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共 134 条记录，以下是81-90 订阅

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Hand-written Digit recognition using Convolutional Neural Network in Python with Tensorflow

Hand-written Digit Recognition using Convolutional Neural Ne...

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Recent Trends in Computer Science and Technology (ICRTCST), international conference on

作者： Prakash Anand Piyush Ranjan Priyanka Srivastava Department of Computer Science Jharkhand Rai University Ranchi India Department of Computer Science Sarala Birla University Ranchi India

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

Recently, deep learning has transformed machine learning by significantly enhancing its artificial intelligence as Artificial Neural Networks (ANN) have become increasingly prevalent. Due of its extensive range of applications in fields such as intelligence, healthcare, medical, athletics, robots, etc., machine learning algorithms is remarkably used in a wide range of industries. At the core of incredible advancements in deep learning are convolutional neural networks (CNN), which integrate artificial neural networks (ANN) and contemporary deep learning algorithms. Across a variety of applications, including pattern classification, phrase classification, voice recognition, image identification, text summarization, documentary analysis, scene recognition, and handwritten digit recognition, it has been used. Our research's objective is to develop a model that can accurately compare image comparisons and identify handwritten numbers. We used the Modified National Institute of Standards and Technology (MNIST) dataset to conduct our experiment CNN.

关键词： Deep learning Handwriting recognition machine learning algorithms Image recognition Text recognition Service robots pattern classification

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Cross-Modality Image Quality Prediction for Time-Resolved CT from Breathing Signals

Cross-Modality Image Quality Prediction for Time-Resolved C...

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Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and Data, LDTM 2024, 5th international Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on machine learning for Multimodal/-sensor Healthcare Data, ML4MHD2024 and Workshop on Multimodal learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

作者： Schwarz, Annette Dickmann, Jannis Hofmann, Christian Szkitsak, Juliane Bert, Christoph Maier, Andreas Arias-Vergara, Tomás Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Siemens Healthineers AG Forchheim Germany Department of Radiation Oncology Universitätsklinikum Erlangen Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Erlangen Germany

ISBN: (纸本)9783031845246

Four-dimensional computed tomography (4DCT) is a time-resolved, multi-modal imaging method that captures respiratory signals synchronised with the CT scan in order to track the movement of the lung. It is routinely used in radiation therapy treatment planning for lung or liver cancer patients. However, image artifacts during 4DCT scans caused by irregular patient breathing negatively impact treatment outcomes. this work proposes a method to automatically detect patients at high risk for severe image artifacts even before the scan is conducted based on an pre-scan analysis of their breathing. this can help to take proactive measures to improve image quality, such as changing the scan mode or providing in-depth patient coaching. A deep neural network is trained to predict the image quality score of 28 lung CT phantom scans, each rated by ten clinical experts. Different pretrained networks are investigated for feature generation and combined with two linear output heads to predict the average expert image quality score of unseen scans. We were able to predict the quality of a 4DCT with a mean absolute error of 8% using only the one-dimensional breathing signal as input. this accuracy is comparable to the rating consistency of our clinical experts, which were rating the images directly. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Radiotherapy

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Enhancing Security with machine learning-based Finger-Vein Biometric Authentication System

Enhancing Security with Machine Learning-based Finger-Vein B...

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Mobile Computing and Sustainable Informatics (ICMCSI), international conference on

作者： G Prakash Babu Upendra Singh Aswal M. Sindhu Vijay Jagdish Upadhye Natrayan L Harshal Patil Department of Computer Science and Engineering Acharya Institute of Technology Bangalore Karnataka India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Business Systems R.M.K. Engineering College Thiruvallur Tamil Nadu India Department of Microbiology Research and Development Cell Parul Institute of Applied Sciences Parul University Waghodia Gujarat India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India

Biometric authentication systems have gained popularity as the necessity for secure individual identification has expanded. Iris, hand geometry, fingerprints, retina, vein patterns on the fingers and palms, and voice patterns are all common biometric features. Biometric recognition systems based on finger-veins are becoming more common due to their numerous advantages, including low danger of duplication or fraud, minimum invasiveness, and excellent identification accuracy. Motivated by these advantages, our research looks into the subject of finger-vein authentication. Similar to identification, exploration of hidden patterns within data is essential for effective finger-vein recognition. the study employs two feature extraction approaches, the Local Binary pattern (LBP) and the Gabor filter, as well as two machine learning models, the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Support Vector machine (SVM). Using finger-vein images from the SDUMLA HMT database, this study evaluates four distinct feature extraction and ML model combinations. the simulation results highlight the excellent accuracy of 97.72%, False Acceptance Rate (FAR) of 3.1%, and False Rejection Rate (FRR) of 1.41% attained by LBP with ANFIS. In addition to performance measurements, the study examines the amount of time required for each task.

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Intelligent DoS and DDoS Detection: A Hybrid GRU-NTM Approach to Network Security

Intelligent DoS and DDoS Detection: A Hybrid GRU-NTM Approac...

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Smart Electronics and Communication (ICOSEC), international conference on

作者： Caroline Panggabean Chandrasekar Venkatachalam Priyanka Shah Sincy John P Renuka Devi Shanmugavalli Venkatachalam Departement of CSE (AI) JAIN (Deemed – to – be University) Bangalore Karnataka Departement of CSE (AIML) JAIN (Deemed – to – be University) Bangalore Karnataka Department of CSE KSR College of Engineering Namakkal Tamil Nadu

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Detecting Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks remains a critical challenge in cybersecurity. this research introduces a hybrid deep learning model combining Gated Recurrent Units (GRUs) and a Neural Turing machine (NTM) for enhanced intrusion detection. Trained on UNSW-NB15 and BoT-IoT datasets, the model employs GRU layers for sequential data processing and an NTM for long-term pattern recognition. the model achieves 99% accuracy in distinguishing between normal, DoS, and DDoS traffic. this approach offers promising advancements in realtime threat detection, potentially improving network security across various domains.

关键词： Accuracy Turing machines Network intrusion detection Network security Denial-of-service attack threat assessment Real-time systems pattern recognition Resource management Computer crime

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Human Activity recognition: Fight Detection using Long term Recurrent Convolutional Network (LRCN)

Human Activity Recognition: Fight Detection using Long term ...

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Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), international conference on

作者： Ngrddy Jhnvi S.K. Bhrdwj Ajay Sharma Shamneesh Sharma Ankur Sodhi Saikat Gochhait School of Computer Science and Engineering Lovely Professional University Phagwara Punjab India Delivery and Student Success upGrad Education Private Limited Bangalore Karnataka India Enterprise Businees and Career Services upGrad Education Private Limited Bangalore Karnataka India Symbiosis Institute of Digital and Telecom Management Constituent of Symbiosis International Deemed University Pune India

ISBN: (数字)9798350368710

ISBN: (纸本)9798350368727

Human Activity recognition for Fight Detection is an important research domain aimed at automatically identifying patterns indicative of physical altercations. Leveraging deep learning models like CNNs and RNNs, this approach extracts features from video frames to recognize fight-related behavioural patterns. the primary objective is to develop a machine learning model capable of autonomously detecting instances of fights in video footage, using techniques such as the Long-term Recurrent Convolutional Network (LRCN). Training involves a comprehensive dataset encompassing examples of both fights and non-fight activities, with model performance evaluated using standard metrics. Each frame undergoes individual analysis by the model to predict the presence of any indications of a fight. the model predicts the action with an accuracy of 98.03% and the movements given as input can be categorized as fights or no fights.

关键词： Training Measurement Industries Predictive models Feature extraction pattern recognition Convolutional neural networks Object recognition Human activity recognition Standards

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Elevated learning based Secured Phishing Website Identification Methodology using Artificial Intelligence Assistance

Elevated Learning based Secured Phishing Website Identificat...

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Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： G. Ramkumar Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Phishing attacks pose a huge threat for online security and therefore need advanced detecting techniques in order to mitigate the damages it causes. this paper introduces a sophisticated model for the detection of phishing websites, named the Blended ResNet-EfficientNet Model (BREM), which unifies the advantages of the ResNet and EfficientNet architectures. To address this challenge, BREM uses the rich hierarchical pattern recognition ability of ResNet-50 and the practical feature extraction capability of EfficientNet-B3 to achieve classification performance in phishing detection. On overall assessment BREM outperforms both traditional machine learning models and standalone deep learning model with an accuracy of 96%, precision at 94%, recall of 95% and FI score of 94.5%. these same authors validate the high specificity (97 %), negative predictive value (95 %) and Matthews correlation coefficient of 0.92 further underlining the robustness and reliability of BREM. this approach does not only improve the accuracy of detection, but also provides much better security against phishing campaigns. In the near future research directions such as real-time deployment, more experiments on different feature sets, adversarial robustness, transfer learning from heterogeneous datasets, model learning models and pattern recognition. they are able to draw from an immense amount of data and address minor deviances, which allows AI's better discrimination of real versus false websites, therefore increasing user cyber protections and preventable breaches [3] [4]. At the heart of securing phishing websites identification is making use of powerful AI capabilities to scan and recognize phishing convincingly. this AI utilizes different methods - natural language processing (NLP), image recognition and behavioral analysis - to keep an eye on things happening to web content and its design. Behavior analysis tracks user-interactions and web behavior to detect for abnormal operations, which co

关键词： Accuracy Phishing Scalability Transfer learning Robustness Natural language processing Real-time systems pattern recognition Security Protection

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Deep learning: Exploring the Depths of Artificial Neural Networks for Advanced Data Analysis and pattern recognition

Deep Learning: Exploring the Depths of Artificial Neural Net...

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Global conference for Advancement in Technology (GCAT)

作者： Rajesh Jagadeesan Ravikumar A. Ammupriya Devi.P.P V.Samuthira Pandi Akhila Akula K. Sankar Software Engineering Senior Advisor Evernorth Health Services Chattanooga TN Department of MBA St Joseph's College of Engineering Chennai Department of Artificial Intelligence and Machine Learning Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Avadi Chennai Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of ECE MLR Institute of Technology Hyderabad Department of Biomedical Engineering Rajalakshmi Engineering College Chennai

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

this research provides a comprehensive exploration of LSTMs, CNNs, and RNNs, with a focus on their application in advanced data analysis and pattern recognition. We begin by examining the theoretical underpinnings of each architecture, highlighting their unique features and the mechanisms by which they process and learn from data. In the process of collecting long-term relationships in sequential data, LSTMs have been demonstrated to perform exceptionally well, making them an excellent choice for applications such as language modeling and time series prediction. CNNs are discussed in the context of their ability to extract hierarchical features from spatial data, rendering them particularly effective for image and video analysis. RNNs, with their inherent ability to process sequences of varying lengths, are analyzed for their applications in speech recognition and machine translation. Following this, the research begins to conduct a comparative examination of different architectures, examining how well they perform on benchmark datasets as well as challenges that are encountered in the real world. We assess their strengths and limitations, considering factors such as accuracy, computational efficiency, and scalability. Additionally, we explore the challenges associated with training deep neural networks, including the need for extensive data preprocessing, the risk of overfitting, and the importance of hyperparameter tuning. Finally, we discuss the implications of these findings for future research and practical applications in data analysis and pattern recognition. the study concludes that while each architecture has its niche, the combination of LSTMs, CNNs, and RNNs offers a powerful toolkit for tackling complex problems across diverse domains. the research underscores the potential of deep learning to unlock insights from data at unprecedented scales and complexities, paving the way for innovative solutions in fields ranging from healthcare to finance.

关键词： Deep learning Training Technological innovation Time series analysis Computer architecture Speech recognition Feature extraction Computational efficiency Tuning Overfitting

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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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Multi-Sensor and Deep learning based Ankle Joint Motion Intention Prediction under Diverse Gait Speed Conditions

Multi-Sensor and Deep Learning based Ankle Joint Motion Inte...

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ieee international conference on Automation Science and Engineering (CASE)

作者： Jumin Gong Dunwen Wei Tao Gao Zekun Liu Department of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Motion intention prediction is an essential challenge in improving the efficacy of exoskeleton human-machine interactions. Traditional gait phase detection and motion pattern recognition methods can only identify the current gait state, lacking the ability to adjust motion parameters based on step speed dynamically, and real-time performance is difficult to guarantee. In this paper, we proposed a deep learning model TCN-LSTM, which integrated Time Convolutional Network (TCN) and Long Short-Term Memory Network (LSTM), to predict human ankle joint angles under different walking speeds based on data from Inertial Measurement Unit (IMU) and Goniometer (GON). We selected 5 subjects in a public dataset for experimental validation. Experimental results demonstrated that the proposed model could accurately and rapidly predict ankle joint angles. the MAE of the individual-specific model was 1.191°, outperforming the 3 baseline models: LSTM (1.750°), Gated Recurrent Unit (GRU) (1.653°), and TCN (1.340°). the Mean Absolute Error (MAE) of the general model is reduced by 14.3% compared to the 3 baseline models. the prediction error of TCN-LSTM with GON is reduced by 13.69%. Furthermore, the average prediction time of our model is only 3.7ms. therefore, the accuracy and real-time performance of the model can meet the continuous prediction of human ankle joint angle.

关键词： Ankle Legged locomotion Deep learning Exoskeletons Predictive models Data models Real-time systems pattern recognition Long short term memory Phase detection

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Hyperdimensional Consumer pattern Analysis with Quantum Neural Architectures using Non-Hermitian Operators 23

Hyperdimensional Consumer Pattern Analysis with Quantum Neur...

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Proceedings of the 5th international conference on Information Management & machine Intelligence

作者： Shivam Goyal Sunil K. Singh Sudhakar Kumar Saket Sarin Brij B. Gupta Varsha Arya Kwok Tai Chui CSE Chandigarh College of Engineering and Technology India Department of Computer Science and Information Engineering (CSIE) Asia University Taichung Taiwan Taiwan Department of Business Administration Asia University Taiwan CSE Hong Kong Metropolitan University China

ISBN: (纸本)9798400709418

In an era inundated with high-dimensional consumer data, the need for advanced hyper-dimensional pattern analysis poses a significant computational challenge. this research pioneers the use of quantum computing to revolutionize consumer technology. Modern data streams, including images, audio, sensor data, and more, require an agile solution to overcome dimensionality challenges that traditional machine learning and classical computing struggle with. Our work combines Quantum Neural Architectures (QNAs) with Non-Hermitian Operators (NHOs), harnessing NHOs’ unique non-unitary properties for quantum speedup in feature extraction, dimensionality reduction, and pattern recognition. this approach allows for simultaneous processing of large datasets through quantum parallelism, demonstrating substantial gains in efficiency and accuracy compared to classical and Hermitian quantum methods. We also explore quantum cryptography, offering insights into quantum-safe encryption and cryptographic primitives. this multidisciplinary effort represents a paradigm shift in quantum technology’s application across consumer domains. this paper presents a quantum-inspired framework for hyperdimensional consumer pattern analysis, supported by mathematical rigor and empirical validation. the fusion of NHOs and QNAs heralds a new era in consumer technology, marked by exceptional computational power, robust security, and transformative potential.

关键词： Consumer Applications

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