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Class Imbalance of Bio-Medical data by Using PCA-Near Miss for Classification 5

Class Imbalance of Bio-Medical Data by Using PCA-Near Miss f...

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5th international conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Tumuluru, Praveen Daniel, Ravuri Mahesh, Gundabathula Lakshmi, Kalavala Deekshitha Mahidhar, Pakalapati Kumar, Muttum Vinay Koneru Lakshmaiah Education Foundation Department of Cse AP Vaddeswaram India Prasad V. Potluri Siddhartha Institute of Technology Department of Cse Vijayawada India

ISBN: (纸本)9798350321425

Class imbalance is a significant problem in many real-time domains dealing with healthcare data, which leads to poor classification performance and may have severe consequences. this work addresses the class imbalance problem in healthcare data, which often leads to poor classification performance and potentially serious consequences. this study proposes a hybrid PNM model that integrates the Near-Miss sampling technique and Principal Component Analysis to overcome this challenge. this study aims to investigate the effectiveness of the PNM model in improving classifier performance and compare it with several baseline classifiers. Further, the experiments are conducted on a real-world healthcare dataset containing various healthcare records. the results showed that the PNM model outperformed the baseline classifiers regarding the precision, recall, F1 score, and area under the curve (AUC). Specifically, the PNM model achieved a precision of - 0.85, recall of - 0.82, F1 score - 0.83, and AUC - 0.91, while the best-performing baseline classifier achieved a precision of 0.72, recall of 0.64, F1 score of 0.68, and AUC of 0.83. Our study demonstrates that the PNM model offers a promising approach to addressing the class imbalance in healthcare data and improving classifier performance. Integrating the Near-Miss sampling technique and Principal Component Analysis enables the model to achieve a better balance among the majority and minority classes, resulting in more accurate classification. the PNM model has the potential to be applied to various healthcare domains, such as disease diagnosis, patient risk stratification, and treatment prediction. © 2023 IEEE.

关键词： Principal component analysis

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Smart Home Garden Monitoring System Based on Internet of things 5

Smart Home Garden Monitoring System Based on Internet of Thi...

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5th IEEE international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Bhargav, M.S.S. Varma, A.K.C. Venkateswara Rao, Ch. Srinivas, Ch.V.V.S. Orugu, Rangarao Shaik, Abdul Rahaman B V Raju Institute Of Technology Department Of Ece Narsapur India Vishnu Institute Of Technology Department Of Ece Bhimavaram India

ISBN: (纸本)9798350330861

A smart home gardening system using IoT cloud technology can greatly enhance the efficiency and convenience of managing garden. It enables the monitor and control various aspects of the garden remotely, making it easier to maintain healthy plants and optimize resource usage. In order to ensure optimum plant development while also using the most amount of irrigation resources, the aim of this effort is to create a prototype for continually managing soil moisture levels and monitoring temperature, humidity, and sun light intensity. the monitoring software blink application has been used, and the sensor data will be available online. Given that water is a need for plant existence, it must not be used excessively. It is possible to program the Arduino Uno to examine some signals from sensors, like moisture and rainfall. With the help of blink user interface has been done which can be used to regulate and monitor the irrigation of water to plants, and an IoT module can be used to display the soil moisture levels and humidity continually on a webpage. the proposed system has various sensors (soil moisture, temperature, humidity, and light depending resistor) for monitoring the garden plants. Controlling and monitoring the water supply and keeping an eye on the plants are the key goals of this work which is envisioned through blink application, where the interfacing between prototype and monitoring environment has been enabled through it. the experimental results shows that the proposed system for garden monitoring is best suitable and economic and accurate in terms of monitoring. © 2023 IEEE.

关键词： Soil moisture

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Independent and Collaborative Demosaicking Neural Networks 5

Independent and Collaborative Demosaicking Neural Networks

引用

5th ACM international conference on Multimedia in Asia, MMAsia 2023

作者： Niu, Yan Zhang, Lixue Li, Chenlai State Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education College of Computer Science and Technology Jilin University Jilin Changchun China College of Computer Science and Technology Jilin University Changchun China School of Artificial Intelligence Shenzhen Polytechnic University Shenzhen China

ISBN: (纸本)9798400702051

Existing demosaicking neural models generally reconstruct the red, green and blue channels by one unified network. this has the advantage of allowing the RGB channels to share latent features. However, it is unnoticed that the training samples are severely unbalanced among the three channels. As a consequence, a unified model trained to fit the red or blue sample distributions is prone to under-fit the green samples. In this paper, we demonstrate the existence of such conflicts, and analyze the reason behind this phenomenon. To solve this disadvantage, we decouple the traditional three-in-one demosaicking to three independent but collaborative sub-tasks, respecting the distribution of samples in each spectrum. We show that, by our decoupling strategy, a very simple architecture can achieve high accuracy with high efficiency. For substantiation, we construct a model that has merely 1.3M parameters in total, and compares its accuracy to state of the art methods of similar or larger model size, across a wide variety of benchmark datasets. the proposed independent and collaborative architecture gains performance improvement over comparable models in both accuracy and inference time. © 2023 Copyright held by the owner/author(s).

关键词： Network architecture

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User Behavior Analysis in Determining the Vulnerable Category of Vishing and Smishing 5

User Behavior Analysis in Determining the Vulnerable Categor...

引用

5th international conference on Advancements in Computing, ICAC 2023

作者： Kumarasinghe, Prasadi Dissanayake, Dushan Gamage, Prarthana Ganegoda, Gamage Upeksha University of Moratuwa Faculty of Information Technology Moratuwa Sri Lanka

ISBN: (纸本)9798350358131

Despite the fact that phishing is frequently addressed, voice phishing and SMS phishing which is also known as vishing and smishing are least addressed social engineering attacks. through vishing and smishing, the attackers try to divulge sensitive information over the phone. As some of the common types of vishing and smishing attacks, bank related attacks, insurance related attacks, charity related attacks, social media related attacks can be pointed out. Most of the time these attacks are conducted targeting specific domains that the user is highly vulnerable to. Such as mobile device users have the higher tendency to be susceptible to a scam attack in the domain where they are frequently engaged in. Hence by observing the behavior of the user to a scam attempt, analyzing the user behavior, the vulnerability of the user to a specific scam attack category can be determined. User's behavior in a scam attempt is further influenced by their own characteristics such as socio demographic attributes, behavior related attributes and perceptual related attributes. So the impact of those user characteristics which their behavior in a scam attempt is influenced by plays a significant importance in determining their susceptibility to a scam attempt. Security Education Training and Awareness(SETA) is a program which is commonly available within organizations as preventive mechanisms. the same concept is applied in this proposed solution by creating a knowledge base of SETA recommendations for each category of attack that the user is highly vulnerable to. So this application determines the category of attack that the user is highly susceptible to and increases awareness and proficiency of the user to ensure prevention. © 2023 IEEE.

关键词： Knowledge based systems

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Sun-tracking Solar Panel with Battery Charger Based on the MPPT technology 5

Sun-tracking Solar Panel with Battery Charger Based on the M...

引用

5th IEEE international conference on Modern Electrical and Energy System, MEES 2023

作者： Jacko, Patrik Kovalchuk, Viktoriia Mlynar, Patrik Prus, Viacheslav Somka, Oleksand Technical University of Košice Department of Theoretical and Industrial Electrical Ingeneering Košice Slovakia Kremenchuk Mykhailo Ostrohradskyi National University Department of Systems of Automatic Control and Electric Drive Kremenchuk Ukraine Kremenchuk Mykhailo Ostrohradskyi National University Department of Electrical Engineering Kremenchuk Ukraine

ISBN: (纸本)9798350359787

the energy crisis is a massive problem in today's world. Conventional energy sources are not only limited but also major contributors to environmental pollution. Renewable energy sources are becoming priorities worldwide. To reduce dependence on conventional energy sources, it is necessary to explore alternative options. Solar energy is rapidly gaining attention as an important means of expanding the use of renewable energy. Solar cells, which convert solar energy into electrical energy, are expensive and relatively inefficient. Various mechanisms and systems are used to increase the efficiency of solar cells and maximize energy generation. A solar tracking system is used to enhance the efficiency of solar cells by tracking the movement of the sun. this article describes the methodology for designing an automatic solar tracker based on the ESP32 microcontroller. Light-dependent resistors (LDRs) are used as sensors to detect the current position of the sun. the proposed tracker has a precise control mechanism that adjusts the orientation of the solar panel based on the measured values from the LDRs to achieve the optimal position perpendicular to the sun, ensuring the highest productivity in energy generation. the result is a constructed small-scale prototype of a solar tracking system, along with a description of its components and the programming part. © 2023 IEEE.

关键词： Solar energy

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Exploration and Practice of data Visualization 24

Exploration and Practice of Data Visualization

引用

2024 5th international conference on big data Economy and Information Management, BDEIM 2024

作者： Yu, Gongzheng Yang, Shaowu College of Computer Science and Technology National University of Defense Technology Hunan Changsha China

ISBN: (纸本)9798400711862

Currently, data visualization is widely used in industries such as smart city display, rail transit monitoring and network traffic analysis, public opinion analysis. the data sources are relatively stable and the data formats are relatively regular. However, in some scenarios, the data is relatively scattered and of low quality, and there is limited introduction to the entire process of collecting, circulating, and aggregating this data into visual applications. Based on this, a comprehensive implementation method for metadata collection, raw data aggregation, and data visualization was explored and practiced, and a clear and scalable solution was proposed, from the backend to the frontend, the ideas and methods of data collection and aggregation, as well as the methods of building websites using the Django framework and implementing typical visualization applications using Echarts tools, were introduced. © 2024 Copyright held by the owner/author(s).

关键词： data aggregation data visualization Django Echarts

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Design and Implementation of a Distributed technology-Based big data Analysis System 24

Design and Implementation of a Distributed Technology-Based ...

引用

2024 5th international conference on big data Economy and Information Management, BDEIM 2024

作者： Song, Zhengju Housing and Urban Rural Construction Administration Bureau of Dongying Shandong Dongying China

ISBN: (纸本)9798400711862

A new big data analysis method for big data is proposed to address the storage, processing, and security issues of massive data in the big data environment. the main research objects are Hadoop HDFS and Spark, with scalability, fault tolerance, and efficiency as the goals. A hierarchical approach is adopted to achieve data collection, storage, processing, and visualization integration. On this basis, it is proposed to improve the overall performance of the system through data block storage and parallel processing, achieving high-speed, efficient, and robust fault tolerance. the system also incorporates security measures for data protection and integrity across multiple nodes. Performance testing reveals that the system excels in data handling, resource allocation, and fault tolerance across various data scales, while identifying areas for optimization. the research results of this project will provide valuable references for practical problems in the field of big data, and also provide new ideas for the scalability development of big data. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： big data analysis system distributed architecture Hadoop HDFS

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Neighborhood node selection strategy for blockchain networks based on Boltzmann method 5

Neighborhood node selection strategy for blockchain networks...

引用

5th international conference on Computer Information Science and Artificial Intelligence, CISAI 2022

作者： Chen, Zhuo Wang, GuoAn Zhou, Chuan Dept of Computer Science and Engineering Chongqing University of Technology 69 Redlight Ave Chongqing China

ISBN: (纸本)9781510662520

In view of the low block propagation efficiency in the blockchain network, which leads to the low throughput and high acknowledgement delay of the blockchain system, a dynamic node neighbor selection strategy is designed. According to the node elimination rate, neighbor nodes are divided into reserved nodes and replacement nodes. First, all possible combinations of neighbor node replacement are found by using the combination rule. then, when determining the strategy of each round, the neighbor node with good transmission timeliness is selected as the reserved node to improve the block transmission efficiency. the selection process of reserved nodes is regarded as a multi arm bandit problem, and a mathematical model is established by using Boltzmann selection strategy. the parameters of the strategy, such as node elimination rate and average propagation delay, are analyzed and verified by simulation experiments. the experimental results show that this strategy can optimize the topology of the blockchain network and effectively reduce the average propagation delay of the blockchain network. © 2023 SPIE.

关键词： Network topology

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IoT with Cloud Based Breast Cancer Diagnosis Using Deep Learning Techniques 5

IoT with Cloud Based Breast Cancer Diagnosis Using Deep Lear...

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5th international conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Ranjith Kumar, G. Ranjani, M. Santhiya, R. thamilselvi, S.S. K.S. Rangasamy College of Technology Dept. of Electronics and Communication Engineering Tiruchengode India

ISBN: (纸本)9798350321425

Breast cancer is a significant public health issue affecting women worldwide. Early detection and accurate diagnosis are crucial for improving survival rates and reducing mortality. Deep learning, an instance of machine learning, has yielded encouraging results in a variety of medical applications, especially breast cancer detection. Deep learning algorithms can automatically learn and identify patterns in medical images, such as mammograms, to detect breast cancer. Moreover, deep learning can also assist in predicting breast cancer sub-types and prognoses. this proposed study provides a review of the current state-of-the-art in deep learning for breast cancer detection and prognosis, including the use of convolutional neural networks (CNNs). To address the issue of breast cancer detection, a deep learning architectures named ResNet-50 and EffNet-50 are used to detect the affected portion. In this proposed method, the Dicom mammogram image is used for diagnosis. the designed architectures are tested with the training dataset of size 19,994 and a validation set of size 8,333 and its accuracy is found separately for both models and also by combining them. the combined ResNet50 and EffNet50 architectures are found to provide a highest accuracy of 98.6% whereas the individual models accuracy is 96.2% and 96.8% for ResNet-50 and EffNet-50 respectively © 2023 IEEE.

关键词： Medical applications

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Innovative Way of Identifying Skin Cancer Model Design with FCNN and LSTM 5

Innovative Way of Identifying Skin Cancer Model Design with ...

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5th IEEE international conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Rao, Addanki Janardhana Babu, M.Raja Durgarao, Guttula Kumar, Gottala Parameswara Lakshmanarao, A. A.P. Surampalem India Aditya Engineering College Department of It Surampalem India Gayatri Vidya Parishad College of Engineering Department of Cse Kommadi India Aditya College of Engineering & Technology Department of It Surampalem India

ISBN: (纸本)9798350383522

Skin cancer is a prevalent and potentially life-threatening disease with rising global incidence rates. Early detection significantly improves prognosis, and the integration of advanced technologies in the medical field holds the promise of enhancing diagnostic accuracy. Visual inspection, which is used by dermatologists to identify skin cancer, is subjective, requires a lot of time, and is reliant on the dermatologist's ability. Diagnostics might be revolutionized by artificial intelligence technology, which offers automated, data-driven insights that increase diagnostic precision and speed. the idea that CNNs and LSTMs may be integrated to provide a dynamic and all-encompassing approach to skin cancer diagnosis served as the basis for this paper. Given sequential data, LSTMs perform better at recognizing temporal patterns, while CNNs are more adept at deriving intricate spatial features from still images. By merging these several designs, we developed a multimodal model system that can both recognize the visual characteristics of skin lesions and show how these lesions evolve over time. To compare the fusion model, first we applied CNN for skin cancer detection and achieved 90.2%. Later, we applied fusion of CNN and LSTM and acquired accuracy of 93.4%. the results shown that integrating CNN and LSTM given more accuracy for skin cancer detection. © 2024 IEEE.

关键词： CNN Fusion LSTM Skin Cancer

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