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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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Smart LPG Monitoring System: Enhanced Safety with Leakage Detection 2

Smart LPG Monitoring System: Enhanced Safety with Leakage De...

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2nd International Conference on Networking, Embedded and Wireless Systems, ICNEWS 2024

作者： Hotur, Varsha P. Prianka, R.R. Lynsha Helena Pratheeba, H.P. Padmavathi, M. Ramaraj, Kottaimalai Thilagaraj, M. MVJ College of Engineering Department of Electronics and Communication Engineering Bengaluru India New Horizon College of Engineering Department of Artificial Intelligence and Machine Learning Bengaluru India CMR Institute of Technology Department of Computer Science and Engineering Bengaluru India Dayananda Sagar College of Engineering Department of Electronics and Communication Engineering Bengaluru India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamilnadu Krishnankoil India MVJ College of Engineering Department of Industrial Internet of Things Bengaluru India

ISBN: (纸本)9798350367461

Gas leaks can lead to significant accidents causing both material damage and harm to individuals. Two significant factors that contribute to gas leaks are inadequate upkeep of systems and inadequate public awareness. Therefore, the detection of Liquefied Petroleum Gas (LPG) leaks is crucial in order to prevent accidents and safeguard human lives. LPG cylinders are so important to our everyday existence, making it essential for users to keep track of their gas levels to manage consumption effectively. Monitoring usage not only helps users stay informed about their LPG levels but also ensures timely booking of a new cylinder when needed. Given the high flammability of LPG, gas leakage poses serious risks, leading to accidents with potential loss of lives and property damage. To address these concerns, this project introduces a comprehensive LPG level monitoring system with leakage detection. These systems often utilize advanced sensors that can quickly and accurately identify the release of LPG. Additionally, smart systems may integrate with other technologies, such as communication modules, to alert users or authorities in real-time when leakage of gas is identified. Employing a load cell, the equipment constantly monitors the gas content of the cylinder and uses a GSM module to notify the consumer when the replacement needs to be scheduled. Upon receiving confirmation, the device takes appropriate action. Additionally, the system features gas sensors to detect LPG leakage, notifying the user via a GSM module, while activating alarms and exhaust mechanisms for added safety. © 2024 IEEE.

关键词： Liquefied petroleum gas

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Dynamic Playlist Recommendations based on Multi-Modal Mood Detection and Contextual Learning 8

Dynamic Playlist Recommendations based on Multi-Modal Mood D...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Sundaravadivel, P. Raj, J. Relin Francis Kumar, S. Senthil Krishnan, R. Santhana Muthu, A. Essaki Karthikeyan, M. Saravana Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Scad College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai Avadi India

ISBN: (纸本)9798350376425

This paper presents a novel system for dynamic playlist recommendations by integrating multi-modal mood detection with contextual learning. The system utilizes three primary data sources: text-based sentiment analysis using BERT, facial expression recognition through ResNet-50, and physiological data analysis with LSTM networks. By processing user input from social media, capturing facial expressions, and analyzing physiological signals such as heart rate and skin conductance, the system effectively determines the user's current mood. Playlist recommendations are then generated through a hybrid approach combining collaborative and content-based filtering, augmented by contextual bandits to adapt dynamically to changing user preferences and time-of-day patterns. The proposed system provides a personalized music experience by continuously learning from user interactions and mood states. Comparative evaluations against existing recommendation systems demonstrate the system's efficacy in enhancing user satisfaction and engagement through accurate and context-aware music suggestions. © 2024 IEEE.

关键词： Collaborative filtering

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A Projected Gradient Descent Algorithm for Designing Low-Resolution Finite-Alphabet Equalizers in All-Digital Massive MU-MIMO communication Systems

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IEEE Access 2023年 11卷 50744-50751页

作者： Chen, Jung-Chieh Lin, Yu-Cheng National Cheng Kung University Department of Electrical Engineering Tainan701401 Taiwan National Cheng Kung University Institute of Computer and Communication Engineering Tainan701401 Taiwan

Incorporating low-resolution finite-alphabet equalizers into all-digital base station architectures is a promising approach to enhance energy efficiency and cost-effectiveness in massive multi-user multiple-input multiple-output uplink systems. These equalizers represent the spatial equalization matrix using low-resolution coefficients. However, designing these coefficients to achieve the desired bit error rate (BER) performance is a computationally challenging task that is classified as NP-hard. A previous study used Riemannian manifold optimization (RMO) to design a low-resolution spatial equalization matrix to address this challenge. Although the RMO-assisted equalizer provides excellent BER performance, it has high computational complexity and runtime. In this study, we propose a computationally efficient algorithm based on the projected gradient descent (PGD) framework to simultaneously reduce the computational time and complexity of the equalization algorithm while maintaining the same BER performance as the RMO-assisted equalizer. Our simulation results demonstrate that the proposed PGD-assisted equalizer achieves almost the same performance as the RMO-assisted equalizer with significantly lower computational complexity. Notably, the proposed PGD-based algorithm has a faster convergence rate than RMO, running approximately 151.75 times faster. © 2013 IEEE.

关键词： Optimization

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A Miniaturized Fan Beam Antenna Based on Spherical Luneburg Lens 11

A Miniaturized Fan Beam Antenna Based on Spherical Luneburg ...

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11th IEEE Asia-Pacific Conference on Antennas and Propagation, APCAP 2023 - Proceedings

作者： Liu, Zhiheng Lin, Shu Li, Pingyu Wang, Yingqiao Zhang, Xinyue Zhang, Xingqi Harbin Institute of Technology School of Electronics and Information Engineering Harbin China Jilin University Department of Communication Engineering Changchun China Maynooth University Department of Electronic Engineering Maynooth Ireland University of Alberta Department of Electrical and Computer Engineering Edmonton Canada

ISBN: (纸本)9798350326277

A beamforming antenna based on a spherical Luneburg lens operating at 3-4 GHz is proposed. The antenna consists of a spherical lens with a radius of 0.8 %0 (free-space wavelength of the central operating frequency) and five double-ridged waveguides. An approximation to an ideal Luneburg lens is realized by controlling the air-filling rate of a series of hollow dielectric cones with common vertices. By adopting the double ridge waveguide as the feed source, the requirement of the fan beam for the smaller aperture feed source is realized. The simulation results show that the aperture efficiency of the designed lens can reach 60 %, the synthetic beam is a fan beam with a width of 145°, and the gain flatness is better than 0.5 dB within ± 60°. The antenna has the advantages of small lens size, high aperture efficiency, and wide synthetic beam, and can be used for the deployment of 5G communication base stations. © 2023 IEEE.

关键词： 5G communication Aperture efficiency Double-ridged waveguides Fan beam Luneburg lens

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Secure and Energy Concise Route Revamp Technique in Wireless Sensor Networks

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Intelligent Automation & Soft Computing 2023年第2期35卷 2337-2351页

作者： S.M.Udhaya Sankar Mary Subaja Christo P.S.Uma Priyadarsini Department of Information Technology Velammal Institute of TechnologyChennai601204India Department of Networking and Communication SRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai600124India

Energy conservation has become a signiﬁcant consideration in wireless sensor networks(WSN).In the sensor network,the sensor nodes have internal batteries,and as a result,they expire after a certain *** a result,expanding the life duration of sensing devices by improving data depletion in an effective and sustainable energy-efﬁcient way remains a ***,the clustering strategy employs to enhance or extend the life cycle of *** identify the supervisory head node(SH)or cluster head(CH)in every grouping considered the feasible strategy for power-saving route discovery in the clustering model,which diminishes the communication overhead in the ***,the critical issue was determining the best SH for ensuring timely communication *** secure and energy concise route revamp technology(SECRET)protocol involves selecting an energy-concise cluster head(ECH)and route revamping to optimize *** sensors transmit information over the ECH,which delivers the information to the base station via the determined optimal path using our strategy for effective data *** modeled our methods to accom-plish power-efﬁcient multi-hop ***,protected navigation helps to preserve energy when *** suggested solution improves energy savings,packet delivery ratio(PDR),route latency(RL),network lifetime(NL),and scalability.

关键词： Wireless sensor network wireless security wireless routing clustering ad hoc network

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Realm of Medical Image Processing Under the Light of Artificial Intelligence 1

Realm of Medical Image Processing Under the Light of Artific...

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1st International Conference on Healthcare Innovations, Software and engineering Technologies, HISET 2024

作者： Tamboli, Asif I. Shah, Samarth Saxena, Gaurav Wagh, S.J. Prashanth, Addagatla Diallo, Mamadou Yero Department of Radio-Diagnosis Karad India Chandigarh University Department of Computer Science Mohali India Government Engineering College Department of Computer Science Karad India Institute of Aeronautical Engineering Electronics and Communication Engineering Hyderabad India Sharda University Computer Science & Engineering Gr.Noida India

ISBN: (纸本)9798350360707

Over the previous century, a fundamental breakthrough in medical imaging processing as well as storage occurred, with the switch from traditional detectors in radiography to digital detectors. As a result, medical imaging digitalization constitutes a big achievement and promises considerable breakthroughs in cancer. Thus, goal of study is to analyze the advanced imaging process in medical field using artificial intelligence (AI). AI, on the other hand, has the potential to go beyond these applications by producing picture-based biomarkers that are able to accurately predicting survival or response to therapy, thereby extracting far more information from photographic images than used to be conceivable. Such biomarkers might be incorporated into existing prognostic as well as predictive models used in clinical treatment, functioning as integrative biomarkers. So, the study helps in understanding importance of medical imaging processes and further helps in diagnosing minor diseases and eliminates it without human intervention with help of AI. © 2024 IEEE.

关键词： Medical image processing

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New Delay Doppler communication Paradigm in 6G Era:A Survey of Orthogonal Time Frequency Space(OTFS)

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China communications 2023年第6期20卷 1-25页

作者： Weijie Yuan Shuangyang Li Zhiqiang Wei Yuanhao Cui Jiamo Jiang Haijun Zhang Pingzhi Fan Department of Electronic and Electrical Engineering Southern University of Science and TechnologyShenzhenChina Faculty of Electrical Engineering and Computer Science Technical University of BerlinBerlinGermany School of Mathematics and Statistics Xi’an Jiaotong UniversityXi’anChina Department of Communication Engineering Beijing University of Posts and TelecommunicationsBeijingChina China Academy of Information and Communications Technology BeijingChina School of Computer and Communication Engineering University of Science and Technology BeijingBeijingChina School of Information Science and Technology Southwest Jiaotong UniversityChengduChina

In the 6G era,Space-Air-Ground Integrated Network(SAGIN)are anticipated to deliver global coverage,necessitating support for a diverse array of emerging applications in high-mobility,hostile *** such conditions,conventional orthogonal frequency division multiplexing(OFDM)modulation,widely employed in cellular and Wi-Fi communication systems,experiences performance degradation due to significant Doppler *** overcome this obstacle,a novel twodimensional(2D)modulation approach,namely orthogonal time frequency space(OTFS),has emerged as a key enabler for future high-mobility use ***,OTFS modulates information within the delay-Doppler(DD)domain,as opposed to the timefrequency(TF)domain utilized by *** offers advantages such as Doppler and delay resilience,reduced signaling latency,a lower peak-to-average ratio(PAPR),and a reduced-complexity *** studies further indicate that the direct interplay between information and the physical world in the DD domain positions OTFS as a promising waveform for achieving integrated sensing and communications(ISAC).In this article,we present an in-depth review of OTFS technology in the context of the 6G era,encompassing fundamentals,recent advancements,and future *** objective is to provide a helpful resource for researchers engaged in the field of OTFS.

关键词： OTFS 6G delay-Doppler(DD)domain

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A Machine Learning Framework for Integrating Multi-Omics Data for Early Leukemia Detection 8

A Machine Learning Framework for Integrating Multi-Omics Dat...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Nikshya, J. Ebens Karthikeyan, M. Saravana Prasad, Shalini Krishnan, R. Santhana Balamurugan, S. Raj, J. Relin Francis Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai Avadi India City Engineering College Department of Electronics and Communication Engineering Doddakalasandra India Velammal College of Engineering and Technology Department of Mathematics Madurai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350376425

The early detection of leukemia remains a critical challenge in medical diagnostics due to the complexity and variability of omics data. This research introduces a novel machine learning framework that integrates multi-omics data for the early detection of leukemia, utilizing the proposed CCA-RFE Selector (CCARS) algorithm. The CCARS algorithm combines Canonical Correlation Analysis (CCA) with Recursive Feature Elimination (RFE) to enhance feature selection and classification accuracy. The effectiveness of CCARS is evaluated through extensive simulation analyses, comparing its performance with existing algorithms, including Principal Component Analysis (PCA), Lasso Regression (LR), and Canonical Correlation Analysis (CCA). The evaluation metrics used include accuracy, F1-score, and computational time. Results demonstrate that the CCARS algorithm significantly improves accuracy and F1-score while reducing computational time compared to the existing methods. The CCARS algorithm achieves notable performance enhancements, validating its potential for integrating multi-omics data in leukemia detection. © 2024 IEEE.

关键词： Contrastive Learning

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Smart Identity Card for Campus Automation

Smart Identity Card for Campus Automation

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Akila, I.S. Pratheek, Paveen Poonia, Aditya Department of Electronic and Communication Engineering Coimbatore Institute of Technology Tamil Nadu Coimbatore641014 India

ISBN: (纸本)9798350325188

It is developed to ease the work of students by implementing a smart card system. Each student is given a card with a unique identity chip, which can be used for identification, security, and transactions. The system provides increased privacy and security, improves transaction speed, and simplifies campus operations. The system is designed to automate identification and provide easy access to controls and services on campus. The smart card is scanned using an RFID reader, allowing students to carry a single card. Data is not stored directly on the smart card, but rather on a server for an account-based system. The proposed work also includes the creation of a webpage for user credentials and consist of user database where details like marksheet, attendance and record of books taken from library, NFC also stores code for Tap-login to campus system. Additionally, a real-time database will be used to maintain the status of fees, and penalties. A secure portal has been created for file sharing between teachers and students. Student and Teacher have different ID card and permission to access the portal features. © 2023 IEEE.

关键词： File Sharing NFC Portal Feature Real-Time Database RFID Smart Card

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