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3rd International Conference on Computational Modelling, Simulation and Optimization, ICCMSO 2024

作者： Chhabra, Deepak Kumar Dhingra, Ashwani Kaushik, Ashish Dhingra, Sunita Kumar Sahdev, Ravinder Dhingra, Sunil Maharshi Dayanand University Department of Mechanical Engineering UIET Rohtak India Deenbandhu Chhotu Ram University of Science and Technology Department of Mechanical Engineering Sonipat India Maharshi Dayanand University Department of Computer Science and Engineering UIET Rohtak India University Institute of Engineering and Technology Kurukshetra University Department of Mechanical Engineering Haryana Kurukshetra India

ISBN: (纸本)9798350361391

Modernizing and enhancing primary dental health care is imperative to address the growing demands of patients suffering from oral diseases to achieve the sustainable development goal of ensuring healthy lives and promoting well-being. Artificial intelligence (AI) is one such emerging technology that can radically transform dental practices by providing AI-driven imaging solutions, advanced diagnostic capabilities, and accurate treatment planning. This study explores the current state of artificial intelligence in dentistry using bibliometric analysis, along with an in-depth discussion on widely used artificial intelligence techniques for minimally invasive dentistry. Furthermore, several applications of AI in dentistry, along with benefits and current challenges, are also described. The study found that AI-powered tools are demonstrably enhancing diagnostic accuracy, streamlining workflows, and offering personalized treatment options. © 2024 IEEE.

关键词： Sustainable development goals

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Deadline-aware and energy efficient IoT task scheduling using fuzzy logic in fog computing

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Multimedia Tools and Applications 2025年第15期84卷 14359-14386页

作者： Thakur, Rahul Sikka, Geeta Bansal, Urvashi Giri, Jayant Mallik, Saurav Department of Computer Science and Engineering Dr. B R Ambedkar National Institute of Technology Punjab Jalandhar144011 India Department of Computer Science and Engineering National Institute of Technology Delhi Delhi110036 India Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Department of Environmental Health Harvard T H Chan School of Public Health BostonMA United States Saveetha University TN Chennai602105 India

Fog computing is an emerging paradigm that extends cloud computing (CC) by providing computation, communication, and storage services at the edge of a network, closer to end devices. It has gained significance due to the rapid development of IoT devices, which generate various types of tasks. Processing these tasks in the cloud can strain its infrastructure and lead to delays in time-sensitive requests. To address this limitation, fog computing (FC) concepts were introduced in 2012 by Cisco. FC is not meant to replace CC but rather to complement and extend its capabilities. One of the challenges in FC is efficiently assigning tasks to appropriate resources to minimize makespan, energy consumption (EC), and increase the number of deadline-satisfied tasks. In this work, the improvement of semi-greedy algorithm has been done by incorporating fuzzy logic (FL). By leveraging FL, the aim is to enhance the algorithm's decision-making process and make it more adaptive to varying conditions and uncertainties in the fog environment. The use of FL allows more nuanced and flexible task scheduling (TS) decisions based on fuzzy sets and fuzzy rules. The simulation experiments demonstrate that the proposed algorithm outperforms PSG (Priority-aware Semi-Greedy) and PSG-M (PSG with multistart), which were identified as the best scheduling algorithms (Algos) in the literature review. The algorithm exhibits better performance in terms of reducing makespan, EC, and increasing the percentage of deadline-satisfied tasks compared to PSG and PSG-M. The inclusion of FL further enhances the algorithm's effectiveness in handling complex scheduling scenarios in a FC environment. To evaluate the performance of the proposed algorithm, different simulation experiments have been conducted using a selected simulator after a systematic review of existing simulators. The experiments involved 300 and 500 random and static tasks, as well as 60 fog nodes in the fog environment. All simulations were impl

关键词： Fog computing

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Design of Mobile Robot Automatic Navigation System Based on ROS 5

Design of Mobile Robot Automatic Navigation System Based on ...

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5th Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2024

作者： Duan, Chenxu Xue, Ling Kumar, Sachin School of Mechanical Engineering Sichuan University Jinjiang College Meishan620680 China Department of Computer Science and Engineering Galgotias College of Engineering and Technology Greater Noida India

ISBN: (纸本)9781643685243

Mobile robot is the key equipment of flexible production system, flexible handling system and automatic storage system. As an important bridge connecting logistics and production, mobile robot can effectively improve the production efficiency of manufacturing enterprises and reduce production costs. Under the background of the close combination of information technology and manufacturing industry, higher requirements are put forward for its automation and intelligence. Aiming at the problem of large error accumulation caused by the existing navigation methods using odometer navigation and GPS navigation technology, this paper takes Turtlebot3-waffle mobile robot as the research object and carries out the research of automatic navigation system. Firstly, the basic motion and attitude are analyzed, and the forward and inverse kinematics models and dead reckoning formulas are established. Then, the core algorithms of map building, autonomous positioning and autonomous navigation are studied, and several simulation environments are established on the ROS simulation platform to verify the algorithms. Finally, an autonomous navigation simulation system is built on the ROS platform, and the laser radar SLAM map based on gmapping algorithm is constructed for the established simulation environment model, and the automatic navigation function is simulated by using the map. In the experiment, the robot successfully planned the corresponding local path and global path and successfully reached the navigation target point, which verified the feasibility and effectiveness of the automatic navigation system. It provides a reference for the research of mobile robot automatic navigation system. © 2024 The Authors.

关键词： Motion planning

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Real-Time Threat Management using Deep Q-Learning and Mininet 3

Real-Time Threat Management using Deep Q-Learning and Minine...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sivakumar, Amrutha Bhavya Reddy, G. Bhanot, Shreya Rajagopal, Shinu M. Prashanth, B.N. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Mechanical Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

The research combines Deep Q-Learning(DQN) with a Mininet-based network simulation and Scapy intrusions detection system (IDS) for malicious traffic prioritizing. The RL agent continuously learns to act based on real-time traffic data and builds an adaptive model that detects and prioritizes threats such as SYN Flood, UDP Flood, Slowloris, HTTP Flood etc. The RL agent then adjusts its policy via the seamless combination of network simulation and live traffic detection to make sure lossy channels representing high-severity attacks are dealt with first, lessening the damage. The system improves the identification of intrusions by stimulating the intrusion prioritization dynamically with continuous learning and feedback, increasing network security. © 2025 IEEE.

关键词： Network intrusion

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Securing Cloud-Encrypted Data:Detecting Ransomware-as-a-Service(RaaS)Attacks through Deep Learning Ensemble

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computers, Materials & Continua 2024年第4期79卷 857-873页

作者： Amardeep Singh Hamad Ali Abosaq Saad Arif Zohaib Mushtaq Muhammad Irfan Ghulam Abbas Arshad Ali Alanoud Al Mazroa School of Computer Information Sciences University of CumberlandsWilliamsburgKentucky40769USA Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Electrical Engineering The University of LahoreLahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware *** study provides a new approach for RaaS attack detection which uses an ensemble of deep learning *** this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is *** the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are ***,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested *** proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%*** empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual *** expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.

关键词： Cloud encryption RaaS ensemble threat detection deep learning cybersecurity

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Smooth Reference Command Generation and Control for Transition Flight of VTOL Aircraft Using Time-Varying Optimization

Smooth Reference Command Generation and Control for Transiti...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Kim, Jinrae Bullock, John L. Cheng, Sheng Hovakimyan, Naira Department of Mechanical Science and Engineering The Grainger College of Engineering University of Illinois Urbana-Champaign United States Department of Electrical and Computer Engineering The Grainger College of Engineering University of Illinois Urbana-Champaign United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Vertical take-off and landing (VTOL) aircraft pose a challenge in generating reference commands during transition flight. While sparsity between hover and cruise flight modes can be promoted for effective transitions by formulating 1-norm minimization problems, solving these problems offline pointwise in time can lead to non-smooth reference commands, resulting in abrupt transitions. This study addresses this limitation by proposing a time-varying optimization method that explicitly considers time dependence. By leveraging a prediction-correction interior- point time-varying optimization framework, the proposed method solves an ordinary differential equation to update reference commands continuously over time, enabling smooth reference command generation in real time. Numerical simulations with a two-dimensional Lift+Cruise vehicle validate the effectiveness of the proposed method, demonstrating its ability to generate smooth reference commands online. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： VTOL/STOL aircraft

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An Industrial Micro Parts Recognition Technology Based on Improved Yolov8 7

An Industrial Micro Parts Recognition Technology Based on Im...

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7th International Conference on computer Network, Electronic and Automation, ICCNEA 2024

作者： Hao, Yafei Jia, Feng School of Computer Science and Engineering Xi'an Technological University Xi'an China School of Mechanical and Electrical Engineering Xi’an Technological University Xi’an China

Aiming at the recognition and classification problem of micro assembly parts in industrial production, the Yolov8 object detection algorithm was used as the base model to improve the C2f class in the backbone. And apply the improved object detection algorithm to the recognition field of detecting assembly parts. By collecting images of gears, nails, and screws from various angles on Roboflow, a training set and a validation set containing three types of gears, nails, and screws are formed. At the same time, in order to improve the robustness of the Yolov8 object detection algorithm, data processing methods such as adding noise and image rotation were adopted for the samples in the data. Due to the use of Yolov8 as the base model, a TXT text file containing label prediction box position information was formed after labelling the dataset on Roboflow. Based on Python (3.10.12) and cloud platform, improved Yolov8 object detection algorithm was trained and network validation was carried out. Use TXT files as input for the model to obtain and learn features of gears, nails, and screws. After 200 epochs, the mAP for detecting gears, nails, and screws increased by about 3% compared to the base model. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Screws

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Direct ink writing of gradient shear-stiffening elastomer for enhanced toughness and impact resistance

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Journal of Industrial and engineering Chemistry 2025年 149卷 593-603页

作者： Gong, Liping Zhao, Chunyu Lu, Hongda Zhang, Qingtian Chen, Zexin Yang, Junjie Yang, Jinyu Sun, Shuaishuai Du, Haiping Li, Weihua School of Mechanical Materials Mechatronic and Biomedical Engineering University of Wollongong Wollongong2522 Australia CAS Key Laboratory of Mechanical Behaviour and Design of Materials Department of Modern Mechanics University of Science and Technology of China Hefei230027 China School of Electrical Computer and Telecommunication Engineering University of Wollongong Wollongong2522 Australia

Shear-stiffening materials have garnered significant attention due to their inherent ability to rapidly respond to changes in strain rate. However, conventional studies on these materials exhibit limited mechanical performance and insufficient structural design, hindering applications in material science and smart mechatronics. Inspired by the gradient structure of a squid beak, this study uses a direct-ink-writing (DIW) technology to develop a novel gradient shear-stiffening elastomer (GSSE). Comprehensive characterizations were conducted to optimize the printability of the shear-stiffening elastomer under various testing conditions, including rheology, compression, and tension. Additionally, the toughness and impact resistance of the elastomer were systematically investigated. It is demonstrated that the biomimetic GSSE exhibits enhanced flexibility, efficient energy dissipation, and outstanding impact resistance. Consequently, this work advances the field of shear-stiffening elastomers for diverse applications by leveraging the gradient structure design strategy to achieve superior mechanical performance under complex loading conditions. © 2025 The Author(s)

关键词： Strain rate

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Multi-variable control to mitigate loads in CRISPRa networks 63

Multi-variable control to mitigate loads in CRISPRa networks

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Manoj, Krishna Grunberg, Theodore W. Vecchio, Domitilla Del Mit Department of Mechanical Engineering MA02139 United States Mit Department of Electrical Engineering and Computer Science MA02139 United States

ISBN: (纸本)9798350316339

The discovery of CRISPR-mediated gene activation (CRISPRa) has transformed the way in which we perform genetic screening, bioproduction and therapeutics through its ability to scale and multiplex. However, the emergence of loads on the key molecular resources constituting CRISPRa by the orthogonal short RNA that guide such resources to gene targets, couple theoretically independent CRISPRa modules. This coupling negates the ability of CRISPRa systems to concurrently regulate multiple genes independent of one another. In this paper, we propose to reduce this coupling by mitigating the loads on the molecular resources that constitute CRISPRa. In particular, we design a multi-variable controller that makes the concentration of these molecular resources robust to variations in the level of the short RNA loads. This work serves as a foundation to design and implement CRISPRa controllers for practical applications. © 2024 IEEE.

关键词： Molecular docking

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A Novel Hybrid AI Framework for Real-Time Prediction and Risk Assessment of Vector-Borne Epidemics using Spatiotemporal Data 4

A Novel Hybrid AI Framework for Real-Time Prediction and Ris...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Munirathnam, Rajesh Maindola, Meenakshi Bhargav, H.K. Patil, Prachi Ramesh Bhatt, Pooja Natrayan, L. TechnoGen Inc. Data & Analytics Division NJ United States Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Department of Computer Science and Engineering Tumakuru India Jagdambha College of Engineering and Technology Department of Electronics and Telecommunication Engineering Yavatmal India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department of Computer science and Engineering Vadodara India Saveetha School of Engineering Simats Department of Mechanical Engineering Chennai India

ISBN: (纸本)9798350352931

Vector-borne epidemics constitute major threats to global health that call for improved modeling systems for managing diseases. In order to incorporate such factors into an actual model, a traditional forecasting process may fail to grasp the nature of disease transmission adequately. We present a hybrid AI system based on LSTM and GCN for forest bug outbreak detection in real time. The framework also integrated a spatial-temporal analysis of climate variability, vector population fluctuations, and human mobility with climate variables across 15 regions over a five-year period (2019-2023). The hybrid framework also produced superior results, achieving a 94.2% prediction rate for health risk disease outbreaks, compared to a 17.3% improvement in traditional machine learning. When used to distinguish between high-risk and low-risk zones, the model demonstrated the following metrics: sensitivity of 92.8% and specificity of 95.1%. Real-time risk assessment demonstrated 89.6% effectiveness in indicating early warning and the best prediction lead time average of 21 days. This framework significantly enhances the prediction of vector-borne diseases by offering a dependable, real-time platform for disease surveillance and risk assessment. Its high accuracy and early warning capabilities make the system a useful tool in epidemiological and public health campaigns and control measures. © 2024 IEEE.

关键词： Prediction models

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