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arXiv 2024年

作者： Islam, Sharfin He, Zhanpeng Ciocarlie, Matei Dept. of Mechanical Engineering Dept. of Computer Science Columbia University New York United States

Underactuated manipulators reduce the number of bulky motors, thereby enabling compact and mechanically robust designs. However, fewer actuators than joints means that the manipulator can only access a specific manifold within the joint space, which is particular to a given hardware configuration and can be low-dimensional and/or discontinuous. Determining an appropriate set of hardware parameters for this class of mechanisms, therefore, is difficult - even for traditional taskbased co-optimization methods. In this paper, our goal is to implement a task-based design and policy co-optimization method for underactuated, tendon-driven manipulators. We first formulate a general model for an underactuated, tendon-driven transmission. We then use this model to co-optimize a three-link, two-actuator kinematic chain using reinforcement learning. We demonstrate that our optimized tendon transmission and control policy can be transferred reliably to physical hardware with real-world reaching experiments. © 2024, CC BY-SA.

关键词： Actuators

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Support Vector Regression Based Traffic Prediction Machine Learning Model*

Support Vector Regression Based Traffic Prediction Machine L...

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Ansh Srivastava Mrigaannkaa Singh Somesh Nandi Dept. of Computer Science and Engineering RV College of Engineering Bengaluru India Dept. of Electronics and Communication RV College of Engineering Bengaluru India Dept. of AI/ML RV College of Engineering Bengaluru India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

To combat the rising issue of urban traffic congestion, optimization of traffic light timings at intersections is crucial. Traditional methods show stagnancy in managing traffic flow dynamically. This study introduces Support Vector Regression (SVR) as an unconventional solution for traffic signal optimization. The model predicts the traffic conditions and alters the green light to turn on and off accordingly. Utilization of a comprehensive dataset consisting of vehicle distributions and critical flow ratios have been implemented to training a machine learning model. The statistics and performance is checked by usage of Mean Squared Error, R Squared, Mean Absolute Error demonstrating that SVR can effectively enhance traffic signal control. This approach pledges significant improvements in reduction in congestion and streamlined traffic flow. Our findings majorly highlight the capability of the SVR Machine Learning model in improving urban traffic management systems and call for further enhancement and collaboration with experts to address dynamic traffic issues.

关键词： Support vector machines Training Computational modeling Urban areas Machine learning Predictive models Vectors Vehicle dynamics Optimization Traffic congestion

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Adaptive Traffic Signal Management Using Real-Time Vehicle Detection and Tracking

Adaptive Traffic Signal Management Using Real-Time Vehicle D...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Sarvesh Shirulkar Rishabhdev Makode Richa Khandelwal Dept. of Electronics and Computer Science Shri Ramdeobaba College of Engineering and Management Nagpur India Dept. of Electronics Engineering Ramdeobaba University Nagpur India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Traffic congestion is a significant issue in urban cities, as it restricts traffic flow, increases fuel consumption, and produces high emissions. Traditional traffic signal systems work on fixed timing without considering real-time variations in traffic and often worsen the problem. This paper describes an adaptive traffic signal control system based on the YOLO (You Only Look Once) algorithm, using object detection for real-time signal timing in response to real-time traffic. The system applies real-time vehicle detection and tracking at intersections for dynamic management of green light time according to volume and vehicle type. The proposed approach makes vehicle recognition significantly efficient and responds quickly to the change in traffic situation. The paper discusses the design, implementation, and evaluation of performance in a simulated environment. The results demonstrate improved traffic management and reduced congestion, offering a promising solution to smart city traffic control.

关键词： YOLO Adaptive systems Smart cities Vehicle detection Green products Traffic control Real-time systems Timing Dynamical systems Vehicle dynamics

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Leveraging Smart Video-Based Learning to Enhance Student Engagement and Collaboration 6

Leveraging Smart Video-Based Learning to Enhance Student Eng...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Serasinghe, Chanukya Perera, Nashali Samarasinghe, Devindu Muthusinghe, Madusha Wijesundara, Malitha Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Humanities and Sciences Malabe Sri Lanka

ISBN: (纸本)9798331517878

The COVID-19 pandemic has expedited the shift in education to online learning, which has exposed shortcomings in virtual learning environments' personalization and engagement. This research addresses these issues by developing a web application designed to enhance student engagement and learning efficiency. The system has gamified collaboration features, video chapter segmentation, and personalized video recommendation system. The methodology comprises models for generating chapter titles and for precise video transcription, and machine learning algorithms for tailored suggestions depending on user input. Interactive Questions and Answers modules and real-time synchronized video playback are included. The results demonstrate how the system's personalized content, interactive tests, and teamwork tools increase user engagement and comprehension. The efficacy of personalized material and real-time synchronization in enhancing learning results is discussed. This innovative approach offers a comprehensive solution to the challenges of online education, creating a more immersive and interactive learning experience. © 2024 IEEE.

关键词： Federated learning

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An Effective Method to Detect DHCP Starvation Attack using Port Scanning

An Effective Method to Detect DHCP Starvation Attack using P...

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Next-Generation Computing, IoT and Machine Learning (NCIM), International Conference on

作者： Asaduzzaman Jony Abu Saleh Musa Miah Muhammad Nazrul Islam Dept. of Computer Science and Engineering Bangladesh University of Professionals (BUP) Dhaka Bangladesh Dept. of Computer Science and Engineering The University of Aizu Aizuwakamatsu Japan Dept. of Computer Science and Engineering Military Institute of Science and Technology (MIST) Dhaka Bangladesh

Nowadays, the DHCP starvation attack is a crucial attack that denied the services for the legitimate network user where the attacker mainly occupies all the IP addresses in the DHCP server pool. Most of the existing studies proposed ICMP (Internet Control Message Protocol) and the ARP (Address Resolution Protocol) based techniques to detect the DHCP starvation attack. However, the ICMP-based detection is ineffective if the client has an active host-based firewall that blocks the ICMP ECHO REQUEST, while the ARP-based detection does not work for DHCP networks separated by a DHCP relay agent and works only within the same or local network. Therefore, the objective of this research is to propose an effective technique to detect DHCP starvation attack detection through port scanning to address the limitations of ICMP and ARP-based detection methods. To attain this objective, this study first highlights the limitations of existing detection methods demonstrating insightful facts. Secondly, the study proposed a port scan-based DHCP starvation attack detection method for local and remote (relay) DHCP networks. Finally, the effectiveness and validation of the proposed techniques were demonstrated using the Multivendor Network Emulation Software (EVE-NG). The study showed that the proposed port scanning-based technique could accurately detect the DHCP starvation attack while overcoming the limitations of the ICMP and ARP techniques.

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Exploration of Ripe Raw and Defective Tomatoes with Deep Learning Techniques 2

Exploration of Ripe Raw and Defective Tomatoes with Deep Lea...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Vanitha, C.N. Sathyamoorthy, Malathy Dhanaraj, Rajesh Kumar Rai, Priti Geetha, V. Manikantan, M. Kongu Engineering College Science and Engineering Department of Computer Erode India Kpr Institute of Engineering and Technology Department of Information Technology Coimbatore India Pune India Gl Bajaj Institute of Management Greater Noida India Reva University School of Applied Sciences Department of Computer Science Bangalore India Kongu Engineering College Dept. of CT-UG Perundurai India

ISBN: (纸本)9798350325799

Due to great need of agricultural operations, there was a shortage of workers during the harvest period. This results in a bottleneck in the operations of agricultural organizations. One of the most common problems in these sectors is the hazardous practice of farming. This type of farming operation makes vegetables very costly and unattractive. The reduction of costs is controlled by the need for labour. This issue is solved by adapting to new technology and compensating the labour deficiency. The algorithms that are processed in this method are Convolutional Neural Network (CNN), Object Detection and Generative Adversarial Network (GAN). These algorithms will enable the model to recognise faulty tomatoes. This model accurately identifies raw, ripe and defective tomatoes using deep learning methods and it is highly helpful for the betterment of agriculture. © 2023 IEEE.

关键词： Object detection

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DistB-VNET: Distributed Cluster-based Blockchain Vehicular Ad-Hoc Networks through SDN-NFV for Smart City

DistB-VNET: Distributed Cluster-based Blockchain Vehicular A...

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International Conference on computer and Information Technology (ICCIT)

作者： Anichur Rahman MD. Zunead Abedin Eidmum Dipanjali Kundu Mahir Hossain MD Tanjum An Tashrif Md Ahsan Karim Md. Jahidul Islam Dept. of Computer Science and Engineering National Institute of Textile Engineering and Research (NITER) Constituent Institute of Dhaka University Savar Dhaka Dept. of Internet of Things and Robotics Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Dept. of Computer Science and Engineering Green University of Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

In the developing topic of smart cities, Vehicular Ad-Hoc Networks (VANETs) are crucial for providing successful interaction between vehicles and infrastructure. This research proposes a distributed Blockchain-based Vehicular Ad-hoc Network ("DistB-VNET") architecture that includes binary malicious traffic classification, Software Defined Networking (SDN), and Network Function Virtualization (NFV) to ensure safe, scalable, and reliable vehicular networks in smart cities. The suggested framework is the decentralized blockchain for safe data management, SDN-NFV for dynamic network management and resource efficiency, and a noble isolation forest algorithm that works as an IDS (Intrusion Detection System). Further, "DistB-VNET" offers a dual-layer blockchain system, where a distributed blockchain provides safe communication between vehicles, while a centralized blockchain in the cloud is in charge of data verification and storage. This improves security, scalability, and adaptability, ensuring better traffic management, data security, and privacy in VANETs. Furthermore, the unsupervised isolation forest model achieves a high accuracy of 99.23% for detecting malicious traffic. Additionally, reveals that our method greatly improves network performance, offering decreased latency, increased security, and reduced congestion, an effective alternative for existing smart city infrastructures.

关键词： Smart cities Scalability Heuristic algorithms Forestry Dynamic scheduling Blockchains Network function virtualization Resource management Vehicle dynamics Software defined networking

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Advancing Skill-Based Pathways to Good Jobs in Artificial Intelligence at Community Colleges 14

Advancing Skill-Based Pathways to Good Jobs in Artificial In...

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14th IEEE Integrated STEM Education Conference, ISEC 2024

作者： Saaid, Yasmin Rathore, Heena Griffith, Henry Trio Ubms San Antonio College San AntonioTX United States Texas State University Dept. of Computer Science San MarcosTX United States San Antonio College Dept. of Engineering San AntonioTX United States

ISBN: (纸本)9798350352801

This paper provides recommendations for developing skill-based employment pathways in artificial intelligence (AI) at community colleges. These recommendations are motivated by a summary assessment of influencing factors, including current AI workforce composition, employer perceptions of skill-based hiring, and existing implementations of comparable models. Our analysis indicates that future development should be expedited to capture favorable trends in labor market demand and current hiring protocols. Leveraging curriculum developed by industry leaders, and ensuring the integration of skill validation mechanisms, are also suggested as a means of addressing existing barriers to entry for individuals without four-year degrees. © 2024 IEEE.

关键词： artificial intelligence education community colleges skill-based hiring

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COVID-19 Diagnosis Algorithm Based on Convolutional Neural Networks

COVID-19 Diagnosis Algorithm Based on Convolutional Neural N...

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2024 International Conference on Electronics and Devices, Computational science, ICEDCS 2024

作者： Han, Xinxin Xiong, Xueguan Wang, Liuling Yang, Yuxin Lu, Dandan Southwest University of Science and Technology School of Computer Science and Technology Mianyang China Southwest University of Science and Technology School of National Defense Science Mianyang China Southwest University of Science and Technology School of Economics and Management Mianyang China Southwest University of Science and Technology School of Information Engineering Mianyang China Southwest University of Science and Technology Dept. Engineering Technology Center Mianyang China

ISBN: (纸本)9798331527624

This study investigates an automated COVID-19 diagnosis system based on convolution networks, which aims to improve the effectiveness and accuracy of diagnosis. In a systematic analysis of 1709 chest X-rays, the research team used the ResNet model of deep learning for training and evaluation. Based on a detailed analysis of the confusion matrix generated by the model, the study found that the weighted average accuracy of the model and the recall rate reached 94,04% with an F1 value of up to 0,9403. This data shows better performance of convolutional neural networks in automated diagnosis. The results of the study show that neural network technology can effectively relieve the workload of doctors and provide diagnostic support for future public health events. © 2024 IEEE.

关键词： COVID-19

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Exploring the Relationship between Rent and Flat Prices Through Random Forest and Grid Search

Exploring the Relationship between Rent and Flat Prices Thro...

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2024 International Conference on Circuit, Systems and Communication, ICCSC 2024

作者： Ghosh, Joyita Maji, Kalyan Mzili, Toufik Roy, Priyanka Chakraborty, Monalisa Gupta, Subir W.B Haldia India India Chouaib Doukkali University Dept of Computer Science El Jadida Morocco Dr. B. C. Roy Engineering College Dept. of It WB Durgapur India Dr. B. C. Roy Engineering College Dept. of Cse WB Durgapur India

ISBN: (纸本)9798350365306

This paper explores the critical relationship between flat rents and prices, an important metric reflecting the state of the housing market and its underlying economic factors. The study introduces predictive models based on Random Forest algorithms, refined through Grid Search optimisation, to provide accurate market forecasts. Such predictive capabilities are crucial for policymakers, investors, and developers, who require reliable data for strategic decision-making and policy formulation. The research methodology employs Random Forest for its robust handling of complex datasets, with Grid Search utilised to calibrate the model parameters for enhanced precision. The resulting models are rigorously evaluated, achieving a training accuracy of 0.948 and testing accuracies of 0.745 for price prediction and 0.885 for rent prediction, indicating predictive solid performance. Low Log Loss metrics further validate the models' reliability. The study identifies the model's strengths and areas for refinement through detailed visualisations - accuracy comparisons, correlation heatmaps, and residual distribution plots. The residual plots, in particular, indicate a better generalisation capability in rent forecasting compared to flat price predictions, which exhibit minor overfitting. The findings provide actionable insights, demonstrating the efficacy of Random Forest and Grid Search in capturing the complex dynamics of flat rents and prices. The success of these models indicates their potential for broader application across different regions and market conditions, suggesting a valuable asset for stakeholders in achieving a balanced and fair housing market. This paper aims to contribute to the literature by offering a novel, data-driven perspective on market analysis and economic forecasting within the real estate sector © 2024 IEEE.

关键词： Prediction models

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