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4th International Conference on Advanced Electronic Materials, computers and Software Engineering, AEMCSE 2021

作者： Ullah, Nazir Al-Dhlan, Kawther A. Al-Rahmi, Waleed Mugahed Nanjing University School of Management and Engineering Department of Management Science and Engineering China University of ha'Il College of Computer Science and Engineering Department of Information and Computer Science Saudi Arabia Universiti Teknologi Faculty of Social Science and Humanities School of Education Malaysia

ISBN: (纸本)9781665415965

in financial institutions, the traditional Know Your Customer (KYC) process is insecure, and costly. The adoption of disruptive technology is a necessary condition for the coming future of financial institutions. This study proposed a Hyperledger Fabric network for KYC optimization. The Performance of the proposed system was tested by using the Hyperledger Composer. The experiment results confirm that due to the strong and identity features of Hyperledger Fabric, the proposed system can speed up the KYC clearing transfers, challenge the inefficiencies that arise from duplicated conduct of similar tasks, secure data sharing, cost-effective, and ultimately bring transparency in the traditional KYC system. © 2021 IEEE.

关键词： Distributed ledger

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Diagnosis of Liver Disease Induced by Hepatitis Virus Using Machine Learning Methods 8

Diagnosis of Liver Disease Induced by Hepatitis Virus Using ...

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8th International Conference on Information Technology Trends, ITT 2022

作者： Hafeez, Muhammad Ahmad Imran, Azhar Khan, Muhammad Imran Khan, Ali Haider Nawaz, Asif Ahmed, Sheeraz Air University Department of Creative Technologies Islamabad Pakistan Air University Faculty of Computing and A.I Department of Creative Technologies Islamabad Pakistan School of Systems and Technology University of Management and Technology Department of Computer Science Lahore Pakistan Faculty of Electrical Engineering Higher Colleges of Technology Dubai United Arab Emirates Iqra National University Department of Computer Sciences Peshawar Pakistan

ISBN: (数字)9781665487429

ISBN: (纸本)9781665487412

Liver, the largest vital organ in the human body, helps in digestion, energy storage, and toxic removal processes. Hepatitis A, B, C, fatty liver, cirrhosis, and liver cancer all cause swelling of the belly and legs, changes in the color of urine, and jaundice. Early identification is necessary to save the lives of those infected with the Hepatitis virus. Although clinical tests such as Abdominal Ultrasound, Chloride Blood Test, Liver Biopsy, Liver Function Tests (LFT), Magnetic Resonance Cholangio Pancreatography (MRCP), Total Protein and Albumin/Globulin (A/G) Ratio, and others are used in the diagnosis of Hepatitis virus, but high-level expertise of the doctors are also required. With continual advancements in Artificial Intelligence (AI) and Machine Learning algorithms, medically intelligent systems for early detection of fatal liver disease can be developed for prediction of deadly liver disease in the early stage. The goal of this paper is to examine the accuracies of several Machine Learning algorithms such as Linear Regression, Decision Tree, Random Forest, KNN, and SVM. For this work, author used the data set accessible at the UCI database and proved that the Support Vector Machine perform better than all other methods with.91.84% accuracy. © 2022 IEEE.

关键词： Decision trees

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Detection of Physical Anomalies in IoT Networks Using Machine Learning Techniques

Detection of Physical Anomalies in IoT Networks Using Machin...

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2024 International Conference on Advancement in Renewable Energy and Intelligent Systems, AREIS 2024

作者： Mukunthan, M.A. Jose Anand, A. Kesavan, Stalin Mutyala, Malini Kesavan, R. Geetha, R. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Cse Tamil Nadu Chennai India Kcg College of Technology Department of Ece Tamil Nadu Chennai India Academy of Maritime Education and Training Department of Marine Engineering Tamil Nadu Chennai India Vignana Bharathi Institute of Technology Department of Csbs Telangana Hyderabad India Department of Computer Science and Engineering Tamilnadu Chennai India SRM Institute of Science and Technology Department of Computing Technologies Tamilnadu Chennai India

ISBN: (纸本)9798350387230

This work focuses on the implementation of a Machine Learning (ML) algorithm for detecting physical anomalies in Internet of Things (IoT) networks. A Wireless Sensor Network (WSN) was deployed, collecting data under two scenarios: normal operation and an altered state exhibiting physical anomalies. The WSN, composed of humidity, Hall effect, barometer, and temperature sensors, communicated using the MQTT protocol with an Edge node for data capture. Data profiling and preprocessing were conducted to clean and optimize the dataset for ML. The K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF) algorithm were employed to classify network traffic, with hyperparameters tuned using Randomized Search CV. The dataset was split into training (75%) and testing (25%) sets, with performance metrics such as accuracy, precision, recall, F1 score, and ROC curve analyzed. The final model achieved high accuracy (99.53 %) and showed excellent precision (1) with a classification time of 0.0012 seconds per packet. These results demonstrate the RF model's effectiveness in detecting physical anomalies in IoT networks, although further optimization may be needed for real-world scalability. © 2024 IEEE.

关键词： Support vector machines

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Image Cryptography Scheme Based on Improvement Chaos Logistic Function, Chaos Arnold Cat Map and Gingerbread Man Process

Image Cryptography Scheme Based on Improvement Chaos Logisti...

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2024 International Conference on Artificial Intelligence and Mechatronics System, AIMS 2024

作者： Hashim, Mohammed Mahdi Almajidi, Abdul Razzaq Jabr Ibrahim, Raed Khalid Jasem, Bashar I. Saad, Mohammed Ayad Nahi, Abdullah A. Ministry of Higher Education and Scientific Research Baghdad Iraq AL-Esraa University Baghdad Iraq Al-Farahidi University Department of Medical Instruments Engineering Baghdad Iraq Al-Hadba University College Department of Computer Technology Engineering Mosul Iraq Jannat Iraq College Department of Medical Instrumentations Technique Engineering Ramadi Iraq Management Technical College Department of Information Technologies Southern Technical University Basrah Iraq

ISBN: (纸本)9798350350524

Recently, information security has emerged as a crucial aspect of information technology and communication considering the Internet's proliferation. The development of image cryptography emerged as a method to enhance the confidentiality of communication. In order to achieve these aims, a framework comprising three primary phases has been developed for the purpose of image cryptography. During the first phase, a fresh key generator was developed, incorporating a substantial degree of randomness by utilizing several chaotic maps and the suggested Improvement Chaos Logistic function (ICLF). In the second phase, the proposed method involves the introduction of a confusion process. This phase entails the sorting of the key generator in ascending order, followed by the permutation of the image pixels depending on the sorting key. Finally, the diffusion phase is described, which involves the utilization of the Gingerbread Man Method (GGM), Arnold Cat Map (ACM) transform, and XOR operation. Based on the findings, it has been confirmed that the suggested image cryptography method has a better result in the field of cryptography when compared with existing methods. © 2024 IEEE.

关键词： Cryptography

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IOT based prediction of rainfall forecast in coastal regions using deep reinforcement model

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Measurement: Sensors 2023年 29卷

作者： Nithyashri, J. Poluru, Ravi Kumar Balakrishnan, S. Ashok Kumar, M. Prabu, P. Nandhini, S. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chengalpet District Kattankulathur603203 India Department of Information Technology Institute of Aeronautical Engineering Hyderabad India Department of Computer Science and Business Systems Sri Krishna College of Engineering and Technology Coimbatore India Faculty of Computer Science Department of Computer Science and Software Engineering Skyline University Nigeria Kano Nigeria Bengaluru560029 India Department of Computer Science and Engineering Karpagam Academy of Higher Education Tamilnadu Coimbatore India

This research proposes an IoT based technique for predicting rainfall forecast in coastal regions using a deep reinforcement learning model. The proposed technique utilizes Long Short-Term Memory (LSTM) networks to capture the temporal dependencies between the rainfall data collected from the coastal regions and the prediction model parameters. The proposed technique is evaluated on a dataset of rainfall data collected from the coastal regions of India and compared to traditional methods of rainfall forecasting. The accuracy and reliability of these models are evaluated by comparing them to prior models. Precipitation in coastal locations may be predicted with an average accuracy of 89% using the suggested model, as shown by the results. The suggested framework is computationally efficient and can be trained with little input. The results of this research give strong evidence that the proposed model is an effective tool for coastal precipitation forecasting. © 2023 The Authors

关键词： Internet of things

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Digital Competence of Specialists: Development Technology in a Higher education Institution 14

Digital Competence of Specialists: Development Technology in...

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14th International Conference on Advanced computer Information technologies, ACIT 2024

作者： Henseruk, Halyna Martyniuk, Serhii Vasylenko, Oksana Henseruk, Yuliia Henseruk, Viktor Habrusiev, Valerii Ternopil Volodymyr Hnatiuk National Pedagogical University Department of Computer Science and Teaching Methods Ternopil Ukraine Anhalt University of Applied Sciences Department of Theory and Practice of Translation Köthen Germany Ternopil Volodymyr Hnatiuk National Pedagogical University Department of English Philology and Methods of Teaching English Ternopil Ukraine Ternopil Volodymyr Hnatiuk National Pedagogical University Department of Pedagogy and Management of Education Ternopil Ukraine

ISBN: (纸本)9798350350036

In a digital society, professionals who are professionally competent and have a variety of skills are increasingly in demand. Digital skills are important and form the basis of the digital competence of specialists. The purpose of the study is to analyse regulatory documents in the context of the development of digital competence of specialists, to develop, substantiate and implement the trajectory of its development in a higher education institution. A questionnaire for self-assessment by bachelor's and master's students and practitioners of their own level of digital competence has been developed, which contains 12 descriptors and their detailed description. Based on the analysis of regulatory documents and the survey results, the author outlines the need to include disciplines in the training programmes for specialists that would contribute to the development of their digital competence. The authors have developed a technology for the system of development of digital competence of specialists. The design stage is substantiated, which includes three blocks: academic disciplines, self-education and professional self-development. The role of professional disciplines in digital technologies in the development of digital competence of specialists is determined. The study emphasises that continuity and continuity in the training of specialists in the context of the use of digital technologies in various aspects of activity is important in this development. © 2024 IEEE.

关键词： Students

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LiDAR-Based Climate Change Imaging in Geoscience Using Spatio Extreme Fuzzy Gradient Model

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 465-472页

作者： Krushnasamy, V.S. Al-Omari, Omaia Sundaram, Ajith Varghese, Issac K. Muniyandy, Elangovan Rao, M. V. A. L. Narasimha Dept. of EIE Dayananda Sagar College of Engineering Bangalore India Information Systems Department College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Amrita School of Business Amrita Vishwa Vidyapeetham Kochi India T A Pai Management Institute Manipal Academy of Higher Education Manipal India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of MBA Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur 522302 India

In order to estimate high-resolution air temperatures, computationally demanding procedures are required because climate models do not provide the resolution required for urban climate studies. Data-driven methods, on the other hand, provide air temperature downscaling that is quicker and more precise. Lidar technology has become a vital tool for researching these atmospheric components because of its capacity to measure backscattered signals and investigate the atmosphere at extremely high spatial and temporal resolution. However, machine learning has become a potent technique for sophisticated data processing and analysis because of the complexity and diversity of lidar technology. This research proposes a novel technique in urban–rural interface analysis in remote sensing by machine learning (ML) model. Here the input is collected as urban–rural landscape data and processed for noise removal as well as normalisation. Then these data features have been extracted and classified using a spatio extreme fuzzy gradient model with reinforcement Markov vector perceptron neural networks. Experimental analysis has been carried out for various geographical regions in terms of training accuracy, average precision, recall, RMSE, and F-1 score. The proposed model attained an F-1 score of 92%, training accuracy of 98%, average precision of 95%, recall of 93%, and RMSE of 55%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Lidar technology Machine learning model Reinforcement Markov vector Remote sensing Spatio extreme fuzzy gradient

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Anomaly Detection for Log using AutoM and Auto-Sklearn Classification Algorithm 15

Anomaly Detection for Log using AutoM and Auto-Sklearn Class...

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15th International Conference on Computing Communication and Networking technologies, ICCCNT 2024

作者： Karthik, G.M. Gupta, Ankur Gupta, Sh Rajesh Jha, Amaresh Sivasangari, A. Mishra, B.P. School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India Department of CSE RIMT University Godindgarh Mandi India Department of Management Sanskriti University Uttar Pradesh Mathura India School of Modern Media University of Petroleum and Energy Studies Dehradun India School of Computing Sathyabama Institute of Science and Technology Chennai India Department of Electronics JSS Academy of Technical Education NoidaSector-62 India

ISBN: (纸本)9798350370249

— In the realm of application development, logs represent a pivotal resource for the identification and comprehension of application performance, as well as the detection of anomalies, all of which are essential for ensuring the overall efficiency of an application. The proliferation of users has resulted in a substantial increase in the volume of logs available for analysis in the field of log analysis research. Typically, these logs exhibit an unstructured format, largely dictated by the developer's design choices. Consequently, there is a need for log parsing techniques to in still structure into these logs, enabling the application of machine learning algorithms for the prediction of anomalies. In the present research, the primary objective is the prediction of anomalies within Hadoop Distributed File System (HDFS) logs utilizing an AutoML (Automated Machine Learning) tool known as Auto-Sklearn. The research outcomes illustrate the effectiveness of the proposed model, which attains an impressive accuracy rate of 99% when applied to the dataset. This study underscores the critical role of logs in application development, highlights the importance of log structure transformation, and demonstrates the substantial predictive capabilities of the Auto-Sklearn model in the context of HDFS log analysis. ©2024 IEEE.

关键词： Anomaly detection

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The Role of Ensemble Learning in Advancing Child Fetal Health: A Focus on Voting and Bagging Techniques

The Role of Ensemble Learning in Advancing Child Fetal Healt...

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Cognitive, Green and Ubiquitous Computing (IC-CGU), International Conference on

作者： Satyaprakash Swain Kumar Janardan Patra Suvendra Kumar Jayasingh Mihir Narayan Mohanty Binod Kumar Pattanayak Department of Computer Science and Engineering Institute of Technical Education and Research Siksha ‘O’ Anusandhan Deemed to be University Bhubaneswar Odisha India Department of Computer Science and Engineering Odisha University of Technology and Research Bhubaneswar Odisha India Department of Computer Science and Engineering Institute of Management and Information Technology BPUT Cuttack Odisha India Department of Electronics and Communication Engineering Institute of Technical Education and Research Siksha ‘O’ Anusandhan Deemed to be University Bhubaneswar Odisha India

ISBN: (数字)9798350361780

ISBN: (纸本)9798350361797

The field of healthcare has witnessed significant advancements in leveraging technology for in-depth analysis of fetal health data, offering profound insights into child development. We meticulously gathered an extensive dataset comprising 1500 instances of child fetal health data sourced from New Born and Pediatric Health Centre, Cuttack, Odisha. We proposed a robust analytical pipeline for effectively classifying and predicting fetal health outcomes. The process initiated with meticulous data preprocessing and refinement, ensuring data integrity and quality. We use clustering techniques unveiled inherent patterns and groupings within the dataset, setting the stage for in-depth analysis and feature extraction. Further enriching the analysis, Principal Component Analysis (PCA) is deployed to streamline data dimensionality while retaining essential information. For prediction we use ensemble learning techniques. To predict the condition, we used voting ensemble method encompassing Decision Trees (DT), Support Vector Machines (SVM) and Naïve Bayes (NB), after predictive models are successfully constructed, showcasing notable accuracies of 92.28%, 83.89% and 82.22%. These results underscore the effectiveness of these models in predicting fetal health outcomes based on the comprehensive dataset. To further refine model performance, a meticulous GridSearchCV approach exhaustively explored optimal parameter values within predefined parameter grids, significantly enhancing predictive capabilities. Additionally, leveraging a bagging method, particularly Random Forest (RF), Logistic Regression (LR), and Gradient Boosting, further bolstered predictive accuracies. These bagging models demonstrated exceptional achieving accuracy of 94.02%, 93.22% and 92.28%. The systematic approach involving clustering, dimensionality reduction, ensemble learning, and parameter optimization resulted in highly accurate predictive models for fetal health assessment. Our research not only highligh

关键词： Support vector machines Radio frequency Dimensionality reduction Pediatrics Data preprocessing Medical services Predictive models

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Forecasting E-Commerce Sales Adoption Based on DE-ELM-RGSO Approach 2

Forecasting E-Commerce Sales Adoption Based on DE-ELM-RGSO A...

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2nd International Conference on Intelligent Data Communication technologies and Internet of Things, IDCIoT 2024

作者： Kumar, Keyur Nayak, Keyurkumar M. Palanisamy, Viswanathan Singh, Gagan Reddy, Y Siva Prabakaran, V. Lakineni, Prasanna Kumar University of Mumbai's Garware Institute of Career Education and Development Mumbai India School of Commerce Reva University Bangalore India Meerut Institute of Technology Meerut India Vishwa Vishwani School of Business Department of MBA Hyderabad India SNS College of Technology Department of Management Studies Coimbatore India Sri Venkateswara College of Engineering Department of Computer Science & Engineering Tirupathi India

ISBN: (纸本)9798350327533

Most agricultural supplies companies have established corporate Web presences and Internet strategies by 1999. Though some purchases were made directly by customers, the great majority were made by vendors (channel partners) and not end users. Business owners that witnessed logistical and stock management improvements through e-commerce were more likely to adopt the practice. It was hoped that farmers would take to e-commerce because of the streamlined experience it provided them with researching and buying goods. The proposed methodology begins with the preliminary processes of preprocessing, feature extraction, and model training. After carefully evaluating the raw data, four stages of preprocessing were implemented: treatment of missing values, rectification of skewed distribution, data analysis of outliers, and application of normalization. Both linguistic and informational features are employed in feature extraction. In order to train DE-ELM-RGSO models, feature extraction must first take place. When compared to DE and RGSO, the two most popular alternatives, the proposed technique emerges as the clear winner. By implementing the plan, they increased accuracy to 95.76 percent. © 2024 IEEE.

关键词： Electronic commerce

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