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Deep Learning Technique for Real-Time Forest Fire Detection from Video Streams

Deep Learning Technique for Real-Time Forest Fire Detection ...

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Intelligent Systems and Embedded Design (ISED), International Conference on

作者： Nidhi Gupta Sujata Chakravarty Satyabrata Dash Dept. of Computer Science & Engineering Centurion University of Technology & Management Odisha India Dep. of Computer Science and Engineering Gandhi Institute of Technology & Management (GITAM Deemed to be University) Andhra Pradesh India

Forest fires are currently the most common issue that we are dealing with. The resources that have been lost are irreplaceable. Forest fires are uncontrollable fires that occur in nature as a result of human involvement or any other natural event. Forest fires can be so large that putting them out takes a long time. Contrasting with previous years, there has been a striking surge in the frequency of forest fires. Changes in climatic conditions are the key factor affecting the scope of forest fires. Long dry spells in the spring and summer make the woodlands particularly vulnerable. The extent of forest fires is mostly determined by weather conditions such as precipitation and wind, as well as the terrain's architecture. Long dry spells in the spring and summer make the woodlands particularly vulnerable. The extent of forest fires is mostly determined by weather conditions such as precipitation and wind, as well as the terrain's architecture. The incorporation of a Video Fire Detection model significantly enhances the efficiency of fire detection systems, particularly in large spaces. Establishing a video fire detection system involves focusing on spatial, spectral, and temporal indicators, as well as flame picture segmentation, recognition, tracking, and prediction. Analytical techniques, specifically utilizing ResNet50 and VGG16 models, can be applied to identify pool fire images through these segmentation processes, contributing to more robust fire detection capabilities.

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Machine learning algorithms in Bigdata Analysis and its applications: A review 4

Machine learning algorithms in Bigdata Analysis and its appl...

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4th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2022

作者： Khoshaba, Farah Kareem, Shahab Awla, Hoshang Mohammed, Chnar Erbil Polytechnic University Technical Engineering College Information System Engineering Department Erbil Iraq Lebanese French University College of Engineering and Computer Science Dep. of Information Technology Kurdistan Erbil Iraq Soran University College of Science Department of Computer Science Erbil Iraq

ISBN: (纸本)9781665468350

A wide range of disparate variety of heterogeneous and even disparate data sources has been integrated into the computer science research principles through the assistance of Artificial Intelligence (AI) and Machine Learning (ML) delivering excellent results in accuracy and data quality. Even so, taking machine learning approaches to very broad and dynamic datasets is both logistically and computationally costly. Some people are generating so much data regularly, which means that advanced analytics platforms have greater importance than ever before. Spark Machine Learning has extensive capabilities for a variety of ML activities such as regression, grouping, dimension reduction, and extraction of rules. While it has numerous datasets of high quality and consistency of structure. The specification of the MapReduce programming model was intended for the massive density of large-volume computation. It accomplishes this by splitting the job into several well-defined sub-sized units. There are various sets of data mining frameworks that link to a distributed device. Hadoop serves as the infrastructure for the distributed system. Several resources claim that Mahout will scale to handle massive datasets. This article discusses and demonstrates the use of algorithms and their significant impact on Bigdata, as well as a comparison between their implementations among Apache Spark and Apache Mahout. © 2022 IEEE.

关键词： Big data

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Cybersecurity Threats in the Era of AI: Detection of Phishing Domains Through Classification Rules 2

Cybersecurity Threats in the Era of AI: Detection of Phishin...

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2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Aljaidi, Mohammad Alsarhan, Ayoub Al-Fraihat, Dimah Al-Arjan, Ahmed Igried, Bashar El-Salhi, Subhieh M. Khalid, Muhammad Al-Na'amneh, Qais Zarqa University Faculty of Information Technology Department of Computer Science Zarqa13110 Jordan The Hashemite University Department of Information Technology Jordan Isra University Faculty of Information Technology Department of Software Engineering Amman11622 Jordan The Hashemite University Faculty of Prince AL-Hussein Bin Abdullah Ii of Information Technology Department of Information Technology Jordan The Hashemite University Department of Computer Science and Applications Zarqa Jordan The Hashemite University P. O Box 330127 Zarqa13133 Jordan University of Hull Department of Computer Sciences and Technology Kingston upon Hull United Kingdom Applied Science Private University Dep. of Cyber Security Amman Jordan

ISBN: (纸本)9798350373363

Phishing is a cyber threat where attackers create deceptive websites or emails to exploit individuals for fraudulent purposes such as providing sensitive information like usernames, passwords, or financial details. Hence, detecting phishing emails is of paramount importance to protect sensitive information, preventing financial losses, protecting against identity theft, and maintaining trust. This study employs machine learning techniques to classify and categorize websites that are used for phishing attacks. To achieve this, we proposed a classification model for the detection of phishing domains based on classification rules. These rules were formulated based on analyzing and distributing them into three classifiers: domain name features, search engine opinion (SEO) information of the domain, and security scans by security vendors. A total of eleven classification rules were formulated which were distributed as follows: six rules for the first classifier, four rules for the second classifier, and one rule for the third classifier. Each classifier feeds its results to the main classifier, which then evaluates the received results based on its rules. Domain names from 222 confirmed phishing URLs were used to test the suggested model. The model demonstrated exceptional performance, achieving an impressive accuracy rate of 99.1% during testing. © 2023 IEEE.

关键词： Websites

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An analysis of the Oracles used in Ethereum’s blockchain

An analysis of the Oracles used in Ethereum’s blockchain

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IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Giuseppe Antonio Pierro Honore Mahugnon Dep. of Mathematics and Computer Science University of Cagliari Cagliari Italy National Institute for Research in Digital Science and Technology INRIA Lille France

Smart contracts are programs whose business logic runs in a decentralized architecture named blockchain where each of the executing nodes trusts and agrees with the execution outcomes. Blockchain is an isolated execution environment, so smart contracts cannot access external data by themselves. The blockchain Oracle, or simply Oracle, is the software that allows smart contracts to interact with the outside world. In the past, a wide variety of studies have examined the oracles from various perspectives such as their implementation characteristics and their reliability. Unlike some other studies, this paper analyzes the use of Oracles through a static analysis of smart contracts. First, we analyze the source code of a corpus of over 40K smart contracts to find the most blockchain Oracles used by the Ethereum blockchain. On the basis of previous works, we examine the features of the most used blockchain Oracles in Ethereum. Lastly, we discussed our findings and the possible reasons why some blockchain oracles are more widely used than others. The purpose of this study is to shed light on how and which oracles are actually used by Ethereum smart contracts, as an alternative to other very interesting studies that analysed the blockchain oracle characteristics.

关键词： Source coding Smart contracts Finance Static analysis computer architecture Software Blockchains

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Real-time Control of UGV Robot in Gazebo Simulator using P300-based Brain-computer Interface

Real-time Control of UGV Robot in Gazebo Simulator using P30...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Nuaimi, Fatima Ali Al Zeddoug, Jamal Belkacem, Abdelkader Nasreddine Autonomous Robotics Research Center Technology Innovation Institute Abu Dhabi United Arab Emirates CIT United Arab Emirates University Dep. Computer and Network Engineering Al Ain United Arab Emirates

ISBN: (纸本)9781665468190

Brain computer interface (BCI)-based virtual environment control has found broad applications in solving and pursuing factual healthcare issues concerning efficiency, safety, and costs. In this technical paper, an unmanned ground vehicle (UGV) robot with a simulator-equipped BCI system was utilized. The Gazebo simulator was employed to develop a simulated setting. The software CitySim World allowed rendering the simulated milieu more down-to-earth. A non-invasive electroencephalogram (EEG)-based BCI was used to follow the brain signals and extract the P300 component, a kind of simultaneous BCI controlling procedure for safe, fast, and inexpensive implementation. This UGV control system using human brain activity can be beneficial for the real UGV platform control. It enables the discovery of the probable errors in the physical implementation. All the steps implementing our BCI system were appropriately provided (data acquisition system, user interface design, BCI data architecture, ROS/ robot Jackal, and implementation and tests). Furthermore, the project implementation and some solutions to possible issues were posed. The project outcomes were assessed by employing BCI in a simulation;before implementing real-time, to determine errors and resolve the validity of the project scope. © 2022 IEEE.

关键词： Electroencephalography

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Clinical Informatics System to Understand Lung Cancer Using Transfer Learning-Based computer-Aided Application

Clinical Informatics System to Understand Lung Cancer Using ...

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International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)

作者： Ahed Abugabah Atif Mehmood Farah Shahid College of Technological Innovation Zayed University Abu Dhabi UAE Dep. of Computer Science and Technology Zhejiang Normal University Jinhua China

Lung cancer is the second leading cause of death worldwide after cardiovascular disease. Due to late diagnosis, lung cancer patients have an extremely low survival rate when compared to other cancer patients. Hence, early lung cancer detection is critical for patients to undergo early therapies, boosting their chances of surviving or potentially becoming cancer-free. Over the past few years, researchers have developed several deep learning-based computer-aided diagnostic tools to detect lung cancer early. As a consequence, deep learning models are readily susceptible to over-fitting for fewer data samples and can be computationally time-consuming to pick hyper-parameters, which usually leads to a reduction in performance. Our proposed technique came up with a solution to this challenge of lung cancer classification tasks that used a layer-wise transfer learning strategy. In this work, three types of data samples such as Lung adenocarcinoma (Lung a), lung squamous cell carcinoma (Lung scc), and lung benign (Lung b) have been taken and implemented models, namely Group-I, II, III with frozen layers and baseline algorithms as VGG-19. To check the validity of the proposed models on lung samples and attain the best classification accuracy on Lung scc vs. Lung b 97.53%, also discriminate between Lung a vs. Lung b testing accuracy 92.59%. In contrast, the remaining Lung scc vs. Lung a attained 89.30%. Finally, a comparison to other studies demonstrating the suggested model superiority over state-of-the-art models in testing accuracy

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Human Dynamic Behavior: Reconstruction Trajectories Using CDRs 2

Human Dynamic Behavior: Reconstruction Trajectories Using CD...

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2nd International Conference on Physics and Applied Sciences, ICPAS 2021

作者： Behadili, Suhad Faisal Mohammed Ali, Israa Abdulqasim University of Baghdad College of Science Computer Science Dep. Baghdad Iraq Middle Technical University Institute of Technology Baghdad Iraq

Investigating the human mobility patterns is a highly interesting field in the 21th century, and it takes vast attention from multi-disciplinary scientists in physics, economic, social, computer, engineering⋯etc. dep.nding on the concept that relates between human mobility patterns and their communications. Hence, the necessity for a rich repository of data has emerged. Therefore, the most powerful solution is the usage of GSM network data, which gives millions of Call Details Records gained from urban regions. However, the available data still have shortcomings, because it gives only the indication of spatio-temporal data at only the moment of mobile communication activities. In this chapter, Gama platform development environment of constructing spatially multi agent simulation is used for modeling and simulating the individual reconstructed trajectories. As a result, the individual trajectories revealed the human daily life patterns. © Published under licence by IOP Publishing Ltd.

关键词： Trajectories

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Experimental Comparison of Global Planners for Trajectory Planning of Mobile Robots in an Unknown Environment with Dynamic Obstacles

Experimental Comparison of Global Planners for Trajectory Pl...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Zehra Tüfekçi Gökhan Erdemir Department of Computer Engineering Istinye University İstanbul Turkiye Department of Computer Science and Engineering Istanbul Sabahattin Zaim University Dep. of Engineering Management & Technology University of Tennessee at Chattanooga Tennessee USA

Interacting with their workspace, detecting obstacles, and real-time path planning in the field to reach the goal in the unknown environment are current working areas of mobile robotics. Working in an unknown environment with dynamic obstacles is a challenging topic for autonomous robots, which need to protect themselves from the dynamic obstacles they encounter, not damage the obstacle, and re-plan their path according to their new position. This study performed an experimental comparison of three different global planners for trajectory planning of mobile robots in an unknown environment with dynamic obstacles. Global Planner, Carrot Planner, and NavfnROS planners in the ROS package were used as global planners in two different mazes with dynamic obstacles. The durations from the start to the goal point in the two designed environments were determined for each global planner.

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Health home-monitoring system based on IoT 7

Health home-monitoring system based on IoT

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7th IEEE International Symposium on Electrical and Electronics Engineering, ISEEE 2021

作者： Ionescu, Bogdan Craciun, Marian Petrea, George Epure, Silviu Arama, Catalin Vlad, Ciprian 'Dunarea de Jos' University of Galati Dep. of IT and Digital Communications Romania 'Dunarea de Jos' University of Galati Dep. of Computer Science and Information Technology Romania 'Dunarea de Jos' University of Galati Dep. of Electronics and Telecommunications Romania 'Dunarea de Jos' University of Galati Dep. of Automation and Electrical Engineering Romania

ISBN: (纸本)9781665437998

The recent COVID-19 pandemic highlighted that there is a lot of work to do regarding the integration of digital technologies in healthcare. Actual trending offers the possibility to patients to have permanent knowledge about their health status and not to go to have a general check when some symptoms appear. Internet of Things (IoT) has also reached the medical area and can offer high quality solutions. After this step is taken the data collected and stored locally or remote in the cloud cand be easily reached, analyzed and health monitoring for each patient is much improved. © 2021 IEEE.

关键词： Internet of things

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Exploring Genetic Programming Models in computer-Aided Diagnosis of Skin Cancer Images

Exploring Genetic Programming Models in Computer-Aided Diagn...

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Congress on Evolutionary Computation

作者： Qurrat UI Ain Harith Al-Sahaf Bing Xue Mengjie Zhang School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand Dep. of Cybersecurity Engineering College of Engineering and Info. Technology AlShaab University Baghdad Iraq

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Extracting important information from complex skin lesion images is vital to effectively distinguish between different types of skin cancer images. In addition to providing high classification performance, such computer-aided diagnostic methods are needed where the models are interpretable and can provide knowledge about the discriminative features in skin lesion images. This underlying information can significantly assist dermatologists in identifying a particular stage or type of cancer. With its flexible representation and global search abilities, Genetic Programming (GP) is an ideal learning al-gorithm to evolve interpretable models and identify important features with significant information to discriminate between skin cancer classes. This paper provides an in-dep.h analysis of a recent GP-based feature learning method where different well-developed feature descriptors are integrated into the learning algorithms to extract high-level features for skin cancer image classification. The study explores the effectiveness of utilizing feature learning for this complex task and designing program structure to suit the problem domain as it has shown promising results compared to commonly used feature descriptors and an existing GP-based feature learning method developed for general image classification. This study analyzes the GP-evolved models to identify the prominent features and most effective feature descriptors important for the classification of these skin cancer images. The evolved models are interpretable, they provide knowledge that can assist dermatologists in making diagnoses in real-time clinical situations by identifying prominent skin cancer characteristics captured by the feature descriptors and learned during the evolutionary process.

关键词： Representation learning Visualization Computational modeling Genetic programming Feature extraction Skin Lesions

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