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ABALearn: An automated Logic-Based learning System for ABA Frameworks 22nd

ABALearn: An Automated Logic-Based Learning System for ABA ...

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22nd international conference of the Italian Association for Artificial Intelligence, AIxIA 2023

作者： Tirsi, Cristina Proietti, Maurizio Toni, Francesca Imperial College London London United Kingdom IASI-CNR Rome Italy

ISBN: (纸本)9783031475450

We introduce ABALearn, an automated algorithm that learns Assumption-Based Argumentation (ABA) frameworks from training data consisting of positive and negative examples, and a given background knowledge. ABALearn’s ability to generate comprehensible rules for decision-making promotes transparency and interpretability, addressing the challenges associated with the black-box nature of traditional machine learning models. This implementation is based on the strategy proposed in a previous work. The learnt ABA frameworks can be mapped onto logic programs with negation as failure. The main advantage of this algorithm is that it requires minimal information about the learning problem and it is also capable of learning circular debates. Our results show that this approach is competitive with state-of-the-art alternatives, demonstrating its potential to be used in real-world applications where low user expertise is available. Overall, this work contributes to the development of automated learning techniques for argumentation frameworks in the context of Explainable AI (XAI) and provides insights into how such learners can be applied to make predictions. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

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Classification of Stenosis and Regurgitation from Seismocardiogram Signals using Convolutional Neural Network 22

Classification of Stenosis and Regurgitation from Seismocard...

引用

22nd international conference on ICT and Knowledge engineering, ICT and KE 2024

作者： Singh, Moirangthem James Sharma, L.N. Dandapat, Samarendra Electronics and Electrical Engineering IIT Guwahati Guwahati India

ISBN: (纸本)9798350391411

Timely detection is critical in preventing the advancement of valvular heart diseases (VHD), such as regurgitation and stenosis. Deep learning frameworks present a robust approach for capturing the intricate features of VHD that are often challenging to discern with traditional manual feature extraction techniques. This research presents a convolutional deep learning framework designed to classify stenosis and regurgitation using seismocardiogram (SCG) signals. Our model incorporates four convolutional neural network (CNN) layers designed to extract and learn detailed characteristics of these conditions effectively. Initially, SCG time-series data are segmented to remove artifacts and then converted into SCG images using the normalized power of continuous wavelet transform (CWT) coefficients with analytic morse mother wavelets. The initial CNN layer processes these images to detect low-level features while the remaining layers extract more intricate patterns. Our approach achieves remarkable results, with an overall accuracy of 99.13%, sensitivity of 98.23%, and specificity of 99.42%, based on evaluations involving 17 patients with regurgitation, 18 with stenosis, and 29 healthy controls. This method outperforms current cardiac disease detection techniques, underscoring the effectiveness of SCG signals for early valve disease screening. © 2024 IEEE.

关键词： Wavelet transforms

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Real- Time Seat Occupancy Detection in Waiting Rooms using Arduino Nano 1oT 33 22

Real- Time Seat Occupancy Detection in Waiting Rooms using A...

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22nd international conference on Emerging elearning Technologies and Applications, ICETA 2024

作者： Ferenčik, Norbert Kolembusová, Petra Danko, Mária Nad'ová, Zuzana Steingartner, William Hudák, Radovan Technical University of Košice Faculty of Mechanical Engineering Košice Slovakia Pavol Jozef Šafárik University in Košice Faculty of Science Košice Slovakia Technical University of Košice Faculty of Electrical Engineering and Informatics Košice Slovakia

ISBN: (纸本)9798331527716

This study presents a seat occupancy detection system for waiting rooms utilizing the Arduino Nano IoT 33 platform. The primary aim is to create an affordable and scalable solution to accurately monitor seat occupancy in real-time, improve space management, and reduce waiting times in healthcare and public service settings. The system uses ultrasonic sensors to detect individuals sitting in seats, which are then connected to the IoT platform to provide real-time data to a central monitoring system. The system underwent testing in a simulated environment and demonstrated a high level of accuracy in detecting occupancy, with an error margin of less than 5%. Its modular design allows for easy expansion and integration with existing IT infrastructures, making it flexible for applications beyond waiting rooms, such as conference halls, classrooms, and public transport systems. The study also discusses the challenges associated with sensor accuracy, energy efficiency, and data privacy. It proposes potential solutions such as integrating additional sensor types, using energy-efficient IoT protocols, and complying with legal standards. The results indicate that the system can significantly improve the management of public spaces, providing a cost-effective tool for organizations to optimize space utilization and enhance user experience. This study contributes to the field of smart environments and demonstrates the potential of IoT technology in addressing practical challenges in public service management. Future work will focus on implementing machine learning algorithms to improve system accuracy and adaptability in dynamic environments. © 2024 IEEE.

关键词： Ultrasonic sensors

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Endoscopic Bladder Tissue Classification Using Seventeen Layered Deep Convolutional Neural Network 2

Endoscopic Bladder Tissue Classification Using Seventeen Lay...

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2nd international conference on Advancements in Smart, Secure and intelligent Computing, ASSIC 2024

作者： Devi, M. Shyamala Pandian, J. Arun Umanandhini, D. Viknesh, V. Vishal, G.P. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Computer Science & Engineering Tamilnadu Chennai India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350370188

After a diagnosis of bladder cancer, endoscopic surgery is carried out as it is thought to be the most efficient and appropriate method of treating superficial tumors. It is essential to classify the bladder tissues accurately using MRI since early diagnosis of the problem can aid in treatment and perhaps save lives. Early-stage diagnosis is still challenging due to the delayed onset of indications and tiny alterations in urinary tract tissue that are imperceptible to the naked eye for diagnosis. The automated approach might be utilized to automatically determine the type of endoscopic bladder tissue by using the proper deep learning model with the necessary convolutional layers. This paper suggests the Seventeen Layered Deep Convolutional Neural Network (17-DCNN) to classify the bladder tissue classes effectively. The Endoscopic bladder tissue dataset from Kaggle is used for the implementation of DOMN that contains 1785 images with four classes as HGC, LGC, NTL, and NST. The 17-DCNN model's initial phase is an analysis of distribution of bladder tissue data images. After data preparation, the existing CNN like DenseNet121, ResNet152, VGG19Net, InceptionV3Net and proposed 17-DCNN model is chosen by fitting the Endoscopic bladder tissue images. The accuracy of the proposed 17-DCNN was 99.32% when compared to other CNN models after implementation. © 2024 IEEE.

关键词： Convolutional neural networks

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Offline Signature Forgery Detection Based on Geometric Measures Using Tensorflow Model 2

Offline Signature Forgery Detection Based on Geometric Measu...

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2nd international conference on Advancements in Smart, Secure and intelligent Computing, ASSIC 2024

作者： Lakshmi, A. Anagha Reddy, G. Siddarth Reddy, M. Sowmya Kathirisetty, Nikhila Vardhaman College of Engineering Department of Information Technology Hyderabad India Vardhaman College of Engineering Department of Computer Science and Data Science Hyderabad India

ISBN: (纸本)9798350370188

The most popular method for identifying people from past signatures is through signatures. By using a TensorFlow model which is a deep learning algorithm, we created a new system to verify signatures on bank checks and other important documents. The main point is that a signature plays a crucial role in banking because it is necessary for money withdrawals. In banks there are no efficient systems to check if a signature is real or fake as it is the most frequently used bio-metric method to verify a person's identity. This can lead to bank fraud. The Project will make it easier to tell whether a signature is genuine or not. Online and offline verification are the two methods of verification. We're going to use various geometric measurements to accomplish offline verification. Python libraries were employed in this case. In the testing phase the model's performance is evaluated. To protect the integrity and legitimacy of handwritten signatures, signature forgery detection is an essential responsi-bility. The capacity to recognize forged signatures with accuracy has grown more important as a result of the rise in digital transactions and the growing reliance on electronic documents. The act of duplicating or copying another person's signature with the purpose to deceive or gain illegal access is known as signature forging. Manually spotting these forgeries can be difficult because expert forgers are adept at closely imitating the visual style and traits of real signatures. Therefore, there has been a lot of interest in the development of automated methods and algorithms to identify fake signatures. © 2024 IEEE.

关键词： data mining

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Q-learning and Neural Networks in automated Driving Systems 2

Q-learning and Neural Networks in Automated Driving Systems

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2nd international conference on data Analytics, Computing and Artificial Intelligence, ICDACAI 2023

作者： Chen, Zeyu Ucl Mechanical Engineering London United Kingdom

ISBN: (纸本)9798350339765

With the development of artificial intelligence, machine learning, and other disciplines, automation in various fields is becoming popular. Therefore, autonomous driving systems in the automotive field represent a major trend in the future of intelligent transportation systems. This paper focuses on the application of Q-learning and deep neural networks in autonomous driving systems. The simulation in Python is used to simulate the condition of the car driving alone on the road. Firstly, the normal car driving situation was analyzed and the actions and states in Q-learning were set. The action set-up ensures the distance sensors' number and direction. Then, this paper draws the neural network and simulates it to get the data. Secondly, it analyzes the resulting reward scores and iteration times and identifies uncertainties through line graphs. In addition, it observes the simulation video and derives the reasons why uncertainty appears. In the simulation, the Best Score, Average generation time and Genetic distance were obtained from the simulation against the number of Gens. The data for genetic distance was found to increase with the number of times it was generated, indicating that the Q-learning component was successfully run in the simulation. And it was found that the uncertainty was due to the birth point of the vehicle. According to the uncertainty, further development of the simulation could include more road environments and different obstacles. © 2023 IEEE.

关键词： Reinforcement learning

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Statistical and Deep Machine learning Techniques to Forecast Cryptocurrency Volatility 22nd

Statistical and Deep Machine Learning Techniques to Forecast...

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22nd international conference on Hybrid intelligent Systems, HIS 2022 and the 18th international conference on Information Assurance and Security, IAS 2022

作者： Cebrián-Hernández, Ángeles Jiménez-Rodríguez, Enrique Tallón-Ballesteros, Antonio J. Department of Applied Economics Seville University Seville Spain Department of Financial Economics Pablo de Olavide University Seville Spain Department of Electronic Computer Systems and Automatic Engineering University of Huelva Huelva Spain

ISBN: (纸本)9783031274084

This paper studies cryptocurrency volatility forecast covering a state-of-the-art review as well empirical comparison through supervised learning. This research has two main objectives. The first objective is the usage of artificial intelligence in the field of predicting the volatility of cryptocurrencies, in particular bitcoin. In this work, supervised machine learning algorithms from two different perspectives, a statistical one and a deep learning one, are compared to predict bitcoin volatility using economic-financial variables as additional information in the models. The second objective is to compare the fit of artificial intelligence models with traditional econometric models such as Multivariate GARCH (traditional generalized heteroscedasticity conditional autoregressive model (M-GARCH). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Stock Market Simulation by Micro-Macro GAN 22

Stock Market Simulation by Micro-Macro GAN

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22nd IEEE/WIC international conference on Web Intelligence and intelligent Agent Technology (WI-IAT)

作者： Naritomi, Yusuke Adachi, Takanori Tokyo Metropolitan Univ Grad Sch Management Tokyo Japan Mitsubishi UFJ Trust Investment Technol Inst Tokyo Japan

ISBN: (纸本)9798350309188

In recent years, research on the fusion of computer simulations with real data, as seen in data assimilation, has been active. In addition, with the development of machine learning, especially deep learning, computer simulations are increasingly using deep learning models in addition to physical models. In the field of finance, research has also been conducted to use machine learning models when simulating markets, for example, to emulate synthetic orders placed by virtual traders. However, it is fundamentally difficult to generate realistic macro dynamics such as the price from micro order dynamics. In this study, we propose a new market simulation method by machine learning model to generate macro dynamics from micro order dynamics. The market simulator built by the machine learning model, which is called Micro-Macro GAN, is trained by coupling two mechanisms. The first mechanism generates micro order dynamics and trains a generator of micro order dynamics to distinguish the real from the synthetic order data using a Wasserstein Generative Adversarial Network (WGAN), which is called the Micro GAN. The second mechanism generates macro dynamics from the micro order dynamics generated by the Micro GAN and trains a generator of macro dynamics to distinguish the real from the synthetic macro data using a Sig-W GAN, which is called the Macro GAN. The macro dynamics data is converted to a signature, and finally, the Sig-W metric is computed as a loss function. As a demonstration, training was performed on Toyota Motor Corporation taken from the Flex Full data provided by the Japan Exchange Group (JPX). The order dynamics data generated from the Micro GAN and the Micro-Macro GAN were compared to the real order dynamics data. The results showed that the Micro-Macro GAN results were more similar to the real dynamics data than the Micro GAN results.

关键词： Generative model data-driven simulation Agent simulation Artificial stock market GAN

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Context-Aware Neural Video Compression on Solar Dynamics Observatory 22

Context-Aware Neural Video Compression on Solar Dynamics Obs...

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22nd IEEE international conference on Machine learning and Applications, ICMLA 2023

作者： Khoshkhahtinat, Atefeh Zafari, Ali Mehta, Piyush M. Nasrabadi, Nasser M. Thompson, Barbara J. Kirk, Michael S. F. Da Silva, Daniel West Virginia University Dept. of Computer Science & Electrical Engineering WV United States West Virginia University Dept. of Mechanical & Aerospace Engineering WV United States Nasa Goddard Space Flight Center MD United States

ISBN: (纸本)9798350345346

NASA's Solar Dynamics Observatory (SDO) mission collects large data volumes of the Sun's daily activity. data compression is crucial for space missions to reduce data storage and video bandwidth requirements by eliminating redundancies in the data. In this paper, we present a novel neural Transformer-based video compression approach specifically designed for the SDO images. Our primary objective is to efficiently exploit the temporal and spatial redundancies inherent in solar images to obtain a high compression ratio. Our proposed architecture benefits from a novel Transformer block called Fused Local-aware Window (FLa Win), which incorporates window-based self-attention modules and an efficient fused local-aware feed-forward (FLaFF) network. This architectural design allows us to simultaneously capture short-range and long-range information while facilitating the extraction of rich and diverse contextual representations. Moreover, this design choice results in reduced computational complexity. Experimental results demonstrate the significant contribution of the FLaWin Transformer block to the compression performance, outperforming conventional hand-engineered video codecs such as H.264 and H.265 in terms of rate-distortion trade-off. © 2023 IEEE.

关键词： FLaWin Neural Video Compression Solar Dynamics Observatory Swin Transformer

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Improving Robustness of Convolutional Networks Through Sleep-Like Replay 22

Improving Robustness of Convolutional Networks Through Sleep...

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22nd IEEE international conference on Machine learning and Applications, ICMLA 2023

作者： Delanois, Jean Erik Ahuja, Aditya Krishnan, Giri P Tadros, Timothy McAuley, Julian Bazhenov, Maxim University of California San Diego Dept. of Computer Science & Engineering La Jolla United States University of California San Diego Dept. of Medicine La Jolla United States

ISBN: (纸本)9798350345346

Convolutional neural networks (CNNs) are a foun-dational model architecture utilized to perform a wide variety of visual tasks. On image classification tasks CNNs achieve high performance, however model accuracy degrades quickly when inputs are perturbed by distortions such as additive noise or blurring. This drop in performance partly arises from incorrect detection of local features by convolutional layers. In this work, we develop a neuroscience-inspired unsupervised Sleep Replay Consolidation (SRC) algorithm for improving convolutional fil-ter's robustness to perturbations. We demonstrate that sleep-based optimization improves the quality of convolutional layers by the selective modification of spatial gradients across filters. We further show that, compared to other approaches such as fine-tuning, a single sleep phase improves robustness across different types of distortions in a data efficient manner. © 2023 IEEE.

关键词： cnn convolution generalization robust-ness sleep

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