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Comparison Between Dependent and Independent Ranked Set Sampling Designs for Parametric Estimation with Applications

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Annals of Data science 2023年第1期10卷 167-182页

作者： Shaaban, M. Yahya, M. The High Institute for Tourism Hotels and Computer El-Seyouf Alexandria Egypt Modern Academy for Computer Science and Management Technology Cairo Egypt

This paper is concerned with the estimation problem using maximum likelihood method of estimation for the unknown parameters of exponetiated gumbel distribution based on neoteric ranked set sampling (NRSS) as a new modification of the usual ranked set sampling (RSS) technique. Numerical study is conducted to compare NRSS as a dependent ranked set sampling technique, with RSS, and median ranked set sampling as independent sampling techniques, and then the performance of RSS and its modifications will be compared with simple random sampling based on their mean square errors and efficiencies. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Neoteric ranked set sampling Median ranked set sampling Ranked set sampling Maximum likelihood estimation Gumbel distribution Maximum likelihood estimation Estimation theory Parametric statistics Simple random sample Estimation

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Semi-analytic solution of the nonlinear Sharma-Tasso-Olver equation via ramadan group integral transform and accelerated adomian decomposition

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Journal of Umm Al-Qura University for Applied sciences 2025年 1-14页

作者： Ramadan, Mohamed A. Mansour, Mariam M. A. El-Shazly, Naglaa M. Mathematics and Computer Science Department Faculty of Science Menoufia University Menoufia Shibin El-Koom Egypt Department of Basic Science Modern Academy of Computer Science and Management Technology Cairo Maadi Egypt

This work focuses on finding an approximate solution to the Sharma-Tasso-Olive (STO) equation. The approach combines accelerated Adomian decomposition (ADM) with the Ramadan group integral transform (RGT) to tackle these nonlinear partial differential equations. Two analytical solution forms are analyzed to evaluate the accuracy and effectiveness of the proposed method. The results demonstrate that the suggested technique yields a rapidly convergent series solution for the STO equation. The paper emphasizes that these semi-analytic methods are powerful tools applicable in engineering and physics for approximating solutions to such nonlinear equations. The resulting series can offer a closed-form solution. Ultimately, the accelerated RGT-ADM variant outperforms the traditional RGT combined with standard Adomian decomposition due to its faster convergence and the elimination of the need for complex Adomian polynomials. © The Author(s) 2025.

关键词： Accelerated variant of Adomain Accuracy Approximate solutions Computational efficiency Convergence rate Nonlinear partial differential equations Numerical methods Polynomial calculations Ramdan group transform Semi-analytic methods Series solutions Sharma-Tasso-Olive equation

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Detection of communicable and non-communicable diseases using hyperparameter optimization with Bi-LSTM model in pathology images

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International Journal of Intelligent Computing and Cybernetics 2023年第4期16卷 649-664页

作者： Shiva Sumanth Reddy C.Nandini Department of Computer Science and Engineering Dayananda Sagar Academy of Technology and ManagementBangaloreIndia Dayananda Sagar Academy of Technology and Management BangaloreIndia

Purpose:The present research work is carried out for determining haemoprotozoan diseases in cattle and breast cancer diseases in humans at early *** combination of LeNet and bidirectional long short-term memory(Bi-LSTM)model is used for the classification of heamoprotazoan samples into three classes such as theileriosis,babesiosis and ***,BreaKHis dataset image samples are classified into two major classes as malignant and *** hyperparameter optimization is used for selecting the prominent *** main objective of this approach is to overcome the manual identification and classification of samples into different haemoprotozoan diseases in *** traditional laboratory approach of identification is time-consuming and requires human *** proposed methodology will help to identify and classify the heamoprotozoan disease in early stage without much of human ***/methodology/approach:LeNet-based Bi-LSTM model is used for the classification of pathology images into babesiosis,anaplasmosis,theileriosis and breast images classified into malignant or *** optimization-based super pixel clustering algorithm is used for segmentation once the normalization of histopathology images is *** edge information in the normalized images is considered for identifying the irregular shape regions of images,which are structurally ***,it is compared with another segmentationapproach circularHough Transform(CHT).The CHT is used toseparatethe *** Canny edge detection and gaussian filter is used for extracting the edges before sending to ***:The existing methods such as artificial neural network(ANN),convolution neural network(CNN),recurrent neural network(RNN),LSTM and Bi-LSTM model have been compared with the proposed hyperparameter optimization approach with LeNET and *** results obtained by the proposed hyperparameter optimization-Bi-LSTM model showed the accuracy of

关键词： Breast cancer detection Convolution neural network Deep neural network Haemoprotozoan disease detection Hyper-parameter optimization LeNet Bi-LSTM

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Knowledge As Not Only Justified True Beliefs in Vision

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 297-299页

作者： Wenbo Zheng Fei-Yue Wang IEEE School of Computer Science and Artificial Intelligence Wuhan University of Technology Sanya Science and Education Innovation Park of Wuhan University of Technology the State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences

COMPUTATIONAL knowledge vision [1] is emphasized as a novel perspective or field in this paper. It first proposes the visual hierarchy and its connection to knowledge, stating that knowledge is a justified true belief. To further the previous research, we concisely summarize our recent works and suggest a new direction that knowledge is also a thought framework in vision.

关键词： Computational modeling Visualization Artificial intelligence Data models Visual perception Visual databases Training data

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A Three Layer authentication using FEO 4

A Three Layer authentication using FEO

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4th IEEE International Conference on Data Engineering and Communication Systems, ICDECS 2024

作者： Hanji, Bhagyashri R Aishwarya, E. Poojitha, B.V. Anushree, H.R. Chinmayi, T.K. Dayananda Sagar Academy of Technology and Management Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350393354

In the contemporary digital landscape, ensuring robust security measures is imperative to safeguard sensitive information and transactions. This work introduces an innovative Three-Level Authentication System that incorporates facial recognition, eye blink password, and OTP (One-Time Password) generation to enhance security and improve the user experience. The initial level of authentication leverages advanced facial recognition technology. Employing sophisticated algorithms, the system analyzes and verifies unique facial features, establishing a secure foundational layer for user identification. The second level introduces the groundbreaking concept of Eye Blink Password, utilizing distinctive eye gestures and blinks for authentication. This biometric approach not only enhances security but also provides a memorable and user-friendly authentication method. The third level further bolsters security by generating dynamic OTPs, introducing a time-sensitive element that significantly reduces the risk of unauthorized access, especially during online transactions. This system proves beneficial in safeguarding against thermal attacks, which exploit heat signatures to capture sensitive information, as well as shoulder surfing, a threat that relies on visual observation. By combining these three authentication levels, the proposed system offers a comprehensive and robust approach to security, addressing various potential vulnerabilities and providing a more secure digital environment. © 2024 IEEE.

关键词： Authentication

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Eco-Detect: A Sustainable Solution to Advanced Waste Sorting using AI and IoT 4

Eco-Detect: A Sustainable Solution to Advanced Waste Sorting...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Nandini Prasad, K.S. Manjula, G. Aishwarya, K.J. Chetankumar Prakash, K. N. Bhanu Chandan, V Sai Dayananda Sagar Academy of Technology and Management Department of Information Science and Engineering India Dayananda Sagar Academy of Technology and Management Department of Computer Science and Design India Dayananda Sagar Academy of Technology and Management Department of Information Science and Engineering Bangalore India

ISBN: (纸本)9798331529635

The traditional waste management system, prevalent in many regions, relies heavily on manual sorting processes carried out by human workers. These processes are often labor- intensive and time-consuming, leading to inefficiencies in waste management operations. Manual sorting methods struggle to cope with the increasing volume and complexity of waste generated, resulting in challenges in accurately categorizing and classifying different types of waste materials. This limitation not only hampers the effectiveness of waste sorting but also impedes efforts to maximize recycling rates and minimize environmental impact. In response to the limitations of traditional waste management approaches, the proposed Eco Detect Advanced Waste Sorting system presents a transformative solution. Leveraging the advancements in artificial intelligence and computer vision, this system introduces the utilization of the YOLOv7 algorithm for real-time interference. YOLOv7, renowned for its exceptional accuracy and speed in object detection, is integrated into the waste sorting process to enable rapid and precise identification and classification of diverse waste materials. By employing deep learning methodologies, the system is capable of recognizing a wide array of waste categories, including plastics, paper, glass, metals, and organic materials, with remarkable efficiency. The integration of the YOLOv7 algorithm into the proposed waste sorting system represents a significant advancement in waste management technology. By enabling swift and accurate identification of various waste types, the system facilitates the optimization of recycling practices, promotes the establishment of a circular economy, and contributes to the overall sustainability agenda. Its adaptability and scalability make it well-suited for widespread implementation, addressing the pressing need for sustainable waste management for clean energy solutions on a global scale. Ultimately, the paper envisions a future where te

关键词： Waste utilization

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Integrity Shield: Defending Genuine Goods

Integrity Shield: Defending Genuine Goods

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2024 IEEE International Conference on Emerging Technologies in computer science for Interdisciplinary Applications, ICETCS 2024

作者： Kshitija Nagareddy, G.B. Param Preetham, R. Bavagi, Raghavendra R. Sushmitha, S. Dayananda Sagar Academy of Technology and Management Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350372502

The rise in counterfeit products poses a significant challenge for consumers, businesses, and governments worldwide. Counterfeit goods not only impact the economy but also pose health and safety risks. Traditional methods of combating counterfeiting have proven inadequate, necessitating the exploration of innovative solutions. This report focuses on the utilization of blockchain technology for fake product identification. © 2024 IEEE.

关键词： Blockchain

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BIOWATCH: Detecting the Lake Pollution to Safeguard Biotic Life

BIOWATCH: Detecting the Lake Pollution to Safeguard Biotic L...

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2024 IEEE International Conference on Emerging Technologies in computer science for Interdisciplinary Applications, ICETCS 2024

作者： Shalini, S. Mounika Sree, K. Prajwal, M.H. Nitin Reddy, N.V. Govardhan Reddy, P. Dayananda Sagar Academy of Technology and Management Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350372502

This study presents an integrated system devised to combat plastic pollution in lakes autonomously, eliminating the need for human intervention. Utilizing sensor data and camera imagery processed through the YOLO algorithm. The detected plastic is then used to compute its density, which is subsequently compared against a predetermined threshold. Upon surpassing the threshold, an automated email notification containing the density values is dispatched to relevant authorities. Furthermore, water quality sensors are integrated to monitor environmental conditions continuously. Regular updates are provided to facilitate proactive measures in pollution prevention. This initiative underscores the application of cutting-edge technology to address environmental issues and uphold the well-being of aquatic ecosystems. Through automated detection and monitoring processes, the system offers a sustainable approach to combat plastic pollution in lakes, promoting environmental conservation efforts. © 2024 IEEE.

关键词： Lakes

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Machine Learning for the Exposure of Malicious URLs 1

Machine Learning for the Exposure of Malicious URLs

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1st International Conference on Recent Advances in Information technology for Sustainable Development, ICRAIS 2023

作者： Hanji, Bhagyashri R. Kanagavalli, R. Dayananda Sagar Academy of Technology and Management Computer Science and Engineering Bangalore India Global Academy of Technology Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350306637

In today's era, as per the statistics for world-wide internet users or digital population as on April 2023, there are 5.18 billion internet users worldwide, which goes to 64.6 percent of global population. Of this total, 4.8 billion, or 59.9 percent of the world's population are social media users. [***/statistics617136/digital-population-worldwide]. Enhancement in technology and internet usage increases the scope for cyber-criminal activities. As the connectivity and the number of users grow, there is a proportional increase in attackers. Vindictive sites or URLs become one of the significant dangers for network protection. Malevolent URLs are most risky danger prompting phishing assault, malware dissemination, Drive-by Downloads, Program Exploits, Data fraud in this computerized world. It's critical to remain cautious and safeguard us on the web. It is vital to be sufficiently mindful and have the option to identify and act against these dangers by not visiting such destinations or giving any private data. In this work we will focus on attacks through malicious URLS, working, preventions and machine learning algorithms used to detect malicious URLs. © 2023 IEEE.

关键词： Cybersecurity

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3TFL-XLnet-CP: A Novel Transformer-Based Crop Yield Prediction Framework with Weighted Loss Based 3-Tier Feature Learning Model

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SN computer science 2025年第3期6卷 1-17页

作者： Anoop, G.L. Nandini, C. Naresh, E. Department of Computer Science and Engineering Christ University Karnataka Bengaluru India Department of Computer Science and Engineering Dayananda Sagar Academy of Technology and Management Karnataka Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

The advancement of crop yield prediction through artificial intelligence (AI) has gained significant attention. However, the existing AI-based approaches for maximizing agricultural productivity, specifically in crop yield prediction, have not consistently delivered satisfactory results. In response to this challenge, we propose a novel framework named as Three Tier Feature Learning with XLnet based Crop Prediction (3TFL-XLnet-CP) that enhances agricultural productivity by accurately predicting crop yield. The 3TFL-XLnet-CP framework employs a three-tier feature learning approach in combination with the powerful XLnet transformer-based crop prediction model. The three-tier feature learning involves the integration of Spiking Neural Network (SNN), Graphical Neural Network (GNN), and Convolutional Neural Network (CNN) to extract distinct feature vectors from the preprocessed data. These feature vectors are then concatenated using Jaccard Similarity to measure their similarity score. Additionally, a weighted Loss function is introduced to optimize feature learning, further enhanced by a novel self-adaptive Spider Monkey Optimization algorithm (SASMO). The concatenated features are subsequently fed into the classification layer for making precise crop yield predictions. The proposed model is implemented using the Python platform and evaluated against existing models such as ANN, RNN, DNN, and BiLSTM. The comparison demonstrates the superiority of our proposed 3TFL-XLnet-CP framework in accurately predicting crop yield. © The Author(s) 2025.

关键词： 3TFL-XLnet-CP Convolutional neural network Crop yield prediction Graphical neural network Spiking neural network

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