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Reshaping the Accountant's Future in the Era of Emerging Technologies 12th

Reshaping the Accountant's Future in the Era of Emerging Tec...

引用

12th World Conference on Information systems and Technologies (WorldCIST)

作者： Ferreira, Ana Pedrosa, Isabel Coimbra Business Sch Polytechn Inst Coimbra Quinta Agricola P-3045231 Coimbra Portugal Polytechn Inst Porto CEOS PP Porto Portugal

ISBN: (纸本)9783031603273;9783031603280

With the advent of the digital age, the accounting profession has undergone a transformation. Adapting to these changes represents an opportunity to showcase accountants' flexibility and perseverance, rather than a mere new challenge. Approaching the new landscape with eagerness and self-assurance will empower accountants to flourish and achieve their professional goals. Emerging Technologies refer to Robotic Process Automation, Artificial intelligence, Big Data and analytics, Cloud, and Blockchain. This study aims to present and analyze the impacts of using emerging technologies on the accounting profession through an exploratory study and interviews with accounting professionals. The results of the interviews revealed that the relationship between emerging technologies and the role and skills of accountants has not yet been established. This work identifies the skills that accountants must detain to be successful, the new professions that will emerge associated with accounting, and activities and processes that will change. The insights presented in this study are highly valuable for those involved in the accounting profession, including professional bodies, regulators, and educational institutions. Researching the use of emerging technologies in the context of accounting may yield valuable knowledge that could inform future modifications to university curricula and instructional approaches for accounting educators.

关键词： Emerging Technologies Robotic Process Automation Accountant Accounting Education Skills Artificial intelligence Automation Big Data Blockchain

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IAM Dataset-Based Author Identification via Convolutional Neural networks 1

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8th Smart Trends in Computing and Communications (SmartCom)

作者： Subramanian, R. Raja Visal, J. Kumar, Betham Raj Yaswanth, Pendyala Raja Gatikoppu, Sai Phanindra Pavan Kumar Kalasalingam Acad Res & Educ Krishnankoil Virudhunagar India

ISBN: (数字)9789819713233

ISBN: (纸本)9789819713226;9789819713233

Optical character recognition (OCR) has emerged as a crucial technology in the field of document analysis and text processing. This paper presents a novel approach to OCR utilizing the IAM dataset, a widely recognized benchmark for handwriting recognition tasks. Our method combines deep learning techniques with advanced pre-processing methods to achieve state-of-the-art results in handwritten text recognition. The IAM dataset offers diverse and challenging handwritten samples, making it an ideal testbed for OCR systems. The proposed model is trained using a large and carefully curated subset of the IAM dataset, ensuring robustness and generalization. Furthermore, this work focuses on reducing the computational complexity and memory footprint of the OCR system, making it suitable for real-time applications. We optimize our model for efficiency while maintaining high accuracy. Our experiments demonstrate superior performance compared to existing OCR methods on the IAM dataset.

关键词： Author net Deep learning Convolutional neural network

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A Categorical Transformer with a Data Science Approach for Recommendation systems Based on Collaborative Filtering 9th

A Categorical Transformer with a Data Science Approach for R...

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9th International Congress on Information and Communication Technology (ICICT)

作者： Hurtado, Remigio Munoz, Arantxa Univ Politecn Salesiana Calle Vieja 12-30 & Elia Liut Cuenca Ecuador Univ Int La Rioja Ave Paz 137 La Rioja Spain

ISBN: (纸本)9789819735556;9789819735563

Recommender systems help predict what customers might like, such as movies, restaurants, or products. Collaborative filtering, a crucial part of these systems, faces challenges when dealing with user, item, and rating data. Traditional machine learning struggles with this data because user and item data are categorical. To solve this, we propose a method that transforms the original data into new variables, making it more suitable for advanced machine learning and deep learning techniques. This approach enhances prediction quality and opens doors for innovative data processing methods in collaborative filtering.

关键词： Recommender systems Collaborative filtering Data science Machine learning Data transformation Categorical transformer

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The Concept ofWireless Energy Transmission Intended for Use in Two-RotorWind Turbines 28th

The Concept ofWireless Energy Transmission Intended for Use ...

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2024 International Conference Automation

作者： Miaskowski, Wojciech Moczulak, Bartosz Kinski, Wojciech Pietkiewicz, Pawel Nalepa, Krzysztof Univ Warmia & Mazury Ul Oczapowskiego 11 PL-10719 Olsztyn Poland Ind Inst Automat & Measurements PIAP Lukasiewicz Res Network Al Jerozolimskie 202 PL-02486 Warsaw Poland

ISBN: (纸本)9783031782657;9783031782664

The process of designing wind turbines requires multi-threaded procedures when developing individual elements, taking into account their mutual influence. The WNT/PIAP research team, whose members include the authors of this article, proposes a solution that improves the efficiency of obtaining electricity by wind turbines, which requires the use of unconventional solutions in the field of energy transmission. The solution described in this article is a system for wireless transmission of electricity from the generator stator windings to the inverter. The article presents the effect of the team's work on solving the said design problem. As a result of the work, a concept of wireless transmission of electricity was proposed, aimed at its use in low-power two-rotor wind turbines.

关键词： Renewable energy wind turbine wireless energy transmission

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Simulation of Onboard Helicopter Radar Signals for Surface Elevation Measurements

Simulation of Onboard Helicopter Radar Signals for Surface E...

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International Scientific and Technical Conference on Integrated Computer Technologies in Mechanical Engineering - Synergetic Engineering (ICTM)

作者： Popov, Anatoliy Zhyla, Semen Inkarbaieva, Olha Bortsova, Mariya Natl Aerosp Univ Kharkiv Aviat Inst Kharkiv Ukraine Ivan Kozhedub Kharkiv Natl Air Force Univ Kharkiv Ukraine

ISBN: (纸本)9783031614149;9783031614156

The stage of landing a helicopter on unequipped fields in low visibility or adverse weather conditions is particularly dangerous for pilots and passengers. One of the possible ways to reduce risks is to use additional safe landing systems. Among the variety of such systems the terrain radars that use space-time signal processing and operate effectively in various environmental conditions deserve special attention. An important and relevant task in developing and analyzing these radars is a preliminary assessment of their general performance and accuracy. To solve this problem, simulation models of the surface and reflected signals that are detected by a two-antenna onboard radar are developed. The surface is represented as a two-dimensional random processwith a given correlation function of elevation variation. The models of space-time fields after their sum and difference processing in the receiver channels are shown and analyzed in detail. The modeling results are the input data for the approbation of heuristic and optimal algorithms formeasuring elevation of the surface selected for a safe landing. The performance and accuracy of the developed model are researched on the example of a three-dimensional surface with a spatially extended obstacle.

关键词： Helicopter Radar Amplitude Direction Finding Surface Elevation Measurements Surface Simulation

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Impacts of Environmental Factors on Wellbeing: Machine Learning-Based Benchmarking of Spatial and Temporal Properties 9th

Impacts of Environmental Factors on Wellbeing: Machine Learn...

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9th International Congress on Information and Communication Technology (ICICT)

作者： Guerrache, Faiza Brown, David J. Mahmud, Mufti Nottingham Trent Univ Dept Comp Sci Nottingham NG11 8NS England Nottingham Trent Univ CIRC Nottingham NG11 8NS England Nottingham Trent Univ MTIF Nottingham NG11 8NS England

ISBN: (纸本)9789819733019;9789819733026

Environmental noise impacts wellbeing with consequences on physical and mental health. Chronic exposure to noise pollution can amplify stress levels, disturb sleep patterns, and increase the risk of cardiovascular issues. Individuals subjected to continuous noise often report high irritability, impaired concentration, and diminished cognitive performance which may lead to anxiety and depression, particularly in vulnerable populations, such as children and the elderly. Recognising the profound impact of environmental noise is crucial for public health, necessitating comprehensive strategies to mitigate and regulate noise pollution for the enhancement of overall wellbeing. Despite the recent advancement of artificial intelligence (AI), this issue is under-addressed as no benchmark dataset is available to perform an in-depth analysis of the factors that subtly and gradually contribute to the deterioration of physical and mental wellbeing. To fill this gap, the current work presents a benchmarking dataset and explores the spatial and temporal properties of the same in understanding how these environmental factors affect wellbeing. Experiments with explainable AI (XAI) indicate that the Random Forest and Gradient Boosting classifiers can be considered as baseline XAI methods to analyse the outdoor and indoor environmental data, respectively.

关键词： Urban noise Wellbeing Physiological response Environmental health Multi sensor fusion Multimodal Machine learning Environment stress prediction Hr ECG Body temperature

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Factors Influencing Organizational Adoption of Artificial Intelligence and Corporate Social Responsibility in a Solar System World

Factors Influencing Organizational Adoption of Artificial In...

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Intelligent systems Conference

作者： Rouhana, Rima Sayegh, May Merhej Sidani, Dina St Joseph Univ Beirut Fac Business Beirut Lebanon

ISBN: (纸本)9783031664304;9783031664311

Smart Cities are Weaving Technology into Urban Fabric. The benefits of this intelligent transformation are multifold, from environmental sustainability, to economic prosperity, and improved quality of life. While AI has the potential to promote CSR, it is important to be aware of the potential risks. By doing so, businesses can help to ensure that AI is used in a way that benefits society and the environment, and then aligned with CSR principles. As AI and CSR are relatively new fields of study, and the SEPs industry is a vast and complex environment, it is difficult to predict how AI will impact this kind of organizations. Based on a detailed review of previous literature, the Technology-Organizations-Environment (TOE) framework, and the Diffusion of Innovation theory (DOI) and the corporate Artificial Intelligence (AI) readiness, our article focuses on the potential impact of AI on Corporate Social Responsibility (CSR). Through a qualitative method on a single case study, our objective is to analyze the relations between the three main concepts: the SEPs emerging industry, the AI readiness/use and its impact on the CSR (protecting the resources/providing clean energy). Our findings show that SEPs heavily rely on AI systems and platforms to customize their products and services to their customers. It further highlights the urgent need for more accurate and comprehensive AI models to improve the prediction of solar production. However, our study is limited to one case, and future researches could be leaded in order to generalize our findings.

关键词： Smart cities Artificial intelligence Corporate social responsibility systems thinking Interactions models Solar system organizations

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Implementation of Chart Creation Functionality in Voice-Based Report Generator for the Visually Impaired 9th

Implementation of Chart Creation Functionality in Voice-Base...

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9th International Congress on Information and Communication Technology (ICICT)

作者： Ok, Yubin Lim, Soon-Bum Lee, Jongwoo Sookmyung Womens Univ Seoul South Korea

ISBN: (纸本)9789819735587;9789819735594

Being able to communicate information is important in the information society, and data visualization is an important way to do so, but there are limitations to software accessibility for the visually impaired. For the visually impaired who have difficulty creating charts, which are essential for information transfer and understanding, we implemented a voice report generator supporting various chart drawing. The system allows users to say the title, type, data, and labels of a chart, and then generates a chart based on this information. The system supports a variety of plot types, has a simple user interface, and can be operated using a Bluetooth remote control. We conducted an experiment to evaluate the usability of this system and found that it is a convenient and efficient system for generating reports without much difficulty for visually impaired people.

关键词： Creating chart Voice recognition Visually impaired

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Embedded Machine Learning for EMG-Based Elbow Motion Recognition 7th

Embedded Machine Learning for EMG-Based Elbow Motion Recogni...

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7th International Conference on Green Technology and Sustainable Development

作者： Khang Hoang Vinh Nguyen Xuan Phu Do Vietnamese German Univ Mechatron & Sensor Syst Technol Binh Duong Vietnam

ISBN: (纸本)9783031761966;9783031761973

This research introduces a novel setup for capturing, processing, and analyzing electromyographic (EMG) signals for elbow motion recognition. The setup, comprising a three-lead EMG cable assembly, an amplifier PCB, and an Arduino Nano BLE board, implements embedded machine learning and power conservation. The study applies Discrete Wavelet Transform (DWT) for spectral analysis and feature extraction, aiding in EMG signal denoising. It utilizes the XGBoost Random Forest system for model training and K-Means Clustering for anomaly detection. The research evaluates the performance of the Edge Impulse framework, focusing on the model's performance, live classification results on the Arduino Nano BLE board, and power consumption during continuous operation. The model's offline performance was assessed using thirty additional samples, achieving an overall accuracy of 74.15%. The high performance in the "Idle" category suggests potential for a power-saving method by transitioning the system to a sleep state when idle. This evaluation provides insights for further model optimization and refinement. During the test runs conducted on the Arduino Nano BLE board, the inferencing time was measured to be approximately 50 ms. The program's power consumption was approximately 2 mAh, suggesting that it could be feasibly powered by a small rechargeable battery (approximately 200 mAh) for daily usage. This research affirms the potential of Edge Impulse and similar platforms in the field of embedded machine learning.

关键词： Electromyography Upper Limb Motion Recognition Embedded Machine Learning Low-power Micro-controller Digital Notch Filter

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Fuzzy Time Series Forecasting on the Example of the Dow Jones Index Dynamics 1

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Intelligent systems Conference (IntelliSys)

作者： Rzayev, Ramin Alizada, Parvin Mehdiyev, Tahir Inst Control Syst ANAS Vahabzadeh Str 9 AZ-1141 Baku Azerbaijan Baku State Univ Khalilov Str 23 AZ-1148 Baku Azerbaijan

ISBN: (数字)9783031477218

ISBN: (纸本)9783031477201;9783031477218

The paper discusses a new predictive model of a fuzzy volatile time series, in the framework of which a new approach to the fuzzification of historical data is proposed as the results of observations based on "soft measurements" of the states of a dynamic system over a certain period of time. As an example, the Dow Jones index was chosen, the readings of which are set based on the results of daily trading on the US stock exchange by the usual arithmetic averaging of contextual indicators. This allows to consider the daily readings of the Dow Jones index as weakly structured, and to interpret the dynamics of its change as a fuzzy time series. The fuzzification procedure is implemented by the fuzzy inference system that provides the values of the membership functions of the corresponding fuzzy subsets of the discrete universe covering the set of index indicators for the period from June 15, 2018 to October 10, 2019. The proposed predictive model is based on the identified internal relationships, designed as first-order fuzzy relations between evaluation criteria or fuzzy sets that describe weakly structuredDowJones indexes. At the end of the study, the proposed model is evaluated for adequacy using the statistical criteria MAPE, MPE and MSE.

关键词： Dow Jones Industrial Average Fuzzy time series Fuzzy Inference

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