Interoperability remains a significant burden to the developers of Internet of Things’ Systems. This is due to the fact that the IoT devices are highly heterogeneous in terms of underlying communication protocols, data formats, and technologies. Secondly due to lack of worldwide acceptable standards, interoperability tools remain limited. In this paper, we proposed an IoT based Semantic Interoperability Model (IoT-SIM) to provide Semantic Interoperability among heterogeneous IoT devices in healthcare domain. Physicians communicate their patients with heterogeneous IoT devices to monitor their current health status. Information between physician and patient is semantically annotated and communicated in a meaningful way. A lightweight model for semantic annotation of data using heterogeneous devices in IoT is proposed to provide annotations for data. Resource Description Framework (RDF) is a semantic web framework that is used to relate things using triples to make it semantically meaningful. RDF annotated patients’ data has made it semantically interoperable. SPARQL query is used to extract records from RDF graph. For simulation of system, we used Tableau, Gruff-6.2.0, and Mysql tools.

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The IoT (Internet of Things) connect systems, applications, data storage, and services that may be a new gateway for cyber-attacks as they continuously offer services in the organization. Currently, software piracy and malware attacks are high risks to compromise the security of IoT. These threats may steal important information that causes economic and reputational damages. In this paper, we have proposed a combined deep learning approach to detect the pirated software and malware-infected files across the IoT network. The TensorFlow deep neural network is proposed to identify pirated software using source code plagiarism. The tokenization and weighting feature methods are used to filter the noisy data and further, to zoom the importance of each token in terms of source code plagiarism. Then, the deep learning approach is used to detect source code plagiarism. The dataset is collected from Google Code Jam (GCJ) to investigate software piracy. Apart from this, the deep convolutional neural network is used to detect malicious infections in IoT network through color image visualization. The malware samples are obtained from Maling dataset for experimentation. The experimental results indicate that the classification performance of the proposed solution to measure the cybersecurity threats in IoT are better than the state of the art methods. INDEX TERMS Internet of Things, data mining, cyber security, software piracy, malware detection.

Amyotrophic lateral sclerosis (ALS) is a progressive fatal neurodegenerative disease characterised by loss of motor neurons that currently has no cure. Omega-3 polyunsaturated fatty acids, such as eicosapentaenoic acid (EPA), have many health benefits including neuroprotective and myoprotective potential. We tested the hypothesis that a high level of dietary EPA could exert beneficial effects in ALS. The dietary exposure to EPA (300 mg/kg/day) in a well-established mouse model of ALS expressing the G93A superoxide dismutase 1 (SOD1) mutation was initiated at a pre-symptomatic or symptomatic stage, and the disease progression was monitored until the end stage. Daily dietary EPA exposure initiated at the disease onset did not significantly alter disease presentation and progression. In contrast, EPA treatment initiated at the pre-symptomatic stage induced a significantly shorter lifespan. In a separate group of animals sacrificed before the end stage, the tissue analysis showed that the vacuolisation detected in G93A-SOD1 mice was significantly increased by exposure to EPA. Although EPA did not alter motor neurone loss, EPA reversed the significant increase in activated microglia and the astrocytic activation seen in G93A-SOD1 mice. The microglia in the spinal cord of G93A-SOD1 mice treated with EPA showed a significant increase in 4-hydroxy-2-hexenal, a highly toxic aldehydic oxidation product of omega-3 fatty acids. These data show that dietary EPA supplementation in ALS has the potential to worsen the condition and accelerate the disease progression. This suggests that great caution should be exerted when considering dietary omega-3 fatty acid supplements in ALS patients.

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Source Code Authorship Attribution (SCAA) is to find the real author of source code in a corpus. Though, it is a privacy threat to open-source programmers, but, it may be significantly helpful to develop forensic based applications. Such as, ghostwriting detection, copyright dispute settlements, and other code analysis applications. The efficient features extraction is the key challenge for classifying real authors of specific source codes. In this paper, the Program Dependence Graph with Deep Learning (PDGDL) methodology is proposed to identify authors from different programming source codes. First, the PDG is implemented to extract control and data dependencies from source codes. Second, the preprocessing technique is applied to convert PDG features into small instances with frequency details. Third, the Term Frequency Inverse Document Frequency (TFIDF) technique is used to zoom the importance of each PDG feature in source code. Fourth, Synthetic Minority Over-sampling Technique (SMOTE) is applied to tackle the class imbalance problem. Finally, the deep learning algorithm is applied to extract coding styles' features for each programmer and to attribute the real authors. The deep learning algorithm is further fine-tuned with drop out layer, learning error rate, loss and activation function, and dense layers for better accuracy of results. The proposed work is analyzed on 1000 programmers' data, collected from Google Code Jam (GCJ). The dataset contains three different programming languages, i.e., C++, Java, C#. The results are appreciable in outperforming the existing techniques from the perspective of classification accuracy, precision, recall, and f-measure metrics.INDEX TERMS Code authorship attribution, program dependence graph, deep learning, software forensics and security, software plagiarism.