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Comparison of Supervised Learning Models for COVID-19 Confirmed Cases Prediction using Correlation Analysis 12

Comparison of Supervised Learning Models for COVID-19 Confir...

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Joint 12th international conference on Soft computing and Intelligent Systems / 23rd international Symposium on Advanced Intelligent Systems (SCIS and ISIS)

作者： Kim, Jun-Su Choi, Byung-Jae Daegu Univ Dept IT Convergence Engn Gyongsan Gyeongsangbuk D South Korea Daegu Univ Dept Elect Engn Gyongsan Gyeongsangbuk D South Korea

ISBN: (纸本)9781665499248

The COVID-19 virus, which first appeared in 2019, has a strong contagious power and is highly spread by people's mobility. In this study, correlation analysis is used in statistical preprocessing of dataset which further used to predict the COVID-19 confirmed cases for next day. Data is divided into two sets by organizing the data set by data preprocessing using correlation analysis. The first dataset is Google Mobility Data of COVID-19 infection with six variables. The second dataset is Google Mobility Data of COVID-19 infection with two variables: (1) Retail stores and leisure facilities (2) Grocery stores and pharmacies. The results of predicting the number of confirmed cases are compared using four supervised machine learning models. Furthermore, the soft voting method is used to show more improved results than the individual performances of each method.

关键词： COVID-19 Correlation Analysis Machine Learning Soft voting Prediction.

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Empirical Study of Rectifier and Dropout in Feedforward Neural Networks

Empirical Study of Rectifier and Dropout in Feedforward Neur...

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international conference on Information and Communication technology convergence (ICTC)

作者： Sungpil Woo Sunhwan Lim Daeyoung Kim Electronics and Telecommunications Research Institute (ETRI) School of Computing Korea Advanced Institute of Science and Technology (KAIST) Daejeon South Korea

In this paper, we analyze the unique sparsity properties of dropout, rectified activation functions, and other activation functions through simple experiments. We explore how the distinctive membership functions of ea... 详细信息

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

关键词： Rectifiers Information and communication technology Feedforward neural networks convergence

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Effective Lung Cancer Detection Framework Employing Adaptive Histogram Equalization and Deep CNN 7

Effective Lung Cancer Detection Framework Employing Adaptive...

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7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Senthil Pandi, S. Bala Subramanian, C. Jenifer, A. Praveen, D.S. Rajalakshmi Engineering College Department of CSE Chennai India Kalasalingam Academy of Research and Education Department of CSE Srivilliputtur India St. Joseph's Institute of Technology Department of AIDS Chennai India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372816

This paper presents a fast image segmentation technique in medical imaging to reduce the time consumed by doctors in analyzing CT scans. The manual analysis of modern medical imaging is hardly possible because of the very huge size and picture' complexity. The accuracy and convergence time are two critical parameters that affect the performance of the segmentation algorithms. Because of the existing challenges in medical image segmentation, the need to study and apply new evolutionary techniques is an imperative. Lung carcinoma is the most frequently occurring cancer in men worldwide and, therefore, calls for early detective measures to enable time-critical, life-saving therapies. Among the numerous medical imaging techniques, CT has shown very high efficiency in detecting lung cancer. This paper evaluates five optimization algorithms' performance in extracting tumors from lung CT images using a deep convolutional neural network for picture classification. Comparatively, the strategies of median, adaptive-median, and average filters have been evaluated;therefore, the adaptive median filter has been selected as the best possible enhancement methodology for medical CT images. Moreover, adaptive histogram equalization is used to enhance the contrast of images. The improved images pass through four different algorithms before being displayed. The research validates results in Python on a dataset of 20 lung images. In this study, it is noteworthy that during validation, the GCPSO algorithm hit the maximum accuracy of 98.78%. © 2024 IEEE.

关键词： Lung cancer

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Graph-Powered Reinforcement Learning for Intelligent Task Offloading in Vehicular Networks 6

Graph-Powered Reinforcement Learning for Intelligent Task Of...

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6th international conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Ullah, Ihsan Lim, Hyun-Kyo Seok, Yeong-Jun Ul Islam, Naeem Ji, Chang-Hun Han, Youn-Hee Advanced Technology Research Center KOREA TECH Cheonan Korea Republic of Quantum Network Research Center KISTI Daejeon Korea Republic of Computer Science and Engineering KOREA TECH Cheonan Korea Republic of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan Future Convergence Engineering KOREA TECH Cheonan Korea Republic of

ISBN: (纸本)9798350344349

In vehicular edge computing, task offloading optimization is crucial for balancing computational demands with minimizing delays and costs. However, the dynamic nature of the vehicular environment, including vehicle mobility, network topology, and available computing resources, poses significant challenges. This paper presents a task offloading scheme that enables vehicles to dynamically decide between local task execution and offloading to nearby vehicles, edge servers, or the cloud. Our objective is to optimize task offloading by minimizing cost and delay. We integrate graph convolutional networks with deep reinforcement learning to optimize task offloading decisions and achieve our goal. The Graph Convolutional Network is integrated with Deep Reinforcement Learning to enhance network representation and decision efficiency of agent. The optimization problem is formally formulated within the framework of a Markov Decision Process. Simulation results demonstrate the superiority of proposed scheme, which achieves cost-efficiency by maximizing resource utilization, minimizing costs, and optimizing task offloading while reducing task rejection. © 2024 IEEE.

关键词： Reinforcement learning

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Malware Detection and Classification for Internet of Things Using Self-Attention Based Long Short-Term Memory 3

Malware Detection and Classification for Internet of Things ...

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3rd IEEE international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Sudhakar, K. Husain, Saif O. Babu, D. Raja Kumar, M. Ashok Dineshkumar, R. Nitte Meenakshi Institute Of Technology Dept. Of Ai & Ds Bengaluru India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Sr University Department Of Electrical And Electronics Engineering Warangal India Skyline University Nigeria Department Of Computer Science Software Engineering And Cyber Security Kano State Nigeria Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Electronics And Communication Engineering Chennai India

ISBN: (纸本)9798350318609

The Internet of Things (IoT) connects numerous intelligent devices providing security features that interact with default settings accessed through applications. Additionally, Deep Learning (DL)-based mechanisms was involved for malware detection to identify and mitigate attacks. The Adaptive Synthetic Sampling (ADASYN) technique addresses class imbalances, especially when splitter class instances are distributed data in complex patterns. The proposed Self-Attention based Long Short-Term Memory (LSTM) classification method facilitates the handling of long-range dependencies in transferred data sequences offering high flexibility and adaptability to varying input lengths. For feature selection Particle Swarm Optimization (PSO) is utilized iteratively optimizes a solution in a high-dimensional space although it may have a low convergence rate due to its iterative process. The obtained results demonstrate the proposed Self-Attention based LSTM model achieves better accuracy reaching 98.05% on the NSL-KDD dataset and 99.01 % on the UNSW-NB15 dataset. These results ensure accurate classification compared to other existing methods, such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). © 2024 IEEE.

关键词： Internet of things

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Advancing Economic Load Dispatch in Power Systems: A Hybrid Cuckoo Search Algorithm with Adaptive Parameter Control and Linear Regression 5

Advancing Economic Load Dispatch in Power Systems: A Hybrid ...

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5th international conference on Circuits, Control, Communication and computing, I4C 2024

作者： Appa Rao, G.V. Madhurl, K Hema Devi, G Rekha Tarshini, K. Dpaul, G Anan Hemanth, B. Sasi Institute of Technology and Engineering Department of Eee Andhra Pradesh India

ISBN: (纸本)9798331528539

The Economic Load Dispatch (ELD) problem is a critical facet of power system optimization, tasked with determining the optimal allocation of power generation among various units to minimize operational costs. This research introduces a novel hybrid algorithm to address the ELD problem, combining the strengths of the Cuckoo Search algorithm with adaptive parameter control and linear regression. The proposed approach aims to enhance the algorithm's convergence and solution quality, particularly variability and complexity into power systems. This paper evaluates the effectiveness of the hybrid algorithm using two test systems: the IEEE 30-bus system and the IEEE 14-bus system. These widely recognized test cases provide diverse and realistic scenarios for assessing the proposed algorithm's performance under different operational conditions. The integration of adaptive parameter control allows the algorithm to dynamically adjust its parameters during optimization, ensuring adaptability to the changing characteristics of the ELD problem. Furthermore, the inclusion of linear regression provides insights into the underlying patterns of the ELD problem, aiding in the modeling of relationships between variables. The research conducts a comprehensive analysis, comparing the proposed hybrid algorithm with existing techniques. Through extensive simulations and performance evaluations, the study demonstrates the algorithm's superiority in achieving optimal economic load dispatch, particularly when faced with the challenges posed by renewable energy sources. This paper contributes to the ongoing efforts to enhance the economic operation of power systems, addressing the evolving landscape of energy resources and fostering sustainability in the face of increasing renewable energy integration. © 2024 IEEE.

关键词： Hybrid power

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Surrogate-Assisted Multi-Class Collaborative Teaching and Learning Optimizer for High-Dimensional Industrial Optimization Problems

Surrogate-Assisted Multi-Class Collaborative Teaching and Le...

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2024 IEEE international conference on Systems, Man, and Cybernetics, SMC 2024

作者： Bi, Jing Wang, Ziqi Yuan, Haitao Yang, Jinhong Zhang, Jia College of Computer Beijing University of Technology Beijing100124 China Beijing University of Technology Beijing Laboratory of Smart Environmental Protection Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Cssc Systems Engineering Research Institute Beijing China Lyle School of Engineering Southern Methodist University Dept. of Computer Science DallasTX United States

ISBN: (纸本)9781665410205

Swarm intelligence and evolutionary algorithms are widely applied in industrial scheduling, mobile edge computing, etc due to their strong robustness and fast optimization speed. However, some real-world industrial optimization problems involve numerous decision variables, known as high-dimensional problems. Current algorithms often require considerable computational resources to evaluate objective function values because of high-dimensional decision spaces. Moreover, they are also prone to be trapped in local optima. To solve the above problems, this work proposes an improved algorithm named Surrogate-assisted Multi-class Collaborative Teaching and learning optimizer (SMCT). A multi-class collaborative teaching and learning optimizer is proposed as a base optimizer to improve exploration and exploitation abilities. Furthermore, an autoencoder-assisted radial basis function is proposed as the surrogate model to replace true function evaluations, thereby saving computational resources and balancing the complexity and accuracy in fitting true models. Finally, experimental results demonstrate that SMCT surpasses its existing peers in both search accuracy and convergence speed across eight high-dimensional benchmark functions. © 2024 IEEE.

关键词： Scheduling algorithms

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A Transdisciplinary Approach to Holographic Indoor Navigation Using Mixed Reality and Cloud computing 31

A Transdisciplinary Approach to Holographic Indoor Navigatio...

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31st ISTE international conference on Transdisciplinary Engineering, TE 2024

作者： Morganti, Alessio Grandi, Fabio Khamaisi, Riccardo Karim Peruzzini, Margherita Department of Engineering "Enzo Ferrari" University of Modena and Reggio Emilia via Vivarelli 10 Modena Italy Department of Sciences and Methods for Engineering University of Modena and Reggio Emilia via Amendola 2 Reggio Emilia Italy Department of Industrial Engineering University of Bologna viale del Risorgimento 2 Bologna Italy

ISBN: (纸本)9781643685502

The advent of Industry 4.0 has led to the need to train highly skilled operators in complex and dynamic environments, through the convergence of physical and digital technologies. In this context, indoor navigation plays a crucial role in the training of Operators 4.0, allowing them to acquire spatial knowledge and specific skills to operate effectively in advanced industrial environments. However, the challenges associated with this technology, such as environmental complexity and obstacles, require an innovative approach to operator training. Mixed Reality (MR) represents a promising technological innovation for the development of these systems. Indeed, MR enables the co-represence of digital and real world, by content overlapping and integrated interaction between virtual and real assets, promoting efficiency, safety and user engagement. Within this scenario, the paper proposes a Holographic Navigation (HoloNav) methodology based on spatial and cloud computing for the development of an indoor navigation application according to a transdisciplinary approach. The application is deployed on the Microsoft HoloLens 2 as MR device, using Microsoft Azure Spatial Anchors (ASA) and Cloud Storage Account resources. This study aims to provide an applied methodology and guidelines for application development, and to analyze the broader social implications and potential for social change of such MR technology to support training processes. The proposed ASA-based HoloNav approach was applied through a case study, where a tour inside the spare parts warehouse was developed for new operators who need to start familiarizing themselves with all the different areas. © 2024 The Authors.

关键词： Mixed reality

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Optimization and Application of a Deep Learning-Based Multi-Class Retinal Vessel Segmentation Model

Optimization and Application of a Deep Learning-Based Multi-...

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2024 international conference on computing, Robotics and System Sciences, ICRSS 2024

作者： Di, Xingyu College of Communications Nanjing Institute of Technology Nanjing China

ISBN: (纸本)9798331511296

With the development of deep learning, its applications in computer vision and medical image processing have become increasingly widespread, particularly excelling in tasks like image segmentation. Convolutional neural networks (CNNs), known for their ability to perform multi-level feature extraction and their adaptable structural design, have become pivotal in the field of medical image analysis. Retinal vessel multi-class segmentation, which is crucial for the early diagnosis of ophthalmic diseases and the extraction of pathological features, has increasingly become a central focus of research. This paper addresses the field of multi-class segmentation of retinal vessels and proposes an optimized deep learning-based model architecture, significantly improving segmentation accuracy and robustness. The model innovatively integrates the feature extraction capabilities of CNN sand incorporates techniques such as the Adam optimizer, dynamic learning rate adjustment, regularization constraints, and early stopping to enhance convergence efficiency and generalization performance. Compared to traditional single-class segmentation methods, this model effectively distinguishes arteries, veins, and their overlapping regions, demonstrating strong adaptability in complex vessel networks and ambiguous areas. Through the application of data augmentation methods, including rotation, flipping, and scaling, the preprocessing process boosts sample diversity, which in turn enhances the model's robustness against varied data. Experimental results reveal that the optimized model delivers outstanding performance across various segmentation tasks and achieves critical breakthroughs in multi-class vessel recognition. While there remains room for improvement in handling intricate details and complex scenarios, the model's multi-class segmentation capabilities and medical application value offer significant insights for future research. © 2024 IEEE.

关键词： Convolutional neural networks

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Implementation of Android-based Smart Speakers in a Smart Home for Dementia Patients 7

Implementation of Android-based Smart Speakers in a Smart Ho...

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7th IEEE international conference for convergence in technology, I2CT 2022

作者： Chattopadhyay, Rhitayu Pandya, Saumitra Kaur, Rajbir The Lnm Institute of Information Technology Computer Science and Engineering Jaipur India

ISBN: (纸本)9781665421683

Dementia is a broad category of brain diseases that cause long-term and often gradual decrease in the ability to think and remember. It affects the day-to-day activities of a patient. The purpose of our work is to implement a cost-effective, IoT solution for patients suffering with dementia. Our work is a fog computing based implementation of the proposal by Boumpa ***. [1]. The paper [1] mentions that having an edge/fog architecture could lead to additional requirements for custom-owned platform and design of new smart speakers. In our implementation work, we use android phones as smart speakers. This removes the need of production of new smart speakers and a custom-made platform. Our system is modular and scalable. The data is saved locally owing to privacy requirements. The system helps locate the patient in the house by using the signal strength of Bluetooth Low Energy devices. After triangulating, the speakers play a sound associated with the relative or caretaker, so that the patient can identify the relative. Subsequently, the system also informs the relative of the location of the patient in the house. © 2022 IEEE.

关键词： Cost effectiveness

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