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3rd International Conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Pyingkodi, M. Thenmozhi, K. Karthikeyan, M. Kalpana, T. Palarimath, Suresh Kumar, G. Bala Ajith Kongu Engineering College Department of Computer Applications Erode India Kristu Jayanti College Department of Computer Science Bengaluru India University of Technology and Applied Sciences Department of It Salalah Oman

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

Soil fertility is an important factor in determining soil quality as it reflects how well the soil can support plant growth in agriculture. Soil sensor and Arduino can be used to quickly determine the nutrient content of the soil. Nitrogen, phosphorus, and potassium are all considered as important nutrient source components. These components should be measured in order to determine how much extra nutrient content should be added to the soil in order to increase the crop fertility. Soil fertility can be detected by using NPK sensors. Soil nutrient concentration data can help us to determine whether the soil used to support plant production is nutrition deficient or abundant. The nutrient content of the soil samples can be obtained in various ways by using sensing element or mass spectrogram. However, the spectral analysis method is inconvenient, where the records are only 60-70% accurate. By comparing the spectrum analysis method with classic wet chemistry methods, the accuracy of the products needs to be fully resolved due to a scarcity of data. Hence, to detect soil nitrogen, phosphorous, and potassium, a soil NPK sensor should be used. By utilizing a soil NPK sensor, which is of limited cost, fast and easy, elevated, and transportable. Its advantage over a standard detection approach is that it provides extremely fast measurements with accurate data. This paper analyzes and compares different nutrient levels in soil by using kernel density estimation algorithm and machine learning. © 2022 IEEE.

关键词： Soils

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Vessels Segmentation in Angiograms Using Convolutional Neural Network: A Deep Learning Based Approach

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computer Modeling in Engineering & sciences 2023年第7期136卷 241-255页

作者： Sanjiban Sekhar Roy Ching-Hsien Hsu Akash Samaran Ranjan Goyal Arindam Pande Valentina E.Balas School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014India Department of Computer Science and Information Engineering Asia UniversityTaichung41354Taiwan Department of Medical Research China Medical University HospitalChina Medical UniversityTaichung406Taiwan Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology School of Mathematics and Big DataFoshan UniversityFoshan528000China Medica Superspecialty Hospital Kolkata700099India Automation and Applied Informatics Aurel Vlaicu University of AradArad310130Romania

Coronary arterydisease(CAD)has become a significant causeof heart attack,especially amongthose 40yearsoldor *** is a need to develop new technologies andmethods to deal with this *** researchers have proposed image processing-based solutions for CADdiagnosis,but achieving highly accurate results for angiogram segmentation is still a *** different types of angiograms are adopted for CAD *** paper proposes an approach for image segmentation using ConvolutionNeuralNetworks(CNN)for diagnosing coronary artery disease to achieve state-of-the-art *** have collected the 2D X-ray images from the hospital,and the proposed model has been applied to *** augmentation has been performed in this research as it’s the most significant task required to be initiated to increase the dataset’s ***,the images have been enhanced using noise removal techniques before being fed to the CNN model for segmentation to achieve high *** the output,different settings of the network architecture undoubtedly have achieved different accuracy,among which the highest accuracy of the model is 97.61%.Compared with the other models,these results have proven to be superior to this proposed method in achieving state-of-the-art results.

关键词： Angiogram convolution neural network coronary artery disease diagnosis of CAD image segmentation

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Optimizing Healthcare Operations With Big Data and AI 1

Optimizing Healthcare Operations With Big Data and AI

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Dubey, Krati Esnaashariyeh, Ali Nahak, Kirti Hodade, Dipali Nagnath Shrivastava, Kapil Jorvekar, Ganesh Manipal University Jaipur Department of Information Technology Jaipur India Institute of Management and Entrepreneurship Development Pune India Kalinga University Dept of Computer Science & Engineering Chhattisgarh Raipur India Shree Ramchandra College of Engineering Department of Engineering and Applied Science Maharashtra Pune412216 India Department of Computer Engineering and Applications GLA University Mathura India Sanjivani K.B.P. Polytechnic Computer Technology Kopargaon India

ISBN: (纸本)9798350330915

To improve patient care and operational effectiveness, this study investigates the implementation of big data along with artificial intelligence (AI) into medical operations. Utilizing a deductive technique and an interpretive approach, this study collects secondary data from reliable sources using a descriptive design. While AI applications concentrate on clinical decision support, tailored treatment strategies, and predictive analytics, Big Data integration includes health records, medical imaging, genome-wide data, and patient-generated information. Private data, interconnection, biases in AI systems, and resource limitations are some of the difficulties. Transparency, justice, and patient autonomy are all important ethical considerations. Strong data security protocols, continuous attempts to mitigate prejudice, interdisciplinary training, and open governance frameworks are among the suggestions offered. Future research should focus on interoperability standards © 2023 IEEE.

关键词： Big data

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Identifying COVID-19 through X-ray and CT scan images using Machine Learning 18

Identifying COVID-19 through X-ray and CT scan images using ...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Shehzadi, Maham Arshad, Usama Abideen, Zainul Ali, Raja Hashim Khan, Ammad Aslam Ijaz, Ali Zeeshan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Virtual University of Pakistan Dept. of Biological Sciences Rawalpindi Pakistan National University of Computer & Emerging Sciences Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350328172

In the current global scenario, marked by the COVID-19 pandemic, it has become imperative for healthcare systems around the globe to swiftly and accurately diagnose the disease. This is where cutting-edge approaches such as machine learning (ML) come into play, specifically, ML-based identification of COVID-19 from chest X-ray and CT-Scan imagery. Our research collective is devotedly working towards devising a robust, accurate ML model capable of deciphering these images to identify COVID-19 effectively. This involves the application of a variety of AI algorithms and vast data resources. Our work is grounded on a large, annotated image dataset that aids in training and refining the model. Our findings indicate that ML methodologies can discern COVID-19 from chest X-ray and CT scan images with a mean accuracy of 90%. Such a tool holds promise in aiding medical practitioners in prompt and precise patient diagnosis, thereby improving patient prognosis. The most notable contribution of our study lies in the creation of an ML model that can accurately detect COVID-19 in chest X-ray and CT-Scan images, potentially aiding in early disease detection and management. © 2023 IEEE.

关键词： COVID-19

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Intelligent Caching Strategies for 5G Edge Networks using Machine Learning 4

Intelligent Caching Strategies for 5G Edge Networks using Ma...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Guduri, Naga Venkata Ramakrishna Sethu, S. Shalinirajan, R. Reena, R. Karthikeyan, M. Uthayakumar, G.S. University of Technology and Applied Sciences-Al Musannah Electrical and Electronics Section Engineering Department Oman Department of Computer Science and Engineering Tamilnadu Chennai India Panimalar Engineering College Department of Computer Science and Business System Tamilnadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Tamilnadu Chennai India St. Joseph's Institute of Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350300888

To reduce latency and improve the user experience, content caching solutions at the network periphery will become increasingly important as 5G networks and data-intensive applications. In this proposed work, the deployment of a caching architecture for 5G periphery networks using machine learning is done. Utilizing information on user behavior, network conditions, and content prominence enables more accurate forecasts and cache management decisions. This trains the prediction models using a wide range of machine-learning techniques to accommodate the ever-changing needs of both networks and content. To evaluate the efficiency of the proposed architecture by measuring its ability to increase cache hit rates and decrease latency. These techniques significantly outperform the standard caching methods. With the intelligent caching architecture of 5G periphery networks, throughput is increased, latency is reduced, and congestion is alleviated. The findings of this study contribute to the growing corpus of literature on AI-driven optimization in 5G networks and shed light on how such solutions could be enhanced for future networks. Neural Networks achieved 96% accuracy than SVM which is 93%. © 2023 IEEE.

关键词： 5G mobile communication systems

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MEASURING COMPUTATIONAL THINKING - DEVELOPING A SHORT PERFORMANCE TEST FOR HIGHER EDUCATION 21

MEASURING COMPUTATIONAL THINKING - DEVELOPING A SHORT PERFOR...

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21st International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2024

作者： Guggemos, Josef Rietsche, Roman Aier, Stephan Strecker, Jannis Mayer, Simon Department of Vocational Education and Training University of Education Schwäbisch Gmünd Germany Institute for Digital Technology Management Bern University of Applied Sciences Switzerland School of Computer Science University of St.Gallen Switzerland

ISBN: (纸本)9789898704610

Technological advancements, particularly in artificial intelligence, significantly transform our society and work practices. Computational thinking (CT) has emerged as a crucial 21st-century skill, enabling individuals to solve problems more effectively through an automation-oriented perspective and fundamental concepts of computer science. To ensure the effective integration of CT into educational curricula, it is crucial to develop efficient assessment frameworks that allow teachers to measure and promote student CT proficiency. Therefore, our aim is to develop a short test to measure CT among undergraduate students. To this end, we consider two performance tests: the Computational Thinking test (CTt) and the Algorithmic Thinking Test for Adults (ATTA). We use items from both instruments to compile a short test. Based on a sample of 290 second-year non-computer science undergraduate students, we provide evidence on the quality of our test. Besides classical test theory, we apply item response theory, namely Rasch modeling, and confirmatory factor analysis. Our test shows favorable properties, e.g., Cronbach's alpha > .75, and may be suitable for the efficient assessment of CT across higher education programs. © 2024 Proceedings of the 21st International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2024. All rights reserved.

关键词： Students

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Transformative Applications and Key Challenges of Generative AI 7

Transformative Applications and Key Challenges of Generative...

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7th IEEE International Conference and Workshop Obuda on Electrical and Power Engineering, CANDO-EPE 2024

作者： Kovari, Attila Katona, Jozsef Eszterházy Károly Catholic University Institute of Digital Technology Faculty of Computer Science Eger Hungary University of Dunaújváros Department of Institute of Engineering Dunaujvaros Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemét Hungary Institute of Computer Engineering University of Dunaujvaros Department of Software Development and Application Dunaujvaros Hungary Budapest Business University Faculty of Finance and Accountancy Department of Applied Quantitative Methods Budapest Hungary

ISBN: (纸本)9798331531119

The paper discusses the integration and use of generative artificial intelligence technologies in education. Generative AI, such as OpenAI, in particular the GPT model, has fundamentally changed the way humans interact with machines. Advanced technologies in the field of generative AI in education are mostly used to personalize learning experiences by dynamically customizing learning materials according to the needs of learners, thus enabling the improvement of outcomes. This article explores the use of generative AI tools with the aim of assessing how useful students perceive these tools to be. The findings show mixed receptivity from users, highlighting the critical balance in using innovative AI capabilities through AI. © 2024 IEEE.

关键词： Generative adversarial networks

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Fruits Quality Detection using Deep Learning Models: A Meta- Analysis 3

Fruits Quality Detection using Deep Learning Models: A Meta-...

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3rd International Conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Pyingkodi, M. Thenmozhi, K. Karthikeyan, M. Chitra, K. Blessing, N.R. Wilfred Kumar, Sunny Kongu Engineering College Department of Computer Applications Erode India Kristu Jayanti College Department of Computer Science Bengaluru India University of Technology and Applied Sciences Department of It Ibri Oman

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

Deep learning has progressed significantly in recent years, making it an ideal choice for image processing applications. Recognition and classification of fruits using deep learning are considered as the most promising techniques for commercial and agricultural applications. Despite this, the researchers are still having difficulty in distinguishing fruits due to their similar colour, shape, and size. This research study intends to address some of the existing challenges by developing a novel framework for fruit identification and classification. Deep learning models such as U-Net, VGG19, and ResNet are used in this work to train and test these models. Here, two sets of data were used: pre-processed and augmented dataset. ResNet performs well with 98% accuracy on the initial set of data, but it consumes more time to compute. As a result, the deep learning models could recognize and classify 122 different types of fruits in real time. However, image augmentation will have no effect on the models' performance. © 2022 IEEE.

关键词： Fruits

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High-Precision Satellite Position Interpolation: A Comparison of Lagrange, Cubic Spline, and Artificial Neural Network Methods 2

High-Precision Satellite Position Interpolation: A Compariso...

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2nd International Microwave and Antenna Symposium, IMAS 2024

作者： El Attiq, Othmane Reboul, Serge Latif, Adnane Abouelouafa, Safae Information Technology and Modeling Team National School of Applied Sciences Cadi Ayyad University Marrakesh Morocco Computer Science Signal and Image Laboratory of the Côte d'Opale Littoral Côte d'Opale University Calais France Information Technology and Modeling Team Computer Science Signal and Image Laboratory of the Côte d'Opale Marrakesh Morocco National School of Applied Sciences Cadi Ayyad University-Littoral Côte d'Opale University Calais France

ISBN: (纸本)9798350356359

For advanced Global Navigation Satellite System (GNSS) applications like determining the bending angle in GNSS radio occultation, it is imperative to obtain high-precision satellite position estimates at millisecond intervals. This study evaluates Lagrange interpolation, cubic spline interpolation, and Artificial Neural Networks (ANN) to achieve such high precision. Although both cubic spline interpolation and ANN are effective, ANN outperforms cubic spline interpolation with lower maximum errors and better handling of boundary conditions. In comparison, Lagrange interpolation demonstrates a lack of stability. ANN have been identified as the most effective technique for the accurate and continuous estimation of satellite positions through interpolation. © 2024 IEEE.

关键词： Lagrange multipliers

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A Survey of Information Security Roles and Trends in the Quranic Perspective 1

A Survey of Information Security Roles and Trends in the Qur...

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1st International Conference on Cyber Security and Computing, CyberComp 2024

作者： Yunus, Mohd Amin Mohd Rahman, Nurul Hidayah Ab Hamid, Isredza Rahmi A. Universiti Tun Hussein Onn Malaysia Applied Information System Faculty of Computer Science and Information Technology Parit Raja Malaysia Centre of Information Security Research Universiti Tun Hussein Onn Malaysia Parit Raja Malaysia

ISBN: (纸本)9798350387728

In this study, we surveyed the existing information security literature related to the Quran to contribute to the knowledge gap(s) in seeking the role of information security in the Quran research area. Materials published in English were located between January 2019 and February 2024 by searching various academic databases, including Google Scholar, IEEEXplore, ACM Digital Library, Springer and scienceDirect. Thirty-two publications were collected and classified into ten domains of information security Common Body of Knowledge to identify the research trends. The results show that Domain 3 (Cryptography and Digital Watermarking) is the most studied area, followed by Domain 2 (Ethical and Legal Issues) with eight publications and Domain 5 (Access Control) with three publications. Other domains consist of only 1 or 2 publications each. Our observation also showed that three recurring themes of studies that are the implementation of information security techniques to preserve digital Quran authenticity, the association of Quran verses in supporting information security approaches;and the development of information security techniques from the Quran epistemology. Overall, exploring the research trends and interconnected areas could lead to advancements in information security practices and Quranic studies, fostering how traditional wisdom influences modern technologies. © 2024 IEEE.

关键词： authenticity ethical information security Quranic

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