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A Novel Deep Ensemble Learning Approach to Predict Eye Diseases from OCT Images 5

A Novel Deep Ensemble Learning Approach to Predict Eye Disea...

引用

5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Uddin, Nazim Mia, Md Sajib Uddin, Mohammed Nasir Mahmud, Prince Chandpur Science and Technology University Dept. of Information and Communication Technology Bangladesh Sylhet Engineering College Dept. of Computer Science and Engineering Bangladesh Jagannath University Dept. of Computer Science and Engineering Bangladesh Chandpur Science and Technology University Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350394290

As the world advances in technology, the classification of eye diseases is thought to have clinical applications since it evaluates and analyzes the results. Optometrists research and diagnose age-related eye problems such as Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), and Drusen using optical coherence tomography (OCT) of the human eye. Because their symptoms are similar, identifying these diseases is challenging and requires hours of expert analysis. The requirements for eye illness classification should be straightforward in nature, structure, and evaluation. Deep Learning Convolutional Neural Network Classification can create a hierarchical representation of images to differentiate between normal and diseased eye patterns for diagnostic purposes. Therefore, this paper aims to implement and define deep learning to classify and identify eye diseases. Using a four-class classification problem, this work investigates the automatic detection of Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), Drusen, and Normal in Optical Coherence Tomography (OCT) pictures. Multi-transfer learning architecture was used in the suggested deep learning Ensemble classification model. The most incredible accuracy of our proposed Ensemble model for multi- class classification was 83%. © 2023 IEEE.

关键词： Optical tomography

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Crowd Digital Twins: High-Level Requirements and Architecture 22

Crowd Digital Twins: High-Level Requirements and Architectur...

引用

22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Casadei, Roberto Delnevo, Giovanni Girau, Roberto Mirri, Silvia University of Bologna Dept. of Computer Science and Engineering Italy

ISBN: (纸本)9798331508050

Crowd management is the problem of dealing with physical crowds of humans, e.g., for safety reasons or to improve services through crowd-awareness. In previous work, the notion of a Crowd Digital Twin (CDT), namely the digital twin associated to physical gatherings of humans, was motivated and proposed as a fundamental component of crowd management systems. In this work, we better characterise the CDT notion, by investigating the functional and non-functional requirements that such a technical solution should satisfy. Then, we consider what are the fundamental components of CDT systems, and accordingly delineate a general architecture aimed at flexibly supporting the corresponding physical-digital thread. © 2025 IEEE.

关键词： Crowdsourcing

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Meteorology Predicting Technology Based on Artificial Intelligence Algorithms

Meteorology Predicting Technology Based on Artificial Intell...

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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent systems, ICETSIS 2024

作者： Abitova, Gulnara Mukashev, Daniyar Aitmukhanbetova, Elvira Rakhimzhanova, Mira Uskenbayeva, Gulzhan Kulniyazova, Korlan Binghamton University Dept Electrical Engineering and Computer Science Binghamton United States Astana It University Dept. of Computer Engineering Astana Kazakhstan L.N. Gumilyov Eurasian National University Dept. System Analysis and Management Astana Kazakhstan

ISBN: (纸本)9798350372229

The research explores the transformative impact of Artificial Intelligence in meteorology, weather forecasting. The study presented historical prediction methods overview, evolution highlighting from traditional models to advanced AI-driven systems. It delves into the integration of machine and deep learning techniques in data analysis, emphasizing technologies that significantly enhanced the accuracy and efficiency of weather predictions. Key aspects covered include the neural networks application in interpreting complex atmospheric data, the big data role in providing comprehensive training sets for AI models, the predictive analytics utilization for short-term and long-term weather forecasts. The study also examines case-study with AI implemented in weather forecasting, capability demonstrating in handling extreme weather events and climate anomalies. The study addresses the challenges and limitations faced in the meteorology AI integration, such as data quality concerns, computational requirements, and the specialized expertise need. It proposes potential solutions, future directions for research domain, suggesting a multidisciplinary approach involving meteorologists, data scientists, and AI experts. The conclusion underscores the revolutionary impact of AI-technology in meteorology, projecting how continuous advancements in AI-technology could redefine the predicting weather patterns approach. The work highlights the AI current state in weather forecasting but also sets the future innovations stage. © 2024 IEEE.

关键词： artificial intelligence data analysis machine learning neural networks predictive analytics weather Forecasting

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Exploring Machine Learning Techniques for Accurate Heart Disease Detection: A Comprehensive Study

Exploring Machine Learning Techniques for Accurate Heart Dis...

引用

2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Singh, Pardeep Chaudhary, Vipin Kumar Bhatia, Arvind Kumar Sharma, Gaurav Kumbhawat, Amit Singh, Suchita Lovely Professional University Dept. of Computer Science Engineering Punjab Phagwara India Buddha Institute of Technology Dept. of Computer Science Engineering Uttar Pradesh Gorakhpur India

ISBN: (纸本)9798350385199

Globally, heart disease poses a significant challenge to public health. Early identification is crucial for effectively managing and treating heart conditions. Machine learning has shown promising results in predicting medical problems for the past few years. In this study, A system was developed that is capable of detecting cardiovascular diseases using machine learning methods. The project consists of Data loading, Exploratory data analysis, preprocessing, training, evaluation and storage. A dataset was introduced that contained an array of characteristics related to the cardiovascular health status (such as age, gender, cholesterol level.). However, training measured the accuracy of 3 algorithms: K Nearest Neighbours (KNN), Decision Tree, and Random Forest in identifying heart ailment. The article also returns an ensemble graph that considers the forecasts of both these models together, saving the best GNB model for future use. Results showed that the machine learning methods are proficient in heart problem identification, as in this hybrid model, with a 97.32% accuracy rate. © 2024 IEEE.

关键词： Adversarial machine learning

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Heads-Up Multitasker: Simulating Atention Switching On Optical Head-Mounted Displays

Heads-Up Multitasker: Simulating Atention Switching On Optic...

引用

2024 CHI Conference on Human Factors in Computing Sytems, CHI 2024

作者： Bai, Yunpeng Ikkala, Aleksi Oulasvirta, Antti Zhao, Shengdong Wang, Lucia J. Yang, Pengzhi Xu, Peisen Dept. of Computer Science National University of Singapore Singapore Dept. of Computer Science Aalto University Espoo Finland Dept. of Information and Communications Engineering Aalto University Helsinki Finland School of Creative Media Dept. of Computer Science City University of Hong Kong Hong Kong Hong Kong Dept. of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge United States Dept. of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Dept. of Computer Science IPAL National University of Singapore Singapore

ISBN: (纸本)9798400703300

Optical Head-Mounted Displays (OHMDs) allow users to read digital content while walking. A better understanding of how users allocate attention between these two tasks is crucial for improving OHMD interfaces. This paper introduces a computational model for simulating users' attention switches between reading and walking. We model users' decision to deploy visual attention as a hierarchical reinforcement learning problem, wherein a supervisory controller optimizes attention allocation while considering both reading activity and walking safety. Our model simulates the control of eye movements and locomotion as an adaptation to the given task priority, design of digital content, and walking speed. The model replicates key multitasking behaviors during OHMD reading while walking, including attention switches, changes in reading and walking speeds, and reading resumptions. © 2024 Copyright held by the owner/author(s)

关键词： Reinforcement learning

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A Comparative Study on Keyword Extraction and Generation of Synonyms in Natural Language Processing

A Comparative Study on Keyword Extraction and Generation of ...

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2023 International Conference in Advances in Power, Signal, and Information Technology, APSIT 2023

作者： Dhala, Rasmi Rani Pavan Kumar, A.V.S. Panda, Soumya Priyadarsini Giet University Dept. of Computer Science and Engineering Gunupur India Sit Dept. of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350339369

Natural Language Processing (NLP) is an emerging field that aims to enable machines to understand and interpret human language. Keyword extraction and synonym generation are essential tasks in natural language processing. They play a significant role in information retrieval, text classification, and sentiment analysis. In this paper, we explore three different approaches to keyword extraction and synonym generation: rule-based model, statistical model, and extreme learning machine (ELM) model. We compare the performance of each method on a corpus of text and analyze the strengths and weaknesses of each approach. Our results show that the ELM model outperforms the other two methods in terms of accuracy and efficiency. © 2023 IEEE.

关键词： Neural network models

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Earth Pit Monitoring system for Petrol Pumps 5

Earth Pit Monitoring System for Petrol Pumps

引用

5th International Conference on IoT Based Control Networks and Intelligent systems, ICICNIS 2024

作者： Guruprasad, Sunitha Nikitha, Amulya Rebello, Glen Noronha, Calisha Madtha, Elena St Joseph Engineering College Dept. of Computer Science & Engineering Mangaluru India

ISBN: (纸本)9798331518097

The oil industry is constantly seeking ways to monitor their facilities and equipment in a systematic way with as little human intervention as possible. Currently, monitoring of earth pits is done manually. The Hindustan Petroleum Corporation Limited (HPCL) Earth Pit Monitoring App is a mobile application that aims to enable HPCL staff to securely log in and monitor their petroleum storage sites and earthing pits. This app will make it easier to monitor all aspects of their operations, including vendors and petrol pump dealers, with timely alerts for any necessary maintenance checks. The research conducted on the modern type of earthing will be beneficial for the petroleum outlets as maintenance time and cost would be reduced due to its durability. Field research at HPCL Petroleum Outlets were conducted to observe the state of earth pits and the method of earthing used. Chemical earthing is a method of grounding electrical equipment by pouring a mixture of bentonite and graphite around a copper or iron rod driven into the ground, creating a low-resistance path for electrical charge to dissipate into the earth. It is used in areas with high soil resistance and where traditional earthing methods are not feasible. Comparison between the GI Earthing used currently, and the Chemical Earthing was drawn by comparing resistance values under different conditions. © 2024 IEEE.

关键词： Gasoline

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Compact Raspberry PI Server with Integrated UPS 5

Compact Raspberry PI Server with Integrated UPS

引用

5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Hemachandran, A. Nithyabharathi, S. IFET College of Engineering Dept. Computer Science Engineering Tamilnadu Villupuram India

ISBN: (纸本)9798331530013

This paper designs and implements a compact server system based on the Raspberry Pi 4B and tailored to specific low-end and budget-constrained environments, such as home offices and small businesses, through the integration of an uninterruptible power supply (UPS) in the system to ensure continuous operation during periods of power failure. Important features include VPN for secure remote access, an ad blocker to increase the efficiency of browsing, a private NAS system with data management in a secured and easily accessible manner, and video streaming capabilities to meet multimedia requirements. Real-time performance monitoring comes through with an OLED display. The paper reflects an integrated approach combining hardware and software components which stress system flexibility, low costs, and reliability. This compact server system allows an efficient, user-friendly, and flexible server infrastructure at small scale by providing a cost-effective alternative to traditional server solutions. © 2024 IEEE.

关键词： Budget control

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Real-Time Object Detection with IOT Using a Smart Cart 18

Real-Time Object Detection with IOT Using a Smart Cart

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18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Omer, Muhammad Ali, Sardar Jaffar Raza, Syed Muhammad Le, Duc-Tai Choo, Hyunseung College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Sungkyunkwan University Dept. of Computer Science and Engineering Suwon Korea Republic of Sungkyunkwan University Dept. of AI System Engineering Suwon Korea Republic of Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9798350331011

With Industry 4.0's technological advancements, the convenience and affordability of Artificial Intelligence (AI) and IoT have increased. However, customers still face long waiting times in payment queues during the smart shopping experience. Current solutions rely on expensive sensor-based RFIDs or simple barcodes, making them impractical and limited in scalability. This paper proposes an economically smart solution with two cameras on NVIDIA JETSON NANO for realtime object monitoring and automated shopping. Communication between modules is facilitated by TCP/IP and HTTP protocols. By utilizing the YOLO v7, real-time monitoring is achieved. This innovative approach revolutionizes the customer experience by providing a seamless interface and time-saving features. Additionally, it offers potential integration with e-commerce businesses and payment gateways, further enhancing the overall customer and vendor experience. © 2024 IEEE.

关键词： Graphical user interfaces

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Framework for Cloud-Based Data Management in Autonomous Vehicles

Framework for Cloud-Based Data Management in Autonomous Vehi...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Pallapothu, Harshith Gopi Krishna, G. Sunil, Digvijay Teja Yadav, S. Nagasundari, S. Pes University Dept. of Computer Science & Engineering Bengaluru India Research Center for Information Security Forensics and Cyber Resilience Pes University Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331529833

The increasing adoption of autonomous vehicles has driven the need for robust data management solutions that support real-time operations and ensure vehicle safety and efficiency. This work introduces a cloud-based framework for management of sensor data from autonomous vehicles, focusing on optimizing payload transmission rates. The framework leverages AWS IoT Core and AWS IoT Analytics to ensure efficient data flow from vehicle sensors to cloud storage. This framework demonstrates its potential for scalable deployment in real-world autonomous vehicle networks, contributing to the evolution of connected vehicle technologies and intelligent transport systems. This framework ensures reliable data transmission up to 250 payloads per second, beyond which data loss occurs. © 2025 IEEE.

关键词： Cloud platforms

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