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Frontiers in Education (FIE) Conference

作者： Abdelkader Nasreddine Belkacem Nuraini Jamil Saed Alrabaee Dept. of Computer and Network Engineering College of IT UAE University Al Ain United Arab Emirates Dept. of Computer Science and Software Engineering College of IT UAE University Al Ain United Arab Emirates Dept. of Information Systems and Security College of IT UAE University Al Ain United Arab Emirates

The outbreak of the coronavirus disease (COVID-19) has had a profound impact on education worldwide. The rise of remote learning is one of the most significant changes in this regard, as many schools and universities were forced to close down by regional health authorities. This has also caused people to become more conservative in trade-offs between healthcare and education. Google Trends is the most common tool for analyzing online search behaviors. It is a free resource that provides information on the trends and changes in users' online interests over time based on certain terms and subjects. The online search queries on Google can be used to assess users' behaviors concerning online learning to forecast their choices regarding online education. This paper examines the frequency of users' web searches for online communication tools, courses, and learning terms. We statistically compared users in the Middle East and North Africa regions by using the volumes of searches recorded on Google Trends from January 2016 to August 2022. Moreover, we used machine learning techniques to identify differences among the keywords used. The findings statistically show that COVID-19 has led to an increase in the extent of students' attention to and interest in online learning.

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Innovative Poverty Estimation through Machine Learning Approaches 18

Innovative Poverty Estimation through Machine Learning Appro...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Mashhood, Ahmad Abideen, Zain Ul Arshad, Usama Ali, Raja Hashim Khan, Ammad Aslam Khan, Bahram BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Virtual University of Pakistan Dept. of Biological Sciences M.A. Road Satellite Town Rawalpindi Pakistan

ISBN: (纸本)9798350328172

This paper delves into the application and capabilities of machine learning methodologies in forecasting poverty scenarios, underlining the importance of varied data sources, along with the interpretability and explainability of models to refine the precision and transparency of poverty prediction mechanisms. It primarily utilizes an iteration of the LightGBM algorithm to infer poverty stages premised on household variables. This investigation showcases the potential of machine learning methodologies in poverty estimation, elucidating the relevance of additional, diverse data resources, and the critical role of model interpretability in enhancing model accuracy and clarity. The LightGBM version employed in our study demonstrated high efficiency on the validation set, affirming its effectiveness in projecting poverty conditions based on household features. In analyzing SHAP values, we unearthed crucial insights into the contributing elements to poverty. These findings can aid in shaping policies aimed at poverty alleviation and elevating social welfare. Our work provides the basis for future research into poverty prediction and affirms the potential of machine learning methodologies in tackling intricate social challenges. © 2023 IEEE.

关键词： Forecasting

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Comparing Actual Measurement and PVsyst Simulation Results for Energy Generation of Microgrid-Connected PV systems

Comparing Actual Measurement and PVsyst Simulation Results f...

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Smart Grid Conference (SGC)

作者： Mohammad Nazififard Hamed Hashemi-Dezaki Kia Nazififard Dept. of Energy Systems Engineering School of Mechanical Engineering Kashan Iran Dept. of Electrical and Computer Engineering Kashan Iran Independent Researcher Tehran Iran

This paper evaluates the uncertainty in energy generation of a 12 kW p microgrid (MG)-connected solar photovoltaic (PV) system located at the University of Kashan campus. The study compares long-term recorded measurements and PVsyst simulations for energy generation of PV systems over the course of a year. It is focused on specific yield and the deviation between the measured net energy generated by the PV facility and the calculated useful produced energy by PVsyst software. The results indicate a significant difference between the output power and the simulation results from PVsyst during colder months. However, in warmer months, the disparity between recorded measurements and simulated results is less than 10%. The largest deviation occurs in November, where the difference exceeds 35%. Generally, PVsyst software estimates the energy output from the PV system with an uncertainty of less than 10%. These findings contribute to understanding the variability in PV power generation and developing effective energy management strategies. Such strategies are crucial for ensuring a reliable power supply within an MG.

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Sinhala Sign Language Learning System for Hearing Impaired Community

Sinhala Sign Language Learning System for Hearing Impaired C...

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2023 4th International Informatics and software engineering Conference (IISEC)

作者： Idushan K. A. S Dilshan P. G. T Jayasundera P. S Madhushan K. L. B Jenny Krishara Dinuka Wijendra Dept. of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

This research aims to develop a comprehensive system for Sinhala Sign Language (SSL) that includes a learning system, dynamic sign detection, audio/video to sign conversion, and vocal training. SSL plays a crucial role in facilitating communication for individuals who are deaf or hard of hearing in Sri Lanka. The learning system provides a platform for learning SSL and includes a text-to-sign language interpreter. The dynamic sign detection system uses computer vision techniques to identify and interpret dynamic signs accurately. The audio/video to sign conversion system bridges the gap between spoken language and SSL by converting auditory information into visual representations. The vocal training system focuses on enhancing the vocal skills of cochlear implanted children. This research contributes to the development of effective communication and language skills for SSL users.

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Movie genres and beyond: An analytical survey of classification techniques 5

Movie genres and beyond: An analytical survey of classificat...

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5th International Conference on Trends in Electronics and Informatics, ICOEI 2021

作者： Mehal, Avleen Singh Meena, Kalpana Singh, Rijul Bir Shambharkar, Prashant Giridhar Delhi Technological University Dept. of Software Engineering New Delhi India Delhi Technological University Dept. of Computer Engineering New Delhi India

ISBN: (纸本)9781665415712

In today's world with a plethora of options available on movie content, it is very important to have models which could classify these large contents based on their genres. Movies are the most widely entertaining source all over the world. Every person has his own taste of movies, some feel action movies are better, some would say romantic. So the online platforms must have the collection of movies according to a particular category i.e. based on genres. This paper has proposed an extensive and exhaustive survey for various models and techniques, which could be applied to solve the challenges involved in performing the genre based classification. This paper summarizes and compares all those techniques by classifying them into four broad domains -through text mining, poster detection, audio and video features of the movies. These domains included various classifying techniques: Bi-directional LSTM, gram layer, scene categorization and CNN respectively. With such an extensive amount of work going in the field in the last not many years, this review paper will help specialists in the field of movie genre classification. © 2021 IEEE.

关键词： Surveys

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Mobile Gateways for LoRa End-Devices: Support Connectivity on the Move

Mobile Gateways for LoRa End-Devices: Support Connectivity o...

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IEEE International Workshop on computer-Aided Modeling, Analysis, and Design of Communication Links and Networks, CAMAD

作者： Vasileios Moysiadis Dimitrios N. Skoutas Charalabos Skianis Thomas Lagkas Vasileios Argyriou Panagiotis Sarigiannidis Dept. of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Dept. of Information and Communication Systems Engineering University of the Aegean Samos Greece Dept. of Computer Science International Hellenic University Greece Dept. of Networks and Digital Media Kingston University Kingston upon Thames United Kingdom

ISBN: (数字)9798350377644

ISBN: (纸本)9798350377651

This paper proposes a method enabling mobile LoRa gateways to support mobile end devices. The approach utilizes trilateration and similar approaches based on RSSI to estimate the position of end devices and specify the movement of the gateways to maintain contact. The evaluation of the proposed method tests various parameters such as speed and number of end devices, and it shows that it is able to retain contact even with speeds up to 12 mps. Moreover, the evaluation shows that the ADR-MIN algorithm performs better in this scenario than the default ADR.

关键词： Performance evaluation Network servers Conferences Computational modeling LoRa Logic gates Low-power wide area networks

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SPORTS CENTER CUSTOMER SEGMENTATION: A CASE STUDY

arXiv

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arXiv 2024年

作者： Soto, Juan Carmenaty, Ramón Lastra, Miguel Fernández-Luna, Juan Manuel Benítez, José M. Computer Science and Artificial Intelligence Dept. DiCITS Lab iMUDS Universidad de Granada Spain Software Engineering Dept. DiCITS Lab iMUDS Universidad de Granada Spain

Customer segmentation is a fundamental process to develop effective marketing strategies, personalize customer experience and boost their retention and loyalty. This problem has been widely addressed in the scientific literature, yet no definitive solution for every case is available. A specific case study characterized by several individualizing features is thoroughly analyzed and discussed in this paper. Because of the case properties a robust and innovative approach to both data handling and analytical processes is required. The study led to a sound proposal for customer segmentation. The highlights of the proposal include a convenient data partition to decompose the problem, an adaptive distance function definition and its optimization through genetic algorithms. These comprehensive data handling strategies not only enhance the dataset reliability for segmentation analysis but also support the operational efficiency and marketing strategies of sports centers, ultimately improving the customer experience. © 2024, CC BY-NC-ND.

关键词： Genetic algorithms

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The Early Detection of Dementia Disease Using Machine Learning Approach

The Early Detection of Dementia Disease Using Machine Learni...

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International Conference on computer Communication and Informatics (ICCCI)

作者： Md Shariar Kabir Jakia Khanom Md. Atik Bhuiyan Zerin Nasrin Tumpa Sk. Fazlee Rabby Saurabh Bilgaiyan Dept. of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Dept. of Software Engineering Daffodil International University Dhaka Bangladesh School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India

Early dementia detection is a crucial but challenging task in Bangladesh. Often, dementia is not recognized until it is too late to receive effective care. This results in part from a lack of knowledge about the illness and its signs and symptoms. Recent improvements in machine learning algorithms, however, may change this. In a recent study, we developed a model that can identify early dementia in Bangladesh using machine learning algorithms. This research paper proposed an efficient machine learning-based approach for early detection of dementia disease A dataset of 199 people with dementia and 175 healthy controls was used to develop the model. In 96% of the cases, the algorithm correctly identified dementia. This is a significant accomplishment that could revolutionize Bangladesh's dementia detection process. For patients to get the care they require, early dementia detection is essential. This study offers a proof-of-concept for the use of machine learning in dementia early detection & The results of this study suggest that machine learning models can be used as a powerful tool for early detection of dementia.

关键词： Surveys Machine learning algorithms Sociology Machine learning Predictive models Prediction algorithms Task analysis

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Motion Profile Optimization in Industrial Robots using Reinforcement Learning

Motion Profile Optimization in Industrial Robots using Reinf...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Yunshi Wen Honglu He Agung Julius John T. Wen Electrical Computer and Systems Engineering Dept. Rensselaer Polytechnic Institute Troy NY USA

Path tracking problems are challenging with the absence of dynamic models and information about robot controllers. This paper presents a method of optimizing a motion profile constructed using a set of pre-defined motion primitives and a speed command to track a spatial trajectory with high accuracy, speed, and uniform motion using industrial robots. We use a bi-level optimization approach that optimizes execution accuracy using reinforcement learning and execution speed using bi-section search. We train and evaluate the reinforcement learning policy in simulation for an ABB robot. Experiment results demonstrate that the learned policy reduces the optimization cost to achieve the desired specifications. Additionally, the trained policy can generalize to trajectories not included in the training set.

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Establishing a Correlation of Clinical Characteristics with the Level of Tumor Mutation Burden in Urothelial Bladder Carcinoma

Establishing a Correlation of Clinical Characteristics with ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Lujain Alshomali Rania Khorma Abdullah Al-Refai Abedalrhman Alkhateeb Software Engineering Dept. Princess Sumaya University for Technology Amman Jordan Computer Science Dept. Lakehead University Thunder Bay Canada

Bladder cancer is a complex disease and one of the most lethal types of cancer. Recently, some malignancies, including bladder carcinomas, have shown better results with immunotherapy using immune checkpoint inhibitors. Tumor mutational burden (TMB) is a potential biomarker for predicting tumor behavior and immunotherapy response as an outcome. Publicly available clinical data from the bladder cancer of TCGA project is used to analyze correlations of clinical variables with an increased tumor mutation burden (TMB) number compared with those with a lower number of mutations. The threshold for the high mutation burden in the analysis was set at 10 mutations (Mut) per Megabase (Mb). The Chi-Square test (χ 2 ) was used to compare categorical data. The Chi-Square "Best first" method was used to find a correlation between clinical variables and TMB, then compared with the p-value of significance (p<0.05). A significant correlation was found between TMB and Race, Neoplasm Histologic Grade, and gender when applying the Best First/Chi-Square method to clinical variables and level of TMB. This enables further investigation and application of the prediction models of the level of TMB, responsiveness to immunotherapy, and prognosis based on the clinical features of the patients.

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