Deep Siamese domain adaptation convolutional neural network-based quaternion fractional order Meixner moments fostered big data analytical method for enhancing cloud data security

In this paper, Quaternion Fractional Order Meixner Moments-based Deep Siamese Domain Adaptation Convolutional Neural Network-based Big Data Analytical Technique is proposed for improving Cloud Data Security (DSDA-CNN-QFOMM-BD-CDS). The proposed methodology comprises six phases: data collection, transmission, pre-processing, storage, analysis, and security of data. Big data analysis methodologies start with the data collection phase. Deep Siamese domain adaptation convolutional Neural Network (DSDA-CNN) is applied to categorize the types of attacks in the cloud database during the data analysis process. During data security phase, Quaternion Fractional Order Meixner Moments (QFOMM) is employed to protect the cloud data for encryption with decryption. The proposed method is implemented in JAVA and assessed using performance metrics, including precision, sensitivity, accuracy, recall, specificity, f-measure, computational complexity information loss, compression ratio, throughput, encryption time, decryption time. The performance of the proposed method offers 23.31%, 15.64%, 18.89% better accuracy and 36.69%, 17.25%, 19.96% less information loss. When compared to existing methods like Fractional order discrete Tchebyshev encryption fostered big data analytical model to maximize the safety of cloud data depend on Enhanced Elman spike neural network (EESNN-FrDTM-BD-CDS), an innovative scheme architecture for safe authentication along data sharing in cloud enabled Big data Environment (LZMA-DBSCAN-BD-CDS).

Challenges and opportunities of big data analytics in healthcare

Data science is an interdisciplinary discipline that employs big data, machine learning algorithms, data mining techniques, and scientific methodologies to extract insights and information from massive amounts of structured and unstructured data. The healthcare industry constantly creates large, important databases on patient demographics, treatment plans, results of medical exams, insurance coverage, and more. The data that IoT (Internet of Things) devices collect is of interest to data scientists. Data science can help with the healthcare industry's massive amounts of disparate, structured, and unstructured data by processing, managing, analyzing, and integrating it. To get reliable findings from this data, proper management and analysis are essential. This article provides a comprehensive study and discussion of process data analysis as it pertains to healthcare applications. The article discusses the advantages and disadvantages of using big data analytics (BDA) in the medical industry. The insights offered by BDA, which can also aid in making strategic decisions, can assist the healthcare system.

A Decision-Aware Ambient Assisted Living System with IoT Embedded Device for In-Home Monitoring of Older Adults

Older adults' independent life is compromised due to various problems, such as memory impairments and decision-making difficulties. This work initially proposes an integrated conceptual model for assisted living systems capable of providing helping means for older adults with mild memory impairments and their caregivers. The proposed model has four main components: (1) an indoor location and heading measurement unit in the local fog layer, (2) an augmented reality (AR) application to make interactions with the user, (3) an IoT-based fuzzy decision-making system to handle the direct and environmental interactions with the user, and (4) a user interface for caregivers to monitor the situation in real time and send reminders once required. Then, a preliminary proof-of-concept implementation is performed to evaluate the suggested mode's feasibility. Functional experiments are carried out based on various factual scenarios, which validate the effectiveness of the proposed approach. The accuracy and response time of the proposed proof-of-concept system are further examined. The results suggest that implementing such a system is feasible and has the potential to promote assisted living. The suggested system has the potential to promote scalable and customizable assisted living systems to reduce the challenges of independent living for older adults.

Transforming healthcare with big data analytics: technologies, techniques and prospects

In different studies in the field of healthcare, big data analytics technology has been shown to be effective in observing the behaviour of data, of which analysed to allow the discovery of relevant insights for strategy and decision making. The objective of this study is to present the results of a systematic review of the literature on big data analytics in healthcare, focussing in technologies, main areas and purposes of adoption. To reach its objective, the study conducts an exploratory research, through a systematic review of the literature, using the Methodi Ordinatio protocol supported by content analysis. The results reveal that the use of tools implies work performance at the clinical and managerial level, improving the cost-benefit ratio and reducing the time factor in the practice of the workforce in health services. Thus, this study hopes to contribute to the technological advancement of computational intelligence applied to healthcare.

Biomedical Signals for Healthcare Using Hadoop Infrastructure with Artificial Intelligence and Fuzzy Logic Interpretation

In all developing countries, the application of biomedical signals has been growing, and there is a potential interest to apply it to healthcare management systems. However, with the existing infrastructure, the system will not provide high-end support for the transfer of signals by using a communication medium, as biomedical signals need to be classified at appropriate stages. Therefore, this article addresses the issues of physical infrastructure, using Hadoop-based systems where a four-layer model is created. The four-layer model is integrated with Fuzzy Interface System Algorithm (FISA) with low robustness, and data transfers in these layers are carried out with reference health data that are collected at various treatment centers. The performance of this new flanged system model aims to minimize the loss functionalities that are present in biomedical signals, and an activation function is introduced at the middle stages. The effectiveness of the proposed model is simulated by using MATLAB, using a biomedical signal processing toolbox, where the performance of FISA proves to be better in terms of signal strength, distance, and cost. As a comparative outcome, the proposed method overlooks the conventional methods for an average percentage of 78% in real-time conditions.

Smart healthcare IoT applications based on fog computing: architecture, applications and challenges

The history of human development has proven that medical and healthcare applications for humanity always are the main driving force behind the development of science and technology. The advent of Cloud technology for the first time allows providing systems infrastructure as a service, platform as a service and software as a service. Cloud technology has dominated healthcare information systems for decades now. However, one limitation of cloud-based applications is the high service response time. In some emergency scenarios, the control and monitoring of patient status, decision-making with related resources are limited such as hospital, ambulance, doctor, medical conditions in seconds and has a direct impact on the life of patients. To solve these challenges, optimal computing technologies have been proposed such as cloud computing, edge computing, and fog computing technologies. In this article, we make a comparison between computing technologies. Then, we present a common architectural framework based on fog computing for Internet of Health Things (Fog-IoHT) applications. Besides, we also indicate possible applications and challenges in integrating fog computing into IoT Healthcare applications. The analysis results indicated that there is huge potential for IoHT applications based on fog computing. We hope, this study will be an important guide for the future development of fog-based Healthcare IoT applications.

Disaster and Pandemic Management Using Machine Learning: A Survey

This article provides a literature review of state-of-the-art machine learning (ML) algorithms for disaster and pandemic management. Most nations are concerned about disasters and pandemics, which, in general, are highly unlikely events. To date, various technologies, such as IoT, object sensing, UAV, 5G, and cellular networks, smartphone-based system, and satellite-based systems have been used for disaster and pandemic management. ML algorithms can handle multidimensional, large volumes of data that occur naturally in environments related to disaster and pandemic management and are particularly well suited for important related tasks, such as recognition and classification. ML algorithms are useful for predicting disasters and assisting in disaster management tasks, such as determining crowd evacuation routes, analyzing social media posts, and handling the post-disaster situation. ML algorithms also find great application in pandemic management scenarios, such as predicting pandemics, monitoring pandemic spread, disease diagnosis, etc. This article first presents a tutorial on ML algorithms. It then presents a detailed review of several ML algorithms and how we can combine these algorithms with other technologies to address disaster and pandemic management. It also discusses various challenges, open issues and, directions for future research.

Psychological health analysis based on fuzzy assisted neural network model for sports person

Stress is indeed a life aspect that influences everyone, even though athletes seem to suffer from it one step ahead of others because of the extent they are expected to balance between coursework, workouts, and competitions, along with everyday life and family stress. Therefore, an efficient psychological health analysis for sportspersons is crucial in sports training. This paper introduces a Fuzzy-assisted Neural Network model for Psychological Health Analysis (FNN-PHA) to assess mental stress by monitoring the Electro Cardio Gram signal (ECG), Electroencephalogram (EEG), and Pulse rate. This paper integrates the fuzzy assisted Petri nets, fuzzy assisted k-complex detector, and fuzzy assisted transient time analyzer to ensure the psychological health analysis neural network model’s adaptive performance. The strength of the proposed fuzzy model demonstrates interpretability against the accuracy of different criteria. The simulation analysis shows that the FNN-PHA model enhances the prediction ratio of 98.7%, emotional stability of 96.7%, personal growth of 95.7%, physical fitness level of 97.8%, and depression ratio of 12.5% when compared to other existing models.

HTwitt: a hadoop-based platform for analysis and visualization of streaming Twitter data

Twitter produces a massive amount of data due to its popularity that is one of the reasons underlying big data problems. One of those problems is the classification of tweets due to use of sophisticated and complex language, which makes the current tools insufficient. We present our framework HTwitt, built on top of the Hadoop ecosystem, which consists of a MapReduce algorithm and a set of machine learning techniques embedded within a big data analytics platform to efficiently address the following problems: (1) traditional data processing techniques are inadequate to handle big data; (2) data preprocessing needs substantial manual effort; (3) domain knowledge is required before the classification; (4) semantic explanation is ignored. In this work, these challenges are overcome by using different algorithms combined with a Naïve Bayes classifier to ensure reliability and highly precise recommendations in virtualization and cloud environments. These features make HTwitt different from others in terms of having an effective and practical design for text classification in big data analytics. The main contribution of the paper is to propose a framework for building landslide early warning systems by pinpointing useful tweets and visualizing them along with the processed information. We demonstrate the results of the experiments which quantify the levels of overfitting in the training stage of the model using different sizes of real-world datasets in machine learning phases. Our results demonstrate that the proposed system provides high-quality results with a score of nearly 95% and meets the requirement of a Hadoop-based classification system.

A Robust and Efficient ECC-based Mutual Authentication and Session Key Generation Scheme for Healthcare Applications

Telecare medicine information system (TMIS) has provided an efficient and convenient way for communications of patients at home and medical staffs at clinical centers. To make these communications secure, user authentication by medical servers is considered as a crucial requirement. For this purpose, many user authentication and key agreement protocols have been put forwrad in order to fulfil this vital necessity. Recently, Arshad and Rasoolzadegan have revealed that not only the authentication and key agreement protocols suggested by Amin and Biswas and Giri et al. are defenseless against the replay attack and do not support the perfect forward secrecy, but also Amin and Biswas's protocol is susceptible to the offline password guessing attack. Nonetheless, in this paper, we demonstrate that Arshad and Rasoolzadegan's and the other existing schemes still fail to resist a well-known attack. Therefore, to cover this security gap, a new user authentication and session key agreement protocol is recommended that can be employed effectively for offering secure communication channels in TMIS. Our comparative security and performance analyses reveal that the proposed scheme can both solve the existing security drawback and, same as Arshad and Rasoolzadegan's scheme, has low communication and computational overheads.