A Comprehensive Review of Recent Research Trends on Unmanned Aerial Vehicles (UAVs)

Systems, Journal Year: 2023, Volume and Issue: 11(8), P. 400 - 400

Published: Aug. 2, 2023

The growing interest in unmanned aerial vehicles (UAVs) from both the scientific and industrial sectors has attracted a wave of new researchers substantial investments this expansive field. However, due to wide range topics subdomains within UAV research, newcomers may find themselves overwhelmed by numerous options available. It is therefore crucial for those involved research recognize its interdisciplinary nature connections with other disciplines. This paper presents comprehensive overview field, highlighting recent trends advancements. Drawing on literature reviews surveys, review begins classifying UAVs based their flight characteristics. then provides an current UAVs, utilizing data Scopus database quantify number documents associated each direction interconnections. also explores potential areas further development including communication, artificial intelligence, remote sensing, miniaturization, swarming cooperative control, transformability. Additionally, it discusses aircraft commonly used control techniques, appropriate algorithms research. Furthermore, addresses general hardware software architecture applications, key issues them. open source projects By presenting view aims enhance our understanding rapidly evolving highly area

Language: Английский

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AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions

Systems, Journal Year: 2023, Volume and Issue: 11(10), P. 519 - 519

Published: Oct. 17, 2023

Artificial intelligence (AI) has significantly impacted thyroid cancer diagnosis in recent years, offering advanced tools and methodologies that promise to revolutionize patient outcomes. This review provides an exhaustive overview of the contemporary frameworks employed field, focusing on objective AI-driven analysis dissecting across supervised, unsupervised, ensemble learning. Specifically, we delve into techniques such as deep learning, artificial neural networks, traditional classification, probabilistic models (PMs) under supervised With its prowess clustering dimensionality reduction, unsupervised learning (USL) is explored alongside methods, including bagging potent boosting algorithms. The datasets (TCDs) are integral our discussion, shedding light vital features elucidating feature selection extraction critical for diagnostic systems. We lay out standard assessment criteria regression, statistical, computer vision, ranking metrics, punctuating discourse with a real-world example detection using AI. Additionally, this study culminates analysis, current limitations delineating path forward by highlighting open challenges prospective research avenues. Through comprehensive exploration, aim offer readers panoramic view AI’s transformative role diagnosis, underscoring potential pointing toward optimistic future.

Language: Английский

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AI‐driven adaptive learning for sustainable educational transformation

Sustainable Development, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 3, 2024

Abstract This paper scrutinizes how adaptive learning technologies and artificial intelligence (AI) are transforming today's education by making it personalized, accessible, efficient as well leading people to accepting, addressing, mitigating sustainable development. Recently, witnessed a remarkable technological surge driven various advances in technology, which can be demonstrated the increase of number scientific publications on this topic from just 1 1990 636 2023. Ongoing digitalization revolution together with novel approach respect each student's unique style abilities paved way for represented innovative tools that personalize educational experiences cater individual learners. All contributes preparing more educated informed citizens, drives innovation, supports economic growth necessary achieving future. Our bibliographic study employs VOSviewer conduct bibliometric analysis total 3518 selected using keywords “adaptive learning” “AI” (represented articles, proceeding papers, book chapters) indexed Web Science (WoS) database 2024. results demonstrate recent changes played key role learning, was rather reinforced “digital surge” brought about COVID‐19 pandemic. findings useful further development field where they employed relevant stakeholders policymakers scholars researchers.

Language: Английский

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Filter bubbles in recommender systems: Fact or fallacy—A systematic review

Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, Journal Year: 2023, Volume and Issue: 13(6)

Published: Aug. 3, 2023

Abstract A filter bubble refers to the phenomenon where Internet customization effectively isolates individuals from diverse opinions or materials, resulting in their exposure only a select set of content. This can lead reinforcement existing attitudes, beliefs, conditions. In this study, our primary focus is investigate impact bubbles recommender systems (RSs). pioneering research aims uncover reasons behind problem, explore potential solutions, and propose an integrated tool help users avoid RSs. To achieve objective, we conduct systematic literature review on topic The reviewed articles are carefully analyzed classified, providing valuable insights that inform development approach. Notably, reveals evidence RSs, highlighting several biases contribute existence. Moreover, mechanisms mitigate demonstrate incorporating diversity into recommendations potentially alleviate issue. findings timely will serve as benchmark for researchers working interdisciplinary fields such privacy, artificial intelligence ethics, Furthermore, it open new avenues future related domains, prompting further exploration advancement critical area. article categorized under: Fundamental Concepts Data Knowledge > Human Centricity User Interaction Application Areas Commercial, Legal, Ethical Issues Considerations Security Privacy

Language: Английский

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Curriculum Recommendations Using Transformer Base Model with InfoNCE Loss and Language Switching Method

Published: Nov. 28, 2023

The Curriculum Recommendations paradigm is dedicated to fostering learning equality within the ever-evolving realms of educational technology and curriculum development. In acknowledging inherent obstacles posed by existing methodologies, such as content conflicts disruptions from language translation, this aims confront overcome these challenges. Notably, it addresses introduced hindrances that can impede creation an all-encompassing personalized experience. paradigm's objective cultivate environment not only embraces diversity but also customizes experiences suit distinct needs each learner. By proactively identifying addressing issues, strives pave way for a more inclusive responsive landscape, ensuring opportunities are equitable tailored individual learners' requirements. To challenges, our approach builds upon notable contributions in development learning, introducing three key innovations. These include integration Transformer Base Model enhance computational efficiency, implementation InfoNCeLoss accurate content-topic matching, adoption switching strategy alleviate translation-related ambiguities. Together, innovations aim collectively tackle challenges contribute forging effective journey diverse range learners. Competitive cross-validation scores underscore efficacy sentence-transformers/LaBSE, achieving 0.66314, showcasing methodology's effectiveness linguistic nuances alignment prediction.

Language: Английский

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Digital Twins and Control Theory: A Critical Review on Revolutionizing Quadrotor UAVs

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 43291 - 43307

Published: Jan. 1, 2024

This work explores the crucial roles that control theory and digital twins play in enhancing performance of underactuated quadrotor unmanned aerial vehicles (QUAVs). It describes how novel idea combined with could alter operations. Some basic ideas, such as UAV model, various schemes, innovative techniques to improve autonomy QUAV missions dynamic circumstances, may also be interest readers. highlights recent developments presents a game-changing combining twin computer vision, amalgamating artificial intelligence internet things like elements sensing perception better for autonomous flight control, human-UAV interaction, energy-efficient flight, swarming UAVs. The reader finally find suggestions applying understandings incorporating technology boost its revolutionary potential.

Language: Английский

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A comparative analysis of student perceptions on the effectiveness of academic advising at king saud university: a focus on experiences, satisfaction, and outcomes

Saudi Pharmaceutical Journal, Journal Year: 2025, Volume and Issue: 33(3)

Published: May 23, 2025

Language: Английский

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AI-Powered Academic Guidance and Counseling System Based on Student Profile and Interests

Applied System Innovation, Journal Year: 2023, Volume and Issue: 7(1), P. 6 - 6

Published: Dec. 28, 2023

Over the past few decades, education sector has achieved impressive advancements by incorporating Artificial Intelligence (AI) into educational environment. Nevertheless, specific processes, particularly counseling, still depend on traditional procedures. The current method of conducting group sessions between counselors and students does not offer personalized assistance or individual attention, which can cause stress to make it difficult for them informed decisions about their coursework career path. This paper proposes a counseling solution designed aid high school seniors in selecting appropriate academic paths at tertiary level. system utilizes predictive model that considers history student preferences determine students’ likelihood admission chosen university recommends similar alternative universities provide more opportunities. We developed based data from 500 graduates 12 public schools Morocco, as well eligibility criteria 31 institutions colleges. comprises two modules: recommendation module uses popularity-based content-based recommendations prediction calculates using Huber Regressor model. outperformed 13 other machine learning modules, with low MSE 0.0017, RMSE 0.0422, highest R-squared value 0.9306. Finally, is accessible through user-friendly web interface.

Language: Английский

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Improving Academic Advising in Engineering Education with Machine Learning Using a Real-World Dataset

Algorithms, Journal Year: 2024, Volume and Issue: 17(2), P. 85 - 85

Published: Feb. 18, 2024

The role of academic advising has been conducted by faculty-student advisors, who often have many students to advise quickly, making the process ineffective. selection incorrect qualification increases risk dropping out, changing qualifications, or not finishing enrolled in minimum time. This study harnesses a real-world dataset comprising student records across four engineering disciplines from 2016 and 2017 years at public South African university. examines relative importance features models for predicting performance determining whether are better suited extended mainstream programmes. employs three-step methodology, encompassing data pre-processing, feature selection, model training with evaluation, predict addressing issues such as imbalance, biases, ethical considerations. By relying exclusively on high school data, predictions based solely students’ abilities, fostering fairness minimising biases predictive tasks. results show that removing demographic like ethnicity nationality reduces bias. study’s findings also highlight significance following features: mathematics, physical sciences, admission point scores when performance. evaluated, demonstrating their ability provide accurate predictions. varying among key contributions, underscoring potential transform enhance decision-making. These can be incorporated into recommender system, thereby improving quality guidance.

Language: Английский

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Combination prediction method of students’ performance based on ant colony algorithm

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(3), P. e0300010 - e0300010

Published: March 11, 2024

Students’ performance is an important factor for the evaluation of teaching quality in colleges. The prediction and analysis students’ can guide learning time. Aiming at low accuracy problem single model prediction, a combination method put forward based on ant colony algorithm. First, considering characteristics behavior models, decision tree (DT), support vector regression (SVR) BP neural network (BP) are selected to establish three models. Then, algorithm (ACO) proposed calculate weight each model. was compared with Machine (ML) models other methods terms running mean square error (MSE) 0.0089 has higher than DT MSE 0.0326, SVR 0.0229 0.0148. To investigate efficacy model, used comparative study. GS-XGBoost 0.0131, PSO-SVR 0.0117 IDA-SVR 0.0092. Meanwhile, speed also faster above methods.

Language: Английский

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