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Securing Sensitive Data: How Blockchain Transforms Privacy in Healthcare, Law, and Education

Securing Sensitive Data: How Blockchain Transforms Privacy i...

引用

2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Bale, Ajay Sudhir Arulananth, T.S. Saranya, S. Gadag, Tejashwini Sathiamoorthy, J. Ponnuru, Sudhakiran New Horizon College of Engineering Dept. of Ece Karnataka Bengaluru India Mlr Institute of Technology Department of Electronics and communication Engineering Telangana Hyderabad500043 India Bms Institute of Technology & Management Dept. of Mathematics Bengaluru India School of Computer Science Engineering Reva University Bengaluru India Rmk Engineering college Department of Computer science and Design Kavarapettai India

ISBN: (纸本)9798331543617

In the field of healthcare, law, and education, Blockchain technology (BT) is emerging as a transformative force. This paper helps to explore the usage of BT to enhance the privacy and security in the above said domains. In healthcare industry, blockchain offers privacy and secure management of the vital electronic health records (EHR) and patient health records (PHR), boosting the data integrity and access control. In the law sector, blockchain-enabled smart contracts are providing new possibilities for the transactions automation, transparency, and security of the transactions, thoroughly facing the challenges in terms of jurisdiction and compliance with regulations like GDPR. In the field of education, blockchain technology is improvising the record-keeping, degree verification, and collaborative learning environments. The paper helps to examine the features like the effectiveness, efficiency, and security aspects of blockchain implementations in these sectors, focusing on both the potential benefits and the challenges imposed that need to be addressed for the widespread adoption in real time. © 2024 IEEE.

关键词： Electronic health record

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Exploring the Impact of Image Filters in Facial Detection through Different Classification Algorithms 4

Exploring the Impact of Image Filters in Facial Detection th...

引用

4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Bisht, Raj Kishor Mohan, Daivik Mohan, Aadershi Joshi, Akshay Kumar Lohumi, Yogesh Graphic Era Hill University School of Computing Dehradun India Graphic Era Hill University Dept. of Mathematics Dehradun India Graphic Era Deemed to be Univeristy Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350354218

In the present work, we have analyzed the effect of applying different filters in face detection through different classification methods. For the purpose, we have created different datasets: first dataset consists of 1600 different unfiltered images of eight students with 200 images of each student and second dataset consists of 8000 filtered images using a mix of filters with 1000 images of each student. Then we created five additional datasets, each dataset created by applying a single filter. Five filters namely blur, cut-out, mosaic, noise and shear are used for the present study. Six different algorithms CNN, Random Forest, SVM, Gaussian Naïve Bayes, KNN, and Logistic Regression were applied and results of unfiltered image dataset and filtered image dataset using a mix of filters have been compared. We found a little improvement in the accuracy in the case of KNN and CNN and a little decline in accuracy of other algorithms applied to filtered dataset created using a mix of filters. The significance of difference between the two results is checked through paired t-test. Based on paired t-test, we conclude that filters in images, in general, do not have much effect in face detection through different classification algorithms. The results of unfiltered dataset and individual filtered datasets show that there is a significant difference in results between the unfiltered dataset and blur filtered dataset, others being identical. This indicates that the filter 'blur' has some effect in face detection and other has no significant effect. © 2024 IEEE.

关键词： Random forests

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Development of a Neural Network System for Predicting Topsoil Moisture Using Remote Sensing and Rainfall Forecast Data 4

Development of a Neural Network System for Predicting Topsoi...

引用

4th International Conference on Embedded and Distributed Systems, EDiS 2024

作者： Martinez, Miriam Zambudio Da Silveira, Larissa Haringer Martins Marin-Perez, Rafael Gomez, Antonio Fernando Skarmeta University of Murcia Faculty of Computer Science Murcia Spain Odin Solutions Sl Murcia Spain University of Murcia Dept. of Information and Communication Engineering Murcia Spain

ISBN: (纸本)9798331519872

With only 1% of the planet's water resources accessible in rivers and lakes, and with droughts on the rise, the scarcity of fresh water is becoming an increasingly serious problem for the world's population. Of this 1%, 70% is used by the agricultural sector, which tends to consume more of this scarce resource due to population growth and the resulting pressure to increase agricultural production. One of the fundamental variables in this context is soil surface moisture, which is particularly important for the management of water resources and has a direct impact on the interaction between the soil and the atmosphere. Therefore, this work discusses the current advances made in the measurement of this variable, which is fundamental for precision agriculture, and creates a model capable of predicting topsoil moisture in a study basin located in the southeast of the Iberian Peninsula, characterised by its predominantly agricultural. To develop the model, we used topsoil moisture data from the Sentinel-1 satellite, probes distributed over the study area, precipitation measurements from meteorological stations and ensemble forecasts from meteorological models. The final model predicted surface soil moisture (SSM) with an average Root Mean Squared Error (RMSE) of 0.15 and a maximum difference between observed and predicted data of 1.37%, demonstrating the ability to predict from one to ten days in advance. © 2024 IEEE.

关键词： Soil moisture

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Comparative Study of Face Tracking Algorithms for Remote Photoplethysmography

Comparative Study of Face Tracking Algorithms for Remote Pho...

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2023 IEEE International Conference on Electrical, computer and Energy Technologies, ICECET 2023

作者： Jayasinghe, J.A.S.Y. Katsigiannis, Stamos Malasinghe, Lakmini Sri Lanka Institute of Information Technology Dept. of Electrical and Electronic Engineering Malabe Sri Lanka Durham University Dept. of Computer Science Durham United Kingdom

ISBN: (纸本)9798350327816

Remote Photoplethysmography (rPPG) is a non-invasive approach for monitoring Heart Rate (HR) that can be used in various applications in healthcare and biometrics. rPPG measurements acquired using facial videos have become very popular and one of the main steps of this technique is facial tracking and Region of Interest (ROI) extraction. This research paper investigates four widely used face tracking algorithms, namely MediaPipe Face Mesh (MPFM), Haar Cascade, Multi-task Cascaded Convolutional Network (MTCNN), and Dlib, concerning their ROI extraction capabilities for rPPG HR measurements. Using evaluation metrics such as accuracy, processing time, and ease of extracting ROIs, this work also recommends the most suitable face tracking algorithm from those mentioned above for rPPG measurements, and presents a compilation of a prioritization list of ROIs based on their sensitivities for rPPG measurements. Experimental results showed that the MPFM algorithm and cheek ROIs provided the best HR measurements. © 2023 IEEE.

关键词： Face Tracking Machine Learning Remote Photoplethysmography ROI Extraction

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An Iterated Local Search Algorithm for Commuting Bus Routing Problem with Latest Arrival Time Constraint

An Iterated Local Search Algorithm for Commuting Bus Routing...

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2022 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2022

作者： Hashimoto, H. Hu, Y. Sugiura, T. Takada, Y. Yagiura, M. Tokyo University of Marine Science and Technology Dept. Logistics and Information Engineering Tokyo Japan Tokyo University of Science Dept. Applied Mathematics Tokyo Japan Nagoya University Graduate School of Information Science Dept. Computer Science and Mathematical Informatics Nagoya Japan

ISBN: (数字)9781665486873

ISBN: (纸本)9781665486873

Given a set of bus stops and a set of employees, the commuting bus routing problem with latest arrival time constraint (CBRP-LATC) aims to determine routes of buses to carry every employee from the company to one of the bus stops at which he/she wants to get off. Each employee walks to his/her destination after getting off the bus and has a desired time when he/she wants to arrive there, which is called the latest arrival time. The objective is to find a set of routes with the minimum total traveling cost that covers all employees' destinations under bus capacity and latest arrival time constraints. We propose an iterated local search algorithm in which we use two kinds of neighborhood operations called set-del/1-ins and VRP-OPT∗. The set-del/1ins operation consists of three neighborhood operations, setdel, 1-ins and set-del-ins to remove or assign employees from or to a bus route. The VRP-OPT∗ is a local search algorithm to improve the distances of routes based on four neighborhood operations, 2-opt, 2-opt∗, relocate and cross-exchange. Computational results show the effectiveness of the two neighborhood operations. © 2022 IEEE.

关键词： Personnel

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Effect of Hyper-Parameter Tuning on the Performance of Augmented Random Search 1

Effect of Hyper-Parameter Tuning on the Performance of Augme...

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1st International Conference on Informatics, ICI 2022

作者： Rana, Misha Kumar, Nimesh Sharma, Himanshi Pandey, Kavita Jaypee Institute of Information Technology Dept. of Computer Science & Engineering and Information Technology Noida India

ISBN: (纸本)9781665437219

Recently, researchers working in the field of Artificial Intelligence have been keen on finding new algorithms that provide optimal results. One such reinforcement learning algorithm is the Augmented Random Search. This algorithm is imperative as it provides better results over other reinforcement learning algorithms. Finding the optimal values of hyper-parameters helps the algorithm to work up to its full potential. In this work, we have made an effort to find out the best hyper-parameter settings for the learning rate and direction, for this specific task. Our results show how a change in these values can impact the final maximum rewards obtained during the training process. We report novel findings of the impact of hyper-parameters on the performance of ARS algorithm. Finally, an effort has been made to find out by what percentage the ARS performs better than other Reinforcement Learning algorithm, TRPO. © 2022 IEEE.

关键词： Reinforcement learning

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Optimal Classification Tiling Algorithm 15

Optimal Classification Tiling Algorithm

引用

15th International Conference on information and Communication Technology Convergence, ICTC 2024

作者： Cheng, Wei-Chen Liou, Cheng-Yuan Polylogarithmic technology Ltd United Kingdom University of Oxford Department of Oncology United Kingdom National Taiwan University Dept. of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350364637

This work presents a max-margin tiling algorithm to construct multi-layer perceptrons with perfect performance for all training patterns. Each perceptron distinguishes a portion of a single class patterns from all the rest patterns, as large as possible. The process is a divide and conquer approach therefore the size of the portion gradually reduced and the algorithm accelerate accordingly. Each layer of such perceptrons encodes different class patterns with different codes, maintaining faithful representations. The max-margin separation boundary is optimal to against noise therefore this construction provides resolution of challenging classification of noisy patterns such as seriously corrupted images. To our knowledge, it gives the best performance for noisy testing patterns among all existing methods. We applied the algorithm on highly sparse data of genetic mutations and identified not only gene NF1, which had been previously found by monogenic analysis, but also a list of genes whose deletions may polygenically associate with a group of breast cancer patients. © 2024 IEEE.

关键词： Genetic algorithms

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A Decision Support Model for Improved Routing in Software Defined Vehicular Ad hoc Networks 18

A Decision Support Model for Improved Routing in Software De...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Kumar, Manish Raw, Ram Shringar University School of Information Communication and Technology Guru Gobind Singh Indraprastha University Dept. of Computer Science and Engineering New Delhi India New Delhi India

ISBN: (纸本)9789380544519

The inherent dynamism of Vehicular Ad hoc Networks (VANETs), distinguished by rapid movement and frequent changes in network structure, imposes substantial obstacles in effective route management and optimization. The use of Software Defined Networking (SDN) in VANETs enables a centralized control mechanism that provides flexibility and adaptability in the administration of the network. The SDN Controller in SDN is responsible for route determination. There are many factors like the distance between vehicles, movement direction, connection duration, data transfer rate, speed, and vehicle data transmission range that are involved to determine the route. This study introduces an innovative decision support model for Software Defined Vehicular Ad hoc Networks (SD-VANETs) that use the Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to improve the route determination which further enhances the VANETs routing. The model estimates the relative importance of the factors to decide the route by the SDN controller in Sd-VANETs. This work enhances the progress of intelligent routing solutions in VANETs, providing a scalable and efficient way for future ITS advancements. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： AHP MCDM in Routing Multi Criteria Decision Making SD-VANET TOPSIS VANET VANET Routing

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Challenges in Working with Unstructured Data in the LLM Era: NER Processes Using Graph Neural Networks 4

Challenges in Working with Unstructured Data in the LLM Era:...

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4th International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2024

作者： Bouquet, Paolo Molinari, Andrea Sandri, Simone University of Trento Department of Information Engineering and Computer Science Trento Italy University of Trento Dept. of Industrial Engineering Trento Italy Okkam S.r.l. Trento Italy

ISBN: (纸本)9798350391183

The application of neural networks and large language models (LLMs) in the financial technology (fintech) sector has significantly enhanced capabilities of providing better services based on unstructured documents, mainly thanks to the astonishing interactive capabilities of large language models (LLMs). However, in these contexts, precisely recognizing named entities (for example, the beneficiary of an insurance policy) and creating reliable, domain-dependent, private knowledge graphs is more crucial than conversational capabilities. Moreover, optimizing these models for dynamic data environments and ensuring Explainability presents notable challenges. This paper aims to present our approach for the creation of a knowledge graph that uses graph neural nets (GNN) to provide a better named-entity recognition (NER) process, to identify entities in the text precisely and to solve other issues in knowledge graph management, like the co-ference resolution problem or focusing on dynamic data environments and the critical need for Explainability of modern AI-based solutions. The paper presents our preliminary results on one of the various pipeline steps that creates the navigable knowledge graph. © 2024 IEEE.

关键词： Knowledge graph

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An Ameliorated Methodology for Water Supply and Management Using Binary Trees 14

An Ameliorated Methodology for Water Supply and Management U...

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

作者： Arunakumari, B.N. Setty, Vishal R Vivek, Villuri Ramesh, Sujan Sowmyashree, P. Kshama, S.B. Bms Institute of Technology and Management Computer Science and Engineering Karnataka560064 India Bms Institute of Technology and Management Computer Science and Engineering Bangalore India Bms Institute of Technology and Management Dept. of Information Science and Engineering Bengaluru India

ISBN: (纸本)9798350335095

Water supply system is an infrastructure for collection, storage, transmission, and distribution of water to homes, factories, irrigation of agricultural fields, etc. Water holds an important factor in growth of the city. Earlier, lakes and rivers are the major source of water supply to the cities and villages. The rivers followed their own path throughout the city and provided water to some specific areas. Due to this the new areas which were being developed would not have direct connection to the new areas, the people would have to look for other alternatives or use the water available from the underground water beds, by building wells. Water connection to different regions is setup in an organized manner. Even though everything is organized, but there are certain drawbacks such as improper detection in the leakage of pipe lines and failure in the proper use of technology to setup and *** this paper, an attempt has been made to rectify these problems by applying the concept of binary search tree. Also, we have discussed about how pipelines can be setup underground in a smooth manner and using IOT devices to detect leakages or breakages in the pipelines, so that the water is not wasted. © 2023 IEEE.

关键词： Water supply

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