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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Venkateswara Reddy, P. Sarasu, P. Kalasalingam Academy of Research and Education Department of Computer Science & Engineering Srivilliputtur Tamil Nadu Krishnankoil626126 India

ISBN: (纸本)9798350313987

Image synthesis offers a way to quickly create huge amounts of datasets for regulating the manufacturing process to ensure optimal airflow as well as material while monitoring the generation of data sets variety for machine learning applications. Artificial information leads a vital role along with the training pipeline in the given requirements of deep learning applications. The present study offers an extensive collection of both the modern techniques and image synthesis for visual machine learning which employ for picture synthesizing. Image synthesis is described based on such a classification that takes modelling and rendering in the context of image production, and a categorization is also established regarding the computer vision applications which are utilized in. In order to encourage future picture production for machine learning, the designers concentrated on the method's computer graphics components. In the present process, each approach is evaluated for quality and reported performance, by suggesting as to what learning potential is anticipated. The present study acts as thorough reference and directs at both the application development and data production sides. © 2023 IEEE.

关键词： deep learning algorithms Image Generator Machine learning algorithms and Image Synthesis

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Design of Data Information Technology Optimization based on deep learning

Design of Data Information Technology Optimization based on ...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Fan, Zhen Guangdong University of Science and Technology College of Management Dongguan China

ISBN: (纸本)9798350315455

System optimization is the main content of big data technology, but in the process of big data technology management, data changes and computing methods will manage big data influence as well as minimize rationality of system optimization. This paper proposes a deep learning algorithm to optimize and analyze the data information technology, enhance a development level of big data technology, and shorten the time of data statistics formulation. Then, the big data is estimated comprehensively. Finally, continuous monitoring is used to optimize the data information technology. deep learning method can identify the relationship between information and data, better analyze data correlation, simplify the complexity between data and improve the accuracy of information analysis. The deep learning (DL) algorithm can accurately calculate parameters, improve the level of data information technology, and the rationality is greater, and better than traditional statistics law. Therefore, the deep learning algorithm can meet the requirements, and is appropriate for continuous examination of the development of information technology. © 2023 IEEE.

关键词： big data management big data technology deep learning algorithms System optimization traditional statistical methods

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Design Thinking –An Effective Method for Enhancing Intrusion Detection Systems through Hyper parameter Tuning in deep learning 2

Design Thinking –An Effective Method for Enhancing Intrusio...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Dinesh, K. Kalaivani, D. Vijayaraghavan, T. Department of Computer Science Dr. SNS Rajalakshmi College of Arts and Science Coimbatore India

ISBN: (纸本)9798350340235

This research study explores the optimization of Intrusion Detection Systems (IDS) by fine-tuning hyperparameters for deep learning (DL) algorithms, incorporating data analytics. The research systematically analyzes the impact of hyperparameters on DL models, including CNNs, RNNs, and their variants. Diverse datasets are utilized to evaluate the robustness and versatility of the tuned models. The results demonstrate substantial improvements in IDS accuracy and efficiency, emphasizing the significance of hyperparameter tuning and data analytics. This research provides valuable insights for optimizing IDS performance, advancing network security in the face of evolving cyber threats. © 2023 IEEE.

关键词： Cyber Threats Data Analytics deep learning algorithms Hyperparameter Tuning Intrusion Detection Systems (IDS)

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Gait Recognition: A Cross - Dataset Evaluation of deep learning Models

Gait Recognition: A Cross - Dataset Evaluation of Deep Learn...

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2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2024

作者： Singloo, Hemal Kumar, Manoj Namdev, Simran Sonbawne, Kinsukh Suni Indoria, Nishant Vit Bhopal University Dept. Computer Science and Engineering Sehore India

ISBN: (纸本)9798350348460

This research paper investigates the effectiveness of deep learning models for gait recognition using a variety of data, including gait phase data and sensor data. This study evaluates the performance of convolutional neural networks (CNN) and long-term memory networks (LSTM), focusing on comparative analysis across multiple datasets to gain insight and contribute to different data analyses. The gait level dataset contains important movement patterns as a main area of research. Additionally, sensor-based data identifies various information needs, and complete analysis, for greater understanding and use. These comprehensive experimental results provide several observations on the variability and performance of CNNs and LSTMs across various datasets. The applicability of these models in recognizing activities in different products is presented, and their advantages and limitations in model recognition, display, and feature extraction are seen. This study distinguishes between different types of deep learning models and demonstrates their suitability for handling different types of data. These results lead to decisions regarding model selection and application, contributing to advances in analytics, biometrics, and human recognition applications. © 2024 IEEE.

关键词： CNN deep learning algorithms Gait data LSTM Machine learning

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Extraction and Mapping of Cropland Parcels in Typical Regions of Southern China Using Unmanned Aerial Vehicle Multispectral Images and deep learning

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DRONES 2023年第5期7卷 285-285页

作者： Wu, Shikun Su, Yingyue Lu, Xiaojun Xu, Han Kang, Shanggui Zhang, Boyu Hu, Yueming Liu, Luo South China Agr Univ Guangdong Prov Key Lab Land Use & Consolidat Guangzhou 510642 Peoples R China Hainan Univ Coll Trop Crops Haikou 570100 Peoples R China

The accurate extraction of cropland distribution is an important issue for precision agriculture and food security worldwide. The complex characteristics in southern China pose great challenges to the extraction. In this study, for the objective of accurate extraction and mapping of cropland parcels in multiple crop growth stages in southern China, we explored a method based on unmanned aerial vehicle (UAV) data and deep learning algorithms. Our method considered cropland size, cultivation patterns, spectral characteristics, and the terrain of the study area. From two aspects-model architecture of deep learning and the data form of UAV-four groups of experiments are performed to explore the optimal method for the extraction of cropland parcels in southern China. The optimal result obtained in October 2021 demonstrated an overall accuracy (OA) of 95.9%, a Kappa coefficient of 89.2%, and an Intersection-over-Union (IoU) of 95.7%. The optimal method also showed remarkable results in the maps of cropland distribution in multiple crop growth stages, with an average OA of 96.9%, an average Kappa coefficient of 89.5%, and an average IoU of 96.7% in August, November, and December of the same year. This study provides a valuable reference for the extraction of cropland parcels in multiple crop growth stages in southern China or regions with similar characteristics.

关键词： unmanned aerial vehicle (UAV) data deep learning algorithms cropland parcels multiple crop growth stages extraction

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deep learning based IoT and cloud-integrated state of charge estimation for battery powered electric vehicles

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JOURNAL OF ENERGY STORAGE 2024年 100卷

作者： Devi, B. Kumar, V. Suresh Karthick, T. Balasundar, C. Thiagarajar Coll Engn Dept EEE Madurai 625015 Tamil Nadu India Quantan Techserv Pvt Ltd Madurai 625016 Tamil Nadu India

Lithium-ion battery packs are widely used for automotive applications. An exact state of charge (SOC) estimation is essential to obstruct the battery from overcharge and discharge, increase the battery lifespan, and know the driving range. Even though a number of SOC estimation strategies have been presented by researchers, further exploration is necessary to identify an appropriate technique that can accommodate a variety of lithium-ion battery chemistries. In recent studies, it has been demonstrated that deep learning (DL), a well-known machine learning algorithm, performs better for SOC estimates than many other strategies. To get the most out of DL models, it is crucial to choose the proper hyperparameters and make effective use of relevant input characteristics. This work proposes a novel cloud-integrated battery management system (CIBMS) based on quantifiable features, including voltage, current, and temperature. The training and testing data is collected from the realtime electric vehicle drive set up using an Internet of Things (IoT) sensor and transferred to the cloud using an internet-connected Raspberry Pi 4B+ processor. The data is analyzed using various DL algorithms such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network with LSTM (CNN-LSTM), Convolutional Neural Network with Bi-directional LSTM (CNN BI-LSTM), and Convolutional Neural Network-Gated Recurrent Unit-Long Short-Term Memory (CNN-GRU-LSTM) for accurate battery status estimation. The proposed algorithm is incorporated to mitigate issues like pre- processing, overfitting challenges in real-time applications rather than conventional methods. The accomplishment of the algorithm has been evaluated in terms of mean square error (MSE) and root mean square error (RMSE) of the SOC. The obtained results clearly show that the proposed CNN-GRU-LSTM algorithm accurately estimated the battery status which is crucial for real-time decision-making in electric vehicles

关键词： Electric vehicle State of charge Internet of things deep learning algorithms Cloud-integrated battery management system

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Single and multiple drones detection and identification using RF based deep learning algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期187卷 115928-115928页

作者： Sazdic-Jotic, Boban Pokrajac, Ivan Bajcetic, Jovan Bondzulic, Boban Obradovic, Danilo Univ Def Belgrade Mil Acad Veljka Lukica Kurjaka 33 Belgrade Serbia Mil Tech Inst Ratka Resanovica 1 Belgrade Serbia

Unmanned aerial systems, especially drones have gone through remarkable improvement and expansion in recent years. Drones have been widely utilized in many applications and scenarios, due to their low price and ease of use. However, in some applications drones can pose a malicious threat. To diminish risks to public security and personal privacy, it is necessary to deploy an effective and affordable anti-drone system in sensitive areas to detect, localize, identify, and defend against intruding malicious drones. This research article presents a new publicly available radio frequency drone dataset and investigates detection and identification methodologies to detect single or multiple drones and identify a single detected drone's type. Moreover, special attention in this paper has been underlined to examine the possibility of using deep learning algorithms, particularly fully connected deep neural networks as an anti-drone solution within two different radio frequency bands. We proposed a supervised deep learning algorithm with fully-connected deep neural network models that use raw drone signals rather than features. Regarding the research results, the proposed algorithm shows a lot of potentials. The probability of detecting a single drone is 99.8%, and the probability of type identification is 96.1%. Moreover, the results of multiple drones detection demonstrate an average accuracy of 97.3%. There have not been such comprehensive publications, to this time, in the open literature that have presented and enlightened the problem of multiple drones detection in the radio frequency domain.

关键词： Anti-drone system Classification deep learning algorithms Detection Drone Multiple drones

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A novel deep learning framework based swin transformer for dermal cancer cell classification

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 133卷

作者： Ramkumar, K. Medeiros, Elias Paulino Dong, Ani de Albuquerque, Victor Hugo C. Hassan, Md Rafiul Hassan, Mohammad Mehedi Rajalakshmi Inst Technol Dept Comp Sci & Engn Chennai India Univ Fed Ceara Dept Teleinformat Engn Fortaleza CE Brazil Dongguan City Univ Sch Artificial Intelligence Dongguan Peoples R China Univ Maine Coll Arts & Sci Dept Comp Sci Presque Isle ME 04769 USA King Saud Univ Coll Comp & Informat Sci Dept Informat Syst Riyadh 11543 Saudi Arabia

Recent studies have demonstrated the efficacy of deep learning architectures in enhancing the interpretation of skin images, thereby aiding in the classification and segmentation of skin cancer. However, the existing deep learning techniques predominantly focus on either segmentation or classification and are designed for normally distributed data. Addressing this limitation, the present study introduces a hybrid ensemble deep learning approach for skin cancer classification and segmentation. This proposed model amalgamates Residual learning Machines, Swin Transformers, and Fast Neural Networks (FNN) to proficiently manage diverse sets of nonuniformly distributed data, thereby augmenting diagnostic accuracy. The efficacy of this approach was evaluated using various skin cancer datasets, including ISIC-2008, PH -2, and HM007. The assessment involved several performance metrics, such as the Matthew correlation coefficient (MCC), recall, F1 -score, specificity, accuracy, and precision. Moreover, to underscore the superiority of the proposed model, its performance was juxtaposed with that of previous efforts. Results from repeated trials indicate that the proposed model achieved 98.78% classification accuracy, 98.7% precision, 98.7% F1 -score, 98.64% average recall, and an MCC of 0.9863 across different skin disease datasets. Demonstrating consistent superiority over existing methods, the proposed approach shows considerable potential in revolutionizing the diagnostics of skin cancer.

关键词： Skin cancer Dermoscopy deep learning algorithms Swin transformers Residual learning networks Fast neural networks

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Application of unsupervised learning and deep learning for rock type prediction and petrophysical characterization using multi-scale data

GEOENERGY SCIENCE AND ENGINEERING

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GEOENERGY SCIENCE AND ENGINEERING 2023年 230卷

作者： Iraji, Shohreh Soltanmohammadi, Ramin Matheus, Gabriela Fernandes Basso, Mateus Vidal, Alexandre Campane State Univ Campinas UNICAMP Dept Mech Engn FEM BR-13083860 Campinas SP Brazil State Univ Campinas UNICAMP Ctr Energy & Petr Studies CEPETRO BR-13083896 Campinas SP Brazil State Univ Campinas UNICAMP Geosci Inst IG Dept Geol & Nat Resources BR-13083855 Campinas SP Brazil

This study integrates well log data, routine core analyses, microcomputed X-ray tomography (mu CT) images, and sedimentary petrography to accurately characterize and evaluate the carbonate reservoirs of the Barra Velha formation (Aptian) of the Santos Basin within Brazilian pre-salt region. In these carbonate reservoirs, the porous system is extremely diverse and variable, making it challenging to establish rock typing with comparable petrophysical properties. Based on this integrated study, the reservoir sequences were characterized and a precise definition of four reservoir rock types (RRTs) was performed by integrating the petrophysical values from the plugs and their corresponding well log data of two cored wells using K-means unsupervised classification algorithm. The classification results were combined with various conventional techniques to evaluate the quality and geological characteristics of the studied sequence. This evaluation encompassed different parameters such as flow and storage capacity, reservoir quality index, flow zone indicator, pore spaces interpretation, and average pore and throat radius. The study involved a detailed analysis of thin sections to identify various facies, including shrubstones, reworked, and spherulitestone, and to classify various forms of porosity such as interparticle, intraparticle, intercrystalline, vug, moldic, fracture, and growth framework porosity. Pore Network Modeling from mu CT analysis of plugs was used specifically for the characterization of pores and throats of plug samples from each RRT. These datasets were utilized as supporting evidence to offer a more accurate and inclusive knowledge of reservoir quality. The study aimed to develop predictive models by implementing deep learning and machine learning algorithms trained on well log data to estimate plug porosity and rock type. Two deep learning models, ResNet and 1D CNN, were trained and evaluated for plug porosity prediction, with the 1D CNN model showing

关键词： Micro -computed tomography ( mu CT) images K -means unsupervised classification algorithm Reservoir rock types (RRTs) Pore network modeling deep learning algorithms ResNet 1D CNN XGBoost algorithm

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A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers

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JOURNAL OF NEURAL ENGINEERING 2021年第3期18卷

作者： Zhang, Xiang Yao, Lina Wang, Xianzhi Monaghan, Jessica McAlpine, David Zhang, Yu Univ New South Wales Sydney NSW Australia Univ Technol Sydney Sydney NSW Australia Macquarie Univ Sydney NSW Australia Lehigh Univ Bethlehem PA 18015 USA Harvard Univ Boston MA 02115 USA

Brain signals refer to the biometric information collected from the human brain. The research on brain signals aims to discover the underlying neurological or physical status of the individuals by signal decoding. The emerging deep learning techniques have improved the study of brain signals significantly in recent years. In this work, we first present a taxonomy of non-invasive brain signals and the basics of deep learning algorithms. Then, we provide the frontiers of applying deep learning for non-invasive brain signals analysis, by summarizing a large number of recent publications. Moreover, upon the deep learning-powered brain signal studies, we report the potential real-world applications which benefit not only disabled people but also normal individuals. Finally, we discuss the opening challenges and future directions.

关键词： brain– computer interface deep learning algorithms survey brain signals

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