Maintaining privacy for a recommender system diagnosis using blockchain and deep learning.

The healthcare sector has been revolutionized by Blockchain and AI technologies. Artificial intelligence uses algorithms, recommender systems, decision-making abilities, and big data to display a patient's health records using blockchain. Healthcare professionals can make use of Blockchain to display a patient's medical records with a secured medical diagnostic process. Traditionally, data owners have been hesitant to share medical and personal information due to concerns about privacy and trustworthiness. Using Blockchain technology, this paper presents an innovative model for integrating healthcare data sharing into a recommender diagnostic computer system. Using the model, medical records can be secured, controlled, authenticated, and kept confidential. In this paper, researchers propose a framework for using the Ethereum Blockchain and x-rays as a mechanism for access control, establishing hierarchical identities, and using pre-processing and deep learning to diagnose COVID-19. Along with solving the challenges associated with centralized access control systems, this mechanism also ensures data transparency and traceability, which will allow for efficient diagnosis and secure data sharing

IMapC: Inner MAPping Combiner to Enhance the Performance of MapReduce in Hadoop

Hadoop is a framework for storing and processing huge amounts of data. With HDFS, large data sets can be managed on commodity hardware. MapReduce is a programming model for processing vast amounts of data in parallel. Mapping and reducing can be performed by using the MapReduce programming framework. A very large amount of data is transferred from Mapper to Reducer without any filtering or recursion, resulting in overdrawn bandwidth. In this paper, we introduce an algorithm called Inner MAPping Combiner (IMapC) for the map phase. This algorithm in the Mapper combines the values of recurring keys. In order to test the efficiency of the algorithm, different approaches were tested. According to the test, MapReduce programs that are implemented with the Default Combiner (DC) of IMapC will be 70% more efficient than those that are implemented without one. To make computations significantly faster, this work can be combined with MapReduce

IMapC: Inner MAPping Combiner to Enhance the Performance of MapReduce in Hadoop

Hadoop is a framework for storing and processing huge amounts of data. With HDFS, large data sets can be managed on commodity hardware. MapReduce is a programming model for processing vast amounts of data in parallel. Mapping and reducing can be performed by using the MapReduce programming framework. A very large amount of data is transferred from Mapper to Reducer without any filtering or recursion, resulting in overdrawn bandwidth. In this paper, we introduce an algorithm called Inner MAPping Combiner (IMapC) for the map phase. This algorithm in the Mapper combines the values of recurring keys. In order to test the efficiency of the algorithm, different approaches were tested. According to the test, MapReduce programs that are implemented with the Default Combiner (DC) of IMapC will be 70% more efficient than those that are implemented without one. To make computations significantly faster, this work can be combined with MapReduce

Air Quality Monitoring and Analysis for Sustainable Development of Solid Waste Dump Yards Using Smart Drones and Geospatial Technology

Air pollution has become a global health issue, affecting millions of people annually. It is essential to obtain accurate and up-to-date information on air quality levels to reduce the impact of air pollution on human health. Traditional air quality monitoring methods are limited by spatial coverage and the time required for sample collection and analysis. However, the Internet of Things (IoT), drone technology, and Geographic Information System (GIS) technology have enabled the development of real-time air quality monitoring systems that provide accurate and up-to-date information on air quality levels across large areas. The study found that gas concentration varied significantly at different heights, with the highest concentration at 3 m from the ground and the lowest at 9 m. The concentration of gases also varied by month, with the highest concentration of CO recorded in March at 45 ppm at 3 m, and the highest concentration of NH4 recorded in September at 66.7 ppm at 3 m. Kodungaiyur dump yard needs environmental monitoring due to the high levels of environmental pollution and health risks caused by unsegregated waste. In conclusion, developing real-time air quality monitoring systems using GIS technology is necessary to obtain accurate and up-to-date information on air quality levels. It is essential to monitor the Kodungaiyur dump yard and other, similar sites regularly to prevent the health risks associated with environmental pollution

A recommendation system based on AI for storing Block data in the Electronic Health Repository

A proliferation of wearable sensors that record physiological signals has resulted in an exponential growth of data on digital health. To select the appropriate repository for the increasing amount of collected data, intelligent procedures are becoming increasingly necessary. However, allocating storage space is a nuanced process. Generally, patients have some input in choosing which repository to use, although they are not always responsible for this decision. Patients are likely to have idiosyncratic storage preferences based on their unique circumstances. The purpose of the current study is to develop a new predictive model of health data storage to meet the needs of patients while ensuring rapid storage decisions, even when data is streaming from wearable devices. To create the machine learning classifier, we used a training set synthesized from small samples of experts who exhibited correlations between health data and storage features. The results confirm the validity of the machine learning methodolog

Analysis of Fault Classifiers to Detect the Faults and Node Failures in a Wireless Sensor Network

Technology evaluation in the electronics field leads to the great development of Wireless Sensor Networks (WSN) for a variety of applications. The sensor nodes are deployed in hazardous environments, and they are operated by isolated battery sources. Network connectivity is purely based on power availability, which impacts the network lifetime. Hence, power must be used wisely to prolong the network lifetime. The sensor nodes that fail due to power have to detect quickly to maintain the network. In a WSN, classifiers are used to detect the faults for checking the data generated by the sensor nodes. In this paper, six classifiers such as Support Vector Machine, Convolutional Neural Network, Multilayer Perceptron, Stochastic Gradient Descent, Random Forest and Probabilistic Neural Network have been taken for analysis. Six different faults (Offset fault, Gain fault, Stuck-at fault, Out of Bounds, Spike fault and Data loss) are injected in the data generated by the sensor nodes. The faulty data are checked by the classifiers. The simulation results show that the Random Forest detected more faults and it also outperformed all other classifiers in that category

Frequency of rare <i>BCR‐ABL1</i> fusion transcripts in chronic myeloid leukemia patients

Introduction: The hallmark of chronic myeloid leukemia (CML) is the presence of Philadelphia chromosome, its resultant fusion transcript (BCR‐ABL1), and fusion protein (p210). Alternate breakpoints in BCR (m‐bcr, &#956;‐bcr, and others) or ABL1 result in the expression of few rare fusion transcripts (e19a2, e1a2, e13a3, e14a3) and fusion proteins (p190, p200, p225) whose exact clinical significance remains to be determined. Methods: Our study was designed to determine the type and frequency of BCR‐ABL1 fusion transcripts in 1260 CML patients and to analyze the prognosis and treatment response in patients harboring rare BCR‐ABL1 fusion transcripts. Results: The frequency of various BCR‐ABL1 fusion transcripts was as follows: e14a2 (60%), e13a2 (34.3%), e1a2 (1.2%), e1a2 + e13a2 (2.0%), e1a2 + e14a2 (1.8%), e19a2 (0.3%), and e14a3 (0.3%). CML patients with e1a2 transcripts had higher rates of disease progression, resistance, or suboptimal response to imatinib and failed to achieve major molecular response. Conclusion: Characterization of the specific fusion transcript in CML patients is important owing to the difference in prognosis and response to therapy in addition to the conventional need for monitoring treatment response. CML patients with e1a2 transcripts have to be closely monitored due to the high incidence of disease progression and treatment resistance/failure

TEQIP - III Sponsored First International Conference on Innovations and Challenges in Computing, Analytics and Security

This book contains abstracts of the various research papers of the academic &amp; research community presented at the International Conference on Innovations and Challenges in Computing, Analytics and Security (ICICCAS-2020). ICICCAS-2020 has served as a platform for researchers, professionals to meet and exchange ideas on computing, data analytics, and security. The conference has invited papers in seven main tracks of Data Science, Networking Technologies, Sequential, Parallel, Distributed and Cloud Computing, Advances in Software Engineering, Multimedia, Image Processing, and Embedded Systems, Security and Privacy, Special Track (IoT, Smart Technologies and Green Engineering). The Technical and Advisory Committee Members were from various countries that have rich Research and Academic experience. Conference Title: TEQIP - III Sponsored First International Conference on Innovations and Challenges in Computing, Analytics and SecurityConference Acronym: ICICCAS-2020Conference Date: 29-30 July 2020Conference Location: Pondicherry Engineering College, Puducherry – 605014, India (Virtual Mode)Conference Organizer: Department of Computer Science and Engineering, Pondicherry Engineering College, Puducherry, India.Conference Sponsor: TEQIP-III NPIU (A Unit of the Ministry of Human Resource Development, India)