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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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Identifying Fake Twitter Trends with Deep Learning 1

引用

International Conference on Artificial Intelligence: Theory and Applications (AITA)

作者： AlBuhairi, Thahab M. Alhakbani, Haya A. Imam Mohammad Ibn Saud Islamic Univ IMSIU Riyadh Saudi Arabia

ISBN: (数字)9789819984794

ISBN: (纸本)9789819984787;9789819984794

This paper describes a model for detecting and distinguishing fake tweets from real tweets. The model uses one of the most commonly used deep learning algorithms in natural language processing, the Bidirectional Long Short-Term Memory (Bi-LSTM) algorithm. The model was trained on two datasets, the PolitiFact dataset from the FakeNewsNet repository and the PHEME dataset. The datasets are divided into 80% for training and 20% for testing. The model contains two merged models (hybrid model), one for text attributes and the other model for numeric/categorical attributes. To achieve the best result, the model balanced the datasets using four balancing techniques: RandomOverSampler, SMOTE, RandomUnderSampler, and NearMiss. In addition, there are two combination strategies that combine two strategies with certain percentages: combineOverUnderSampling and combineSMOTEUnderSampling balancing strategies. This work is one of the first works to use these combinations and obtain very good results, more than most related studies. For the PHEME dataset, the model was trained with a variety of epochs (10, 20, 50, and 100) to obtain the best result. For the PolitiFact dataset, only 10 epochs were trained at a time. The best result obtained with the PolitiFact dataset is 0.96 for accuracy and precision, 0.95 for recall, F1-score, and ROC-AUC. The best result obtained with the PHEME dataset is 0.87 for accuracy and 0.86 for precision and 0.85 for recall, F1-score, and ROC-AUC.

关键词： Fake Tweets Twitter Trends Deep learning NLP AI Bi-LSTM

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Use of Fuzzy Sets in Process Capability Analysis: A Comparative Literature Analysis

Use of Fuzzy Sets in Process Capability Analysis: A Comparat...

引用

International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Gundogdu, Fatma Kutlu Ilbahar, Esra Yazir, Kubra Karasan, Ali Kaya, Ihsan Natl Def Univ Turkish Air Force Acad Dept Ind Engn Istanbul Turkiye Yildiz Tech Univ Dept Ind Engn Istanbul Turkiye Presidency Republ Turkiye Undersecretariat Def Ind Ankara Turkiye

ISBN: (纸本)9783031671944;9783031671951

Process capability analysis (PCA) is the assessment of a process's ability to meet customer requirements specified by constraints. It is a crucial aspect of statistical process control used to evaluate process variability. PCA provides data on both conforming and nonconforming production rates, which represent the quantity of items that meet and do not meet SLs, respectively. Processes can be categorized as "capable", or "incapable" based on their process capability indices (PCIs). The fuzzy set theory can effectively address ambiguity and enhance flexibility and sensitivity in classical PCIs. To achieve this goal, upper and lower specification limitations can be represented using linguistic variables. Fuzzy process capability indexes (FPCIs) are generated by utilizing fuzzy mean and fuzzy variance. This study aims to provide an extensive literature evaluation on publications about FPCIs. The research was assessed based on many parameters. The study presented classifications such as FPCI, application area, fuzzy parameters, and type of fuzzy sets. We aimed to create an outline for researchers in this subject and discuss recent advancements in FPCIs and fuzzy PCA.

关键词： Fuzzy set theory Linguistic variables Process capability indices

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Towards an Adaptive Approach to a Personalised Design of Intelligent Learning Assistants (ILAs)

Towards an Adaptive Approach to a Personalised Design of Int...

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Intelligent systems Conference

作者： Schelter, Nicole Veiel, Dirk Fernuniv Hagen Germany

ISBN: (纸本)9783031664274;9783031664281

This paper presents an innovative approach to designing Intelligent Learning Assistants (ILAs) named GITTE (Great Individual TuTor Embodiment), focusing on their visual representation and personalisation. The significance of visual representations for ILAs is explored, underlining their impact on learning outcomes and student perceptions. The presented approach introduces a novel, exploratory, and participatory method for identifying influential design features in ILAs using the Pedagogical Agents-Levels of Design (PALD) model and generative AI models like DALL-E and ChatGPT. This method allows for the exploration of design features not previously considered, capturing unconscious student preferences. The paper documents the initial test involving a manual approach to evaluate the applicability of generative AI models in designing GITTE, emphasizing the interaction between students and the generative model. The results indicate the potential of this approach in enhancing the personalisation and effectiveness of ILAs, suggesting new directions for research in this emerging field. The concept presented is poised to make significant contributions to the development of more engaging and personalized digital learning aids.

关键词： Intelligent learning assistants Personalization Generative AI

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Experimental Demonstration for Learning Analytics and Data Mining on e-assessment

Experimental Demonstration for Learning Analytics and Data M...

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2024 International Conference on Computing, Communication, Cybersecurity and AI

作者： Bhurre, Shraddha Prajapat, Shaligram Raikwar, Sunny Goswami, Prakshep Kothari, Soham Khanuja, Gurpreet Yadav, Kartikey Choure, Purvi DAVV Int Inst Profess Studies Indore MP India Dr APJ Kalam Univ Indore India

ISBN: (纸本)9783031744426;9783031744433

Every subject and course has a learning goal that we work towards during the teaching-learning process, whether it be online or offline. Under the current system, assignments, project analysis, semester exams, and internal exams are used to evaluate students' learning. Several factors, including participation in class, prior knowledge, engagement time, activity logs, forum posts, understanding level, etc., influence how well students learn throughout a course or session. All these factors will be difficult to analyze in offline mode. All of these activities determine whether or not the learning objective is met. This study examines an experiment that provided blended learning data on students' academic details, learning behaviors, and evaluation modules to determine the success of the two subjects. It discussed the details of each phase involved in the experiment. Students enrolled in integrated courses, MTech(PG-1) and MCA(PG-2) were analyzed in this study procedure through 4 weeks of offline classes. Moodle Gnomio was used for the online processes related to the Registration process, Quizzes, and discussion forums. A total of 6 quizzes for PG-1(M-Tech) and PG-2(MCA) were organized, and learning resources were provided before the exam. This study discusses detailed experiment setup, and Statistical analysis involving difficulty level of quizzes. This study proposed a framework for the dashboard that utilize this dataset and involves analysis related to student performance, grade distribution, inferring learning patterns, and course success.

关键词： Online Learning Moodle Blended Learning Learning Analytics

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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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Balancing Immersion and Simplicity: Unlocking Extended Reality's Potential for Dynamic Learning in Industries 14th

Balancing Immersion and Simplicity: Unlocking Extended Reali...

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14th International Conference on Learning Factories (CLF)

作者： Koenigs, Lisa Chloe Hoehne, Benjamin Longmuss, Jorg Sustainum Inst Sustainable Econ D-10965 Berlin Germany Berlin Univ Appl Sci & Technol Inst Distance Learning D-13353 Berlin Germany

ISBN: (纸本)9783031653995;9783031654008

Extended Reality (XR - comprising virtual and augmented reality) holds promise for dynamic learning, particularly in learning factories and other on-the-job training arrangements. It fits the need for flexible learning materials, e.g. substituting costly or hazardous real life processes. Despite its potential, adoption is still limited in workplace settings and vocational training. Two recent explorative case studies show the obstacles in more detail. These include the costly programming, the low level of software development competencies in SMEs and therefore the hurdle of creating and editing XR applications. Promising solution strategies are also offered. A comparison of XR and paper instructions (first case study) for an industrial assembly task showed that XR enhances work performance but lacks intuitiveness, leading users to rely only on embedded media. This indicates an unbalanced cost-benefit-ratio. More low-threshold (using open-source software programs and already available devices) digital instructions with intuitive XR features (easy to create and use) could bridge experience gaps while benefiting users, SMEs, and trainers with low digital expertise. This need for low-threshold creation and adaptation has been met in a second case study with a digital learning template with XR-features. It facilitates a modular compilation of existing images, videos, 360-degree views and 3-D models. It allows easy realization, manageable also for teachers with limited IT knowledge. In a first test case, trainers and participants rated this approach as very promising.

关键词： XR Dynamic Learning On-the-job Training Immersion and Simplicity Digital Competency

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Application of Flipped Learning for the Development of Autonomous Learning for Higher Education Students 1

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11st World Conference on Information systems and Technologies (WorldCIST)

作者： Huacca-Incacutipal, Ronald Arenas-Rojas, Luis Alberto Jesus Leo-Rossi, Ernesto Alessandro Carver Res 604 Courtland StSuite 131 Orlando FL 32804 USA Escuela Posgrad Newman 987 Bolognesi Ave Tacna Peru

ISBN: (数字)9783031456459

ISBN: (纸本)9783031456442;9783031456459

The purpose of the study was to determine if flipped learning influences the autonomous learning of students of the professional career in accounting of the John Von Neuman Institute of Higher Technological Education - Tacna 2022. The research was of a quantitative approach, of a basic type, explanatory level causal and non-experimental design. The sample consisted of 70 students in the third cycle of the professional accounting career of the aforementioned institute, two questionnaires validated by experts were applied to measure the variables flipped learning and autonomous learning. The results were analyzed using the SPSS 25 statistical program. Non-parametric statistics were used, applying Spearman's Rho correlation coefficient with a significance level of 0.05.

关键词： Flipped Learning Autonomous Learning Virtual Learning Environments Accounting

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A Comparative Study on Ant-Colony Algorithm and Genetic Algorithm for Mobile Robot Planning 6th

A Comparative Study on Ant-Colony Algorithm and Genetic Algo...

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6th International Conference on Soft Computing and Data Mining (SCDM)

作者： Rajendran, Piraviendran A. L. Othman, Muhaini Univ Tun Hussein Onn Malaysia Parit Raja 86400 Batu Pahat Joho Malaysia

ISBN: (纸本)9783031669644;9783031669651

This paper investigates the optimization of routing for robotics vehicles in automated warehouses in Malaysia. Focusing on routing optimization, the study evaluates Ant-Colony Optimization (ACO) and Genetic Algorithm (GA) in Mobile Robot Planning. Key challenges includes efficient routing among task scheduling, and path planning complexities. Objectives include analyzing features of mobile robot planning and representing them in ACO and GA, implementation ACO and GA algorithms for solving routing problems using dataset, and evaluating their performance. The research anticipates significant contributions to algorithmic solutions, utilizing Python-based experiments aligned with Software Engineering practice, providing practical insights for routing optimization in automated warehouses. Results indicates that ACO outperforms GA in minimizing travel distance, establishing it as the superior routing algorithm for both case studies. Case study 1, the ACO algorithm achieved a best distance of 1036 (u) with execution time 1.67 (s), while the GA algorithm resulted in a best distance 1062 (u) with execution time 0.08 (s). For case study 2, the ACO algorithm achieved a best distance of 1071 (u) with execution time 1.91 (s), while the GA algorithm resulted in a best distance of 1082 (u) with execution time 0.08 (s). Multiple code execution cycles are conducted to provide average findings, ensuring the strength and consistency of the assessment. In conclusion, the study successfully identifies key features in warehouses routing, implements ACO and GA algorithms, and evaluates the performance based on achieved routes and distance.

关键词： Automated Warehouse Ant-Colony Optimization (ACO) Genetic Algorithm (GA) Routing Optimization Mobile Robot Planning Travel Distance Execution Time

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An Automated Quasi-Identification (QID) for Re-identification 6th

An Automated Quasi-Identification (QID) for Re-identificatio...

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6th International Conference on Soft Computing and Data Mining (SCDM)

作者： Roslan, Saida Nafisah Hamid, Isredza Rahmi A. Darem, Abdulbasit A. Rahim, Nordiana Univ Tun Hussein Onn Malaysia Dept Informat Secur & Web Technol Batu Pahat Johor Malaysia Northern Border Univ Dept Comp Sci Ar Ar Saudi Arabia

ISBN: (纸本)9783031669644;9783031669651

The rapid growth of data generation and technology has led to concerns about security and privacy. Personal and sensitive information can be disclosed by unauthorized individuals, necessitating the use of privacy-preserving techniques. In this study, an automated quasi-identification (QID) approach for privacy preservation is proposed. The approach aims to select appropriate QID based on re-identification risk in each attribute, reducing subjective judgment and minimizing data loss. The methodology involves data processing, frequency calculation, re-identification risk score computation, QID selection, and evaluation using data anonymization tool used for privacy-preserving data processing and analysis. The selection process considers the likelihood of information being disclosed through a single record in each attribute based on an attack where attackers are aware of the victim's background or a randomly selected victim whose information is available in a published or external dataset. The effectiveness of the approach is evaluated on two datasets: the adult dataset and the bank marketing dataset. The results demonstrate a significant reduction in re-identification risks and success rates of attacker models after preserving QID attributes. The proposed approach offers a valuable solution for privacy preservation while minimizing data loss and maintaining data utility.

关键词： Quasi-identifier re-identification risk privacy preserving

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