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Integrated Circuits and Communication systems (ICICACS), IEEE International Conference on

作者： B. Seetha Lakshmi Vijay Anand R Habeeb Omotunde M. Birundadevi Mahalakshmi S Madala Guru Brahmam Department of Artificial Intelligence and Data Science Sri Shanmugha College of Engineering and Technology Namakkal Tamilnadu India School of Computer Science Engineering & Information Systems Vellore Institute of Technology Vellore India Department of Information Systems College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University Riyadh Saudi Arabia Department of Maruthuvam Sri Sairam Siddha Medical College and Research Centre Chennai Department of Computer Science and Design R.M.K. Engineering College Chennai Tamilnadu India

Deep Learning was utilized to autonomously classify renal abnormalities, which improved the diagnostic accuracy for individuals with chronic kidney disease (CKD), according to this study. During training on a meticulously selected dataset, the proposed model outperformed the most recent approaches. By utilizing medical imaging data— specifically quantitative and qualitative data—to differentiate between discrete kidney abnormalities, the model demonstrated exceptional levels of accuracy, precision, and recall. Furthermore, apart from effectively resolving ethical considerations pertaining to data privacy, this innovative methodology has the potential to fundamentally transform the existing landscape of chronic kidney disease (CKD) diagnosis and treatment. The results demonstrate that the model effectively manages intricate renal disorders, signifying a noteworthy progression in the field of medical image processing. This research paves the way for additional fruitful lines of inquiry, which ought to enhance nephrology diagnostics and patient care.

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Narrow-Linewidth and Frequency-Agile FMCW Laser on the Si3N4-on-SOI Photonic Platform

Narrow-Linewidth and Frequency-Agile FMCW Laser on the Si3N4...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Chuxin Liu Xinhang Li Yuyao Guo Liangjun Lu Jianping Chen Linjie Zhou Department of Electronic Engineering State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab of Navigation and Location Services Shanghai Institute for Advanced Communication and Data Science Shanghai Jiao Tong University Shanghai China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu China

We demonstrate a hybrid-integrated self-injection-locked laser on the Si 3 N 4 -on-SOI platform to generate FMCW signal with 9 kHz linewidth, 2 MHz repetition rate, and high linearity, supporting FMCW-LiDAR with refre... 详细信息

ISBN: (纸本)9798350369311

关键词： Linearity Electro-optical waveguides Photonics

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Hybrid Optimization Algorithm to Mitigate Phishing URL Attacks In Smart Cities

Hybrid Optimization Algorithm to Mitigate Phishing URL Attac...

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Innovative Practices in Technology and Management (ICIPTM), International Conference on

作者： G. Sujatha Manimuthu Ayyannan S. Gayathri Priya V. Arun A. N. Arularasan M Jogendra Kumar Department of Electronics and Communication Sri Venkateswara College of Engineering Tirupati Andhra Pradesh Senior Software Engineering Manager AI/ML and Data Expert San Francisco Bay Area California USA Department of Electronics and Communication R. M. D. Engineering College Kavaraipettai Tamil Nadu Department of Computer Science &Engineering Madanapalle Institute of Technology Madanapalle Andhra Pradesh Department of Artificial Intelligence &Data Science Panimalar Engineering College Chennai Tamil Nadu Department of Computer Science Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh

To improve the detection of phishing sites, this proposal introduces feature weights for intelligent phishing site detection based on hybrid bio-inspired algorithms. The proposed approach uses Gray Wolf Optimization (GWO) and Firefly Algorithm (FF), which examines a wide range of website attributes, to more precisely identify phishing sites. Then employs Artificial Neural Network (ANN) to classify different website elements according to the importance of each component in differentiating between legitimate and phishing websites using bioinspired-based recommended site feature weights. According to experimental findings, the suggested hybrid bioinspired-based feature weighting greatly improved classification accuracy, true positive (TPR), and negative rates (TNR), as well as precision and F1 score. Phishing is an online crime that entails the gathering of private information like passwords, account numbers, and credit card numbers. Attackers use alluring URLs to entice phony website visitors. Recently, Artificial Intelligence-based phishing detection has seen some success, and in this study, ANN was used to detect phishing. This ANN classifier may make phishing websites simpler to spot. However, it has been shown that the effectiveness of detection can be increased by applying a genetic algorithm to improve feature selection.

关键词： Uniform resource locators Smart cities Phishing Biological system modeling Artificial neural networks Feature extraction Classification algorithms

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Incorporating Transfer Learning in Engine Health Assessment for Accurate Remaining Useful Life Prediction

Incorporating Transfer Learning in Engine Health Assessment ...

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

作者： C. Karpagavalli Nelson A R. Raja Saranya E Manzoore Elahi M. Soudagar Christu Paul Ramaian Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajapalayam Tamil Nadu India Department of Mathematics Panimalar Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering Pandian Saraswathi Yadav Engineering College Sivagangai Tamil Nadu India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Automobile Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798350396157

ISBN: (纸本)9798350396164

Aircraft engines are critical, therefore any move to make them safer, more reliable, and fuel-efficient is highly encouraged. To solve the challenges of flight safety and maintenance expense during aircraft engine operation, a prognostics and health management system is deployed. This system is primarily concerned with flaw detection, health evaluation, and life prediction. Making decisions about aircraft engine operation and maintenance is strongly reliant on estimating the remaining useful life (RUL). This study provides an ensemble transfer learning model for RUL prediction using the C-MAPSS turbofan engine dataset. Pre-processing procedures such as filtering and normalization are used to improve the dataset. The proposed ensemble model for RUL prediction is then trained on the processed data. Some of the well-known transfer learning models that are compared include Xception, ResNet-50, and VGG-16. The ensemble model outperforms the other models, with the highest R2 value of 0.9901. Furthermore, the ensemble model is very effective; it can predict RUL in just 37 seconds, which suggests that it could be useful for keeping an eye on engine health and making decisions about aircraft.

关键词： Training Accuracy Atmospheric modeling Computational modeling Transfer learning Predictive models Maintenance

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Cloud Computing based Smart Traffic Management System with Priority Switching for Health Care Services

Cloud Computing based Smart Traffic Management System with P...

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Applied Artificial Intelligence and Computing (ICAAIC), International Conference on

作者： X.S. Asha Shiny D. Ravikumar A. Chinnasamy S. Hemavathi Department of Computer Science and Engineering Nalla Malla Reddy Engineering College (Autonomous Institution) Hyderabad Telangana India Department of Electronics and Communication Engineering Kings Engineering College Chennai Tamil Nadu India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science Engineering Sri Sairam Engineering College Chennai Tamil Nadu India

Regular cities can be transformed into intelligent structures by leveraging information and communication technologies. Innovative city development could be significantly impacted by the Internet of Things paradigm, commonly called cloud computing. All urban locations have a lot of traffic. In this study, Internet-of- Things (IoT) based system is proposed for health care services to organize and to establish the traffic signaling and pick up a route under which road congestion can be administrated. Vehicle accident contraventions are identified by the traffic officers using an online service that is hierarchically carefully monitored or constrained. However, the suggested approach is generic and may be utilized in any major metropolis without losing its generality. Traffic lights linked to cameras in metropolitan areas can be upgraded by connecting to IoT. During a pandemic, this approach is precious. The police can easily regulate traffic from their homes using their cell phones and identify defaulters. The suggested technique aids in the separation of ambulance and rescue engines from ordinary traffic.

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An Improved Differential Evolution Algorithm for Parameter Extraction of Photovoltaic Models

An Improved Differential Evolution Algorithm for Parameter E...

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： Jai Keerthy Chowlur Revanna Dilli. Mahitha Kalakonda Jyothsna A. Mahendar Manjula B. M Information Systems Engineering and Management Harrisburg University of Science and Technology Harrisburg USA Malla Reddy Engineering College for Women Autonomous Coimbatore India Department of Electronics and Communication Engineering Vaageshwari College of Engineering Karimnagar India Department of Comuter Science and Engineering (Data Science) CMR Technical Campus Hyderabad India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

In photovoltaic systems, extracting parameters from generated current-voltage data is critical for simulating, controlling, and optimizing their performance. While there are several strategies for accomplishing this task, but they all have limitations. This work provides an improved adaptive differential evolution approach for solar system parameter extraction. A crossover rate sorting mechanism is developed in this system that allocates different crossover rates to individuals based on their fitness, allowing better-performing individuals to have a greater impact on the following generation. A Pelican optimization strategy used to speed up convergence and balance exploration and exploitation. The performance of the proposed method is validated by extracting characteristics from several solar models, including single diode, double diode, and photovoltaic modules. As a result, the proposed method is a viable and efficient option for parameter extraction in photovoltaic models.

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A study on the deviations in performance of FNNs and CNNs in the realm of grayscale adversarial images

arXiv

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arXiv 2022年

作者： Nagabushanam, Durga Shree Mathew, Steve D.A. Chowdhary, Chiranji Lal Department of Software and Systems Engineering School of Information Technology and Engineering Vellore Institute of Technology Vellore632014 India Data Science and Applications Indian Institute of Technology Madras Chennai600036 India

Neural Networks are prone to having lesser accuracy in the classification of images with noise perturbation. Convolutional Neural Networks, CNNs are known for their unparalleled accuracy in the classification of benign images. But our study shows that they are extremely vulnerable to noise addition while Feed-forward Neural Networks, FNNs show very less correspondence with noise perturbation, maintaining their accuracy almost undisturbed. FNNs are observed to be better at classifying noise-intensive, single-channeled images that are just sheer noise to human vision. In our study, we have used the hand-written digits dataset, MNIST with the following architectures: FNNs with 1 and 2 hidden layers and CNNs with 3, 4, 6 and 8 convolutions and analyzed their accuracies. FNNs stand out to show that irrespective of the intensity of noise, they have a classification accuracy of more than 85%. In our analysis of CNNs with this data, the deceleration of classification accuracy of CNN with 8 convolutions was half of that of the rest of the CNNs. Correlation analysis and mathematical modelling of the accuracy trends act as roadmaps to these conclusions. © 2022, CC BY.

关键词： Convolutional neural networks

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SVM-based Predictive Modeling of Drowsiness in Hospital Staff for Occupational Safety Solution via IoT Infrastructure

SVM-based Predictive Modeling of Drowsiness in Hospital Staf...

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Computer, Communication and Control (IC4), International Conference on

作者： B. Jai Ganesh P. Vijayan V. Vaidehi S. Murugan R. Meenakshi M. Rajmohan Department of Electronics and Communication Engineering R.M.D. Engineering College Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Saveetha Engineering College Chennai Tamil Nadu India Department of Computer Science Vinayaka Mission’s Kirupananda Variyar Arts and Science College Vinayaka Mission’s Research Foundation (DU) Salem Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

This research proposes an efficient approach to improve hospital worker safety on the job by detecting and preventing drowsiness. The system incorporates data from hospital-deployed Internet of Things (IoT) infrastructure and using Support Vector Machines (SVM) as its primary predictive modeling tool. data pertaining to ambient factors, work habits, and physiological indications of employees are collected in real-time by the IoT infrastructure. To identify patterns linked to sleepiness, the SVM model is trained on an extensive dataset. The next step is to implement the model in a healthcare setting so that it can detect signs of sleepiness and act accordingly in a timely manner. With the help of the IoT the predictive model can be dynamically adjusted and monitored in real-time, making it useful in a wide range of situations. The recommended approach is to enhance workplace safety by reducing the likelihood of accidents caused by sleepiness among healthcare workers. The SVM-based prediction model can correctly detect and forecast cases of sleepiness, the research subjected it to extensive testing and validation. This system's adoption helps with the creation of data-driven, proactive methods to improve hospital occupational safety.

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DC Voltage Prediction based on CNN with Physics Information Embedding for MVDC Distribution systems

DC Voltage Prediction based on CNN with Physics Information ...

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International Conference on Power Electronics and Motion Control (IPEMC)

作者： Pingyang Sun Rongcheng Wu Hongyi Wang Zhiwei Shen Gen Li Muhammad Khalid Georgios Konstantinou School of Electrical Engineering and Telecommunications UNSW Sydney Australia Data Science Institute University of Technology Sydney Australia Department of AAU Energy Alborg University Denmark Department of Engineering Technology and Didactics Electric Energy DTU Denmark Electrical Engineering Department & Interdisciplinary Research Center for Sustainable Energy Systems King Fahd University of Petroleum and Minerals Saudi Arabia

ISBN: (数字)9798350351330

ISBN: (纸本)9798350351347

Conventional neural networks (NNs), though efficient in rapid dc voltage calculations for medium-voltage direct current (MVDC) distribution systems with diverse converter control schemes, face accuracy challenges with untrained system parameter distributions and topology structures. This paper proposes a physics-embedded convolutional NN (PECNN) to address this issue. The PECNN is enhanced with two additional layers, including a multi-channel combination layer and a physics operation layer. The former channel combination layer strengthens the model feature extraction capability by converting all input channels constituted by initial data matrix into dc voltage, current and line conductance matrix channels. The latter physics operation layer reformulates the combined input channels by physical connections in MVDC systems. The new layers enhance the prediction accuracy and allow generalization of the model. Three MVDC distribution networks with different dc voltage levels and network layouts are used to verify the superiority of proposed PECNN compared to other NNs in different topologies.

关键词： Accuracy Network topology Artificial neural networks Medium voltage Distribution networks Feature extraction Topology

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Digital Innovations Development: Causal factors and relations - A Case Study of Montenegro 28

Digital Innovations Development: Causal factors and relation...

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2024 28th International Conference on Information Technology, IT 2024

作者： Ognjanovic, Ivana Lakovic, Luka Sendelj, Ramo Bokor, Laszlo Kara, Peter A. Reich, Christoph Madan, Manav Roganovic, Milovan Zouilas, Emmanouil Mantas, John Erakovic, Jevto Radusinovic, Tanja Rakocevic, Nada University of Donja Gorica Faculty for Information Systems and Technologies Podgorica81000 Montenegro University of Donja Gorica Humanistic Studies Podgorica81000 Montenegro Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Networked Systems and Services Budapest1111 Hungary Furtwangen University Institute of Data Science Cloud Computing and It Security Furtwangen78120 Germany Clinic for Neurology Clinical Centre of Montenegro Podgorica81000 Montenegro National and Kapodistrian University of Athens Faculty of Nursing School of Health Sciences Goudi11527 Greece Chamber of Economy of Montenegro Podgorica81000 Montenegro

ISBN: (纸本)9798350369618

In order for economies to expand and develop globally, innovation is essential. A popular statistic for evaluating innovation between countries is the Global Innovation Index (GII), which provides a thorough analysis of the state of innovation worldwide. In this context, this paper aims to contribute to the literature by examining the multi-factoral effects of the innovation ecosystem on innovation development (internal causal effects), and the impact of global trends and crises on the innovation ecosystem (external causal effects) in Montenegro. The resources provided in this paper offer valuable insights into the innovation landscape in Montenegro and the Western Balkans, as well as the impact of globalization, global trends, and crises on innovation in the region. The paper also discusses the function of innovation hubs such as the DigNest project, in promoting cooperation between academic institutions, government agencies and corporations, and supporting the exchange of knowledge and fresh concepts. © 2024 IEEE.

关键词： Ecosystems

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