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1st International Conference on Smart Mechatronics, ICSMech 2024

作者： Jusman, Yessi Nur 'Aini, Masayu Alya Naufal, Zaidan Ahmad Ridho, Prajarino Rushartini, Hesti Wimala, Galuh Ananda, Indra Pradani, Nisha Alifa Universitas Muhammadiyah Yogyakarta Center of Artificial Intelligence and Robotics Studies Department of Electrical Engineering Bantul Indonesia Universiti Malaysia Perlis Faculty of Electrical Engineering and Technology Perlis Malaysia Universitas Muhammadiyah Yogyakarta Faculty of Engineering Department of Electrical Engineering Bantul Indonesia

ISBN: (纸本)9798350368918

Breast cancer remains one of the most common and deadly forms of cancer affecting women globally. Early detection through mammogram screening plays a vital role in improving survival rates by enabling timely diagnosis and treatment. This study explored the application of deep learning techniques, particularly GoogLeNet and ResNet-50 architectures, to classify mammogram images into benign and malignant categories. The dataset, sourced from Kaggle, consisted of 479 mammogram images. These images were preprocessed and divided into training and testing sets, with 90% allocated for training and 10% for testing. Both GoogLeNet and ResNet-50 models were trained and tested, yielding different performance metrics. GoogLeNet achieved the highest testing accuracy of 70.83%, while ResNet-50 attained 68.75% accuracy. GoogLeNet demonstrated better sensitivity at 92.85%, but ResNet-50 outperformed in terms of precision at 69.69%. Although both models displayed competitive results, GoogLeNet exhibited superior performance in classifying cancer cells based on sensitivity, making it more effective in identifying true positives. These findings demonstrated the potential of deep learning models to enhance breast cancer detection and classification using mammogram images, contributing to improved diagnostic accuracy and patient outcomes. © 2024 IEEE.

关键词： Lung cancer

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Urban traffic signal control optimization through Deep Q Learning and double Deep Q Learning: a novel approach for efficient traffic management

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Multimedia Tools and Applications 2024年 1-24页

作者： Jamil, Qazi Umer Kallu, Karam Dad Khan, Muhammad Jawad Safdar, Muhammad Zafar, Amad Ali, Muhammad Umair Sector H-12 Islamabad44000 Pakistan School of Mechanical Engineering Chonnam National University Gwangju Korea Republic of Department of Electrical Engineering School of Engineering Prince Sattam Bin Abdul Aziz University Al Kharj Saudi Arabia Sector H-12 Islamabad44000 Pakistan Department of Artificial Intelligence and Robotics Sejong University Seoul05006 Korea Republic of

Traffic congestion remains a persistent challenge in urban areas, necessitating efficient traffic control strategies. This research explores the application of advanced reinforcement learning techniques, specifically Deep Q-Learning (DQN) and Double Deep Q-Learning (DDQN), to address this issue at a four-way traffic intersection. The RL agents are trained using a reward function based on minimizing waiting times, enabling them to learn effective traffic signal control policies. The study focuses on comparing the performance of a simple non-reinforcement learning (Non RL) agent, a Deep Q-Network (DQN) agent, and an improved Double Deep Q-Learning (DDQN) agent in different traffic scenarios. The Non RL agent, which follows a fixed order of traffic phases, demonstrates limitations in both low and high traffic situations, leading to inefficiencies and imbalanced queue lengths. On the other hand, the DQN agent exhibits promising results in low traffic conditions but struggles in high traffic due to its greedy behavior. The DDQN agent, with an extended green light base time, outperforms both the Non RL agent and the original DQN agent in high traffic scenarios, making it more suitable for real-world traffic conditions. However, it shows some inefficiencies in low traffic scenarios. Future research is recommended to address multi-agent deep reinforcement learning challenges, incorporate attention mechanisms and hierarchical reinforcement learning, explore graph theory applications, and develop efficient communication protocols among agents to further enhance traffic control solutions. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Traffic congestion

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Clustering-Based artificial Rabbit Optimization: Approach for Enhanced Data Clustering

Clustering-Based Artificial Rabbit Optimization: Approach fo...

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Computational intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Balqees Ayal-Awwad Jamil Al-Sawwa Mohammad Almseidin Maen Alzubi Computer Science Department Tafila Technical University Tafila Jordan Department of Robotics and Artificial Intelligence Jadara University Irbid Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Data clustering is a data mining task used to group data points based on their similarity. The goal of clustering is to divide $N$ data points into $K$ clusters while minimizing the intra-cluster distance and maximizing the inter-cluster distance. Data clustering can be viewed as a combinatorial optimization problem, which is known as NP-hard. Thus, nature-inspired optimization algorithms have been applied to find better solutions for clustering problems. In this paper, the clustering-based artificial rabbit optimization (CARO) method is proposed. The main idea is to find the optimal centroids for the $K$ clusters by minimizing the sum of squared errors. Using seven benchmark datasets, the experimental results showed that the CARO achieved outstanding results compared to six nature-inspired optimization algorithms in terms of purity and entropy.

关键词： Rabbits Clustering algorithms Machine learning Benchmark testing Feature extraction Entropy Reliability Data mining Optimization Convergence

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Robust Practical Stabilization for Complex Dynamical Networks With DoS Attacks and Actuator Saturation

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3108-3118页

作者： Shi, Xueya Peng, Zhinan Yu, Junzhi Li, Hongfei Southwest University College of Electronic and Information Engineering Chongqing400715 China Sichuan Artificial Intelligence Research Institute Yibin644000 China University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Beijing100871 China

This article addresses the problem of designing an attack-resilient adaptive event-triggered (AET) controller for complex dynamical networks (CDNs) under DoS attacks and actuator saturation, with a focus on robust practical stability (RPS). First, considering the impact of DoS attacks on closed-loop systems, an AET controller against DoS attacks is designed. Unlike other event-triggered controllers, the complete timeline is divided, and the AET controller is built with two switching modes based on the intervals of dormant and active periods of DoS attacks in which the system is located. Second, to reconcile AET controller with actuator saturation, a switched system modeling approach is established that explicitly incorporates saturation constraints into the coupled network dynamics. Third, a switched Lyapunov-Krasovskii functional (LKF) is proposed, with which sufficient conditions are provided to ensured the RPS, and a joint design strategy is developed for the desired triggered matrix and feedback gain using linear matrix inequalities (LMIs). Moreover, the results are generalized to the case of actuator faults, indicating that the system is able to achieve RPS with actuator faults. Finally, the proposed method is verified through an example. © 2013 IEEE.

关键词： Lyapunov functions

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Smart Home Appliance Control in the IoT Era

Lecture Notes on Data Engineering and Communications Technol...

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Lecture Notes on Data Engineering and Communications Technologies 2022年 129卷 392-397页

作者： Khalfalla, Osman Abdalla Ali, Suleiman Abdullahi Altrjman, Chadi Mubarak, Auwalu Saleh Artificial Intelligence Engineering Department Research Center for AI and IoT AI and Robotics Institute Near East University Mersin 10 Turkey Center for Research and Development Jamhuriya University of Science and Technology Mogadishu Somalia

Many nations are trying to develop smart cities. In these cities, all appliances will be connected through a communication network, the main challenge is building a system that will be flexible and efficient at the same time. In this study a mobile application was developed to control home appliances through ESP32 controller. The mobile application was developed in such a way that the user can use fingerprint, voice control, and switch buttons control. The mobile application was tested on several phones from a different manufacturer and it performs greatly based on the performance criteria specified in this work. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Internet of things

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Wi-Fi Attendance System in the IoT Era

Lecture Notes on Data Engineering and Communications Technol...

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Lecture Notes on Data Engineering and Communications Technologies 2022年 130卷 19-29页

作者： Abidemi, Sarumi Usman Oghenetega, Omoraka Gershion Daniel, Sekyere-Asiedu Al-Turjman, Fadi Artificial Intelligence Engineering Department Research Center for AI and IoT AI and Robotics Institute Near East University Mersin 10 Nicosia Turkey Department of Computer Information Systems Near East University Mersin 10 Nicosia Turkey

Traditionally, attendance is marked manually by students writing down their name on a paper. This whole process wastes some of lecture time and might even get lost. Maintaining the attendance of the students in an institution is a hefty task. This project aims at designing a smart attendance system that automatically monitors and manages attendance of the students in an institution efficiently. The system is developed with an Arduino ESP8266 Wi-Fi microcontroller deployed in the class room. Further, Wi-Fi communication modules was used to make convenient communication between the student’s devices e.g. phone or laptop and the ESP8266 Wi-Fi microcontroller. Database was created where the attendance submitted will be logged and saved. Internet is utilized to transfer the information collected by the micro controller to the database and from the database to the front-end web page GUI graphical user interface. The students and admin will make one-time registration and subsequently establishing a Wi-Fi connection between the student’s device and the microcontroller in the class to mark an attendance. This system will arrange the attendance in a list with a real time timestamp and save it to database, and also be accessible to the lecturer/Admin over internet any time with uses of web browser e.g. Google chrome through the front end graphic user interface (GUI). This system is useful to reduce manual method of attendance by teachers and to alleviate the spread of COVID-19. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Students

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THE GENERALIZED MATRIX NORM PROBLEM

arXiv

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arXiv 2023年

作者： Kulmburg, Adrian Department of Robotics Artificial Intelligence and Embedded Systems Technical University of Munich Germany

We study the computability of the operator norm of a matrix with respect to norms induced by linear operators. Our findings reveal that this problem can be solved exactly in polynomial time in certain situations, and we discuss how it can be approximated for other cases. Along the way, we investigate the concept of push-forward and pull-back of seminorms, which leads us to uncover novel duality principles that come into play when optimizing over the unit ball of *** Codes 15A60 (Primary), 65F35, 68Q25 Copyright © 2023, The Authors. All rights reserved.

关键词： Polynomial approximation

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Investigation on Mechanical and Tribological Characteristics of Al–Zn Alloys Under Different Conditions

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Journal of The Institution of Engineers (India): Series D 2024年 1-9页

作者： Pruthvi, H.M. Biradar, Srikumar Chetan Kumar, D.S. Biradar, Iranna M. Manjunatha, M.C. Prem Chand, R. Anil Kumar, B.N. Shenoy, Ghanashyam Department of Mechanical Engineering Dr. T Thimmaiah Institute of Technology Karnataka KGF India Department of Mechanical Engineering Atria Institute of Technology Karnataka Bengaluru India Department of Mechanical Engineering New Horizon College of Engineering Karnataka Bengaluru India BAZE University Abuja Nigeria Department of Mechanical Engineering Bangalore Institute of Technology Karnataka Bengaluru India Department of Robotics and Artificial Intelligence Bangalore Institute of Technology Karnataka Bengaluru India

Aluminum alloys are a crucial material due to their high resistance to corrosion and strength to weight ratio, particularly in pressure systems and transport applications. However, they have low melting temperatures and are relatively soft compared to other engineering materials. Utilising secondary reinforcing components such as tungsten carbide, fly ash, mica, boron carbide, silicon carbide, graphite, e-glass, and aluminium oxide, researchers have recently concentrated on creating metal matrix composites. Stir casting was used to create aluminium alloys with different weight percentages of magnesium and zinc. The alloys were then re-aged at 150 °C for different holding times. SEM image of a worn-out surface reveals material flow, scratches, and narrow grooves that are caused by wear parameters including applied stress, sliding velocity, and sliding distance. Only 5.57% more ultimate tensile strength was achieved for the highest weight percentage of magnesium and zinc after 7 h of ageing. Improved mechanical qualities were seen in SEM pictures, and regression analysis confirmed the experimental findings. © The Institution of Engineers (India) 2024.

关键词： Heat treatment

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Research on Path Planning of Mobile Robots Based on Snow Ablation Optimizer

Research on Path Planning of Mobile Robots Based on Snow Abl...

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第43届中国控制会议

作者： Wenlong Chen Zile Wang Yuhang Yang Siqi Wang Heng Wang Yasar Ayaz School of Automation and Electrical Engineering University of Science and Technology Beijing National Center of Artificial Intelligence (NCAI) Department of Robotics and Artificial Intelligence National University of Sciences and Technology (NUST)

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Path planning is one of the key technologies for mobile robots. Metaheuristic algorithms hold significant promise in generating an effective and reliable path for mobile robots. However, most metaheuristic algorithms do not balance exploration and exploitation in the solution space, which may lead to premature convergence and cannot guarantee to find a satisfying *** ablation optimizer(SAO) is a novel metaheuristic technique based on the sublimation and melting phenomenon of snow,which can overcome the shortcomings of most other metaheuristic algorithms. This paper proposes a path planning method for mobile robots based on the SAO algorithm and undertakes a comparative study with the planning method based on particle swarm optimization(PSO) using simulation experiments. The results show that the proposed path planner is superior to the PSO-based path planner in both planning speed and path quality.

关键词： Path Planning Mobile Robots Snow Ablation Optimizer Particle Swarm Optimization

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A Metaheuristic Technique for Cluster-Based Feature Selection of DNA Methylation Data for Cancer

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Computers, Materials & Continua 2023年第2期74卷 2817-2838页

作者： Noureldin Eissa Uswah Khairuddin Rubiyah Yusof Ahmed Madani Centre for Artificial Intelligence&Robotics Malaysia-Japan International Institute of TechnologyUniversiti Teknologi MalaysiaKuala Lumpur54100Malaysia Department of Computer Engineering Arab Academy for ScienceTechnology and Maritime TransportCairo2033Egypt Department of Mechanical Precision Engineering Malaysia-Japan International Institute of TechnologyUniversiti Teknologi MalaysiaKuala Lumpur54100Malaysia

Epigenetics is the study of phenotypic variations that do not alter DNA *** epigenetics has grown rapidly over the past few years as epigenetic alterations exist in all human *** of these alterations is DNA methylation;an epigenetic process that regulates gene expression and often occurs at tumor suppressor gene loci in ***,studying this methylation process may shed light on different gene functions that cannot otherwise be interpreted using the changes that occur in DNA ***,microarray technologies;such as Illumina Infinium BeadChip assays;are used to study DNA methylation at an extremely large number of varying *** each DNA methylation site,a beta value(β)is used to reflect the methylation ***,clustering this data from various types of cancers may lead to the discovery of large partitions that can help objectively classify different types of cancers aswell as identify the relevant loci without user *** study proposed a Nested Big Data Clustering Genetic Algorithm(NBDC-GA);a novel evolutionary metaheuristic technique that can perform cluster-based feature selection based on the DNA methylation *** efficacy of the NBDC-GA was tested using real-world data sets retrieved from The Cancer Genome Atlas(TCGA);a cancer genomics program created by the NationalCancer Institute(NCI)and the NationalHuman Genome Research *** performance of the NBDC-GA was then compared with that of a recently developed metaheuristic Immuno-Genetic Algorithm(IGA)that was tested using the same data *** NBDC-GA outperformed the IGA in terms of convergence ***,the NBDC-GA produced a more robust clustering configuration while simultaneously decreasing the dimensionality of features to a maximumof 67%and of 94.5%for individual cancer type and collective cancer,*** proposed NBDC-GA was also able to identify two chromosomes with highly contrastingDNAmethylations activities th

关键词： Cancer clustering DNA methylation feature selection metaheuristic technique the cancer genome atlas

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