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Regulatory Support to Prevent Health data Breaches

Regulatory Support to Prevent Health Data Breaches

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international conference on computing, Engineering, and Design (ICCED)

作者： Warmanto Firmansyah Teddy Mantoro Pratama Dahlian Persadha Medical Intelligence Postgraduate Program State Intelligence College Bogor Indonesia Department of Computer Science Media-Tech Lab Sampoerna University and State Intelligence College Jakarta Indonesia

ISBN: (纸本)9781665453905

Digitalization of health data is one of the advances in the era of information technology. It is very useful for both the patient and the paramedic. Unfortunately, it also has a hole because there are more breaches due to the leakage of health data nowadays. Electronic health data is particularly vulnerable, as it is most often targeted by attackers or hackers. Health data were considered to have the most sensitive personal data. In Indonesia, there have been several leaks of health data. The breached data are from government-owned applications including BPJS Kesehatan, eHAC, and vaccine certificates. The health data needed to be protected, so there is no misuse of it. The breached health data can be harmful to the person affected. This study is to capture the situation and problem of health data breaches on the government-owned application in Indonesia. The result of this paper is to propose a recommendation to minimize the occurrence of health data breaches. Strategic moves are needed in anticipating the occurrence of health data breaches. One of the steps that can be taken is the preparation of regulations to secure health data. With the relevant regulations, it is projected that the breach of health data can be minimized.

关键词： Regulators data security Government data protection data breach Regulation Vaccines

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Face Mask Detection using Single Shot Multibox Detector and Mobile Net

Face Mask Detection using Single Shot Multibox Detector and ...

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Advanced technologies in Intelligent Control, Environment, computing & Communication Engineering (ICATIECE), international conference on

作者： P. Ranjana K. Ramesh Shivanandhan.J. S Manoj. B Department of CSE Hindustan Institute of Technology and Science Chennai India

ISBN: (纸本)9781665493970

During the pandemic time government took many safety measures to protect the public at common gathering places. People are insisted on wearing a face mask to protect themselves from COVID. Even then many people were roaming without a mask in public places. The proposed technique to detect the face mask is to identify the person's face with mask and person's face without mask and reporting to the safety officers about the persons without mask for further action. The proposed Face mask detection is developed using the ML technique which can be used to classify the people wearing masks and not wearing masks with the input given to the model. The proposed face mask detector is a one-stage detector that focuses on detecting the face mask alone. This work is implemented using the Tensor flow and Computer vision libraries. NumPy is used for image processing. The data set used in MAFA dataset. The model is trained using this data set to get the accurate results. To enable multiple detection here the single shot with multi box detector is used. The base model used for this process is Mobile Net V2. The proposed model is simple and it can be integrated with several other technologies to provide high accuracy percentage of output in the minimum possible time.

关键词： Tensors Pandemics Computational modeling Surveillance Image processing Detectors Time measurement

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Age-Based Ranking of YouTube Videos for Improved Parental Controls in Smart TV Environment

Age-Based Ranking of YouTube Videos for Improved Parental Co...

引用

Emerging technologies in Electronics, computing and Communication (ICETECC), international conference on

作者： Iftikhar Alam Azhar Uddin Shah Khusro Department of Computer Science City University of Science & Information Technology Peshawar Pakistan Department of Computer Science University of Peshawar Peshawar Pakistan

YouTube is a popular social media networking site that contains billions of videos. Many YouTube videos target children of different ages with offensive, inappropriate, violent, etc. Numerous one-size-fits-all countermeasures and research work have been deployed and suggested. However, these solutions are ineffective in accurately detecting inappropriate content for a diverse audience of different needs and requirements. In this research work, instead of one-size-fits-all, we consider a mutable age-based context, where different Age-Groups (AG) have different choices and needs. A novel and real-time approach have been proposed to prevent and allow the audience of varying AG towards the diverse content of YouTube, specifically in a smart TV-watching scenario. The proposed system analyses the running video through its metadata for teenagers, children, and adults. In parallel with data, the proposed model captures the viewers in real-time, detects their age, and checks the displayed video against the detected AG for appropriateness. The same system responds to parental consent and overrides its stop/play policies according to the direct input provided by parents/guardians with a diverse psychological setup, developed by their beliefs, religion, and cultural sensitivities. The proposed solution leverages Random Forest Classifier–a supervised text classification approach with 80% accuracy and Convolutional Neural Network for age determination using the Caffe model.

关键词： Video on demand Sensitivity Smart TV Social networking (online) Text categorization Psychology Metadata

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Stud app—student data management application 1st

Stud app—student data management application

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1st international conference on Frontiers in Smart Systems technologies, ICFSST 2019

作者： B, Robin P Kurian, Pavan Mathew, Justin Department of Computer Science & Engineering Saintgits College of Engineering Kottayam India

ISBN: (纸本)9789811550287

Nowadays, in most of the engineering colleges, students are using the conventional methods for applying for duty leaves, medical leaves, other permissions, etc. Also, the students’ data regarding their personal information, academic performance, etc., are kept in computer databases or in special record books. The students submit the hard copy of the certificates of their activities to their faculty advisors. The activity points earned by each student have to be calculated and uploaded to the university websites by the faculty staff. As the number of students is high, it is a tedious task. It will be easier for the students and faculties if there is a facility which enables them to apply for leaves, submit certificates, store and access academic details of students, etc., via an application. This paper explains a web application that will enable the students and faculties to quickly process the leave applications and other requests, update and access students’ data, upload certificates for activity points, etc. © Springer Nature Singapore Pte Ltd 2021.

关键词： Students

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Ant Colony Algorithm for Food Analysis data Processing

Ant Colony Algorithm for Food Analysis Data Processing

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international conference on Multi-modal Information Analytics, MMIA 2021

作者： Shi, Jing Xi’an Medical University Xi’an Shaanxi710021 China

ISBN: (纸本)9783030748135

With the development and popularization of computer technology, computer technology plays an important role in food science. For example, SAS, Excel and other software are used to process experimental data, in engineering design, food formula and instrument analysis, simulation technology, the automatic detection and control of food processing have different degrees of application. With the development of information technology and computer technology, simulation technology plays an increasingly important role in food industry. At present, simulation technology has been successfully used in petrochemical process, coal mine production process, construction process, fire control discipline, traffic safety and other fields involving safety issues. Simulation technology has become one of the key technologies in system security engineering. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Food processing

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Application and Development of Big data in the Field of Construction Engineering

Application and Development of Big Data in the Field of Cons...

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international conference on Multi-modal Information Analytics, MMIA 2021

作者： Liu, Jia Huang, Mingfei Chen, Jinfeng Wu, Zhengfei Li, Shanshan Chongqing College of Architecture and Technology Chongqing China Department of Military Facility Army Logistics University Chongqing China China Construction Fifth Engineering Division Wall and Decoration Co. Ltd. Changsha Hunan China

ISBN: (纸本)9783030748104

In the social development and transformation, the progress of science and technology has played an important role in promoting. In the field of intelligent building research, with the comprehensive application of various technologies in it, the application range of intelligent buildings has also been expanded, and the application of intelligent buildings has begun to be applied. It develops and forms different application systems to provide people with various services in production and life, and also makes the building operation process more green and environmentally friendly, and fully meets the needs of sustainable development. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Big data

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Smart Plant Leaf Disease Detection System using Internet of Thing (IOT) and PLDP Net-RF Model

Smart Plant Leaf Disease Detection System using Internet of ...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： R. Santhoshkumar B. Rajalingam P. Deepan P. Santosh Kumar Patra Department of Computer Science and Engineering St. Martins Engineering College Secunderabad Department of Artificial Intelligence and Data Science St. Martins Engineering College Secunderabad Department of CSE (AI & ML) St. Martins Engineering College Secunderabad St. Martins Engineering College Secunderabad

The decline in apple yield is largely due to diseases that harm the apple's leaves. As a result, it is critical that diseases of citrus plants be detected using an intelligent detection technology. Many artificial intelligence problems can now be solved using deep learning methodologies. Consequently, we decided to use this technology to identify diseases that can impact citrus fruit and leaves. A model based on convolutional neural networks (CNNs) is proposed in this study utilising an integrated strategy. There was a need to construct a model to discriminate healthy vegetables and plants from those with typical apple leaf diseases like black rot and apple scab, therefore the Random Forest (RF) model was devised. The PLDP Net-RF model, which was introduced, may extract complementary discriminative qualities by merging many different layers of data. A number of cutting-edge deep learning models were tested against the RF model on the PlantVillage datasets. The PLDP Net-RF model outperforms its competitors in a variety of evaluation metrics, according to the results of the tests. For farmers who are concerned in detecting apple leaf diseases, the PLDP Net-RF model is a beneficial tool.

关键词： Radio frequency Deep learning Measurement Image segmentation Neural networks Merging Internet of Things

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The Parallel Reversible Pebbling Game: Analyzing the Post-quantum Security of iMHFs 20th

The Parallel Reversible Pebbling Game: Analyzing the Post-qu...

引用

20th international conference on Theory of Cryptography (TCC)

作者： Blocki, Jeremiah Holman, Blake Lee, Seunghoon Purdue Univ W Lafayette IN 47906 USA

ISBN: (纸本)9783031223174;9783031223181

The classical (parallel) black pebbling game is a useful abstraction which allows us to analyze the resources (space, space-time, cumulative space) necessary to evaluate a function f with a static datadependency graph G. Of particular interest in the field of cryptography are data-independent memory-hard functions f(G,H) which are defined by a directed acyclic graph (DAG) G and a cryptographic hash function H. The pebbling complexity of the graph G characterizes the amortized cost of evaluating f(G,H) multiple times as well as the total cost to run a brute-force preimage attack over a fixed domain X, i.e., given y is an element of{0, 1}* find x is an element of X such that f(G,H)(x) = y. While a classical attacker will need to evaluate the function f(G,H) at least m = vertical bar X vertical bar times a quantum attacker running Grover's algorithm only requires O(root m) blackbox calls to a quantum circuit C-G,C-H evaluating the function f(G,H). Thus, to analyze the cost of a quantum attack it is crucial to understand the space-time cost (equivalently width times depth) of the quantum circuit C-G,C-H. We first observe that a legal black pebbling strategy for the graph G does not necessarily imply the existence of a quantum circuit with comparable complexity-in contrast to the classical setting where any efficient pebbling strategy for G corresponds to an algorithm with comparable complexity for evaluating f(G,H). Motivated by this observation we introduce a new parallel reversible pebbling game which captures additional restrictions imposed by the No-Deletion Theorem in Quantum computing. We apply our new reversible pebbling game to analyze the reversible space-time complexity of several important graphs: Line Graphs, Argon2i-A, Argon2i-B, and DRSample. Specifically, (1) we show that a line graph of size N has reversible space-time complexity at most O (N-1+ 2/root log N). (2) We show that any (e, d)-reducible DAG has reversible space-time complexity at most O (Ne +

关键词： Parallel reversible pebbling Argon2i DRSample data-independent memory-hard function

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Anomaly Detection of ECG Signals using Attention-derived Convolutional Long Short-term Memory

Anomaly Detection of ECG Signals using Attention-derived Con...

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data science and Intelligent computing (ICDSIC), international conference on

作者： Rawa Ali Hasan Lamia Abed Noor Ali Aaied College of Computer Science and Information Technology University of Al-Qadissiya Diwaniya Iraq

An electrocardiogram (ECG) is a diagnostic procedure that measures electrical activity in the heart and helps doctors detect any irregularities. Early detection of anomalies in ECG signals is critical to offer patients the best treatment options. An electrocardiogram, or ECG, is time-series data generated by the electrical activity of the heart. Applying machine learning techniques to anomaly detection in these time series data has been the focus of recent research initiatives. These techniques necessitate feature extraction, which introduces complications such as the discrepancy in the features extracted and variability in the ECG parameters. Avoiding the need for human-crafted features from ECG signals is possible with the help of deep learning techniques, such as approaches built on Convolution Neural Networks (CNN). Given the volume of the ECG datasets and the sophistication of recent CNN technologies, However, CNN's performance in detecting anomalies in ECG was hindered by its inability to capture ECG signals of varying lengths. To tackle this issue, we presented C2LSTM, a deep learning model consisting of CNN, LSTM cells, and an attention mechanism. This model has more flexibility in terms of input length and uses half as many parameters compared to the CNN models. We conducted a considerable number of tests on the MIT-BIH dataset in order to give a thorough evaluation of the C2LSTM model against the AAMI standards; the C2LSTM achieved a sensitivity of 96%, positive predictivity of 99%, and F1-score of 96%.

关键词： Heart Deep learning Sensitivity Convolution Computational modeling Electrocardiography Predictive models

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Critical Features Selection of Training Model for Intrusion Detection System 6th

Critical Features Selection of Training Model for Intrusion ...

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6th international conference on Advanced Machine Learning technologies and Applications, AMLTA 2021

作者： Li, Jian Zheng, Weimin Chang, Kuo -Chi Wang, Shuai Luo, Yi School of Information Science and Engineering Fujian University of Technology Fuzhou China College of Computer Science and Technology Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9783030697167

The purpose of this paper is to determine the key characteristics required for intrusion detection model construction to achieve the greatest accuracy in detection. In this paper, Two-Class Neural Network, Two-Class Locally-Deep Support Vector Machine, and Two-Class Averaged Perceptron are selected. To test the accuracy of intrusion detection. This paper uses the NSL-KDD data set to discuss the accuracy of binary classification in classification, uses permutation feature importance to rank features, and according to the analysis, results show that ("Service","dst_host_rerror_rate","Flag",) features are the most important. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Intrusion detection

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