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Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKiiE), International conference on

作者： Rahul Ghotekar Kailash Shaw Minakhi Rout Department of Computer Science and Engineering KIIT University Odisha India Department of Artificial Intelligence and Machine Learning Symbiosis Institute of Technology Symbiosis International (Deemed University) Lavale Pune Maharashtra India

Hyperspectral image (HSI) classification holds immense significance in remote sensing applications like land cover mapping and environmental monitoring. This study introduces Graph Convolutional neural networks (GCNN) with the SWIN Transformer model (G_SwTr) to enhance HSI classification accuracy. GCNNs are utilized to incorporate spatial information through graph representations, capturing spectral-spatial dependencies. Graph convolutions, effectively harnesses spectral and spatial attributes, further the integration of the SWIN Transformer, recognized for its proficiency in capturing long-range dependencies via shifted windows and hierarchical processing, elevates feature extraction and contextual understanding. The Proposed G_SwTr model is evaluated across three HSI datasets (Pavia-University, Indian-Pines, and Salinas) and is compared against traditional Machine Learning and Deep Learning methods. The results achieved outperform state-of-art methods, with an outstanding Overall Accuracy (O.A) of 99.7, 99.9, and 99.5, an average accuracy (A.A) of 99.5, 99.6, and 98.2, and a kappa coefficient (K) of 99.5, 99.6, and 98.3 for all three datasets.

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Self-Tuning PID Controller for an Arm-Angle Adjustable Quadrotor Using an Actor-Critic-Based neural Network

Self-Tuning PID Controller for an Arm-Angle Adjustable Quadr...

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Iranian conference on Intelligent Systems (ICIS)

作者： Arshia Rezaei Amin Talaeizadeh Aria Alasty Department of Mechanical Engineering Advanced Research Lab for Control and Agricultural Robotics (Sharif AgRoLab) Sharif University of Technology Tehran Iran

ISBN: (数字)9798331507565

ISBN: (纸本)9798331507572

The increasing prevalence of multirotors necessitates the development of new configurations for specific applications. Innovative morphing of multirotors has enhanced their stability, maneuverability, agility, and overall performance. The utilization of artificial Intelligence (AI) in robotics has enabled robots to exhibit autonomous behaviors in areas such as control, path planning, state estimation, system identification, and image processing. This paper introduces a novel approach to improving the control system of an adjustable arm-angle quadrotor through the integration of a self-tuning Proportional-Integral-Derivative (PID) controller, utilizing an Actor-Critic-based neural Network (NN). Arm-angle adjustable quadrotors, with their complex and variable dynamics, present challenges in achieving optimal control performance across different arm angles. Traditional PID controllers often struggle to adapt efficiently to these dynamics. To address this issue, the Actor-Critic framework from reinforcement learning is employed to develop a self-tuning mechanism for the PID controller. The Actor network learns the optimal control actions, while the Critic network evaluates the performance of the control system, enabling continuous improvement. Extensive Software-in-the-Loop (SIL) and Hardware-in-the-Loop (HIL) tests demonstrate the effectiveness and feasibility of the proposed approach in achieving robust and adaptive control for tilt-arm quadrotors under diverse operating conditions. The results highlight the superiority of the Actor-Critic-based self-tuning PID controller over conventional methods, showcasing its potential for real-world devlopment in autonomous aerial systems.

关键词： Uncertainty Optimal control Reinforcement learning artificial neural networks Autonomous aerial vehicles Real-time systems Adaptive control Robots Tuning Quadrotors

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Generating Pedestrian Training Dataset using DCGAN 19

Generating Pedestrian Training Dataset using DCGAN

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Proceedings of the 2019 3rd International conference on Advances in image processing

作者： Daeun Dana Kim Muhammad Tanseef Shahid Yunseong Kim Won Jun Lee Hyun Chul Song Francesco Piccialli Kwang Nam Choi Dept. of computer science and engineering Chung-Ang University Seoul South Korea Dept. of Electrical Engineering and Information Technology University of Naples Federico II Italy

ISBN: (纸本)9781450376754

Recently, as autonomous cars are developing very fast, it is the most crucial task to detect pedestrians for autonomous driving. Convolution neural network based on pedestrian detection models has gained enormous success in many applications. However, these models need a large amount of annotated and labeled datasets for training process which requires lots of time and human effort. For training samples, the diversity and quantity of datasets are very important. The proposed framework is based on Deep Convolutional Generative Adversarial networks (DCGAN), able to generate realistic pedestrians. Experimental results show that DCGAN framework is able to synthesize real pedestrian images with diversity. The synthesized samples can be included in training data to improve the performance of pedestrian detectors. 24,770 images including PETA dataset, Inria dataset were used for the training process.

关键词： Deep Convolutional GAN Dataset Pedestrian Generative Adversarial networks image Generation

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artificial Intelligence for Automatic Pain Assessment: Research Methods and Perspectives

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PAIN RESEARCH & MANAGEMENT 2023年第1期2023卷 6018736页

作者： Cascella, Marco Schiavo, Daniela Cuomo, Arturo Ottaiano, Alessandro Perri, Francesco Patrone, Renato Migliarelli, Sara Bignami, Elena Giovanna Vittori, Alessandro Cutugno, Francesco Ist Nazl Tumori IRCCS Fdn G Pascale Div Anesthesia & Pain Med I-80131 Naples Italy Ist Nazl Tumori Napoli IRCCS G Pascale SSD Innovat Therapies Abdominal Metastases Via M Semmola I-80131 Naples Italy Ist Nazl Tumori IRCCS Fdn G Pascale Head & Neck Oncol Unit I-80131 Naples Italy Univ Naples Federico II Dieti Dept Naples Italy Fdn Pascale IRCCS Napoli Ist Nazl Tumori IRCCS Div Hepatobiliary Surg Oncol Naples Italy Univ Sapienza Rome Fac Med & Psychol Dept Pharmacol Rome Italy Univ Parma Dept Med & Surg Anesthesiol Crit Care & Pain Med Div Parma Italy Osped Pediatr Bambino Gesu IRCCS Dept Anesthesia & Crit Care ARCO ROMA I-00165 Rome Italy Univ Naples Federico II Dept Elect Engn & Informat Technol I-80100 Naples Italy

Although proper pain evaluation is mandatory for establishing the appropriate therapy, self-reported pain level assessment has several limitations. Data-driven artificial intelligence (AI) methods can be employed for research on automatic pain assessment (APA). The goal is the development of objective, standardized, and generalizable instruments useful for pain assessment in different clinical contexts. The purpose of this article is to discuss the state of the art of research and perspectives on APA applications in both research and clinical scenarios. Principles of AI functioning will be addressed. For narrative purposes, AI-based methods are grouped into behavioral-based approaches and neurophysiology-based pain detection methods. Since pain is generally accompanied by spontaneous facial behaviors, several approaches for APA are based on image classification and feature extraction. Language features through natural language strategies, body postures, and respiratory-derived elements are other investigated behavioral-based approaches. Neurophysiology-based pain detection is obtained through electroencephalography, electromyography, electrodermal activity, and other biosignals. Recent approaches involve multimode strategies by combining behaviors with neurophysiological findings. Concerning methods, early studies were conducted by machine learning algorithms such as support vector machine, decision tree, and random forest classifiers. More recently, artificial neural networks such as convolutional and recurrent neural network algorithms are implemented, even in combination. Collaboration programs involving clinicians and computer scientists must be aimed at structuring and processing robust datasets that can be used in various settings, from acute to different chronic pain conditions. Finally, it is crucial to apply the concepts of explainability and ethics when examining AI applications for pain research and management.

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VGG Models in image Captioning: Which Architecture Delivers Better Descriptions?

VGG Models in Image Captioning: Which Architecture Delivers ...

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International conference on Open Source Systems and Technologies (ICOSST)

作者： Ahmad Maaz Shaheer Abbas Raja Hashim Ali Iftikhar Ahmed Talha Ali Khan Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi Pakistan Department of Business University of Europe for Applied Sciences Think Campus Potsdam Germany

ISBN: (数字)9798331508692

ISBN: (纸本)9798331508708

The recent advancements and a flurry of deep learning architectures in the fields of computer vision and natural language processing have greatly benefited the task of creating natural language descriptions for images in recent years. These advancements have proved to be the turning point with popular practical applications in artificial intelligence, as well as for driving cross-disciplinary developments. However, not many studies have investigated the performance of different convolutional and LSTM architectures for image captioning, motivating our study to bridge this gap. This study compares the performance of VGG16 and VGG19 models on the Flickr8k dataset. image features are extracted using pretrained VGG16 and VGG19 models, textual input is tokenized, and a unique Encoder-Decoder architecture is built. We determine the effect of training length on captioning proficiency by conducting comprehensive tests with varied epochs. The BLEU ratings reveal detailed insights into the models' language generating capabilities, demonstrating comparable performance across VGG16 and VGG19. Our work fills this gap by assessing the efficiency of several neural designs on a given dataset. The findings highlight the complexities of model convergence, and the need for different evaluation measures and defined benchmarks in the picture captioning sector. We determine the relationship between training epochs and model performance in this study, and provides useful observations for researchers and practitioners. We systematically examine the design of various convolutional neural networks, and provide a basis for future research in the ever-advancing world of image captioning.

关键词： Training Computer architecture Benchmark testing Performance gain Feature extraction Turning Natural language processing Complexity theory Long short term memory Convergence

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AI-Powered Early Detection of Sepsis using Infrared image Multi-Target based Faster Region-based Convolutional neural Network

AI-Powered Early Detection of Sepsis using Infrared Image Mu...

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Software, Systems and Information Technology (SSITCON), International conference on

作者： Muntather Almusawi Vidya Kamma Arelli Madhavi S. Jeyanthi B Santhiya Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science and Engineering Neil Gogte Institute of Technology Rangareddy Hyderabad India Department of Artificial Intelligence & Machine Learning Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Hyderabad India Department of Computer Applications New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of information Technology Bannari Amman Institute of Technology Sathyamangalam India

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

Sepsis is a complex and heterogeneous syndrome that continues to pose significant challenges to healthcare globally, particularly in realm of Al-powered early detection. The increasing demand for Intensive Care Unit (ICU) admissions and associated rising costs have urged the need for effective management strategies. The problem of the sepsis addresses the issues of misclassification and insurance organizations that arise from inefficient the medical recorder. This paper, proposed Infrared image multi-target based Faster Region-based Convolutional neural Network (FR-CNN) technique efficiently achieve better performance in the sepsis detection. Initially data obtained from the Best care database at KAMC with a focus on patient care in ICU through analysis of medical records. The pre-processing utilized min-max normalization and enhance the image quality which is uniformly to process the feature vector. The FR-CNN architecture is involved to identify the sepsis in multiple within infrared image. The model is trained using labelled data that include various infrared image depicting sepsis related feature and detection the complex patterns associated with sepsis, which is achieve better accuracy. The performance of proposed method is evaluated with various metrics such as accuracy, precision, recall and f1-score metrics on Best care database.

关键词： Training Measurement Accuracy Databases Sepsis Software systems Vectors Robustness Convolutional neural networks Medical diagnostic imaging

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A new massively parallel and distributed virtual machine model using mobile agents

A new massively parallel and distributed virtual machine mod...

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IEEE International conference on Multimedia Computing and Systems (ICMCS)

作者： Mohamed Youssfi Omar Bouattane Jamila Bakkoury Mohammed Ouadi Bensalah Lab. SSDIA University Hassan II Mohammedia Casablanca Mohammedia Morocco FSR University Mohammed V AGDAL Rabat Morocco

ISBN: (纸本)9781479938254

The goal of this paper, is to present a new massively parallel virtual machine model, designed for parallel and distributed high performance computing on a distributed system. The proposed model allows us to build a polymorphic grid computing assigned to solve fine grained parallel problems over different machine structures. This model is built using distributed Virtual Processors Units (VPU). Each VPU corresponds to a mobile agent deployed in a physical processing unit. Each physical processing unit is associated to a node of the distributed system. The VPUs are designed to communicate with each other asynchronously by exchanging, in local or remote way, ACL messages (Agent Communication Language) containing data, instructions or any task to be performed. In this model a special agent is designed to represent the host of the parallel virtual machine. This agent manages the life cycle of VPUs, the load balancing system, and parallel applications to run. In this model VPUs can also use a virtual shared memory represented by hierarchical mobile agents. All the properties offered to the proposed model, are easily designed thanks to the flexibility and the mobility of the multi agent systems.

关键词： Computational modeling Graphics processing units Irrigation Load modeling artificial neural networks Virtual machining Message systems

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NeuroNet: A Deep Learning Framework for Stroke Analysis

NeuroNet: A Deep Learning Framework for Stroke Analysis

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International conference on

作者： Arpana Prasad V Asha Neethu Tressa Divya Sharma Deepthi V Divyashree S Master of Computer Applications New Horizon College of Engineering Bengaluru India

The revolutionary potential of a deep learning framework for stroke analysis and prediction in the healthcare industry is examined in this paper. The suggested framework uses large datasets, such as patient records, medical images, and pertinent health information, to create histicated neural networks in order to address the critical need for early detection and intervention in stroke cases. Important areas like stroke type classification, natural language processing, image analysis, treatment optimization, outcome prediction, telemedicine, and explainable artificial intelligence are highlighted in the thorough review of related work. The steps involved in data collection, preprocessing, model selection, and result interpretation are all carefully laid out in the methodology. A thorough evaluation of machine learning models, such as Random Forest, SVM, XGBoost, and Logistic Regression, is carried out; Random Forest continuously performs well. The results highlight how difficult it is to predict strokes and how crucial it is to choose models carefully. With the ultimate goal of improving stroke diagnosis, treatment, and prevention for improved patient care through ongoing research and technological advancements, ethical considerations and adherence to healthcare regulations in AI applications are emphasized.

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Automatic person identification system using handwritten signatures

Automatic person identification system using handwritten sig...

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International conference on Computer and Communication Engineering, ICCCE

作者： A.A.M. Abushariah T.S. Gunawan J. Chebil M.A.M. Abushariah ECE Department Faculty of Engineering International Islamic University Malaysia Kuala Lumpur Malaysia Department of Computer Information Systems King Abdullah II School for Information Technology University of Jordan Amman Jordan

This paper reports the design, implementation, and evaluation of a research work for developing an automatic person identification system using hand signatures biometric. The developed automatic person identification system mainly used toolboxes provided by MATLAB environment.. In order to train and test the developed automatic person identification system, an in-house hand signatures database is created, which contains hand signatures of 100 persons (50 males and 50 females) each of which is repeated 30 times. Therefore, a total of 3000 hand signatures are collected. The collected hand signatures have gone through pre-processing steps such as producing a digitized version of the signatures using a scanner, converting input images type to a standard binary images type, cropping, normalizing images size, and reshaping in order to produce a ready-to-use hand signatures database for training and testing the automatic person identification system. Global features such as signature height, image area, pure width, and pure height are then selected to be used in the system, which reflect information about the structure of the hand signature image. For features training and classification, the Multi-Layer Perceptron (MLP) architecture of artificial neural Network (ANN) is used. This paper also investigates the effect of the persons' gender on the overall performance of the system. For performance optimization, the effect of modifying values of basic parameters in ANN such as the number of hidden neurons and the number of epochs are investigated in this work. The handwritten signature data collected from male persons outperformed those collected from the female persons, whereby the system obtained average recognition rates of 76.20% and74.20% for male and female persons, respectively. Overall, the handwritten signatures based system obtained an average recognition rate of 75.20% for all persons.

关键词： Feature extraction artificial neural networks Testing Training Accuracy Databases Computer architecture

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SE3D: A Framework for Saliency Method Evaluation in 3D Imaging

SE3D: A Framework for Saliency Method Evaluation in 3D Imagi...

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IEEE International conference on image processing

作者： Mariusz Wiśniewski Loris Giulivi Giacomo Boracchi DEIB Politecnico di Milano Italy

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

For more than a decade, deep learning models have been dominating in various 2D imaging tasks. Their application is now extending to 3D imaging, with 3D Convolutional neural networks (3D CNNs) being able to process LIDAR, MRI, and CT scans, with significant implications for fields such as autonomous driving and medical imaging. In these critical settings, explaining the model’s decisions is fundamental. Despite recent advances in Explainable artificial Intelligence, however, little effort has been devoted to explaining 3D CNNs, and many works explain these models via inadequate extensions of 2D saliency methods. A fundamental limitation to the development of 3D saliency methods is the lack of a benchmark to quantitatively assess these on 3D data. To address this issue, we propose SE3D: a framework for Saliency method Evaluation in 3D imaging. We propose modifications to ShapeNet, ScanNet, and BraTS datasets, and evaluation metrics to assess saliency methods for 3D CNNs. We evaluate both state-of-the-art saliency methods designed for 3D data and extensions of popular 2D saliency methods to 3D. Our experiments show that 3D saliency methods do not provide explanations of sufficient quality, and that there is margin for future improvements and safer applications of 3D CNNs in critical fields.

关键词： Measurement Deep learning Solid modeling Three-dimensional displays Laser radar Magnetic resonance imaging image processing

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