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9th International Conference on Smart Computing and Communications, ICSCC 2023

作者： James, Jisha Anand, H.S. Dept. of Computer Science and Engineering Kochi India Muthoot Institute of Technology and Science Kochi India Cvv Institute of Science and Technology Dept. of Computer Science Ernakulam India

ISBN: (纸本)9798350314090

Thyroid nodules are a common clinical finding, and their accurate risk stratification is essential for determining appropriate management strategies. The Thyroid Imaging Reporting and Data System (TIRADS) has emerged as a valuable tool for evaluating thyroid nodules by integrating various ultrasound features to predict malignancy. This research article aims to assess the efficacy of TIRADS scoring in detecting thyroid nodule malignancy and its potential as a standardized diagnostic tool. A comprehensive literature review was conducted to identify studies that evaluated the diagnostic performance of TIRADS scoring systems in different patient populations. The included studies demonstrated that TIRADS scoring systems exhibited substantial accuracy, sensitivity, and specificity in predicting thyroid nodule malignancy. The incorporation of ultrasound features, such as nodule size, composition, echogenicity, margins, and presence of calcifications, allowed for a standardized and reproducible risk stratification. TIRADS scoring systems also demonstrated the ability to optimize the decision-making process for further diagnostic workup, reducing unnecessary fine-needle aspiration biopsies and surgical interventions. The article also discusses the limitations and challenges associated with TIRADS scoring. © 2023 IEEE.

关键词： Machine learning

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A Sustainable Smart Irrigation and Crop Protection System for Bangladesh 8

A Sustainable Smart Irrigation and Crop Protection System fo...

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8th IEEE International Conference on Computational System and Information technology for Sustainable Solutions, CSITSS 2024

作者： Ahmed, Khandakar Rabbi Salam, Towfika Miraz, Shafiun Jahan, Israt Hoque, Aminul Al Noman, Abdullah Miyan Research Institute International University of Business Agriculture and Technology Uttara Model Town Dhaka Bangladesh Dept. of Mechatronics Engineering Kazla Rajshahi6204 Bangladesh Dept. of Computer Science and Engineering Rajshahi Bangladesh International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Uttara Model Town Dhaka Bangladesh Asian University of Bangladesh Dept. of Computer Science and Engineering Bangabandhu Road Tongabari Ashulia Dhaka Bangladesh Jahangirnagar University Dept. of Institute of Information Technology Savar Dhaka Bangladesh

ISBN: (纸本)9798331505462

Bangladesh's most crucial sector for development is agriculture. Rainfall has been irregular in recent years due to climate change. In our country, the untimely rain and hailstorms harm crops and vegetables, especially winter vegetables. Surface water is mainly used for agriculture, but its availability constantly declines. Our current key challenge is to save surface water while protecting crops from rain and hail. IoT is cutting-edge technology in many applications, such as smart homes, smart cities, and intelligent traffic management. The IoT may be implemented in so many different industries and sectors. This research work is about smart irrigation with crop protection systems in agriculture. The system's primary parts are the sensors, water pump, servo motors, embedded systems, and internet connectivity. IoT sensors like soil moisture sensors and rain sensors are used in the suggested methodology. The fields of crops have received water from a pump. A motor has covered the crops. The proposed solution is a straightforward Internet of Things setup that sends data to the Arduino after detecting rainfall and soil moisture levels. Depending on the sensor data, the Arduino sends signals to the motor and pump to irrigate the crops and cover crops as necessary, revealing the crops after rain. The system is constructed economically using bamboo, and poly and plastic nets have been utilized to protect the crops from rain and fog. The system's primary effects are increased productivity, decreased crop damage rate, and saved surface water. © 2024 IEEE.

关键词： Soil moisture

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Device Identification using Network Traces in a Smart Home IoT Network 17

Device Identification using Network Traces in a Smart Home I...

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17th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2023

作者： Brahma, Jwngfu Matam, Rakesh Choudhury, Nikumani Indian Institute of Information Technology Dept. of Computer Science & Engineering Guwahati India Birla Institute of Technology & Science Dept. of Computer Science & Information Systems Hyderabad India

ISBN: (纸本)9798350307672

Device identification is a crucial aspect of securing networks, particularly in the context of the Internet of Things (IoT), where a vast variety of devices are interconnected. Recently, there has been significant research on developing techniques for identifying IoT devices based on their unique characteristics, called as device fingerprints. These techniques use machine learning algorithms, which can effectively learn and classify devices based on their features. However, device identification remains a challenging task due to the diversity of IoT devices and the constant evolution of their characteristics. This paper presents our attempt at device identification by analyzing distinct device characteristics observed during network communication and using different machine learning techniques. © 2023 IEEE.

关键词： Internet of things

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Machine Learning Classifiers for Predicting the Risk of Cardiovascular Disease in Healthcare 4.0 3

Machine Learning Classifiers for Predicting the Risk of Card...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Kumari, Palak Kumar, Ashish Chatterjee, Kakali Master of Computer Applications Patna Women's College Patna India National Institute of Technology Patna Dept. of Computer Science and Engineering Patna India

ISBN: (纸本)9798331528201

Cardiovascular disease (CVD) generic phrase used to describe heart or blood vessel illness. Conditions that impact the body's, brain's, or heart's blood flow. Reduced blood flow can result from factors such as blood clots, leading to heart attack, angina, or stroke. Other potential meanings of CVD. These models offer key benefits like clear interpretability and the ability to analyze complex patterns, significantly enhancing the accuracy of cardiovascular disease risk predictions. The demerits of using SVM, LR, K-NN, and MLP for CVD prediction include inter-pretability issues, overfitting risks, data requirements, feature selection challenges, scalability concerns, algorithm complexity, and class imbalance. Collaboration between machine learning experts and healthcare professionals is essential for developing accurate and reliable CVD prediction models that enhance patient care. CVD is a significant health threat, often leading to fatal complications. Early detection is crucial for effective intervention. This study employs SVM (accuracy is 0.84%), LR (accuracy is 0.86%), K-NN (accuracy is 0.94%), and MLP (accuracy is 0.79%) machine learning techniques to enhance CVD detection by addressing outliers and null values, contributing to advancing automatic detection initiatives. © 2024 IEEE.

关键词： Nearest neighbor search

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An Investigation of Deep Neural Network based Techniques for Object Detection and Recognition Task in computer Vision 2

An Investigation of Deep Neural Network based Techniques for...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Sunil Kumar, Aithal S Mahesh, Kavitha St Joseph Engineering College Dept. of Computer Science and Engineering Mangalore India Nmam Institute of Technology Nitte Dept. of Computer Science and Engineering India Science and Engineering Nagarjuna College of Engineering & Technology Mudugurki Bengaluru India

ISBN: (纸本)9798350347579

Detection of objects and its recognition in visual sequences are the two critical tasks in the computer vision field. Various real-time applications such as autonomous vehicles, face recognition, health-care systems and space exploration requires highly reliable and precise object detection models. Traditional object detection and recognition algorithms are based on hand crafted and are considered to be erroneous, time consuming and expensive leading to the significant reduction of accuracy rate for object detection in large datasets. Recently, large number of promising deep neural networks models have been emerged for facilitating automated and accurate detection of varying scale objects and its precise recognition across various computer vision applications. Several GPU based neural models thereby incorporating context-aware capabilities have shown effective performance, which overcomes the drawbacks of traditional techniques. This research study provides an investigation of several popular deep learning models that exists for accurate object detection in various forms of visual sequences. Varying scale objects are detected frequently available in popular MS COCO and PASCAL datasets and their performance are evaluated utilizing one stage Yolo family and two stage Faster RCNN deep learning object detectors. At the end of the study, several future research directions for object detection task are discussed. © 2023 IEEE.

关键词： Object detection

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Robust decentralized plug and play voltage tracker of islanded microgrids under loads and lines uncertainties by the invariant ellipsoids

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Journal of King Saud University - engineering Sciences 2024年第6期36卷 409-419页

作者： Bayoumi, Ehab H.E. Soliman, Hisham M. El-Sheikhi, Farag Ali Shafiq, M. Awad, Hilmy Energy and Renewable Energy Department Egyptian Chinese University Cairo Egypt Dept. of Electrical Power Eng. Faculty of Eng Cairo University Cairo Egypt Dept. of Electrical and Electronics Engineering Istanbul Esenyurt University Istanbul Turkey Dept. of Electrical and Computer Engineering Sultan Qaboos University Muscat Oman Dept. of Electrical Technology Faculty of Technology and Education Helwan University Egypt

Microgrids (MGs) comprising of distributed generation units (DGs) are subjected to plug-and-play operation (PnP), lines (connecting the DGs) parameters uncertainty, and load changes. Robust stability and an authentic operation for islanded microgrids can be guaranteed through a robust decentralized voltage tracker developed in this paper. The proposed controller design has the following merits: 1) fully decentralized, 2) scalable, and 3) maintains robust stability against PnP of DGs, load changes, and lines parameters’ uncertainties. A sufficient condition is developed by linear matrix inequality convex optimization is exploited to solve the problem. The MGs’ changes in load and the line's parameters are modelled as norm-bounded uncertainties. The suggested controller uses local measurements from DGs, i.e., decentralized by the decomposition of the global system into subsystems. For each subsystem, the rest of the system's impact is considered disturbances, whose influence must be minimized. The proposed disturbance rejection control algorithm is based on the method of invariant-ellipsoids. Several time-domain scenarios such as connecting and disconnecting a number of DGs, local load changes, and variation of transmission line parameters are executed to assess the suggested controller's effectiveness, using MATLAB /Sim PowerSystems Toolbox. © 2021 The Authors

关键词： Controllers

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Credit Card Fraud Detection Using Machine Learning- A Comprehensive Review 3

Credit Card Fraud Detection Using Machine Learning- A Compre...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Jaswant, T. Venkata Manoj, G. Sai Vamisdhar, V. Aravind, A. Reddy, S. Vasudeva Patni, Jagdish Chandra Bahadure, Nilesh Kumar, Rohit Alliance School of Advanced Computing Alliance University Dept. of Computer Science Engineering Bengaluru India School of Technology Gsfc University Department of Computer Science and Engineering Gujarat India

ISBN: (纸本)9798331518592

Credit card fraud is an omnipresent threat in the digital era that cannot be effectively managed without complex detection and prevention strategies. Machine learning has become instrumental for the industry, principally, due to providing tools to analyze transactional information and identify patterns of fraud. The article dwells on the methods, problems, and prospects of their future use. The algorithms are analyzed according to their potential applicability, scalability, and interpretability for fraud detection. This study includes convolutional neural networks and recurrent neural networks are used for utilization of anomaly exposure detection methods to detect unseen fraud with models. © 2024 IEEE.

关键词： Convolutional neural networks

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Youth Unemployment Mitigation: Leveraging Machine Learning and Exploratory Data Analysis for Evidence-Driven Strategies

Youth Unemployment Mitigation: Leveraging Machine Learning a...

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2023 IEEE Conference on engineering informatics, EI 2023

作者： Aarthi, S. Dharmita, A. Pragathi, G. Elakkiya, S. Srm Institute of Science and Technology Dept. of Computer Science Engineering Chennai India Srm Institute of Science and Technology Dept. Computer Science Engineering Specialization with Big Data Analytics Chennai India

ISBN: (纸本)9798350338522

Youth unemployment represents an urgent global socioeconomic challenge. This study combines machine learning and exploratory data analysis to understand and solve this problem comprehensively. Traditional approaches often rely on qualitative assessments and historical data requiring a data-driven transition to more effective solutions. Our approach integrates machine learning algorithms and Exploratory Data Analysis techniques to analyze diverse data sets, identify influencing factors, and recommend targeted interventions. The analysis process includes data collection, preprocessing, feature selection, machine learning modeling, and Exploratory Data Analysis techniques to understand the dynamics of the Better understanding unemployment among young people. Preliminary results reveal precise predictive patterns and previously unrecognized relationships, providing evidence-based solutions to inform policy and support the economic well-being of younger generations. Further research and validation are needed to increase applicability. © 2023 IEEE.

关键词： Data visualization

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A Comprehensive Analysis of Music Genre Classification with Audio Spectrograms using Deep Learning Techniques 5

A Comprehensive Analysis of Music Genre Classification with ...

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5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Abbas, Mohammad Kazim Gupta, Kunal Mudassir, Mohammad Jain, Rishabh Galgotias College Of Engineering And Technology Dept. Of Computer Science And Engineering Greater Noida India Kiet Group Of Institutions Dept. Of Computer Science Delhi-NCR Ghaziabad India

ISBN: (纸本)9798350330861

This review paper investigates the application of audio spectrograms for categorization of musical genres. The use of feature extraction technique in convolutional neural networks (CNNs) for music genre analysis is highlighted in the discussion of these techniques. The relationship between audio spectrograms and musical subgenres is also observed in this paper, as well as how well audio segmentation methods perform in terms of increasing classification precision. The findings of recent research employing various approaches like CNN, PRCNN, LSTM, SVM, etc are used to conduct a comparative analysis of their effectiveness, highlighting the strengths and weaknesses of each approach. The use of spectrogram for feature extraction and multi-layer CNN proved to be ideal for future researches. © 2023 IEEE.

关键词： Feature extraction

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Classification of Musical Genres Using Audio Spectrograms 5

Classification of Musical Genres Using Audio Spectrograms

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5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

ISBN: (纸本)9798350330861

The classification of music into distinct genres is a valuable undertaking in the realm of multimedia study, as it enables artists, albums, and songs to be categorized based on shared musical characteristics. This study aims to contribute to the field of music genre categorization by proposing an approach that can more accurately classify musical genres compared to existing methodologies. Our approach leverages the attributes extracted from the MK2 audio dataset, which is a compilation of musical genre data that has been collected and processed to facilitate the prediction of song genres. The proposed method employs convolutional neural networks (CNN) and utilizes audio spectrograms as input features. Additionally, this study presents a comparative analysis of our proposed approach with previous research papers, providing insights into the effectiveness of our proposed model. © 2023 IEEE.

关键词： Classification (of information)

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