy. Our study highlights the significance of data scaling and normalization in improving the model's performance. the results demonstrate that ensemble methods, specifically Random Forest and Extra Trees Classifier, outper-form traditional methods, exhibiting an accuracy of 94 % for PCOS detection. Feature selection analysis reveals that clinical and demographic features, including age, body mass index (BMI), and hormonal levels, play a pivotal role in PCOS detection, with the combination of features yielding superior performance. Model optimization through parameter fine-tuning and experimentation with different ensemble methods enhance the models' predictive capability. Our proposed methodology addresses the limitations of existing algorithms, ensuring robust and accurate PCOS diagnosis. the implementation of our approach demonstrates efficiency in achieving high accuracy and performance for PCOS detection. therefore, the future for early diagnosis of PCOS with this innovative and compatible machine-learning approach is promising, offering the potential to revolutionize PCOS diagnosis and aid in timely intervention and personalized management strategies. thus, machine learning is a valuable adjunct to clinical examination, hormonal assays, and ultrasound, as it holds great promise as a powerful tool for early detection and diagnosis of PCOS, enhancing healthcare outcomes for women with this disorder. © 2023 IEEE.

关键词： machine learning

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7th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2023

7th International Conference on Information and Communicatio...

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7th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2023

ISBN: (纸本)9789819937608

the proceedings contain 57 papers. the special focus in this conference is on international conference on Information and Communication Technology for Intelligent Systems. the topics include: Segmentation of Brain Tumor Images Using Morphological Reconstruction;navigation Planning for Efficient Area Covering for Surface Cleaning;pathfinding Visualizer: A Survey of the State-of-Art;a Study on Cyber Security and its Challenges in India;Implementation of AES Algorithm;feature Extractor Techniques for Alzheimer's Predictive Model in Brain Imaging;user Preference Recommendation System and Analytics for News Articles;Design and Development of Focus Controller for the 50/80 cm ARIES Schmidt Telescope;an Efficient Abstractive Summarization of Research Articles Using Pegasus Model;predicting Fishing Effort: Data Collection for machine learning Model Using Scientific and Indigenous Method;a Secure Data Dynamics and Public Auditing Scheme for Cloud Storage;Model Design to Analyse Coronary Artery Disease Using machine learning Techniques (MDACADMLT);impact of Geo-positioning Information in Limiting Forest Fire Damage;Automatic Music Melody Generation Using LSTM and Markov Chain Model;federated learning Approaches to Diverse machine learning Model: A Review;A Real-Time Human Computer Interaction Using Hand Gestures in OpenCV;GAN (Generative Adversarial Network)-Based Image Super-Resolution: A Technical Perspective;mental Wellness—Mind Matters;smart Pantry System Using IoT Based Cloud;performance Analysis of Classification Techniques in Heart Disease Prediction;analytics of Retail Store Consumption Using Physical computing;An Exploratory Study of SMT Versus NMT for the Resource Constraint English to Manipuri Translation;a Framework for Opinion Extraction and Analysis;spatio-Temporal Prediction in Epidemiology Using Graph Convolution Network.

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7th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2023

7th International Conference on Information and Communicatio...

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7th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2023

ISBN: (纸本)9789819937578

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