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22nd IEEE International Symposium on Intelligent Systems and informatics, SISY 2024

作者： Safarpour Motealegh Mahalegi, Homayoun Farhadi, Amirfarhad Molnar, Gyorgy Nagy, Eniko Obuda University Institute of Mechatronics and Vehicle Engineering Budapest Hungary Islamic Azad University Department of Computer Engineering Science and Research Branch Tehran Iran Obuda University Hungary Szechenyi University Apaczai Csere János Faculty of Humanities Education and Social Sciences Kando Kalman Faculty of Electrical Engineering Budapest Gyor Hungary Obuda University Antal Bejczy Center for Intelligent Robotics John von Neumann Faculty of Informatics Budapest Hungary

ISBN: (纸本)9798350385601

In recent years, the number of unmanned aerial vehicle (UAV) applications has increased. However, navigating them indoors is still tricky because no GPS signals are available, and the obstacles constantly change. This study exploits the collaboration between convolutional neural networks (CNNs) and reinforcement learning (RL) to overcome these challenges, improving the level of independence of UAVs in indoor settings. We present a new technique known as potential-based reward shaping, which directly incorporates expert knowledge into the reinforcement learning framework. This technique efficiently addresses the problem of sparse rewards that often hinder learning in conventional RL configurations. In addition, we have created a reflective reinforcement learning agent that systematically assesses its actions to prioritize those that result in the most substantial enhancements. We have extensively evaluated the performance of our method with the High-Definition Indoor Navigation (HDIN) dataset to show that our method continuously outperformed current non-introspective RL techniques in terms of decision-making speed and navigation accuracy in this evaluation. The results show that this new CNN-RL combination is feasible and suggests a scalable method for complex indoor navigation problems. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep-Net:Fine-Tuned Deep Neural Network Multi-Features Fusion for Brain Tumor Recognition

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computers, Materials & Continua 2023年第9期76卷 3029-3047页

作者： Muhammad Attique Khan Reham R.Mostafa Yu-Dong Zhang Jamel Baili Majed Alhaisoni Usman Tariq Junaid Ali Khan Ye Jin Kim Jaehyuk Cha Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Informatics University of LeicesterLeicesterUK Information Systems Department Faculty of Computers and Information SciencesMansoura UniversityMansouraEgypt Department of Computer Engineering College of Computer ScienceKing Khalid UniversityAbha61413Saudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Management Information System Department College of Business AdministrationPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea

Manual diagnosis of brain tumors usingmagnetic resonance images(MRI)is a hectic process and ***,it always requires an expert person for the ***,many computer-controlled methods for diagnosing and classifying brain tumors have been introduced in the *** paper proposes a novel multimodal brain tumor classification framework based on two-way deep learning feature extraction and a hybrid feature optimization ***-Mobile,a pre-trained deep learning model,has been fine-tuned and twoway trained on original and enhancedMRI *** haze-convolutional neural network(haze-CNN)approach is developed and employed on the original images for contrast ***,transfer learning(TL)is utilized for training two-way fine-tuned models and extracting feature vectors from the global average pooling ***,using a multiset canonical correlation analysis(CCA)method,features of both deep learning models are fused into a single feature matrix—this technique aims to enhance the information in terms of features for better *** the information was increased,computational time also *** issue is resolved using a hybrid feature optimization algorithm that chooses the best classification *** experiments were done on two publicly available datasets—BraTs2018 and BraTs2019—and yielded accuracy rates of 94.8%and 95.7%,*** proposedmethod is comparedwith several recent studies andoutperformed *** addition,we analyze the performance of each middle step of the proposed approach and find the selection technique strengthens the proposed framework.

关键词： Brain tumor haze contrast enhancement deep learning transfer learning features optimization

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Variant of Implementation of a Grid-Tied Solar-Wind System with a Storage Battery for Self-Consumption of Local Object 8

Variant of Implementation of a Grid-Tied Solar-Wind System w...

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8th IEEE International Conference on Energy Smart Systems, ESS 2022

作者： Shavolkin, Olexandr Shvedchykova, Iryna Kolcun, Michal Medved, Dusan Kyiv National University of Technologies and Design Department of Computer Engineering and Electromechanics Kyiv Ukraine Technical University of Kosice Faculty of Electrical Engineering and Informatics Košice Slovakia

ISBN: (数字)9798350334418

ISBN: (纸本)9798350399202

The purpose of the work is the improvement the technique of the parameter's choice and control of the solar-wind system. The calculation of the system parameters for the taken schedule of the load is considered in the example. The given values are the degree of reduction of electrical energy consumption from the grid and the use of installed power of the photovoltaic battery. The boundary modes of renewable energy sources generation are taken into account. The base is the calculated values of the average monthly generation of renewable energy sources and the dependency of distribution of energy of the photovoltaic battery for a month for five years according to the archival data. With a coefficient of installed power use close to 0.9, it is possible to have a ratio of the powers of a photovoltaic battery and a wind generator of 6.9:1 with an average value over 5 years of a decrease in consumption from the grid in December by 3.48 times. When generation is below the monthly average one, it is proposed to regulate the consumption of active power according to the forecast. This allows equalizing power consumption in the daytime with a reduction during peak hours. The formation of the SOC battery graph is carried out by taking into account the forecast for the next day. The modeling confirms the possibility of reducing the consumption of electricity from the grid from 2.5 times to the full exclusion of consumption. © 2022 IEEE.

关键词： Energy utilization

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Hyperparameter Optimization of MobileNet Architecture on Balinese Mask Classification

Hyperparameter Optimization of MobileNet Architecture on Bal...

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IEEE International Symposium on Consumer Electronics (ISCE)

作者： I Gede Andika Ayu Manik Dirgayusari I Nyoman Arnawan Made Dona Wahyu Aristana Dewa Putu Yudhi Ardiana Computer Systems Engineering Faculty of Technology and Informatics Institut Bisnis dan Teknologi Indonesia Denpasar Bali Indonesia Infomatics Engineering Faculty of Technology and Informatics Institut Bisnis dan Teknologi Indonesia Denpasar Bali Indonesia

ISBN: (数字)9798350365191

ISBN: (纸本)9798350365207

This study utilized MobileN et to surpass the accuracy of previous research in the field of Balinese mask image classification. Balinese masks as one of the objects of Balinese cultural heritage must be preserved. Based on the data collected, most of young generation in Bali who has no interest in Balinese masks. The approach in the field of technology is known to have a very effective impact on the younger generation because of its rapid and efficient deployment. To improve accuracy, we analyze hyperparameter combinations such as learning rate, batch size, and dropout. Based on 16 scenarios, we got an accuracy of 98.57% for the combination of learning rate 0.00001, batch size 8, and dropout 40%. This value exceeds previous studies using the VGG16 architecture with an accuracy of 85%.

关键词： Accuracy Hyperparameter optimization Cultural differences Image classification

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Real-time Emotion Recognition in Smart Homes 17

Real-time Emotion Recognition in Smart Homes

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17th IEEE International Symposium on Applied Computational Intelligence and informatics, SACI 2023

作者： Fodor, Kristian Balogh, Zoltan Molnar, Gyorgy Constantine the Philosopher University in Nitra Faculty of Natural Sciences and Informatics Department of Computer Science Slovakia Óbuda University Kándó Kálmán Faculty of Electrical Engineering Hungary Széchenyi University János Csere Apáczai Faculty Győr Hungary

ISBN: (纸本)9798350321104

To make machines emotionally more intelligent, it is necessary to choose the right measuring devices that are able to collect physiological biosignals, which can be used to recognize the emotional state of the user. These devices should not restrict the users in their everyday life, therefore commercial, non-invasive Internet of Things (IoT) devices must be used. Thanks to the interoperability of IoT devices, it is possible to send the obtained data to a server, where it can be used in the predict method of a trained machine learning algorithm. Based on the recognized emotion, it is possible to create different instructions for smart home elements. © 2023 IEEE.

关键词： Emotion Recognition

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Arabic Sentiment Analysis of Food Delivery Services Reviews

Arabic Sentiment Analysis of Food Delivery Services Reviews

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2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Mustafa, Dheya Khabour, Safaa M. Shatnawi, Ahmed S. Taqieddin, Eyad The Hashemite University Faculty of Engineering Dept. of Computer Engineering Zarqa13133 Jordan Yarmouk University Faculty of Information Technology and Computer Sciences Dept. of Information Systems Irbid21163 Jordan Jordan University of Science & Technology Dept. of Software Engineering Irbid21110 Jordan School of Computing and Informatics Dept. of Cyber Security Al-Hussein Technical University Amman Jordan

ISBN: (纸本)9798350335590

Customer reviews on online platforms have grown to become an important source of insight into a company's performance. Food delivery services (FDS) companies aim to effectively use customers' feedback to identify areas for improvement of customer satisfaction. Although Arabic is becoming one of the most widely used languages on the Internet, only a few studies have focused on Arabic sentiment analysis to date. The present study conducts an extensive emotion mining and sentiment analysis on FDS-related reviews in Arabic, exploiting natural language processing, and machine learning techniques to extract information that reflects customers' subjective viewpoints, recognize their feelings, and determine polarity in the FDS domain. This work begins with collecting the FDS dataset (Talabat), and then extracting and creating a dialects lexicon for Arabic dialects, followed by walking the reader through detailed steps of cleaning and pre-processing a manually annotated dataset. Finally, we examined classification algorithms including Decision Tree (DT), and Support Vector Machine (SVM). We achieved a maximum accuracy of about 82% using the SVM classifier. © 2023 IEEE.

关键词： Sentiment analysis

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Chromosome Feature Extraction and Ideogram-Powered Chromosome Categorization

Chromosome Feature Extraction and Ideogram-Powered Chromosom...

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作者： Pysarchuk, Oleksii Mironov, Yurii Faculty of Informatics and Computer Science National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Kiev03056 Ukraine Faculty of Cybersecurity Computer and Software Engineering National Aviation University Kiev03058 Ukraine

Chromosomal diseases diagnostics is based on identifying chromosomes and detecting abnormalities in them. It is a sophisticated procedure that is performed manually or partially automated, therefore is prone to human error. Automation of such diagnostics is a complex multistage task that presents many challenges. One of such challenges is chromosome feature extraction and classification. Categorizing chromosomes is necessary for determining a diagnosis. This problem has been previously covered by research papers, but there is no complete solution by now. Moreover, the vast majority of proposals rely on Machine Learning (ML) and Neural Networks (NN). Usage of ML-powered solutions may pose a problem due to difficulty of dataset collection - medical data may be diverse, heterogeneous and hard to collect because of its sensitive nature. Given paper presents a proposal of chromosome feature extraction and categorization without NN. It uses computer Vision (CV) techniques for chromosome feature extraction: medial axis is extracted from a chromosome to identify its direction, and than chromosome bands (colored segments that make up distinct chromosome pattern) are identified along the axis. Having transformed chromosome image into a discrete piece of data, it is passed to a proposed multiple-criteria decision-making algorithm. This algorithm is designed to categorize chromosomes without learning dataset, instead making use of ideogram (schematic reference chromosome) data. The algorithm is focused on processing a chromosome as a set of segments, where each segment can be recognized by a special predicate. A predefined combination of such predicates should allow recognizing chromosome type. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Feature extraction

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E-Warung Systems Development 4

E-Warung Systems Development

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4th International Conference on informatics, Multimedia, Cyber and Information System, ICIMCIS 2022

作者： Tri Wahyuni, Cornelia Antoni, Darius Cholil, Widya Universitas Bina Darma Departement of Informatics Engineering Palembang Indonesia Universitas Indo Global Mandiri Faculty of Computer Science Palembang Indonesia Universitas Pembangunan Nasional Veteran Jakarta Faculty of Computer Science Jakarta Indonesia

ISBN: (纸本)9781665473279

The development of information technology is currently growing rapidly and has been implemented in many sectors including trade. Warung is one of the economic drivers of an area that sells basic needs, daily needs. The wa are relatively small and are located in residential areas, with extended working hours seven days a week and very limited availability of raw materials. This study aims to develop a warung information system that is integrated to all stakeholders. The method used to develop ewarung systems is the Prototype method, where this method describes customer needs in more detail. prototype is an unfinished program that provides an interface with a software flow simulation so that it looks like ready-made software. The prototype has three stages, namely Listen to Customer collects user needs, then Build/revice mock-ups, at this stage design and prototype systems are carried out, lastly Customer Test Drives/Mock-ups, Prototypes of the system are tested by users who will use the system until found specifications that match the wishes of the customer or user. In addition, to get maximum results, this study conducted observations used to find out information about business processes, culture and daily warung activities in the field. Interviews were conducted to obtain variations of authentic research results with the process of verifying and validating the data obtained and the last one being to do documentation, which is a record of every event that has occurred or can be said to be an event that has passed. The results of this study were in the form of an e-warung prototype design by obtaining results using alpha testing to respondents, namely running 15 processes and the results obtained were that the entire process was running accordingly. The next test, namely beta testing, was carried out on 5 respondents by asking 6 questions on the questionnaire that had been distributed, the results were that respondents who answered 'Yes' were 83.3% and respondents wh

关键词： Digital storage

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Sine Cosine Algorithm for Simple recurrent neural network Tuning for Stock Market Prediction 30

Sine Cosine Algorithm for Simple recurrent neural network Tu...

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30th Telecommunications Forum, TELFOR 2022

作者： Jovanovic, Luka Milutinovic, Nemanja Gajevic, Masa Krstovic, Jelena Rashid, Tarik A. Petrovic, Aleksandar Technical Faculty Singidunum University Belgrade Serbia University of Kurdistan Computer Science and Engineering Department Hewler44017 Iraq Singidunum University Faculty of Informatics and Computing Belgrade Serbia

ISBN: (数字)9781665472739

ISBN: (纸本)9781665472739

Deep artificial neural networks have recently gained popularity in the time series forecasting literature. Recurrent neural networks' higher suitability for this type of problem is the reason why this type of network has been chosen over other deep neural network approaches. Due to the number of parameters used the simplicity of these networks is considerable. This characteristic makes deep recurrent neural networks highly suitable for the problems of forecasting. Unfortunately, finding recurrent neural architecture for each specific task is NP-hard, therefore employment of metaheuristics is appropriate. Accordingly, the research proposed in this paper tackles tuning simple recurrent neural networks by sine cosine algorithm for stock market prediction. The proposed method's performance was compared with other metaheuristics and validated against the Nikkei stock exchange. © 2022 IEEE.

关键词： Recurrent neural networks

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BHJO: A Novel Hybrid Metaheuristic Algorithm Combining the Beluga Whale, Honey Badger, and Jellyfish Search Optimizers for Solving engineering Design Problems

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computer Modeling in engineering & Sciences 2024年第10期141卷 219-265页

作者： Farouq Zitouni Saad Harous Abdulaziz S.Almazyad Ali Wagdy Mohamed Guojiang Xiong Fatima Zohra Khechiba Khadidja Â Kherchouche Department of Computer Science and Information Technology Kasdi Merbah UniversityP.O.Box 30000OuarglaAlgeria Department of Computer Science College of Computing and InformaticsUniversity of SharjahP.O.Box 27272SharjahUnited Arab Emirates Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China

Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization *** approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization *** this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)*** proposed hybrid algorithm will be referred to as *** this fusion,the BHJO algorithm aims to leverage the strengths of each *** this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and *** meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization *** addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid ***,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem ***,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation *** rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedd

关键词： Global optimization hybridization of metaheuristics beluga whale optimization honey badger algorithm jellyfish search optimizer chaotic maps opposition-based learning

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