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Classification of Melanocytic Nevus Images using BigTransfer (BiT): A study on a novel transfer learning-based method to classify Melanocytic Nevus Images 3

Classification of Melanocytic Nevus Images using BigTransfer...

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3rd IEEE International Conference on Computing, Communication, and Intelligent systems, ICCCIS 2022

作者： Sinha, Sanya Gupta, Nilay Birla Institute of Technology Mesra Patna Dept. of Electronics and Communications Engineering Patna India Sikkim Manipal Institute of Technology Dept. of Computer Science and Engineering Majitar India

ISBN: (纸本)9781665462006

Skin cancer is a fatal disease that takes a heavy toll over human lives annually. The colored skin images show a significant degree of resemblance between different skin lesions such as melanoma and nevus, making identification and diagnosis more challenging. Melanocytic nevi may mature to cause fatal melanoma. Therefore, the current management protocol involves the removal of those nevi that appear intimidating. However, this necessitates resilient classification paradigms for classifying benign and malignant melanocytic nevi. Early diagnosis necessitates a dependable automated system for melanocytic nevi classification to render diagnosis efficient, timely, and successful. An automated classification algorithm is proposed in the given research. A neural network previously-trained on a separate problem statement is leveraged in this technique for classifying melanocytic nevus images. The suggested method uses BigTransfer (BiT), a ResNet-based transfer learning approach for classifying melanocytic nevi as malignant or benign. The results obtained are compared to that of current techniques, and the new method's classification rate is proven to outperform that of existing methods. © 2022 IEEE.

关键词： Diseases

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Imaging – based Detection of Bone Injuries using Hybrid Models

Imaging – based Detection of Bone Injuries using Hybrid Mod...

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International Conference on ent Computing and Control systems (ICICCS)

作者： Vineela Madireddy Bhagya Lakshmi Medam Sai Vardhan Reddy Manne Navaneeth Yadav Madhugani Dept. Computer Science and Engineering -AI&ML Vignan Institute of Technology and Science Nalgonda India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Bone fractures are among the most common physical injuries, affecting individuals across all age groups and requiring prompt and precise diagnosis. However, radiologists often face challenges in accurate fracture identification due to variations in image quality and complexity. The present work investigates a robust system for the automatic detection and classification of fractures in X-radiation (X-ray) images. The proposed model integrates Convolutional Neural Networks (CNN) and MobileNet with a hybrid approach that combines MobileNet and Random Forest to improve classification accuracy. The system is designed to be user-friendly, incorporating modules for registration, data processing, and real-time classification of fractures into 11 distinct types, including avulsion fracture, impacted fracture, longitudinal fracture, comminuted fracture, greenstick fracture, oblique fracture, pathological fracture, spiral fracture, and others. Experimental results demonstrate that the hybrid model significantly enhance diagnostic accuracy, underscoring its potential application in medical settings.

关键词： Pathology Accuracy Spirals Computational modeling Bones Real-time systems Convolutional neural networks X-ray imaging Random forests Injuries

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A Study on Text Feature Selection Using Ant Colony and Grey Wolf Optimization 7

A Study on Text Feature Selection Using Ant Colony and Grey ...

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7th International Conference on Informatics and Computing, ICIC 2022

作者： Widians, Joan Angelina Wardoyo, Retantyo Hartati, Sri Universitas Gadjah Mada Doctoral Program Dept. of Computer Science and Electronics Indonesia Universitas Mulawarman Faculty of Engineering Dept. of Informatics Samarinda Indonesia Universitas Gadjah Mada Faculty of Mathematics and Natural Sciences Dept. of Computer Science and Electronics Yogyakarta Indonesia

ISBN: (纸本)9798350345711

Text classification (TC) is widely used for organizing digital documents. The issues in TC are numerous characteristics and high-element dimensions. Many pattern classification issues require feature selection (FS), which is pertinent. FS removes unneeded and redundant data from the dataset. The Ant Colony Optimization (ACO) and Grey Wolf Optimizer (GWO) for FS are the main topics of our thorough assessment of the literature on the Swarm Intelligence (SI) algorithm. Furthermore, it illustrates how the hybrid SI technique is used in FS across various sectors. The hybrid SI technique uses applicable data from various FS methods to find feature subsets with smaller sizes and better classification performance than those found by regular FS algorithms. © 2022 IEEE.

关键词： Ant colony optimization

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Predicting Grid-Level Urban Population Dynamics Based on POI and Road Networks: A Random Forest Approach and SHAP Evaluation 24

Predicting Grid-Level Urban Population Dynamics Based on POI...

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24th COTA International Conference of Transportation Professionals: Resilient, Intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Yao, Yao Jiang, Yinghong Li, Jiaxing Xu, Jian Yu, Qing Zhang, Haoran Co. Ltd. Shanghai China School of Urban Planning and Design Peking Univ. Shenzhen Graduate School Shenzhen China Dept. of Computer Science and Engineering Southern Univ. of Science and Technology Shenzhen China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

Predicting urban population dynamics encounters challenges like intricate spatiotemporal variations, privacy concerns tied to user data, and a lack of model interpretability, all impeding sustainable urban development. To address these issues, this paper introduces a method for hourly urban population prediction based on Points of Interest (POI) and road network data. By modeling POIs and road networks, the approach reconstructs grid-level counts of accessible POIs across different time intervals. These features are key inputs for a Random Forest algorithm, demonstrating high precision and consistent stability in forecasting population dynamics at an hourly level. Additionally, SHAP analysis assesses the spatiotemporal significance of different POI types and their accessibility concerning the prediction model. Results highlight the substantial influence of office spaces, residential areas, and shopping facilities on the population prediction model, with local POIs and those accessible within a 60-min radius making noteworthy contributions. This research establishes a foundation for urban population forecasting, providing valuable insights for traffic planning, management, and future urban development strategies. © ASCE.

关键词： Prediction models

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Embodied AI in Mobile Robots: Coverage Path Planning with Large Language Models

arXiv

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arXiv 2024年

作者： Kong, Xiangrui Zhang, Wenxiao Hong, Jin Braunl, Thomas Dept. of Electrical Electronic and Computer Engineering The University of Western Australia Perth Australia Dept. of Computer Science and Software Engineering The University of Western Australia Perth Australia

In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities in understanding and solving mathematical problems, leading to advancements in various fields. We propose an LLM-embodied path planning framework for mobile agents, focusing on solving high-level coverage path planning issues and low-level control. Our proposed multilayer architecture uses prompted LLMs in the path planning phase and integrates them with the mobile agents' low-level actuators. To evaluate the performance of various LLMs, we propose a coverage-weighted path planning metric to assess the performance of the embodied models. Our experiments show that the proposed framework improves LLMs' spatial inference abilities. We demonstrate that the proposed multi-layer framework significantly enhances the efficiency and accuracy of these tasks by leveraging the natural language understanding and generative capabilities of LLMs. Our experiments show that this framework can improve LLMs' 2D plane reasoning abilities and complete coverage path planning tasks. We also tested three LLM kernels: gpt-4o, gemini-1.5-flash, and claude- 3.5-sonnet. The experimental results show that claude-3.5 can complete the coverage planning task in different scenarios, and its indicators are better than those of the other models. © 2024, CC BY-NC-SA.

关键词： Motion planning

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To Predict the Fire Outbreak in Australia using Historical Database 10

To Predict the Fire Outbreak in Australia using Historical D...

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10th International Conference on Reliability, Infocom Technologies and Optimization ,Trends and Future Directions, ICRITO 2022

作者： Tayal, Devendra Kumar Agarwal, Nidhi Jha, Anjali Deepakshi Abrol, Vrinda Indira Gandhi Delhi Technical University for Women Dept. of Computer Science & Engineering New Delhi India

ISBN: (数字)9781665474337

ISBN: (纸本)9781665474337

Australian bush fires have caused huge damage, not only to the environment but also to the community. In southeastern Australia, 11.5 million hectares (28.4 million acres) of bushland and forest was affected during the 'Black Summer' Bushfire of 2019-2020. The frequency of bushfires gives context for modelling different climate data to accurately anticipate future hot regions for bushfires. In this study, we have implemented a Machine Learning based Decision Tree Model to construct a Forest Fire Prediction Model using data from the last 20 years. This algorithm is derived from a collection of unrelated decision trees. Additionally, we converted observed fire spots into a continuous density of fire spots and made a choropleth map. The prediction model's structure makes it possible to produce predictions with a higher degree of accuracy. Additionally, it helps to increase assistance for fire crews at the front-line. © 2022 IEEE.

关键词： Fires

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Performance Analysis of Single Ended Primary Inductor Converter with Adaptive and Static Proportional Integral Control Variants

Performance Analysis of Single Ended Primary Inductor Conver...

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International Conference on Electrical engineering, computer science and Informatics (EECSI)

作者： A. Nur Annisa Alif Panawan Faizal Arya Samman Ida Rachmaniar Sahali Kayue M. Smedley Dept. of Electrical Engineering Hasanuddin University Gowa Indonesia Dept. of Electrical Engineering and Computer Science University of California Irvine California

ISBN: (数字)9798350355314

ISBN: (纸本)9798350355321

The Single-Ended Primary-Inductor Converter (SEPIC) is one type of DC-DC converter that has been widely utilized. In certain conditions, this DC-DC converter may exhibit inappropriate behavior regarding stability and reliability, thus requiring a controller to ensure that the voltage, current, or power values they produce align with the desired one. In this research, we examine and simulate the performance of the SEPIC converter using a Proportional Integral (PI) controller and compare the use of tuning methods: Ziegler-Nichols (ZN), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Fuzzy Logic in regulating the PI control parameters. Simulations will be performed through MATLAB. From the simulation results, it is observed that the PI tuning methods using GA, PSO, and Fuzzy Logic are superior to the ZN method, and with the advantage of adaptive process for PI tuning, the fuzzy logic method outperforms in terms of convenience to the other three methods.

关键词： Fuzzy logic Simulation DC-DC power converters Stability analysis Steady-state Reliability Particle swarm optimization Voltage control Tuning Genetic algorithms

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Adaptive Traffic Signal Timing: Leveraging YOLOv10 and computer Vision for Real-Time Optimization

Adaptive Traffic Signal Timing: Leveraging YOLOv10 and Compu...

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

作者： Suhas Papanashi Manya Chadaga Kshithi R Santosh S Huddar K Sreelakshmi Ramakanth Kumar P Dept. of Electronics and Telecomm RV College of Engineering Bengaluru Dept. of Computer Science & Eng. RV College of Engineering Bengaluru

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

Traffic congestion poses a significant challenge in modern urban environments, particularly in densely populated cities. Traditional traffic signals, which operate on fixed timings, often require manual adjustments to respond to varying traffic densities, leading to inefficient traffic flow. This issue arises because fixed intervals do not account for the dynamic nature of traffic patterns throughout the day. While developing intelligent systems that adjust signal durations dynamically offers a potential solution, such systems involve substantial financial investment and extended implementation timelines. This paper proposes a method to optimize green light durations by analyzing footage collected from a traffic junction in Bengaluru. This analysis can be performed using existing CCTV footage or manually gathered data. Even limited sample sizes can provide valuable insights for preliminary adjustments. The approach utilizes YOLOv10, a state-of-the-art computer vision algorithm, to extract relevant traffic data. The data is processed by an algorithm designed to reallocate green light durations, which are then compared with the original timings. An experimental setup involved recording one-minute videos over three days at the selected junction and applying the proposed algorithm to assess and refine signal durations. The results demonstrate the feasibility of using such algorithms to improve traffic signal management effectively.

关键词： computer vision Urban areas Green products Traffic control Real-time systems Timing Data mining Junctions Vehicle dynamics Videos

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Data-Enhanced Prediction with Decomposition and Amplitude-Aware Permutation Entropy in Distributed Computing systems

Data-Enhanced Prediction with Decomposition and Amplitude-Aw...

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IEEE International Conference on systems, Man and Cybernetics

作者： Haitao Yuan Qinglong Hu Jing Bi Wei Zhang Jia Zhang MengChu Zhou School of Automation Science and Electrical Engineering Beihang University Beijing China Dept. of Computer Science City University of Hong Kong Hong Kong China College of Computer Beijing University of Technology Beijing China Dept. of Computer Science and Engineering University of Connecticut Storrs CT USA Dept. of Computer Science Southern Methodist University Dallas TX USA Dept. of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In recent years, distributed computing has wit-nessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in quality of service and cost efficiency. However, these data often exhibit non-linearity, high volatility, and inter-dependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVI for achieving high-accuracy workload prediction in distributed computing systems. SVI employs the Savitzky-Golay filter and variational mode decomposition for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVI improves the prediction accuracy by 35.4% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVI enhances the prediction accuracy of workload, CPU, and memory by 62.5%, 65.6%, and 69.1 %, respectively.

关键词： Training Accuracy Costs Computational modeling Memory management Organizations Quality of service Predictive models Market research Internet

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Secure and Traceable QR Code Using Blockchain-enabled Certificates 11

Secure and Traceable QR Code Using Blockchain-enabled Certif...

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11th IEEE International Conference on Communication systems and Network Technologies, CSNT 2022

作者： Bhadoria, Robin Singh Goyal, Akshat Das, Arka Prabha Bashar, Abul Zikria, Mohammed Gla University Dept. of Computer Engineering and Applications Mathura India Dept. of Cse Mathura India Bhopal India Prince Mohammad Bin Fahd University College of Computer Engineering and Science Khobar Saudi Arabia

ISBN: (纸本)9781665480383

The blockchain has been considered to be a breakthrough technology solution for problems based on security aspects. It is a Distributed Ledger Technology (DLT) which can be used to established trust among different trustless entities or nodes present in the network. Furthermore, Quick Response (QR) Codes are likewise one of the most promising innovations going on in the markets. In QR code, small dots are present which get converted to binary numbers and get validated with error correcting algorithms. Hence, QR codes are beneficial for both storage and authentication. In this article, we discuss the prospects of combination of these two technologies for better results. In this paper, we aim to develop permission-less Blockchain, which is relatively easier to compute and have shown to increase its data capacity. It also endeavored to add the QR Code incorporation to a block and make it more scalable with considering the limited space in a such block. © 2022 IEEE.

关键词： Blockchain

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