Smart Algorithms for Hierarchical Clustering in Optical Network

Network design process is a very important in order to balance between the investment in the network and the supervises offered to the network user, taking into consideration, both minimizing the network investment cost, on the other hand, maximizing the quality of service offered to the customers as well.Partitioning the network to smaller sub-networks called clusters is required during the design process inorder to ease studying the whole network and achieve the design process as a trade-off between several atrtributes such as quality of service, reliability,cost, and management. Under CANON, a large scale optical network is partitioned into a number of geographically limited areas taking into account many different criteria like administrative domains, topological characteristics, traffic patterns, legacy infrastructure etc. An important consideration is that each of these clusters is comprised of a group of nodes in geographical proximity. The clusters can coincide with administrative domains but there could be many cases where two or more clusters belong to the same administrative domain. Therefore, in the most general case the partitioning into specific clusters can be either a off-line or a on-line process. In this work only the off-line case is considered. In this Study, we look at the problem of designing efficient 2- level Hierarchical Optical Networks (HON), in the context of network costs optimization. 2-level HON paradigm only have local rings to connect disjoint sets of nodes and a global sub mesh to interconnect all the local rings. We present an Hierarchical algorithm that is based on two phases. We present results for scenarios containing a set of real optical topologies

Improving CNN-based Person Re-identification using score Normalization

Person re-identification (PRe-ID) is a crucial task in security, surveillance, and retail analysis, which involves identifying an individual across multiple cameras and views. However, it is a challenging task due to changes in illumination, background, and viewpoint. Efficient feature extraction and metric learning algorithms are essential for a successful PRe-ID system. This paper proposes a novel approach for PRe-ID, which combines a Convolutional Neural Network (CNN) based feature extraction method with Cross-view Quadratic Discriminant Analysis (XQDA) for metric learning. Additionally, a matching algorithm that employs Mahalanobis distance and a score normalization process to address inconsistencies between camera scores is implemented. The proposed approach is tested on four challenging datasets, including VIPeR, GRID, CUHK01, and PRID450S, and promising results are obtained. For example, without normalization, the rank-20 rate accuracies of the GRID, CUHK01, VIPeR and PRID450S datasets were 61.92%, 83.90%, 92.03%, 96.22%; however, after score normalization, they have increased to 64.64%, 89.30%, 92.78%, and 98.76%, respectively. Accordingly, the promising results on four challenging datasets indicate the effectiveness of the proposed approach.Comment: 5 pages, 6 figures and 2 table

Computer Numerical Control-PCB Drilling Machine with Efficient Path Planning – Case Study_2

In Printed Circuit Board (PCB) drilling machines, the location of the drill holes are fed into the machine and the PCB will be drilled at the corresponding coordinates. Some machines do not choose the optimal route when completing their tasks. Hence, this paper proposes an approach, which is based on the Algorithm Shortest Path Search Algorithm (SPSA), for finding the optimal route in PCB holes drilling process. In SPSA, when the robotic arm at the initial position, the algorithm calculates the nearest point to the initial position from all points that the wires starts or ends with. If the nearest point is a start-of-wire point, it will use SPS algorithm 1. If the nearest point is an end-of-wire point, it will use SPS algorithm 2. This process is repeated until drilling all the lines. Then, the robotic arm will drill all the holes according to the proposed Simulated Annealing Algorithm (AS) in order to determine the optimal machining parameters for milling operations. The results of the different optimization algorithms Genetic Algorithm (GA) and AS are compared and conclusions are presented. . The proposed Computer Numerical Control (CNC) machine consists of driver, drill, three stepper motors, cables and microcontroller PIC16f877A to control the movement of the machine. The SPSA algorithm optimizes the use of the motors and other mechanical paths involved in the process while reducing total time taken to traverse all the drill holes. This paper also explains the detailed problem of interest and the mathematical formulation of the problem is defined. Experimental result indicates that the proposed SPSA-based approach is capable to efficiently find the optimal route for PCB holes drilling process

Blockchain-based secure and intelligent data dissemination framework for UAVs in battlefield applications

The modern warfare scenario has immense challenges that can risk personnel's lives, highlighting the need for data acquisition to win a military operation successfully. In this context, unmanned aerial vehicles (UAVs) play a significant role by covertly acquiring reconnaissance data from an enemy location to make the friendly troops aware. The acquired data is mission-critical and needs to be secured from the intruders, which can implicitly manipulate it for their benefit. Moreover, UAVs collect a large amount of data, including high-definition images and surveillance videos; handling such a massive amount of data is a bottleneck on traditional communication networks. To mitigate these issues, this article proposes a blockchain and machine learning (ML)-based secure and intelligent UAV communication underlying sixth-generation (6G) networks, that is, Block-USB. The proposed system refrain the disclosure of highly-sensitive military operations from intruders (either a rogue UAV or a malicious controller). The proposed system uses off-chain storage, that is, Interplanetary file system (IPFS), to improve the blockchain storage capacity. We also present a case study on securing UAV-based military operations by considering multiple scenarios considering controller/UAV malicious. The performance of the proposed system outperforms the traditional baseline 4G/5G and non IPFS-based systems in terms of classification accuracy, communication latency, and data scalability

Social Media for Teaching and Learning within Higher Education Institution: A Bibliometric Analysis of the Literature (2008-2018)

Research on the topic of social media for teaching and learning in the higher education have received much attention from academia and practitioners. Social media technology was acknowledged not only as an important communication tool between faculty members and students but also shows great potential as the next social learning platform that better serves the teaching, learning and administration of a higher education institute. Its informal setting allows more flexibility for both students and faculty members to teach and learn anywhere and everywhere. Despite its important, little is known about how this research topic is evolving within the literature. Hence, using a bibliometric analysis technique this study examines the trends, topics, and challenges addressed by previous research for the past ten years (2008-2018). The paper ends by discussing future research directions related to this topic

Social Media for Teaching and Learning within Higher Education Institution: A Bibliometric Analysis of the Literature (2008-2018)

Research on the topic of social media for teaching and learning in the higher education have received much attention from academia and practitioners. Social media technology was acknowledged not only as an important communication tool between faculty members and students but also shows great potential as the next social learning platform that better serves the teaching, learning and administration of a higher education institute. Its informal setting allows more flexibility for both students and faculty members to teach and learn anywhere and everywhere. Despite its important, little is known about how this research topic is evolving within the literature. Hence, using a bibliometric analysis technique this study examines the trends, topics, and challenges addressed by previous research for the past ten years (2008-2018). The paper ends by discussing future research directions related to this topic

Scientometric Analysis and Classification of Research Using Convolutional Neural Networks: A Case Study in Data Science and Analytics

With the increasing development of published literature, classification methods based on bibliometric information and traditional machine learning approaches encounter performance challenges related to overly coarse classifications and low accuracy. This study presents a deep learning approach for scientometric analysis and classification of scientific literature based on convolutional neural networks (CNN). Three dimensions, namely publication features, author features, and content features, were divided into explicit and implicit features to form a set of scientometric terms through explicit feature extraction and implicit feature mapping. The weighted scientometric term vectors are fitted into a CNN model to achieve dual-label classification of literature based on research content and methods. The effectiveness of the proposed model is demonstrated using an application example from the data science and analytics literature. The empirical results show that the scientometric classification model proposed in this study performs better than comparable machine learning classification methods in terms of precision, recognition, and F1-score. It also exhibits higher accuracy than deep learning classification based solely on explicit and dominant features. This study provides a methodological guide for fine-grained classification of scientific literature and a thorough investigation of its practice

Scientometric Analysis and Classification of Research Using Convolutional Neural Networks: A Case Study in Data Science and Analytics

With the increasing development of published literature, classification methods based on bibliometric information and traditional machine learning approaches encounter performance challenges related to overly coarse classifications and low accuracy. This study presents a deep learning approach for scientometric analysis and classification of scientific literature based on convolutional neural networks (CNN). Three dimensions, namely publication features, author features, and content features, were divided into explicit and implicit features to form a set of scientometric terms through explicit feature extraction and implicit feature mapping. The weighted scientometric term vectors are fitted into a CNN model to achieve dual-label classification of literature based on research content and methods. The effectiveness of the proposed model is demonstrated using an application example from the data science and analytics literature. The empirical results show that the scientometric classification model proposed in this study performs better than comparable machine learning classification methods in terms of precision, recognition, and F1-score. It also exhibits higher accuracy than deep learning classification based solely on explicit and dominant features. This study provides a methodological guide for fine-grained classification of scientific literature and a thorough investigation of its practice