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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Thanigaivelu, P.S. Sairam, A. Vijayan, P. Kumar, A. Suresh Mohankumar, N. Ganeshbabu, T.R. Srm Institute Of Science And Technology Department Of Computing Technologies Kattankulathur Tamil Nadu Chennai India Saveetha Institute Of Medical And Technical Sciences Department Of Computer Science And Engineering Saveetha School Of Engineering Tamil Nadu Chennai India Saveetha Engineering College Department Of Artificial Intelligence And Data Science Tamil Nadu Chennai India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department Of Management Studies Tamil Nadu Chennai India Maharashtra Nagpur India Department Of Electronics And Communication Tamil Nadu Namakkal India

ISBN: (纸本)9798350371567

The management of stormwater in urban areas across the globe is becoming an increasingly difficult problem because of the increased risk of floods caused by climate change. Typical drainage methods often turn out to be insufficient, which calls for the development of novel approaches. This study suggests a smart stormwater management system powered by the Internet of Things and aims to boost urban resilience and reduce floods. The system continually monitors critical drainage characteristics, such as water levels, flow rates, and water quality, thanks to a network of Internet of Things sensors. To accurately predict probable flooding occurrences, advanced predictive algorithms examine this data together with incoming meteorological information. The technology performs real-time dynamic management of the drainage network, improving the efficiency of pumps and valves to reduce the likelihood of flooding. When potential flood hazards are identified, those who need to be informed, such as local authorities and citizens, are notified through an early warning system. The findings of this study lead to a more effective allocation of resources, an improvement in water quality, and an increase in the resilience of metropolitan areas to severe weather events. This approach, powered by the Internet of Things, promises a route toward sustainable and flood-resilient communities by transforming urban drainage. © 2024 IEEE.

关键词： Internet of things

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Opto-Video Encryption Based on Logistic Adjusted Sine map in FrFT

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computers, Materials & Continua 2022年第8期72卷 3651-3663页

作者： Osama S.Faragallah Ashraf Afifi Ibrahim F.Elashry Ensherah A.Naeem Heba M.El-Hoseny Ahmed I.Sallam Hala S.El-sayed Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Electrical Engineering Kafrelsheikh UniversityKafrelsheikh 61519Egypt Electrical Department Faculty of Industrial EducationSuez UniversitySuez 43527Egypt Department of Electronics and Electrical Communication Engineering Al-Obour High Institute for Engineering and Technology 3036Egypt Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf 32952Egypt Department of Electrical Engineering Faculty of EngineeringMenoufia UniversityShebin El-Kom 32511Egypt

In the last few years,videos became the most common form of information transmitted over the internet,and a lot of the traffic is confidential and must be protected and delivered safely to its intended *** introduces the challenges of presenting encryption systems that can encode videos securely and efficiently at the same *** paper presents an efficient opto-video encryption system using Logistic Adjusted Sine map(LASM)in the Fractional Fourier Transform(FrFT).In the presented opto-video LASM-based FrFT scheme,the encoded video is split into distinct frames and transformed into optical signals utilizing an optical *** of the developed optical video frames is ciphered by utilizing the LASM in optical FrFT system using two-phase modulation forms on the video frame,the first in the time-domain and the second in the FrFT *** the end,the ciphervideo frame is spotted utilizing a CCD digital camera and transformed into a digital structure that can be managed using a *** test the proposed opto-video LASM-based FrFT scheme using various security *** outcomes demonstrate that the presented scheme can effectively encrypt and decrypt video *** addition,it encrypts videos with a high level of encryption qualitywithout sacrificing its resistance to noise ***,the test outcomes demonstrate that the presented scheme is immune to known attacks.

关键词： Optical encryption FrFT logistic-adjusted sine map

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Human Stress Detection in and through Sleep using Machine Learning

Human Stress Detection in and through Sleep using Machine Le...

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Recent Advances in Electrical, electronics, Ubiquitous communication, and Computational Intelligence (RAEEUCCI), International Conference on

作者： Harshini V Harisudha Kuresan Omkar Kancharlawar V.M. Madhan S. Balamithra Department of Computer Science Engineering Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Tamilnadu India Bachelor of Dental Surgery Sathyabama Dental College and Hospital Chennai India

ISBN: (数字)9798350354539

ISBN: (纸本)9798350354546

The effects of stress on the body and mind may be devastating, and it is a prevalent health problem. The quality of one's sleep is an important health indicator, and stress is a known disruptor of that quality. As a result, gauging a person's stress levels and health risks based on their sleep patterns may be quite informative. One's capacity for making decisions, paying attention, learning, and solving problems are all significantly impacted by stress. Both the disciplines of psychology and computer science have recently dedicated significant resources to the study of stress detection and modelling. Affective states, or the actual experience of having that emotion, are the measuring stick that psychologists use to assess the magnitude of stress. The majority of the progress in stress classification has been made using user-dependent models that cannot generalize to a new user. This results in substantial time investment from new users in training the algorithm to forecast their emotional states. Common mental health issues in children need to be addressed immediately since they may escalate into more serious issues if left untreated. The analysis and diagnosis of medical data are areas where machine learning techniques now excel. Different machine learning algorithms have had their accuracy across the chosen attribute set compared. Data on sleep-related factors, such as heart rate variability and sleep architecture, will be collected by wearable devices and analyzed using machine learning methods such support vector machine (SVM), decision tree (DT), MLP, and Ada Boost classifier. By analyzing a person's sleep patterns with the help of a machine learning model, researchers have shown they can effectively identify signs of stress in people.

关键词： Support vector machines Training Machine learning algorithms Computational modeling Human factors Machine learning Vectors

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Design and Simulation of 2.45 GHz Microstrip Patch Antenna for S-Band Wireless Applications

Design and Simulation of 2.45 GHz Microstrip Patch Antenna f...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Md. Sohel Rana Sheikh Md. Rabiul Islam Sifat Hossain Md. Mostafizur Rahman Department of Electronics and Communication Engineering Khulna University of Engineering and Technology Khulna Bangladesh Department of Computer Science and Engineering Bangladesh University of Professionals Dhaka Bangladesh

In this research paper, discusses the examination, design, and analysis of a rectangular microstrip patch operating at a frequency of 2.45 GHz. The antenna is constructed using a lossy material called Rogers RT5880, with specific characteristics including a dielectric permittivity of 2.2, a thickness of 1.6 mm, and a loss tangent of 0.0001. To design and simulate the antenna, the researchers utilized Studio 2019, a software suite within the computer simulation technology field. Additionally, Origin Software was employed for various plot designs. The simulation results yielded several outcomes, including return loss, voltage standing wave ratio (VSWR), gain, directivity, bandwidth, and efficiency. These parameters were measured and found to have - 46.453 dBi, 1.0096, 5.202 dBi, 5.590 dBi, 54.5 MHz, and values, 93.05% respectively. The study's main objective is to reduce the return loss to a typical VSWR value close to 1. The researchers also aimed to enhance the antenna's gain, directivity, and efficiency.

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Towards Real Time Traffic Analysis with Reduced Image Quality

Towards Real Time Traffic Analysis with Reduced Image Qualit...

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IEEE India Conference (INDICON)

作者： Rohit Mohanraj Erunika Oshani Dayaratna Department of Electronics and Communication Engineering National Institute of Technology Warangal Warangal India Department of Statistics and Computer Science Faculty or science University of peradeniya SriLanka

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

This paper presents a detailed study of two computationally efficient object recognition models-YOLOv5 and Fast R-CNN with a MobileNet backbone-focusing on their performance under different degrees of image / video qualities. Blurring is a common problem in autonomous driving due to motion, weather conditions, and other factors, which can greatly reduce picture quality and, consequently, the accuracy of object recognition. Also, the low-quality images produced by on-car devices suffer when trying to utilize them for advanced detection applications. Utilizing the Zenseact Open Dataset (ZOD), images are generated by varying kernel sizes of Gaussian blur to examine how each model's recognition accuracy is affected by picture degradation. Our study shows key differences in the stability of these models, showing the trade-offs between processing speed and recognition confidence in poor visual settings. The results provide helpful guidance for choosing object recognition systems that keep performance in difficult situations, crucial for the safety and efficiency of driverless cars.

关键词： YOLO Performance evaluation Degradation Training Adaptation models Visualization Accuracy Real-time systems Object recognition Video recording

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Car Damage Identification System Using Convolutional Neural Network 3

Car Damage Identification System Using Convolutional Neural ...

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3rd IEEE International Conference on Industrial electronics: Developments and Applications, ICIDeA 2025

作者： Krishna, Raji Sariki, Tulasi Prasad Sigamsetty, Siri Mahalakshmi Kumar, G. Bharadwaja Jayanthi, D. Asha, A. School of Electrical Engineering Vellore Institute of Technology Chennai Campus Chennai India School of Computer Science Engineering Vellore Institute of Technology Chennai Campus Chennai India Rajalakshmi Engineering College Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331533205

The number of individuals driving has increased daily, leading to a rise in the number of auto insurance claims. The lengthy process of registering, processing, and determining the outcome of each claim includes the service engineer's manual assessment, which serves as the basis for the damage report, and the physical examination carried out by an insurance company surveyor. It would be advantageous for both the organization and the client if this entire procedure were automated. This system utilizes images of the car in a damaged state as input. It then generates pertinent data, including the locations of the damaged sections (front, rear, and sides) and an assessment of the extent of the damage (none, minor, or substantial). This system uses the Convolutional Neural Network (CNN) Model to analyze images automatically for auto insurance, providing the user with information on the location and extent of the vehicle damage. ImageNet weights and the VGG16 architecture are also used here. Transfer learning is used for a variety of tasks, such as finding and classifying different kinds of auto parts and issues. The logistic regression classifier is trained using feature and label extraction methods. © 2025 IEEE.

关键词： Convolutional neural networks

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Query-Based Image Retrieval Using SVM 5th

Query-Based Image Retrieval Using SVM

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5th International Conference on Emerging Technologies in computer engineering: Cognitive Computing and Intelligent IoT, ICETCE 2022

作者： Janu, Neha Gupta, Sunita Nawal, Meenakshi Choudhary, Pooja Department of Computer Science and Engineering Swami Keshvanand Institute of Technology Management and Gramothan Jaipur India Department of Information Technology Swami Keshvanand Institute of Technology Management and Gramothan Jaipur India Department of Electronics and Communication Engineering MNIT Jaipur India Department of Electronics and Communication Engineering Swami Keshvanand Institute of Technology Management and Gramothan Jaipur India

ISBN: (纸本)9783031070112

In today’s world, images are used to extract information about objects in a variety of industries. To retrieve images, many traditional methods have been used. It determines the user’s query interactively by asking the user whether the image is relevant or not. Graphics have become an important part of information processing in this day and age. The image is used in image registration processing to extract information about an item in a variety of fields such as tourism, medical and geological, and weather systems calling. There are numerous approaches that people use to recover images. It determines an individual’s query interactively by asking users whether the image is relevant (similar) or not. In a content-based image retrieval (CBIR) system, effective management of this image database is used to improve the procedure’s performance. The study of the content-based image retrieval (CBIR) technique has grown in importance. As individuals, we have studied and investigated various features in this manner or in combinations. We discovered that image Registration Processing (IRP) is a critical area in the aforementioned industries. Several research papers examining color feature and texture feature extraction concluded that point cloud data structure is best for image registration using the Iterative Closest Point (ICP) algorithm. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Feature extraction

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Using String Data to Estimate the Shading Loss of Solar Power Plants 6

Using String Data to Estimate the Shading Loss of Solar Powe...

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

作者： Tripathi, R.K. Rana, Kuldeep Yadav, Komal Yuvaraj, K. Premananthan, G. Deshmukh, Vishwesh School of Mechanical Engineering Dev Bhoomi Uttarakhand University Dehradun India Invertis University Department of Computer Applications Bareilly India School of Medical & Allied Sciences K. R. Mangalam University Gurugram India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Karpagam College of Engineering Department of Electronics and Communication Engineering Coimbatore India Vishwakarma Institute of Information Technology Electrical Engineering Pune India

ISBN: (纸本)9798350304480

Aside from a greater emphasis on energy rationalization and efficiency, decentralized electricity supply methods are becoming increasingly significant for the world's energy supply, and their role in power consumption is growing. Examples of these sources include solar and wind energy. While installing photovoltaic (PV) systems, it is imperative to be cognizant of the great nation regulatory backdrop because energy projects typically require significant long-term financial investments. In the recent years, there has been a tendency towards the construction of new photovoltaic power plants mostly employing conventional crystalline PV to pinpoint the system's faulty components. This article uses a PV system in Hungary to illustrate the energy correlations of shading. This study's objective is to present a method for classifying the operational features of strings and PV power plants based on monitoring data, allowing for the estimation of the yearly energy loss. Since the solution is simple to adapt to real-time management and monitoring platforms but it makes the internationalization of Photovoltaic system easier, the model's creative novelty is that its application can provide practical assistance for the operator of Photovoltaic systems across the world potentially troublesome strings, enabling a more targeted examination of PV power installations. The model's new practical advantage is that it makes it easy to identify any power losses due to improper operation (e.g., incorrect inverter operation, adverse effects of trees as well as other objects, or improper spacing among both chords of PV power plants) through a more straightforward calculation mechanism. For the safety of the Photovoltaic panels, the later reconstruction of a strands, or even the solution to issues under warranty, early fault detection is crucial. Also, it is possible to identify any unfavorable changes in the fund's economic characteristics by calculating the annual wasted energy from shading.

关键词： Energy generation Photovoltaic Shading loss Solar power plant String data

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Optimising Diabetic Retinopathy Classification using EfficientNetB7

Optimising Diabetic Retinopathy Classification using Efficie...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Ajisha Mathias J. Relin Francis Raj S R Raja R. Santhana Krishnan C. Puvanadevi S. Sujatha Electronics and Communication Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Avadi Chennai India Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Master of Computer Applications Hindustan Institute of Technology and Science Chennai India Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Information Technology Adhiyamaan College of Engineering Hosur India Department of Information Technology Adhiyamaan College of Engineering Hosur India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Diabetic retinopathy (DR) is a significant cause of vision impairment and blindness in individuals with diabetes, necessitating early and accurate detection of its various stages for effective treatment. This study applies EfficientN etB7, an advanced deep learning model, to classify different stages of diabetic retinopathy and compares its performance with other established models such as DenseN et121, Inception V3,and VGG16. The study uses datasets like the Kaggle Diabetic Retinopathy Detection and Messidor datasets, which provide annotated retinal images across multiple stages of DR: mild, moderate, severe, proliferative diabetic retinopathy, and normal. Key preprocessing techniques are employed to enhance the quality of the input data. These include noise reduction, contrast enhancement using CLAHE, and data augmentation to enhance the model's reslience. EfficientNetB7 is selected due to its optimal network depth, width, and resolution balance, which facilitates high accuracy with fewer computational resources. The model's performance is evaluated using metrics including Fl score, accuracy, precision, and recall, which are crucial for assessing the reliability of medical image classification models. Comparative analysis reveals that EfficientN etB7 outperforms DenseNet121, InceptionV3, and VGG16 across all evaluation metrics. Specifically, EfficientN etB7 achieves superior accuracy, precision, recall, and Fl score in classifying the different stages of diabetic retinopathy. These results highlight the effectiveness of EfficientN etB7 in providing reliable and efficient detection of diabetic retinopathy, highlighting its potential for improving clinical outcomes through early and precise diagnosis.

关键词： Measurement Deep learning Diabetic retinopathy Accuracy Computational modeling Visual impairment Data augmentation Data models Robustness Medical diagnostic imaging

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Smart Agriculture: Explainable Deep Learning Approach with Gradient-Weighted Class Activation Mapping

Smart Agriculture: Explainable Deep Learning Approach with G...

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computer, electronics & Electrical engineering & their Applications (IC2E3), International Conference on

作者： Mohammad Khaja Shaik Mudarakola Lakshmi Prasad Y Sowmya Reddy S Asif D Kalpana Pundru Chandra Shaker Reddy Electronics and Communication Engineering St. Ann's College of Engineering and Technology Chirala Andhra Pradesh India Computer Science and Engineering Institute of Aeronautical Engineering Dundigal Telangana India Computer Science and Engineering (AI&ML) CVR College of Engineering Hyderabad India Computer Science and Engineering Koneru Lakshmaiah Education Foundation Bowrampet Hyderabad Telangana India Computer Science and Engineering (Data Science) Malla Reddy Engineering College (Autonomous) Hyderabad Telangana India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

ISBN: (数字)9798350388534

ISBN: (纸本)9798350388541

In light of the pressing need to guarantee food security in the face of threats posed by climate change, an increasing global population together with rising incomes, worsening soil erosion, and dwindling water resources, crop yield forecasting has assumed more significance in recent years. A relatively new area of study, “Explainable AI” seeks to provide context for the findings produced by Deep learning models. Nevertheless, there is a pressing need for more comprehensive evaluations of deep learning models for object classification in images. Due to this, the research suggests two steps for implementing Explainable AI: (1) assessing the deep-learning(DL)-strategies accuracy utilizing evaluation methods, and (2) evaluating the feature recognition skill of an scan when recognized by DL utilizing Grad-CAM for model construal. Compared to EfficientNetV2S and Xception, the results reveal that MobieNet model has lower dependability but higher accuracy. When taking the ratio match rate into account, though, MobileNetV2 achieves the best accuracy. Building intelligent agriculture support systems (with the help of automated fruit-picking robots that discard bad fruits) is one application of the study's findings. Another is the utilization of the Explainable AI model's output to determine the best deep learning model for processing.

关键词： Climate change Deep learning Extended reality Agriculture Food security Smart agriculture Agricultural robots Resource management Crop yield Weather forecasting

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