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Detection and Classification of Human Emotion Using Deep Learning Model

Detection and Classification of Human Emotion Using Deep Lea...

2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Betageri, Deepa Yelamali, Vani School of Electronics and Communication Engineering KLE Technological University Hubli India Indian Institute of Information Technology Department of Computer Science & Engineering Dharwad India

ISBN: (纸本)9798331540685

Human emotions are the mental states resulting from neurophysiological changes that are diversely linked to human thoughts, feelings, and behavioral reactions. Facial expressions, eye movement and gestures are several nonverbal communication methods used in many human-machine interactions. Facial emotion recognition has been an essential field because it has many applications that convey the mental state and emotions of each individual. As there is no clear way to distinguish between emotions on a face and the complicated neural system that is connected to the face, recognizing human emotions is a difficult undertaking. In this paper, for students' facial expression recognition, we presented a model called Convolutional Neural Network (CNN). The proposed model consists of six convolutional layers, six max pooling with two fully connected layers. Using a Haar cascade detector, the system identifies the faces of students from input images. It categorizes them into seven different facial expressions, including surprised, fearful, disgusted, sadness, happiness, anger, and neutral. The FER2013 dataset yielded an accuracy rate of 77.22% for the proposed model. The comparative study is carried out with four other deep learning models. The models are Inception V3, VGG-16, VGG-19, Resnet50 and the accuracies achieved are 40%, 37%, 22.50%, 22.09%, respectively. © 2024 IEEE.

关键词： Contrastive Learning

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Lifelong Dynamic Timed A∗ (LTA∗) for Fastest Path Retrieval in Congested Road Networks Using Predicted Speeds 10

Lifelong Dynamic Timed A∗ (LTA∗) for Fastest Path Retrieva...

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10th International Conference on Vehicle technology and Intelligent Transport Systems, VEHITS 2024

作者： Sondhi, Kartikey Dalapati, Poulami Kumar, Saurabh Department of Computer Science and Engineering The LNM Institute of Information Technology Jaipur India

ISBN: (纸本)9789897587030

Efficient transportation systems are crucial for the ever-growing smart cities. With the increasing urbanization and growth in vehicular traffic, congestion has become a significant challenge. This research paper addresses the critical issue of identifying the fastest, least congested path in road transport networks, aiming to enhance overall travel efficiency and reduce the negative impact of traffic congestion. The study employs an improved version of the Lifelong Planning A∗ (LPA∗) that helps find the fastest route between two points in dynamic changing environments. The proposed methodology is called the Lifelong Dynamic Timed A∗(LTA∗) algorithm with an optimal bound weight factor integrated with it to make the search more guided and efficiently predict optimized traffic paths to provide real-time recommendations. To validate the effectiveness of the developed algorithm, extensive simulations and case studies are conducted on a small area inWashington as well as on GridWorlds. The experimental results show that LTA∗, within accurate weight bounds, always managed to find the fastest path, and in some cases, the time taken was close to half of that produced by A∗. Copyright © 2024 by SCITEPRESS - science and technology Publications, Lda.

关键词： Traffic congestion

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Recognition of Human Emotions Through The Use of Facial Expressions

Recognition of Human Emotions Through The Use of Facial Expr...

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2023 IEEE Global Conference on information Technologies and Communications, GCITC 2023

作者： Singh, Nongmeikapam Thoiba Chaudhary, Amrita Dhiman, Richa Singh, Akanksha Chanu, Asem Debala Chandigarh University Department of Computer Science and Engineering Punjab India Indian Institute of Information Technology Department of Computer Science and Engineering Assam Guwahati India

ISBN: (纸本)9798350308167

Emotions play a pivotal role in human life, influencing communication, relationships, and productivity. Recognizing and understanding these emotions is crucial, and various methods have been explored, including monitoring brain activity. However, traditional approaches, such as electroencephalography (EEG), are costly and time-consuming, often yielding accuracy rates ranging from 31% to 81%. In this study, we propose an alternative approach: accurately detecting human emotions through facial expressions. Leveraging an existing simulator and neural networks, we achieved remarkable accuracy rates of up to 96%. We present the methodology, including image processing, differential readings, and facial expression detection, and compare our approach to other methods. The study sheds light on the potential of neural network-based systems for high-accuracy emotion recognition from facial expressions, offering a promising avenue for human-computer interaction and intelligent surveillance systems. © 2023 IEEE.

关键词： brain activity emotion recognition facial expression fuzzy logic human-computer interaction neural network

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Onboard Class Incremental Learning for Resource-Constrained scenarios using Genetic Algorithm and TinyML

Onboard Class Incremental Learning for Resource-Constrained ...

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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Pandey, Suraj Kumar Nair, Shivashankar B Department of Computer Science and Engineering Indian Institute of Technology Guwahati Assam Guwahati India

ISBN: (纸本)9798400704956

Deploying Machine Learning (ML) models in real-world settings over resource-constrained edge devices has always been a challenging task. While TinyML tackles this issue to an extent, by mostly using pre-trained Deep Learning (DL) models, the static nature of such models renders them ineffective for non-stationary data. A model having a low memory footprint that could allow onboard Class Incremental Learning (CIL) so as to accommodate data from new classes and also avoid catastrophic forgetting, is thus, the need of the day. The work described in this paper endeavours to meet this need by providing a method that utilises TinyML to accommodate a DL model onboard a resource-constrained device. To enable onboard CIL over the DL model, the method leverages Latent Replays (as exemplars) and a Genetic Algorithm (GA) to create a multi-fitness landscape that facilitates the inclusion of new class data, suppresses catastrophic forgetting and keeps a check on the quality of exemplars. Experiments and comparisons conducted for a gesture recognition task using time-series data with the proposed method deployed on a microcontroller, show the effectiveness of augmenting TinyML DL models with the GA for onboard CIL. © 2024 Copyright held by the owner/author(s).

关键词： Genetic algorithms

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Dimensionality Reduction and Gradient Boosting for In-Vivo Hyperspectral Brain Image Classification

Dimensionality Reduction and Gradient Boosting for In-Vivo H...

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2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Singh, Raj Bahadur Datta, Aloke The Lnm Institute of Information Technology Department of Computer Science and Engineering Jaipur India

ISBN: (纸本)9798350376876

Hyperspectral imaging (HSI) datasets contain hundreds of contiguous narrow spectral bands, which can create challenges in data analysis due to the curse of dimensionality. However, much of this data is redundant, necessitating the application of dimensionality reduction techniques to extract the most relevant features before the classification task. Two feature extraction methods, principal component analysis (PCA) and non-negative matrix factorization (NMF), are explored. Then, the reduced feature sets are used to train three gradient-boosting algorithms, namely extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost), for the classification of in-vivo hyperspectral brain images. The experimental results show the effectiveness of gradient boosting over support vector machine (SVM) and random forest (RF) in performance measures like accuracy, sensitivity, specificity, and F1-score. © 2024 IEEE.

关键词： Non-negative matrix factorization

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Service Quality and Experience Based Service Selection Framework in Cloud 4

Service Quality and Experience Based Service Selection Frame...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Yadav, Anurag Yadav, Neeraj Goraya, Major Singh Indian Institute of Technology Department of Computer Science and Engineering Roorkee India Bennett University Department of Computer Science and Engineering Greater Noida India Sant Longowal Institute of Engineering and Technology Department of Computer Science and Engineering Sangrur India

ISBN: (纸本)9798350395914

Selecting a cloud service provider (CSP) from a wide range of options with varying offerings poses a crucial challenge for service requesting consumers (SRCs). The decision criteria outlined in Service Level Agreement (SLA), which defines the Quality of Service (QoS) offered by the CSP, help in selecting a suitable provider. However, relying solely on the SLA for evaluating the merits of a CSP is insufficient. Therefore, the Quality of Experience (QoE) has gained significance in assessing the overall satisfaction of the (SRC). Additionally, monitoring consumer buying behavior is essential from a business perspective, as all aspects of a business transaction should be mutually beneficial for both the CSP and SRC. Therefore, a new framework is proposed to determine the relative rankings of CSPs and SRCs. Effectiveness of the framewo rk is established through experiments on the QWS cloud dataset, which reveal positive results in terms of implementation complexity and time required. © 2024 IEEE.

关键词： Cloud platforms

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Estimation of Particle Froude Number in Deposited Bed Condition Using Hybrid Machine Learning Models 1

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2nd International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Kumar, Sanjit Agarwal, Mayank Deshpande, Vishal Department of Computer Science and Engineering Indian Institute of Technology Patna Patna India Department of Civil and Environmental Engineering Indian Institute of Technology Patna Patna India

ISBN: (数字)9789819995189

ISBN: (纸本)9789819995172

In hydrology, maintaining self-cleaning capabilities in drainage systems is crucial to prevent sediment deposition at the bottom of channels. This deposition can disrupt the hydraulic capacity of the channels. This study accurately estimates the particle Froude number in non-deposition with deposited beds condition. A dataset was collected from various studies and analyzed using hybrid machine learning (ML) models. Specifically, Additive Regression (AR) and Random Committee (RC) models were employed. These ML models utilized Random Forest (RF) and Random Tree (RT) as base regressors, enhancing the estimation performance. The results were evaluated using multiple performance metrics. Through comparisons, it was determined that the proposed AR-RF (MAE = 0.456, NSE = 0.930, RMSE = 0.603, and R2 = 0.931) ML model outperformed the other models. The AR-RF accurately estimates the particle Froude number, which is important for self-cleaning sewer systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Froude number

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A Secure Framework for WSN-IoT Using Deep Learning for Enhanced Intrusion Detection

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computers, Materials & Continua 2024年第10期81卷 471-501页

作者： Chandraumakantham Om Kumar Sudhakaran Gajendran Suguna Marappan Mohammed Zakariah Abdulaziz S.Almazyad School of Computer Science&Engineering Vellore Institute of TechnologyChennai CampusChennai600127India School of Electronics&Engineering Vellore Institute of TechnologyChennai CampusChennai600127India Department of Computer Sciences and Engineering College of Applied ScienceKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

The security of the wireless sensor network-Internet of Things(WSN-IoT)network is more challenging due to its randomness and self-organized *** detection is one of the key methodologies utilized to ensure the security of the *** intrusion detection mechanisms have issues such as higher misclassification rates,increased model complexity,insignificant feature extraction,increased training time,increased run time complexity,computation overhead,failure to identify new attacks,increased energy consumption,and a variety of other factors that limit the performance of the intrusion system *** this research a security framework for WSN-IoT,through a deep learning technique is introduced using Modified Fuzzy-Adaptive DenseNet(MF_AdaDenseNet)and is benchmarked with datasets like NSL-KDD,UNSWNB15,CIDDS-001,Edge IIoT,Bot *** this,the optimal feature selection using Capturing Dingo Optimization(CDO)is devised to acquire relevant features by removing redundant *** proposed MF_AdaDenseNet intrusion detection model offers significant benefits by utilizing optimal feature selection with the CDO *** results in enhanced Detection Capacity with minimal computation complexity,as well as a reduction in False Alarm Rate(FAR)due to the consideration of classification error in the fitness *** a result,the combined CDO-based feature selection and MF_AdaDenseNet intrusion detection mechanism outperform other state-of-the-art techniques,achieving maximal Detection Capacity,precision,recall,and F-Measure of 99.46%,99.54%,99.91%,and 99.68%,respectively,along with minimal FAR and Mean Absolute Error(MAE)of 0.9%and 0.11.

关键词： Deep learning intrusion detection fuzzy rules feature selection false alarm rate accuracy wireless sensor networks

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An In-Depth Study of Handwritten Digit Recognition Methods: An Exploration of Deep Learning and Machine Learning Approaches

An In-Depth Study of Handwritten Digit Recognition Methods: ...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Singh, Nongmeikapam Thoiba Tiwary, Priyanshu Chanu, Asem Debala Chandigarh University Department of Computer Science and Engineering Punjab India Indian Institute of Information Technology Department of Computer Science and Engineering Assam Guwahati India

ISBN: (纸本)9798350319200

This article explains how deep learning and machine learning techniques are being utilized to replace humans in a variety of activities, like item classification and sound addition. It emphasizes handwritten text recognition (HTR), which involves computers interpreting handwritten content from sources like paper documents and touchscreens. For recognizing handwritten digits, the study analyzes three algorithms: support vector machines (SVM), multilayer perception (MLP), and convolutional neural networks (CNN). This comparison aims to evaluate the correctness and execution times of different approaches. The evaluation is based on parameters like the dataset used, the number of epochs, algorithm complexity, accuracy, and hardware specifications. The study compares the effectiveness of CNN, Multilayer Perception (MLP), and Support Vector Machines using handwritten digits from the MNIST dataset. It evaluates these algorithms based on criteria such as runtime, complexity, rate of precision, the count of epochs, and the quantity of hidden layers specifically within deep learning techniques. SVM had the highest training data accuracy, but CNN outperformed others on the test dataset. The article's primary goal is to identify the most precise model for recognizing handwritten digits. It achieves this by assessing the performance of different algorithms and comparing them based on various characteristics. © 2023 IEEE.

关键词： convolutional neural networks execution time handwritten digits multilayer perceptron overfitting visualization

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Complex Social Networks: Dynamics, Domains, and Dimensions 1

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International Conference on Electrical and Electronics engineering, ICEEE 2023

作者： Gera, Suruchi Sinha, Adwitiya Department of Computer Science and Engineering JSS Academy of Technical Education Noida India Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Noida India

ISBN: (数字)9789819986613

ISBN: (纸本)9789819986606

The online social platforms witnessed enormous growth in its networked structure as users continue to connect and interact through e-social dialogues. This eventually causes the transformational emergence of online social systems into complex networks. These large-scale networks inherently allow social entities to get engaged in diverse forms of interactions, thereby resulting in dynamic and complex user behavior. This raises many distinct challenges in social networks, which requires cross-dimensional analysis of the complex information for societal benefits. Understanding these complex systems has become crucial for analyzing the behavioral characteristics for drawing essential inferences. Analysis and evaluation of social complex networks in the context of several parameters are performed with prerequisites of graph theory, computational statistics, and probabilistic models. Our research provides a review of the evolving challenges in the social domain with case studies being conducted over multiple social systems. The study covers major aspects of social network theory that reveal communities and the impact of the central behavior of users. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Complex networks

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