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An integrated GIS-based multivariate adaptive regression splines-cat swarm optimization for improving the accuracy of wildfire susceptibility mapping

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Geocarto International 2023年第1期38卷

作者： Hai, Tao Theruvil Sayed, Biju Majdi, Ali Zhou, Jincheng Sagban, Rafid Band, Shahab S. Mosavi, Amir School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Guizhou Duyun China Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Selangor Shah Alam Malaysia Department of Computer Science Dhofar University Salalah Oman Department of Building and Construction Technologies Engineering Al-Mustaqbal University College Hilla Iraq Department of Computer Technology Engineering Technical Engineering College Al-Ayen University Thi-Qar Iraq Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou Taiwan John von Neumann Faculty of Informatics Obuda University Budapest Hungary German Research Center for Artificial Intelligence Oldenburg Germany Institute of the Information Society University of Public Service Budapest Hungary

A hybrid machine learning method is proposed for wildfire susceptibility mapping. For modeling a geographical information system (GIS) database including 11 influencing factors and 262 fire locations from 2013 to 2018 is used for developing an integrated multivariate adaptive regression splines (MARS). The cat swarm optimization (CSO) algorithm tunes the parameters of the MARS in order to generate accurate susceptibility maps. From the Pearson correlation results, it is observed that land use, temperature, and slope angle have strong correlation with the fire severity. The results demonstrate that the prediction capability of the MARS-CSO model outperforms model tree, reduced error pruning tree and MARS. The resulting wildfire risk map using MARS-CSO reveals that 20% of the study areas is categorized in the very low wildfire risk class, whereas 40% is under the very high class of fire hazard. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： artificial intelligence cat swarm optimization geospatial machine learning natural hazard Wildfire susceptibility

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Enhancing UAV Autonomous Navigation in Indoor Environments Using Reinforcement Learning and Convolutional Neural Networks

Enhancing UAV Autonomous Navigation in Indoor Environments U...

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International Symposium on Intelligent Systems and informatics (SISY)

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

ISBN: (数字)9798350385601

ISBN: (纸本)9798350385618

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.

关键词： Indoor navigation Refining Reinforcement learning Autonomous aerial vehicles Indoor environment Convolutional neural networks Intelligent systems informatics Autonomous robots Global Positioning System

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A New Stock Price Forecasting Method Using Active Deep Learning Approach

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Journal of Open Innovation: technology, Market, and Complexity 2022年第2期8卷

作者： Alkhatib, Khalid Khazaleh, Huthaifa Alkhazaleh, Hamzah Ali Alsoud, Anas Ratib Abualigah, Laith Department of Computer Information Systems Jordan University of Science and Technology Irbid 22110 Jordan College of Engineering and IT University of Dubai Academic City Dubai 14143 United Arab Emirates Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan Faculty of Computer Sciences and Informatics Amman Arab University Amman 11953 Jordan

Stock price prediction is a significant research field due to its importance in terms of benefits for individuals, corporations, and governments. This research explores the application of the new approach to predict the adjusted closing price of a specific corporation. A new set of features is used to enhance the possibility of giving more accurate results with fewer losses by creating a six-feature set (that includes High, Low, Volume, Open, HiLo, OpSe), rather than the traditional four-feature set (High, Low, Volume, Open). The study also investigates the effect of data size by using datasets (Apple, ExxonMobil, Tesla, Snapchat) of different sizes to boost open innovation dynamics. The effect of the business sector in terms of the loss result is also considered. Finally, the study included six deep learning models, MLP, GRU, LSTM, Bi-LSTM, CNN, and CNN-LSTM, to predict the adjusted closing price of the stocks. The six variables used (High, Low, Open, Volume, HiLo, and OpSe) are evaluated according to the model’s outcome, showing fewer losses than the original approach, which utilizes the original feature set. The results show that LSTM-based models improved using the new approach, even though all models showed a comparative result wherein no model showed better results or continuously outperformed other models. Finally, the added new features positively affected the prediction models’ performance. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： Bi-LSTM CNN deep learning GRU LSTM MLP stock price prediction

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Imaging Intracellular Metabolic Activity of Mammalian Oocytes by Dynamic Full-Field Optical Coherence Microscopy

Imaging Intracellular Metabolic Activity of Mammalian Oocyte...

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Conference on Lasers and Electro-Optics Europe (CLEO EUROPE)

作者： Seweryn Morawiec Patrycjusz Stremplewski Anna Ajduk Brendan Kennedy Maciej Szkulmowski Faculty of Physics Institute of Physics Astronomy and Informatics Nicolaus Copernicus University in Torun Torun Poland BRITElab Harry Perkins Institute of Medical Research QEII Medical Centre Nedlands Western Australia Australia Department of Embryology Faculty of Biology University of Warsaw Warsaw Poland Department of Electrical Electronic & Computer Engineering School of Engineering The University of Western Australia Perth Western Australia Australia Centre for Medical Research The University of Western Australia Crawley Western Australia Australia

ISBN: (纸本)9798350345995

Full-field optical coherence microscopy (FF-OCM) is ideally suited to study living cells as it provides high-resolution cross-section images with no need for fluorescent labelling [1], [2]. The OCM signal is proportional both to the scattering potential of the sample, providing structural information, and to the motion amplitude of the inter-cellular structures. Thus, the metabolic activity information can be extracted from the spatiotemporal analysis of the OCM images. This technique is particularly appealing for embryology to noninvasively assess the development potential of living cells, e.g., oocytes [3].

关键词： Color spaces Image analysis Optical microscopy Phase modulation Spatial resolution Speckle noise

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Power of sequential protocols in hidden quantum channel discrimination

arXiv

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arXiv 2023年

作者： Sugiura, Sho Dutt, Arkopal Munro, William J. Zeytinoǧlu, Sina Chuang, Isaac L. Physics and Informatics Laboratory Ntt Research Inc. 940 Stewart Dr. SunnyvaleCA94085 United States Laboratory for Nuclear Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Co-Design Center for Quantum Advantage Massachusetts Institute of Technology CambridgeMA02139 United States Ntt Basic Research Laboratories Research Center for Theoretical Quantum Physics 3-1 Morinosato-Wakamiya Kanagawa Atsugi243-0198 Japan Department of Physics Harvard University CambridgeMA02138 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

In many natural and engineered systems, unknown quantum channels act on a subsystem that cannot be directly controlled and measured, but is instead learned through a controllable subsystem that weakly interacts with it. We study quantum channel discrimination (QCD) under these restrictions, which we call hidden system QCD (HQCD). We find that sequential protocols achieve perfect discrimination and saturate the Heisenberg limit. In contrast, depth-1 parallel and multi-shot protocols cannot solve HQCD. This suggests that sequential protocols are superior in experimentally realistic situations. Copyright © 2023, The Authors. All rights reserved.

关键词： Communication channels (information theory)

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Applications of Machine Learning Techniques in Detecting Skin Cancer

Applications of Machine Learning Techniques in Detecting Ski...

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International Conference on Contemporary Computing and informatics (IC3I)

作者： Pankaj Kunekar Rounak Vaishnav E. Kalaivani Durgaprasad Gangodkar Chamandeep Kaur Joshuva Arockia Dhanraj Vishwakarma Institute of Technology Pune Department of Medical Oncology Sri Ram Cancer and Super specialty centre Clinical Research Manager Mahatma Gandhi University of medical sciences and technology Jaipur Bannari Amman Institute of Technology Sathyamangalam Tamil Nadu Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science & Information Technology Jazan University Saudi Arabia Department of Mechatronics Engineering Centre for Automation & Robotics (ANRO) Hindustan Institute of Technology and Science Padur Chennai India

The most lethal kind of skin cancer is called malignant melanoma. Accurate diagnosis of skin malignancies are aided by dermoscopy, a non-invasive high-resolution imaging procedure. Melanoma is a very dangerous kind of skin cancer because of how quickly it spreads and how aggressive it is. For this reason, malignant melanoma maintains its place as one of the tumors with the highest rates of growth across the globe. Second-order texture feature data may be obtained by the GLCM (Grey Level Co-occurrence Matrix) technique. The reference pixel and neighboring pixels are considered by GLCM to ensure an accurate calculation. Grayscale pictures have a decent number of levels, which is straightforwardly relative to the number of segments and columns in the frameworks used to characterize the scale. Relative recurrence is addressed by the grid component P I j | Dx, Dy), where I and j are force values and Dx and Dy are pixel distances. In total, the co-occurrence matrix considers 14 different attributes, including energy, efficiency, latency, correlation, homogeneity, and contrast. Convolutional Neural networks (CNN) and Gaussian Filters (GF) classifiers were then used to sort these vectors. Comparisons of the results these classifiers provide are also made.

关键词： Force Medical treatment High-resolution imaging Melanoma Machine learning Gray-scale Skin

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Expression of Concern for: Applications of Machine Learning Techniques in Detecting Skin Cancer

Expression of Concern for: Applications of Machine Learning ...

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International Conference on Contemporary Computing and informatics (IC3I)

ISBN: (数字)9798350398267

ISBN: (纸本)9798350398274

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The Block Chain technology to protect Data Access using Intelligent Contracts Mechanism Security Framework for 5G Networks

The Block Chain Technology to protect Data Access using Inte...

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International Conference on Contemporary Computing and informatics (IC3I)

作者： Ranjeet Yadav Ritambhara Karthik Kumar Vaigandla G S Pradeep Ghantasala Rajesh Singh Durgaprasad Gangodkar Lecturer in Electronics and Communication Engineering Government Polytechnic Daman Varkund Nani Daman UT Administration of Dadra and Nagar Haveli & Daman and Diu India Department of Electronics and Communication Engineering Jaipur Engineering College and Research centre Jaipur Rajasthan India Department of Electronics and Communication Engineering Balaji Institute of Technology and Science Warangal Telangana India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Uttaranchal Institute of Technology Uttaranchal University Dehradun Uttarakhand India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

The introduction of the study primarily emphasises the significance of utilising block chain technologies with the possibility of privacy and security benefits from the 5G Network. One may state that the study’s primary focus is on all the advantages of adopting block chain technology to safeguard everyone’s access to crucial data by utilizing intelligent contracts to enhance the 5G network security model on information security *** literature evaluation for the study focuses primarily on the advantages advantages of utilizing block chain technology advance data security and privacy, as well as their development and growth. The whole study paper has covered both the benefits and drawbacks of employing the block chain technology. The literature study part of this research article has, on the contrary hand, also studied several approaches and tactics for using the blockchain technology facilities. To fully understand the circumstances in this specific case, a poll was undertaken. It was possible for the researchers to get some real-world data in this specific situation by conducting a survey with 51 randomly selected participants.

关键词： Data privacy Privacy 5G mobile communication Data security Information security Network security Blockchains

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Canonical Computations in Cellular Automata and Their Application for Reservoir Computing

Canonical Computations in Cellular Automata and Their Applic...

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Conference on Artificial Life, ALIFE 2023

作者： Lindell, Trym A. E. Hudcová, Barbora Nichele, Stefano Artificial Intelligence Lab Department of Computer Science Faculty of Technology Art and Design Oslo Metropolitan University Oslo Norway Machine Learning Research Group Østfold University College Halden Norway Department of Algebra Faculty of Mathematics Charles University Prague Czech Republic Czech Institute of Informatics Robotics and Cybernetics CTU Prague Czech Republic

Cellular Automata (CAs) have potential as powerful parallel computational systems, which has lead to the use of CAs as reservoirs in reservoir computing. However, why certain Cellular Automaton (CA) rules, sizes and input encodings are better or worse at a given task is not well understood. We present a method that enables identification and visualization of the specific information content, flow and transformations within the space-time diagram of CA. We interpret each spatio-temporal location in CA’s space-time diagram as a function of its input and call this novel notion the CA’s Canonical Computations (CCs). This allows us to analyze the available information from the space-time diagrams as partitions of the input set. The method also reveals how inputencoder-rule interactions transform the information flow by changing features like spatial and temporal location stability as well as the specific information produced. This general approach for analysing CA is discussed for the engineering of reservoir computing systems. © 2023, Massachusetts institute of technology. All rights reserved.

关键词： Sequential machines

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Exploring Machine Learning Techniques to Identify Important Factors Leading to Injury in Curve Related Crashes

arXiv

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arXiv 2023年

作者： Moeinaddini, Mehdi Pourmoradnasseri, Mozhgan Hadachi, Amnir Cools, Mario ITS Lab Institute of Computer Science University of Tartu Narva mnt 18 Tartu51009 Estonia LEMA Research Group Urban & Environmental Engineering Department University of Liège Liège Belgium Department of Informatics Simulation and Modelling KULeuven Campus Brussels Brussels Belgium Faculty of Business Economics Hasselt University Diepenbeek Belgium

Different factors have effects on traffic crashes and crash-related injuries. These factors include segment characteristics, crash-level characteristics, occupant level characteristics, environment characteristics, and vehicle level characteristics. There are several studies regarding these factors’ effects on crash injuries. However, limited studies have examined the effects of pre-crash events on injuries, especially for curve-related crashes. The majority of previous studies for curve-related crashes focused on the impact of geometric features or street design factors. The current study tries to eliminate the aforementioned shortcomings by considering important pre-crash events related factors as selected variables and the number of vehicles with or without injury as the predicted variable. This research used CRSS data from the National Highway Traffic Safety Administration (NHTSA), which includes traffic crash-related data for different states in the USA. The relationships are explored using different machine learning algorithms like the random forest, C5.0, CHAID, Bayesian Network, Neural Network, C&R Tree, Quest, etc. The random forest and SHAP values are used to identify the most effective variables. The C5.0 algorithm, which has the highest accuracy rate among the other algorithms, is used to develop the final model. Analysis results revealed that the extent of the damage, critical pre-crash event, pre-impact location, the trafficway description, roadway surface condition, the month of the crash, the first harmful event, number of motor vehicles, attempted avoidance maneuver, and roadway grade affect the number of vehicles with or without injury in curve-related crashes. © 2023, CC BY.

关键词： Machine learning

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