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Empowering Healthcare with AI: Advancements in Medical Image Analysis, Electronic Health Records Analysis, and AI-Driven Chatbots 3

Empowering Healthcare with AI: Advancements in Medical Image...

引用

3rd International Conference for Innovation in technology, INOCON 2024

作者： Sahithya, Balla Guru Prasad, M.S. Sahithi, Balla Madhu Krishna, K. Devarlla, Ajay Charan Yashavanth, T.R. Dept. of Computer Science and Engg. Dehradun India Reva University School of Computing and Information Technology Bengaluru India

ISBN: (纸本)9798350381931

The integration of Artificial Intelligence (AI) into healthcare has ushered in a new era of transformative advancements, addressing critical aspects such as Medical Image Analysis (MIA), Electronic Health Records (EHR), and AI-driven Chatbots. In this research paper, we present comprehensive developments in these domains, demonstrating their synergistic potential. In the realm of Medical Image Analysis, we have employed transfer learning techniques and harnessed diverse datasets, which include Pneumonia Chest X-Ray Images, and the Tuberculosis (TB) Chest X-ray Database, and the bone cancer Chest X-ray Database. Our models, built using transfer learning, have achieved remarkable accuracy, with some reaching an impressive 98.4% on the datasets collected from various sources. Notably, among the selected models, including InceptionV3, InceptionResNetV2, DenseNet121, DenseNet169, DenseNet201, ResNet50V2, ResNet101V2, VGG16, and VGG19. DenseNet201 has displayed exceptional performance, attaining an accuracy of 98.4%. The proposed work encapsulates significant advancements in AI within the healthcare domain, particularly in the fields of Medical Image Analysis, Electronic Health Records, and AI-driven Chatbots. Through rigorous experimentation and innovation, we demonstrate the potential to reshape healthcare delivery, diagnosis, and patient engagement. These developments underscore the transformative power of AI in redefining the healthcare landscape for the better. © 2024 IEEE.

关键词： Diagnosis

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Cloud-Based Smart Home Monitoring using ThingSpeak and ESP32 5

Cloud-Based Smart Home Monitoring using ThingSpeak and ESP32

引用

5th IEEE International Conference on Communications, information, Electronic and Energy Systems, CIEES 2024

作者： Komitov, Nikolay Terziyska, Margarita Terziyski, Zhelyazko University of Food Technologies Information and Control Technology Dept. of Automation Plovdiv Bulgaria University of Food Technologies Physics and Information Technologies Dept. of Mathematics Plovdiv Bulgaria Trakia University Stara Dept. of Computer Science and Mathematics Zagora Bulgaria

ISBN: (纸本)9798350352863

This paper presents an approach to implementing a cloud-based smart home monitoring system using the ESP32 microcontroller and ThingSpeak as the cloud platform. The proposed system provides an efficient solution for real-time monitoring of various environmental parameters, which are collected through sensors connected to the ESP32 and transmitted wirelessly to the ThingSpeak cloud. By leveraging the Internet of Things (IoT), this system enables users to remotely monitor their home environment from anywhere using a web interface. The article discusses the hardware setup, software implementation, and data visualization techniques, offering a practical guide for those interested in smart home technology and IoT integration. © 2024 IEEE.

关键词： Smart homes

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A Birds Species Detection Utilizing an Effective Hybrid Model 21

A Birds Species Detection Utilizing an Effective Hybrid Mode...

引用

21st International Multi-Conference on Systems, Signals and Devices, SSD 2024

作者： Rafa, Sara Abbas Al-Qfail, Zohaa Mubarak Adil Nafea, Ahmed Abd-Hood, Samia F. Al-Ani, Mohammed M Alameri, Saeed Amer College of Law and Political Science University of Anbar Dept. Law Ramadi Iraq College of Computer Science and Information Technology University of Anbar Dept. Artificail Inteligence Ramadi Iraq Selangor Bangi Malaysia Seiyun University Dept. Computer Science Faculty of Applied Sciences Hadhramout Yemen Hadhramout University Faculty of Computers and Information Almukalla Hadhramout Yemen Seiyun University Dept. Department of Information Technology Hadhramout Yemen

ISBN: (纸本)9798350374131

The aim of this study is to improve the performance of detection Birds Species by proposed a hybrid model using a combination of MobileNetV2 for feature extraction, an Autoencoder for dimensionality reduction, and Support Vector Machines (SVM) for classification. The methodology includes preprocessing, augmentation, feature extraction, and classification phases. The proposed model is evaluated using the Bird Species dataset, consisting of 525 bird species the dataset is carefully curated to ensure cleanliness and absence of leakage between sets. The evaluation metrics include precision, recall, accuracy, and F-measure. The results have shown the efficiency of the proposed model in bird species classification. © 2024 IEEE.

关键词： Support vector machines

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Prioritization of Medical Image Security Features: Fuzzy AHP Approaches 6

Prioritization of Medical Image Security Features: Fuzzy AHP...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Kumar, Satish Devi, Mausmi Singh, Sunil Chaurasia, Pawan Kumar Khan, Raees Ahmad BBA University Dept. of Information Technology Lucknow India PSIT Dept. of Computer Science & Engineering AKTU Lucknow India

ISBN: (纸本)9798350304480

Medical images are sensitive data about patients, hospitals, healthcare sector and, therefore, require secure transmission to protect their privacy. To ensure the security of medical image transmission, three essential factors are vitally employed: integrity, confidentiality, and authentication. In this study, we propose an F-AHP technique to determine the relative importance of these security factors. The technique was implemented using 'Octave MATLAB tools'to obtain the weights for each factor, and the results showed that integrity is the most critical factor for the transmission of medical image security than confidentiality and authentication. This technique can help healthcare organizations to choose the appropriate security measures for transmitting medical images while ensuring that patient data is protected during transmission. Overall, the researcher highlights the importance of security in medical image transmission and provides a framework for healthcare organizations to implement effective security measures. © 2023 IEEE.

关键词： Authentication Confidentiality Fuzzy AHP information Security Integrity Medical Image Security

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Semantic clustering of images using semantic similarity measures: A comparative study 8

Semantic clustering of images using semantic similarity meas...

引用

8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024

作者： Youcefa, Abdelmadjid Aiadi, Oussama Khaldi, Belal Kherfi, Mohammed Lamine University Kasdi Marbah Ouargla Dept. of Electronics and Telecommunications Ouargla Algeria University Kasdi Marbah Ouargla Dept. of Computer Science and Information Technology Ouargla Algeria

ISBN: (纸本)9798350309249

In the last decade, the increasing popularity of image sharing applications over the web has led to a huge rising in the size of the personal image collections. While conventional content-based image retrieval systems suffer from the commonly acknowledged semantic difference between low-level image features and high-level semantics, using textual information associated with images could be a good alternative. Therefore, in order to facilitate the navigation through these collections, and extracting meaningful information from them rapidly and accurately, semantic clustering of images based on textual information could help performing such an important task. In this study, we present a comparative study of several semantic similarity metrics for image datasets clustering. In particular, we evaluate the performance of eight measures namely Path, Resnik, Wu-Palmer, Lin, Jiang-Conrath, Leacock-Chodorow, Li, and Wpath. We conduct our experiments on three public datasets. The experimental results revealed that Resnik and Wpath Similarity measures whith accuracy (78% and 77.67% respectively) outperform the remaining metrics and yield more coherent and fast clustering solutions. © 2024 IEEE.

关键词： Semantics

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Optimizing Engagement Dynamics in E-Learning Environments with Insights and Strategic Approaches 3

Optimizing Engagement Dynamics in E-Learning Environments wi...

引用

3rd International Conference on Creative Communication and Innovative technology, ICCIT 2024

作者： Cholisoh, Novi Anugrah, Rio Wahyudin Fazri, Muhammad Faizal Wahid, Syahrul Mu'Arif Pramudya, Reza Dani University of Raharja Dept. of Information Management Tangerang Indonesia Pandawan Sejahtera Indonesia Dept. of Computer Systems Tangerang Indonesia Bosindo Dept. of Business Intelligence Tangerang Indonesia Sesindo Dept. of Digital Business Tangerang Indonesia Pandawan Sejahtera Indonesia Dept. of Information Technology Tangerang Indonesia

ISBN: (纸本)9798350367492

This study explores the factors influencing student engagement in e-learning and proposes strategies for improvement. It incorporates a mixed-method approach, combining quantitative data from a learner survey with qualitative insights from interviews and focus groups involving educators and students. The survey investigates technological, pedagogical, and personal factors that impact engagement, while qualitative data provide deeper insights into experiences and perceptions. The findings underscore that student engagement in e-learning is shaped by the quality of digital content, opportunities for interaction, feedback mechanisms, and individual learner characteristics. Personalized learning experiences and robust support systems emerge as crucial for enhancing engagement. The study recommends practical strategies to strengthen e-learning engagement, including adopting adaptive learning technologies, promoting collaborative learning communities, and providing targeted support for diverse learner needs. These insights contribute to optimizing engagement within digital learning environments. © 2024 IEEE.

关键词： Contrastive Learning

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Near-Optimal Content Service Algorithm with Procurement and Placement in Edge Networks

Near-Optimal Content Service Algorithm with Procurement and ...

引用

2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Yang, Chun-An Chen, Shih-Chieh Peng, Yi-Hsuan Chen, Yu-Wen Kuo, Jian-Jhih Tsai, Ming-Jer National Tsing Hua University Dept. of Computer Science Hsinchu Taiwan National Chung Cheng University Dept. of Computer Science and Information Engineering Chiayi Taiwan New York City College of Technology Computer Systems Technology BrooklynNY United States

ISBN: (纸本)9798350351255

Telecom carriers have announced new content services by making a partnership with content providers and offered popular video streaming to customers. Via the integration with edge networks, telecom carriers can procure various contents from content providers and place them on edge servers proximate to end users to serve real-time requests with high bandwidth and ultra-low latency. Nevertheless, it is challenging to consider the content procurement, placement, and services jointly due to the user preference, user distribution, storage capacity of edge server, economic costs, etc. Telecom carriers would like to balance the procuring, placing, and transfer costs. To address this problem, the paper formulates an optimization problem and then proposes an approximation algorithm. Finally, the simulation results manifest that our algorithm outperforms other baselines. © 2024 IEEE.

关键词： Video streaming

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Energy Efficient Coverage Optimization in Mobile Wireless Sensor Network Using Grey Wolf Algorithm 14

Energy Efficient Coverage Optimization in Mobile Wireless Se...

引用

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

作者： Gupta, Ankit Mamatha, K.M. Kiran, M. National Institute of Technology Karnataka Dept. of Information Technology Surathkal India Viseveshvaraya Technological University Dept. of Computer Science and Engineering Belgaum India

ISBN: (纸本)9798350335095

The issue of decreased coverage rate in Mobile Wireless Sensor Networks (MWSNs), caused by mobile sensor nodes being randomly placed inside a monitoring area. Additionally, it becomes extremely important to utilise a sensor node's energy very effectively due to the finite energy of sensor nodes. Hence, to provide optimised positions for the sensor nodes while using the energy of sensor nodes adept.y authors propose an energy efficient coverage algorithm. Initially, article focus on optimal placement of the sensor nodes within a area to achieve the maximum coverage and later authors have focused on improvising the network lifetime. Article presents a combination of Grey Wolf Optimization and Virtual Force algorithm for optimization of coverage in MWSN. Further, to improve the network lifetime, a GWO-based clustering algorithm is presented using distance and energy as a parameter. The algorithms are implemented and simulated on Matlab. The efficiency of the presented algorithm is observed comparing with other Swarm Intelligence (SI) based optimization algorithms, like GWO, VFA, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Ant-lion Optimization (ALO) and the results of the GWO-based clustering is compared with the traditional LEACH algorithm and energy-balanced clustering based on PSO. Simulation results demonstrate that the presented algorithms outperform the considered algorithms. © 2023 IEEE.

关键词： Sensor nodes

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An Effective Hybrid Model for Skin Cancer Detection Using Transfer Learning 16

An Effective Hybrid Model for Skin Cancer Detection Using Tr...

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16th International Conference on Developments in eSystems Engineering, DeSE 2023

作者： Mohammed, Hussam J. Nafea, Ahmed Adil Almulla, Hussein K. Aliesawi, Salah A. S. Al-Ani, Mohammed M University of Anbar College of Computer Science and Information Technology Dept. Artificail Inteligence Ramadi Iraq University of Anbar Computer Center Ramadi Iraq University of Anbar College of Computer Science and Information Technology Dept. Computer Science Ramadi Iraq Selangor Bangi Malaysia

ISBN: (纸本)9798350381344

Skin cancer is considered one of the most fatal illnesses in the human population. Within the current healthcare system, the procedure of identifying skin cancer is time-consuming and poses a potential risk to human life if not recognized promptly. Early identification of skin cancer is imperative to maximize the likelihood of achieving complete recovery. This research proposed using a hybrid model including DenseNet201 and auto-encoder for feature extraction, and a support vector machine (SVM) as the classifier. The proposed model was evaluated in the ISIC 2016 dataset, which consists of nine different classes. The hybrid model achieved a classification accuracy of 91.09% percent in accurately identifying nine different forms of skin cancer. The findings indicate that the proposed model is competitive compared to the baseline models. This outcome offers substantial assistance to dermatologists and health professionals in the field of skin cancer detection. © 2023 IEEE.

关键词： Support vector machines

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A Hybrid CNN-SEU-Net Approach for Improved Melanoma Detection in Dermoscopy Images : Combining CNN and SEU-Net for Robust Melanoma Detection 4

A Hybrid CNN-SEU-Net Approach for Improved Melanoma Detectio...

引用

4th International Conference on Electronic information Engineering and computer Communication, EIECC 2024

作者： Ahmed, Asaad Sun, Guangmin Li, Yu Saadeldin, Mohamed Beijing University of Technology School of Information Science and Technology Beijing China University College Dublin Dept. School of Computer Science Dublin Ireland

ISBN: (纸本)9798331534622

Melanoma is a highly aggressive form of skin cancer with high mortality rates if not diagnosed early. Accurate diagnosis poses significant challenges, particularly for nonspecialist healthcare providers, due to the complexity and variability of skin lesions. Current diagnostic methods often lack the precision needed for reliable early detection, highlighting the need for innovative solutions. This paper proposes a novel hybrid deep learning framework for melanoma detection that integrates SEU-Net and Convolutional Neural Networks (CNNs) to address these challenges. The framework begins with SEU-Net, an encoder-decoder segmentation model enhanced with skip connections and SE attention mechanisms, to generate a segmentation mask that identifies the region of interest (ROI) in dermoscopic images. The segmentation mask is multiplied with the original image to isolate the lesion area, creating a segmented image that minimizes irrelevant background information. This segmented image is then passed to a CNN, classifying the lesion as malignant or benign. The proposed approach was evaluated on the ISBI 2016 dataset, achieving an accuracy of 93.60\%, outperforming existing state-of-the-art methods. These results demonstrate the potential of the proposed framework to significantly improve the precision and reliability of melanoma diagnosis, enabling earlier detection and ultimately reducing mortality rates. © 2024 IEEE.

关键词： Convolutional neural networks

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