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Deep Belief Network based machine learning for Daily Activities Classification 2021

Deep Belief Network based Machine Learning for Daily Activit...

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5th international conference on Artificial Intelligence and Virtual Reality (AIVR)

作者： Phiasai, Tejtasin Chinpanthana, Nutchanun Sukhothai Thammathirat Open Univ Sch Sci & Technol Pak Kret Thailand Dhurakij Pundit Univ Coll Innovat Technol & Engn Bangkok Thailand

ISBN: (纸本)9781450384148

Human activity recognition has been a very active topic in pervasive computing for several years for its important applications in assisted living, healthcare, and security surveillance. Many researchers are finding and representing the details of human body gestures to determine human activity. While simple activities can be easily recognized only by acceleration data, our research has focused on the recognition and understanding the various activities in daily living. In this work, we address this problem by proposing approach theory of deep learning with the Deep belief network. Deep belief network comprises a series of Restricted Boltzmann machines will be formed by superimposed multiple Restricted Boltzmann machines and training the model parameters for data reconstruction, feature construction and classification. We tested our approach on PASCAL VOC datasets. the experimental results indicate that our proposed approach offers significant performance improvements with the maximum of 79.8%.

关键词： Image processing classification deep learning human activity classification

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data Expansion for Named Entity recognition based on migration learning

Data Expansion for Named Entity Recognition based on migrati...

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Computer Vision, Image and Deep learning (CVIDL), international conference on

作者： Qingyu Li Chao Zhang Gang Zhou School of Cyber Science and Engineering Zhengzhou University Zhengzhou Henan China Information Engineering University Zhengzhou Henan China

ISBN: (数字)9798350373820

ISBN: (纸本)9798350373837

the pivotal role of named entity recognition in the field of natural language comprehension significantly influences numerous tasks, including the likes of machine translation, the distillation of events, and the fabrication of knowledge graphs. Confronting the issue of data scarcity, the method initially entails the acquisition of dense semantic insights through the preliminary training of the BERT framework on an expansive textual database. Subsequent refinement of these initially trained models is tailored specifically for nuanced NER assignments, enhancing the detection of sector-specific entities. this refinement is complemented by the integration of character-level feature depiction and the employment of Conditional Random Field layers, augmenting the precision of entity demarcation. A thorough evaluation conducted using datasets from NER that are accessible to the public corroborates the efficacy of this approach, showcasing commendable results in precision and retrieval rates. When juxtaposed with the conventional methodologies predicated on rules or feature engineering, this innovative approach demonstrates notable enhancements in the identification of a diverse array of entity categories, evidenced by an increment of $0.02 \%$ in precision and a rise of $3.21 \%$ in retrieval.

关键词： Training Adaptation models Target recognition Transfer learning Semantics Named entity recognition Conditional random fields

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A Quick Overview of Recent Developments in Image Restoration using machine learning

A Quick Overview of Recent Developments in Image Restoration...

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India Council international Subsections conference (INDISCON), IEEE

作者： Neelam Kumari Isha Kansal Preeti Sharma Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350376753

ISBN: (纸本)9798350376760

Digital images are gradually getting importance in every industry, including computer science, medical, communications, transport, security, and surveillance. Degraded Images can be restored from a variety of distortions. Distortions occur due to malfunctioning imaging equipment transmission channels, atmospheric and environmental conditions, etc. Image restoration techniques have applications across various domains like image processing, data mining, and predictive analysis. Today machine learning is used in almost every area by taking into consideration its excellence efficiency, decision making, to automate task and to retrieve pertinent data for analysis. In an effort to solve issues, numerous academics, mathematicians, and programmers have conducted research and used a variety of machine learning techniques. this paper centers on a comprehensive overview of machine learning, techniques used for image restoration methodologies. the purpose of the study is to compare evolution of various parameters like Peak Signal to Noise Ratio (PSNR), Structure Similarity Index Matrix (SSIM), Mean Structure Similarity Index Matrix (MSSIM), Mean Square Error (MSE) and Natural Image Quality Evaluator (NIQE) between machine learning (ML) and traditional techniques.

关键词： PSNR Surveillance Noise reduction machine learning Distortion Image restoration Indexes Security Predictive analytics Smart phones

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Face recognition System Using Haar Algorithm

Face Recognition System Using Haar Algorithm

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Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： Samriddhi Nikhil Jaiswal Pranjal Dubey EEE Department GCET Greater Noida Greater Noida India

ISBN: (数字)9798350330861

ISBN: (纸本)9798350330878

Face recognition has been the most demanding field over the past two decades. We can analyze the reason stems from the need for automated visual and monitoring systems which have power to replace all human efforts done manually. these researches involve knowledge such as neuroscience, computer vision, pattern recognition, machine learning and many other techniques. Face recognition is better than other diagnostic procedures namely: fingerprint, iris scanning, signature etc. the face recognition system uses two techniques face detection and face recognition and combine both to build a face recognition system. the first face detection process is performed on acquired images and it will find the object. the second face recognition process uses algorithms to find details about a person’s face. thereafter these distinctive details, such as the distance between the eyes, the distance from the mouth to the nose and eyes or the shape of chin and all the specific details is then converted into statistical representation and compared with the data stored in the facial recognition database.

关键词： Visualization Neuroscience Shape Face recognition Nose Mouth machine learning

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5th international conference on Recent Trends in Image Processing and pattern recognition, RTIP2R 2022

5th International Conference on Recent Trends in Image Proce...

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5th international conference on Recent Trends in Image Processing and pattern recognition, RTIP2R 2022

ISBN: (纸本)9783031235986

the proceedings contain 31 papers. the special focus in this conference is on Recent Trends in Image Processing and pattern recognition. the topics include: Image Processing and pattern recognition of Micropores of Polysulfone Membrane for the Bio-separation of Viruses from Whole Blood;An Extreme learning machine-Based AutoEncoder (ELM-AE) for Denoising Knee X-ray Images and Grading Knee Osteoarthritis Severity;motor Imagery Classification Combining Riemannian Geometry and Artificial Neural Networks;Autism Spectrum Disorder Detection Using Transfer learning with VGG 19, Inception V3 and DenseNet 201;shrimp Shape Analysis by a Chord Length Function Based Methodology;supervised Neural Networks for Fruit Identification;targeted Clean-Label Poisoning Attacks on Federated learning;building Marathi SentiWordNet;A Computational Study on Calibrated VGG19 for Multimodal learning and Representation in Surveillance;alzheimer’s Disease Detection Using Ensemble learning and Artificial Neural Networks;automated Deep learning Based Approach for Albinism Detection;a Deep learning-Based Regression Scheme for Angle Estimation in Image dataset;the Classification of Native and Invasive Species in North America: A Transfer learning and Random Forest Pipeline;Towards a Digital Twin Integrated DLT and IoT-Based Automated Healthcare Ecosystem;enabling Edge Devices Using Federated learning and Big data for Proactive Decisions;ioT and Blockchain Oriented Gender Determination of Bangladeshi Populations;Federated learning Based Secured Computational Offloading in Cyber-Physical IoST Systems;a Hybrid Campus Security System Combined of Face, Number-Plate, and Voice recognition;single-Trial Detection of Event-Related Potentials with Artificial Examples Based on Coloring Transformation;identifying the Relationship Between Hypothesis and Premise;semi-supervised Multi-domain learning for Medical Image Classification;data Poisoning Attack by Label Flipping on SplitFed learning;a Deep learning-Powered

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A Network Monitoring Model based on Convolutional Neural Networks for Unbalanced Network Activity

A Network Monitoring Model based on Convolutional Neural Net...

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international conference on Smart Systems and Inventive Technology (ICSSIT)

作者： A Srinath Reddy S.Phani Praveen G Bhargav Ramudu A Bhanu Anish A Mahadev D Swapna Computer Science Department P.V.P. Siddhartha Institute of Technology Vijayawada India Computer Science Department PVP Siddhartha Institute of Technology Vijayawada India

Networks play an important part in present life. Cybersecurity is critical to network information security. A network monitoring mechanism that observes and recognizes every type of potential problem in technology as well as the infrastructure on the node and that square measure several current systems out there; however, they still face challenges not the accuracy of detective work vulnerabilities, an excellent job scaling back vigilance and finding intrusion attacks. machine learning (ML) is growing in quality. a comprehensive methodology to think about innovative penetration with a surprising pattern. machine learning methodology recognizes knowledge from expertise and distinguishes between traditional and anomalous knowledge. In our work, CNN model implementation and optimization methods for pattern recognition, Mistreatment of public NSL-KDD records, solve identification issues with the Adam optimized Network Intrusion. this research study has trained a CNN algorithmic rule. the planned IDS model classify all packets to find network intrusions and traffic as an everyday or malicious category. Recursive Feature elimination (RFE) is employed for feature choice. CNN fared substantially better than other classifiers in terms of accuracy, with a CNN result of 0.9672%. the three most ordinarily used metrics to assess IDS performance accuracy, square measure exactitude, and false positive rate (FPR).

关键词： Measurement machine learning algorithms Face recognition Current measurement Optimization methods Intrusion detection machine learning

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the Future of Skin Cancer Detection: machine learning and Convolutional Neural Networks

The Future of Skin Cancer Detection: Machine Learning and Co...

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data Intelligence and Cognitive Informatics (ICDICI), international conference on

作者： Kuldeep Vayadande Owee Mirajkar Satyam Mhetre Harsh Mehta Sahil Markande Siddhi Mohol IT Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

Cancers such as melanoma and other skin cancers are among the most frequent cancers occurring among people across the world. In such a circumstances, chances of survival are very much possible if such a cancer could be diagnosed early. As for that, to achieve the faster diagnosis than with morphology and color in question it is possible to employ computer-aided diagnostic systems developed with the use of deep learning models including CNN. We have proposed a Hybrid Model which detects the skin cancer efficiently by training images with high resolution using classifier like CNN coupled with autoencoders for feature extraction. therefore, it assists the dermatologist to identify cancerous tumors and produces accurate diagnosis results because it always learns from fresh data. By continuously upgrading through machine learning, the platform can improve detection and diagnose skin cancer better for better outcomes. It uses such advanced algorithms with large amounts of skin imaging data to point at lesions indicative of cancer. the platform hopes to help dermatologists detect and diagnose skin cancer better eventually reducing its mortality rate. High-resolution algorithms are learned on large sets containing hundreds of images to detect small differences and patterns observed in lesions. this is flexible enough to keep pace with the current clinical practice and research literature. It is an excellent teaching aid for residents and medical students, with an in-depth discussion of all kinds of dermatological diseases and their analysis and visualizations.

关键词： Deep learning Training Computational modeling Autoencoders Feature extraction Skin pattern recognition Convolutional neural networks Lesions Skin cancer

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Improving a Speech Emotion recognition System for Saudi Dialect

Improving a Speech Emotion Recognition System for Saudi Dial...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),conference on

作者： Reham Al-Dayil Wided Bouchelligua Areej Algaith Computer Engineering Department College of Computer and Information Sciences King Saud University (KSU) Riyadh Saudi Arabia Applied College IMAM Mohammed Ibn Saud Islamic University (IMSIU) Riyadh Saudi Arabia

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

Speech Emotion recognition (SER) is a challenging and essential task, particularly for underrepresented languages and dialects. this study introduces an improved SER system specifically designed for the Saudi dialect, addressing significant challenges such as data scarcity and imbalanced datasets. To tackle these issues, advanced data augmentation techniques—such as noise addition, volume adjustment, and time shifting—are applied to enhance dataset diversity and model robustness. the feature extraction process utilizes Mel-Frequency Cepstral Coefficients (MFCC), which effectively capture the key characteristics of speech signals. these features are then processed using traditional machine learning classifiers, including KNN, SVM, and LDA, as well as deep learning models such as CNN and MLP. the experimental results reveal significant improvements in accuracy, precision, recall, and F1-scores across all classifiers after implementing data augmentation. Among the classifiers, LDA and CNN achieve the highest accuracy of 96.19%, showcasing the efficacy of tailored data augmentation strategies in advancing SER for the Saudi dialect.

关键词： Deep learning Emotion recognition Accuracy Noise Speech recognition Speech enhancement Linguistics data augmentation Feature extraction Robustness

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FPGA Architecture to Enhance Hardware Acceleration for machine learning Applications 5

FPGA Architecture to Enhance Hardware Acceleration for Machi...

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5th international conference on Computing Methodologies and Communication, ICCMC 2021

作者： Itagi, Anirudh Krishvadana, S. Bharath, K P Rajesh Kumar, M. Vellore Institute of Technology Alumni School of Electronics Engineering VelloreTN India Vellore Institute of Technology School of Electronics Engineering VelloreTN India

ISBN: (纸本)9781665403603

Many algorithms have been developed in the field of machine learning and its sub-fields such as neural networks, Deep learning and so on, for applications such as pattern classification, image and video processing, statistical data mining and so forth. these algorithms perform such tasks with remarkable accuracy. However, when implemented in traditional processor core software, many of these algorithms get choked when the size of the network or computational demand of the algorithm scales up. FPGAs and ASICs offer higher computation capability, throughput and bandwidth. Features such as massive parallel processing, high performance and reliability, are strong arguments for FPGAs for Deep Neural Network applications. FPGAs offer reconfigurable, flexible architectures where even the most popular GPUs fall short. this paper proposes a robust, re-configurable architecture for deploying machine learning algorithms and presents its advantages by implementing a Neural Network in an FPGA and comparing its results with an implementation in a Raspberry Pi. © 2021 IEEE.

关键词： Field programmable gate arrays (FPGA)

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machine learning Models for Identifying patterns in GNSS Meteorological data 1

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5th international conference on Applied Technologies, ICAT 2023

作者： Alvarez-Castillo, Luis Fernando Torres-Carrión, Pablo Serrano-Agila, Richard Universidad Técnica Particular de Loja Loja1101608 Ecuador

ISBN: (数字)9783031589539

ISBN: (纸本)9783031589522

this research is centered on the comprehensive analysis of meteorological data sourced from strategically positioned Global Navigation Satellite System (GNSS) stations located in Ecuador. Meteorological data of LJEC, PLEC, CUEC, and GZEC was collected and analyzed. For each station, three years (2017–2019) meteorological data recorded throughout each year at one-second intervals were analyzed. data mining techniques are employed for in-depth analysis, utilizing machine learning algorithms to discern these stations behavior patterns. A machine learning model has been meticulously developed and fine-tuned to harmonize with the data’s underlying structure, thereby yielding precise results with a minimal margin of error. After model development and requisite testing, a satisfaction rate of 90% has been achieved, affirming formulated hypotheses validation. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： machine learning

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