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An Experimental Investigation on the Emotion recognition Using Power Spectrum Density and machine learning Algorithms in EEG Signals 5th

An Experimental Investigation on the Emotion Recognition Usi...

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5th EAI international conference on Big data Innovation for Sustainable Cognitive Computing, BDCC 2022

作者： Babu, Nirmal Varghese Kanaga, E. Grace Mary Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India

ISBN: (纸本)9783031283239

Emotion recognition is a technique for identifying and classifying human emotions by combining biosensing, face recognition, speech and voice recognition, machine learning, and pattern recognition. the electroencephalogram (EEG), which is based on brain signals, is used in the proposed work to identify the emotions. Here, the DEAP dataset is used to gather information about human emotional states, and machine learning algorithms like SVM, KNN, and MLP were used as the classifiers. In this proposed work, we used Power Spectrum Density as the Feature Extraction Technique and Welch’s Periodogram as the Signal Decomposition Technique. It was observed that gamma signals show more Valence, whereas Alpha signals show Arousal. To Cross validation system has been used to validate the performance of classifiers used in the work. Based on the locations of the electrode, EEG bands, and classifiers, accuracy comparisons were done. Out of the three classifiers, KNN scored more accuracy than other classifiers used. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Feature extraction

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FRAMS: Facial recognition Attendance Management System 6th

FRAMS: Facial Recognition Attendance Management System

引用

6th international conference on Advances in Computing and data Sciences (ICACDS)

作者： Vaidya, Anagha Tyagi, Vipin Sharma, Sarika Symbiosis Int Symbiosis Inst Comp Studies & Res Pune 411016 India Jaypee Univ Engn & Technol Raghogarh Guna 473226 Madhya Pradesh India

ISBN: (纸本)9783031126383;9783031126376

Student attendance monitoring is one of the most important activity in the education domain. In the traditional attendance system it is rollcall calling method which leads to human errors and chance of marking proxy attendance is very high. the teacher also spends a considerable amount of time on attendance task. this paper introduced new model Facial recognition Attendance Management System (FRAMS) for marking students' attendance through facial recognition. the machine learning algorithms are developed and proposed by the authors. Two models are created with train images of each students. the Face Detection and Face Recognizer models are developed in this study by using open source software libraries on static images. the experiment results show 90% accuracy is recorded by the model for marking the student's attendance successfully. the model is comparing the images with different students and marking accordingly.

关键词： Face recognition RFID Figure print learning Analytics Educational data mining

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Smart Cyber Intrusion Detection System Using Hyperparameter Tuned Deep learning with Feature Subset Selection Model 6

Smart Cyber Intrusion Detection System Using Hyperparameter ...

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6th Iraqi international conference on Engineering Technology and its Applications, IICETA 2023

作者： Hadi, Maysam Reyad Hassan, Ahmed R. Mohammed, Ibraheem Hatem Alzubaidi, Laith H. Obaid, Mohammed Kadhim Mohammed, Aymen Altoosi University College Najaf Iraq National University of Science and Technology Dhi Qar Iraq Al Rafidain University College Department of Computer Communications Engineering Bagdad Iraq The Islamic University Najaf Iraq College of Arts Ahl Al Bayt University Karbala Iraq Medical Technical College Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350303339

Since drastic increase in network connectivity and a considerable number of computer oriented applications in recent times, the challenging issue of satisfying cybersecurity gets raised. It is needed to design an effective defence system for several cyberattacks. therefore, the detection of instability and attacks requires the development of intrusion detection systems (IDS) to accomplish cybersecurity. Artificial intelligence (AI) techniques, specifically, machine learning (ML) and deep learning (DL) can be employed for the design of effectual data-driven IDS models. this manuscript introduces a Smart Cyber Intrusion Detection System using Hyperparameter Tuned Deep learning Model with Feature Subset Selection (HTDLM-FSS). the presented HTDLM-FSS technique focuses majorly on the detection and classification of cyber intrusions in the smart environment. To transform the input data into meaningful format, the initial stage of data pre-processing is carried out. then, the presented HTDLM-FSS technique accomplishes feature selection process using the wolf pack optimization (WPO) algorithm. Next, modified denoising autoencoder (MDAE) is employed for cyber intrusion recognition process. At last, gravitational search optimization (GSO) algorithm is utilized for effectual hyperparameter adjustment process. the stimulation analysis of the HTDLM-FSS technique can be tested by using a benchmark dataset and the outcomes implied the promising performance of the HTDLM-FSS approach. © 2023 IEEE.

关键词： Intrusion detection

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Quantification of Epileptic Electrical Sustained Activity during Sleep in BECT Patients 6

Quantification of Epileptic Electrical Sustained Activity du...

引用

6th IEEE international conference on pattern recognition and Artificial Intelligence, PRAI 2023

作者： Mei, Guohan Zhang, Zhezhao Hu, Dinghan Wang, Danping Jiang, Tiejia Jiang, Tao Zhang, Junfeng Cao, Jiuwen Hangzhou Dianzi University Machine Learning and I-Health International Cooperation Base of Zhejiang Province 310018 China Hangzhou Dianzi University Artificial Intelligence Institute Zhejiang310018 China BioMedTech Facilities Paris75270 France Zhejiang University School of Medicine Children's Hospital Department of Neurology 310018 China The First Affiliated Hospital of Anhui Medical University Department of Neurosurgery 230002 China Anhui Public Health Clinical Center Department of Neurosurgery 230002 China Hainan University School of Information and Communication Engineer 570228 China

ISBN: (纸本)9798350325485

Electrical status epilepticus during sleep (ESES) is a specific EEG phenomenon induced by sleep with near-constant spike and slow wave emission. In the clinical diagnosis of its associated syndromes, the quantification of abnormal electroencephalography (EEG) during sleep, i.e. spike-wave index (SWI), is often used as an important reference standard. It is based on the characteristics of spikes and slow-spikes in the central temporal region. EEG signals can identify the presence of seizures for patients and provide information about the severity and extent of abnormal EEG activity during sleep, which helps doctors to better understand the patient's physiological function and to develop individualised treatment and prognosis plans. In this paper, we proposed a novel method that combines deep learning and morphological operations to identify and quantify epileptic electrical sustained activity during sleep. the proposed method provides the mean SWI error of 6.04%, the Recall of 87.37% and the Precision of 56.11%. Besides, 15.2%, 64.6% and 85.3% could be achieved for PCT (1%), PCT (5%) and PCT (10%), respectively. the experimental results show that the proposed method has great potential for the clinical diagnosis and prognosis of children with epilepsy. It will help to provide long-range EEG detection for patients with ESES syndrome, thus offering the possibility of early treatment for patients. © 2023 IEEE.

关键词： Semantics

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Hierarchical Clustering and Measure for Tourism Profiling 1

引用

6th Asia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint international conference on Web and Big data (APWeb-WAIM)

作者： Djebali, Sonia Gabot, Quentin Guerard, Guillaume Leonard De Vinci Res Ctr F-92916 Paris France

ISBN: (数字)9783031251986

ISBN: (纸本)9783031251979;9783031251986

Social network analysis has become widespread in recent years, especially in digital tourism. Indeed, the vast amount of data that tourists produce during their travels represents an effective source for interpreting their behaviors (geographics, demographics, psychographics, movement patterns). Since the classic measures unfit to those kind of information, this article presents a new measure to determine tourist profiles thanks to the digital traces left on social networks. this measure is based on geographic, demographic and pattern's behaviors of the tourists as the context and the content of their trips. the approach is simulated and evaluated experimentally with a hierarchical clustering on the traces left by tourists on TripAdvisor in the French capital Paris. Clusters found correspond to tourism segment determined by the Tourism Office of Paris.

关键词： Tourism profiling machine learning Distance measure

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Search-Time Efficient Device Constraints-Aware Neural Architecture Search 10th

Search-Time Efficient Device Constraints-Aware Neural Archit...

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10th Biennial international conference on pattern recognition and machine Intelligence (PReMI)

作者： Dutta, Oshin Kanvar, Tanu Agarwal, Sumeet Indian Inst Technol Delhi India

ISBN: (纸本)9783031451690;9783031451706

Edge computing aims to enable edge devices, such as IoT devices, to process data locally instead of relying on the cloud. However, deep learning techniques like computer vision and natural language processing can be computationally expensive and memory-intensive. Creating manual architectures specialized for each device is infeasible due to their varying memory and computational constraints. To address these concerns, we automate the construction of task-specific deep learning architectures optimized for device constraints through Neural Architecture Search (NAS). We present DCA-NAS, a principled method of fast neural network architecture search that incorporates edge-device constraints such as model size and floating-point operations. It incorporates weight sharing and channel bottleneck techniques to speed up the search time. Based on our experiments, we see that DCA-NAS outperforms manual architectures for similar sized models and is comparable to popular mobile architectures on various image classification datasets like CIFAR-10, CIFAR-100, and Imagenet-1k. Experiments with search spaces-DARTS and NAS-Bench-201 show the generalization capabilities of DCA-NAS. On further evaluating our approach on HardwareNAS-Bench, device-specific architectures with low inference latency and state-of-the-art performance were discovered.

关键词： Neural Architecture Search DARTS Meta-learning Edge Inference Constrained Optimization

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Conventional Neural Network for Maths Handwritten Digit Categorization 6

Conventional Neural Network for Maths Handwritten Digit Cate...

引用

6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Garg, Shivya Kuchhal, Navya Barmola, Jigyasa Rawat, Vandana Singh, Neelam Graphic Era Deemed to Be University Department of Computer Application Dehradun India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350304480

Handwritten digit recognition plays a crucial role in various sectors such as document processing, automatic form identification, and postal automation. this task involves accurately identifying and classifying handwritten digits into their respective numerical values. this presentation provides a summary of current developments in machine learning techniques for handwritten digit recognition. the primary objective of this research is to develop an advanced system for recognizing handwritten digits by harnessing state-of-the-art machine learning techniques. To train and test the suggested system, a sizable dataset of handwritten digits is used. We are utilizing the MNIST dataset here. To improve the quality of the input pictures and guarantee uniform feature representation, preprocessing techniques like image enhancement and normalization are used. Experimental results indicate that the CNN model surpasses all other investigated techniques in accuracy, boasting an impressive accuracy rate of over 98%. the capacity of the CNN model to automatically deduce pertinent characteristics from the input pictures, catching both local and global patterns, is credited with its better performance. Additionally, the model can successfully handle spatial relationships in the digit pictures because to CNNs'usage of convolutional layers. A number of practical applications, such as computerized form processing, digital document analysis, and intelligent character recognition, are possible with the suggested handwritten digit recognition system. the CNN model's high accuracy enables accurate digit categorization, enhancing efficiency and accuracy across a variety of applications. Future research can concentrate on investigating ensemble approaches, data augmentation methods, and fine-tuning techniques to improve the handwritten digit recognition system's performance even further. © 2023 IEEE.

关键词： CNN digit recognition epochs Handwritten recognition hidden layers machine learning neural network

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On Trace of PGD-Like Adversarial Attacks 27th

On Trace of PGD-Like Adversarial Attacks

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27th international conference on pattern recognition, ICPR 2024

作者： Zhou, Mo Patel, Vishal M. Johns Hopkins University BaltimoreMD21218 United States

ISBN: (纸本)9783031781216

Adversarial attacks pose security concerns to deep learning applications, but their characteristics are under-explored. Yet largely imperceptible, a strong trace could have been left by PGD-like attacks in an adversarial example. Recall that PGD-like attacks trigger the "local linearity" of a network, which implies different extents of linearity for benign or adversarial examples. Inspired by this, we construct an Adversarial Response Characteristics (ARC) feature to reflect the model’s gradient consistency around the input to indicate the extent of linearity. Under certain conditions, it qualitatively shows a gradually varying pattern from benign example to adversarial example, as the latter leads to Sequel Attack Effect (SAE). To quantitatively evaluate the effectiveness of ARC, we conduct experiments on CIFAR-10 and ImageNet in a challenging setting. the results suggest that SAE, reflected through the ARC feature, is an effective and unique trace of PGD-like attacks. Our method is designed to generalize with a scarce amount of data, which remains feasible even when access to the full training dataset is impossible. Code: https://***/cdluminate/advtrace. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Exploring the Role of Artificial Intelligence for pattern recognition of Textile Sorting and Recycling for Circular Economy 6

Exploring the Role of Artificial Intelligence for Pattern Re...

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6th international conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Karmali, Irfan Mohammed Valilai, Omid Fatahi Constructor University Bremen Campus Ring 1 Bremen28759 Germany

In an era where sustainability is a vital concern, the textile industry is a major obstacle that stands in the way of addressing environmental challenges, climate change, pollution, and biodiversity loss. the integration of Artificial Intelligence (AI) into the textile recycling process holds significant promise for advancing the circular economy agenda. AI technologies offer innovative solutions for automating pattern recognition tasks, thereby enhancing the efficiency and accuracy of textile sorting and recycling processes. this paper endeavors to explore AI algorithms specifically tailored for soiled cloth detection in textile products. through a comprehensive analysis of existing literature studies, the paper aims to propose an approach that addresses the unique challenges associated with identifying and classifying soiled cloth. this research focuses on investigating and developing unsupervised computer vision techniques for the initial detection and segmentation of stains on textile materials. the paper focuses on the capabilities of two methods, (1) Otsu's thresholding and (2) K-Means clustering, which do not require extensive training data or deep learning models. Practical conditions are examined for complex backgrounds and uneven lighting conditions and capability for accurate separation of stains from fabrics with similar colors or textures. the results show that for both methods to work efficiently, they require even and bright lighting conditions on the textiles. Moreover, the paper has discussed the challenges faced for real implementation of models for textiles being sorted on factory line. © 2025 the Authors. Published by Elsevier B.V.

关键词： Circular economy

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Automated Detection of Dysgraphia Symptoms in Primary and Middle School Children 6

Automated Detection of Dysgraphia Symptoms in Primary and Mi...

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6th IEEE international conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Tushar, B.T. Goel, Umang Ms, Varun Urs Kulkarni, Vinay Sooda, Kavitha B.M.S. College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350306613

Dysgraphia, a learning disability impacts hand-writing fluency and legibility, which significantly hinder's a child's academic progress and self-esteem. the proposed work utilizes pattern recognition techniques, employing preprocessing techniques like Canny Edge Detection, WaterShed Segmentation and various image enhancement techniques to analyze written characters and extract relevant features using character-wise bounding boxes. this detailed analysis allows the system to capture subtle variations in letter formation and spacing that are often indicative of dysgraphia. Furthermore, A MobileNet architecture has been proposed to assess the system's performance and classify dysgraphia risk levels into three categories: Below Average(BA), Average(AV) and Above Average(AA). this categorization system provides valuable insights for educators and parents, enabling early intervention and tailored support for students struggling with dysgraphia. the model was trained using a data set of 940 images of handwriting samples from students studying in grades one to seven. Students were asked to write 20 words through dictation and another 20 words copied from the board. the proposed system aims to demonstrate immediate results, highlighting its potential for early detection and intervention. © 2024 IEEE.

关键词： Deep learning Dysgraphia Kannada Words learning Disability MobileNet

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