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10th International Conference on Automation, Robotics, and Applications, ICARA 2024

作者： Sousa, Ricardo B. Sobreira, Heber Miguel Silva, Manuel F. Moreira, Antonio Paulo University of Porto Faculty of Engineering Porto4200-465 Portugal INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto4200-465 Portugal ISEP Polytechnic of Porto R. Dr. António Bernardinode Almeida Porto4249-015 Portugal

ISBN: (纸本)9798350394245

The extraction of geometric information from the environment may be of interest to localisation and mapping algorithms. Existent literature on extracting geometric features from 2D laser data focuses mainly on detecting lines. Regarding corners, most methodologies use the intersection of line segment features. This paper presents a feature extraction algorithm for corner-like points in the 2D laser scan. The proposed methodol-ogy defines arrival and departure neighbourhoods around each scan point and performs local line fitting evaluated in multiple distance-based scales. Then, a set of indicators based on line fitting error, the angle between arrival and departure lines, and consecutive observation of the same keypoint across different scales determine the existence of a corner-like feature. The experiments evaluated the corner-like features regarding their relative position and observability, achieving standard deviations on the relative position lower than the sensor noise and visibility ratios higher than 75% with very low false positives rates. © 2024 IEEE.

关键词： Feature extraction

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Reversing BLE Remote Control Protocols of Dual Channel LED Lamps with CCT and Illumination Control

Reversing BLE Remote Control Protocols of Dual Channel LED L...

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2023 International Conference Automatics and Informatics, ICAI 2023

作者： Kandilarov, Rostislav Yordanov, Yordan University of Ruse 'Angel Kanchev' Faculty of Transport Department of Machine Science Machine Elements Engineering Graphics and Physics Ruse Bulgaria University of Ruse 'Angel Kanchev' Computer Systems and Technologies Faculty of Electrical Engeneering Electronics and Automation Ruse Bulgaria

ISBN: (纸本)9798350312911

In this paper various BLE protocols for communication between remote or Android applications and intelligent dual channel (cool and warm white) LED lamps were reverse engineered. Three protocols were fully decoded and several partially. Replay attack is tested and confirmed working. Demo C++ library was developed and a proof-of-concept firmware for esp32 based development board was used to verify the decoding and the attacks applicability. © 2023 IEEE.

关键词： Light emitting diodes

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Modified aquila optimizer for forecasting oil production

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Geo-Spatial Information science 2022年第4期25卷 519-535页

作者： Mohammed A.A.Al-qaness Ahmed A.Ewees Hong Fan Ayman Mutahar AlRassas Mohamed Abd Elaziz State Key Laboratory for Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanChina Systems University of Bisha Department of e-BishaKingdom of Saudi Arabia Department of Computer Damietta UniversityDamiettaEgypt School of Petroleum Engineering China University of Petroleum(East China)QingdaoChina Department of Mathematics Faculty of ScienceZagazig UniversityZagazigEgypt Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman 346United Arab Emirates Faculty of Computer Science&Engineering Galala UniversitySuze 435611Egypt

Oil production estimation plays a critical role in economic plans for local governments and ***,many studies applied different Artificial Intelligence(AI)based meth-ods to estimate oil production in different *** Adaptive Neuro-Fuzzy Inference System(ANFIS)is a well-known model that has been successfully employed in various applica-tions,including time-series ***,the ANFIS model faces critical shortcomings in its parameters during the configuration *** this point,this paper works to solve the drawbacks of the ANFIS by optimizing ANFIS parameters using a modified Aquila Optimizer(AO)with the Opposition-Based Learning(OBL)*** main idea of the developed model,AOOBL-ANFIS,is to enhance the search process of the AO and use the AOOBL to boost the performance of the *** proposed model is evaluated using real-world oil produc-tion datasets collected from different oilfields using several performance metrics,including Root Mean Square Error(RMSE),Mean Absolute Error(MAE),coefficient of determination(R2),Standard Deviation(Std),and computational ***,the AOOBL-ANFIS model is compared to several modified ANFIS models include Particle Swarm Optimization(PSO)-ANFIS,Grey Wolf Optimizer(GWO)-ANFIS,Sine Cosine Algorithm(SCA)-ANFIS,Slime Mold Algorithm(SMA)-ANFIS,and Genetic Algorithm(GA)-ANFIS,***,it is compared to well-known time series forecasting methods,namely,Autoregressive Integrated Moving Average(ARIMA),Long Short-Term Memory(LSTM),Seasonal Autoregressive Integrated Moving Average(SARIMA),and Neural Network(NN).The outcomes verified the high performance of the AOOBL-ANFIS,which outperformed the classic ANFIS model and the compared models.

关键词： Oil production ANFIS opposition-based learning(OBL) Aquila Optimizer(AO) time series forecasting Tahe oilfield Sunah oilfield

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Search and Rescue Optimization with Machine Learning Enabled Cybersecurity Model

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computer systems science & engineering 2023年第5期45卷 1393-1407页

作者： Hanan Abdullah Mengash Jaber S.Alzahrani Majdy M.Eltahir Fahd N.Al-Wesabi Abdullah Mohamed Manar Ahmed Hamza Radwa Marzouk Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Research Centre Future University in EgyptNew Cairo11745Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt

Presently,smart cities play a vital role to enhance the quality of living among human beings in several ways such as online shopping,e-learning,ehealthcare,*** the benefits of advanced technologies,issues are also existed from the transformation of the physical word into digital word,particularly in online social networks(OSN).Cyberbullying(CB)is a major problem in OSN which needs to be addressed by the use of automated natural language processing(NLP)and machine learning(ML)*** article devises a novel search and rescue optimization with machine learning enabled cybersecurity model for online social networks,named SRO-MLCOSN *** presented SRO-MLCOSN model focuses on the identification of CB that occurred in social networking *** SRO-MLCOSN model initially employs Glove technique for word embedding ***,a multiclass-weighted kernel extreme learning machine(M-WKELM)model is utilized for effectual identification and categorization of ***,Search and Rescue Optimization(SRO)algorithm is exploited to fine tune the parameters involved in the M-WKELM *** experimental validation of the SRO-MLCOSN model on the benchmark dataset reported significant outcomes over the other approaches with precision,recall,and F1-score of 96.24%,98.71%,and 97.46%respectively.

关键词： Cybersecurity cyberbullying social networking machine learning search and rescue optimization

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An Optimal Scheme for WSN Based on Compressed Sensing

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computers, Materials & Continua 2022年第7期72卷 1053-1069页

作者： Firas Ibrahim AlZobi Ahmad Ali AlZubi Kulakov Yurii Abdullah Alharbi Jazem Mutared Alanazi Sami Smadi Department of Information Systems and Networks Faculty of Information TechnologyThe World Islamic Sciences&Education UniversityJordan Computer Science Department Community CollegeKing Saud UniversitySaudi Arabia Department of Computer Engineering National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic InstituteUkraine

Wireless sensor networks(WSNs)is one of the renowned ad hoc network technology that has vast varieties of applications such as in computer networks,bio-medical engineering,agriculture,industry and many *** has been used in the internet-of-things(IoTs)applications.A method for data collecting utilizing hybrid compressive sensing(CS)is developed in order to reduce the quantity of data transmission in the clustered sensor network and balance the network *** cluster head nodes are chosen first from each temporary cluster that is closest to the cluster centroid of the nodes,and then the cluster heads are selected in order based on the distance between the determined cluster head node and the undetermined candidate cluster head ***,each ordinary node joins the cluster that is nearest to *** greedy CS is used to compress data transmission for nodes whose data transmission volume is greater than the threshold in a data transmission tree with the Sink node as the root node and linking all cluster head *** simulation results demonstrate that when the compression ratio is set to ten,the data transfer volume is reduced by a factor of *** compared to clustering and SPT without CS,it is reduced by 75%and 65%,*** compared to SPT with Hybrid CS and Clustering with hybrid CS,it is reduced by 35%and 20%,*** and SPT without CS are compared in terms of node data transfer volume standard *** with Hybrid CS and clustering with Hybrid CS were both reduced by 62%and 80%,*** compared to SPT with hybrid CS and clustering with hybrid CS,the latter two were reduced by 41%and 19%,respectively.

关键词： Compressed sensing computer networks sensor networks ad hoc networks

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A Unified Decision-Making Technique for Analysing Treatments in Pandemic Context

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computers, Materials & Continua 2022年第11期73卷 2591-2618页

作者： Fawaz Alsolami Abdullah Saad Al-Malaise Alghamdi Asif Irshad Khan Yoosef B.Abushark Abdulmohsen Almalawi Farrukh Saleem Alka Agrawal Rajeev Kumar Raees Ahmad Khan Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknow226025Uttar PradeshIndia Department of Computer Science and Engineering Babu Banarasi Das UniversityLucknow226028India

The COVID-19 pandemic has triggered a global humanitarian disaster that has never been seen *** experts,on the other hand,are undecided on the most valuable treatments of therapy because people ill with this infection exhibit a wide range of illness indications at different phases of ***,this project aims to undertake an experimental investigation to determine which treatments for COVID-19 disease is the most effective and *** research analysis is based on vast data gathered from professionals and research journals,making this study a comprehensive *** solve this challenging task,the researchers used the HF AHPTOPSIS Methodology,which is a well-known and highly effective MultiCriteria Decision Making(MCDM)*** technique assesses the many treatment options identified through various research papers and guidelines proposed by various countries,based on the recommendations of medical practitioners and *** review process begins with a ranking of different treatments based on their effectiveness using the HF-AHP approach and then evaluates the results in five different hospitals chosen by the authors as *** also perform robustness analysis to validate the conclusions of our *** a result,we obtained highly corroborative results that can be used as a *** results suggest that convalescent plasma has the greatest rank and priority in terms of effectiveness and demand,implying that convalescent plasma is the most effective treatment for SARS-CoV-2 in our *** also has the lowest priority in the estimation.

关键词： AHP-TOPSIS hesitant fuzzy sets SARS-CoV-2 healthcare sector preventive drugs

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Improving Transparency of Hardware Breakpoints with Virtual Machine Introspection 12

Improving Transparency of Hardware Breakpoints with Virtual ...

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12th International Congress on Advanced Applied Informatics, IIAI-AAI 2022

作者： Sato, Masaya Nakamura, Ryosuke Yamauchi, Toshihiro Taniguchi, Hideo Okayama Prefectural University Faculty of Computer Science and Systems Engineering Soja Japan Okayama University Graduate School of Natural Science and Technology Okayama Japan Okayama University Faculty of Natural Science and Technology Okayama Japan

ISBN: (数字)9781665497558

ISBN: (纸本)9781665497558

Hardware breakpoints are used to monitor the behavior of a program on a virtual machine (VM). Although a virtual machine monitor (VMM) can inspect programs on a VM at hardware breakpoints, the programs themselves can detect hardware breakpoints by reading debug registers. Malicious programs may change their behavior to avoid introspection and other security mechanisms if a hardware breakpoint is detected. To prevent introspection evasion, methods for hiding hardware breakpoints by returning a fake value to the VM are proposed. These methods detect the read and write operations of the debug register from the VM and then return the processing to the VM as if their access has succeeded. However, VM introspection remains detectable from the VM by confirming the availability of the debug exception in the address set. While the previous work handles the read and write operations of the debug register, the debug exception is not delivered to the VM program. To address this problem, this study presents a method for making hardware breakpoints compatible with VM introspection. The proposed method uses surplus debug address registers to deliver the debug exception at the hardware breakpoint set by the VM program. If a VM program attempts to write a value to a debug register, the VMM detects and stores the value in a real debug register that is not used for VM introspection. Because debug exception at the hardware breakpoint was delivered to the VM, hardware breakpoints set by the VM were compatible with VM introspection. The evaluation results showed that the proposed method had a low performance overhead. © 2022 IEEE.

关键词： Virtual machine

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Determining the Critical Factors of Antenatal Care Follow-Up Using Two-Phase Machine Learning

Determining the Critical Factors of Antenatal Care Follow-Up...

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International Conference on Soft Computing and Measurements (SCM)

作者： Igor A. Bessmertny Bidru C. Enkomaryam Adane L. Mamuye Faculty of Software Engineering and Computer Systems ITMO University Saint Petersburg Russia Department of Computer Science University of Gondar Gondar Ethiopia

ISBN: (数字)9798350363708

ISBN: (纸本)9798350363715

Determining the critical factors affecting antenatal visits will greatly contribute to reducing maternal and infant mortality. This study, thus attempted to construct a cluster-based predictive model to determine the critical factors of antenatal care follow-up among women in Ethiopia using two-step machine learning techniques. The 5,990 preprocessed obstetric dataset with 13 selected attributes were used that was collected from the Dabat Research Center at the University of Gondar, Ethiopia. To solve the study problem, six cluster models were developed using K-means, Expectation Maximization and Make Density clustering algorithms, which are well-suited for analyzing health datasets, providing scalability, flexibility, robustness to noise, and the ability to handle uneven cluster sizes. K-means out-formers Density based and Expectation Maximization clustering algorithms with minimum number of sum of square errors (17094) and number of iteration (3). Then, output of the K-means cluster model was used as input to segment the dataset and build a total of 21 predictive models using the tree-based J48 classifier, PART and JRip rule generators while adjusting their parameters. The model for predicting antenatal care follow-up is constructed by induction of the truncated PART rule (accuracy of 99.93%) and extracts attributes of interest such as antenatal care attendance, antenatal care location, antenatal care frequency, current vaccination, woman’s age, and HIV testing to predict antenatal care follow-up in the study area.

关键词： Machine learning algorithms Accuracy Scalability Clustering algorithms Machine learning Predictive models Prediction algorithms

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Exploiting Partial Observability and Optimized Simple State Representations in Deep Q-Learning 20

Exploiting Partial Observability and Optimized Simple State ...

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20th International Conference on Frontiers of Information Technology, FIT 2023

作者： Mahmood, Danyaal Arshad, Usama Ali, Raja Hashim Ul Abideen, Zain Shah, Muhammad Huzaifa Khan, Talha Ali Ijaz, Ali Zeeshan Ali, Nisar Siddique, Abu Bakar Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Applications & Nanostructure BCMaterials Basque Center for Materials Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350395785

The advent of deep Q-learning has opened up new possibilities in training autonomous agents to perform intelligently in intricate settings. This research work examines the potential of deep Q-learning in the paradigmatic Snake game, which requires an agent to navigate a grid, ingest food items, and avoid collisions. We incorporated a partially observable game state, introducing a novel level of difficulty to the task. Our proposed approach utilizes a straightforward feed-forward neural network to estimate Q-values of potential actions, given the current state. The agent progressively learns to balance between exploration and exploitation, allowing it to navigate optimally in the game scenario. Most existing studies in deep Q-learning for the Snake game overlook the complexities posed by partial observability, focusing mainly on fully observable state representations. Furthermore, the majority of the investigations delve into the efficacy of complex neural network architectures, leaving a gap in the understanding of the performance of more simplistic feed-forward networks. Our study fills this gap by offering an in-depth exploration and analysis of deep Q-learning strategies, especially when applied to partially observable game states and simple neural networks. Simulations and results reveal that adopting simpler state representations can significantly boost the agent's performance in the Snake game. A more detailed and intricate reward function is also observed to enhance the agent's decision-making skills and overall proficiency in the game. These observations underscore the significance of optimizing state representations and reward functions for maximizing the performance of deep Q-learning agents in the Snake game. © 2023 IEEE.

关键词： Autonomous agents

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SpamVis: A Visual Interactive System for Spam Review Detection 28th

SpamVis: A Visual Interactive System for Spam Review Detecti...

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28th International Conference on Knowledge Based and Intelligent information and engineering systems, KES 2024

作者： Lam, Nguyen Thanh Thao Le, Nu Uyen Phuong Islam, Md Rafiqul Sakib, Md KowsarHossain Prome, Shanjita Akter Sanin, Cesar Szczerbicki, Edward Zhou, Jianlong College of Business & Management VinUniversity Hanoi12426 Viet Nam Faculty of Engineering Architecture and Information Technology University of Queensland BrisbaneQLD4072 Australia Business Information Systems Australian Institute of Higher Education SydneyNSW Australia School of Computer Science Taylor's University Malaysia Faculty of Management and Economics Gdansk University of Technology Gdansk Poland School of Computer Science University of Technology Sydney Australia

In recent times, the number of spam reviews through various online platforms has emerged as a prime challenge, profoundly impacting businesses and consumers. These fake reviews not only distort clients' perceptions of products and services but also erode trust within the digital ecosystem. Despite the advent of machine learning (ML) techniques for identifying spam reviews, comparing text, and pinpointing groups of spammers, there remain notable gaps in both accuracy and the combination of interactive visualization for real-time decision-making. This paper presents SpamVis, a visual interactive system that leverages deep learning (DL) and ML blended with advanced visualization techniques for spam review detection, enabling analysts to conduct complicated analytical queries. The system allows users to input via click-on or touch to generate interactive charts and plots tailored for spam review analysis. The findings of the baseline test carried out on 67,395 review texts demonstrate that Bidirectional Encoder Representations from Transformers (BERT) carried out the best accuracy (86%) compared to other models. Our outcomes suggest that SpamVis can alleviate the gaps concerning accuracy and visualization needs in contemporary techniques, guiding analysts to make informed decisions for mitigating spam reviews and enhancing consumer trust. Furthermore, SpamVis empowers users to seamlessly discover the online reviews of various social media platforms in real-time, such as Facebook, Youtube, etc., giving them practical insights to navigate the online marketplace effectively. © 2024 The Authors.

关键词： Adversarial machine learning

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