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Novel machine learning investigation for Buongiorno fluidic model with Sutterby nanomaterial

Tribology International 2024年 199卷

作者： Khan, Maryam Pervaiz Chang, Chuan-Yu Raja, Muhammad Asif Zahoor Shoaib, Muhammad Department of Computer Science and Information Engineering Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology Yunlin 64002 Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin 64002 Taiwan Department of Data Science and AI Applications Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology Yunlin 64002 Taiwan AI center Yuan Ze University Taoyuan320 Taiwan

The nanomaterials are frequently employed in a variety of heat transfer applications arising in energy generation, engine cooling, extrusion procedures, heat exchanger, thermos-chemical systems, manufacturing structures, powered plants etc. Experts and researchers in these fields often encounter such materials, leading to discernible impacts on velocity, temperature and concentration profiles. The objective of this study is to represent the mathematical structures for Sutterby nanomaterial fluidic model involving porous medium BFM-SNM using nonlinear autoregressive exogeneous networks backpropagated with Levenberg-Marquardt technique (NARX-LMT). The mathematical design of the model is originally presented by PDEs, which are converted into ODEs system through suitable modifications with alternative transformation incorporating numbers, i.e., Deborah, Prandtl and Reynold, as well as parameters, i.e., magnetic thermophoresis, radiation and temperature. The synthetic data is produced numerically by simulating Adams numerical method for BFM-SNM and the supervised computing paradigm of NARX-LMT is applied to the obtained datasets, and the results of NARX-LMT consistently align with numerical observations for each variant of the presented model, exhibiting negligible errors. The NARX-LMT performance on exhaustive experimentation is effectively illustrated through iterative convergence curves on mean squared error, control metrics of the optimization, distribution of error on histograms and regression outputs for Sutterby nanomaterial fluidic model. © 2024 Elsevier Ltd

关键词： Ordinary differential equations

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Ensuring Web Application Security: An OWASP Driven Development Methodology

Ensuring Web Application Security: An OWASP Driven Developme...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Sakthivel, M. Sivanantham, S. Bharathiraja, N. Bala Krishna, N. Kamalraj, R. Kumar, V. Saravana Sri Shanmugha College of Engineering and Technology Department of Artificial Intelligence and Data Science Salem Sankari India Saveetha Institute of Medical and Technical Sciences Department of Cse Saveetha School of Engineering Chennai India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Biomedical Engineering Chennai India Mohan Babu University School of Computing Department of Ai&ml Andhra Pradesh Tirupati India University School of Cs and It Jayanagar Bangalore India Sreenidhi Institute of Science and Technology Department of It Hyderabad India

ISBN: (纸本)9798350359688

Website security is a big threat for both large companies and individual developers, the more advanced the technology employed, the more challenging it is to build secure website development procedures. Hackers can easily access vulnerabilities that were not patched during development and that were thereafter distributed. This could result in significant financial losses for the business or the person. Users who visit vulnerable websites may be subject to XSS attacks that could compromise their systems, or an unsecure database configuration may result in a potential data leak, in which case the password of every user who has registered on the website is compromised. Users who use the same password on numerous websites run the danger of identity theft. This study offers the most common vulnerabilities that should be fixed in any application, giving developers the crucial place to start. The overwhelming number of vulnerabilities in each application that is being developed served as the impetus for this project. Every developer, regardless of expertise level, needs a place to start in order to fix any vulnerabilities that may have appeared in their program. The goal of this project is to create a safe online application in accordance with the recommendations of the Open Web Application Security Project (OWSAP). This demonstrates how web application vulnerabilities can be mitigated through coding changes, configuration adjustments, and patch applications. The top ten vulnerabilities according to OWASP are SQL injections, faulty authentication, sensitive data exposure, faulty access control, and Xml external entities. The web application's security has been examined and shown to have defenses in place for those vulnerabilities. © 2024 IEEE.

关键词： Information leakage

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Real-Time Gesture Recognition for Volume Control Using Python and ml Techniques

Real-Time Gesture Recognition for Volume Control Using Pytho...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Syed Musthafa Lavanya Murugan M. Mutharasu T. Vadivel Fathima H M. Leelavathi Department of Information Technology M.Kumarasamy College of Engineering Karur Tamil Nadu India Computer Science American International University Kuwait Department of Computer Science and Engineering (Cyber Security) Madanapalle Institute of Technology &Science Madanapalle Andhra Pradesh Department of Computer Applications KSR College of Arts and Science Tiruchengode Tamil Nadu Department of AI & ML Sree Saraswathi Thyagaraja College Pollachi Tamil Nadu

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

This research study proposes a gesture-based volume control system that combines computer vision and machine learning to improve user interaction with audio devices. By applying the OpenCV library for real-time video processing and MediaPipe's hand-tracking module, the system interprets hand gestures to control the volume. This system measures the distance between the index finger and thumb. Using this distance, the system adjusts the system's audio volume by using the Pycaw library. The purpose is to have a touchless interface wherein the user can control audio levels by simple hand movements to enhance accessibility and usability across various environments. This is an promising alternative to traditional input methods as the demand for more natural human-computer interaction grows, this gesture-based approach represents a leveraging computer vision and machine learning technique, which accurately detects gestures in volume control. The goal of this project is to design a touchless volume control system that relies on gesture input, computer vision, and machine learning techniques in order to bring an intuitive user experience. The proposed system integrates OpenCV for real-time video processing and makes use of Media Pipe's advanced hand-tracking abilities to identify gestures, which are the distance between the thumb and index finger, to control the volume of audio. Pycaw is used to interact with the audio output of the system to dynamically control the volume. This gesture-based interface is designed to improve accessibility, usability, and user experience by providing a modern, intuitive alternative to traditional volume control methods.

关键词： Hands computer vision Thumb Process control Machine learning Streaming media Control systems Real-time systems User experience Usability

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A Novel Approach to Identify Pituitary Tumor Using an Optimal CNN Model with Deep Learning

A Novel Approach to Identify Pituitary Tumor Using an Optima...

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Computational Intelligence, Networks and Security (ICCINS), International Conference on

作者： Ebin P M Roja Thomas Safad Ismail Kavitha Nair R Alliance School of Advance Computing Alliance University Banglore India Department of Computer Science and Engineering Mangalam College of Engineering Ettumanoor India Department of Computer Engineering College of Engineering Chengannur Chengannur India Department of AI &ML Acharya Institute of Technology Banglore India

The technical paper introduces a novel approach to identifying pituitary tumors using an Optimized Convolutional Neural Network (OCNN) model with deep learning. Pituitary tumors rank among the most prevalent variety of brain tumors, and accurate identification is critical for proper diagnosis and treatment. The suggested method makes use of a deep learning framework that combines an ideal feature extraction method with a pre-trained CNN model. The pulled out features are then used to train the CNN model to classify pituitary tumors accurately. The effectiveness of the suggested method is assessed using a publicly accessible dataset, revealing that it surpasses the performance of current leading techniques. The suggested method possesses the capacity to act as a valuable asset for making clinical decisions in the field of neuroimaging, particularly for the accurate identification of pituitary tumors. The results of the experiment indicate that our suggested model successfully detects and classifies tumors with high accuracy, sensitivity, and specificity. Our model specifically achieves a 99.55% overall accuracy, 99% sensitivity, 99% specificity and 99% F1 score. These outcomes show that our suggested CNN design for brain tumor identification and classification performs better than several state-of-the-art techniques.

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Medical Insurance Fraud Detection Based on Block Chain and Machine Learning Approach

Medical Insurance Fraud Detection Based on Block Chain and M...

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International Conference on Emerging Research in Electronics, computer science and Technology (ICERECT)

作者： Bijaya Kumar Sethi Prakash Kumar Sarangi Adepu Sai Aashrith Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India Department of CSE (AI & ML) Vardhaman College of Engineering Hyderabad India

With the significant rise in medical costs, the Health Insurance department's duty of controlling medical expenses has become increasingly vital. Traditional medical insurance settlements are paid per-service, which results in a lot of unnecessary costs. Now a day, the single-disease payment mechanism has been frequently employed to address this issue. However, there is a possibility of fraud with single-disease payments. In this work, the authors have presented a methodology for detecting the health insurance fraud entrenched block chain and Machine learning techniques like Support Vector Machine (SVM) and logistic Regression, that can automatically recognize apprehensive medical records to assure sustainable execution of single-disease payment and reduce medical insurance worker's workload. The authors have also proposed a medical record storage and management procedure based on consortium block chain to assure data security, immutability, traceability, and audit ability. The suggested system may effectively identify fraud and considerably increase the efficiency of medical insurance evaluations, as demonstrated by experiments on two real datasets from two 3A hospitals.

关键词： computer science Costs Hospitals Data security Insurance Support vector machine classification Machine learning

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The use of machine learning (ml) in sustainable systems-An Overview 1

The use of machine learning (ML) in sustainable systems-An O...

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Salama, Ramiz Al-Turjman, Fadi Altrjman, Chadi Bordoloi, Dibyahash Ai and Robotics Institute Research Center for Ai and IoT Near East University Nicosia Department of Computer Engineering Mersin10 Turkey Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept. Mersin Nicosia10 Turkey University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Mersin10 Turkey Waterloo University Canada Graphic Era Hill University Dehradun Rs Cse Graphic Era University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350338027

Machine learning has the ability to dramatically improve sustainable systems by anticipating needs, maximizing resource use, raising output, and lowering waste. An overview of earlier studies on the incorporation of machine learning into sustainable systems is presented together with a case study of how machine learning was used to lower energy use in a residential structure. The results show that machine learning may be used to increase energy efficiency and save a lot of money. Wearable technology has added a whole new dimension to the already vast field of personal electronics. The mobile phone gave devices their true individuality. Because so many services are designed around mobile phones, the market has opened up for a brand-new personalized experience utilizing wearable technologies. Fabric sensors may now be combined with wearable microcontrollers like the flora and lily pad to monitor stretch, pressure, bend, and even the direction that the body is being braced. The connections between them are based on conductive threads that follow the curve of the fabric. We'll examine how different teams used their in-depth understanding of wearable technologies to accomplish their goals in this study. © 2023 IEEE.

关键词： Machine learning

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Fingerprint Liveness Detection Using Denoised-Bayes Shrink Wavelet and Aggregated Local Spatial and Frequency Features 21

Fingerprint Liveness Detection Using Denoised-Bayes Shrink W...

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21st International Conference on Machine Learning and Cybernetics, ICmlC 2022

作者： Raswa, Farchan Hakim Kinarta, Indra Yusuf Pulungan, Reza Harjoko, Agus Lee, Chungting Li, Yung-Hui Wang, Jia-Ching National Central University Department of Computer Science and Information Engineering Taiwan Universitas Gadjah Mada Department of Computer Science and Electronics Indonesia Hon Hai Research Institute Ai Research Center Taiwan

ISBN: (数字)9781665488327

ISBN: (纸本)9781665488327

Fingerprint has a competent level of uniqueness because the various features can form different patterns in humans. It is a verification requirement in various aspects, such as mobile phone, banking accounts, attendance, etc. One of the preventive measures in maintaining performance is liveness detection. We deep exploited the handcrafted method to achieve adequate performance. To encapsulate the noise possibility, we added the Bayes shrink-wavelet transform as the noise removal. So, the noise obtained in the fingerprint image can be minimized but keep the quality of the fingerprint image is in good condition. Then, we conjugated the spatial and frequency domain in pixel neighborhood distribution using the local binary pattern (LBP) and local phase quantization (LPQ) feature. Finally, we mapped the learning stage using a prominent classifier, i.e., a support vector machine (SVM). Our experiment was evaluated with LivDet 2015 dataset. The proposed method has achieved sustainable results regarding average error rate (AER). © 2022 IEEE.

关键词： Local binary pattern

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Octacleaner: Underwater Trash Detection Through YOLO

Octacleaner: Underwater Trash Detection Through YOLO

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Dhanni Pavani A Nymisha Nandini Reddy Neha Saw Siva Prasad Santosh Madeva Naik Computer science with AI & ML Hyderabad Institute of Technology and Management Hyderabad India Mechanical Engineering Hyderabad Institute of Technology and Management Hyderabad India

Million tons of trash is generated every day, which is often inadequately managed and sent to landfills. When it rains, trash along with agricultural waste and sediment, washes into water bodies, leading to underwater pollution. The heavy plastics, metals, glass, and organic waste settle at the water body's bottom, compounding the problem. To solve this,Octacleaner was developed which is an underwater trash-collecting robot equipped with a YOLO object detection model. In our study, the performance of YOLOv5 and YOLOv8 were compared. YOLOv5 boasts efficient and rapid object detection, making it suitable for real-time applications. It utilizes a streamlined architecture, making it computationally efficient while maintaining accuracy. YOLOv8, on the other hand, takes this a step further, achieving high precision of 90°/0, offering faster and highly accurate results. Its strength lies in its ability to swiftly and effectively identify objects. However, the unique challenge in underwater environments is that trash can change its shape due to water currents and decomposition. Additionally, factors like reduced sunlight and the presence of underwater mud can make the predictions more challenging. Despite this, YOLOv8 shows promise in addressing these difficulties, with its superior accuracy and speed.

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YOLOv8- Based Helmet and Vest Detection System for Safety Assessment

YOLOv8- Based Helmet and Vest Detection System for Safety As...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Divyanka Thakur Priya Pal Amogh Jadhav Numaira Kable Bhagyalakshmi V Sonali Deshpande Department of Computer Science Engineering (AI&ML) Smt. Indira Gandhi College of Engineering Navi Mumbai India Department of Electronics and Telecommunication KC College of Engineering Management and Research Thane(E) India

Helmet and vest detection systems ensure worker safety in high-risk professions like construction, mining, and law enforcement. The latest version of the You Only Look Once (YOLO) object detection model, YOLOv8, offers significant advancements over its predecessors. YOLOv8 utilizes a fully convolutional neural network, comprising two major components: the backbone and the head, incorporating a self-attention mechanism to identify objects at various scales in the image. This model eliminates anchors, reducing box predictions and speeding up post-processing phases. Among the YOLOv8 family, YOLOv8-s stands out as the smallest and fastest model, with a size of 21 MB and a single epoch runtime of only 11 seconds. In this study, the YOLOv8-s model achieved an impressive 99.5% mean average precision (mAP) accuracy rate in real time. By implementing this system, the construction industry can significantly enhance safety practices through efficient and reliable monitoring of personal protective equipment usage at construction sites.

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Stock Price Prediction using Bi-directional Long-Short Term Memory based Deep Neural Network

Stock Price Prediction using Bi-directional Long-Short Term ...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Sowmya, M. Al-Farouni, Mohammed Reddy Panga, Naresh Kumar Indira, R. Saranya, N. Naga Nitte Meenakshi Institute of Technology Department of Ai & Ds Bengaluru India The Islamic University Engineering College of Technical Engineering Department of Computers Techniques Najaf Iraq Virtusa Corporation New York United States Panimalar Engineering College Department of Ece Chennai India Saveetha College of Liberal Arts & Science Simats Department of Computer Applications Chennai India

ISBN: (纸本)9798350360660

In the area of finance, forecasting of stock price is becoming very popular increasingly. Predicting the stock price of a firm is very important to increase speculators interest in investing for a company and for growth of shareholders in that stock. In addition to a unique investment portfolio model based on the projections produced, this article suggests a novel method for time-series stock price prediction. To achieve this, a Bidirectional Long-Short Term Memory-based Deep Neural Network (Bi-LSTM-DNN) is equipped with novel regression technique. Once made, the predictions are utilized to build investment portfolio, more precisely, a projected portfolio. Numerous tests have been conducted using NIFTY-50 stock data that was acquired from Indian National Stock Exchange. The outcomes verify that suggested model performs better than a number of conventional prediction models and a number of conventional portfolio optimization algorithms. Reducing investing risks and increasing rewards are possible with an accurate stock price prediction. The Root Mean Square Error (RMSE) value for the proposed deep learning network for stock prediction using Bi-LSTM-DNN is 0.81. © 2024 IEEE.

关键词： Prediction models

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