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Facial emotion recognition based on Textural pattern and Convolutional Neural Network 4

Facial emotion recognition based on Textural pattern and Con...

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4th IEEE international conference on computing, Power and Communication Technologies, GUCON 2021

作者： Mukhopadhyay, Moutan Dey, Aniruddha Shaw, Rabindra Nath Ghosh, Ankush Bengal Institute of Technology Department of Computer Science Engineering Kolkata India MAKAUT Department of Information Technology Kolkata India Galgotias University Department of EC and Communication Engineering India School of Engineering and Applied Science Neotia University Kolkata India

ISBN: (纸本)9781728199511

Human emotional states can be detected by various ways such as analysingthe brain signals (EEG), speech, skin responses, facial expressions, body gestures etc. But facial expressions recognition is one of the popular techniquesand easy to detect with computers using various computer visions techniques. In this work, we have proposed a new approach to detect basic facial expressions from textural images using deep learning model Convolution neural network (CNN). In the proposed approach textural images of human faces are generated using Local binary pattern(LBP) method and trained the CNN model with the LBPimages. In the proposed approach all the images of extended Cohn-Kanade(CK+)dataset are converted into LBP images that are used to train the CNN model. The performance of proposed facial expression recognition system has been validated on modifiedCK+dataset. Facial emotionrecognition Accuracy of conventional CNN model with CK+ dataset and proposed method on modified CK+ dataset is also compared. © 2021 IEEE.

关键词： Neural network models

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Anomolous Behavioral pattern Analysis for IoT Application Using Firewall

Anomolous Behavioral Pattern Analysis for IoT Application Us...

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Electrical Electronics and computing Technologies (ICEECT), international conference on

作者： Mayank Agrawal Preeti Badhani Neha Tripathi Neeraj Kumar Pandey Amit Kumar Mishra Ankur Dumka Department of Computer Engineering & Applications GLA University Mathura India Department of CSE Graphic Era Hill University Dehradun Uttarakhand India Department of CSE Graphic Era (Deemed to be University) Dehradun India Department of CSE Women Institute of Technology Dehradun Uttarakhand India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

As Internet of Things (IoT) devices grow in many areas, such as smart homes, healthcare, business automation, and transportation, providing a security mechanism also becomes important. This brief presents a security solution that uses electronic technology to enhance IoT applications against various cyber threats. The solution begins by identifying the unique security challenges posed by the IoT ecosystem, including device diversity, access restrictions, and communication gaps. Recognizing the important role of firewalls in network security, our approach includes firewall resources designed for IoT environments. This article includes a behavioral analysis to identify negative behavioral patterns that indicate potential security hazards resulting in threat and response. It proposes a new concept that integrates machine learning algorithms with an electronic engine to crack code. Extensive experiments have been conducted to evaluate the effectiveness of the Framework. Using IoT traffic data from various applications. The results show that it is possible to identify bad patterns, including interference of illegal equipment, unreliable information, and poor communication.

关键词： Firewalls (computing) Transportation Telecommunication traffic Smart homes Real-time systems Internet of Things Security Object recognition pattern analysis Anomaly detection

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(Street) Lights Will Guide You: Georeferencing Nighttime Astronaut Photography of Earth

(Street) Lights Will Guide You: Georeferencing Nighttime Ast...

引用

IEEE Computer Society conference on Computer Vision and pattern recognition Workshops (CVPRW)

作者： Alex Stoken Peter Ilhardt Mark Lambert Kenton Fisher Jacobs Technology NASA Johnson Space Center NASA Johnson Space Center

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Astronaut photography from the international Space Station provides the highest spatial resolution nighttime Earth observations imagery publicly available, offering up to a 150x increase in resolution over other freely accessible satellite data sources. Yet, this imagery is underutilized in science applications because it lacks the geolocation meta-data required for downstream analysis. We present Night-Match, a fast and accurate method for localizing and geo-rectifying nighttime astronaut photography. By combining street network data with daytime satellite imagery, we produce a reliable reference target for similarity detection via pairwise image matching. We curate and release the Astronaut Imagery Matching Subset - Night (AIMS-Night), a collection of 363 images and ground truth localizations, and benchmark our method against this set to establish a robust localization pipeline. Our method correctly localizes 81.8% of AIMS-Night, and can be quickly deployed on the over 2 million nighttime astronaut photographs to produce a high quality analysis-ready data product.

关键词： Photography Location awareness Earth Roads Geology soft sensors Urban areas

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Enhancing Graph Database Interaction through Generative AI-Driven Natural Language Interface for Financial Fraud Detection

Enhancing Graph Database Interaction through Generative AI-D...

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international conference on computing and Networking Technology (ICCNT)

作者： T Simran J Geetha Department of Computer Science and Engineering Ramaiah Institute of Technology Bangalore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The rapid evolution of financial services has led to an increase in fraudulent activities, necessitating advanced detection methods. Traditional techniques often struggle with the volume and complexity of financial transactions, leading to inaccuracies and delays. This research addresses the enhancement of graph database interaction through a generative AI-driven natural language interface for more accurate fraud detection. We employ the T5 and LLaMA-2 models, trained on a balanced dataset of financial transactions, to recognize fraud patterns. The $\mathbf{T 5}$ model handles natural language queries, extracting key details such as transaction amounts and account numbers. The LLaMA-2 model generates predictions on the likelihood of transactions being fraudulent. The system then converts these processed queries into Cypher queries for the Neo4j graph database to retrieve relevant transaction data. This approach enables efficient pattern analysis and interactive data exploration. Experimental results demonstrate that our system accurately identifies fraudulent transactions, significantly reducing the time and expertise needed for detection. This approach makes use of Neo4j’s powerful features to increase the efficacy and precision of financial detection systems for fraud.

关键词： Databases Computational modeling Natural languages Finance Predictive models Feature extraction Fraud pattern recognition pattern analysis Financial services

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Knock Detecting Door Unlock System using IOT

Knock Detecting Door Unlock System using IOT

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Intelligent and Innovative Technologies in computing, Electrical and Electronics (IITCEE), international conference

作者： Divyam Arora Anika Kirana Venkat Sudarshan Manjunath G Asuti Dept. of ECE B N M Institute of Technology Bengaluru India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

This paper presents the design and implementation of a Knock Detecting Door Lock System, aimed at enhancing security by eliminating traditional lock and key mechanisms. The proposed system utilizes an ESP32-based setup to detect specific knocking patterns, ensuring that only authorized individuals can unlock the door. The system addresses common security issues associated with key duplication and passcode vulnerabilities by relying on a personalized knocking sequence. Key features include the ability to set, and update knocking patterns, detect discrepancies, and maintain a balance between security and convenience. The hardware components include an ESP32, piezo sensors, and a servo motor, while the software is developed using the Arduino IDE. It can also incorporate two-factor authentication by generating a One-Time Password (OTP) when a valid knocking pattern is recognized. The OTP is sent to the user’s mobile device as an additional verification step before access is granted. By integrating the knock-based mechanism with the OTP feature, this system offers a robust, user-friendly solution that addresses both analog and digital security needs. The system’s innovative approach offers a cost-effective, efficient, and secure solution for modern door locking needs, with potential applications in various settings, including residential and commercial properties.

关键词： Vibrations Accuracy Authentication Sensor systems software pattern recognition Security Intelligent sensors Smart phones Testing

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Analysis of Eye Fixations During Emotion recognition in Talking Faces 9

Analysis of Eye Fixations During Emotion Recognition in Talk...

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9th international conference on Affective computing and Intelligent Interaction (ACII)

作者： Cao, Houwei Elliott, Forest New York Inst Technol Dept Comp Sci New York NY 10023 USA Oberlin Coll Dept Comp Sci Oberlin OH 44074 USA

ISBN: (纸本)9781665400190

Research on emotion recognition from cues expressed in facial expression has a long-standing tradition. In this study, we investigate human's visual attention and fixation patterns when identifying six basic emotions on expressive talking faces. Stimuli for the current experiments consisted of 92 video clips of facial expression during talking. The whole experiments were divided into two sessions. The video stimuli in the first session were presented in random order across different face identities, while in the second session the video from the same face identity were be played sequentially. The participants' eye movements were recorded by the Tobii X3-120 screen-based eye-tracking system. We defined a set of area-of-interest (AOI) regions, including 4 AOIs of general face areas and 12 AOIs related to specific Action Units (AUs) involved in the coding of the six basic emotions. The gaze pattern analysis was done by looking at the fixation time on this predetermined set of AOIs. Based on the ANOVA analysis, we did not find significant differences in mean fixation time on any AOI for discriminating the six basic emotions, but a subset of significant AOIs was found when we sectioned the six basic emotions into positive, negative, and neutral. Next, we propose to develop a novel emotion perception classifier which can automatically classify an observer's emotion perception based on her gaze patterns and fixation sequence when identifying the basic emotions on expressive talking faces. The fixation time on the 16 predetermined AOIs were used as features to train support vector machine (SVM) models. The proposed models achieved the overall classification accuracy of 84.1% on recognizing 3-way emotions of negative, positive and neutral, suggesting that the proposed eye gaze patterns - the fixation time on 16 predetermined AOIs, are very promising for automatic classification of the perceived emotions. Finally, we divided the data into different gender and race groups, and

关键词： eye fixation emotional face gaze pattern emotion recognition gaze diversity

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Advanced Forecasting of Wind Energy Generation Through Integration of AE-CLSTM

Advanced Forecasting of Wind Energy Generation Through Integ...

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Green Energy, computing and Sustainable Technology (GECOST), international conference on

作者： Md Saidur Rashid Mahin Shaharier Kabir Md Ashikul Islam Abu Shufian Dept. of Electrical Engineering Lamar University Texas USA Dept. of Electrical and Electronic Engineering American International University-Bangladesh Dhaka Bangladesh

The advent of sustainable energy solutions marks a pivotal shift in the global energy landscape, with wind energy playing a crucial role due to its minimal environmental impact. However, the inherent unpredictability of wind necessitates advanced forecasting methods for effective grid management. This research introduces the Autoencoder Convolutional Long Short-Term Memory (AE-CLSTM) model, a novel approach that synergizes the dimensionality reduction prowess of Autoencoders with the spatial and temporal pattern recognition capabilities of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. This integrated model aims to enhance the accuracy and reliability of wind energy forecasting, addressing the challenges posed by the stochastic nature of wind behavior. Employing an extensive dataset from a SCADA system of the wind farm, the AE-CLSTM model outperforms conventional CNN Model, LSTM Model, and CNN-LSTM model in terms of accuracy. The key findings of this research are encouraging: the model achieved a Correlation Coefficient of 95.43%, indicating a strong positive relationship between predicted and actual energy outputs, signifying the model's effectiveness in providing reliable forecasts than other models. These outcomes highlight the potential of AE-CLSTM in transforming wind power prediction, contributing significantly to the effective management of smart grids and the expansion of sustainable energy integration.

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Machine Learning Models for the Classification of Skin Cancer

Machine Learning Models for the Classification of Skin Cance...

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2022 international conference on Business Analytics for Technology and Security, ICBATS 2022

作者： Arooj, Sahar Khan, Muhammad Farhan Khan, Muhammad Adnan Khan, Muhammad Saleem Taleb, Nasser Riphah International University Riphah School of Computing and Innovation Faculty of Computing Lahore Pakistan University of Health Sciences Department of Forensic Sciences Lahore Pakistan Gachon University Pattern Recognition and Machine Learning Lab Department of Software Seongnam13120 Korea Republic of Ncba School of Computer Science Lahore Pakistan Canadian University Dubai United Arab Emirates

ISBN: (纸本)9781665409209

Skin cancer is a serious illness that requires early identification in order to improve survival rates. Deep learning algorithms for computerized skin cancer detection have now become popular in recent years. These models may increase their performance by having access to additional data, and their prime objective is image categorization. This activity is extremely useful in the realm of health since it may help physicians and experts make the best decisions and accurately assess a patient's condition. Early detection of skin cancer helps patients to receive appropriate treatment and so enhance their survival rate. This proposed methodology is generated to detect and classify skin cancers. In this study, we employed four pre-trained deep learning models (Squeeze net, Alex net, Res net 101, VGG 19) for the classification of four types of skin cancers in more than 6000 skin images including actinic keratoses, intraepithelial carcinoma Bowen's disease (akiec), basal cell carcinoma (bcc), benign keratosis-like lesions (bkl) and melanocytic nevi (nv). The objective was the identification of the best model in the classification of these breast cancer images with highest accuracy. Experimental results reveal that the Squeeze net model achieved an accuracy of 92.5% which is highest when compared with all other models while Alex net, Res net 101, VGG 19 acquired 91.1%, 83.2%, and 90.4% respectively. © 2022 IEEE.

关键词： Convolutional neural networks

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Quantum Based Deep Learning Models for pattern recognition 6th

Quantum Based Deep Learning Models for Pattern Recognition

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6th international conference on Information, Communication and computing Technology (ICICCT)

作者： Shrivastava, Prakhar Soni, Kapil Kumar Rasool, Akhtar MANIT Dept Comp Sci Engn Bhopal Madhya Pradesh India

ISBN: (纸本)9783030883782;9783030883775

The machine learning model influences the pattern recognition based on extraction of relative patterns, but not enough capable of efficiently processing the data set that needs layered interaction. So, deep learning model takes the advantage of artificial neural network for processing the data in layered abstraction by exploring massive parallelism, although the classical implementations of such model may not be competent due to the processing and storage of large neural networks. Current research explores quantum computation potentials and utilizes its significance for supporting inherent parallelism, as the machine perform exponential operations in single step of execution. The possible classical designs of pattern recognition using deep learning are Hopfield network and Boltzmann machine and their equivalent quantum models can remain effective and overcome the processing limitations of classical model by incorporating Grover's search method and quantum Hebbian learning. The aim of writing this article is to introduce the necessity of deep learning model, an emergence of quantum computations, discussion over requisites of data preprocessing techniques, then to propose the classical equivalent quantum deep learning model along with the algorithms, complexity comparison and speedup analysis followed by conclusive aspects that proves effectiveness of quantum deep learning model and future works.

关键词： Quantum pattern recognition Machine learning Deep learning Grover's search Neural networks Superposition Quantum Hebbian learning

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A Ensemble Deep Learning approach on risk management in finance and marketing sector

A Ensemble Deep Learning approach on risk management in fina...

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international conference on computing and Networking Technology (ICCNT)

作者： K R Chairma Lakshmi P. Ratnaraju Sumit Kumar Sharma Rambabu Nalagandla Manjula Pattnaik P Kamalarajan Department of Electronics and Instrumentation Engineering R.M.K. Engineering College School of Management and Commerce Malla Reddy University Hyderabad Telangana India Department of Information Technology Ajay Kumar Garg Engineering College Ghaziabad Uttar Pradesh India Solution Architect USA Accounting Department College of Business Administration Princess Nourah Bint Abdulrahman University Saudi Arabia Department of Chemistry R.M.D. Engineering College

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The ensemble deep getting to know technique is a powerful and innovative tool for danger control inside the finance and advertising quarter. It combines the strengths of more than one deep getting to know models to provide accurate and comprehensive risk tests. By using harnessing the electricity of artificial intelligence and device learning, this approach can efficiently analyze extensive amounts of facts and perceive capability risks in actual time. This enables monetary and advertising establishments to make informed and well timed selections to mitigate dangers and enhance their normal overall performance. With its potential to conform and learn from new data, the A Ensemble Deep getting to know method is reworking the way threat is controlled in those industries.

关键词： Performance evaluation Accuracy Target recognition Social networking (online) Profitability Finance Market research pattern recognition Risk management Advertising

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