A Proposed Internet of Everything Framework for Disease Prediction

Social networks and Internet of things are two paradigms when integrated a new paradigm Internet of Everything is established that has its impact on revolutionizing various fields such as engineering, industry and healthcare. Social networks became nowadays of the most important web services on which people heavily rely, thus became a major source for information extraction for rational decision making considering individuals as social or socio sensors. Furthermore, people using sensors especially biological sensors enabled the use of internet of things technology in building intelligent healthcare systems. One of the challenges facing the design of such systems is the design of an intelligent recommender system that is able to deal with such big data. For that, this paper proposes a framework to develop an enhanced intelligent expert advisor-based health monitoring and disease awareness system. The proposed framework enables the researchers to design advisory systems that are able to observe physiological signals through the use of different bio sensors and integrate it with historical medical data together with   the massive data collected from social networks to provide accurate alerts and recommendations for many ailments inspected. The proposed Framework is designed to facilitate generic, dynamic and scalable process of integrating different types of social networks and bio sensors

A Modified Cloud-Based Cryptographic Agent for Cloud Data Integrity

In spite of all the advantages delivered by cloud computing, several challenges are hindering the migration of customer software and data into the cloud. On top of the list is the security and privacy concerns arising from the storage and processing of sensitive data on remote machines that are not owned, or even managed by the customers themselves. In this paper, initially a homomorphic encryption-based Cryptographic Agent is proposed. The proposed Cryptographic Agent is based on Paillier scheme, and is supported by user-configurable software protection and data privacy categorization agents, as well as set of accountable auditing services required to achieve legal compliance and certification. This scheme was tested using different text documents with different sizes. Testing results showed that as the size of the document increases, the size of the generated key increases dramatically causing a major problem in regards to the processing time and the file size especially for large documents. This leaded us to the second part of this research which is: a modified security architecture that adds two major autonomic security detective agents to the multi-agent architecture of cloud data storage. In this paper, we focus on the first agent namely (Automated Master Agent, AMA) that is added to the Multi Agent System Architecture (MASA) layer (cloud client-side) by which any changes happen in the document are mapped in a QR code encoded key print (KP). Experimental results after integrating these agents showed a 100% alternation detection accuracy and a superiority in extracting the KP of large and very large size documents which exceeds the currently available products and leverage the tamper-proof capabilities of cryptographic coprocessors to establish a secure execution domain in the computing cloud that is physically and logically protected from unauthorized access

Analytical expressions, modeling, and simulations of intensity and frequency fluctuations in directly modulated semiconductor lasers

Analytical expressions for the intensity and frequency/phase noise of single-mode semiconductor lasers based on quantum-mechanical rate equations are derived. Correlated photons, electrons, and phase Langevin noise sources and their auto and cross-correlation relations are presented, along with a novel self-consistent normalized laser model that includes the laser’s correlated noise sources. A symbolically defined device (SDD) is constructed using the proposed normalized model and implemented in Agilent’s advanced design system (ADS) CAD tool. Dynamic laser characteristics are predicted using the SDD implementation for 1300-nm InGaAsP/InP lasers. The results of time domain dynamic simulations of photons, carriers, optical output power, and phase—with and without the effects of noise—are presented. Simulation results are used to show the effects of random noise on both the phase and optical power output of semiconductor lasers. Simulation results are analyzed to demonstrate the resonance frequency shift dependence on the bias current levels, the relation between the frequency response and the bias current, and the dependence of the laser line width broadening on the frequency fluctuations. Comparison between the presented results and other published results (simulations and measurements) show good agreement while achieving simulation time enhancement. The suitability of the proposed models for the study and characterization of the performance of complete systems in both circuit and system simulations is examined

An Automatic Generation of Heterogeneous Knowledge Graph for Global Disease Support: A Demonstration of a Cancer Use Case

Semantic data integration provides the ability to interrelate and analyze information from multiple heterogeneous resources. With the growing complexity of medical ontologies and the big data generated from different resources, there is a need for integrating medical ontologies and finding relationships between distinct concepts from different ontologies where these concepts have logical medical relationships. Standardized Medical Ontologies are explicit specifications of shared conceptualization, which provide predefined medical vocabulary that serves as a stable conceptual interface to medical data sources. Intelligent Healthcare systems such as disease prediction systems require a reliable knowledge base that is based on Standardized medical ontologies. Knowledge graphs have emerged as a powerful dynamic representation of a knowledge base. In this paper, a framework is proposed for automatic knowledge graph generation integrating two medical standardized ontologies- Human Disease Ontology (DO), and Symptom Ontology (SYMP) using a medical online website and encyclopedia. The framework and methodologies adopted for automatically generating this knowledge graph fully integrated the two standardized ontologies. The graph is dynamic, scalable, easily reproducible, reliable, and practically efficient. A subgraph for cancer terms is also extracted and studied for modeling and representing cancer diseases, their symptoms, prevention, and risk factors

A Modified Cloud-Based Cryptographic Agent for Cloud Data Integrity

In spite of all the advantages delivered by cloud computing, several challenges are hindering the migration of customer software and data into the cloud. On top of the list is the security and privacy concerns arising from the storage and processing of sensitive data on remote machines that are not owned, or even managed by the customers themselves. In this paper, initially a homomorphic encryption-based Cryptographic Agent is proposed. The proposed Cryptographic Agent is based on Paillier scheme, and is supported by user-configurable software protection and data privacy categorization agents, as well as set of accountable auditing services required to achieve legal compliance and certification. This scheme was tested using different text documents with different sizes. Testing results showed that as the size of the document increases, the size of the generated key increases dramatically causing a major problem in regards to the processing time and the file size especially for large documents. This leaded us to the second part of this research which is: a modified security architecture that adds two major autonomic security detective agents to the multi-agent architecture of cloud data storage. In this paper, we focus on the first agent namely (Automated Master Agent, AMA) that is added to the Multi Agent System Architecture (MASA) layer (cloud client-side) by which any changes happen in the document are mapped in a QR code encoded key print (KP). Experimental results after integrating these agents showed a 100% alternation detection accuracy and a superiority in extracting the KP of large and very large size documents which exceeds the currently available products and leverage the tamper-proof capabilities of cryptographic coprocessors to establish a secure execution domain in the computing cloud that is physically and logically protected from unauthorized access

Sentiment-Based Spatiotemporal Prediction Framework for Pandemic Outbreaks Awareness Using Social Networks Data Classification

According to the World Health Organization, several factors have affected the accurate reporting of SARS-CoV-2 outbreak status, such as limited data collection resources, cultural and educational diversity, and inconsistent outbreak reporting from different sectors. Driven by this challenging situation, this study investigates the potential expediency of using social network data to develop reliable early information surveillance and warning system for pandemic outbreaks. As such, an enhanced framework of three inherently interlinked subsystems is proposed. The first subsystem includes data collection and integration mechanisms, data preprocessing, and hybrid sentiment analysis tools to identify tweet sentiment taxonomies and quantitatively estimate public awareness. The second subsystem comprises the feature extraction unit that identifies, selects, embeds, and balances feature vectors and the classifier fitting and training unit. This subsystem is designed to capture the most effective linguistic feature combinations with more spatial evidence by using a variety of approaches, including linear classifiers, MLPs, RNNs, and CNNs, as well as pre-trained word embedding algorithms. The last is the modeling and situational awareness evaluation subsystem, which measures temporal associations between pandemic-relevant social network activities and officially announced infection counts in the most hazardous geolocations. The proposed framework was developed and tested using a combination of static datasets and real-time scraped Twitter data. The results of these experiments showed the remarkable performance of the framework in assessing the temporal associations between public awareness and outbreak status. It also showed that the Decision Tree Classifier with Unigram+TF-IDF feature vectors outperformed other conventional models for sentiment classification and geolocation classification with an accuracy of 94.3% and 80.8, respectively. As indicated, conventional machine learning algorithms didn’t achieve a precision of more than 80%, while, for instance, MLP with self-embedding layer, Word2Vec, and GloVe pre-trained word embedding resulted in very poor accuracy of 10%, 36%, and 32%, respectively. However, adding the PoS tag one-hot encoding embedding increased the validation accuracy from 36% to approximately 89%, while the best performance for the second subsystem was achieved by Bi-LSTM with RoBERTa word embedding, with an accuracy of 96%. The achieved results reveal that the proposed framework can proactively capture the potential hazards associated with the prevalence of infectious diseases as an effective early detection and info-surveillance awareness system