Wireless sensor network technology is widely used in the western world for improving agriculture output. However, in the developing countries, the adaptation of technology is very slow due to various factors such as cost and unawareness of farmers with the technology. There are reports in the literature related to the precision agriculture and hopefully, this paper will add to the knowledge of the use of Wireless sensor network (WSN) for monitoring agriculture fields for pest detection. The literature related to pest monitoring and detection using wireless sensor networking technologies are reviewed. Then, the advanced sensing technologies are currently in use for the detection of a pest has been described. The existing techniques about pest detection and disease monitoring are evaluated on the basis of some key parameters such as the type of sensors used, their cost, processing tools, etc. Finally, the sensing technologies and the possibility of using third generation sensing technology for monitoring and detection of cotton crops are analyzed.

Preservation of privacy of users’ personal data has always been a critical issue to deal with. This issue in the Internet of Things (IoT), which facilitates millions of applications, has become even more challenging. Currently, several approaches and methods are available to safeguard privacy but each of them suffers from one or more anomalies. In particular, Trusted Third-Party approach relies on the trust of a third-party server, Cooperation needs the trust of other peers, Obfuscation is known to return inaccurate results, and Dummy generates too much overhead. Moreover, these and most of the other well-known approaches deal only with specific types of applications linked to the location-based services. In this paper, we present two new methods, namely: Blind Third Party (BTP) and Blind Peers ( B L P ), and combine them to form a new one to be known as the Blind Approach ( B L A ). With the help of simulation results we shall demonstrate the effectiveness and superiority of B L A over the other available methods. The simulation results also exhibit that B L A is free from all the existing problems of the other approaches. However, B L A causes a slight increase in the average (response) time, which we consider to be a minor issue. We shall also discuss the capability and superiority of the Blind Approach in the cases of E-health, Smart Transportation, and Smart Home systems.

Businesses need trust to confidently perform trade among each other. Centralized business models are the only mature solutions available to perform trades over the Internet. However, they have many problems which includes but are not limited to the fact that these create bottleneck on the server as well as requires trusted third parties. Recently, decentralized solutions have gained significant popularity and acceptance for future businesses. The wide acceptance of such systems is indeed due to the trust management among various untrusted business stakeholders. Many solutions have been proposed in this regard to provide decentralized infrastructure for various business models. A standard solution that is acceptable to the industry is still in demand. Hyperledger umbrella Blockchain projects, that are supported by IBM and many other industry big players are gaining popularity due to its efficient and pluggable design. In this study, the author present the idea of utilizing Blockchain to design a Value-Added Tax (VAT) system for Saudi Arabia's newly introduced tax system. The reason to select this business model for VAT is twofold. First, it provides an untampered distributed ledger, which cannot be deceived by any party. Each transaction in the system cannot go unnoticed by the smart contract. Secondly, it provides a transparent record, and updates all involved parties regarding each activity performed by stakeholders. The newly proposed system will provide a transparent database of VAT transactions according to our smart contract design and at each stage of supply chain, tax will be deducted and stored on peer-to-peer network via consensus process. The author believes that the proposed solution will have significant impact on VAT collection in the Kingdom of Saudi Arabia.

Connected vehicles and smart cars have become highly reliant on location-based services (i.e. LBS) to provide accurate, personalized and intelligent services. However, location-based services have endangered its users to considerable risks concerning the privacy and security of users' personal data. Although existing research provides a myriad of methods to improve and protect user privacy in LBS applications, most of these methods are concerned with handling static queries and non-mobile objects only. Moreover, various issues and challenges still persist with regards to the need to trust third parties, overloading of the user, and low accuracy of the returned results. This paper contributes a Double Obfuscation Approach (referred to as DOA) that applies two phases of obfuscation consecutively whilst integrating two differing privacy protection approaches, namely Obfuscation and Trusted Third Party, and two techniques, namely fog caching technology and mix zone. In essence, the DOA obfuscates and hides the identity and location of its users using the fog nodes, which operate as a trusted third party (TTP), and without the need to reveal the identity of the users or trust the cooperating nodes. Moreover, this paper presents a DOA algorithm that improves the overall user privacy and system performance using the fog nodes, which split the responses of each query into five parts, thus reducing the processing time of the results by the user and enhancing the overall accuracy where the user directly selects the most suitable parts based on his current location. Overall, the hybrid DOA approach empowers the users of connected vehicle applications to protect their privacy through an algorithm that caters for the dynamic nature of user queries and mobility of objects. The results of our comparative simulations against well-known hybrid privacy protection methods demonstrate the superiority of the proposed Double Obfuscation Approach especially with respect to user privacy whilst maintaining a nominal overhead on the user, reduced response time and high accuracy of the obtained results.

Traffic congestion is still a challenge faced by most countries of the world. However, it can be solved most effectively by integrating modern technologies such as Internet of Things (IoT), fog computing, cloud computing, data analytics, and so on, into a framework that exploits the strengths of these technologies to address specific problems faced in traffic management. Unfortunately, no such framework that addresses the reliability, flexibility, and efficiency issues of smart-traffic management exists. Therefore, this paper proposes a comprehensive framework to achieve a reliable, flexible, and efficient solution for the problem of traffic congestion. The proposed framework has four layers. The first layer, namely, the sensing layer, uses multiple data sources to ensure a reliable and accurate measurement of the traffic status of the streets, and forwards these data to the second layer. The second layer, namely, the fog layer, consumes these data to make efficient decisions and also forwards them to the third layer. The third layer, the cloud layer, permanently stores these data for analytics and knowledge discoveries. Finally, the fourth layer, the services layer, provides assistant services for traffic management. We also discuss the functional model of the framework and the technologies that can be used at each level of the model. We propose a smart-traffic light algorithm at level 1 for the efficient management of congestion at intersections, tweet-classification and image-processing algorithms at level 2 for reliable and accurate decision-making, and support services at level 4 of the functional model. We also evaluated the proposed smart-traffic light algorithm for its efficiency, and the tweet classification and image-processing algorithms for their accuracy.

The main motivation of this study is to forecast the performance of Indian banks using multiple regression analysis and artificial neural network and to compare these two methods for the accuracy. To achieve this goal, financial data spread over 10 years from 2010 to 2019 was collected from 19 Indian public sector banks. The data consists of 17 financial ratios collected from financial statements and other publications of the sample organizations. Capital Adequacy Ratio (CRAR) has been chosen as dependent variable for measuring and predicting the financial strength of banks. Identification of significant ratios that are determinants of CRAR are identified by using regression technique then these identified ratios are used as input for a developing a neural network model. The findings of multi-linear regression analysis identified 7 financial ratios that have a positive relationship between with dependent variable (CRAR). These 7 dependent variables were used to predict the financial strength (CRAR) of the Banks. Then a feed forward back propagation neural network was developed with these 7 dependent variables to predict the CRAR. Finally, the performances of these two methods were measured by using the Mean Square Error (MSE), Root Mean Squared Error (RMSE) and Mean Absolute Percent Error (MAPE). The results indicate that ANN model scores an improvement of 55.67% in MSE over regression model. In RMSE which re-scales the errors in order to keep the errors’ dimension as the predicted value, ANN model scores an improvement of 33.425% over regression model. It also indicates that ANN model scores an improvement of 99.32% over regression model in MAPE which measures the magnitude of absolute errors in relative terms. Results show that ANN model outperforms the regression model and is superior technique of forecasting CRAR of Indian Banks.

Long unpunctuated texts containing complex linguistic sentences are a stumbling block to processing any low-resource languages. Thus, approaches that attempt to segment lengthy texts with no proper punctuation into simple candidate sentences are a vitally important preprocessing task in many hard-to-solve NLP applications. To this end, we propose a preprocessing solution for segmenting unpunctuated Arabic texts into potentially independent clauses. This solution consists of: (1) a punctuation detection model built on top of a multilingual BERT-based model, and (2) some generic linguistic rules for validating the resulting segmentation. Furthermore, we optimize the strategy of applying these linguistic rules using our suggested greedy-like algorithm. We call the proposed solution PDTS (standing for Punctuation Detector for Text Segmentation). Concerning the evaluation, we showcase how PDTS can be effectively employed as a text tokenizer for unpunctuated documents (i.e., mimicking the transcribed audio-to-text documents). Experimental findings across two evaluation protocols (involving an ablation study and a human-based judgment) demonstrate that PDTS is practically effective in both performance quality and computational cost. In particular, PDTS can reach an average F-Measure score of approximately 75%, indicating a minimum improvement of roughly 13% (i.e., compared to the performance of the state-of-the-art competitor models).

Conventional paper currency and modern electronic currency are two important modes of transactions. In several parts of the world, conventional methodology has clear precedence over its electronic counterpart. However, the identification of forged currency paper notes is now becoming an increasingly crucial problem because of the new and improved tactics employed by counterfeiters. In this paper, a machine assisted system-dubbed DeepMoney-is proposed which has been developed to discriminate fake notes from genuine ones. For this purpose, state-of-the-art models of machine learning called Generative Adversarial Networks (GANs) are employed. GANs use unsupervised learning to train a model that can then be used to perform supervised predictions. This flexibility provides the best of both worlds by allowing unlabelled data to be trained on whilst still making concrete predictions. This technique was applied to Pakistani banknotes. State-of-the-art image processing and feature recognition techniques were used to design the overall approach of a valid input. Augmented samples of images were used in the experiments which show that a high-precision machine can be developed to recognize genuine paper money. An accuracy of 80% has been achieved. The code is available as an open source to allow others to reproduce and build upon the efforts already made.