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Knowledge-Based Model for Detecting Neurodegenerative Diseases Using Text Complexity Measures 1

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Munkova, Dasa Munk, Michal Zozuk, Nataliia Casnochova Mistecky, Michal Constantine Philosopher Univ Nitra Tr A Hlinku 1 Nitra 94901 Slovakia Univ Pardubice Studentska 95 Pardubice 53210 Czech Republic Univ Ostrava Realni 5 Ostrava 70103 Czech Republic

ISBN: (数字)9783031832079

ISBN: (纸本)9783031832062;9783031832079

The incidence of neurodegenerative diseases affecting the brain and its cognitive functions, including language and speech, is increasing in society. These diseases impact the manner and quality of speech and can be detected through non-invasive methods. Understanding language involves analyzing internal linguistic features such as text readability and complexity. Language complexity is a significant measure of an individual's linguistic development, representing an independent dimension of utterance (whether written or spoken) and manifesting across all linguistic levels (phonological, morphological, syntactic, and semantic). The aim of this research is to identify linguistic features - measures of text complexity - that may serve as predictors for a knowledge-based model to detect neurodegenerative diseases such as Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), and Parkinson's Disease (PD) in the context of the inflectional Slovak language. The results indicate that lexical measures of language complexity that are independent of text length are unsuitable for predicting neurodegenerative diseases such as AD/MCI or PD. However, they can be useful in distinguishing between AD/MCI and PD. The rate of action in describing a situational picture is a strong predictor for distinguishing AD/MCI but not PD. The sequence of two verbs is a strong predictor for diagnosing both AD/MCI and PD, but does not distinguish between these diseases. Last but not least, vocabulary range and diversity influence not only the diagnosis of neurodegenerative diseases, but also help differentiate between AD and PD.

关键词： artificial intelligence healthcare neurodegenerative diseases language complexity

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Prediction of Blood Oxygen Saturation by Physiological Variables Using Machine Learning

Prediction of Blood Oxygen Saturation by Physiological Varia...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Rovira, Ronald H. Gomez, Oscar W. Montano, Manuel Bayas, Marcia M. Figueroa, Junior Efrain Andrade, Carlos Univ Estatal Peninsula Santa Elena TECED Res Grp La Libertad Ecuador Univ Nacl Colombia Manizales Colombia

ISBN: (纸本)9783031834318;9783031834325

In recent times, the integration of emerging technologies such as the Internet of Things and artificial intelligence have considerably transformed healthcare. As technology advances, the healthcare sector faces growing challenges due to ever-increasing demand and traditional invasive procedures. In this context, this article presents a model as an effective alternative to conventional oximetry especially useful in places where medical equipment is limited. The model utilizes deep learning to estimate blood oxygen levels through Long Short-Term Memory (LSTM) networks, which consist of two main layers. These layers include key indicators such as heart rate (HR), respiratory impedance (RESP), and pulse, witch were sampled 53 individuals for these indicators. The first is an LSTM layer with 100 cells and 41,600 parameters with a hyperbolic tangent activation function, designed to capture complex temporal patterns. The second linearly activated, dense layer with 101 parameters converts these representations into a clear and useful output. The results provide a mean absolute error of 1.68 and a root mean square error of 2.45, which shows that the model is quite accurate and suitable for applications that require continuous monitoring. In this work, a study was done to see how the main body functions are related. It found that heart rate and pulse are strongly connected, but not as much with respiratory rate and oxygen saturation. Also, used a Random Forest algorithm to determine the relationship between these factors and SpO2 values. The analysis revealed that heart rate and pulse are highly correlated, while the correlation with respiratory impedance and oxygen saturation is low. Finally, when implementing the model with Tensor-Flow Keras, we observed that it generalizes well and shows no signs of overfitting, which is a positive indication of its reliability. The results of this study not only demonstrate how emerging technology can improve the accuracy of health data,

关键词： Oxygen saturation Heart rate Respiratory impedance Pulse Machine learning LSTM

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Wavelength Calibration of Historical Spectrographic Plates with Dynamic Time Warping 12th

Wavelength Calibration of Historical Spectrographic Plates w...

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12th Conference on Cloud Computing, Big Data and Emerging Topics

作者： Ponte Ahon, Santiago Andres Seery, Juan Martin Quiroga, Facundo Ronchetti, Franco Stanchi, Oscar Dal Bianco, Pedro Hasperue, Waldo Aidelman, Yael Gamen, Roberto UNLP Fac Informat III LIDI RA-1900 La Plata Buenos Aires Argentina UNLP La Plata Argentina Comis Invest Cient Pcia De Bs As CIC PBA La Plata Argentina UNLP Fac Ciencias Astronom & Geofis La Plata Argentina CONICET UNLP Inst Astrofis Plata La Plata Argentina

ISBN: (纸本)9783031708060;9783031708077

The Facultad de Ciencias Astronomicas y Geofisicas of the Universidad Nacional de La Plata counts with 15,000 spectroscopic records on glass plates with valuable and unique astronomical data. Currently, processing these plates requires a complex manual process that involves several stages, requiring several hours to process a single plate. In particular, the wavelength calibration requires the determination of the wavelength range in which the data were observed. This is achieved by matching the spectrum of the comparison lamp on the plate to the reference spectrum. Since many times neither the metadata of the lamps nor the physical lamps are available, automating the tasks requires a semi-blind approach that uses simulated data as a reference. However, the simulated data differs significantly from the physical lamps, given that many peaks determined by theoretical calculations are rarely observed in practice, and conversely the physical lamps and spectrograph carry imperfections that cause unexpected peaks. In this work, we propose an wavelength calibration pipeline that enables automated matching of the wavelength of the comparison lamps via Dynamic Time Warping (DTW) between the samples and simulated data. Our best model achieves a 93% average Intersection-over-Union (IoU) over a set of 32 manually calibrated plates.

关键词： Dynamic Time Warping Spectroscopic records Spectroscopic plates Optimization Wavelength calibration

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Learning-Based Traversability Costmap for Autonomous Off-Road Navigation 5th

Learning-Based Traversability Costmap for Autonomous Off-Roa...

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5th China Intelligent Robotics Academic Conference

作者： Zhu, Qiumin Sun, Zhen Xia, Songpengcheng Liu, Guoqing Ma, Kehui Pei, Ling Gong, Zheng Jin, Cheng Shanghai Jiao Tong Univ Shanghai Key Lab Nav & Locat Based Serv Shanghai Peoples R China China Acad Informat & Commun Technol Beijing Peoples R China Key Lab Smart Earth Beijing Peoples R China

ISBN: (纸本)9789819616138;9789819616145

Traversability estimation in off-road terrains is an essential procedure for autonomous navigation. However, creating reliable labels for complex interactions between the robot and the surface is still a challenging problem in learningbased costmap generation. To address this, we propose a method that predicts traversability costmaps by leveraging both visual and geometric information of the environment. To quantify the surface properties like roughness and bumpiness, we introduce a novel way of risk-aware labelling with proprioceptive information for network training. We validate our method in costmap prediction and navigation tasks for complex off-road scenarios. Our results demonstrate that our costmap prediction method excels in terms of average accuracy and MSE. The navigation results indicate that using our learned costmaps leads to safer and smoother driving, outperforming previous methods in terms of the highest success rate, lowest normalized trajectory length, lowest time cost, and highest mean stability across two scenarios.

关键词： Autonomous Navigation Traversability Off-road Environments Unmanned Ground Vehicles Inertial Measurement Unit (IMU)

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2-bit Multiplication Quantum Circuit with Addition 2nd

2-bit Multiplication Quantum Circuit with Addition

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2nd International Conference on information Technologies and Their Applications

作者： Andishmand, Saadi Akleylek, Sedat Ondokuz Mayis Univ TR-55200 Samsun Turkiye Univ Tartu Tartu Estonia

ISBN: (纸本)9783031734168;9783031734175

This paper presents a groundbreaking exploration into quantum computing's capacity to revolutionize computational arithmetic through the development of a 2-bit multiplication quantum circuit. Leveraging the unique properties of quantum mechanics, particularly the phenomena of superposition and entanglement, this study navigates beyond classical computing limits. It intricately designs and implements a quantum circuit capable of multiplying two binary numbers, demonstrating a significant advancement in quantum computational technology. Utilizing qubits, the quantum analogs of bits, the circuit fundamentally redefines the multiplication process, adhering to quantum mechanics principles rather than classical binary operations. Our approach emphasizes the intricate orchestration of quantum gates within the circuit, with a particular focus on Controlled NOT (CNOT) and Toffoli gates. These are crucial for the arithmetic logic underpinning our multiplication task, illustrating the nuanced application of quantum theory to practical computation. The circuit showcases a pragmatic application of quantum computing principles, contributing to the burgeoning field of quantum algorithms. It serves as a vital stepping stone toward realizing more complex quantum arithmetic operations and their broader applications. Implemented using IBM's Qiskit, a versatile quantum computing software development framework, the circuit's design and functionality were rigorously tested in simulated environments. The results validate the circuit's efficiency and accuracy, confirming the successful quantum multiplication of 2-bit numbers. This work not only enriches the existing quantum algorithm repository but also sets a precedent for future quantum computing endeavors, particularly in optimizing and scaling quantum arithmetic operations.

关键词： Quantum Computing Quantum Circuits Qubits Qiskit Multiplication

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Blockchain-Enabled Green Supply Chain Management: Innovating Agricultural Plastic Waste Recovery and Reverse Logistics 5th

Blockchain-Enabled Green Supply Chain Management: Innovating...

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5th 5th Olympus - International Conference on Supply Chain

作者： Tzanetou, Dimitra Ponis, Stavros T. Plakas, George Maroutas, Theodoros Nikolaos Natl Tech Univ Athens Sch Mech Engn Heroon Polytech 9 Athens 15780 Greece

ISBN: (纸本)9783031693434;9783031693441

In the scientific field of green supply chain management and reverse logistics, addressing the escalating issue of plastic pollution stands as a critical challenge, particularly within the agricultural sector. The integration of sustainable practices and innovative technologies in managing agricultural plastic waste is paramount to advancing environmental stewardship and operational efficiency. In that direction, the research presented in this paper delves into the development of a novel approach that harmonizes with the principles of reverse logistics and green supply chain management, aiming to mitigate the environmental footprint of agricultural plastics. Central to this study is the exploration of an advanced system for themanagement of agriplasticwaste, leveraging Blockchain technology to foster a transparent, efficient, and sustainable supply chain. This system proposes a decentralized platform that enables seamless interaction between stakeholders, including farmers and waste collectors, facilitating the effective tracking, collection, and recycling or energy recovery of agricultural plastics. By establishing a reliable network for the detailed reporting of agriplastic usage and disposal, the research underscores the potential of digital ledger technologies to enhance traceability and accountability in waste management processes. Furthermore, this research emphasizes the importance of reverse logistics in the lifecycle management of agricultural plastics, from their initial deployment to their final disposal or repurposing. Through the creation of a mobile application and the formulation of a comprehensive business model, the study aims to facilitate the recovery and recycling of materials and thus, to contribute to the reduction of environmental pollution, the promotion of sustainable agricultural practice and the broader objectives of environmental conservation and sustainability.

关键词： Green Supply Chain Management Blockchain Agricultural plastic waste Reverse Logistics Circular Economy Waste-to-energy

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Citation Based Scientific Document Summarization Using Deep Learning 4th

Citation Based Scientific Document Summarization Using Deep ...

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4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Jyoti, Divya Srivastava, Jyoti Mahato, Dharmendra Prasad Natl Inst Technol Hamirpur Dept Comp Sci & Engn Hamirpur 177005 Himachal Prades India

ISBN: (纸本)9783031837920;9783031837937

With the rapid increase in the volume of scientific literature, researchers face challenges in keeping up with the latest advancements while summarizing the documents. Scientific document text summarization offers a solution by providing concise and informative summaries that highlight the key contributions from original texts. This study introduces a novel method leveraging deep learning, specifically the sBERT model to summarize scientific documents. The proposed approach treats the extractive summarization as a classification problem using a dual BERT model setup. The methodology is evaluated using data set from CL-SciSumm. Results indicate that our approach significantly outperforms the existing methods in terms of ROUGE scores, demonstrating its effectiveness in generating accurate summaries of scientific literature.

关键词： Citation Based Summarization Scientific Documentation Deep Learning BERT and sBERT model Natural Language Processing

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The Effect of Generative Artificial Intelligence on Cognitive Thinking Skills in Higher Education Institutions: A Systematic Literature Review 5th

The Effect of Generative Artificial Intelligence on Cognitiv...

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5th Southern African Conference for Artificial Intelligence Research

作者： Moongela, Harry Matthee, Machdel Turpin, Marita van der Merwe, Alta Univ Pretoria Dept Informat ZA-0001 Pretoria South Africa

ISBN: (纸本)9783031782541;9783031782558

Generative Artificial Intelligence (GenAI) tools have gained popularity in Higher Education Institutions (HEIs), and tools such as ChatGPT have been reported to impact students' cognitive thinking skills. This study addresses a gap in the literature regarding the effect of GenAI on the cognitive thinking skills of students and scholars in HEIs. A Systematic Literature Review (SLR) was conducted that yielded relevant research articles, on which a thematic analysis was performed. Cognitive thinking skills identified in the articles include critical thinking, creative thinking, reflective thinking, computational thinking and problem-solving. The study summarised teaching interventions reported in the various studies, which aimed to engage with and enhance cognitive thinking skills. The findings revealed positive and negative impacts of GenAI on cognitive thinking skills. The findings indicated that GenAI such as ChatGPT can help improve, boost, and develop cognitive thinking skills and enhance learning and the overall higher education experience under well-planned guidance. The study subsequently provides guidelines on how students and scholars can use GenAI to improve cognitive thinking skills in HEIs. HEIs are encouraged to create special programs to teach students how to correctly use GenAI tools like ChatGPT. It is worth noting that students need to use their knowledge, thinking skills, and good judgment to use GenAI properly.

关键词： Cognitive Thinking Critical Thinking Creative Thinking Reflective Thinking Computational Thinking Problem Solving Generative AI Higher Education Institutions

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PF-BiCGAN: An Abnormal Values Replacement Approach for Port Electrical Load Forecasting 5th

PF-BiCGAN: An Abnormal Values Replacement Approach for Port ...

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Du, Jiepeng Chen, Yujia Cao, Jingling Wuhan Univ Technol Sch Transportat & Logist Engn Wuhan 430072 Peoples R China Wuhan Univ Technol State Key Lab Maritime Technol Wuhan 430072 Peoples R China

ISBN: (纸本)9789819770069;9789819770076

Accurately predicting electricity loads is vital for optimizing energy usage plans of ports. However, terminal electricity load data is characterized by high noise, significant fluctuations, and complexity, posing challenges for precise load forecasting. Therefore, this study proposes a data-driven approach to thoroughly analyze terminal electricity load data and designs an anomaly replacement method based on Generative Adversarial Networks (GANs) to address anomalies in port electricity load data, thereby enhancing energy utilization efficiency. Firstly, using spectral residual analysis to detect anomalies in port electricity load data and proposing the Positional Fourier Transform Bi-directional and Conditional Generative Adversarial Net (PF-BiCGAN) method based on GANs for anomaly replacement. Secondly, to generate more accurate synthetic data, PF-BiCGAN introduces a positional encoder and frequency domain enhancement module. The positional encoder helps random noise acquire sequential information to generate synthetic data that mimics the distribution of original data, and the frequency domain enhancement module improves the discriminator's ability to distinguish between real and synthetic data. Extensive experimental analyses on real port electricity load data in a certain domestic port validate the effectiveness of the proposed methods. Particularly in anomaly handling, compared with other data generation methods, our approach demonstrates significant advantages. Additionally, comparative experiments in electricity load forecasting further confirm the model's superiority in predicting energy consumption.

关键词： Port Electricity Load Forecasting Anomaly Detection Generative Adversarial Network Frequency Domain Enhancement

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Planning to Avoid Ambiguous States Through Gaussian Approximations to Non-linear Sensors in Active Inference Agents 5th

Planning to Avoid Ambiguous States Through Gaussian Approxim...

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5th International Workshop on Active Inference

作者： Kouw, Wouter M. TU Eindhoven Bayesian Intelligent Autonomous Syst Lab Eindhoven Netherlands

ISBN: (纸本)9783031771378;9783031771385

In nature, active inference agents must learn how observations of the world represent the state of the agent. In engineering, the physics behind sensors is often known reasonably accurately and measurement functions can be incorporated into generative models. When a measurement function is non-linear, the transformed variable is typically approximated with a Gaussian distribution to ensure tractable inference. We show that Gaussian approximations that are sensitive to the curvature of the measurement function, such as a second-order Taylor approximation, produce a state-dependent ambiguity term. This induces a preference over states, based on how accurately the state can be inferred from the observation. We demonstrate this preference with a robot navigation experiment where agents plan trajectories.

关键词： Active inference Free energy minimization Bayesian filtering Non-linear sensing Control systems Planning Navigation

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