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Between No-Code, Low-Code, Custom-Code, and AI. Hyperautomation Strategies at Nordic Enterprises 20th

Between No-Code, Low-Code, Custom-Code, and AI. Hyperautomat...

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20th International Workshop on Combinatorial Image Analysis (IWCIA)

作者： Kedziora, Damian Aunimo, Lili Kortesalmi, Heli LUT Univ Lahti 15210 Finland Kozminski Univ PL-03301 Warsaw Poland Haaga Helia Univ Appl Sci Helsinki 15210 Finland

ISBN: (纸本)9783031665936;9783031665943

With the growing importance of business process automation practices, companies deploy a variety of tools and policies to reduce manual workload and achieve strategic goals. Our study explored the perception of strategic approaches to enterprise hyperautomation among 136 responders, representing 52 leading companies from Nordic Europe. Findings highlight the importance of custom code, as well as low-code and RPA solutions, occasionally accompanied by AI and no-code. The strongest impact of hyperautomation success was related to governance, security and compliance, as well as engagement of all stakeholders, and celebration of achievements among persons taking part at the transition.

关键词： Strategy Low Code No Code Custom Code Robotic Process Automation Hyperautomation

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A Chaotic Equilibrium Optimization for Electric Vehicle Charging Scheduling in Distribution networks with Integrated Battery Energy Storage systems 7th

A Chaotic Equilibrium Optimization for Electric Vehicle Char...

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7th International Conference on Green Technology and Sustainable Development

作者： Thien Vo Minh Long Diep Hoan Van Pham Luu Huynh Trung Dieu Vo Ngoc Ho Chi Minh City Univ Technol HCMUT Dept Power Syst 268 Ly Thuong Kiet StDist 10 Ho Chi Minh City Vietnam Can Tho Univ Technol Dept Elect Elect Telecommun Can Tho City Vietnam Can Tho Univ Dept Elect Engn Coll Engn Technol Can Tho City Vietnam Power Generation Joint Stock Corp 2 Can Tho City Vietnam Kien Giang Coll Rach Gia City Rach Gia City Kien Giang Prov Vietnam Vietnam Natl Univ Ho Chi Minh Linh Trung Ward Ho Chi Minh City Vietnam

ISBN: (纸本)9783031762314;9783031762321

The transition towards transportation electrification has led to the gradual replacement of conventional fuel-based vehicles with electric vehicles (EVs), driven by their notable advantages. However, the rapid proliferation of electric vehicle charging stations (EVCSs) poses challenges in managing, coordinating, and optimizing their operational capacities due to inherent limitations. In this research, we propose an enhanced chaotic equilibrium optimization (CEO) algorithm to address the scheduling complexities of EVCSs within a distribution network integrated with a battery energy storage system (BESS). By integrating distributed generations (DGs) and aiming to minimize power loss and voltage deviation, our approach seeks to optimize the deployment of EVCSs across different charging scenarios (fast, medium, and slow) while considering varying levels of EVCS load penetration. Leveraging the IEEE 33 and 69-bus systems for simulation and utilizing Matlab software, our model determines optimal values for EVCSs' active power, BESS mobilization capacity, and DG deployment based on cost-effectiveness. The obtained results demonstrate the efficacy of our proposed CEO model in efficiently coordinating EVCSs' power flow within BESS-integrated distribution networks, thereby providing multiple benefits to end-users.

关键词： Electric vehicle charging schedules transportation power system Distributed generation EVCS CEO BESS

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Application of Machine Learning Methods with Reinforcement in Planning Innovative Projects 13th

Application of Machine Learning Methods with Reinforcement i...

引用

13th Computer Science Online Conference

作者： Sudakov, Vladimir A. Sivakova, Tatiana V. Keldysh Inst Appl Math Miusskaya Sq 4 Moscow 125047 Russia

ISBN: (纸本)9783031702846;9783031702853

In the paper examines the problem of planning innovative projects taking into account resource limitations and project priorities. This problem belongs to the class of discrete programming problems and to solve it is proposed to use machine learning methods with reinforcement. As part of the study, a model of the environment and reward of an intelligent agent was created. The developed software shows good scalability on a computing cluster.

关键词： Aircraft Innovative projects Machine reinforcement learning Discrete optimization Agent reward Learning environment

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Dimensional Reduction to Improve Learning Recommender systems Precision Based on Grades 9th

Dimensional Reduction to Improve Learning Recommender System...

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

作者： Bojorque, Rodolfo Plaza, Andrea Moscoso, Fernando Univ Politecn Salesiana Cuenca Ecuador

ISBN: (纸本)9789819733019;9789819733026

The incorporation of optional subjects into higher education curricula is a ubiquitous practice, affording students the latitude to pursue coursework aligned with their interests across diverse knowledge domains such as humanities, arts, sciences, and technology, along with supplementary offerings. Despite this expansive selection, students often grapple with an inundation of information when confronted with selecting optional subjects. Recognizing the extensive dataset encompassing student grades and subject enrollment, this study proposes the implementation of a Learning Recommender System, based on collaborative filtering, a machine-learning technique. This method harnesses students' academic success in particular subjects to proffer judicious recommendations to their counterparts. A challenge encountered by recommender systems is the issue of data sparsity, given that not all students undertake every available course, resulting in a sparse matrix. To surmount this challenge, the study advocates for the application of dimensional reduction techniques, which serve to transform the sparse matrix into a more condensed yet informative representation. This reduction augments the predictive efficacy, and versatility for employing a spectrum of machine-learning techniques. The Matrix Factorization model emerges as the preeminent choice in contemporary recommender systems. The research substantiates the efficacy of these methodologies through an empirical examination of data sourced from Salesian Technical University, comprising records of ten thousand students and one thousand subjects over a five-year duration. In essence, the study conclusively demonstrates that dimensional reduction is indispensable for facilitating the data sparsity predicament in recommender systems, thereby enhancing the efficiency and efficacy of the proposed Learning Recommender System.

关键词： Learning recommender systems Artificial intelligence Machine learning

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Multimodal Early Fusion Strategy Based on Deep Learning Methods for Cervical Cancer Identification 1

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4th Congress on Intelligent systems (CIS)

作者： Mukku, Lalasa Thomas, Jyothi CHRIST Kengeri 560074 India

ISBN: (数字)9789819990436

ISBN: (纸本)9789819990429;9789819990436

It is essential to enhance the accuracy of automatic cervical cancer diagnosis by combining multiple forms of information obtained from a patient's primary examination. However, existing multimodal systems are not very effective in detecting correlations between different types of data, leading to low sensitivity but high specificity. This study introduces a deep learning system for automatic diagnosis of cervical cancer by incorporating multiple sources of data. First, a convolutional neural network (CNN) to transform the image database to a vector that can be combined with non-image datasets is used. Subsequently, an investigation of jointly the nonlinear connections between all image and non-image data in a deep neural network is performed. Proposed deep learning-based method creates a unified system that takes advantage of both image and non-image data. It achieves an impressive 89.32% sensitivity at 91.6% specificity when diagnosing cervical intraepithelial neoplasia on a wide-ranging dataset. This result is far superior to any single-source system or prior multimodal approaches.

关键词： Cervical cancer Deep learning Early fusion

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SmartCrop: An IoT-Powered Crop Health Monitoring System 1

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8th Smart Trends in Computing and Communications (SmartCom)

作者： Sai, P. Sarvan Sri Sunil, Abhinav Mukesh, Koraganji Matcha, Mukhesh Balaji Valsan, Vipina Pandi, V. Ravikumar Sathyan, Soumya Suresh, Kavya Amrita Vishwa Vidyapeetham Dept Comp Sci & Engn Amrita Sch Comp Amritapuri India Amrita Vishwa Vidyapeetham Dept Elect & Elect Engn Amritapuri India

ISBN: (数字)9789819713233

ISBN: (纸本)9789819713226;9789819713233

SmartCrop is an innovative IoT-based crop health monitoring system that integrates crucial sensors and cutting-edge technology to transform modern agriculture. It excels in monitoring environmental conditions and crop health, employing advanced algorithms for precision irrigation and rapid threat detection while providing real-time data on temperature, humidity, and soil moisture. Farmers can access this valuable information through a user-friendly cloud platform, enabling informed decisions for sustainable and resource-efficient farming. SmartCrop not only boosts crop quality and yields but also aligns with eco-conscious farming practices, conserving water, and reducing pesticide use. In a world marked by climate uncertainty and rising food demand, SmartCrop represents a beacon of hope, enhancing food security, reducing environmental impact, and promoting agricultural sustainability. Moreover, SmartCrop aligns with several United Nations Sustainable Development Goals (SDGs). It contributes to SDG 2 (Zero Hunger) by optimizing crop yields and quality, SDG 13 (Climate Action) by enhancing agricultural resilience, SDG 15 (Life on Land) through biodiversity-friendly practices, SDG 12 (Responsible Consumption and Production) by promoting resource-efficient farming, and SDG 9 (Industry, Innovation, and Infrastructure) as a testament to technology-driven agricultural innovation. SmartCrop showcases the power of technology to shape a more environmentally responsible and economically viable future for agriculture while exemplifying innovation for a more sustainable planet.

关键词： IoT agriculture Crop health monitoring Sensor integration Precision farming Sustainable agriculture Smart farming

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Enhancing Decision Support systems for the Energy Sector with Sustainable Artificial Intelligence Solutions 1

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Intelligent systems Conference (IntelliSys)

作者： Mouzakitis, Spiros Markaki, Ourania Papapostolou, Katerina Karakolis, Evangelos Pelekis, Sotiris Psarras, John Natl Tech Univ Athens Decis Support Syst Lab Athens Greece

ISBN: (数字)9783031477249

ISBN: (纸本)9783031477232;9783031477249

Artificial intelligence (AI) can significantly enhance the decisionmaking processes in a plethora of key energy-related challenges such as energy supply and demand prediction, grid flexibility, asset maintenance and operation, and load forecasting. As such, it is expected to play a crucial role in the digitalisation and sustainability of the energy industry. Despite the potential benefits of AI and the wealth of research in the related fields, several factors, including a lack of in-house AI expertise, data interoperability issues, unclear regulations, and ethical concerns, prevent stakeholders from the Electric Power and Energy systems (EPES) sector from taking a universal approach to the EPES value chain and fully utilizingAI's potential. This paper presents ourwork that aims to address the former challenges by proposing a holistic approach to utilizing, developing, and deploying state-of-the-art AI and data analytic services for the decision makers of the energy sector. The framework will be seamlessly integrated with the AI-on-Demand platform, i.e. Europe's one-stop-shop for AI assets. The paper introduces the I-NERGY framework and describes its bidirectional relation to the AIoD platform under the light of contributing to the EPES value chain optimal management, particularly for SMEs and non-tech industries.

关键词： Decision Support Artificial Intelligence Big Data Energy Machine Learning Analytics

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Addressing the Business Disruption Through the Development and Pilot Testing of Caviturismo: A Web Portal for Cavite Tourism 9th

Addressing the Business Disruption Through the Development a...

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

作者： Muyot, Allen Jhon C. Nabablit, Karlo Jose E. Nolledo, Mark Edriane F. Victa, Maria Socorro D. Abad, Jenerry Y. Cavite State Univ CCAT Campus Dept Comp Studies Rosario Cavite Philippines

ISBN: (纸本)9789819732982;9789819732999

Tourism is a vital economic driver in the Philippines, contributing significantly to the nation's GDP and employment rates. Cavite is one of the promising tourist destinations in the CALABARZON region, known for its natural beauty, rich cultural heritage, and historical significance. In recent years, business disruption accelerated caused by the COVID-19 pandemic when consumer behavior shifted toward online platforms. However, Filipino micro, small, and medium enterprises (MSMEs) faced a lot of challenges in establishing and keeping up with their online presence, mainly due to technology and financial constraints. The study focused on the development and pilot testing of a web portal for the promotion of tourism in Cavite. In addition, this aimed to support the MSMEs by providing them with costfree, well-designed, and user-friendly business web pages. The website modules underwent rigorous functional and accuracy testing, yielding promising results. This project demonstrates that Caviturismo is a functional and reliable solution capable of effectively promoting the province of Cavite as a tourist destination and the stakeholders.

关键词： Cavite tourism Business pages Web development MSMEs Promoting tourism Business disruption

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Identification of Phishing URLs Using Machine Learning Models 1

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4th Congress on Intelligent systems (CIS)

作者： Vivek, Meghashyam Premjith, Nithin Johnson, Aaron Antonio Maurya, Ashutosh Kumar Jingle, I. Diana Jeba Christ Bangalore Karnataka India

ISBN: (数字)9789819990436

ISBN: (纸本)9789819990429;9789819990436

In this study, we provide a machine learning-based method for identifying phishing URLs. Sixteen features, including Have IP, Have At, URL Length, URL Depth, Non-standard double slash, HTTPS domain, Shortened URL, Hyphen Count, DNS Record, Domain age, Domain active, iFrame, Mouse Over, Right click, Web Forwards, and Label, were extracted from the 600,000 URLs we gathered as a dataset of legitimate and phishing URLs. We then used this dataset to train a variety of machine learning models. These included standalone models such Naive Bayes, Logistic Regression, Decision Trees, and K-Nearest Neighbors (KNN). We also used ensemble models like Hard Voting, XGBoost, Random Forests, and AdaBoost. Finally, we used deep learning models such as Artificial Neural networks (ANN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU) and Convolutional Neural networks (CNN). On evaluation of performance metrics like accuracy, precision, recall, train time and prediction time it was found that XGBoost provides the best performance across all categories.

关键词： XGBoost Phishing Prediction Machine learning Classifier

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Approaches of Safe and Efficient 3D UAM Traffic Management Using VR, AR, and MR with AI;HCI Requirements for Digital Twin Application 18th

Approaches of Safe and Efficient 3D UAM Traffic Management U...

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18th International Conference on Intelligent Autonomous systems (IAS)

作者： Oh, Chang-Geun Lee, Mikyoung Hanseo Univ Taean South Korea DfR Consulting Inc Seoul South Korea

ISBN: (纸本)9783031449802;9783031449819

This paper highlights the digital twin (DT) application that utilizes VR/ AR/MR to develop high-level urban air mobility (UAM) operation monitoring systems. UAM operations may require precision 3D maps for planning, monitoring, and debriefing purposes. Recent VR and MR technologies have implemented vivid 3D graphics, and these are subject to the digital twin (DT) for the UAM monitoring system. Digital twin (DT) is a virtual content that visualizes synchronized features of an existing system of interest. DT has two paradigms-functional DT and physio-functional DT for its usage. This paper proceeds with a discussion of six human-DT interaction issues-the reality of 3D graphics, AI functions, DT users' allocentric versus egocentric viewpoints, accuracy of spatial synchronization, and systems engineering process-for safe and efficient UAM operations.

关键词： Virtual reality Augmented reality Mixed reality AI Digital twin Urban air mobility Human-computer interaction

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