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Enhancement of Photovoltaic Dust Detection Using Deep Learning Technique

Enhancement of Photovoltaic Dust Detection Using Deep Learni...

引用

Advances in Modern Age Technologies for Health and engineering Science (AMATHE), International Conference on

作者： R. Lillian Gracia Priyadharsini. C B.Elizabeth Caroline Division Of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Electronics and Communication Engineering IFET College of Engineering Villupuram India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

The dust collection on solar panels situated in solar parks is a common problem faced by PV system operators, particularly in arid and dusty regions. Dust deposition reduces the transparency of the panel surface, leading to decreased sunlight absorption and thus reduced energy conversion efficiency. Traditional methods for detecting dust accumulation involve manual inspection or periodic cleaning schedules, both of which are labor-intensive, time-consuming, and often inefficient. Recent developments in deep learning and machine vision have made autonomous dust detection techniquesfeasible. Numerous techniques have been employed to identify and measure dust on solar panels, such as machine learning models and image processing algorithms. In this paper, the dataset of clean and dusty images of the solar panels utilized wasapplied to four state of the art models and then the EfficientnetB0 model was proposed to detect the dust over the solar panels. On comparing the EfficientnetB0 model with the other state of the art models, EfficientnetB0 yielded an impressive accuracy rate of approximately 85.92%. Further research and development in this field hold the potential to revolutionize the maintenance and operation of solar power plants, contributing to a greener and more sustainable future.

关键词： Deep learning Schedules Machine learning algorithms Machine vision Neural networks Manuals Maintenance

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Femoropopliteal Artery Stent Detection in Fluoroscopy Using Otsu's Preprocessing and CNN Segmentation 4

Femoropopliteal Artery Stent Detection in Fluoroscopy Using ...

引用

4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Rajinikanth, Venkatesan Damaševičius, Robertas Taniar, David Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Division of Research and Innovation Department of Computer Science and Engineering TN Chennai602105 India Kaunas University of Technology Centre of Real Time Computer Systems Kaunas44249 Lithuania Monash University Faculty of Information Technolog MelbourneVIC3800 Australia

ISBN: (纸本)9798350387490

computer algorithm supported medical image analysis is a prevalent method in clinics, which effectively alleviates the diagnostic burden associated with traditional image assessment methods. This study aims to create a method that utilizes a Convolutional-Neural-Network (CNN) to accurately segment the Femoropopliteal Artery Stent (FAS) in Digital Fluoroscopy Images (DFI). In order to improve the accuracy of segmentation, this study used the combination of Otsu's tri-level thresholding and the Lévy-Flight Distribution Algorithm (LFDA). The suggested technique consists of many stages: collecting and resizing images and masks, boosting the DFI using Otsu and LFDA, using CNN-segmentation to extract the stent region, comparing segmented stent with mask, and calculating the required performance metrics. The efficacy of the suggested system is validated by analyzing the raw and thresholded DFI. The investigational results of this work affirm that this method achieves a higher level of accuracy (>96%) in segmenting images when the Otsu's preprocessed image is utilized. The experimental study employs the UNet-variants, and in the future, alternative CNN-segmentation methodologies can be utilized to validate the effectiveness of the presented scheme. © 2024 IEEE.

关键词： Convolutional neural networks

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Movement analysis of Individuals Using ML for different window size on raw accelerometer Time series Data 9

Movement analysis of Individuals Using ML for different wind...

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9th International Conference on Information technology Trends, ITT 2023

作者： Radhika, N. Mathew, Ashams Paul, Sujni Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India Higher Colleges of Technology CIS Division Dubai United Arab Emirates

ISBN: (纸本)9798350327502

Individual movement analysis is becoming more open and common as sensors incorporated in mobile devices and machine learning algorithms progress. In the proposed framework, different classification techniques of machine learning algorithms such as Logistic Classifier and Support vector machine (SVM) are used to recognize human activities. The Fast Fourier transform (FFT) of tri-axial accelerometers that measure acceleration in all three spatial dimensions of smartphones is considered with different window sizes which correspond to the different number of samples from the sensor to predict the activities of an individual. The Performance analysis of individual classification for different window sizes is evaluated using accuracy, recall, precision, and F1-score. Results show that SVM performed better when compared to other model but there is a significant increase in accuracy as the window size increase for both the ML classification techniques. © 2023 IEEE.

关键词： Accelerometers

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Observer-Based Control of Second-Order Multi-vehicle Systems in Bearing-Persistently Exciting Formations 63

Observer-Based Control of Second-Order Multi-vehicle Systems...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Tang, Zhiqi Fidan, Baris Johansson, Karl H. Martensson, Jonas Hamel, Tarek Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science StockholmSE-10044 Sweden University of Waterloo Mechanical and Mechatronics Engineering Department ON Canada I3S-UCA Cnrs France

ISBN: (纸本)9798350316339

This paper proposes an observer-based formation tracking control approach for multi-vehicle systems with second-order motion dynamics, assuming that vehicles' relative or global position and velocity measurements are unavailable. It is assumed that all vehicles are equipped with sensors capable of sensing the bearings relative to neighboring vehicles and only one leader vehicle has access to its global position. Each vehicle estimates its absolute position and velocity using relative bearing measurements and the estimates of neighboring vehicles received over a communication network. A distributed observer-based controller is designed, relying only on bearing and acceleration measurements. This work further explores the concept of the Bearing Persistently Exciting (BPE) formation by proposing new algorithms for bearing-based localization and state estimation of second-order systems in centralized and decentralized manners. It also examines conditions on the desired formation to guarantee the exponential stability of distributed observer-based formation tracking controllers. In support of our theoretical results, some simulation results are presented to illustrate the performance of the proposed observers as well as the observer-based tracking controllers. © 2024 IEEE.

关键词： Acceleration measurement

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On the Application of Synthetic Media to Penetration Testing 2

On the Application of Synthetic Media to Penetration Testing

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2nd Workshop on the security implications of Deepfakes and Cheapfakes, WDC 2023, held in conjunction with ACM AsiaCCS 2023

作者： Soares, Nathalia Seiden, Steven Baggili, Ibrahim Webb, Andrew Lab Center of Computation and Technology Baton RougeLA United States Division of Computer Science and Engineering Louisiana State University Baton RougeLA United States

ISBN: (纸本)9798400700989

Artificial Intelligence (AI) is currently a hot topic. The explosion of AI technologies that create Synthetic Media (SM), such as Deep Fakes (DF), is worrisome, especially since they can easily spread false information over social networks. Our work explores a positive use case for SM (fake text, website and app design, audio, and videos) to aid in reconnaissance, vulnerability assessment, and exploitation during penetration testing. We contest that using SM during various penetration testing phases is useful for uncovering security weaknesses in an organization. Our work results in constructing a primary mind map of the various penetration testing phases and the potential uses of SM in each of them. © 2023 ACM.

关键词： Artificial intelligence

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Advanced Urban Air Quality Control and Management Using XM and RFE Models for Enhanced Predictive Analysis 4

Advanced Urban Air Quality Control and Management Using XM a...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Sreelatha, Bolisetty Bhoopathy, V. Saravanan, B. Vattikolla, Tulasirao Asrani, Deepak Kaliappan, S. Geethanjali College of Engineering and Technology Department of Electronics and Communication Engineering Medchal India Sree Rama Engineering College Department of Cse Tirupati India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Department of Englis Hyderabad India School of Information Technology Institute of Management Studies Noida India Lovely Professional University Division of Research and Development Phagwara India

ISBN: (纸本)9798350352931

Urban air pollution, particularly in large cities, has historically been exacerbated by construction activity, resulting in significant surges. Pollutants such as Sulphur dioxide and soot have, however, been significantly reduced in the industrialized Western world as a result of developments in abatement technologies. As a result, the attention has shifted to pollutants related with mobility, such as nitrogen oxides, organic compounds, and fine particulate matter. This work uses a machine learning approach to analyze urban air quality, with a focus on feature selection and data preparation. Data preprocessing, feature selection using Principal Component Analysis (PCA), and model training are all important steps. Normalization of Life Cycle Assessment (LCA) results was also added as a preprocessing step. The model had a maximum accuracy of 91.93% in categorizing and predicting urban air quality. This outcome was greatly influenced by the selection of important features using PCA and model refining by feature deletion. The findings highlight the efficacy of sophisticated machine learning approaches, specifically PCA and normalization, in improving air quality prediction models. The findings show that, while traditional pollutants have been significantly reduced, new concerns in urban air pollution necessitate specific solutions centered on transportation-related particles and other minute pollutants. This paper establishes a paradigm for better urban air quality management and emphasizes the value of adaptive approaches in environmental monitoring. © 2024 IEEE.

关键词： Nitrogen oxides

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Brownian-Distribution Algorithm Optimized Efficientnet Features for Normal/Glaucoma Retinal Fundus Image Classification 4

Brownian-Distribution Algorithm Optimized Efficientnet Featu...

引用

4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

ISBN: (纸本)9798350387490

The eye is a critical sensory organ in human physiology, and any abnormality in the eye will result in vision problems ranging from mild to severe. Image-guided methods are frequently employed to conduct clinical-level eye health assessments, and retinal fundus imaging (RFI) is among the most frequently employed modalities for eye analysis. This research aims to implement a Deep-Learning (DL)-based technique for classifying the Normal/Glaucoma RFI database. This scheme consist following phases, image collection and labelling, feature extraction with EfficientNet, feature optimization using a novel Brownian-Distribution Algorithm (BDA), and binary classification with 3-fold cross validation. Initially, the proposed approach performs the classification with EfficientNet-model features and documents the results. Subsequently, the BDA is employed to optimize the features in order to identify the most significant ones. Serial features-fusion is then implemented to generate a new features vector. The results of this study confirm that this approach is capable of achieving a 100 % accuracy rate when the BDA optimized feature vector is utilized for the classification task. In the future, this application may be utilized to analyze the RFI collected from hospital. © 2024 IEEE.

关键词： Ophthalmology

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M3GNAS: Multi-modal Multi-view Graph Neural Architecture Search for Medical Outcome Predictions

M3GNAS: Multi-modal Multi-view Graph Neural Architecture Sea...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Al-Sabri, Raeed Gao, Jianliang Chen, Jiamin Oloulade, Babatounde Moctard Wu, Zhenpeng Abdullah, Monir Hu, Xiaohua Central South University School of Computer Science and Engineering Changsha China College of Science and Engineering Hamad Bin Khlifa University Division of Information and Computing Technology Doha Qatar College of Computing and Information Technology University of Bisha Saudi Arabia College of Computing & Informatics Drexel University United States

ISBN: (纸本)9798350386226

Multi-modal multi-view graph learning models have achieved significant success in medical outcome prediction, combining various modalities to enhance the performance of various medical tasks. However, current architectures for multi-modal multi-view graph learning (M3GL) models heavily depend on manual design, demanding significant effort and expert experience. Meanwhile, significant advancements have been achieved in the field of graph neural architecture search (GNAS), contributing to the automated design of learning architectures based on graphs. However, GNAS faces challenges in automating multimodal multi-view graph learning (M3GL) models, as existing frameworks cannot handle M3GL architecture topology, and current search spaces do not consider M3GL models. To address the above challenges, we propose, for the first time, a multi-modal multi-view graph neural architecture search (M3GNAS) framework that automates the construction of the optimal M3GL models, enabling the integration of multi-modal features from different views. We also design an effective multi-modal multi-view learning (M3L) search space to develop inner-view and outer-view graph representation learning in the context of graph learning, obtaining a latent graph representation tailored to the specific requirements of downstream tasks. To examine the effectiveness of M3GNAS, it is evaluated on medical outcome prediction tasks. The experimental findings demonstrate our proposed framework's superior performance compared to state-of-the-art models. © 2024 IEEE.

关键词： Contrastive Learning

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Early-Stage Detection of Colorectal Cancer Using Image Classification

Early-Stage Detection of Colorectal Cancer Using Image Class...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Ashlin Santhosh A M Anusha Bamini M Rajeswari D Brindha R Chitra S Stewart Kirubakaran Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Early detection of colorectal cancer is important as it is one of the most common and deadliest types of cancer worldwide. This project proposes an image classification model based on deep learning architecture that could detect colorectal cancer in its preliminary stages by using an EfficientNetB0. By using the Colorectal Cancer Whole Slide Images (WSI) dataset, a classification model using the WSI dataset to classify tissue samples into six distinct categories of varying grades of cancerous growths and normal. In this model, transfer learning is included to maintain computational efficiency while enhancing accuracy. A user interface with Streamlit, which has real-time image upload, analysis, and prediction display, was also developed. The predictions come with confidence scores, and the Interface also provides educational content for each cancer stage to help raise awareness and prevent the disease. Applications with potential for medical diagnosis and as an educational tool for early detection of colorectal cancer are possible for this system.

关键词： Deep learning Analytical models Adaptation models Computational modeling Transfer learning Colorectal cancer Streaming media Predictive models User interfaces Image classification

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BiLSTM Classifier: A New Approach for Detecting Cyber-Attacks in MITRE ATTACK Framework 6

BiLSTM Classifier: A New Approach for Detecting Cyber-Attack...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Singh, Gurinder Pal Sandhu, Jasminder Kaur Hooda, Manish Kumar Chandigarh University Computer Science and Engineering Punjab Gharuan India Semiconductor Laboratory Technology Development Division Punjab S. A. S. Nagar India

ISBN: (纸本)9798350304480

Cyber-attacks have become a significant challenge to organizations worldwide. MITRE ATT&CK framework is a widely adopted methodology for identifying, categorizing, and describing different types of cyber-attacks. However, detecting and preventing these attacks remain a significant challenge for cybersecurity researchers and practitioners. In this study, we assessed the ability of the BiLSTM Classifier to detect attacks in different stages of MITRE ATT&CK frameworks. Our results show that the BiLSTM classifier can achieve high accuracy in detecting attacks across various stages of the framework, including reconnaissance, initial access, execution, persistence, and exfiltration. © 2023 IEEE.

关键词： Attack Detection BiLSTM Classifier Cybersecurity Deep learning Machine learning MITRE ATT&CK framework Neural networks

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