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2023 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2023

作者： de Carvalho, Thiego Buenos Aires martins, Denis mayr Lima de Lima Neto, Fernando Buarque Computing Engineering Program University of Pernambuco Recife Brazil Machine Learning and Data Engineering University of Münster ERCIS Münster Germany

ISBN: (纸本)9798350348071

Under the increasingly complex and interconnected world we live in, data has become extremely valuable. Nevertheless, exploring even a simple database is not a trivial task, as it requires technical knowledge which many new and non-technical data users do not have. This task includes writing database queries to retrieve data, uncover insights, and exploit patterns. Furthermore, in large volumes of data, finding valuable data that matches a certain user’s purpose requirement is challenging, especially under restrictive time constraints. Typically, this task is manual, ad-hoc, and time-consuming. To address these challenges, researches have proposed tools to support data exploration tasks, especially by means of View Recommendation. However, current recommendation approaches require enumerating all candidate views that can be defined over an input database. In this work, we introduce a novel view recommendation method that leverages a human-in-the-loop approach to create, evaluate, and tailor views to match user intent. Our experimental evaluation on real-world data demonstrates that our approach suitably recommends views that capture multiple perspectives on the data at hand. © 2023 IEEE.

关键词： data visualization

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Recommending View Bundles in data marketplaces

Recommending View Bundles in Data Marketplaces

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2022 IEEE International Conference on Systems, man, and Cybernetics, SmC 2022

作者： De Carvalho, Thiego Buenos Aires martins, Denis m. Lima De Lima Neto, Fernando B. Vossen, Gottfried University of Pernambuco Computing Engineering Program Recife Brazil University of Münster Ercis Machine Learning and Data Engineering Münster Germany University of Münster Ercis Databases and Information Systems Münster Germany

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

For companies to have a competitive advantage, they need to extract relevant information from data and for that, they need to complement their own data with other data sources. data marketplaces are platforms on which data providers and data consumers do business. However, every data interaction incurs a monetary cost. Therefore, data consumers are interested in buying a set of interesting views that fit their (goal and) budget. Views allow data to be represented visually, enabling users to make sense of patterns and insights. Besides allowing for easier cost control, buying a set of views bundled together increases the chance of finding what consumers want over buying them view-by-view. Selecting the suitable views to compose an interesting bundle is non-trivial, due to the vast number of view combinations that potentially meet the data consumer's needs. In this paper, we address the problem of view bundle recommendation in data marketplaces, in which the utility of a bundle depends on the interplay among candidate views. We propose the use of Self-Organizing maps as a means to compute this interplay and use a Genetic Algorithm to design near-optimal bundles. Our empirical results demonstrate that our approach can effectively aid data consumers to find relevant view bundles under budget constraints. © 2022 IEEE.

关键词： Genetic algorithms

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Human-in-the-Loop Evolution of database Views for data Exploration

Human-in-the-Loop Evolution of Database Views for Data Explo...

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Latin America Congress on Computational Intelligence (LA-CCI)

作者： Thiego Buenos Aires de Carvalho Denis mayr Lima martins Fernando Buarque de Lima Neto Computing Engineering Program University of Pernambuco Recife Brazil Machine Learning and Data Engineering University of Münster ERCIS Müsnter Germany

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Leveraging machine learning for intelligent agriculture

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Discover Internet of Things 2025年第1期5卷 1-21页

作者： Sowmya, B.J. meeradevi, A.K. Supreeth, S. Pradeep Kumar, D. Ravi Kumar, B.N. Rohith, S. mishra, Divyansh Koushik, Abhishek Patil, Ankit U. Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru560054 India Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bengaluru560054 India School of Computer Science and Engineering REVA University Bengaluru560064 India Department of Computer Science Ramaiah Institute of Technology Bengaluru560054 India Department of Information Science and Engineering BMS Institute of Technology & Management Bengaluru560119 India Department of Electronics and Engineering Nagarjuna College of Engineering and Technology Bengaluru562164 India

Agriculture, the backbone of many economies, faces challenges like lack of information, outdated practices, and limited access to technology, hindering farmer productivity. This work proposes a user-friendly, multilingual platform leveraging Generative AI to address farmers' diverse needs. The platform encompasses various features to enhance agricultural practices. An LLm-powered Government Scheme Advisor functions as a multilingual chatbot offering intelligent guidance on government agricultural schemes and subsidies. The Disease Detection module utilizes AI technology for real-time identification and treatment recommendations, minimizing crop diseases and yield losses. The Soil Testing Centre feature locates nearby soil testing centers, providing essential information based on geographical data to assist farmers in optimizing soil quality. A Crop Recommendation feature employs machine learning algorithms to offer personalized crop recommendations, considering various factors and aiding informed decision-making. The Crop Planning Tool, with its intuitive user interface, simplifies planning planting schedules and managing resources. Additionally, the platform includes an mSP Center Locator to find nearby minimum Support Price (mSP) centers based on location. By integrating these innovative solutions, this platform bridges the gap between conventional agricultural techniques and contemporary technology, equipping farmers with the resources and expertise essential for advancing productivity and sustainability. multilingual support ensures accessibility for a wider audience, breaking down language barriers and promoting inclusivity in the agricultural sector. This work proposes an innovative, multilingual platform powered by Generative AI to address these issues. Key features include an LLm-driven chatbot for government scheme guidance, AI-based real-time disease detection, and location-based tools for soil testing and mSP center identification. Additionally, the platf

关键词： Smart agriculture

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ACTIONS ON CLASSIFIABLE C*-ALGEBRAS WITHOUT EQUIVARIANT PROPERTY (SI)

arXiv

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arXiv 2024年

作者： Gardella, Eusebio Kranz, Julian Vaccaro, Andrea Department of Mathematical Sciences Chalmers University of Technology University of Gothenburg GothenburgSE-412 96 Sweden Data Science: Machine Learning and Data Engineering Prof. Dr. Fabian Gieseke Leonardo-Campus 3 Münster48149 Germany Mathematisches Institut Fachbereich Mathematik Informatik der Universität Münster Einsteinstrasse 62 Münster48149 Germany

We exhibit examples of actions of countable discrete groups on both simple and non-simple nuclear stably finite C*-algebras that are tracially amenable but not amenable. We furthermore obtain that, under the additional assumption of strict comparison, amenability is equivalent to tracial amenability plus the equivariant analogue of matui-Sato's property (SI). By virtue of this equivalence, our construction yields the first known examples of actions on classifiable C*-algebras that do not have equivariant property (SI). We moreover show that such actions can be chosen to absorb the trivial action on the universal UHF algebra, thus proving that equivariant Z-stability does not in general imply equivariant property (SI). Copyright © 2024, The Authors. All rights reserved.

关键词： Algebra

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machine learning-Enhanced Self-management for Energy-Effective and Secure Statistics Assortment in Unattended WSNs

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SN Computer Science 2025年第2期6卷 1-10页

作者： Biswas, Preesat mishra, Aishwarya Dixit, Rinku Sharma Dwivedi, Abhishek Choudhary, Shailee Lohmor Tiwari, Shweta Chakravarthi, m. Ayyappa Electronics & Telecommunications Government Engineering College C. G Jagdalpur India Department of Computer Science & Engineering IES College of Technology M.P Bhopal India Department of Artificial Intelligence and Machine Learning New Delhi Institute of Management Delhi India Department of Data Science and Engineering School of AIML IoT & IS CCE DS and Computer Applications Manipal University Jaipur Jaipur Rajasthan India Department of Computer Science and Engineering Gyan Ganga Institute of Technology and Sciences M.P Jabalpur India Department of CSE-Data Science KKR and KSR Institute of Technology and Sciences Guntur India

Unattended Wireless Sensor Networks (UWSNs) operate without human supervision and have limited resources. In a conventional WSN with a fixed sink for data collection, nodes one hop away from the sink consume more energy, causing a hotspot issue. This leads to network fragmentation and reduced sensing efficiency. To address this, a mobile sink is proposed. This study details the development and implementation of Secure mRL, focusing on legitimate message communication to the mobile sink via convex nodes. By using these convex nodes as data collection points, the mobile sink avoids traversing the entire network. We employ an enhanced system to generate keys and authenticate messages, assessing its resilience against various attacks, including sleep deprivation, snooze, network substitution, and modification. Findings indicate that Secure mRL maintains consistent performance across different node counts and significantly reduces energy consumption by 84%, improving the speed of attack detection. This improvement is achieved by monitoring the digital signature and residual energy of nodes. The proposed SVm-based network training establishes a residual energy cut-off, while convex nodes mitigate synchronization issues among other sensor nodes in Secure mRL. Additionally, convex nodes manage the availability checks for the mobile sink. The proposed approach demonstrates a 92% improvement in packet delivery ratio (PDR) and an 89% reduction in end-to-end delay, highlighting its efficacy in enhancing network performance. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Duty-cycled UWSN mRL Sec-mRL SEC-TmP SVm UWSN

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Evolving Interpretable Classification models via Readability-Enhanced Genetic Programming

Evolving Interpretable Classification Models via Readability...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： João Victor T. De Souza Abreu Denis mayr Lima martins Fernando Buarque De Lima Neto Polytechnic School - PPGEC University of Pernambuco Recife Brazil Machine Learning and Data Engineering University of Münster ERCIS Muesnter Germany

ISBN: (纸本)9781665487696

As the impact of machine learning (mL) on busi-ness and society grows, there is a need for making opaque mL models transparent and interpretable, especially in the light of fairness, bias, and discrimination. Nevertheless, interpreting complex opaque models is not trivial. Current interpretability approaches rely on local explanations or produce long explanations that tend to overload the user's cognitive abilities. In this paper, we address this problem by extracting interpretable, transparent models from opaque ones via a new readability-enhanced multi-objective Genetic Programming approach called REmO-GP. To achieve that, we adapt text readability metrics into model complexity proxies that support evaluating mL interpretability. We demonstrate that our approach can generate global interpretable models that mimic the decisions of complex opaque models over several datasets, while keeping model complexity low.

关键词： Adaptation models Computational modeling Genetic programming machine learning Readability metrics Complexity theory Computational intelligence

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Visual Computing Unified Application Using Deep learning and Computer Vision Techniques

International Journal of Interactive Mobile Technologies

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International Journal of Interactive mobile Technologies 2024年第1期18卷 59-74页

作者： Sowmya, B.J. meeradevi Seema, S. Dayananda, P. Supreeth, S. Shruthi, G. Rohith, S. Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Karnataka Bengaluru India Department of Artificial Intelligence and Machine Learning Ramaiah Institute of Technology Karnataka Bengaluru India Department of Computer Science and Engineering M S Ramaiah Institute of Technology Karnataka Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal India School of Computer Science and Engineering REVA University Karnataka Bengaluru India Department of Electronics & Communication Engineering Nagarjuna College of Engineering & Technology Karnataka Bengaluru India

Vision Studio aims to utilize a diverse range of modern deep learning and computer vision principles and techniques to provide a broad array of functionalities in image and video processing. Deep learning is a distinct class of machine learning algorithms that utilize multiple layers to gradually extract more advanced features from raw input. This is beneficial when using a matrix as input for pixels in a photo or frames in a video. Computer vision is a field of artificial intelligence that teaches computers to interpret and comprehend the visual domain. The main functions implemented include deepfake creation, digital ageing (de-ageing), image animation, and deepfake detection. Deepfake creation allows users to utilize deep learning methods, particularly autoencoders, to overlay source images onto a target video. This creates a video of the source person imitating or saying things that the target person does. Digital aging utilizes generative adversarial networks (GANs) to digitally simulate the aging process of an individual. Image animation utilizes first-order motion models to create highly realistic animations from a source image and driving video. Deepfake detection is achieved by using advanced and highly efficient convolutional neural networks (CNNs), primarily employing the EfficientNet family of models. © 2024 by the authors of this article. Published under CC-BY.

关键词： Animation

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AI-Optimized Placement and Routing for PCB Design 9

AI-Optimized Placement and Routing for PCB Design

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9th International Conference on Science, Technology, engineering and mathematics, ICONSTEm 2024

作者： Sathyaraj, P. Arulkumar, S. Ajina, A. Beshir, Kedir Umasankar, L. Arularasan, A.N. Rmk College of Engineering And Technology Department of Electronics And Communication Engineering Tamil Nadu Puduvoyal601206 India Wachemo University Department of Electrical And Computer Engineering Ethiopia M S Ramaiah Institute of Technology Department of Artificial Intelligence And Machine Learning Bangalore560054 India S.A. Engineering College Department of Electrical And Electronics Engineering Tamil Nadu Tiruvallur600077 India Panimalar Engineering College Department of Artificial Intelligence And Data Science Tamil Nadu Chennai600123 India

ISBN: (纸本)9798350365092

Rapid electronic device development requires more complicated and densely packed PCB designs. These systems need properly placed and connected electrical components for best performance and reliability. Complexity and time restrictions are rendering manual methods ineffective. Therefore, AI-based PCB insertion and routing automation methods have been developed. This research suggests using AI to optimize PCB component arrangement and routing. The system uses machine learning and optimization to improve design efficiency. The AI system creates layouts that match performance requirements, decrease signal interference, and meet manufacturing limits using previous design data and user preferences. A learning module that updates its knowledge base, an algorithm that chooses between design objectives, and real-time user input are examples. Signal integrity, thermal issues, and manufacturability are considered while modifying design requirements. Evaluation of the suggested methodology shows considerable gains in design efficiency, time-to-market, and electronic system performance. This research improves AI-automated PCB design, enabling more complex and effective electrical systems. AI in PCB design will revolutionize the industry by solving complicated design problems and speeding up the development of cutting-edge electronics. © 2024 IEEE.

关键词： Reinforcement learning

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Parkinson's Disease Detection using Hand Drawn Spirals and Waves

Parkinson's Disease Detection using Hand Drawn Spirals and W...

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2023 International Conference on Recent Advances in Science and engineering Technology, ICRASET 2023

作者： Shashidhara, H.S. Kalbag, Saish K mathias, Tanya Srinivasa, K.G. Niranjanamurthy, m. Affiliated to Visvesvaraya Technological University M S Ramaiah Institute of Techology and Engineering Department of Informaition Science and Engineering Belgaum Bangalore India International Institute of Information Technology Department of Data Science and Artififial Intelligece Chhattisgarh Naya Raipur India Affiliated to Visvesvaraya Technological University B M S Institute of Technology and Management Department of Artificial Intelligence and Machine Learning Belgaum Bangalore India

ISBN: (纸本)9798350306927

Parkinson's disease, a neurodegenerative disorder, affects millions of people worldwide. Early detection is critical for effective treatment and management. Early detection of Parkinson's disease is challenging due to the lack of accessible and non-invasive screening methods. Current diagnostic techniques often require specialized equipment and invasive procedures, limiting their feasibility for widespread screening. There is a need for cost-effective and accessible approaches that can accurately detect early stages of Parkinson's disease. The model requires a high degree of precision in distinguishing between these patterns, which is a significant challenge to overcome. In this research work, we proposed a novel approach for detecting early stages of Parkinson's disease by analyzing hand-drawn spirals and waves in photographs. © 2023 IEEE.

关键词： Diagnosis

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