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Forecasting the Remaining Useful Life of Proton Exchange Membrane Fuel Cells by Utilizing Nonlinear Autoregressive Exogenous Networks Enhanced by Genetic Algorithms

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SSRN 2023年

作者： Shen, Yitong Alzayed, Mohamad Chaoui, Hicham the Department of Electronics Carleton University OttawaONK1S 5B6 Canada The Department of Electrical and Computer Engineering Texas Tech University LubbockTX79409 United States

The Proton Exchange Membrane Fuel Cell (PEMFC), known for its efficient energy conversion, minimal electrolyte leakage, and low operating temperature, shows great potential as a clean energy source. However, its lifespan is limited due to degradation during normal operation, which, if uncontrolled, can result in dangerous failures such as explosions. Hence, accurately estimating the remaining useful life (RUL) is vital. In this research, a combined prediction method using genetic algorithms (GA) and nonlinear autoregressive neural networks (NARX) with external inputs is proposed. The method's performance was trained and validated using the 2014 IEEE PHM Data Challenge dataset, and it was compared to two commonly used artificial neural network algorithms: GA-based backpropagation neural network (GA-bPNN) and GA-based time delay neural network (GA-TDNN). The findings demonstrate that the proposed approach surpasses the other two artificial neural network algorithms in terms of prediction accuracy. © 2023, The Authors. All rights reserved.

关键词： Genetic algorithms

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IoT-Genomics Analysis for Personal Health with Cybergenomics and Cloud technology

IoT-Genomics Analysis for Personal Health with Cybergenomics...

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International Conference on Communications and Signal Processing

作者： T. Prabakaran A. Ramesh babu S. Hemalatha Jayabharathi Ramasamy Rajesh Kambattan Kovarasan b Elango Department of Computer Science and Engineering Joginpally B.R. Engineering College Hyderabad Telangana India School of Computer Applications Vidya Vikas Institute of Engineering And Technology Mysore Karnataka India Department of Artificial Intelligence and Data Science Tagore Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Adhiparasakthi Engineering College Melmaruvathur Tamil Nadu India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

Recent innovations at the convergence of genomics and the Internet of Things (IoT) have opened the way for an innovative strategy for administering individualized health care. This research study combines the potential of wearable health monitoring technologies with genetic insights to analyze the personal healthcare. A comprehensive knowledge of health dynamics may be achieved by seamlessly integrating real-time health data collected from IoT-enabled wearables with individual genetic profiles. Using algorithms for machine learning, connections between genetic differences and instantaneous health responses may be uncovered, paving the way for the development of individualized therapies and lifestyle recommendations. The suggested framework allows people to improve their health and gives medical professionals access to data-driven insights that can be used for the early diagnosis of diseases and the development of individualized treatment plans. The work that has been done sheds light on the revolutionary potential of merging genetics with the Internet of Things to establish a new paradigm of preventative and individualized medical treatment.

关键词： technological innovation Merging Medical treatment Genomics Real-time systems Internet of Things bioinformatics

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blockchain based Identity Verification Model

Blockchain Based Identity Verification Model

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2019 International Conference on Vision Towards Emerging Trends in Communication and Networking, ViTECoN 2019

作者： Malik, Gunit Parasrampuria, Kshitij Reddy, Sai Prasanth Shah, Seema NMIMS MPSTME Mumbai Campus B.Tech Computer Engineering India

ISBN: (纸本)9781538693537

blockchain is peer-to-peer decentralized system that provides security, reliability, authenticity, immutability and transparency. It is an expandable list of records called blocks which are linked using cryptography. blockchain by design, is resilient to modifications, it consists of an open distributed ledger that records all the transactions made between two parties which cannot be forged. This paper provides a unique model for document verification with use case of the government. Currently all issuing authorities are separate cells providing identity documents to a citizen. Due to this cellular nature document verification is a tedious and long process. The problem of falsifying documents is another issue within government bodies. We discuss the use of blockchain to improve efficiency and security. The purpose of this project is to develop a private permissioned blockchain network where an individuals official documents can be shared by government bodies, organizations and educational institutions. Using the blockchain technology, our platform achieves a decentralized system to share authenticated government documents between government bodies and private organizations without the need of a certain level of human intervention. With the concluding facts of this paper, it is observed that the process of document verification and issuing process's efficiency is drastically improved with our blockchain model. Along with this we reap benefits of security, reliability and transparency. © 2019 IEEE.

关键词： Smart contract

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Jellyfish Search Optimizer with Deep Learning Driven Cervical Cancer Recognition and Classification Model on biomedical Images

Jellyfish Search Optimizer with Deep Learning Driven Cervica...

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International Conference on Advances in Information technology (ICAIT)

作者： M. Suresh Anand S. Venkatramulu Sesha Vidhya S W T Chembian Yashaswini b M Magesh Kumar b Department of Computing Technologies School of Computing SRM Institute of Science & Technology Kattankulathur Department of CSE kakatiya Institute of Technology and science Warangal Department of ECE RMK College of Engineering and Technology Department of CSE Vel Tech High Tech Dr Rangarajan Dr Sakuntala Engineering College Morai Avadi Electronics & Communication Engineering JSS Academy of Technical Education JSSATE-B Campus Bengaluru Karnataka Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam Erode

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

The emergence of Cervical Cancer (CC) Recognition and Classification Model for biomedical Images is a main advancement in fields of medical imaging and diagnostics. Leveraging state-of-the-art image processing and machine learning algorithms, this model can accurately detect and classify CC-related abnormalities in medical images, namely colposcopy or Pap smear images. by providing automated and precise assessments, the model empowers medical experts to make timely and informed decisions, which leads to earlier interference and potential life-saving treatment. With its potential to improve diagnostic accuracy and reduce human error, the model holds great promise in the fight against CC, especially in resource-constrained healthcare settings where medical practitioners may be limited. This study presents a Jellyfish Search Optimizer with Deep Learning Driven CC Recognition and Classification (JSODL-CCRC) model on biomedical Images. The JSODL-CCRC technique is mainly developed to investigate the medical images for detection and classification of CC. The JSODL-CCRC approach comprises median filtering (MF) for noise reduction, followed by feature extraction utilizing Inceptionv3 to derive complex image patterns. besides, the JSO based hyperparameter selection process is performed for the Inception v3 model. At last, Extreme Learning Machine (ELM) algorithm is applied to categorize the images which validates efficacy in handling complex biomedical image data. The experimental results stated that the JSODL-CCRC model enables effective detection results over other models.

关键词： Deep learning Image recognition Jellyfish biological system modeling Noise reduction Medical services Feature extraction Medical diagnostic imaging Cervical cancer Tuning

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Challenges in Cyber Physical Social Systems and Internet of Things

Challenges in Cyber Physical Social Systems and Internet of ...

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Smart electronics and Communication (ICOSEC), International Conference on

作者： S. Revathi J. Raja M Mohanraj K. Malathi balasubbareddy Mallala R.G. Vidhya Department of Computer Science Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Business Systems Sri Eshwar college of engineering Coimbatore India Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Chennai India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Hyderabad Telangana India Department of ECE HKBK College of Engineering Bangalore India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

This research analyses the complex dynamics of Cyber-Physical-Social Systems (CPSS), encompassing cyber-physical systems, cybersecurity, the Internet of Things (IoT), and social media. by exploring the interactions among these elements, this study analyses the vulnerabilities, risks, and potential benefits within the CPSS framework. This research study analyses the evolving landscape of cybersecurity by considering various aspects such as data integrity, privacy in the IoT era, and the influence of social media on user behaviour and influence dynamics. The study also introduces innovative strategies to enhance CPSS resilience, including methods to mitigate cyber threats and enhance overall system integrity. Additionally, we investigate how social media shapes CPSS behaviours, offering valuable insights into influence dynamics. This research advances the comprehensive understanding of CPSS to support future studies and advancements in the protection and improvement of interconnected systems.

关键词： Data privacy Social networking (online) Shape Media Real-time systems Internet of Things Protection Predictive analytics Monitoring Resilience

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Predicting the Quantity of Municipal Solid Waste using XGboost Model 3

Predicting the Quantity of Municipal Solid Waste using XGBoo...

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3rd International Conference on Inventive Research in Computing Applications, ICIRCA 2021

作者： Jayaraman, Vaishnavi Parthasarathy, Saravanan Lakshminarayanan, Arun Raj Singh, Hiran Kumar B.S.Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9780738146270

The quantity of Municipal Solid Waste (MSW) gets intensified, based on various factors such as population growth, monetary status and consumption patterns. The insufficiency of elementary trash data is a critical problem for managing the MSW. In this study, the goal is to forecast the MSW generation of Northern Ireland. A precise model was developed to estimate the total amount of waste produced for every quarter of the year, by employing the Machine Learning techniques. The seasonal ARIMA (s-ARIMA) and Extreme Gradient boosting (XGboost) models were employed to estimate the amount of waste produced. On comparing both the models, XGboost performed better. Thus, the parameters of the XGboost were tuned to yield the optimal outcome. The XGboost with the tuned hyperparameters achieved an optimum result with the higher coefficient of determination (R2) value as 0.5325 and lower RMSE value of 13215.97. The prediction of the MSW weight would help the decision-makers in treating and disposing solid waste appropriately. © 2021 IEEE.

关键词： Decision making

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Ambient Parametric Monitoring of Farms Using Embedded IoT LoRa

Ambient Parametric Monitoring of Farms Using Embedded IoT Lo...

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2019 IEEE bombay Section Signature Conference, IbSSC 2019

作者： Raj, Tejaswi Johny, Thoms A Khetawat, Suyash Rajeshwari, b. Prasad, Sathya PES University B.Tech Department of Electronics Communication Engineering Bengaluru560085 India

ISBN: (纸本)9781538674017

This paper presents the development of a cost effective, self-sustainable and solar rechargeable smart farming product based on embedded IoT and providing end to end solution using the cutting edge LoRa technology. It portrays the innovative methods developed to monitor ambient parameters using the LoRa LPWAN. The product leverages the capabilities of the Arduino Mini MCU capitalizing the low power and ultra-low power optimization techniques to enhance self-sustainability of the device. It also provides an insight of the alternative, creative approaches developed for some soil sensors (NPK and pH) to make the product cost-effective. These real time ambient data parametric trends can be monitored over the cloud at user's convenience on an android application. © 2019 IEEE.

关键词： Cost effectiveness

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Face Spoof Detection Using Gray Level Co-Occurrence Matrix and Discrete Wavelet Transform Feature Extractor

Face Spoof Detection Using Gray Level Co-Occurrence Matrix a...

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International Conference on Computing Sciences (ICCS)

作者： Amal Mathew Kaushik Daiv Polkumpally Rohan Goud Piyush Talreja Sai Sanjana Reddy Vatte B.tech Computer Science Ms Ramaiah University of Applied Sciences Bangalore Karnataka India B.E. Computer Science MIT College of Engineering Pune Maharashtra India B.E Computer Science Chaitanya Bharathi Institute of Technology Hyderabad India B.E Computer Science Maharashtra Institute of Technology Pune Maharashtra India

ISBN: (纸本)9781665494465

Face identification using ML (machine learning) is well-known. Attendance structures may benefit from this method. Using this method, you may achieve the desired area, as well as beneficial attributes and a dataset, by preparing two sets of data again for test and training phases. To distinguish between a testing set and a test sets, a photograph is used as a testing set. An ensemble classification method is used to sort the test images into categories like “identified” and “unidentified.” This model can't provide reliable findings since it simply divides data into two categories. The development of GLCM was motivated by the need to use texture properties to identify faces. The existence of the query picture is noted once face detection has taken place. In simulation findings, the new model outperforms the baseline models in terms of accuracy. Keywords—Ensemble classifier, GLCM, Face Spoof, SVM, DWT

关键词： Training Image segmentation Face recognition Training data Machine learning Transforms Feature extraction

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Cloud Computing-Enhanced and Raspberry Pi-Powered Solution for Remote Ear, Nose, and Throat Consultations with AI-Driven Diagnostics

Cloud Computing-Enhanced and Raspberry Pi-Powered Solution f...

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Emerging technology (INCET), International Conference for

作者： Giriprasad. S T. Karpagam V. Nalini Nilamadhab Mishra N. Mohankumar N. Malathi Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science R.M.K College of Engineering and Technology Chennai Tamil Nadu India Department of Mathematics Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College Chennai Tamil Nadu India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India Department of Electronics and Communication Engineering B.V. Raju Institute of Technology Telangana India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

The cutting-edge telemedicine program presents a game-changing alternative for patients seeking ENT (ear, nose, and throat) consultations from a distant location. Utilizing the capabilities of Raspberry Pi devices, powerful artificial intelligence, and cloud computing, the system allows otoscopic tests to be performed without interruption while securely transmitting data to the cloud. On a web-based platform, interactive patient-professional consultations are made possible via real-time communication, which is made possible by WebRTC. Cloud services are very important because of their ability to provide scalable storage as well as AI-driven diagnostics for accurate evaluations. The system complies with the requirements for healthcare compliance, emphasizes security and privacy, and features encryption from beginning to finish. This integrated approach promises scalability, reliability, and advanced diagnostic capabilities, and it reshapes the landscape of remote ENT care with accessible and technologically advanced healthcare solutions. With a user-friendly interface for patients and a cloud-hosted application for healthcare professionals, this integrated approach provides patients with advanced healthcare solutions.

关键词： Cloud computing Privacy Telemedicine Scalability Nose Medical services Ear

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Innovative techniques in the Construction of Very Large Scale Integration Circuits for Low-Power and High-Performance Circuits

Innovative Techniques in the Construction of Very Large Scal...

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Communications and Information technologies (GCCIT), Global Conference on

作者： V. Saravanan G. Sasikala P. Gomathi D. Kirubakaran A. Rajalingam Jubert R. Pagunuran Department of Nano Electronics Materials and Sensors Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Department of ECE V.S.B. Engineering College Department of Computer Science and Engineering R.M.K Engineering College Engineering Department College of Engineering and Technology University of Technology and Applied Sciences Shinas Sultanate of Oman

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

Portable electronic gadgets and high-performance computing systems have increased demand for low-power, high-performance integrated circuits (ICs). VLSI technology, which creates integrated circuits with dozens to millions of transistors on a chip, is leading these efforts. This research investigates low-power, high-performance VLSI circuit construction methods. We start with VLSI design fundamentals and power management and performance optimization concerns. The constraints of traditional VLSI design methods in satisfying current electronics' strict requirements are examined. VLSI design methodology development and evaluation are the focus of this research. Advanced power optimization methods include MTCMOS, adaptive body biasing, and power gating. We also study clock tree improvement, signal integrity enhancement, and new materials and device topologies for high performance. In addition, we study how machine learning techniques automate and optimize circuit placement, routing, and timing in VLSI design. AI handles the complexity of current VLSI designs and finds optimal design parameters for minimal power and high performance. Scaling VLSI circuits to newer technology nodes raises process variance, leakage current, and thermal management difficulties, which the research tackles. These issues can be overcome with new ways to keep VLSI circuits scaling while preserving performance and power efficiency. Finally, case studies and experimental findings show that the offered methods work in real-world applications. These findings demonstrate significant power usage and performance benefits over typical VLSI design. In conclusion, our research advances VLSI technology by developing low-power, high-performance integrated circuits. The proposed VLSI design methods and solutions could lead to more efficient and powerful electronic systems.

关键词： Performance evaluation Design methodology Machine learning Very large scale integration Thermal management Routing Topology Timing Leakage currents Signal integrity

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