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A Global Best-guided Firefly Algorithm for engineering Problems

Journal of Bionic engineering 2023年第5期20卷 2359-2388页

作者： Mohsen Zare Mojtaba Ghasemi Amir Zahedi Keyvan Golalipour Soleiman Kadkhoda Mohammadi Seyedali Mirjalili Laith Abualigah Department of Electrical Engineering Faculty of EngineeringJahrom UniversityJahrom***FrasIran Department of Electronics and Electrical Engineering Shiraz University of TechnologyShiraz***Iran Department of Electrical and Computer Engineering Tarbiat Modares UniversityTehran***Iran Department of Computer Engineering Sari BranchIslamic Azad UniversitySari***Iran Department of Electrical Engineering Urmia BranchIslamic Azad UniversityUrmia571696896Iran Centre for Artificial Intelligence Research and Optimisation Torrens University AustraliaBrisbaneQLD4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722South Korea Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan Faculty of Information Technology Middle East UniversityAmman11831Jordan School of Computer Sciences Universiti Sains Malaysia11800George TownPulau PinangMalaysia University Research and Innovation Center Obuda University1034BudapestHungary School of Engineering and Technology Sunway University MalaysiaPetaling Jaya27500Malaysia Applied science research center Applied science private universityAmman11931Jordan

The Firefly Algorithm(FA)is a highly efficient population-based optimization technique developed by mimicking the flashing behavior of fireflies when *** article proposes a method based on Differential Evolution(DE)/current-to-best/1 for enhancing the FA's movement *** proposed modification increases the global search ability and the convergence rates while maintaining a balance between exploration and exploitation by deploying the global best ***,employing the best solution can lead to premature algorithm convergence,but this study handles this issue using a loop adjacent to the algorithm's main ***,the suggested algorithm’s sensitivity to the alpha parameter is reduced compared to the original *** GbFA surpasses both the original and five-version of enhanced FAs in finding the optimal solution to 30 CEC2014 real parameter benchmark problems with all selected alpha ***,the CEC 2017 benchmark functions and the eight engineering optimization challenges are also utilized to evaluate GbFA’s efficacy and robustness on real-world problems against several enhanced *** all cases,GbFA provides the optimal result compared to other *** that the source code of the GbFA algorithm is publicly available at https://***/projects/gbfa.

关键词： Firefly algorithm New movement vector Global best-guided firefly algorithm Global optimization engineering design

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Enhancing crop yield prediction in Senegal using advanced machine learning techniques and synthetic data

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Artificial Intelligence in Agriculture 2024年第4期14卷 99-114页

作者： Mohammad Amin Razavi A.Pouyan Nejadhashemi Babak Majidi Hoda S.Razavi Josue Kpodo Rasu Eeswaran Ignacio Ciampitti P.V.Vara Prasad School of Electrical and Computer Engineering University of TehranTehranIran Department of Biosystems and Agricuttural Engineering Michigan State UniversityMIUSA Department of Computer Engineering Khatam UniversityTehranIran Department of Computer Science and Engineering Michigan State UniversityEast LansingMIUSA Department of Agronomy Faculty of AgricultureUniversity of JafinaKilinochchSri Lanka Department of Agronomy Kansas State UniversityManhattanKSUA Feed the Future Sustainable Intensification Innovation Lab Kansas State UniversityManhattanKSUA

In this study,we employ advanced data-driven techniques to investigate the complex relationships between the yields of five major crops and various geographical and spatiotemporal features in *** analyze how these features influence crop yields by utilizing remotely sensed *** methodology incorporates clustering algorithms and correlation matrix analysis to identify significant patterns and dependencies,offering a comprehensive understanding of the factors affecting agricultural productivity in *** optimize the model's performance and identify the optimal hyperparameters,we implemented a comprehensive grid search across four distinct machine learning regressors:Random Forest,Extreme Gradient Boosting(XGBoost),Categorical Boosting(CatBoost),and Light Gradient-Boosting Machine(LightGBM).Each regressor offers unique functionalities,enhancing our exploration of potential model *** top-performing models were selected based on evaluating multiple performance metrics,ensuring robust and accurate predictive *** results demonstrated that XGBoost and CatBoost perform better than the other *** introduce synthetic crop data generated using a Variational Auto Encoder to address the challenges posed by limited agricultural *** achieving high similarity scores with real-world data,our synthetic samples enhance model robustness,mitigate overfitting,and provide a viable solution for small dataset issues in *** approach distinguishes itself by creating a flexible model applicable to various crops *** integrating five crop datasets and generating high-quality synthetic data,we improve model performance,reduce overfitting,and enhance *** findings provide crucial insights for productivity drivers in key cropping systems,enabling robust recommendations and strengthening the decision-making capabilities of policymakers and farmers in datascarce regions.

关键词： Crop yield prediction Variational auto encoder Pattern recognition on spatiotemporal and physiographical variables Synthetic tabular data generation Ensemble learning

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An Optimized Algorithm for Renewable Energy Forecasting Based on Machine Learning

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Intelligent Automation & Soft Computing 2023年第1期35卷 755-767页

作者： Ziad M.Ali Ahmed M.Galal Salem Alkhalaf Imran Khan College of Engineering at Wadi Addawaser Prince Sattam bin Abdulaziz UniversityWadi Aldawaser11991Saudi Arabia Electrical Engineering Department Aswan Faculty of EngineeringAswan UniversityAswan81542Egypt Production Engineering and Mechanical Design Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer College of Science and Arts in Ar RassQassim UniversityAr RassSaudi Arabia Department of Electrical Engineering University of Engineering and TechnologyPeshawar814Pakistan

The large-scale application of renewable energy power generation technology brings new challenges to the operation of traditional power grids andenergy management on the load side. Microgrid can effectively solve this problemby using its regulation and flexibility, and is considered to be an ideal *** traditional method of computing total transfer capability is difficult due tothe central integration of wind farms. As a result, the differential evolutionextreme learning machine is offered as a data mining approach for extractingoperating rules for the total transfer capability of tie-lines in wind-integratedpower systems. K-medoids clustering under the two-dimensional “wind power-load consumption” feature space is used to define representative operational scenarios initially. Then, using stochastic sampling and repetitive power flow, aknowledge base for total transfer capability operating rule mining is ***, a novel method is used to filter redundant characteristics and find featuresthat are closely associated to the total transfer capability in order to decrease theultra-high dimensionality of operational features. Finally, by feeding the trainingdata into the proposed algorithm, the total transfer capability operation rules arederived from the knowledge base. It can be seen that, the proposed algorithmcan optimize the system performance with good accuracy and generality, according to numerical data.

关键词： Load forecasting distribution network machine learning renewable energy

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Accurate Proximity Sensor for Parking Assistance 8

Accurate Proximity Sensor for Parking Assistance

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8th Human-computer Interaction Slovenia Conference, HCI SI 2023

作者： Klopčič, Matic Pečečnik, Kristina Stojmenova University of Ljubljana Faculty of Electrical Engineering Tržaška cesta 25 Ljubljana1000 Slovenia University of Ljubljana Faculty of Computer and Information Science Večna pot 113 Ljubljana1000 Slovenia

The distance to obstacles is crucial for safe driving and parking. Currently, cars are equipped with sensors that measure the distance to obstacles, but the distance is only displayed using different colors and sounds. Therefore, we have assembled a device that, with a distance measuring sensor, thermometer, and humidity sensor, measures and calculates the distance to the nearest object. It accurately displays the exact distance down to the centimeter on the screen, providing the driver with more precise information about the proximity to obstacles. © 2024 Copyright for this paper by its authors.

关键词： DHT22 HC - SP04 proximity sensor Raspberry Pi relative humidity temperature

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Natural Language Processing with Optimal Deep Learning Based Fake News Classification

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computers, Materials & Continua 2022年第11期73卷 3529-3544页

作者： Sara AAlthubiti Fayadh Alenezi Romany F.Mansour Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

The recent advancements made in World Wide Web and social networking have eased the spread of fake news among people at a faster *** most of the times,the intention of fake news is to misinform the people and make manipulated societal *** spread of low-quality news in social networking sites has a negative influence upon people as well as the *** order to overcome the ever-increasing dissemination of fake news,automated detection models are developed using Artificial Intelligence(AI)and Machine Learning(ML)*** latest advancements in Deep Learning(DL)models and complex Natural Language Processing(NLP)tasks make the former,a significant solution to achieve Fake News Detection(FND).In this background,the current study focuses on design and development of Natural Language Processing with Sea Turtle Foraging Optimizationbased Deep Learning Technique for Fake News Detection and Classification(STODL-FNDC)*** aim of the proposed STODL-FNDC model is to discriminate fake news from legitimate news in an effectual *** the proposed STODL-FNDC model,the input data primarily undergoes pre-processing and Glove-based word ***,STODL-FNDC model employs Deep Belief Network(DBN)approach for detection as well as classification of fake ***,STO algorithm is utilized after adjusting the hyperparameters involved in DBN model,in an optimal *** novelty of the study lies in the design of STO algorithm with DBN model for *** order to improve the detection performance of STODL-FNDC technique,a series of simulations was carried out on benchmark *** experimental outcomes established the better performance of STODL-FNDC approach over other methods with a maximum accuracy of 95.50%.

关键词： Natural language processing text mining fake news detection deep belief network machine learning evolutionary algorithm

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A Hybrid Algorithm ACS-GA for Solving the Traveling Salesman Problem 1

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15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： Bai, Zhonghai Snášel, Václav Vo, Bay Kong, Lingping Wang, Xiaopeng Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava Ostrava70800 Czech Republic Faculty of Information Technology HUTECH University Ho Chi Minh City Viet Nam

ISBN: (数字)9789819700684

ISBN: (纸本)9789819700677

Traveling Salesman Problem (TSP), a well-known combinatorial optimization problem, aims to find the shortest path for a salesman to visit all the given cities and return to the departure city, with each city visited once and only once. To solve the problem, we propose a hybrid metaheuristic algorithm that combines the ant colony system(ACS) and genetic algorithm(GA). Distinguishing from previous hybrid algorithms, the two algorithms in our proposed hybrid algorithm run independently in parallel, exchange each other’s optimal solutions in a fixed iteration cycle and incorporate the exchanged optimal solutions into the independently running algorithm through update pheromone or add to the candidate parent. The experimental part uses data instances with a city size distribution between 51 and 226. The results of running the ACS, GA, Tabu Search (TS), and hybrid algorithms ACS-GA were compared. The results show that our hybrid algorithm ACS-GA outperforms the results of the single algorithm in terms of solution quality and stability overall. This research provides a better solution to optimizing TSP by proposing a novel hybrid metaheuristic algorithm. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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Analysis of Google Play Store's Sentiment Review on Indonesia's P2P Fintech Platform

Analysis of Google Play Store's Sentiment Review on Indonesi...

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2022 IEEE Delhi Section Conference, DELCON 2022

作者： Amrie, Syahrul Kurniawan, Sandy Windiatmaja, Jauzak Hussaini Ruldeviyani, Yova Universitas Indonesia Faculty of Computer Science Department of Information Technology Jakarta Indonesia Universitas Indonesia Faculty of Computer Science Department of Computer Science Jakarta Indonesia Universitas Indonesia Faculty of Engineering Department of Electrical Engineering Jakarta Indonesia Universitas Indonesia Faculty of Computer Science Department of Computer Science Depok Indonesia

ISBN: (纸本)9781665458832

Nowadays there are so many mobile phone-based investment applications, ranging from mutual funds, stocks, and P2P lending. While these investment applications are gaining huge attraction among the general masses, sometimes selecting the right platform still becomes a hot issue. This research aimed to analyze the sentiment on P2P lending applications and to determine the user's response due to the increase in the number of funds distribution during the COVID-19 pandemic. By doing so, this research could give some insight into the new and existing user. Data was obtained through assessment reviews on the Play store platform for the P2P A, P2P B, and P2P C applications. Assessment reviews were classified by using a data mining approach, TF-IDF feature extraction, and Naïve-Bayes classification method. This research showed that P2P A got 77% positive sentiment and 23% negative sentiment, P2P B got 36% positive sentiment and 64% negative sentiment, and P2P C got 68% positive sentiment and 32% negative sentiment. From the results of the study, it was found that P2P A got better results than both P2P B and P2P C. those were 77% positive sentiment with 23% negative sentiment in finance topic, 56% positive sentiment with 44 % negative sentiment in account verification topic, 79% positive sentiment with 21% negative sentiment in apps review, and 99% positive sentiment with 1% negative sentiment in referral topic. © 2022 IEEE.

关键词： Sentiment analysis

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Bridging the Physical and Virtual Worlds: A Hand Tracking Gesture Recognition System for XR Applications 1

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32nd International Conference on Organization and Technology of Maintenance, OTO 2023

作者： Fumić, Matija Livada, Časlav Faculty of Electrical Engineering Computer Science and Information Technology Osijek Josip Juraj Strossmayer University in Osijek Kneza Trpimira 2B Osijek31000 Croatia

ISBN: (数字)9783031514944

ISBN: (纸本)9783031514937

Extended reality (XR) is a term that encompasses augmented reality (AR), virtual reality (VR), and mixed reality (MR). These technologies allow users to interact with digital content in immersive and realistic ways. One of the challenges of XR is to provide natural and intuitive ways to control and manipulate the virtual environment. This paper presents the development of a gesture recognition system for hand tracking in extended reality technologies (XR) and its practical applications. The system uses a gesture definition language that allows the user to create custom gestures based on the hand skeleton model. The system was implemented using the Oculus Quest 2 device and the Oculus SDK in the Unity game engine and is compatible with all devices that support hand tracking. The gesture recognition system was implemented using a three-bone thumb and little finger model that is compatible with all hand models that contain four bones. The implementation of this gesture recognition system in XR has several benefits, including increased immersion, improved interaction with virtual objects, and reduced physical fatigue compared to traditional input methods such as controllers or keyboards. This system provides users with a more intuitive and natural way to interact with their virtual environment. Overall, this work demonstrates the feasibility and potential of integrating gesture recognition into XR technologies and its ability to enhance user experience and interaction. The system presented in this work can serve as a foundation for future developments in this area, with potential applications in gaming, education, healthcare, and other fields. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Augmented reality

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Securing Primary Users in IoT 5G Cognitive Radio Networks 32

Securing Primary Users in IoT 5G Cognitive Radio Networks

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32nd International Conference on Software, Telecommunications and computer Networks, SoftCOM 2024

作者： Alrjoob, Ayat M. Ababnah, Ahmad A. Al-Mistarihi, Mamoun F. Darabkh, Khalid A. German-Jordanian University School of Electrical Engineering and Information Technology Electrical and Communication Engineering Department Amman11180 Jordan Jordan University of Science and Technology Faculty of Engineering Electrical Engineering Department P.o.Box 3030 Irbid22110 Jordan The University of Jordan School of Engineering Computer Engineering Department Amman11942 Jordan

ISBN: (纸本)9789532901382

The Internet of Things (IoT) has transformed device communication, necessitating reliable information security, especially physical layer security (PLS). Protecting data confidentiality in wireless transfers is critical in IoT environments. Cognitive Radio Networks (CRNs) optimize spectrum utilization, enhancing network efficiency. This study investigates PLS in CRNs, featuring a primary source-destination pair and a secondary user (SU) that performs cooperative jamming to secure the primary user's (PU) communication. The SU generates artificial interference, with transmit power determined by the primary network's interference tolerance. We develop mathematical formulas to assess outage probabilities, aiming to enhance security and efficiency in IoT and spectrum management. © 2024 University of Split, FESB.

关键词： Risk assessment

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Autonomous Vehicle Control in CARLA Simulator Using Reinforcement Learning 66

Autonomous Vehicle Control in CARLA Simulator Using Reinforc...

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66th International Symposium ELMAR, ELMAR 2024

作者： Cincurak, Dorian Grbic, Ratko Vranjcs, Mario Vranjes, Denis TTTech Auto Central and Eastern Europe OsijekHR-31000 Croatia Computer Science and Information Technology Osijek Faculty of Electrical Engineering OsijekHR-31000 Croatia

ISBN: (纸本)9798350375411

Autonomous vehicles (AVs) have emerged as a transformative technology that promises safer and more efficient transportation systems. Central to the development of autonomous driving is the ability to make real-time decisions in complex and dynamic environments. Reinforcement learning (RL) has gained considerable attention as a promising approach that enables AVs to learn control policies directly from interaction with their environment. In this paper, two RL algorithms for autonomous driving were developed in the CARLA autonomous driving simulator: one based on the Deep Q-Learning (DQN) algorithm and the other based on the Deep Deterministic Policy Gradient (DDPG) algorithm. The evaluation of the developed algorithms was carried out using the user-defined metrics and shows promising results in learning from interaction. © 2024 IEEE.

关键词： Deep reinforcement learning

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