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Framework for securing EPR records with Searchable encryption AES

Framework for securing EPR records with Searchable encryptio...

引用

2022 IEEE Delhi Section Conference, DELCON 2022

作者： Negi, Lokesh Bhatt, Shobha Netaji Subhas University of Technology Department of Computer Science and Engineering East Campus Delhi India

ISBN: (纸本)9781665458832

Electronic Patient records (EPR) are very sensitive to individuals, so it becomes very important to manage the security with preciseness. Recent attacks show that improper security management to EPR causes a serious violation of HIPPA by seeking Protected Health Information. Managing the security of EPR records is very time and cost constraint, so we need cost-efficient methods for securing the EPR records. EPR records have attributes that need various levels of security, so to encrypt the whole EPR with the same encryption algorithm is a wastage of time and power. To overcome this issue, we proposed a framework that first classify the EPR attributes into three class and then employs the appropriate encryption method to that attribute based on the class. The proposed framework reduces the burden of encryption time and cost. © 2022 IEEE.

关键词： Cryptography

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Improvisation in Water Quality Prediction using H20 ViML: A comparative study

Improvisation in Water Quality Prediction using H20 ViML: A ...

引用

2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Mallika Singh, Nanhay Lathar, Pankaj Netaji Subhas University of Technology Department of Computer Science & Engineering East Campus Delhi India Delhi Skill & Entrepreneurship University School of Information Technology & Information Technology Enabled Services Delhi India

ISBN: (纸本)9798350373783

Water is the source of life. The concern however rounds about to availability of safe and healthy drinking water to beings. With increasing industrialization and urbanization pollution levels are increasing. Relying on age old long calculative methods for water quality prediction is simply wastage of our resources. Nowadays collected data is also a resource. If we use Machine Learning models, dependency on manual calculation and identification of degraded parameter can pave way to enhancement. This can be a breakthrough in global concern of water management. Currently Water Quality Indexing is used which is a lengthy, data dependent and trouble some process. Machine Learning models for regression to increase accuracy can be used. Comparison can be drawn between Random forest, XG Boost, Decision tree and other regression focused algorithm, Further using ensemble models using H2O ML stacked ensemble automation accuracy can be increased. Further proper identification of parameters can be achieved by using genetic algorithms such as PSO, sparrow searching. The main objective is to improve accuracy and broaden the spectrum of parameter consideration using Machine Learning algorithms. © 2024 IEEE.

关键词： Genetic algorithms

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A Model for Teaching Geometry to Schoolchildren Using Smartphone App 1

A Model for Teaching Geometry to Schoolchildren Using Smartp...

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1st International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2022

作者： Yadav, Savita Chakraborty, Pinaki Yadav, Sourabh Netaji Subhas University of Technology Department of Computer Science and Engineering New Delhi110078 India

ISBN: (纸本)9781665436472

Smartphones are popular among children of all age groups and have the potential to make learning process interactive as well as engaging. Educational smartphone apps for children should counterbalance the limited abilities of children. An iterative model for developing educational apps for schoolchildren has been proposed and substantiated by designing an app to teach about triangles in the present study. We conducted a pretest-posttest control group study. The app developed by us along with traditional methods was used to make 26 children in the experimental group aware of the concept and properties of triangles while the control group with equal number of children was taught about triangles using only chalk and blackboard. The performance of children in answering questions related to triangles was then assessed in the two groups. The study did not notice any significant difference in the performance of the children in the two groups. However, the feedback of the children in the experimental group was found to be positive in terms of the overall environment created by learning through the app in the classroom. Meticulously planned use of geometry apps may augment classroom teaching and stimulate children to be actively involved. Parents, practitioners and teachers need to be aware of the requirements of children and facilitate to provide conducive, age appropriate smartphone apps for learning geometry to them. © 2022 IEEE.

关键词： Smartphones

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A Comparative Study of Machine Learning Algorithms for Photovoltaic Degradation Rate Prediction 3

A Comparative Study of Machine Learning Algorithms for Photo...

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3rd International Conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Dhingra, Bhavya Tyagi, Shivam Tomar, Anuradha Netaji Subhas University of Technology Department of Electrical Engineering Delhi78 India Netaji Subhas University of Technology Department of Computer Science Delhi78 India Netaji Subhas University of Technology Department of Inst. and Control Engg. Delhi78 India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Solar energy is the most versatile, harmless, and non-exhaustive energy present in nature because of this, the number of photovoltaic modules that have been integrated into the electrical grid is increasing every day. As a result, reliable forecasting of falling power output over the period of time is required for an acceptable return on investment made for these interactions, to estimate the power delivered to the power system by these photovoltaic modules, photovoltaic degradation rates must be known. In this study degradation rates of photovoltaic modules are estimated using the application of nine machine learning models and the effectiveness of these models is compared in order to determine which model is most efficient. All the models are tested on various evaluation metrics like mean absolute error, root mean squared error, and mean percentage error for an unbiased evaluation, and the run time of these models is also calculated and compared to determine the overall efficiency of the models. © 2022 IEEE.

关键词： Machine learning

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An Analysis of Ethical Dimension of Machine Learning Models in Software Defect Prediction in Healthcare Sector 3rd

An Analysis of Ethical Dimension of Machine Learning Models ...

引用

3rd International Conference on Electronic Governance with Emerging Technologies, EGETC 2024

作者： Kansal, Mrignainy Sibal, Ritu Ram, Pancham Department of Computer Science and Engineering Netaji Subhas University of Technology Delhi India

ISBN: (纸本)9783031770289

Due to the rapid growth of Machine Learning and its applications in various sectors, especially in the Healthcare Sector, ethical considerations become a critical factor. This research paper analyses various ethical concerns related to machine learning applications in healthcare and its challenges. Previous research has focused on different types of ethical issues that are faced while implementing machine learning techniques and algorithms in the healthcare sector. These issues are related to patient privacy, algorithmic biases, etc. In this research paper, we analyze different types of machine learning algorithms that are most frequently applied in the healthcare sector and compare them based on their accuracy, precision, and other factors. After that, we perform a comprehensive review of various ethical guidelines and concerns related to machine learning in healthcare. This research paper combines Software fault prediction and the Ethical use of Machine Learning algorithms to improve the reliability and efficiency of Healthcare Software Systems. It evaluates the performance of different ML techniques used in the Healthcare Sector and gives the results. By clearly identifying and understanding the ethical concerns related to ML applications, we can ensure the proper development of Healthcare facilities and follow ethical guidelines at the same time. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Artificial Intelligence for Alzheimer’s Disease Diagnosis: A Review of Recent Advancements, Obstacles, and Future Prospects

Artificial Intelligence for Alzheimer’s Disease Diagnosis: ...

引用

IEEE Students' Conference on Electrical, Electronics and computer Science (SCEECS)

作者： Ashima Jain Bijendra Kumar Department of Computer Science and Engineering Netaji Subhas University of Technology Delhi India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

Alzheimer’s disease (AD) is a common chronic ailment that predominantly affects elderly people. AD is a neurological condition that causes a steady deterioration in cognitive function. There is no cure for AD; however, early detection and timely treatment can slow its progression. The development and implementation of artificial intelligence (AI) technology in medicine has greatly improved the accuracy and precision of the detection and diagnosis of AD. Machine learning (ML) and deep learning (DL) models have grown in popularity in recent years, particularly for medical image analysis. The application of ML and DL approaches to diagnose AD has grown significantly in recent years. This study reviews numerous studies from 2020 to 2024 that used ML and DL to detect AD. It gives a complete review of the ML/DL models used in these studies, as well as their limitations and future prospects.

关键词： Deep learning Accuracy Reviews Transforms Multisensory integration Medical diagnosis Artificial intelligence Alzheimer's disease Older adults Medical diagnostic imaging

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HydroPro: An IoT-Based Smart Water Bottle

HydroPro: An IoT-Based Smart Water Bottle

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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Nirbhavane, Aadi Kushwaha, Riti Singal, Gaurav Tomar, Abhinav Pal, Vipin Gupta, Ankur Amity International School Delhi India School of Computer Science and Technology Bennett University Greater Noida India Netaji Subhas University of Technology Computer Science and Engineering Department Delhi India Rishihood University Newton School of Technology Department of Computer Science Sonipat India

ISBN: (纸本)9798350364866

Water is a necessity for the correct functioning of the human body. In the modern era, most people have hectic schedules, making it difficult for them to remember to drink enough water and track their water consumption. This problem of not being able to track the daily water intake becomes even more pronounced for people of older age groups. In addition, people on medications also require strict water intake to dilute the toxins that the medicines introduce in their body. We present our product - HydroPro, a smart water bottle aimed at encouraging our target audiences to better their hydration habits. Our product gives a more personalized water tracking facility to the user wherein we take the user's environment parameters into account (namely temperature and humidity) to notify the user whether they have consumed enough water. The objective is to enable effective management of daily water requirements and to ensure users drink good quality water. The result shows the working effectiveness of smart water bottle system which is also tested and validated on mobile application. © 2024 IEEE.

关键词： Bottles

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Deepfake Detection: Leveraging InceptionResNetV2 and LSTM for Enhanced Accuracy

Deepfake Detection: Leveraging InceptionResNetV2 and LSTM fo...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Saini, Aradhna Attiuttama Naaz, Sheenam Shivhare, Anushka Dhuriya, Gaurav Yadav, Neha G L Bajaj Institute of Technology and Management Computer Science and Engineering Greater Noida India G L Bajaj Institute of Technology and Management Artificial Intelligence and Machine Learning Greater Noida India Sharda University Computer Science and Engineering Greater Noida India Netaji Subhas University of Technology Computer Science and Engineering Delhi India

ISBN: (纸本)9798331508685

The antique adage "seeing is believing"is simply no longer actual, which has profound implications for a ramification of sides of our lives. Deepfakes are becoming less complicated and simpler to create as technology advances. In fact, with the right software program, you can do a part of it right out of your palm. it is challenging to pick out deepfakes. The capability of the human eye to differentiate among deepfakes has reduced. however, a few academics have tried to identify deepfake. Deepfakes are media that can be generated by synthetic intelligence (AI) using algorithms. Artificial intelligence algorithms are designed to understand the traits of both the supply and the target photo. Subsequently, the target image is positioned on the pinnacle of the supply photograph. Our goal is to pick out video deepfakes by way of utilizing deep getting-to-know neural networks which include InceptionResNetV2 and LSTM. With the usage of transfer learning and the pre-trained InceptionResNetV2 CNN for characteristic extraction and vector production, we were able to efficaciously assemble a deepfake detection version. The capabilities had been used to train the LSTM layer, and the confusion matrix that was produced offers us the accuracy of testing and validation. The corresponding version's accuracy for 20 and 40 epochs was 84.75% and 91.48%, respectively. © 2025 IEEE.

关键词： Generative adversarial networks

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Maximum Power Point Tracking (MPPT) Control of Grid Connected Solar-Wind Energy Conversion System

Maximum Power Point Tracking (MPPT) Control of Grid Connecte...

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2023 International Conference on Power, Instrumentation, Energy and Control, PIECON 2023

作者： Yadav, Dharmendra Singh Ali, Javed Netaji Subhas University of Technology Department of Electrical Engineering Delhi India Indian Institute of Technology Department of W.R.D. & Management Roorkee India

ISBN: (纸本)9798350399769

Grid-connected solar-wind energy conversion systems are distinguished by their dependability and operational efficiency. Modeling, simulation, and control of a system that converts energy from solar and wind and feeds it into the grid are described in this paper. The system contains of a wind turbine, a synchronous generator with permanent magnets, a solar array, a controller, and converters. An MPPT control technique with optimal tip speed ratio (OTSR) delivers maximum power in wind energy systems independent of variations in wind flow. The solar system's highest power point is monitored with the use of the Perturb and Observe (P&O) Maximum Power Point Tracking (MPPT) algorithm. The control technique works on the inverter to keep the DC link voltage constant and controls the injected power to maintain the constant voltage and frequency fluctuations within the limit at the grid side. In the MATLAB/Simulink software, the suggested system's dynamic behaviour and control are examined. © 2023 IEEE.

关键词： Wind turbines

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Analysis of Search Based Algorithms for Prediction of Aging Related Bugs 7th

Analysis of Search Based Algorithms for Prediction of Aging ...

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7th International Conference on Smart Trends in Computing and Communications, SmartCom 2023

作者： Bansal, Ankita Khanna, Megha Dhawan, Laavanaya Krishnamurthy, Juhi Department of Information Technology Netaji Subhas University of Technology Dwarka India Department of Computer Science Sri Guru Gobind Singh College of Commerce University of Delhi New Delhi India Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India

ISBN: (纸本)9789819908370

Genetic algorithms (GAs) and Search based algorithms (SBAs) are very powerful optimization methods which are inspired by the success of evolutionary processes in the natural world. These optimization methods have been used to develop effective classifiers and have been successfully applied to various domains. This paper aims to evaluate GA based methods for the task of ARB prediction and analyze their effectiveness for the same. ARBs are software defects that manifest in a software module after prolonged usage. Such bugs are extremely hard to detect through traditional software testing methods and can have a very high impact when encountered during the operation of a software. Predictive models that can analyze a software code and flag the possibility of an ARB can be useful in mitigating the impact of ARBs. In this paper, we present an empirical study, that analyses statistically, the performance of GA/SBA based classifiers for ARB prediction on five datasets. We account for the data imbalance by creating synthetic minority samples using SMOTE. Results of this study show that SBA algorithms are effective in developing predictive models for ARB detection and their performance is comparable to machine learning algorithms (ML) for the same. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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