http://hdl.handle.net/10366/145966 2026-05-02T20:51:33Z http://hdl.handle.net/10366/146106 In the world of computation, the encryption is a technique by which the plaintext or any type of data which is converted from the readable form is transformed into an encoded form. That encoded form can only be read by another entity if they have corrected key for decryption. The proposed technique providing the security to the data in inefficient way that can be further use in implementation in new upcoming task and enhancement in current running projects of SOA (Service Oriented Architecture) BPEL (Business Process Execution Language). 2020-11-08T00:00:00Z http://hdl.handle.net/10366/146107 2020-12-30T00:00:00Z http://hdl.handle.net/10366/146104 In compare to Authentication for identification and relationship of an identity of a user with its task and process within the system, authorization in access control is much anxious about confirming that user and its task in the form of system process, access to the assets of any particular domain is only approved when proven obedient to the identified policies. Access control and authorization is always an area of interest for researchers for enhancing security of critical assets from many decades. Our prime focus and interest is in the field of access control model based on Attribute base access control (ABAC) and with this paper we tried to integrate ABAC with openstack cloud for achieving finer level of granularity in access policies for domain like smart grid. Technical advancement of current era demands that critical infrastructure like traditional electrical grid open ups to the modern information and communication technology to get the benefit in terms of efficiency, scalability, accessibility and transparency for better adaptability in real world. Incorporation of ICT with electric grid makes it possible to do greater level of bi-directional interaction among stake holders like customer, generation units, distribution units and administrations and these leads international organization to contribute for standardization of smart grid concepts and technology so that the realization of smart grid becomes reality. Smart grid is a distributed system of very large scale by its nature and needs to integrate available legacy systems with its own security requirements. Cloud computing proven to be most efficient approach for said requirements and we have identified openstack as our cloud platform. We have integrated ABAC approach with default RBAC approach of openstack and provide a frame work that supports and integrate multiple access control polices in making authorization decisions. Smart grid domain in considered as case study which requires support of multiple access policies (RBAC, ABAC or DAC etc) with our model for access control and authorization. 2020-11-04T00:00:00Z http://hdl.handle.net/10366/146103 The abundance of photos on the internet, along with smartphones that could implement computer vision technologies allow for a unique way to browse the web. These technologies have potential used in many widely accessible and globally available reverse-image search applications. One of these applications is the use of reverse-image search to help people finding items which they're interested in, but they can't name it. This is where Awjedni was born. Awjedni is a reverse-image search application compatible with iOS and Android smartphones built to provide an efficient way to search millions of products on the internet using images only. Awjedni utilizes a computer vision technology through implementing multiple libraries and frameworks to process images, recognize objects, and crawl the web. Users simply upload/take a photo of a desired item and the application returns visually similar items and a direct link to the websites that sell them. 2020-09-13T00:00:00Z http://hdl.handle.net/10366/146105 Human life is a serious matter, so we should not neglect anything that might threaten it. It must be protected in all possible ways. Consequently, all health services such as hospitals, medicines, ambulances and so on need to evolve continuously to overcome life-threatening problems. Since many people could lose their life because of an ambulance delay. We proposed a system that provides a way to overcome the ambulance delay problem. With the current traffic light system, the ambulance can get stuck in the traffic or may cause an accident while it crosses the red light. To avoid that, the proposed system enables the ambulance to control the traffic light. Thus, the system will facilitate the ambulance movement to save people's life. The hardware used to implement this project includes Arduino UNO and mega with network shield (ZigBee). We have used C++ language and Arduino IDE to program the Arduino and the ZigBee. 2020-11-07T00:00:00Z http://hdl.handle.net/10366/146102 A Mobile Ad-hoc Network (MANET) is a group of nodes connected via ad-hoc fashion for communicating with each other through wireless interface. The communication among the nodes in such network take place by using multi-hop in the absence of fixed infrastructure. TCP faces some hurdles and complexities in multi-hop ad-hoc networks particularly congestion and route failures. The incompatibility between the MAC and TCP are previously noticed by the research community. This research study focuses on the impact of MAC layer contention window on TCP in MANET by using variation in network density and velocity of nodes respectively. Simulation has been carried out to quantify and analyze the impact of Contention Window (CW) sizes that affects the performance of TCP by using NS-2 simulator. The impact of CW is investigated on TCP in multi-hop networks by means of performance evaluation parameters i.e. average delay, average packet drops and average throughput. 2020-08-07T00:00:00Z http://hdl.handle.net/10366/146100 Social media has become an important part of our everyday life due to the widespread use of the Internet. Of the social media services, Twitter is among the most used ones around the world. People share their opinions by writing tweets about numerous subjects, such as politics, sports, economy, etc. Millions of tweets per day create a huge dataset, which drew attention of the data scientists to focus on these data for sentiment analysis. The sentiment analysis focuses to identify the social media posts of users about a specific topic and categorize them as positive, negative or neutral. Thus, the study aims to investigate the effect of types of text representation on the performance of sentiment analysis. In this study, two datasets were used in the experiments. The first one is the user reviews about movies from the IMDB, which has been labeled by Kotzias, and the second one is the Twitter tweets, including the tweets of users about health topic in English in 2019, collected using the Twitter API. The Python programming language was used in the study both for implementing the classification models using the Naïve Bayes (NB), Support Vector Machines (SVM) and Artificial Neural Networks (ANN) algorithms, and for categorizing the sentiments as positive, negative and neutral. The feature extraction from the dataset was performed using Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec (W2V) modeling techniques. The success percentages of the classification algorithms were compared at the end. According to the experimental results, Artificial Neural Network had the best accuracy performance in both datasets compared to the others. 2020-09-17T00:00:00Z http://hdl.handle.net/10366/146101 In recent years Petri Nets has been in demand due to its visual depiction. Petri Nets are used as an effective method for portraying synchronization, a concurrency between different system activities. In queuing models Petri networks are used to represent distributed modeling of the system and thus evaluate their performance. By specifying suitable stochastic Petri Nets models, the authors concentrate on representing multi-class queuing systems of various queuing disciplines. The key idea is to define SPN models that simulate a given queue discipline 's behavior with some acceptable random choice. Authors have find system queuing with both a single server and multiple servers with load-dependent service rate. Petri networks in the queuing model have enhanced scalability by combining queuing and modeling power expressiveness of 'petri networks.' Examples of application of SPN models to performance evaluation of multiprocessor systems demonstrate the utility and effectiveness of this modeling method. In this paper, authors have made use of Stochastic Petri nets in queuing models to evaluate the performance of the system. 2020-10-06T00:00:00Z http://hdl.handle.net/10366/146099 2020-12-30T00:00:00Z http://hdl.handle.net/10366/146094 The blockchain technology firstly presented by Haber and Stornetta in the year 1990, and first time blockchain technology used in Bitcoin by Satoshi Nakamoto in 2008. The blockchain technology is truly decentralized technology. In blockchain technology, every block has consisted three main parts that is data, hash block, and the previous hash block. Hash is controls the uniqueness of each block and it is unique for each block. Each block also contains the hash of the previous block; thus, the blocks are connected to each other. A blockchain can divided into three categories public blockchain, consortium blockchain and private blockchain. The proposed paper provided the comparative and analytical review on the blockchain consensus algorithms. 2020-06-01T00:00:00Z http://hdl.handle.net/10366/146093 Adopting the most suitable optimization algorithm (optimizer) for a Neural Network Model is among the most important ventures in Deep Learning and all classes of Neural Networks. It's a case of trial and error experimentation. In this paper, we will experiment with seven of the most popular optimization algorithms namely: sgd, rmsprop, adagrad, adadelta, adam, adamax and nadam on four unrelated datasets discretely, to conclude which one dispenses the best accuracy, efficiency and performance to our deep neural network. This work will provide insightful analysis to a data scientist in choosing the best optimizer while modelling their deep neural network. 2020-06-20T00:00:00Z http://hdl.handle.net/10366/146095 2020-11-30T00:00:00Z http://hdl.handle.net/10366/146092 Friction Stir Welding process is an advanced solid-state joining process which finds application in various industries like automobiles, manufacturing, aerospace and railway firms. Input parameters like tool rotational speed, welding speed, axial force and tilt angle govern the quality of Friction Stir Welded joint. Improper selection of these parameters further leads to fabrication of the joint of bad quality resulting groove edges, flash formation and various other surface defects. In the present work, a texture based analytic machine learning algorithm known as Local Binary Pattern (LBP) is used for the extraction of texture features of the Friction Stir Welded joints which are welded at a different rotational speed. It was observed that LBP algorithm can accurately detect any irregularities present on the surface of Friction Stir Welded joint. 2020-06-19T00:00:00Z http://hdl.handle.net/10366/146090 Geography is the study of local and spatial variations in physical and human events on Earth. Studies of the world's geography have grown together with human developments and revolutions. Atlases often present geographic features and boundaries of areas; an atlas is a compilation of different Earth maps or Earth regions, such as the Middle East, and the continents of Asia, and North America. Most teachers still use classical methods of teaching. Geographical concepts and map-reading skills are the most common aspects of learning that early-stage students find challenging. Hence, the objective of this application is to develop a geography application for children between the ages of 9 and 12 years that would allow them to learn maps. Nowadays, smartphones and mobile apps are drawing closer to becoming acceptable learning tools. To facilitate this, Kids' Atlas is an android application, the main purpose of which is to help children to learn easily and test their knowledge. The application improves learning through entertainment by adding technologies that will help children to learning geography. It captures their attention to learn by visualizing objects and allows them to interact more effectively than traditional methods teaching by visualizing the 3D items. The application intends to improve the individual's ability to understand by providing a training section containing simple quizzes, listening/voice recognition capability, and it has the ability to search for a country by voice recognition and zooming for searched country. The methodology involves a set of software development phases, beginning with the planning; analyze data, design, implementation, testing and maintenance phases. The result of this project is a geography learning application that assists children to enjoy learning geography. The result has shown positive indicators that improve children's ability and knowledge of geography. Learning geography also becomes enjoyable; encouraging and motivating children to continue learning. This project contributes to the growth of education in early childhood, which is essential to shape the nation for the future. Therefore, this project is significant and relevant, as it contributes to the knowledge society for Saudi Arabia. 2020-11-30T00:00:00Z http://hdl.handle.net/10366/146091 Data analytics and its associated applications have recently become impor-tant fields of study. The subject of concern for researchers now-a-days is a massive amount of data produced every minute and second as people con-stantly sharing thoughts, opinions about things that are associated with them. Social media info, however, is still unstructured, disseminated and hard to handle and need to be developed a strong foundation so that they can be utilized as valuable information on a particular topic. Processing such unstructured data in this area in terms of noise, co-relevance, emoticons, folksonomies and slangs is really quite challenging and therefore requires proper data pre-processing before getting the right sentiments. The dataset is extracted from Kaggle and Twitter, pre-processing performed using NLTK and Scikit-learn and features selection and extraction is done for Bag of Words (BOW), Term Frequency (TF) and Inverse Document Frequency (IDF) scheme. /nFor polarity identification, we evaluated five different Machine Learning (ML) algorithms viz Multinomial Naive Bayes (MNB), Logistic Regression (LR), Decision Trees (DT), XGBoost (XGB) and Support Vector Machines (SVM). We have performed a comparative analysis of the success for these algorithms in order to decide which algorithm works best for the given data-set in terms of recall, accuracy, F1-score and precision. We assess the effects of various pre-processing techniques on two datasets; one with domain and other not. It is demonstrated that SVM classifier outperformed the other classifiers with superior evaluations of 73.12% and 94.91% for accuracy and precision respectively. It is also highlighted in this research that the selection and representation of features along with various pre-processing techniques have a positive impact on the performance of the classification. The ultimate outcome indicates an improvement in sentiment classification and we noted that pre-processing approaches obviously suggest an improvement in the efficiency of the classifiers. 2020-06-18T00:00:00Z http://hdl.handle.net/10366/146089 Timely diagnosis is important for saving the life of epileptic patients. In past few years, a lot of treatment are available for epilepsy. These treatments involve use of medicines. But these are not effective in controlling frequency of seizure. There is need of removal of affected region using surgery. Electroencephalogram (EEG) is a widely used technique for monitoring the brain activity and widely popular for seizure region detection. It is used before surgery for locating affected region. This manual process using EEG graphs is time consuming and requires deep expertise. In the present paper, a model has been proposed that preserves the true nature of EEG signal in form of textual one dimensional vector. The proposed model achieves a state of art performance for Bonn University dataset giving an average sensitivity, specificity of 81% and 81.4% respectively for classification among all five classes. Also for binary classification achieving 99.9%, 99.5% score value for specificity and sensitivity instead of 2D models used by other researchers. Thus developed system will significantly help neurosurgeons in increasing their performance. 2020-11-12T00:00:00Z