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Vision Transformer-based Automatic Detection of COVID-19 in Chest X-ray Images 2

Vision Transformer-based Automatic Detection of COVID-19 in ...

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2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Yurdakul, Mustafa Tasdemir, Sakir Kirikkale University Engineering Faculty Computer Engineering Dept. Kirikkale Turkey Selçuk University Technology Faculty Computer Engineering Dept. Konya Turkey

ISBN: (纸本)9798350373363

The COVID-19 virus, which first emerged in the city of Wuhan in China, rapidly spread across the globe due to its high contagiousness. Detecting the virus early is crucial to stop its spread and to provide timely treatment to affected individuals. Chest X-ray (CXR) images are a quick, cost- effective, and non-invasive method commonly used for the diagnosis of COVID-19. CXR images are manually inspected by experts for diagnosis. However manually detection is not only time-consuming but also prone to errors due to human fatigue. For these reasons, there is an urgent need for a system that can detect COVID-19 from CXR images. In this study, the Vision Transformer (ViT) model was used to classify Normal, Pneumonia, and COVID-19 from CXR images. Experimental results show that the Vision Transformer (ViT) possesses a robust and high generalization capability, with an accuracy rate of 97%, indicating its significant potential in medical image analysis. © 2023 IEEE.

关键词： COVID-19

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Exploring the Integration of Generative AI in Modern Education Systems: A Comprehensive Analysis

Exploring the Integration of Generative AI in Modern Educati...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Dey, Avick Kumar Chauhan, Vineet Kumar Singh, Pradeep Kumar Sarkar, Ananya Dept. of Computer Applications West Bengal Durgapur India Galgotias College of Engg. & Tech Dept. of Information Technology Greater Noida India School of Computer Science and Engineering Galgotias University Greater Noida India DSMS College of Tourism and Management Dept. of Computer Applications West Bengal India

ISBN: (纸本)9798331517953

This paper aims at filling this gap through dissecting the applicability of the Generative AI in the current learning and teaching system. The paper offers an introduction to Generative AI technologies: GPT and DALL-E, and describes the chronological history and current progress of its educational applications. Other objectives include discussing about the uses of Generative AI in adaptive learning environment, smart learning systems, automation of organizational work and in talented minds improvement. Analyzing key successful AI cases, the paper reveals the challenges implementers encountered, and provides debates about the appropriate practices of AI. Research evidence points to the prospect of Generative AI benefiting students and educators in teaching and learning, which indicates the innovative potential of the technology, yet at the same time reveals the potential risks and challenges that need to be addressed concerning the ethical and privacy implications of Generative AI in education. Others may follow general recommendations for policymakers and educators on how to integrate artificial intelligence responsibly, as well as future work for this quickly burgeoning domain. © 2024 IEEE.

关键词： Contrastive Learning

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PrecisionCardio: A Comprehensive Machine Learning Approach for Accurate Prediction of Heart Failure Trajectory 30

PrecisionCardio: A Comprehensive Machine Learning Approach f...

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30th IEEE International Conference on Telecommunications, ICT 2024

作者： Hossain, Muhammad Anwar Bin Shawkat, Siam Sharif, Kazi Shaharair Hossain, Md Ikbal Asmani, Herick Rahman, Md Minhazur Ministry of Posts Telecommunications and Information Technology ICT Division Dhaka Bangladesh American International University-Bangladesh Department of Computer Science Dhaka Bangladesh Oklahama State University Dept. of Computer Science StillwaterOK United States University of South Dakota Dept. of Computer Science VermillionSD United States Illinois Institute of Technology Chicago Dept. of Computer Science ChicagoIL United States

ISBN: (纸本)9798350356694

Accurate prediction of cardiac disease can prevent potentially fatal situations. For providing cardiologists with assistance to predict the trajectory of heart failure (HF), we introduce a system based on machine learning that employs medical patient history to estimate the severity of disease advancement. Using a labeled dataset comprising 918 records, we evaluated six models based on its performance in predicting heart disease in this study. Comparable results to our own were obtained from studies that utilized longitudinal multi-class prediction and utilized much larger datasets. A number of machine learning strategies for predicting cardiovascular diseases using patient data on important health factors are described in this work. We presented six unique classification techniques that were applied to develop the prediction models: Support Vector Machine(SVM), NaAve Bayes (NB), Random forest Classifier(RFC), K-Nearest Neighbours(KNN), Logistic Regression and Gradient Boosting Classifier. Before these models were constructed, the data under-went preprocessing and feature selection. The evaluation of the models incorporated the following metrics: F1-score, accuracy, precision, and recall. The SVM model achieved the maximum degree of accuracy, 92.39%. © 2024 IEEE.

关键词： Risk analysis

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Automatically Suggesting Suitable Crop from Soil Samples Using Optimized Neural Networks 3

Automatically Suggesting Suitable Crop from Soil Samples Usi...

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Pasha, Syed Nawaz Ramesh, D. Mohmmad, Sallauddin Deepak, Gujjula School of Computer Science and Artificial Intelligence Sr University Warangal India Kamala Institute of Technology and Science Dept. of Computer Science and Engineering Huzurabad India

ISBN: (纸本)9798350381931

A soil-centric crop recommendation system recommends suitable crops utilizing vital soil attributes as input factors. This sophisticated system incorporates parameters like Nitrogen (N), Phosphorus (P), Potassium (K) levels, temperature, humidity, pH, and rainfall to suggest ideal crops for cultivation. The dataset comprises diverse crops and their specific requirements for each input parameter. We implemented an optimized neural network to provide precise crop recommendations based on the provided input values. By analyzing this data, the system provides farmers with the information necessary to make well-informed decisions regarding their choice of crops, enhancing the potential for optimal yields. Our model performed well on training and testing data with an accuracy of 0.94. These results demonstrate its effectiveness and potential as a valuable tool for agricultural decision-making. This system offers farmers tailored recommendations based on soil properties, climatic conditions, and other environmental factors. © 2024 IEEE.

关键词： Crops

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Detecting Helmets Of The Bike Riders Using Deep Learning Algorithms 14

Detecting Helmets Of The Bike Riders Using Deep Learning Alg...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Mia, Yousuf Rahman, Md. Solaimanur Basak, Animesh Hossain, Sheikh Mufrad Zulfiker, Md. Sabab Sumaia, Merina Akter Daffodil International University Dept. of Computer Science & Engineering Dhaka Bangladesh Model Institute of Science & Technology Dept. of Computer Science & Engineering Gazipur Bangladesh

ISBN: (纸本)9798350335095

We propose real-time bike helmet detection in our study. A lot of people ride bikes in our nation. Motorbikes are more popular than vehicles because they are cheaper to maintain, take up less parking space, and provide more mobility and adaptability in urban situations. Bike riding is entertaining yet risky. Bicyclist safety is the planned system's main purpose. Many drivers don't wear helmets even though they're mandated by law. In emerging countries, mortality has been growing steadily. A helmet detection technology that identifies drivers without helmets is needed to safeguard the public. We employ a real-time 3202 dataset for this approach. We gather wearing helmet 1911 and no helmet 1291 data and utilize algorithms like VGG16, Resnet50, MobileNet v.02, Inception V3, EfficientNet, and CNN. The EfficientNet achieved 98% accuracy. Each person's comparison statement techniques are in the implementation section. To construct the optimum model for the conditions, this inquiry uses model validation approaches. © 2023 IEEE.

关键词： Deep learning

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5G the Missing Piece in Digital Transformation of the Construction Sector 28

5G the Missing Piece in Digital Transformation of the Constr...

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28th International computer Science and Engineering Conference, ICSEC 2024

作者： Chnaidini, Shaik Khan, M. Jebran Ullah, Fasee Khan, Arafat Ahmad Prashar, Deepak Gulf American University Dept. of Civil Engineering Fujairah United Arab Emirates Sarhad University of Science and Information Technology Dept of Computing and Information Technology Peshawar Pakistan Uinversiti Teknology Petronas Dept. of Computer and Information Sciences Seri Iskandar Malaysia Khonkaen University Dept. of Computer Science Khon Kaen Thailand Lovely Professional University Dept. of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350366860

The 5th generation of wireless technology with a continuum leap rushing towards the future in the world of communication has a puzzle that enables the autonomous machine to work in site making a louder voice that shows the era where even if we cannot be fully robotic, we can make the machines do the work by sitting on a faraway control room. Making ways clear to see the work done miles away on construction sites the 5th generation has not only brought the people close through communication but also made them work collegiately with a clearer view front. The paper discusses 5G and the other related technologies that are emerging all over the world and how it is needed specifically in the construction industry. In the present paper, we tried to shed light on the acceptance and implementation of 5G technology in the construction sector. It is seen that the construction sector in India is lagging behind when it comes to implementation and availability of required support and resources for the 5G implementation. © 2024 IEEE.

关键词： Construction industry

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Exploring the Potential of MANETs: A Study on Raspberry Pi and UAV Integration 3

Exploring the Potential of MANETs: A Study on Raspberry Pi a...

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

作者： Archana, B. Mahalakshmi, A. Revathi, K. Ashok Kumar, K. Hema, A.B. Dept. of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore India Dept. of Information Technology SNS College of Engineering Coimbatore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

This research investigates the feasibility of utilizing Mobile Ad Hoc Networks (MANETs) in conjunction with Raspberry Pi-equipped Unmanned Aerial Vehicles (UAV) swarms. The primary objective is to overcome the limitations of traditional communication infrastructures in dynamic and decentralized environments. Specifically, we address the challenge of establishing reliable and efficient communication links within a swarm of UAVs engaged in various applications, including disaster response, military reconnaissance, and environmental monitoring. By integrating Raspberry Pi devices with UAVs, we aim to create a self-organizing communication network that enables seamless data transmission, coordinated swarm operations, and enhanced mission effectiveness. The research explores different synchronization strategies, including master-slave and decentralized approaches, to optimize swarm coordination. Furthermore, we investigate the potential of incorporating advanced technologies such as machine learning, computer vision, and swarm intelligence to further enhance the autonomy, adaptability, and overall performance of the UAV swarm. © 2025 IEEE.

关键词： Mobile ad hoc networks

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A Deep Learning Approach for Recognizing Malayalam Sign Language Gestures 7

A Deep Learning Approach for Recognizing Malayalam Sign Lang...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Issac, Kevin Daniel, Esther Varghese, Bright Gee Karunya Institute of Technology and Sciences Dept. of Computer Science & Engineering Coimbatore India Maharishi International University Dept. of Computer Science Fairfield United States

ISBN: (纸本)9798350397253

Communication involves the sharing or exchanging of information, ideas, and emotions, which requires a common language between individuals. For those with speech impairments, sign language serves as a means of communication. The lack of understanding of sign language can create difficulties in communication between individuals with speech impairments and those without, posing a significant challenge. One solution to this challenge is to create a machine learning-based model that recognizes various sign language motions and converts them into a shared language. This model can be trained to benefit those seeking to interact and communicate with individuals with speech impairments. Although Sign Language Recognition systems exist, they are limited in the number of languages that can be recognized. This article introduces a real-time Sign Language Recognition system that employs transfer learning with TensorFlow and utilizes a webcam for the purpose of detecting and interpreting sign language gestures specific to the Malayalam language. © 2023 IEEE.

关键词： computer vision

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VisionAid Band: Real-Time Smart Navigational and Alert Support System for Visually Impaired Persons Using Wearable technology 2

VisionAid Band: Real-Time Smart Navigational and Alert Suppo...

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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Kumar, Kanak Verma, Anshul Verma, Pradeepika Dept. of Computer Science Banaras Hindu University Varanasi India Technology Innovation Hub Indian Institute of Technology Patna India

ISBN: (纸本)9798331530389

The"VisionAid Band" represents a groundbreaking advancement in assistive technology, designed to empower visually impaired individuals by enhancing their spatial awareness and mobility through an innovative, low-cost solution. Utilising an Arduino-UNO, ultrasonic sensor, and vibrator sensors to interpret the environment, translating physical obstacles into auditory or tactile feedback that guides users safely around their surroundings. The device's affordability and simplicity are pivotal in addressing the accessibility gap in current assistive technologies, which are often prohibitively expensive or complex. This paper details the development process of the VisionAid Band, from conceptualisation to prototype testing. It discusses the selection of the microcontroller platform, sensor integration, and the development of algorithms that efficiently process sensor data to provide real-time, intuitive feedback to the user. Emphasis is placed on the device's design considerations, including ergonomics, power consumption, and user interface, ensuring that the VisionAid Band is effective but also comfortable and user-friendly. Through a series of controlled experiments and user feedback sessions, the effectiveness of the VisionAid Band in improving navigational abilities among visually impaired participants is evaluated. Preliminary results indicate a significant enhancement in users' ability to navigate complex environments safely and confidently. The paper concludes with a discussion of potential improvements, future enhancements, and the broader implications of the VisionAid Band on the accessibility and independence of individuals with visual impairments. The VisionAid Band exemplifies how low-cost, technology-driven solutions can substantially impact the quality of life for people with disabilities. Its development underscores the potential of innovative engineering to address critical challenges in accessibility, paving the way for more inclusive technology solutions

关键词： Assistive technology

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Proposed Real-Time Data Aggregation Scheme for Cluster-based WSN Sensor Nodes 23

Proposed Real-Time Data Aggregation Scheme for Cluster-based...

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18th Asian Internet Engineering Conference, AINTEC 2023

作者： Cruzada, Carla Francisco, Piolo Loyola, Rainier Asaba, Hiroki Flores, Fritz Kevin Peradilla, Marnel Dept. of Computer Technology De La Salle University Manila Philippines

ISBN: (纸本)9798400709395

Wireless Sensor Networks (WSNs) are continuously used in different fields of application. As data transmission increases, it becomes necessary to suggest methods for enhancing energy efficiency, reducing data latency, and alleviating network congestion. Data Aggregation is an approach to enhance network lifetime and reduce data latency when data is transmitted across the network. Data gathered in WSNs must be aggregated properly and arrive in time at the gateway, as these are usually deployed to collect real-time data. Although different protocols have been developed to improve data aggregation, few have only been implemented in real-time WSNs. This study proposes a two-level cluster-based data aggregation protocol for real-time WSN clusters. The proposed data aggregation protocol is implemented in a testbed scenario to explore its feasibility and was evaluated using metrics that cover power consumption, packet transmissions between the sensor nodes and cluster head, percent of aggregation, total data points, and the resources identified for the protocol to operate properly. Overall, based on the test scenarios implemented, the protocol can significantly lessen the amount of data points to be sent to the sink wherein a minimum of 50% of the data points can be aggregated for accelerometer data while 40% for temperature data. Additionally, the aggregation protocol was able to retain a minimum of 33% of the original accelerometer data and a minimum of 51% of the temperature data. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Sensor nodes

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