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Multilingual Facial Emotion Decoding and Posture Recognition using Random Forest Classifier

Multilingual Facial Emotion Decoding and Posture Recognition...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Sangeetha, K. Balaji, V.S. Manju, M. Renuka Devi, S. Velayutham, P. Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology Chennai India

ISBN: (纸本)9798350374933

Effective communication is crucial for success in various interactions, including personal and online interviews. The work proposed is to refine the communication effectiveness and extend the understanding of interactions by incorporating new system functions of real-time emotion and body language capture. Facial and body landmarks are analyzed using Google’s Mediapipe to ensure accurate identification of the frequently performed emotions and frequently posed postures. The proposed system uses three classifiers, namely Random Forest, Gradient Boosting, and Ridge Classifier, all integrated in one pipeline;this makes our work distinct. In the testing phase the classifiers are checked to avoid misidentification of emotions or incorrect posture in different situations. One of the major novelties of this work is its ability to address users of different languages, thus it is relevant anywhere in the world. This dynamic and robust system provides accurate emotion analysis achieving the accuracy of 92%, enhancing understanding in various communication contexts and setting a new standard for interview and interaction analysis. © 2024 IEEE.

关键词： Emotion Recognition

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Automatic Human Life Rescue Analysis Model with Incorporation of Deep learning Algorithm 5

Automatic Human Life Rescue Analysis Model with Incorporatio...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Kalaiselvi, V.K.G. Prithikhaa, S. Divya Darshini, J. Reddy, S Bharath Anandkumar, B. Hariharan, Shanmugasundaram Sri Sairam Engineering College Department of Information Technology Tamilnadu India KG Reddy College of Engineering and Technology Department of CSE Telangana India Vardhaman College of Engineering Department of Artificial Intelligence and Machine Learning India Vardhaman College of Engineering Department of Artificial Intelligence and Data Science Hyderabad India

ISBN: (纸本)9798331530013

In today's fast-paced world, every passing moment can mean the difference between life and death. To address this critical issue, we have developed an innovative application designed to provide rapid emergency medical response. Ambulance services play a pivotal role in emergency healthcare by offering vital transportation and care. However, limited access to ambulance contact information often hampers patients' ability to secure timely assistance during emergencies. With the widespread adoption of smartphones and mobile applications, the proposed solution integrates React Native with a Convolutional Neural Network (CNN) to create an advanced mobile app for law enforcement, capable of real-time car accident detection. Utilizing datasets from Kaggle, images are resized to optimize CNN training. Through transfer learning, the CNN architecture effectively extracts spatial features, enabling accurate accident prediction, validated by robust evaluation metrics such as precision and recall. The React Native app facilitates swift notifications to law enforcement, utilizing cutting-edge deep learning technology to enhance road safety and improve emergency response efficiency. This project aims to streamline emergency services, minimizing response and transport times, thereby saving lives and fostering safer communities. © 2024 IEEE.

关键词： Convolutional neural networks

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A Machine learning Approach to Exploring Ecological Determinacy of Socially Significant Diseases in Moscow 16

A Machine Learning Approach to Exploring Ecological Determin...

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16th International Conference Management of Large-Scale System Development, MLSD 2023

作者： Marunko, Anna Zolotova, Tatiana Financial University under the Government of the Russian Federation Faculty of Information Technology and Big Data Analysis Moscow Russia Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia

ISBN: (纸本)9798350337907

A study of the relationship between some selected environmental indicators and the presence of socially significant diseases in the population of Moscow was carried out. A statistically significant correlation was confirmed. Machine learning models were built during the regression analysis. A web interface was developed to automate the processes of predicting the population's morbidity. © 2023 IEEE.

关键词： Ecology

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Customer Segmentation Using Clustering Analysis

Customer Segmentation Using Clustering Analysis

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2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Jiet, Moses Makuei Kamble, Aahash Puri, Chetan Yesankar, Prajyot Verma, Prateek Rewatkar, Rajendra Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Data Science Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Machine Learning Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Biomedical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798350382693

This research focuses on the crucial role of the clustering technique in data mining, specifically in market forecasting and planning. The study presents a comprehensive report on utilizing the k-means clustering technique and data visualization tools to develop a real- time system for a supermarket, predicting sales across various annual cycles. An open-source dataset of a mall with n = 200 customers divided into k = 3 groups, demonstrated high accuracy in predicting day-to-day sales statistics when compared with actual results. The research highlights the effectiveness of the clustering technique in real-time sales prediction for supermarkets. © 2024 IEEE.

关键词： Sales

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A Review on the Developments in the Field of AI-Based Gait Analysis 2

A Review on the Developments in the Field of AI-Based Gait A...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Wankhede, Vidhi Verma, Prateek Gahane, Shailesh Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Sciences and Technology Maharashtra Wardha442001 India

ISBN: (纸本)9798331528713

The examination of human gait is essential for comprehending motor function, the evolution of diseases, and the effectiveness of rehabilitation in different clinical and research environments. Artificial Intelligence (AI) advancements have recently improved gait analysis methodologies, resulting in increased accuracy, efficiency, and therapeutic effectiveness. This review article compiles the existing literature on AI-based gait analysis. The review emphasizes the role of AI in gait analysis, namely via the use of vision-based approaches, wearable sensor technologies, markerless motion capture systems, and inertial sensor-based methodologies. AI has shown potential in identifying musculoskeletal problems including osteopenia and sarcopenia, forecasting the start of Parkinson's disease, assessing the effects of post-stroke rehabilitation, and diagnosing neurological conditions. In addition, the integration of AI with new technologies such as augmented reality (AR), smart sensors, and wearable devices presents intriguing opportunities to improve gait rehabilitation, enable individualized therapies, and facilitate remote patient monitoring. Although there have been significant breakthroughs, there are still obstacles in creating AI models that can be easily understood for clinical decision-making, establishing standardized procedures for collecting data, and verifying AI algorithms across various populations. This study underlines the transformational potential of AI-based gait analysis in customized healthcare, rehabilitation techniques, and clinical results. In order to fully harness the potential of AI in enhancing human health and well-being, it is imperative to do more research and foster multidisciplinary cooperation. © 2024 IEEE.

关键词： Clinical research

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Giant Trevally Optimizer (GTO): Enhancing HVDC Transmission Capacity with Optimized Fault Current Limiters and HVDC Circuit Breaker Parameters 3

Giant Trevally Optimizer (GTO): Enhancing HVDC Transmission ...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Ramkumar, M. Siva Saranya, N. Gopan, Gokul Baig, Rahmath Ulla Josha Daniel, S. Babu, M. Sns College of Technology Department of Ece India Karpagam College of Engineering Department of Artificial Intelligence and Data Science India Department of Mechanical Engineering Chennai602105 India Cambridge Institute of Technology Department of Artificial Intelligence and Machine Learning K R Puram Bangalore560086 India Hindustan College of Engineering and Technology Department of Eee India Hindusthan College of Engineering and Technology Master of Engineering in Embedded Systems India

ISBN: (纸本)9798331509675

The multi-terminal HVDC system relies heavily on circuit breakers (CBs) and fault current limiters (FCLs) for protection and performance reliability. This research describes a new Giant Trevally optimizer-based strategy for improving the performance of 4-terminal HVDC systems by optimizing crucial circuit breakers and fault current limiters. The proposed method uses time domain estimation to correctly capture fault dynamics, such as traveling waves and reflection coefficients and finds the worst-case fault locations. The multi-objective optimization seeks to optimize the overcurrent, overvoltage handling, fault clearance time, and energy absorption in arresters. This technology provides an effective, dependable protection solution that reduces fault recovery time and increases system stability. The output pulse of the HVDC system shows very low THD results 1.98% for the minimum load condition, 0.87% for median load, and 0.04% for peak load conditions. The results established the simulation of the model in a MATLAB environment. © 2025 IEEE.

关键词： MATLAB

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Application of data Mining Techniques in Automobile Insurance Fraud Detection 23

Application of Data Mining Techniques in Automobile Insuranc...

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6th International Conference on Mathematics and Statistics, ICoMS 2023

作者： Na Bangchang, Kannat Wongsai, Sangdao Simmachan, Teerawat Department of Mathematics and Statistics Faculty of Science and Technology Thammasat University Pathum Thani Thailand Department of Mathematics and Statistics Thammasat University Research Unit in Data Learning Faculty of Science and Technology Thammasat University Pathum Thani Thailand

ISBN: (纸本)9798400700187

The insurance industry is a fast-growing industry and handles substantial amounts of data. Fraudulent claims are the main problem in the industry. Auto insurance fraud is one of the most prominent types of insurance fraud. Numerous fraudulent claims affect not only the insurance company but also the sincere policy holders because of the increasing in premium amounts. Therefore, detection of insurance fraud is a challenging problem. Traditional approaches are hard to handle and inefficient. data mining has recently offered significant contributions to insurance analysis. To overcome this, data mining techniques are used to predict fraudulent claims. This work would help in a screening process to investigate claims, thus minimizing human resources and monetary losses. Three sets of features are obtained by logistic regression models: one with forward selection, one with backward elimination, and one without variable selection. Three algorithms including Naïve Bayes, random forest and adaptive boosting are employed as classifiers. K-fold cross validation is used to evaluate the algorithm performance. The results suggest that the smaller number of features, the better performance. The random forest performs the best with highest accuracy (85.28%), sensitivity (93.85%), and precision (97.91%) whereas the adaptive boosting provides the highest specificity (70.41%) and F-score (89.86%). © 2023 ACM.

关键词： Adaptive Boosting Naïve Bayes Random Forest Variable Selection

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Identifying General Mechanism Shifts in Linear Causal Representations 38

Identifying General Mechanism Shifts in Linear Causal Repres...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Tianyu Bello, Kevin Locatello, Francesco Aragam, Bryon Ravikumar, Pradeep Department of Statistics and Data Sciences University of Texas Austin United States Booth School of Business University of Chicago United States Machine Learning Department Carnegie Mellon University United States Institute of Science and Technology Austria Austria

We consider the linear causal representation learning setting where we observe a linear mixing of d unknown latent factors, which follow a linear structural causal model. Recent work has shown that it is possible to recover the latent factors as well as the underlying structural causal model over them, up to permutation and scaling, provided that we have at least d environments, each of which corresponds to perfect interventions on a single latent node (factor). After this powerful result, a key open problem faced by the community has been to relax these conditions: allow for coarser than perfect single-node interventions, and allow for fewer than d of them, since the number of latent factors d could be very large. In this work, we consider precisely such a setting, where we allow a smaller than d number of environments, and also allow for very coarse interventions that can very coarsely change the entire causal graph over the latent factors. On the flip side, we relax what we wish to extract to simply the list of nodes that have shifted between one or more environments. We provide a surprising identifiability result that it is indeed possible, under some very mild standard assumptions, to identify the set of shifted nodes. Our identifiability proof moreover is a constructive one: we explicitly provide necessary and sufficient conditions for a node to be a shifted node, and show that we can check these conditions given observed data. Our algorithm lends itself very naturally to the sample setting where instead of just interventional distributions, we are provided datasets of samples from each of these distributions. We corroborate our results on both synthetic experiments as well as an interesting psychometric dataset. The code can be found at https://***/TianyuCodings/iLCS. © 2024 Neural information processing systems foundation. All rights reserved.

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A Comprehensive Review of Supervised learning Algorithms in Healthcare Applications 2

A Comprehensive Review of Supervised Learning Algorithms in ...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Barhate, Aditya Tale, Abhay Jikar, Nayan Verma, Prateek Kumar, Praveen Yesankar, Prajyot Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India

ISBN: (纸本)9798331528713

Supervised learning has revolutionized the concept of personalization in treatment with the development of Precision Medicine. This review aims to provide a systematic analysis of the utilization of supervised learning in healthcare and its functions in diagnostics and prognoses, recommendations for treatment, and patient surveillance. Classification algorithms such as Logistic Regression, Support Vector Machines, and Decision Trees. Regression techniques such as Linear Regression, Polynomial Regression, and Neural Networks. Ensemble methods such as Bagging, Boosting, and Stacking. Deep learning techniques such as Convolutional Neural Networks, Recurrent Neural Networks, and Long Short-Term Memory are discussed for their efficiency and applicability in various healthcare applications. There are strengths highlighted by the review including enhanced diagnostic capabilities and patient-targeted therapy alongside weaknesses such as quality of data, interpretability as well as compatibility with existing healthcare systems. This review gives a more generalized approach to how supervised learning algorithms help to enhance healthcare results and identifies the likely topic that requires more research in the future. © 2024 IEEE.

关键词： Self-supervised learning

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Band selection in Hyperspectral Images using information similarity ranking

Band selection in Hyperspectral Images using information sim...

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2024 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing, MIGARS 2024

作者： Hitendra Sarma, T. Dharma Reddy, R. Mrudula, K. Rammohan Rao, K. Dhondi, Sankalp Kanthi, Murali Vasavi College of Engineering Department of Information Technology Hyderabad India Gnits Department of Mathematics Hyderabad India Brane Enterprises Pvt. Ltd. Machine Learning Engineer Hyderabad India Cmr Technical Campus Department of Data Science Hyderabad India

ISBN: (纸本)9798350389678

Band selection in hyperspectral images is crucial for reducing redundant features and improving subsequent tasks like classification and segmentation. This article presents a novel band ranking method based on information similarity measures from the concepts of information theory. We introduce the Normalized Mutual Information for similarity and Variation of Information for dissimilarity to create a ranking scheme based on the affinity of a band to become a cluster center. We explore hybrid schemes combining structural and informational attributes. Supervised approaches using label-wise similarities using ground truth supervision are also investigated. Evaluation against state-of-the-art band selection algorithms on popular datasets, using both traditional Machine learning and Deep learning models, demonstrates superior results. This study underscores the significance of informational and structural attributes in unsupervised band selection and the potential for further enhancement through supervised similarity with ground truth information. © 2024 IEEE.

关键词： Remote sensing

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