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Secure Sharing of Student Credentials Using Blockchain 4th

Secure Sharing of Student Credentials Using Blockchain

引用

4th International Conference on Communication, Devices and Computing (ICCDC 2023)

作者： Sarkar, Puja Kalita, Psarag Jyoti Goswami, Mugdhatanu Dev Saha, Sanchita Nag, Amitava Department of Computer Science and Engineering Central Institute of Technology Assam Kokrajhar783370 India Department of Computer Science and Engineering Haldia Institute of Technology Haldia721657 India

ISBN: (纸本)9789819927098

Sharing students’ credentials is an essential process for any educational ecosystem that includes diverse stakeholders such as students, teachers, various committees of institutes, administrations, government agencies, and many more. All of these stakeholders take extensive measures to ensure the authenticity and confidentiality of students’ testimonials when those are being shared. In spite of these concerns, the distribution of students’ credentials is a vulnerable, potentially complex, and sometimes insecure process. The goal is to reduce the existing security-related issues when sharing the students’ credentials with the help of blockchain technology. The technology of blockchain can be used effectively to solve the issue of securely exchanging student credentials. We proposed a secure, immutable, authentic, tamper-proof, privacy-preserving, and easily shareable blockchain-based mechanism for securely exchanging students’ credentials. In order to make the process of sharing credentials scalable, we proposed a secure off-chain store mechanism. A decentralized application based on Ethereum technology is used for conducting an analysis of the architecture with regard to its performance as well as its potential feasibility. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smart contract

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Web based Payment Tracking and Accounting Application 7

Web based Payment Tracking and Accounting Application

引用

7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Baibhav, Jayesh Ghatak, Jyotishman Mayan, J. Albert Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397253

The Payment Tracking System (PTS) is a sophisticated web-based platform designed for proficiently tracking and managing payments from various vendors. It serves as a centralized hub for consolidating payment requests from the accounts department to upper management, thereby ensuring timely delivery of supplier payments through the implementation of integrated best practices for managing operations and services. The system boasts advanced Transaction Management functionality such as Invoice Generation and Payment Reminders, providing unparalleled visibility, insight, isolation, and swift resolution of payment-related issues for any type of organization by delivering the necessary information in a timely manner to the user. In today's fast-paced business environment, managing invoicing and chasing payments from clients can be a challenging task. This innovative application offers a solution to this problem by providing businesses with the capability to track payments from clients and gain valuable insights into their earnings. The system not only facilitates the creation, sending, and receiving of invoices, but also generates automated follow-up reminders for overdue payments. By utilizing this application, clients can also avoid incurring late fees © 2023 IEEE.

关键词： Websites

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Optimizing Waste Management through Intelligent Machine Learning Systems: A Comparative Study 14

Optimizing Waste Management through Intelligent Machine Lear...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Shravani, Nandikante Vigneshwari, S. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350335095

The purpose of this research study is to examine the use of intelligent machine learning systems in the process of optimising waste management practises. Several other approaches, such as Artificial Neural Networks, K-nearest neighbours, Logistic regression, Decision Trees, Support Vector Machine, and Random Forest, are being investigated as potential ways to improve the currently utilised waste management paradigm. On the other hand, with the implementation of algorithms for machine learning, these measures saw a huge improvement. The use of these methods brought in a remarkable increase in classification accuracy, which reached 92.349%, and a recall rate of 91.04%. This significant improvement in performance demonstrates the usefulness of machine learning in the process of optimising waste management. The Random Forest classifier proved to be the strategy that offered the best results after being compared to the other approaches investigated. In particular, the use of this classifier helped to improve the accuracy of estimates for the amount of time required to empty recycling containers. In a nutshell, the purpose of this study article was to demonstrate how effective intelligent machine learning systems can be in improving existing waste management procedures. © 2023 IEEE.

关键词： Neural networks

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A Comprehensive Review of Machine Learning Techniques for Malaria Diagnosis from Blood Samples

A Comprehensive Review of Machine Learning Techniques for Ma...

引用

2023 IEEE Conference on engineering Informatics, EI 2023

作者： Muhammad Shameem, P. Malarvel, Muthukumaran Hindustan Institute of Technology and Science Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350338522

ISBN: (纸本)9798350338522

This study paper provides an overview of the use of machine learning techniques for malaria diagnosis. The article looks at a variety of machine learning algorithms, such as supervised and unsupervised learning, deep learning techniques, and hybrid approaches, and how they might be used to diagnose malaria. In addition, widely used data sets and preprocessing methods for detecting malaria are discussed, as well as evaluation metrics for determining how well machine learning models work. The paper highlights how machine learning might improve patient outcomes and change the diagnosis of malaria, particularly in areas with limited resources. The paper concludes with a summary of key findings and contributions, limitations, and open research questions for future work. © 2023 IEEE.

关键词： Support vector machines

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A comparative review on machine learning based medical diagnostic systems for Chikungunya and Zika virus 4th

A comparative review on machine learning based medical diagn...

引用

4th International Conference on Computational Methods in science and technology, ICCMST 2024

作者： Gupta, Nisha Singh, Harjeet Singla, Jimmy Chitkara Institute of Engineering and Technology Chitkara University India School of Computer Science and Engineering Lovely Professional University India

ISBN: (纸本)9781032911571

The Chikungunya arbovirus and zika virus have recently experienced widespread outbreaks, which have allowed the virus to spread rapidly worldwide. These illnesses initially manifest as an acute stage of fever and severe joint inflammation. However, this stage gradually turns into a chronic stage, and during this time, a person experiences debilitating articular pain that is persistent and long-lasting. Additionally, the traditional methodologies used to identify it are not very effective and time-consuming. However, in the current years, the advancement in technology assists in monitoring, diagnosing, and treating individuals suffering from this virus remotely. For healthcare systems, Machine Learning (ML) is a godsend since it helps experts identify, diagnose, treat, and control dangerous diseases. In order to detect Zika and Chikungunya virus, this review study evaluates and compares the performance parameters of various ML approaches utilized in existing studies. This paper discusses the top techniques for diagnosing the selected viruses and offers a thorough evaluation of medical expert systems. © 2025 the Author(s).

关键词： Expert systems

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Automatic Recognition of Cucumber Leaf Disease based on Convolution Neural Networks 2

Automatic Recognition of Cucumber Leaf Disease based on Conv...

引用

2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Thangaraj, Rajasekaran Mohan, M. Moulik, M. Logeshwari, A. Loganathan, T. Koushika, P. Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350325799

Weeds, pests, and other plant diseases are the leading causes of financial losses in the agriculture farming industry worldwide. It is a critical factor since it reduces crop quality and capacity significantly. As a result, detecting and classifying diverse plant diseases is critical, and it requires special attention. This impact can be reduced by diagnosing crop diseases at an earlier stage. This work will largely focus on the detection of cucumber leaf diseases. Manual methods of identifying cucumber infections are sometimes time-consuming, difficult, and subjective. Plant disease detection by diseased leaf image processing algorithms is a significant study topic due to the complexity of images of diseased leaves. The ability to diagnose plant disease from leaf images using deep learning algorithms is promising. Strong recognition results were obtained using the suggested transfer learning-based DenseNet121 model, with an accuracy of 95.45%. Conventional classifiers like ResNet50, Inception-ResnetV2, and InceptionV3 were tested in order to compare the CNN findings. The outcomes showed that the CNN was a qualified technique for spotting cucumber diseases in the wild. © 2023 IEEE.

关键词： Convolutional neural networks

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Influence of Opinion Formation on Polarization in Social Networks 14

Influence of Opinion Formation on Polarization in Social Net...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Das, Susmita Dutta, Sangita Chakraborty, Susanta Biswas, Samit Indian Institute of Engineering Science and Technology Department of Computer Science and Technology Shibpur711103 India

ISBN: (纸本)9798350335095

Online social networks have introduced diverse discussions regarding different topics. At the same time, these platforms have also garnered the idea of shaping opinions. The increase in user engagement in case of arguments has led to the issue of polarization in online social media platforms. In this paper, we have studied the influence of opinion change on homophily in social networks and the variation in polarization. Our own new method has been proposed for analysing the modification of user opinions dynamically in different timestamps. The probability of information sharing varies depending on the opinion similarity and homophily in social networks alters accordingly. Our approach portrays the opinion modification dynamics for different network features using complex networks concepts. We have observed that on social interaction networks like Twitter, opinion, information sharing and homophily perform a significant role in the determination of online polarization. Our approach has been compared with an established approach over external datasets and the results obtained are promising. © 2023 IEEE.

关键词： Polarization

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Enhanced Convolutional Neural Network for Sign Character Recognition with Mixed Pooling and Mish Activation Function

Enhanced Convolutional Neural Network for Sign Character Rec...

引用

2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Sur, Hritajit Mandal, Gourab Das, Koushik Nandia, Apurba Das, Avik Kumar Ghosh, Arijeet Institute of Engineering and Management University of Engineering and Management Cybersecurity & Blockchain Technology Department of Computer Science Engineering Kolkata India

ISBN: (纸本)9798331539948

Communication is an essential part of our daily lives, especially for deaf and dumb people who face difficulty in communicating with others. This paper introduces a method of using sign languages, i.e., to convert various hand gestures to appropriate text. The proposed method will help people with disabilities communicate or express their thoughts and opinions to others. By offering cutting-edge methods for sign character recognition, this neural network model aims to close the communication gap and enhance the quality of life for those it serves. In this work, a dataset of hand gestures is divided into training, validation, and testing sets to apply a convolutional neural network (CNN) that has been used to properly recognise the characters on the image. The CNN is further tested on random test data to obtain a reliable interpretation of the sign language we want. The uniqueness of this model lies in the fact that it makes use of mixed pooling operation and mish activation function which train the data more smoothly and in a strong, robust way, which overall improves the accuracy and precision in recognising sign characters. © 2024 IEEE.

关键词： Convolutional neural networks

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Optimized Machine Learning Model for Identifying and Predicting Attention Deficit Hyperactivity Disorder(ADHD) 14

Optimized Machine Learning Model for Identifying and Predict...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Balasubramanian, Sriram Karthikeyan, S. Karthikeyan, M. SRM Institute of Science and Technology Department of Computer Science Engineering Chennai India

ISBN: (纸本)9798350335095

Attention Deficit/Hyperactivity Disorder (ADHD) a form of neurodevelopmental disorder often manifests in young age. As per a 2016 WHO Survey for 10 countries, the ubiquity percentage of adult ADHD was determined to be 2.8% globally, showing a high percentage in countries with above average wealth and a strong correlation with male gender [1]. Inattention, impulsivity, and hyperactivity are the three main symptoms of ADHD that children and adults with the disorder often exhibit. However, these symptoms can vary widely from person to person, and some people with ADHD are classified as having inattentional, hyperactive/impulsive, or combined subtypes of the disorder. Futures seem bright for the development of systems that can use phenotypic and neuroimaging data to anticipate a diagnosis. This paper suggests a system for categorizing ADHD issues using fMRI data. The usage of 3D convolutional neural networks (CNNs) in the processing of fMRI has made numerous significant advances. The 3D spatial data of the fMRI data may be retained thanks to these cutting-edge techniques. Additionally, 3D generative adversarial neural networks (GANs) have made considerable advancements in the creation of normal MRI data. In order to predict ADHD using fMRI, this study uses multi modal 3D CNNs and employing 3D GAN for data augmentation. A time distributed single modal 3D CNN model will be compared to the improved multi modal model utilizing MRI data for classification. © 2023 IEEE.

关键词： Magnetic resonance imaging

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On-Road Drivers and People with Visual Impairments: Real-Time Machine Learning-based Speed Breaker and Pothole Detection 15

On-Road Drivers and People with Visual Impairments: Real-Tim...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Fathima, Chahath A. Suhas, G.K. Bhagappa Deepak, N.R. Research center for Computer & Information Sciences Akshaya Institute of Technology Affiliated to Visvesvaraya Technological University Tumkur India Research center for Computer & Information Sciences Brindavan College of Engineering Affiliated to visvesvaraya technological university Bangalore India Department of Computer Science Engineering Atria Institute of Technology Karnataka Bengaluru India

ISBN: (纸本)9798331300579

Although well-maintained roads are an important source of transportation, their economic impact is rarely felt in a ***, drivers who aren't convinced of their safety are seriously at risk from potholes and speed ***, poor night vision, inaccurate speed breaker signage, and the driver's carelessness all contributed to this *** of these elements harm the vehicle's suspension system in addition to having an impact on drivers who are on the *** days, cars with reduced ground clearance have trouble on uneven *** this paper, we proposed to use a Recurrent Neural Network (RNN) in a machine learning method to create a device that can identify bumps and *** device uses an ESP32 GPS Speedometer to locate these bumps and potholes and transmits the position, along with speed, latitude, and longitude values, to a database via *** device also chooses to save the data it collects in the *** the aid of machine learning, it can be classified into various categories based on *** also has a speech module that allows audio notifications to be played over the *** research suggests using a Recurrent Neural Network (RNN), a machine learning method, to train parameters autonomously in order to enhance vehicle suspension health and prevent driver fatigue. © Grenze Scientific Society, 2024.

关键词： Recurrent neural networks

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