Logistics in Yemen: Identifying Demerits on Lack of Railways in Yemen

If one asks today whether Yemen can be extended by railway lines, it is natural to answer that the geographical nature of Yemen does not allow the extension of the railway line. It is surprising that the Ottoman Empire decided more than 100 years ago to complete this project, but the fall of the empire deprived Yemenis of this Great achievement. Under the title of establishing a modern network of railways for metals, goods and passengers, in recent years, practical steps have been taken to establish a railway network in Yemen and linking it to an international network of economic feasibility studies, but the railway project in Yemen is still just studies prepared by several companies. In 2013 there were talks between the Yemeni government and Russia, the outcome of the Yemeni-Russian talks committee was for the planning of the establishment of a local company in Yemen specializing in railways Under the supervision of the Russian leadership, a project that is the first of its kind as it aims to build an infrastructure. In this research paper the author attempt to use quantitative and qualitative research method, plan and execute fieldwork, collecting data from Competent ministerial bodies, ministry of transportation, Visit the specified area of the project) mineral wealth areas, coastal areas) etc. in Yemen, during fieldwork. Compile preliminary maps of the first alternative - Shahn / Aden and the second alternative - Muscat / Salalah / Sarfeet / Algheiza / Aden. During fieldwork and data collection, the author will visit ministry of transportation to meet the representative to get the requested data of the Proposed railway project along with Yemen land transportation affairs authority, then the author will visit designated area of the proposed project to meet the local authorities of each area to collect the data and discussing the main causes of the project's failure and delay along with that inspecting the security and services of the project’s designated area, although the government has conducted several studies on this project. After that the author will visit a the Minister of Transportation or a senior figure from the ministry for an interview to discuss the main challenges and constraints that causes the failure of the project and the delay to support the study, finally the author will visit Ministry of Public works and roads to collect preliminary maps of the International Coastal Railway (from Haradh to Shahn) and the economic feasibility study of the railway linking the mineral wealth areas (Al-Jouf and Marib Shabwa Balahaf). After analysing data and identifying findings that led to the projects failure, the author found three main challenges at present to build the railway project in Yemen are the absence of government economy, security and political stability

STRATEGIC PROFILING & ANALYTIC MODELLING OF NODE MISBEHAVIOR IN MANET BASED IOT PARADIGM THEORY

Ubiquitous Computing and Internet of Things (IoT) are extremely popular in recent age and therefore imparting high level security mechanism is highly indispensable for such advanced technical systems. Game Theory acts as a suitable tool offering promising solutions to securityrelated concerns in Mobile Ad-Hoc Networks (i.e., MANETs). In MANETs, security forms a prominent concern as it includes nodes which are usually portable and require significant coordination between them. Further, the absence of physical organisation makes such networks susceptible to security breaches, hindering secure routing and execution among nodes. Coordination among nodes during communication and working without control of any central manager truly ensembles them to be applied in IoT. However, the identification and later mitigation of malicious nodes becomes an immensely difficult task especially when Selfish/Erroneous nodes exist along with normal Collaborative nodes in the Regular camp. Game Theory approach has been manipulated in the current study to achieve an analytical view while addressing the security concerns in MANETs. This study considers selfish nodes in the regular node camp while modelling the Regular versus Malicious node game and thereby enhancing the prior mathematical schema of strategical decision making to accommodate for the same. The proposed study performs statistical analysis and presents a mathematical model to mimic the multi-stage game between regular and malicious node using Game Theory. The simulation of the model has proved that the Perfect Bayesian Equilibrium outshines other approaches used in this study, specifically pure strategy and mixed strategy. The utility of both regular and malicious node has improved noticeably when nodes adopt PBE strategy. The framework tries to effectively represent the various unpredictable actions of node cooperation, node declination, node attacks as well as node reporting that can model the strategic profiling of various mobile nodes. Understanding the patterns and then deploying the algorithms in security products can reduce intrusion to a greater extent

Big data analysis solutions using mapReduce framework

Recently, data that generated from variety of sources with massive volumes, high rates, and different data structure, data with these characteristics is called Big Data. Big Data processing and analyzing is a challenge for the current systems because they were designed without Big Data requirements in mind and most of them were built on centralized architecture, which is not suitable for Big Data processing because it results on high processing cost and low processing performance and quality. MapReduce framework was built as a parallel distributed programming model to process such large-scale datasets effectively and efficiently. This paper presents six successful Big Data software analysis solutions implemented on MapReduce framework, describing their datasets structures and how they were implemented, so that it can guide and help other researchers in their own Big Data solutions

Increasing the speed of convergence of an artificial neural network based ARMA coefficients determination technique

In this paper, novel techniques in increasing the accuracy and speed of convergence of a Feed forward Back propagation Artificial Neural Network (FFBPNN) with polynomial activation function reported in literature is presented. These technique was subsequently used to determine the coefficients of Autoregressive Moving Average (ARMA) and Autoregressive (AR) system. The results obtained by introducing sequential and batch method of weight initialization, batch method of weight and coefficient update, adaptive momentum and learning rate technique gives more accurate result and significant reduction in convergence time when compared t the traditional method of back propagation algorithm, thereby making FFBPNN an appropriate technique for online ARMA coefficient determination

MRI reconstruction using discrete fourier transform: a tutorial

The use of Inverse Discrete Fourier Transform (IDFT) implemented in the form of Inverse Fourier Transform (IFFT) is one of the standard method of reconstructing Magnetic Resonance Imaging (MRI) from uniformly sampled K-space data. In this tutorial, three of the major problems associated with the use of IFFT in MRI reconstruction are highlighted. The tutorial also gives brief introduction to MRI physics; MRI system from instrumentation point of view; K-space signal and the process of IDFT and IFFT for One and two dimensional (1D and 2D) data

Optimal model order selection for transient error autoregressive moving average (TERA) MRI reconstruction method

An alternative approach to the use of Discrete Fourier Transform (DFT) for Magnetic Resonance Imaging (MRI) reconstruction is the use of parametric modeling technique. This method is suitable for problems in which the image can be modeled by explicit known source functions with a few adjustable parameters. Despite the success reported in the use of modeling technique as an alternative MRI reconstruction technique, two important problems constitutes challenges to the applicability of this method, these are estimation of Model order and model coefficient determination. In this paper, five of the suggested method of evaluating the model order have been evaluated, these are: The Final Prediction Error (FPE), Akaike Information Criterion (AIC), Residual Variance (RV), Minimum Description Length (MDL) and Hannan and Quinn (HNQ) criterion. These criteria were evaluated on MRI data sets based on the method of Transient Error Reconstruction Algorithm (TERA). The result for each criterion is compared to result obtained by the use of a fixed order technique and three measures of similarity were evaluated. Result obtained shows that the use of MDL gives the highest measure of similarity to that use by a fixed order techniqu

A survey on MANETs: architecture, evolution, applications, security issues and solutions

Mobile ad hoc networks or MANETs, also referred to as mobile mesh networks at times, are self-configuring networks of mobile devices that are joined using wireless channels. These represent convoluted distributed systems comprising of wireless mobile nodes which are free to move and self-organise dynamically into temporary and arbitrary, ad hoc topologies. This makes it possible for devices as well as people to internetwork seamlessly in such regions that have no communication infrastructure in place. Conventionally, the single communication networking application following the ad hoc concept had been tactical networks. Lately, new technologies have been introduced such as IEEE 802.11, Hyperlan and Bluetooth that are assisting in the deployment of commercial MANETs external to the military realm. Such topical evolutions infuse a new and rising interest in MANET research and development. This paper provides an overview of the dynamic domain of MANETs. It begins with the discussion on the evolution of MANETs followed by its significance in various fields. Besides, the MANETs have been analysed from the security perspective, particularly the work performed in the node misbehaviour paradigm has been elaborated

A comparative analysis of machine learning approaches for plant disease identification

Background: The problems to leaf in plants are very severe and they usually shorten the lifespan of plants. Leaf diseases are mainly caused due to three types of attacks including viral, bacterial or fungal. Diseased leaves reduce the crop production and affect the agricultural economy. Since agriculture plays a vital role in the economy, thus effective mechanism is required to detect the problem in early stages.Methods: Traditional approaches used for the identification of diseased plants are based on field visits which is time consuming and tedious. In this paper a comparative analysis of machine learning approaches has been presented for the identification of healthy and non-healthy plant leaves. For experimental purpose three different types of plant leaves have been selected namely, cabbage, citrus and sorghum. In order to classify healthy and non-healthy plant leaves color based features such as pixels, statistical features such as mean, standard deviation, min, max and descriptors such as Histogram of Oriented Gradients (HOG) have been used.Results:  382 images of cabbage, 539 images of citrus and 262 images of sorghum were used as the primary dataset. The 40% data was utilized for testing and 60% were used for training which consisted of both healthy and damaged leaves. The results showed that random forest classifier is the best machine method for classification of healthy and diseased plant leaves.Conclusion:  From the extensive experimentation it is concluded that features such as color information, statistical distribution and histogram of gradients provides sufficient clue for the classification of healthy and non-healthy plants