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Semantic Interoperability in Heterogeneous IoT Infrastructure for Healthcare
Jabbar, Sohail,Ullah, Farhan,Khalid, Shehzad,Khan, Murad,Han, Kijun WILEY INTERSCIENCE 2017 WIRELESS COMMUNICATIONS AND MOBILE COMPUTING Vol.2017 No.-
<P>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.</P>
Analysis of Factors Affecting Energy Aware Routing in Wireless Sensor Network
Jabbar, Sohail,Asif Habib, Muhammad,Minhas, Abid Ali,Ahmad, Mudassar,Ashraf, Rehan,Khalid, Shehzad,Han, Kijun WILEY INTERSCIENCE 2018 WIRELESS COMMUNICATIONS AND MOBILE COMPUTING Vol.2018 No.-
<P>Among constituents of communication architecture, routing is the most energy squeezing process. In this survey article, we are targeting an innovative aspect of analysis on routing in wireless sensor network (WSN) that has never been seen in the available literature before. This article can be a guiding light for new researchers to comprehend the WSN technology, energy aware routing, and the factors that affect the energy aware routing in WSN. This insight comprehension then makes the ways easy for them in designing such types of algorithms as well as evaluating the authenticity and extending the existing algorithms of this category, since algebraic and graphical modelling of these factors is also demonstrated. Various available techniques used by existing routing algorithms to handle these factors in making themselves energy aware are also given. Further, they are analyzed along with the suggested improvements for the researchers. At the end, we presented our previously published research work as an example and case study of discussed factors. A rich list of references is also cited for interested readers to explore the related given points.</P>
Optimized Deep Learning Techniques for Disease Detection in Rice Crop using Merged Datasets
Muhammad Junaid,Sohail Jabbar,Muhammad Munwar Iqbal,Saqib Majeed,Mubarak Albathan,Qaisar Abbas,Ayyaz Hussain International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.3
Rice is an important food crop for most of the population in the world and it is largely cultivated in Pakistan. It not only fulfills food demand in the country but also contributes to the wealth of Pakistan. But its production can be affected by climate change. The irregularities in the climate can cause several diseases such as brown spots, bacterial blight, tungro and leaf blasts, etc. Detection of these diseases is necessary for suitable treatment. These diseases can be effectively detected using deep learning such as Convolution Neural networks. Due to the small dataset, transfer learning models such as vgg16 model can effectively detect the diseases. In this paper, vgg16, inception and xception models are used. Vgg16, inception and xception models have achieved 99.22%, 88.48% and 93.92% validation accuracies when the epoch value is set to 10. Evaluation of models has also been done using accuracy, recall, precision, and confusion matrix.
Khalid, Shehzad,Arshad, Sannia,Jabbar, Sohail,Rho, Seungmin Hindawi Publishing Corporation 2014 The Scientific World Journal Vol.2014 No.-
<P>We have presented a classification framework that combines multiple heterogeneous classifiers in the presence of class label noise. An extension of <I>m</I>-Mediods based modeling is presented that generates model of various classes whilst identifying and filtering noisy training data. This noise free data is further used to learn model for other classifiers such as GMM and SVM. A weight learning method is then introduced to learn weights on each class for different classifiers to construct an ensemble. For this purpose, we applied genetic algorithm to search for an optimal weight vector on which classifier ensemble is expected to give the best accuracy. The proposed approach is evaluated on variety of real life datasets. It is also compared with existing standard ensemble techniques such as Adaboost, Bagging, and Random Subspace Methods. Experimental results show the superiority of proposed ensemble method as compared to its competitors, especially in the presence of class label noise and imbalance classes.</P>
Towards Designing Efficient Lightweight Ciphers for Internet of Things
( Muhammad Tausif ),( Javed Ferzund ),( Sohail Jabbar ),( Raheela Shahzadi ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.8
Internet of Things (IoT) will transform our daily life by making different aspects of life smart like smart home, smart workplace, smart health and smart city etc. IoT is based on network of physical objects equipped with sensors and actuators that can gather and share data with other objects or humans. Secure communication is required for successful working of IoT. In this paper, a total of 13 lightweight cryptographic algorithms are evaluated based on their implementation results on 8-bit, 16-bit, and 32-bit microcontrollers and their appropriateness is examined for resource-constrained scenarios like IoT. These algorithms are analysed by dissecting them into their logical and structural elements. This paper tries to investigate the relationships between the structural elements of an algorithm and its performance. Association rule mining is used to find association patterns among the constituent elements of the selected ciphers and their performance. Interesting results are found on the type of element used to improve the cipher in terms of code size, RAM requirement and execution time. This paper will serve as a guideline for cryptographic designers to design improved ciphers for resource constrained environments like IoT.