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Expression of Concern for: Detection of Lung Inflammation using Chest X-ray with Deep Learning Algorithm

Expression of Concern for: Detection of Lung Inflammation us...

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International Conference on Communication and Electronics Systems (ICCES)

作者： K Purushotham Singh B Niranjan Kumar A Anil Selvi Rajendran Dept.of Computer Science and Engineering Hindustan institute of technology and science Chennai India

ISBN: (数字)9781665496346

ISBN: (纸本)9781665496353

Lung Inflammation causes the passing of around 700,000 youngsters consistently and influences 7% of the worldwide populace. Chest X-ray images are principally utilized for the analysis of this infection. Notwithstanding, in any event, for a prepared radiologist, it is a moving errand to look at chest X-ray. There is a need to further develop the determination precision. In this work, an effective model is developed for the recognition of pneumonia from the computerized chest X-ray pictures is proposed. Several methods are utilized to expand the preparation of dataset in a decent manner. Consequently, it is more advantageous to fabricate a computerized indicator to anticipate the pneumonia utilizing the large information profound learning strategies. Among the wide range of various procedures, Convolutional Neural Networks stand tall and high in this forecast alongside different classifiers. Convolutional brain network comprising of convolution and pooling layers and completely associated Softmax layers toward the finish to give the last forecast.

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The Frontier of Geospatial AI Deep Learning Applications in Data Mining and Spatial Analysis

The Frontier of Geospatial AI Deep Learning Applications in ...

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Mishra, Ajit Kumar Verma, Vikash Manivannan, R. Kumar, N. Krishna Srividya, Mudunuru Sharma, Sanskriti IES College of Technology Department of Electrical & Electronics Engineering M.P Bhopal462044 India GGITS Dept of Artificial Intelligence & Machine Learning M.P Jabalpur India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology School of Computing Computer Science & Engineering TN Chennai India Guru Nanak Institutions Technical Campus Ibrahimpatam Dept.of CSE Telangana Hyderabad India MLR Institute of Technology Dept. of CSE Telangana Hyderabad India GGITS Dept of Computer Science & Business System M.P Jabalpur India

ISBN: (纸本)9798331539948

Modern artificial intelligence uses geographical data mining and regional analysis to analyze large datasets. Deep learning is a new geographic AI method. Example: Geographic Deep Mining Network (GDMN). The Spatial Feature Extractor (SFE), Temporal Geospatial Attention Mechanism (TGAM), and Geospatial Data Fusion Module (GDFM) comprise this technique. We examined six popular approaches and found GDMN performed best. It worked successfully for several reasons. GDMN always outperforms its competitors in memory, F1 score, IoU, training time, and inference time. This is always true. These findings suggest that GDMN might boost geospatial AI's data mining and location analysis abilities. After all the effort, we have developed a solid basis for location intelligence, making it useful in various subject areas. Because it's versatile, mapping specialists may use GDMN for better environmental monitoring or city development. Deep learning helps GDMN make better space choices and uncover insights in large, challenging data sets that no one knew about. GDMN's cutting-edge approaches make space and time research and prediction simpler. This wide perspective offers a more full and all-encompassing picture of today's challenges than previous techniques. This makes it impossible to overstate how significant this work is to location intelligence. Its unmatched research and insights usher in a new era. © 2024 IEEE.

关键词： Adversarial machine learning

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Real-Time Medical IoT for Continuous Glucose Monitoring

Real-Time Medical IoT for Continuous Glucose Monitoring

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Goel, Akanksha Tamrakar, Poi Lal, Mily Bhende, Manisha Wani, Minal Saoji, Saurabh Dr.D.Y. Patil School of Science & Technology Dr.D.Y. Patil Vidyapeeth Pimpri Pune India Dr.D.Y. Patil Biotechnology and bioinformatics Institute Dr.D.Y. Patil Vidyapeeth Pimpri Pune India Maharashtra Institute of Engineering and Technology Dept.of Computer Engineering Nutan Pune India

ISBN: (纸本)9798331539948

This research is about Continuous Glucose Monitoring (CGM) technology and its pivotal role in revolutionizing diabetes care. Leveraging data obtained from a comprehensive study focused on evaluating CGM's efficacy in diabetes management, this study investigates the impact and potential benefits of CGM usage on patients' health after collecting the real secondary data through online forms. It encompasses a diverse array of patient-centric variables, including medical history, medication intake, physiological parameters, and CGM-derived glucose *** integrating machine learning techniques and statistical analysis, this study aims to derive valuable insights into the interplay between CGM data patterns and real-time activities, patients' physiological profiles, and diabetes management outcomes. The extracted features and processed data serve as a foundation for subsequent predictive modeling, classification, and regression analysis. This research aims to contribute to the expansion of knowledge regarding the effectiveness of CGM technology in diabetes treatment. By exploring correlations between CGM data, patient attributes, and clinical outcomes, this study endeavors to pave the way for personalized and optimized diabetes management strategies. © 2024 IEEE.

关键词： CGM Diabetes IoT Data Monitoring Real-time

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Monolithic 3D Printed Micro-Optofluidic Device for Two-phase Flow Monitoring and Bioapplications

Monolithic 3D Printed Micro-Optofluidic Device for Two-phase...

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Electric And Power engineering (EPEi), IEEE International Conference And Exposition On

作者： Lorena Saitta Emanuela Cutuli Giovanni Celano G Claudio Tosto Francesca Guarino Gianluca Cicala Maide Bucolo dept.of Civil Engineering and Architecture University of Catania Catania Italy dept. of Electrical Electronic and Computer Science Engineering University of Catania Catania Italy dept. of Biomedical and Biotechnological Science University of Catania Catania Italy

ISBN: (数字)9798350356199

ISBN: (纸本)9798350356205

In this work, a biocompatible 3D printed micro-optofluidic (MoF) device designed for monitoring two-phase flows is presented. It was manufactured using the Projection Micro-Stereolithography (PμSL) 3D printing technique combined with a biocompatible resin. This device successfully monitored both an air-water slug-flow and a two-phase mixture of micrometric eukaryotic yeast cells (Saccharomyces cerevisiae) suspended in a phosphate-buffered saline (PBS) solution. Its working principle relies on the absorption phenomenon, since different light transmissions are correlated to either the fluid or the cells interfering with a laser beam in a microchannel section. To accomplish this, the MoF device was designed with two micrometric slots for the insertion of optical fibers to capture the light signal. Both the micro-optical and microfluidic components, each 200 μm in size, were integrated into the MoF device as a monolithic structure. A wide experimental campaign was performed on the MoF device investigating different flow rates and cells concentrations. The optimal operative condition for achieving the most robust working ability were determined using a Design of Experiments (DoE) approach.

关键词： Performance evaluation Fluids Biomedical optical imaging Voltage measurement Cells (biology) Three-dimensional printing Stability analysis Real-time systems Biomedical monitoring Monitoring

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Optimizing AODV Routing Protocol to Improve Quality of Service Performance for V2V Communication

Optimizing AODV Routing Protocol to Improve Quality of Servi...

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Smart Computing, IoT and Machine Learning (SIML), International Conference on

作者： Ketut Bayu Yogha Bintoro Tri Kuntoro Priyambodo Muhammad Nurul Musthofa Dept.of Computer Science and Electronics Faculty of Mathematics and Computer Sciences Gadjah Mada University Yogyakarta Indonesia Dept. of Informatic Engineering Faculty of Sciences Technology and Design Universitas Trilogi Jakarta Indonesia

ISBN: (数字)9798350364101

ISBN: (纸本)9798350364118

The study focuses on Quality of Service (QoS) issues in learning automata-based AODV (LA-AODV) for V2V communication protocols. It analyzes and compares the LAAODV protocol with the original AODV protocol in dynamic traffic situations. The study evaluates critical metrics such as the number of vehicles, average speed, congestion spots, and vehicle time loss across various traffic scenarios, including smooth traffic, steady flow traffic, and heavy traffic. NS3 and SUMO are used as essential tools for this evaluation. The study finds that the LA-AODV protocol performs better in smooth traffic conditions than AODV. It demonstrates a $38.27 \%$ higher packet delivery ratio (PDR), an $\mathbf{8 5 . 2 6 \%}$ lower packet loss ratio (PLR), and a remarkable $56.62 \%$ increase in average throughput compared to AODV. Both protocols exhibit similar performance in steady flow traffic with high PDRs $(97.60 \%$ and $96.87 \%$, respectively) and low PLRs ($2 \%$ and $3 \%$, respectively). However, AODV outshines LA-AODV with a higher PDR $\mathbf{(} \mathbf{9 7 . 6 0 \% )}$) than LA-AODV $(\mathbf{9 6 . 8 7 \%}$) in heavy traffic. LA-AODV, on the other hand, excels in average throughput by $17.24 \%$ in heavy traffic. The study offers valuable insights for improving V2V communication protocols and enhancing communication efficiency to handle dynamic and traffic situations.

关键词： Protocols Mobility models Urban areas Packet loss Quality of service Machine learning Throughput

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On the Enhancement of Energy Efficiency of Wireless Rechargeable Sensor Networks through Mobile Charger Scheduling

On the Enhancement of Energy Efficiency of Wireless Recharge...

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Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： Md. Kamaruzzaman Abhijit Chandra Md. Azharuddin Dept.of Robotics and Automation The Neotia University Kolkata India Dept. of Instrumentation and Electronics Engineering Jadavpur University Kolkata India Dept. of Computer Science and Engineering Aliah University Kolkata India

In recent years, wireless rechargeable sensor networks (WRSNs) have become more popular because of its diverse array of applications. However, the limited energy resources of individual sensors pose a significant challenge in maintaining network operations over an extended period. To address this challenge, this research paper presents an energy-efficient scheduling scheme based on distance based scheduling for mobile charging vehicle (MCV) in WRSNs. The proposed scheme capitalizes on the MCV, allowing them to dynamically adjust their charging schedules to cater to the energy demands of sensor nodes (SNs). By efficiently allocating charging tasks to MCV, the scheme aims to balance the energy distribution and mitigate the energy imbalance across the network. By conducting comprehensive simulations with various scenarios, the proposed scheme has been compared with some existing schemes in relation to charging path length, total energy received and charging utility. The simulation results manifest significant advantages of the proposed scheme.

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Prediction of COVID-19 Through Chest X-Ray Images Employing Various Machine Learning Techniques 1

Prediction of COVID-19 Through Chest X-Ray Images Employing ...

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1st International Conference on Intelligent Controller and Computing for Smart Power, ICICCSP 2022

作者： Saheb, Shaik Khasim Narayanan, B. Rao, Thota Venkat Narayana Annamalai University Dept.of Cse Tamilnadu Chidambaram India Sreenidhi Institute of Science and Technology Department of Cse Hyderabad India Annamalai Univeristy Computer Science and Engineering Wing D.D.E Annamalai Nagar Tamilnadu Chidambaram India Sreenidhi Institute of Science and Technology Department of Computer Science and Engineering and HoD-CSE Iot Telangana Hyderabad India

ISBN: (数字)9781665472586

ISBN: (纸本)9781665472586

The COVID-19 is the most infectious disease which is recently discovered. The COVID-19 pandemic has led to excruciating loss to human life and it also caused mild to severe respiratory illness, including death. Detecting the infected patients and taking special care is crucial in fighting covid-19. Radiography and Radiology images are used to diagnose the patients. These are the fastest ways to identify COVID-19 disease. It is observed that patients affected with COVID-19 have specific abnormalities in their chest radiograms. Initially there were few limited set of CT images are available publicly in performing research. Board-certified radiologist role is to perform identification of images exhibiting COVID-19 disease. Chest CT scans are helpful to diagnose COVID-19 disease in individuals. COVID-19 directly shows impact on lungs and it damages and the tiny air sacs. In this paper we have used deep transfer learning models Residual Network (ResNet50) and VGG19 (Visual Geometry Group) to predict the disease at earlier stages. These models obtained a specificity rate of 90% and achieved a highest sensitivity rate of 98 %. In addition to sensitivity and specificity rates ROC curve, average prediction and confusion matrix of each model are presented in the papers. While this achieved performance is very encouraging if we have large set of COVID-19 images then it may give more reliable estimation of accuracy rates. © 2022 IEEE.

关键词： COVID-19

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Plagiarism Detection and Similarity Checking Program using Machine Learning and String Matching Algorithm

Plagiarism Detection and Similarity Checking Program using M...

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International Conference on Communication and Electronics Systems (ICCES)

作者： Munugapati Bhavana Koppula Srinivas Rao Gagan Kumar Koduru K.Vijay Krupa Vatsal S. Parvathi Mogiligidda Sravani Dept. of Computer Science & Information Technology MLR Institute of Technology Affiliated to JNTU Hyderabad Department of Computer Science & Engineering MLR Institute of Technology Affiliated to JNTU Hyderabad MLR Institute of Technology Hyderabad Department of Computer Science & Engineering Malla Reddy University Hyderabad Dept.of Computer Science & Information Technology MLR Institute of Technology Hyderabad Department of Information Technology MVSR Engineering College

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

This research proposes a system for detecting plagiarism in academic submissions. The system utilizes advanced text comparison algorithms to identify instances of plagiarism, ensuring academic integrity and promoting a culture of originality among students. By providing educators with a tool to efficiently detect plagiarism, this system aims to streamline the evaluation process and enhance the overall learning experience for students. This system has the potential to significantly reduce plagiarism incidents and foster a more ethical and productive academic environment.

关键词： Measurement Ethics Machine learning algorithms Plagiarism Machine learning Complexity theory

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Comparative Analysis of Deep Learning Algorithms for Handwritten Digit Recognition

Comparative Analysis of Deep Learning Algorithms for Handwri...

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Delhi Section Conference (DELCON), IEEE

作者： Padmaja Savaram Sasidhar Bandu Lakshitha B. Dept.of Computer Science Prince Sattam bin Abdulaziz University Al-kharj KSA Dept. of English Prince Sattam bin Abdulaziz University Al-kharj KSA Dept. of Computer Science and Engineering VNRVJIET JNTU Hyderabad India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

Handwritten digit recognition presents a considerable challenge in pattern recognition due to the inherent variability in digit size, stroke width, orientation, and margin alignment. The difficulty is further exacerbated when different individuals write the same digit with varied stylistic nuances. Discriminating between visually similar digits, such as 1 and 7, 5 and 6, 3 and 8, 2 and 5, and 2 and 7, adds an additional layer of complexity. The offline handwritten digit recognition approach discussed in this paper leverages a range of machine learning methodologies. The objective is to establish robust and accurate digit recognition techniques, utilizing several algorithms including Artificial Neural Networks, Convolutional Neural Networks, K-Nearest Neighbors, and Recurrent Neural Networks. These algorithms are evaluated using the Modified National Institute of Standards and Technology (MNIST) dataset and the US Postal Service (USPS) dataset to identify the most effective model. The selection of the optimal machine learning approach is determined by factors such as dataset size, data complexity, and required accuracy. The Convolutional Neural Networks model demonstrated superior performance, achieving an accuracy of 98.58% with 10,000 MNIST images and 94.7% accuracy with 2007 USPS images when compared with other models.

关键词： Measurement Handwriting recognition Accuracy Machine learning algorithms Recurrent neural networks Nearest neighbor methods NIST Convolutional neural networks Standards Postal services

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A Transfer Learning-Based Intelligent Judiciary System for Public Safety

A Transfer Learning-Based Intelligent Judiciary System for P...

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2024 IEEE International Conference on Communications, Computing, Cybersecurity and Informatics, CCCI 2024

作者： Pathak, Lakshin Virani, Mili Obaidat, Mohammad S. Patel, Prachita Hathaliya, Jigna J. Jadav, Nilesh Kumar Tanwar, Sudeep Yamsani, Nagendar Nirma University Dept.of CSE Ahmedabad India School of Computer & Communication Engineering University of Science and Technology Beijing Beijing100083 China School of Computing SRM University Department of Computational Intelligence TN 603203 India School of Engineering The Amity University UP Noida201301 India Marwadi University Dept. of CE-AI Rajkot India SR University Department of CS & AI Telangana India

ISBN: (纸本)9798350349832

With the increasing prevalence of criminal activities, there is a growing need for effective crime detection methods leveraging modern technologies, such as artificial intelligence (AI), the Internet of Things (IoT), and cloud computing. In this paper, we propose a transfer learning (TL)-based solution for criminal face detection using convolutional neural networks (CNNs) to improve public safety. Leveraging pre-Trained CNN models, we fine-Tune them on a dataset comprising images of known criminals and non-criminals. The proposed framework aims to mitigate the challenges associated with training CNNs from scratch, such as data scarcity and computational inefficien-cies. Experimental results demonstrate the effectiveness of the proposed framework in accurately identifying criminal faces, showcasing its potential for enhancing law enforcement efforts. We achieved the highest accuracy of 97% with the TL scheme. © 2024 IEEE.

关键词： Convolutional neural networks

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