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检索条件"丛书名=Lecture Notes in Networks and Systems"

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Leveraging Machine Learning for Identifying Occupancy Patterns from Power Data with a Moving Window Feature Extraction Method 9th

Leveraging Machine Learning for Identifying Occupancy Patter...

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

作者： Sayed, Aya Nabil Bensaali, Faycal Himeur, Yassine Houchati, Mandi Qatar Univ Dept Elect Engn Doha Qatar Univ Dubai Coll Engn & Informat Technol Dubai U Arab Emirates Iberdrola Innovat Middle East Doha Qatar

ISBN: (纸本)9789819733019;9789819733026

Accurate and timely occupancy detection is pivotal for achieving energy efficiency in buildings, as it enables the fine-tuning of heating, cooling, lighting, and other systems, ultimately reducing energy waste and environmental impact while enhancing user comfort and cost savings. Leveraging power consumption data for occupancy detection presents a compelling advantage, as it offers a non-intrusive, cost-effective, and readily available solution, eliminating the need for extensive sensor or camera installations and enabling a more scalable and practical approach to building management. This paper presents an innovative approach for identifying occupancy patterns through utilizing machine learning (ML) techniques applied to power consumption data. We analyze two datasets, each representing diverse real-world scenarios, to uncover hidden occupancy patterns and their temporal variations. To enhance the discriminative power of the model, we incorporate a moving window methodology, which allows us to extract valuable features such as the average, standard deviation (SD), and range over specific time intervals. The motivation behind employing ML approaches in this context stems from the growing need for energy-efficient building management and the potential for ML to capture intricate temporal relationships within power consumption data. The results of our study offer valuable insights for applications in intelligent buildings, energy conservation, and occupancy-aware systems.

关键词： Occupancy detection Machine learning Power consumption data Moving window

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Comparative Analysis of the Income and Wealth Tax systems of Argentina and Colombia: An Approach from the Perspective of International Taxation and Evasion 9th

Comparative Analysis of the Income and Wealth Tax Systems of...

引用

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

作者： Prada Segura, Jasleidy Astrid Corredor Garcia, Maria del Pilar Salazar Acosta, Luisa Maria Corp Univ Minuto Dios Bogota Colombia Univ Catolica Salta Salta Argentina

ISBN: (纸本)9789819735617;9789819735624

Income and wealth tax systems are key tools for governments to raise the funds needed to finance their public policies, especially for Latin American and Caribbean countries with a fiscal focus. However, tax evasion and international taxation pose significant challenges to ensure fair and efficient collection. This article focuses on analyzing the income tax systems of Argentina and Colombia, through the mechanisms of international tax planning applied to tax evasion and the existence of tax havens. The similarities and differences between both countries are examined, as well as the policies implemented to address these challenges, through mixed research that brings together literature review and field work, qualitative approach, and inductive method, using content analysis as a technique, which results in a correlation of categories that allow establishing that Argentina and Colombia have a lower taxation than the OECD average, whose tax revenues are equivalent to 33.8% of GDP.

关键词： Equity Taxation Progressivity Collectability Sustainability

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Material Classification of Underground Objects from GPR Recordings Using Deep Learning Approach 1

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2nd Serbian International Conference on Applied Artificial Intelligence (SICAAI)

作者： Stifanic, Daniel Stifanic, Jelena Segota, Sandi Baressi Andelic, Nikola Car, Zlatan Univ Rijeka Fac Engn Vukovarska 58 Rijeka 51000 Croatia

ISBN: (数字)9783031608407

ISBN: (纸本)9783031608391;9783031608407

Exploration and detection of underground objectswithout excavation can be achieved by utilizing ground penetrating radar. Since such an approach is nondestructive, electromagnetic radiation has been used in order to accomplish sub-surface surveying. The correct interpretation of acquired ground penetrating radar data can be demanding, time-consuming and very challenging especially when the observed environment is noisy. However, with the assistance of artificial intelligence algorithms, such data can be processed and analyzed at high speed and with high accuracy. The aim of this research is to develop a deep learning model for the material classification of underground objects from ground penetrating radar recordings. Within the recordings, the pipes are usually visually represented as hyperbola-shaped features of different characteristics. Annotated by experts and preprocessed, ground penetrating radar recordings are used as input to the deep convolutional neural networks. In order to estimate the performances of the models, stratified 5-fold cross-validation is utilized along with the area under the ROC curve and confusion matrix. The results showed that the best performing model architecture (EfficientNetB3) can be used for pipe material classification from ground penetrating radar recordings with satisfactory results.

关键词： ground penetrating radar artificial intelligence deep learning convolutional neural network stratified 5-fold cross-validation classification

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Customers and Employees' Perception Toward CRM Practices Adopted by Banks: A Bibliometric Analysis 8th

Customers and Employees' Perception Toward CRM Practices Ado...

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8th Smart Trends in Computing and Communications (SmartCom)

作者： Goel, Rajat Kalotra, Anil Chandigarh Univ Univ Sch Business Mohali 140413 Punjab India

ISBN: (纸本)9789819713127;9789819713134

Banks in India have been using CRM to understand diverse needs of banking customers and even trying to use the strategy of relationship building with the customers for attracting new customers and retaining existing customers. However, convincing customers for technology adoption to interact with bank is a major challenge. The objective of this research is to assess the progress made in the field of CRM in banking. This study aims to provide readers with a full understanding of the current dynamics and shed light on the current status of the literature pertaining to CRM in banking sector. Furthermore, this study offers a comprehensive examination of the existing body of literature on CRM in banking through the utilization of bibliometric analysis. The Scopus database was utilized for data collecting in this study. The database utilized for this research consisted of a total of 418 documents spanning the years 1998 to 2023. This study examines the theoretical foundations, publication patterns, and citation trends within this field. Additionally, the study investigates commonly referenced journals, frequently employed research keywords, and the formation of research clusters. Based on an analysis conducted using Scopus, it has been determined that the domains of management and business research in the India hold significant prominence in the examination of CRM studies. The analysis was conducted using VOSviewer, which revealed that research on CRM has exhibited inconsistent progress in scholarly publications.

关键词： CRM ICT Banks India Scopus database VOSviewer

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An Exploratory Big Data Approach to Understanding Commitment in Projects 12th

An Exploratory Big Data Approach to Understanding Commitment...

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

作者： Vajjhala, Narasimha Rao Strang, Kenneth David Univ New York Tirana Tirana Albania W3 Res New York NY USA Univ Cumberlands Williamsburg KY USA

ISBN: (纸本)9783031602269;9783031602276

This study addresses the twin challenges of talent retention and high project failure rates (40-70%) by harnessing machine learning (ML) techniques to analyze retrospective big data. The study's objective was to ascertain whether project performance indicators can be a reliable gauge of project manager (PM) organizational commitment. This approach sidesteps the inherent bias and small effect sizes associated with survey self-report responses. Our innovative methodology leverages secondary big data, transforming the values into structured features that predict PM organizational commitment. This study proposes a novel conceptual framework, focusing on actual behavioral evidence rather than traditional, self-reported attitudes to assess the fuzzy predictors of organizational commitment. Among the three developed ML models, one demonstrated a significant 24% effect size, uncovering key features correlating PM tenure and organizational commitment with success. The insights gained from this research have broad implications for global stakeholders in projects and programs, offering a more objective and big data-driven understanding of PM commitment.

关键词： Project Management Organizational Commitment Machine Learning Big Data Analysis Talent Retention Project Failure Rates Data-Driven Management Predictive Modeling Stakeholder Implications Program Management Performance Indicators

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Using Clustering Ensembles and Heuristic Search to Estimate the Number of Clusters in Datasets

Using Clustering Ensembles and Heuristic Search to Estimate ...

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Intelligent systems Conference (IntelliSys)

作者： Odebode, Afees Adegoke Arzoky, Mahir Tucker, Allan Mann, Ashley Maramazi, Faisal Swift, Stephen Brunel Univ London England

ISBN: (纸本)9783031477140;9783031477157

This paper presents a heuristic search-based approach for estimating the number of clusters within a dataset based on an ensemble of clustering methods. We combine a number of clustering results into near-optimal subsets using two distinct approaches. Firstly, a Gray code-based implementation evaluates the quality of all possible subsets;the quality and consistency were excellent, but the search was exhaustive, leading to exponential run-time as the volume and dimension of the dataset increased. For this reason, a Random Mutation Hill Climbing-based alternative is introduced, which evaluates the subsets in small increments, mimicking the Gray code implementation, with a minimum of ninety-two per cent accuracy (mean 96%) and a significant gain in speed (linear as opposed to exponential run-time). Our algorithms are tested on real-world and benchmark datasets, and their performance is compared to other state-of-the-art estimators with promising results. Additionally, a heuristic is presented to guide when to use the exhaustive or heuristic search.

关键词： Random mutation hill climbing Ensembles Convergence point Estimating the number of clusters

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Design and Optimization of a 1-DOF Compliant Mechanism for Vibration-Assisted 3D Printing 7th

Design and Optimization of a 1-DOF Compliant Mechanism for V...

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7th International Conference on Green Technology and Sustainable Development

作者： Van-Khien Nguyen Huy-Tuan Pham Pham-Ngoc-Tuyen Nguyen Quang-Khoa Dang Van-Son Nguyen Ngoc-Dat Nguyen Ngoc-Anh-Thu Le Thai-Khang Tang Ho Chi Minh City Univ Technol & Educ Ho Chi Minh City Vietnam Ho Chi Minh City Univ Ind & Trade Ho Chi Minh City Vietnam

ISBN: (纸本)9783031762314;9783031762321

Compliant mechanisms (CMs) are well-known and widely applied across various disciplines due to their advantages, such as compact design, simplicity in manufacturing and precision. This study proposes a modern design of a 1-DOF vibration-assisted displacement using semi-circular elastic hinges, leaf springs, and utilizing the vibration of a piezo actuator amplified by lever mechanism for the 3D-printing application. In addition to maximizing the natural frequencies and the amplification ratio at the output, minimizing the parasitic motion was considered. The mechanism's geometric design was calculated employing finite element analysis (FEA) and an evolutionary algorithm optimization approach.

关键词： Compliant mechanism vibration-assisted machining FEM vibration-assisted 3D printing

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Application of Mean-Variance Cloning Technique to Investigate the Comparative Performance Analysis of Classical Classifiers on Imbalance and Balanced Data 1

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Intelligent systems Conference (IntelliSys)

作者： Okwonu, Friday Zinzendoff Ahad, Nor Aishah Apanapudor, Joshua Sarduana Arunaye, Festus Irimisose Sharipov, Olimjon Shukurovich Delta State Univ Dept Math Fac Sci PMB 1 Abraka Nigeria Univ Utara Malaysia Sch Quantitat Sci Coll Arts & Sci Sintok 06010 Kedah Malaysia Natl Univ Uzbekistan Sch Math Dept Stat Tashkent Uzbekistan

ISBN: (数字)9783031477157

ISBN: (纸本)9783031477140;9783031477157

Group imbalance data often occur in high dimensional practical classification problems where the number of attributes exceeds the number of instances. In this case, the researcher is faced with dual problems such as (i) biasedness towards the majority group over the minority group and (ii) dimensionality or singularity of the covariance matrix. In such situations, the classical classifiers dependent on the sample mean and covariance matrix are impracticable for classifications. This study focused on the effects of group imbalance data on n > p classification problems. First, we develop a procedure that could transform the minority group into the majority group before the classifiers are applied. This study aims to determine whether there are observable effects on the classifiers' performance for imbalanced and balanced data sets. We also investigated whether the over and under-sampling influence the computational time of the classifiers. The results revealed that the Fisher linear classification method (FLCM) performed comparably for imbalanced and balanced data and outperformed the nearest mean classifier (NMC) and the independent classification rule (ICR). The study demonstrated that the MVCT effects on the classifiers are data-dependent. Therefore, the investigation showed that sample size balancing irrespective of the data dimension does not have a strong impact on the classifier's performance. This analysis concludes that for the n > p classification problem, the FLCM classifier has comparable performance on the imbalanced and balanced data, similar results were observed for the NMC and ICR classifiers. The over and under-sampling of the data set has insignificant effects on the computational time of the classifiers.

关键词： Imbalance Balanced Sample size Over-sampling Under-sampling Classifiers

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Typical Testor Selection Process for Classification Models

Typical Testor Selection Process for Classification Models

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Intelligent systems Conference

作者： Martinez-Mejia, Mateo Alba-Cabrera, Eduardo Perez-Perez, Noel Univ San Francisco Quito Colegio Ciencia & Ingn Quito Ecuador

ISBN: (纸本)9783031664304;9783031664311

This work develops a process based on Testor Theory and new ideas related to this field that allows a ranking of typical testors based on the similarity between objects from the same class and the dissimilarity between objects from different classes. This process allows us to select the typical testors that will perform effectively to reduce the number of features in a dataset. We validate this process by examining the results obtained from a classification model for three different datasets, using the best and worst-ranked typical testors selected by the algorithm. Lastly, we analyze the results obtained and present the effectiveness of the method, as well as the advantages it brings.

关键词： Testor theory Typical testor Similarity Dissimilarity Classification model

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Discovering Social Interactions Between Caregivers and Frail Individuals Using Indoor Localization 1

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16th International Conference on Ubiquitous Computing and Ambient Intelligence

作者： Polo-Rodriguez, Aurora Anguita-Molina, Miguel A. Gil, David Romero, Jose Fernandez, Elena Paloma, Olga Porcel, Ana Medina-Quero, Javier Univ Granada Dept Comp Engn Automat & Robot Res Ctr Informat & Commun Technol CITIC UGR Granada Spain Univ Alicante Dept Comp Sci & Technol Alicante Spain Univ Cadiz Dept Nursing & Physiotherapy Cadiz Spain Univ Seville Dept Nursing Seville Spain

ISBN: (数字)9783031775710

ISBN: (纸本)9783031775703;9783031775710

Social interaction is a key indicator of health in frail populations, as it not only helps alleviate loneliness, but also fosters emotional support, mental stimulation, and a sense of belonging, which are crucial to their overall well-being. This work analyses such interactions by focusing on two critical aspects: (i) the duration of the interaction between caregivers and individuals with cognitive disabilities and (ii) the specific areas where these interactions occur. We used an improved indoor Ultra-Wideband location system to facilitate this analysis. Our results, which span three days, have been thoroughly evaluated and demonstrate extensive coverage and detailed insights into the personal relationships among inhabitants of assisted living facilities.

关键词： Social Interaction Frail Individuals Indoor Location Caregiver Interaction Ultra-Wideband

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