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Smart automated highway lighting system using IoT: a survey

Energy Informatics 2024年第1期7卷 76页

作者： Achar, Tejaswini Eshwar Rekha, C. Shreyas, J. Department of Computer Science and Engineering Government Engineering College Koppal Talakal India Department of Information Science and Engineering Bapuji Institute of Engineering and Technology Davanagere India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

Efficient highway lighting is crucial for ensuring road safety and reducing energy consumption and costs. Traditional highway lighting systems rely on timers or simple photosensors, leading to inefficient operation by illuminating lights when not needed or failing to adjust to changing conditions. The emergence of the Internet of Things (IoT) and related technologies has enabled the development of smart automated highway lighting systems that can dynamically control illumination levels based on real-time data. This paper provides a comprehensive review of the current state-of-the-art in smart automated highway lighting systems employing IoT technologies. Key components, communication protocols, data processing techniques, and lighting control strategies are discussed. The integration of renewable energy sources and energy storage systems is explored for environmentally sustainable operations. Practical implementation case studies are analyzed to highlight benefits and challenges. Open research issues and future directions for further enhancements are identified. © The Author(s) 2024.

关键词： Street lighting

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Advancements in EEG-Based Emotion and Consciousness Recognition: Techniques, Challenges, and Innovations 1

Advancements in EEG-Based Emotion and Consciousness Recognit...

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1st International Conference on Innovations in Communications, Electrical and Computer engineering, ICICEC 2024

作者： Jadekar, Ranjana B. Poornima, B. Gowramma, B.H. Puneeth, S.P. Visvesvaraya Technological University Bapuji Institute of Engineering and Technology Dept. of Computer Science and Engineering Karnataka Belagavi India Visvesvaraya Technological University Bapuji Institute of Engineering and Technology Dept. of Information Science and Engineering Karnataka Belagavi India

ISBN: (纸本)9798350376517

This study delves into the application of Electroencephalogram (EEG)-based techniques for emotion and consciousness recognition, focusing on disorders of consciousness (DOC). By evaluating existing approaches in terms of techniques, methodologies, and accuracies, the research highlights the efficacy of both machine learning and deep learning models. The key challenges were identified such as noise removal due to artifacts and variability in patient responses. The findings underscore the potential of advanced computational methods in enhancing the reliability and precision of EEG-based diagnostic tools, paving the way for better clinical interventions and deeper understanding of human emotions. Also, the findings show that using deep learning, the current works have achieved better results for identification of emotion. © 2024 IEEE.

关键词： Electroencephalography

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Enhancing Silkworm Breeding Through Advanced Image Segmentation: A Comparative Study of U-Net, Mask R-CNN, And FCN 1

Enhancing Silkworm Breeding Through Advanced Image Segmentat...

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1st International Conference on Innovations in Communications, Electrical and Computer engineering, ICICEC 2024

作者： Gowramma, B.H. Poornima, B. Jadekar, Ranjana B. Davanagere India Bapuji Institute of Engineering and Technology Department of Is&e Davanagere India

ISBN: (纸本)9798350376517

Accurate segmentation of male and female silkworms is a crucial task in the sericulture industry, where efficient sex differentiation contributes to optimized breeding and enhanced productivity. Traditionally, this task has been performed manually, which is labour-intensive and error-prone. To address these challenges, this paper presents a comprehensive approach to automated segmentation of silkworm images using advanced image processing techniques. Three different deep learning-based segmentation methods are explored: U-Net, Mask R-CNN, and Fully Convolutional Networks (FCNs). The U-Net model, known for its robust performance in biomedical image segmentation, is employed to delineate the contours of silkworms, ensuring precise boundary detection. Mask R-CNN, a region-based convolutional neural network, is used for instance segmentation, handling multiple silkworms within a single image and providing pixel-wise masks for each instance. Lastly, Fully Convolutional Networks (FCNs) offer a pixel-to-pixel mapping for semantic segmentation, assigning each pixel in the image a category related to the silkworm body or background. Results from these three methods are compared using metrics such as Intersection over Union (IoU) and pixel-wise accuracy, with U-Net achieving the highest IoU of 0.89, closely followed by Mask R-CNN with 0.87, and FCN with 0.85. These findings demonstrate that deep learning techniques can provide effective and reliable solutions for silkworm segmentation, significantly reducing manual effort and improving efficiency in large-scale sericulture operations. © 2024 IEEE.

关键词： Convolutional neural networks

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An Effective Noise Removal Technique for Enhancing Ischemic Brain Stroke Computed Tomography Images 1

An Effective Noise Removal Technique for Enhancing Ischemic ...

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1st International Conference on Innovations in Communications, Electrical and Computer engineering, ICICEC 2024

作者： Puneeth, S.P. Poornima, B. PremaSudha, B.G. Patil, N.S. Puneetha, B.H. Jadekar, Ranjana B. Visvesvaraya Technological University Bapuji Institute of Engineering and Technology Davangere Department of Information Science and Engineering Karnataka Belagavi India Visvesvaraya Technological University Siddaganga Institute of Technology Tumkur Department of Mca Karnataka Belagavi India Visvesvaraya Technological University Bapuji Institute of Engineering and Technology Davangere Department of Computer Science and Business System Karnataka Belagavi India Visvesvaraya Technological University Bapuji Institute of Engineering and Technology Davangere Department of Computer Science and Engineering Karnataka Belagavi India

ISBN: (纸本)9798350376517

Ischemic stroke significantly affects millions of people motor and cognitive skill. Early identification of ischemic stroke helps in preventing loss of life. Usually, noise is present in Computed-Tomography (CT) during acquisition and the noise degrades the visibility of anatomical structures and subtle abnormalities, making it difficult for radiologists to accurately diagnose and interpret medical conditions. Thus, denoising CT medical images is crucial in preserving information and restoring images contaminated with noise. The Convolution-Neural-Network (CNN) has been used to remove the noise with good effect;however, the performance resulted in a loss of clarity and fine details preservation that rendered the CT images unsuitable. In addressing this, presented an enhanced noise removal techniques namely Morphology-Aware CNN (MA-CNN) to enhance brain CT image of ischemic stroke patients for better segmentation, classification to enhance diagnosis. The MA-CNN performance is studied in term of Peak-signal-to-noise-ratio (PSNR);the overall result attained shows the proposed model exhibit higher PSNR in comparison with existing denoiser methods and assures good performance in maintaining the quality of the image and fine details' preservation. © 2024 IEEE.

关键词： Convolutional neural networks

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Diabetic Retinopathy Detection from Retina Image Using Machine Learning 8

Diabetic Retinopathy Detection from Retina Image Using Machi...

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8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Manasa, G.R. Anchan, Anusha Santhosh, T. Hemashree, H.C. Pinnapati, Surekha Shetty, Suvith Suresh NMAM Institute of Technology NITTE Deemed to be University Nitte Karkala India Bapuji Institute of Engineering & Technolgy Visvesvaraya technological university Davanagere India Bapuji Institute of Engineering & Technology Vivesvaraya technological university Davangere India STJ Institute of Technology Vivesvaraya technological university Ranebennur India

ISBN: (纸本)9798350350593

Diabetic Retinopathy is a severe complication of diabetes mellitus and one of the primary causes of blindness on a global scale. Early detection and timely intervention are crucial for preventing irreversible vision loss in affected individuals. This paper focuses on the development of an efficient Diabetic Retinopathy (DR) detection system utilizing machine learning techniques. Leveraging a dataset of high-resolution retina images, the system employs preprocessing methods such as image enhancement and normalization, followed by feature extraction to identify distinctive patterns within the images. Different machine learning algorithms, such as Support Vector Machine (SVM), logistic regression, random forest, and Naive Bayes (NB), undergo training and optimization to categorize the progression of Diabetic Retinopathy (DR) across its mild to severe stages. Additionally, explainable AI techniques are integrated to provide insights into the decision-making process of the models, enhancing transparency and trust in the system's predictions. Moreover, the proposed DR detection system offers a non-invasive, cost-effective, and scalable solution for early diagnosis, facilitating timely medical intervention and preventing the progression of diabetic retinopathy. The paper's integration with Gardio interface enables seamless output for new images, further enhancing its usability. This paper underscores the potential of machine learning in revolutionizing healthcare by contributing to the field of medical image analysis. It aims to improve diagnostic capabilities and, ultimately, enhance patient outcomes. © 2024 IEEE.

关键词： Support vector machines

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Deep Learning Approach for Automated Skin Disease Diagnosis 1

Deep Learning Approach for Automated Skin Disease Diagnosis

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1st International Conference on Innovations in Communications, Electrical and Computer engineering, ICICEC 2024

作者： Prashanth, B.S. Puneeth, B.H. Manoj Kumar, M.V. Bhavana, R.K. Arshitha, K. Santhosh, S. Puneeth, S.P. Sneha, H.R. Nitte Meenakshi Institute of Technology Department of Information Science & Engineering Karnataka Bangalore560064 India Bapuji Institute of Engineering and Technology Department of Computer Science and Business Systems Karnataka Davanagere577004 India Bapuji Institute of Engineering and Technology Department of Information Science and Engineering Karnataka Davanagere577004 India

ISBN: (纸本)9798350376517

Skin diseases has been a significant challenge in healthcare, necessitating accurate and timely diagnosis for effective treatment. This paper presents research on the use of Convolutional Neural Networks (CNNs) for the categorization of skin illnesses. CNNs are a viable tool for automated skin disease detection since they successfully leverage the deep learning capabilities in image-based tasks. The research begins with a detailed review of existing methodologies and datasets in the domain of skin disease classification. Subsequently, we propose a novel CNN architecture tailored to address the unique challenges posed by dermatological images, such as variability in lesion size, color, and texture. The proposed model is trained and evaluated on a diverse and extensive dataset, ensuring its robustness and generalization capabilities across various skin conditions. The study delves into the interpretability of the CNN model, providing insights into the features learned during the training process. This analysis contributes to the understanding of how deep learning models make decisions in the context of dermatological diagnosis, enhancing the trustworthiness and acceptance of such automated systems in clinical settings. Furthermore, the paper investigates the impact of data augmentation techniques on model performance, addressing the scarcity of labeled dermatological datasets. By increasing the training data, we explore strategies to improve the model's ability to generalize to unseen cases, thereby enhancing its practical utility. Experimental results are presented and compared with state-of-the-art approaches, proving the viability of the suggested CNN architecture. © 2024 IEEE.

关键词： Convolutional neural networks

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Spatial Dynamics for Identification of Individuals through Gait and Other Locomotion Activities

Spatial Dynamics for Identification of Individuals through G...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Anusha, R. Sanshi, Shridhar Bapuji Institute of Engineering and Technology Dept. of Computer Science and Engineering Davanagere India National Institute of Technology Dept. of Computer Science and Engineering Karnataka Surathkal India

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379525

Gait, the pattern of walking, has been extensively studied and various methods have been developed to use it as a biometric for individual recognition. Despite this, the potential to identify individuals through running videos has not been thoroughly explored. The paper introduces a novel method that expands the feature-based approach for identifying individuals based on their running style. This work focuses on extracting the mutual information and location specific metric from the key gait poses of subjects in the testing and training datasets. Later on, the assignment of a testing sample to the training sample is accomplished using the proposed classification method. The experiments are conducted on KTH, OU-ISIR A, and Weizmann database. The efficiency of this method is demonstrated by the obtained experimental results. © 2024 IEEE.

关键词： Feature extraction

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Removing/Minimising Gaussian noise from real time CCTV footages using GAN 1

Removing/Minimising Gaussian noise from real time CCTV foota...

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1st International Conference on Innovations in Communications, Electrical and Computer engineering, ICICEC 2024

作者： Prashanth, B.S. Puneeth, B.H. Manoj Kumar, M.V. Sreenivasa, B.R. Suraj, Adyapadi Hegde, Akshay Prashant Pranava Aithal, K.S. Nitte Meenakshi Institute of Technology Department of Information Science & Engineering Karnataka Bangalore560064 India Bapuji Institute of Engineering and Technology Department of Computer Science and Business Systems Karnataka Davanagere577004 India Bapuji Institute of Engineering and Technology Department of Computer Science and Design Karnataka Davanagere577004 India

ISBN: (纸本)9798350376517

This paper describes a system that uses generative adversarial networks (GANs) to eliminate gaussian noise from CCTV of footages. Unwanted noise like gaussian noise frequently degrades the quality of images and makes them more difficult to interpret. In our approach, a discriminator network is used to direct the training of a generator network, whose job it is to produce denoised images from noisy inputs. This framework includes operations like picture enhancement, noise reduction, and evaluation with metrics like the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR). The resultant denoised images show enhanced visual quality and have potential uses in image analysis and computer vision. © 2024 IEEE.

关键词： Generative adversarial networks

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Degree-Based Entropy Descriptors of Graphenylene Using Topological Indices

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Computer Modeling in engineering & Sciences 2023年第10期137卷 939-964页

作者： M.C.Shanmukha Sokjoon Lee A.Usha K.C.Shilpa Muhammad Azeem Department of Mathematics Bapuji Institute of Engineering&TechnologyDavanagere577004India Department of Computer Engineering(Smart Security) Gachon UniversitySeongnam13120South Korea Department of Mathematics Alliance School of Applied MathematicsAlliance UniversityBangalore562106India Department of Computer Science and Engineering Bapuji Institute of Engineering&TechnologyDavanagere577004India Department of Mathematics Riphah Institute of Computing and Applied SciencesRiphah International UniversityLahore54660Pakistan

Graph theory plays a significant role in the applications of chemistry,pharmacy,communication,maps,and aeronautical *** molecules of chemical compounds are modelled as a graph to study the properties of the *** geometric structure of the compound relates to a few physical properties such as boiling point,enthalpy,π-electron energy,andmolecular *** article aims to determine the practical application of graph theory by solving one of the interdisciplinary problems describing the structures of benzenoid hydrocarbons and *** topological index is an invariant of a molecular graph associated with the chemical structure,which shows the correlation of chemical structures using many physical,chemical properties and biological *** study aims to introduce some novel degree-based entropy descriptors such as ENTSO,ENTGH,ENTHG,ENTSS,ENTNSO,ENTNReZ1,ENTNReZ2 and ENTNSS using the respective topological ***,the above-mentioned entropy measures and physico-chemical properties of benzenoid hydrocarbons are fitted using linear regression models and calculated for graphenylene structure.

关键词： Topological descriptors weighted entropy graphenylene

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An Evaluation of E-Learning and User Satisfaction

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International Journal of Web-Based Learning and Teaching Technologies 2022年第2期17卷 1-11页

作者： Rajasekaran, Vijay Anand Kumar, K.R. Susi, S. Mohan, Y.C. Raju, Muntha Hssain, Mohammed Waheeduddin Vellore Institute of Technology India India Shadan Women’s College of Engineering and Technology India Bapuji Institute of Engineering and Technology India Shadan College of Engineering and Technology India

The qualification between learning substance and site content should be parallel whereby both will impact one another and influence user satisfaction and fulfillment. After the examination has closed, concern emerges on how the elements of e-learning in service, system, and information quality influence the nature of the e-learning framework in keeping fit. Realizing that client is a mainstay of e-learning framework, this exploration expected to see if the user fulfillment has any impact on e-learning framework quality. Framework plan, framework conveyance, and framework result have been utilized as a stage for estimating the accomplishment of the e-learning framework. © 2022 IGI Global. All rights reserved.

关键词： Information analysis

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