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lecture notes in computer science (including subseries lecture notes in Artificial Intelligence and lecture notes in Bioinformatics) 2014年 8559 LNCS卷 XI-XII页

作者： Biere, Armin Bloem, Roderick Johannes Kepler University Linz Altenbergerstr. 69 4040 Linz Austria IAIK Graz University of Technology Inffeldgasse 16a 8010 Graz Austria

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A Comprehensive Study on Plant Classification Using Machine Learning Models 1

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8th International Conference on Information and Communication Technology for Competitive Strategies, ICTCS 2023

作者： Karnan, A. Ragupathy, R. Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Tamil Nadu Annamalainagar608002 India

ISBN: (数字)9789819994892

ISBN: (纸本)9789819994885

Plant classification is a critical task with many practical applications, including agriculture, environmental management, and biodiversity conservation. Traditional methods of plant classification can be time-consuming and require considerable expertise. In recent years, machine learning models have shown great promise for automating the plant classification process. This paper provides a comprehensive survey of the existing literature on plant classification using machine learning models. This paper discusses the most commonly used machine learning algorithms, feature extraction and selection techniques, publicly available datasets, and evaluation metrics, also examines the potential applications of plant classification using machine learning models, and identifies the major challenges and future directions for research in this field. Overall, this review highlights the importance of machine learning methods for plant classification and their potential to revolutionize this field in the future. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Improving Breast Cancer Prognosis with DL-Based Image Classification 1

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5th International Conference on computer and Communication Technologies, IC3T 2023

作者： Vinta, Surendra Reddy Dhanalaxmi, B. Aadilakshmi, S. Kumar, Pendyala Kamal Raju, Yeligeti School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Telangana Hyderabad India Telangana Hyderabad India

ISBN: (数字)9789819997077

ISBN: (纸本)9789819997060

Breast cancer is a serious global health problem, particularly among women, highlighting the crucial need for novel diagnostic and prognostic therapies. In this research, we delve into the potential of DL models, with a focus on ResNet50, VGG16, and AlexNet, to classify mammographic images, aiming to improve the prognosis of breast cancer. Leveraging the (DDSM) and its refined version, we engineered and evaluated models to proficiently distinguish mammographic images into normal, benign, and malignant classifications. According to our findings, all three deep learning architectures performed well on the test, with ResNet50 achieving the highest validation accuracy of 96.23%. The VGG16 and AlexNet models also performed well, with accuracies of 96.23% and 95.99%, respectively. These findings indicate that deep learning networks, specifically ResNet50, may efficiently identify mammography pictures, potentially boosting breast cancer prognosis accuracy. While our findings are encouraging, they also underscore the need for additional research. Future research should strive to refine these models further, possibly by using huge and more various datasets, and to investigate the utility of these models in a clinical setting. Furthermore, the development of interpretable models that can explain their conclusions could be a valuable path for future research. This would not only raise trust in these models, but it would also yield vital insights that could improve breast cancer prognosis even more. Finally, this study highlights the potential of DL models in improving breast cancer prognosis through correct mammography picture classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image classification

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Hate Speech Detection on Twitter: A Comparative Evaluation of Different Machine Learning Techniques 1

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3rd International Conference on Artificial-Business Analytics, Quantum and Machine Learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Rastogi, Aryan Kumar, Arjit Dwivedi, Daarshik Singh, Abhishek Pratap Saberwal, Suruchi Alam, Mehboob Department of Computer Science JSS Academy of Technical Education Uttar Pradesh Noida India

ISBN: (数字)9789819725083

ISBN: (纸本)9789819725076

The necessity for robust and fast detection techniques has become critical as social media hate speech has grown. This study investigates ways to identify hate speech comments present on Twitter using language processing methods. In this work, we suggest a cutting-edge method for effectively identify hate speech in tweets that combines linguistic elements and machine learning techniques. Using a sizable dataset of annotated tweets, we test our model, and we get good F1-score and accuracy. The findings of this study present the possibilities of using techniques for processing natural language to identify hateful speech on Twitter and can assist in direct the creation of efficient regulations and interventions to lessen the negative consequences of hate speech on social media sites. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Tweets

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Nafion-Based Chipless RFID Humidity Sensor for Smart Tag Applications 1

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21st National Conference on Sensors and Microsystems, AISEM 2022

作者： Marchi, Giada Mulloni, Viviana Donelli, Massimo Lorenzelli, Leandro Sensors and Devices Fondazione Bruno Kessler Trento Italy Department of Civil Environmental and Mechanical Engineering University of Trento Trento Italy Department of Information Engineering and Computer Science University of Trento Trento Italy

ISBN: (数字)9783031257063

ISBN: (纸本)9783031257056

A humidity sensor based on frequency-domain microwave chipless technology is studied and experimentally characterized in this paper. The chiplessmicrowave sensor is composed of amicrowave resonator coupled with a 50 Ω microstrip transmission line, covered with a 180 μm-thick Nafion layer, which acts as sensitive material. The response of the sensor at 25 °C and for a relative humidity (RH) variation in the range of 0-40% has been experimentally assessed. The sensor shows good sensitivity both in terms of frequency and intensity variations and good linearity. The sensor follows the RH variations in a fully reversible way and the response time is estimated in a few seconds. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Humidity sensors

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lecture notes in computer science (including subseries lecture notes in Artificial Intelligence and lecture notes in Bioinformatics): Preface

Lecture Notes in Computer Science (including subseries Lectu...

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lecture notes in computer science (including subseries lecture notes in Artificial Intelligence and lecture notes in Bioinformatics) 2014年 8735 LNCS卷 VI页

作者： De Decker, Bart Zúquete, André KU Leuven Department of Computer Science iMinds-DistriNet Celestijnenlaan 200A 3001 Leuven Belgium University of Aveiro IEETA Campus Universitário de Santiago 3810-193 Aveiro Portugal

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Detecting Human Walking Direction Using Wi-Fi Signals 1

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International Conference on Applied Soft Computing and Communication Networks, ACN 2023

作者： Ali, Hanan Awad Hassan Seytnazarov, Shinnazar Faculty of Computer Science and Engineering Innopolis University Innopolis420500 Russia Faculty of Computers and Informatics Suez Canal University Ismailia Egypt

ISBN: (数字)9789819720040

ISBN: (纸本)9789819720033

Using wireless signals, it is possible to map some patterns in the received signal to human activities in the vicinity of the wireless receiver. One of the potential applications of this is to detect the direction of human movement in a user device-free manner. To achieve it, we propose to use the channel state information (CSI) of received Wi-Fi signals and process the raw CSI using calibration, Hampel filter, and discrete wavelet transform to minimize the noise and retrieve the useful features of phase and amplitude components of the CSI. Then machine learning algorithms are applied to processed CSI to classify the direction of human walk. Our experimental study with off-the-shelf commodity Wi-Fi hardware and diverse users showed that the proposed system can produce 92.9%, 95.1%, and 89% accuracy for data from two different environments, combined data, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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A Deep Meta-model for Environmental Sound Recognition 1

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3rd International Conference on Data science, Machine Learning and Applications, ICDSMLA 2021

作者： Arun, K.S. Department of Computer Science and Engineering Amal Jyothi College of Engineering Kottayam India

ISBN: (数字)9789811959363

ISBN: (纸本)9789811959356

Nowadays, sound serves as a crucial factor in all facets of human life. Staring from automating personal security systems to critical surveillance systems, sound is an indispensable component. The practical implementation of the present day automatic sound recognition systems in real-life settings is inadmissible due to their poor detection accuracy. However, deep learning-based systems overcome the incompetence of the traditional machine learning-based models, and it can be used to develop automatic sound classification systems. This work proposes a deep meta-model for categorizing environmental sounds on the basis of the spectrogram images generated from these sounds. In the proposed approach, spectrogram images of environmental sounds are used to train five different deep learning models, and the predictions from these base models are then stacked using the proposed deep meta-model. Experimental results on two benchmark datasets such as ESC-50 and UrbanSound 8K demonstrate the fact that the proposed deep meta-model is a promising alternative to the conventional approaches for environmental sound recognition. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Spectrographs

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lecture notes in computer science (including subseries lecture notes in Artificial Intelligence and lecture notes in Bioinformatics): Preface

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lecture notes in computer science (including subseries lecture notes in Artificial Intelligence and lecture notes in Bioinformatics) 2014年 8454 LNCS卷 V-VII页

作者： George, Laurent Vardanega, Tullio University of Paris-Est Champs sur Marne France University of Padua Italy

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Static Analysis Approach of Malware Using Machine Learning 1

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International Conference on Recent Developments in Cyber Security, ReDCySec 2023

作者： Pandey, Aman Raj Sharma, Tushar Basnet, Subarna Setia, Sonia Department of Computer Science Engineering Sharda University Knowledge Park III Greater Noida India

ISBN: (数字)9789819998111

ISBN: (纸本)9789819998104

The malware is continually changing in today’s Internet-dependent environment, with an estimated 560,000 new cases being produced each day. As a result, conventional detection technologies are no longer adequate, which presents a problem for security experts. Because it can learn and adapt to new threats, machine learning has become a potential method for enhancing security measures. This research suggests a machine learning-based approach for identifying different kinds of malware. The technology attempts to improve threat detection’s effectiveness and reactivity. A dataset of 96,724 malware samples is used to evaluate the suggested system, and several machine learning methodologies are contrasted. The outcomes show how machine learning may enhance security systems’ ability to combat continually changing malware threats. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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