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This paper introduces the novel idea of using human social norms and human emotions to improve the collision avoidance of Autonomous Vehicles (AVs). Until now, the literature has been concerned with theoretical debates regarding ethical issues connected to AVs, while no practical steps have yet been undertaken. This paper introduces the concept of an artificial society of AVs with different personalities and with social norms coded into their autopilot so that they act like well-behaved drivers. For proof of concept, the standard agent modelling tool Netlogo is utilized to simulate the artificial society of AVs. Furthermore, comparisons are made with random walk-based non-social-based collision avoidance techniques. Extensive testing has been carried out using the behaviour space tool to determine the performance of the proposed approach regarding the number of collisions. A comparative study undertaken with a random walk method indicates that the proposed approach provides a better option for tailoring the autopilots of future AVs, while also promising to be more socially acceptable and trustworthy regarding safe road travel. [ABSTRACT FROM AUTHOR]

Interoperability remains a major burden to the developers of Internet of Things systems. It is due to IoT devices are extremely heterogeneous regarding basic communication protocols, data formats, and technologies. Furthermore, due to the absence of worldwide satisfactory standards, Interoperability tools remains imperfect. In this paper, we have proposed Semantic Interoperability Model for Big-data in IoT (SIMB-IoT) to deliver semantic interoperability among heterogeneous IoT devices in health care domain. This model is used to recommend medicine with side effects for different symptoms collected from heterogeneous IoT sensors. Two datasets are taken for the analysis of big-data. One dataset contains diseases with drug details and the second dataset contains medicines with side effects. Information between physician and patient are semantically annotated and transferred in a meaningful way. A Lightweight Model for Semantic annotation of Big-data using heterogeneous devices in IoT is proposed to provide annotations for big data. Resource Description Framework (RDF) is a semantic web framework that is recycled to communicate things using Triples to make it semantically significant. RDF annotated patients’ data and made it semantically interoperable. SPARQL query is used to extract records from RDF graph. Tableau, Gruff-6.2.0, and Mysql tools are used in simulation in this article. [ABSTRACT FROM AUTHOR]

• Extracting the abstract view features from different programming codes using abstract syntax tree. • SCAA-AST approach is proposed for programmers' classification. • TFIDF method is proposed to analyze the significance of each feature in terms of local and global weights. • Implementing the deep learning approach to classify programmers from different source codes. Source Code Authorship Attribution (SCAA) is a direct challenge to the privacy and anonymity of developers. However, it is important to recognize the malicious authors and the origin of the attack. In this paper, we proposed Source Code Authorship Attribution using Abstract Syntax Tree (SCAA-AST) for efficient classification of programmers. First, the AST hierarchal features are generated from different programming codes. Second, preprocessing techniques are used to obtain useful features without sound data. Third, the Term Frequency Inverse Document Frequency (TFIDF) weighting technique is used to zoom in on the significance of each feature. Fourth, the Adaptive Synthetic (ADASYN) oversampling method is used to solve the imbalanced class problem. Finally, a deep learning algorithm is designed with the TensorFlow framework, and the Keras API is used to classify programming authors. A deep learning algorithm is further configured with a dropout layer, learning error rate, loss and activation function, and dense layers to enhance the classification results. The results are appreciable in outperforming the existing techniques from the perspective of classification accuracy. [ABSTRACT FROM AUTHOR]

The object tracking in video surveillance for intelligent traffic handling in smart cities requires an enormous amount of data called big data to be transmitted over the network using the Internet of Things. Manual monitoring and surveillance are impossible because traditional computer vision technologies are no more useful for massive processing and intelligent decision making. In this paper, a framework is proposed which enables both on spot data processing and intelligent decision making by using cloud computing. The developed application is a trained on Artificial Neural Network, which can handle different traffic techniques with congested traffic scenario and priorities traffic such as ambulance handling. The Message Queue Telemetry Transport protocol is used for green transmission with mobile access to traffic data. The results analyzed with thirty videos processed data which handle real-time data prioritization for the people for smart surveillance to fastest route and enhance the intelligent data transmission. [ABSTRACT FROM AUTHOR]

Constructing a smart home is not a task without intricate challenges due to involvement of various tools and technologies. Therefore, this research work presents a concept of context-aware low power intelligent SmartHome (CLP i SmartHome). For CLP i SmartHome, we propose a communication model, which provides a common medium for communication, i.e., same communication language. Moreover, an architecture is also proposed that welcomes all the electronic devices to communicate with each other using a single platform service. The proposed architecture describes the application, analysis and visualization aspects of the CLP i SmartHome. Furthermore, the feasibility and efficiency of the proposed system are implemented on Hadoop single node setup on UBUNTU 14.04 LTS core TM i5 machine with 3.2 GHz processor and 4 GB memory. Sample medical sensory data sets and fire detection datasets are tested on the proposed system. Finally, the results show that the proposed system architecture efficiently processes, analyzes, and integrates different datasets and triggers actions to provide safety measurements for elderly age people, patients, and others. [ABSTRACT FROM AUTHOR]