Search results for: regular network d-dimensional

Authors: Iwan Cony Setiadi, Aulia M. T. Nasution

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The multispectral imaging (MSI) technique has been used for skin analysis, especially for distant mapping of in-vivo skin chromophores by analyzing spectral data at each reflected image pixel. For ergonomic purpose, our multispectral imaging system is decomposed in two parts: a light source compartment based on LED with 11 different wavelenghts and a monochromatic 8-Bit CCD camera with C-Mount Objective Lens. The software based on GUI MATLAB to control the system was also developed. Our system provides 11 monoband images and is coupled with a software reconstructing hyperspectral cubes from these multispectral images. In this paper, we proposed a new method to build a hyperspectral reflectance cube based on artificial neural network algorithm. After preliminary corrections, a neural network is trained using the 32 natural color from X-Rite Color Checker Passport. The learning procedure involves acquisition, by a spectrophotometer. This neural network is then used to retrieve a megapixel multispectral cube between 380 and 880 nm with a 5 nm resolution from a low-spectral-resolution multispectral acquisition. As hyperspectral cubes contain spectra for each pixel; comparison should be done between the theoretical values from the spectrophotometer and the reconstructed spectrum. To evaluate the performance of reconstruction, we used the Goodness of Fit Coefficient (GFC) and Root Mean Squared Error (RMSE). To validate reconstruction, the set of 8 colour patches reconstructed by our MSI system and the one recorded by the spectrophotometer were compared. The average GFC was 0.9990 (standard deviation = 0.0010) and the average RMSE is 0.2167 (standard deviation = 0.064).

Keywords: multispectral imaging, reflectance cube, spectral reconstruction, artificial neural network

Procedia PDF Downloads 322

Authors: Zahra Khalid, Gul Muhammad Khan, Arbab Masood Ahmad

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Cartesian Genetic Programming (CGP) is explored to design an optimal circuit capable of early stage breast cancer detection. CGP is used to evolve simple multiplexer circuits for detection of malignancy in the Fine Needle Aspiration (FNA) samples of breast. The data set used is extracted from Wisconsins Breast Cancer Database (WBCD). A range of experiments were performed, each with different set of network parameters. The best evolved network detected malignancy with an accuracy of 99.14%, which is higher than that produced with most of the contemporary non-linear techniques that are computational expensive than the proposed system. The evolved network comprises of simple multiplexers and can be implemented easily in hardware without any further complications or inaccuracy, being the digital circuit.

Keywords: breast cancer detection, cartesian genetic programming, evolvable hardware, fine needle aspiration

Procedia PDF Downloads 216

Authors: Gautam Kumar Saharia, Sagardeep Talukdar, Riki Dutta, Sudipta Nandy

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The physics informed neural network (PINN) method opens up an approach for numerically solving nonlinear partial differential equations leveraging fast calculating speed and high precession of modern computing systems. We construct the PINN based on strong universal approximation theorem and apply the initial-boundary value data and residual collocation points to weekly impose initial and boundary condition to the neural network and choose the optimization algorithms adaptive moment estimation (ADAM) and Limited-memory Broyden-Fletcher-Golfard-Shanno (L-BFGS) algorithm to optimize learnable parameter of the neural network. Next, we improve the PINN with a weighted loss function to obtain both the bright and dark soliton solutions of Fokas-Lenells equation (FLE). We find the proposed scheme of adjustable weight coefficients into PINN has a better convergence rate and generalizability than the basic PINN algorithm. We believe that the PINN approach to solve the partial differential equation appearing in nonlinear optics would be useful to study various optical phenomena.

Keywords: deep learning, optical Soliton, neural network, partial differential equation

Procedia PDF Downloads 126

Authors: Abdel Hadi N. Ebraheim, Eman Azomi, Nefisa A. Fahmy

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This paper designs and implements a computer-aided system (CAS) to help detect and diagnose liver cirrhosis in patients with Chronic Hepatitis C. Our system reduces the required features (tests) the patient is asked to do to tests to their minimal best most informative subset of tests, with a diagnostic accuracy above 99%, and hence saving both time and costs. We use the Support Vector Machine (SVM) with cross-validation, a Multilayer Perceptron Neural Network (MLP), and a Generalized Regression Neural Network (GRNN) that employs a base of radial functions for functional approximation, as classifiers. Our system is tested on 199 subjects, of them 99 Chronic Hepatitis C.The subjects were selected from among the outpatient clinic in National Herpetology and Tropical Medicine Research Institute (NHTMRI).

Keywords: liver cirrhosis, artificial neural network, support vector machine, multi-layer perceptron, classification, accuracy

Procedia PDF Downloads 461

Authors: Rahul Malhotra

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The field of wireless sensor networks (WSN) engages a lot of associates in the research community as an interdisciplinary field of interest. This type of network is inexpensive, multifunctionally attributable to advances in micro-electromechanical systems and conjointly the explosion and expansion of wireless communications. A mobile ad hoc network is a wireless network without fastened infrastructure or federal management. Due to the infrastructure-less mode of operation, mobile ad-hoc networks are gaining quality. During this work, we have performed an efficient performance study of the two major routing protocols: Ad hoc On-Demand Distance Vector Routing (AODV) and Dynamic Source Routing (DSR) protocols. We have used an accurate simulation model supported NS2 for this purpose. Our simulation results showed that AODV mitigates the drawbacks of the DSDV and provides better performance as compared to DSDV.

Keywords: routing protocol, MANET, AODV, On Demand Distance Vector Routing, DSR, Dynamic Source Routing

Procedia PDF Downloads 275

Authors: Mayank Raj, V. G. Narendra

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Ever since the DARPA challenge on autonomous vehicles in 2005, there has been a lot of buzz about ‘Autonomous Vehicles’ amongst the major tech giants such as Google, Uber, and Tesla. Numerous approaches have been adopted to solve this problem, which can have a long-lasting impact on mankind. In this paper, we have used Deep Learning techniques and TensorFlow framework with the goal of building a neural network model to predict (speed, acceleration, steering angle, and brake) features needed for navigation of autonomous vehicles. The Deep Neural Network has been trained on images and sensor data obtained from the comma.ai dataset. A heatmap was used to check for correlation among the features, and finally, four important features were selected. This was a multivariate regression problem. The final model had five convolutional layers, followed by five dense layers. Finally, the calculated values were tested against the labeled data, where the mean squared error was used as a performance metric.

Keywords: autonomous vehicles, deep learning, computer vision, artificial intelligence

Procedia PDF Downloads 158

Authors: Dauda Ibrahim Adagye

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Effective internal control system in the bursary unit of tertiary educational institutions is geared toward achieving quality teaching, learning, and research environment and as well assist the management of the institutions, particularly when decisions are to be made. While internal control system exists in all institutions, the outlined objectives above are far from being achieved. The paper; therefore, assesses the effectiveness of internal control system in tertiary educational institutions in Nasarawa State, Nigeria with the specific focus on the Nasarawa state Polytechnic, Lafia. The study is survey; hence, a simple closed-ended questionnaire was developed and administered to a sample of twenty-seven (27) member staff from the Bursary and the internal audit unit of the Nasarawa State Polytechnic, Lafia to obtain data for analysis purposes and to test the study hypothesis. Responses from the questionnaire were analyzed using a simple percentage and chi-square. Findings shows that the right people are not assigned to the right job in the department, budget, and management accounting were never used in the institution’s operations and checking of subordinate by their superior officers is not regular. This renders the current internal control structure of the Polytechnic as ineffective and weak. The paper therefore, recommends that: transparency should be seen as significant, as the institution work toward meeting its objectives, therefore, it means that the right staff is assigned to the right job and regular checking of the subordinates by their ensued superiors.

Keywords: internal control, tertiary educational intuitions, efficiency

Procedia PDF Downloads 213

Authors: Fadwa Haraka, Ahmad Elouatouati, Mourad Taha Janan

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In this study, we propose a neural network based method in order to calculate the heat source temperature of isotropic plate with lower surface forced cooling. To validate the proposed model, the heat source temperatures values will be compared to the analytical method -variables separation- and finite element model. The mathematical simulation is done through 3D numerical simulation by COMSOL software considering three different materials: Aluminum, Copper, and Graphite. The proposed method will lead to a formulation of the heat source temperature based on the thermal and geometric properties of the base plate.

Keywords: thermal model, thermal resistance, finite element simulation, neural network

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Authors: Elnaz Kanani-Kuchesfehani, Andrea Schiffauerova

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Due to the novelty of the nanotechnology science, its highly knowledge intensive content, and its invaluable application in almost all technological fields, the close interaction between university and industry is essential. A possible gap between academic strengths to generate good nanotechnology ideas and industrial capacity to receive them can thus have far-reaching consequences. In order to be able to enhance the collaboration between the two parties, a better understanding of knowledge transfer within the university-industry relationship is needed. The objective of this research is to investigate the research collaboration between academia and industry in Canadian nanotechnology and to propose the best cooperative strategy to maximize the quality of the produced knowledge. First, a network of all Canadian academic and industrial nanotechnology inventors is constructed using the patent data from the USPTO (United States Patent and Trademark Office), and it is analyzed with social network analysis software. The actual level of university-industry collaboration in Canadian nanotechnology is determined and the significance of each group of actors in the network (academic vs. industrial inventors) is assessed. Second, a novel methodology is proposed, in which the network of nanotechnology inventors is assessed from a game theoretic perspective. It involves studying a cooperative game with n players each having at most n-1 decisions to choose from. The equilibrium leads to a strategy for all the players to choose their co-worker in the next period in order to maximize the correlated payoff of the game. The payoffs of the game represent the quality of the produced knowledge based on the citations of the patents. The best suggestion for the next collaborative relationship is provided for each actor from a game theoretic point of view in order to maximize the quality of the produced knowledge. One of the major contributions of this work is the novel approach which combines game theory and social network analysis for the case of large networks. This approach can serve as a powerful tool in the analysis of the strategic interactions of the network actors within the innovation systems and other large scale networks.

Keywords: cooperative strategy, game theory, industry-university collaboration, knowledge production, social network analysis

Procedia PDF Downloads 258

Authors: Mei-Feng, Yude-Fan, Min-Zhu

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Based on PLC and kingview, this paper proposed a method that designed a set of the automatic control system according to the craft flow and demands for granulation product line. There were the main station and subordinate stations in PLC which were communicated by PROFIBUS network. PLC and computer were communicated by Ethernet network. The conversation function between human and machine was realized by kingview software, including actual time craft flows, historic report curves and product report forms. The construction of the control system, hardware collocation and software design were introduced. Besides these, PROFIBUS network frequency conversion control, the difficult points and configuration software design were elaborated. The running results showed that there were several advantages in the control system. They were high automatic degree, perfect function, perfect steady and convenient operation.

Keywords: PLC, PROFIBUS, configuration, frequency

Procedia PDF Downloads 402

Authors: Mark Harmon, Abdolghani Ebrahimi, Patrick Lucey, Diego Klabjan

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In this paper, we predict the likelihood of a player making a shot in basketball from multiagent trajectories. Previous approaches to similar problems center on hand-crafting features to capture domain-specific knowledge. Although intuitive, recent work in deep learning has shown, this approach is prone to missing important predictive features. To circumvent this issue, we present a convolutional neural network (CNN) approach where we initially represent the multiagent behavior as an image. To encode the adversarial nature of basketball, we use a multichannel image which we then feed into a CNN. Additionally, to capture the temporal aspect of the trajectories, we use “fading.” We find that this approach is superior to a traditional FFN model. By using gradient ascent, we were able to discover what the CNN filters look for during training. Last, we find that a combined FFN+CNN is the best performing network with an error rate of 39%.

Keywords: basketball, computer vision, image processing, convolutional neural network

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Authors: Salim Yusif

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Fiber optic technology has revolutionized telecommunications and data transmission, providing unmatched speed, bandwidth, and reliability. This paper presents the latest advancements in fiber optic technology, focusing on innovations in fiber materials, transmission techniques, and network architectures that enhance the performance of high-speed data transmission systems. Key advancements include the development of ultra-low-loss optical fibers, multi-core fibers, advanced modulation formats, and the integration of fiber optics into next-generation network architectures such as Software-Defined Networking (SDN) and Network Function Virtualization (NFV). Additionally, recent developments in fiber optic sensors are discussed, extending the utility of optical fibers beyond data transmission. Through comprehensive analysis and experimental validation, this research offers valuable insights into the future directions of fiber optic technology, highlighting its potential to drive innovation across various industries.

Keywords: fiber optics, high-speed data transmission, ultra-low-loss optical fibers, multi-core fibers, modulation formats, coherent detection, software-defined networking, network function virtualization, fiber optic sensors

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Authors: Aria Teimourzadeh, Marc Favier, Samaneh Kakavand

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The emergence of social networks has revolutionized the exchange of information. Every behavior on these platforms contributes to the generation of data known as social network data that are processed, stored and published by the social network service providers. Hence, it is vital to investigate the role of these platforms in user data by considering the privacy measures, especially when we observe the increased number of individuals and organizations engaging with the current virtual platforms without being aware that the data related to their positioning, connections and behavior is uncovered and used by third parties. Performing analytics on social network datasets may result in the disclosure of confidential information about the individuals or organizations which are the members of these virtual environments. Analyzing separate datasets can reveal private information about relationships, interests and more, especially when the datasets are analyzed jointly. Intentional breaches of privacy is the result of such analysis. Addressing these privacy concerns requires an understanding of the nature of data being accumulated and relevant data privacy regulations, as well as motivations for disclosure of personal information on social network platforms. Some significant points about how user's online information is controlled by the influence of social factors and to what extent the users are concerned about future use of their personal information by the organizations, are highlighted in this paper. Firstly, this research presents a short literature review about the structure of a network and concept of privacy in Online Social Networks. Secondly, the factors of user behavior related to privacy protection and self-disclosure on these virtual communities are presented. In other words, we seek to demonstrates the impact of identified variables on user information disclosure that could be taken into account to explain the privacy preservation of individuals on social networking platforms. Thirdly, a few research directions are discussed to address this topic for new researchers.

Keywords: information disclosure, privacy measures, privacy preservation, social network analysis, user experience

Procedia PDF Downloads 281

Authors: Tejinder Singh

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Information system is the flow of data from different levels to different directions for decision making and data operations in information system (IS). Data can be violated by different manner like manual or technical errors, data tampering or loss of integrity. Security system called firewall of IS is effected by such type of violations. The flow of data among various levels of Information System is done by networking system. The flow of data on network is in form of packets or frames. To protect these packets from unauthorized access, virus attacks, and to maintain the integrity level, network security is an important factor. To protect the data to get pirated, various security techniques are used. This paper represents the various security techniques and signifies different harmful attacks with the help of detailed data analysis. This paper will be beneficial for the organizations to make the system more secure, effective, and beneficial for future decisions making.

Keywords: information systems, data integrity, TCP/IP network, vulnerability, decision, data

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Authors: Rimmy Yadav, Anmol Preet Kaur

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Big Data has demonstrated the vast potential in streamlining, deciding, spotting business drifts in different fields, for example, producing, fund, Information Technology. This paper gives a multi-disciplinary diagram of the research issues in enormous information and its procedures, instruments, and system identified with the privacy, data storage management, network and energy utilization, adaptation to non-critical failure and information representations. Other than this, result difficulties and openings accessible in this Big Data platform have made.

Keywords: big data, privacy, data management, network and energy consumption

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Authors: Hafdaoui Hichem, Mehadjebia Cherifa, Benatia Djamel

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In this paper, we propose a new method for Bulk detection of an acoustic microwave signal during the propagation of acoustic microwaves in a piezoelectric substrate (Lithium Niobate LiNbO3). We have used the classification by probabilistic neural network (PNN) as a means of numerical analysis in which we classify all the values of the real part and the imaginary part of the coefficient attenuation with the acoustic velocity in order to build a model from which we note the Bulk waves easily. These singularities inform us of presence of Bulk waves in piezoelectric materials. By which we obtain accurate values for each of the coefficient attenuation and acoustic velocity for Bulk waves. This study will be very interesting in modeling and realization of acoustic microwaves devices (ultrasound) based on the propagation of acoustic microwaves.

Keywords: piezoelectric material, probabilistic neural network (PNN), classification, acoustic microwaves, bulk waves, the attenuation coefficient

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Authors: Ahmadu Girgiri, Lawan Gana Ali, Mamman M. Baba

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Environmental security clearly articulates the perfections and developments of various communities around the world irrespective of the region, culture, religion or social inclination. Although, the present state of insecurity has become serious issue devastating the peace, unity, stability and progress of man and his physical environment particularly in developing countries. Recently, measure of security and it management in Nigeria has been a bottle-neck to the effectiveness and advancement of various sectors that include; business, education, social relations, politics and above all an economy. Several measures have been considered on mitigating environment insecurity such as surveillance, demarcation, security personnel empowerment and the likes, but still the issue remains disturbing. In this paper, we present the application of new technology that contributes to the improvement of security surveillance known as “Wireless Sensor Network (WSN)”. The system is new, smart and emerging technology that provides monitoring, detection and aggregation of information using sensor nodes and wireless network. WSN detects, monitors and stores information or activities in the deployed area such as schools, environment, business centers, public squares, industries, and outskirts and transmit to end users. This will reduce the cost of security funding and eases security surveillance depending on the nature and the requirement of the deployment.

Keywords: application, environment, insecurity, sensor, wireless sensor network

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Authors: Abdullah Al Mamun, Tarek R. Sheltami

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Video conferencing is a highly demanding facility now a days in order to its real time characteristics, but faster communication is the prior requirement of this technology. Multi Protocol Label Switching (MPLS) IP Virtual Private Network (VPN) address this problem and it is able to make a communication faster than others techniques. However, this paper studies the performance comparison of video traffic between two routing protocols namely the Enhanced Interior Gateway Protocol(EIGRP) and Open Shortest Path First (OSPF). The combination of traditional routing and MPLS improve the forwarding mechanism, scalability and overall network performance. We will use GNS3 and OPNET Modeler 14.5 to simulate many different scenarios and metrics such as delay, jitter and mean opinion score (MOS) value are measured. The simulation result will show that OSPF and BGP-MPLS VPN offers best performance for video conferencing application.

Keywords: OSPF, BGP, EIGRP, MPLS, Video conference, Provider router, edge router, layer3 VPN

Procedia PDF Downloads 331

Authors: Jayanti Gyawali, Deepak Adhikari, Mukesh Mallik, Sanjay Sah

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Radiation is defined as an emission or transmission of energy in form of waves or particles through space or material medium. The major imaging tools used in diagnostic radiology is based on the use of ionizing radiation. A cross-sectional study was carried out during July- August, 2015 among technicians in 15 different hospitals of Bharatpur, Chitwan, Nepal to assess awareness regarding radiation protection and their current practice. The researcher was directly engaged for data collection using self-administered semi-structured questionnaire. The findings of the study are presented in socio-demographic characteristics of respondents, current practice of respondents and knowledge regarding radiation protection. The result of this study demonstrated that despite the importance of radiation and its consequent hazards, the level of knowledge among technicians is only 60.23% and their current practice is 76.84%. The difference in the mean score of knowledge and practice might have resulted due to technicians’s regular work and lack of updates. The study also revealed that there is no significant (p>0.05) difference in knowledge level of technicians practicing in different hospitals. But the mean difference in practice scores of different hospital is significant (p<0.05) i.e. i.e. the cancer hospital with large volumes of regular radiological cases and radiation therapies for cancer treatment has better practice in comparison to other hospitals. The deficiency in knowledge of technicians might alter the expected benefits, compared to the risk involved, and can cause erroneous medical diagnosis and radiation hazard. Therefore, this study emphasizes the need for all technicians to update themselves with the appropriate knowledge and current practice about ionizing and non-ionizing radiation.

Keywords: technicians, knowledge, Nepal, radiation

Procedia PDF Downloads 330

Authors: M. Adjabi, A. Amiar, P. O. Logerais

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Flexible Alternative Current Systems Transmission (FACTS) are used since nearly four decades and present very good dynamic performances. The purpose of this work is to study the behavior of a system where Static Compensator (STATCOM) is located at the midpoint of a transmission line which is the idea of the project functioning in disturbed modes with various levels of load. The studied model and starting from the analysis of various alternatives will lead to the checking of the aptitude of the STATCOM to maintain the voltage plan and to improve the power flow in electro-energetic system which is the east region of Algerian 400 kV transmission network. The steady state performance of STATCOM’s controller is analyzed through computer simulations with Matlab/Simulink program. The simulation results have demonstrated that STATCOM can be effectively applied in power transmission systems to solve the problems of poor dynamic performance and voltage regulation.

Keywords: STATCOM, reactive power, power flow, voltage plan, Algerian network

Procedia PDF Downloads 569

Authors: M. Adjabi, A. Amiar, P. O. Logerais

Abstract:

Flexible Alternative Current Systems Transmission (FACTS) are used since nearly four decades and present very good dynamic performances. The purpose of this work is to study the behavior of a system where Static Compensator (STATCOM) is located at the midpoint of a transmission line which is the idea of the project functioning in disturbed modes with various levels of load. The studied model and starting from the analysis of various alternatives will lead to the checking of the aptitude of the STATCOM to maintain the voltage plan and to improve the power flow in electro-energetic system which is the east region of Algerian 400 kV transmission network. The steady state performance of STATCOM’s controller is analyzed through computer simulations with Matlab/Simulink program. The simulation results have demonstrated that STATCOM can be effectively applied in power transmission systems to solve the problems of poor dynamic performance and voltage regulation.

Keywords: STATCOM, reactive power, power flow, voltage plan, Algerian network

Procedia PDF Downloads 599

Authors: Mouhammd Alkasassbeh, Alaa Lasasmeh

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We live in an era of rapid development as a result of significant scientific growth. Like other technologies, wireless sensor networks (WSNs) are playing one of the main roles. Based on WSNs, ZigBee adds many features to devices, such as minimum cost and power consumption, and increasing the range and connect ability of sensor nodes. ZigBee technology has come to be used in various fields, including science, engineering, and networks, and even in medicinal aspects of intelligence building. In this work, we generated two main datasets, the first being based on tree topology and the second on star topology. The datasets were evaluated by three machine learning (ML) algorithms: J48, meta.j48 and multilayer perceptron (MLP). Each topology was classified into normal and abnormal (attack) network traffic. The dataset used in our work contained simulated data from network simulation 2 (NS2). In each database, the Bayesian network meta.j48 classifier achieved the highest accuracy level among other classifiers, of 99.7% and 99.2% respectively.

Keywords: IDS, Machine learning, WSN, ZigBee technology

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Authors: Reza Rezaeipour Honarmandzad

Abstract:

In this paper we propose an ANN for detection and localization of fatigue damage in aircraft structures. We used network of piezoelectric transducers for Lamb-wave measurements in order to calculate damage indices. Data gathered by the sensors was given to neural network classifier. A set of neural network electors of different architecture cooperates to achieve consensus concerning the state of each monitored path. Sensed signal variations in the ROI, detected by the networks at each path, were used to assess the state of the structure as well as to localize detected damage and to filter out ambient changes. The classifier has been extensively tested on large data sets acquired in the tests of specimens with artificially introduced notches as well as the results of numerous fatigue experiments. Effect of the classifier structure and test data used for training on the results was evaluated.

Keywords: ANN, fatigue damage, aircraft structures, piezoelectric transducers, lamb-wave measurements

Procedia PDF Downloads 417

Authors: Yash Jain

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The US Center for Disease Control has recommended wearing masks to slow the spread of the virus. The research uses a video feed from a camera to conduct real-time classifications of whether or not a human is correctly wearing a mask, incorrectly wearing a mask, or not wearing a mask at all. Utilizing two distinct datasets from the open-source website Kaggle, a mask detection network had been trained. The first dataset that was used to train the model was titled 'Face Mask Detection' on Kaggle, where the dataset was retrieved from and the second dataset was titled 'Face Mask Dataset, which provided the data in a (YOLO Format)' so that the TinyYoloV3 model could be trained. Based on the data from Kaggle, two machine learning models were implemented and trained: a Tiny YoloV3 Real-time model and a two-stage neural network classifier. The two-stage neural network classifier had a first step of identifying distinct faces within the image, and the second step was a classifier to detect the state of the mask on the face and whether it was worn correctly, incorrectly, or no mask at all. The TinyYoloV3 was used for the live feed as well as for a comparison standpoint against the previous two-stage classifier and was trained using the darknet neural network framework. The two-stage classifier attained a mean average precision (MAP) of 80%, while the model trained using TinyYoloV3 real-time detection had a mean average precision (MAP) of 59%. Overall, both models were able to correctly classify stages/scenarios of no mask, mask, and incorrectly worn masks.

Keywords: datasets, classifier, mask-detection, real-time, TinyYoloV3, two-stage neural network classifier

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Authors: Vimala Rani P, Narasimha Malikarjunan, Mercy Shalinie S

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Data Networking was brought forth as an instantiation of information-centric networking. The attackers can send a colossal number of spoofs to take hold of the Pending Interest Table (PIT) named an Interest Flooding attack (IFA) since the in- interests are recorded in the PITs of the intermediate routers until they receive corresponding Data Packets are go beyond the time limit. These attacks can be detrimental to network performance. PIT expiration rate or the Interest satisfaction rate, which cannot differentiate the IFA from attacks, is the criterion Traditional IFA detection techniques are concerned with. Threshold values can casually affect Threshold-based traditional methods. This article proposes an accurate IFA detection mechanism based on a Multiple Feature-based Extreme Learning Machine (MF-ELM). Accuracy of the attack detection can be increased by presenting the entropy of Internet names, Interest satisfaction rate and PIT usage as features extracted in the MF-ELM classifier. Furthermore, we deploy a queue-based hostile Interest prefix mitigation mechanism. The inference of this real-time test bed is that the mechanism can help the network to resist IFA with higher accuracy and efficiency.

Keywords: information-centric network, pending interest table, interest flooding attack, MF-ELM classifier, queue-based mitigation strategy

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Authors: Antonio Pizzarello, Oris Friesen

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Networks, such as the electric power grid, must demonstrate exemplary performance and integrity. Integrity depends on the quality of both the system design model and the deployed software. Integrity of the deployed software is key, for both the original versions and the many that occur throughout numerous maintenance activity. Current software engineering technology and practice do not produce adequate integrity. Distributed systems utilize networks where each node is an independent computer system. The connections between them is realized via a network that is normally redundantly connected to guarantee the presence of a path between two nodes in the case of failure of some branch. Furthermore, at each node, there is software which may fail. Self-stabilizing protocols are usually present that recognize failure in the network and perform a repair action that will bring the node back to a correct state. These protocols first introduced by E. W. Dijkstra are currently present in almost all Ethernets. Super stabilization protocols capable of reacting to a change in the network topology due to the removal or addition of a branch in the network are less common but are theoretically defined and available. This paper describes how to use the Software Integrity Assessment (SIA) methodology to analyze self-stabilizing software. SIA is based on the UNITY formalism for parallel and distributed programming, which allows the analysis of code for verifying the progress property p leads-to q that describes the progress of all computations starting in a state satisfying p to a state satisfying q via the execution of one or more system modules. As opposed to demonstrably inadequate test and evaluation methods SIA allows the analysis and verification of any network self-stabilizing software as well as any other software that is designed to recover from failure without external intervention of maintenance personnel. The model to be analyzed is obtained by automatic translation of the system code to a transition system that is based on the use of the weakest precondition.

Keywords: network, power grid, self-stabilization, software integrity assessment, UNITY, weakest precondition

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Authors: Anu Malhotra

Abstract:

An Ad-hoc network is one that is an autonomous, self configuring network made up of mobile nodes connected via wireless links. Ad-hoc networks often consist of nodes, mobile hosts (MH) or mobile stations (MS, also serving as routers) connected by wireless links. Different routing protocols are used for data transmission in between the nodes in an adhoc network. In this paper two protocols (OLSR and AODV) are analyzed on the basis of two parameters i.e. time delay and throughput with different data rates. On the basis of these analysis, we observed that with same data rate, AODV protocol is having more time delay than the OLSR protocol whereas throughput for the OLSR protocol is less compared to the AODV protocol.

Keywords: routing adhoc, mobile hosts, mobile stations, OLSR protocol, AODV protocol

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Authors: C. Pislaru, A. Shebani

Abstract:

This paper uses the radial basis function neural network (RBFNN) for system identification of nonlinear systems. Five nonlinear systems are used to examine the activity of RBFNN in system modeling of nonlinear systems; the five nonlinear systems are dual tank system, single tank system, DC motor system, and two academic models. The feed forward method is considered in this work for modelling the non-linear dynamic models, where the K-Means clustering algorithm used in this paper to select the centers of radial basis function network, because it is reliable, offers fast convergence and can handle large data sets. The least mean square method is used to adjust the weights to the output layer, and Euclidean distance method used to measure the width of the Gaussian function.

Keywords: system identification, nonlinear systems, neural networks, radial basis function, K-means clustering algorithm

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Authors: Slim Chokri

Abstract:

Load forecasting is one of the central functions in power systems operations. Electricity cannot be stored, which means that for electric utility, the estimate of the future demand is necessary in managing the production and purchasing in an economically reasonable way. A majority of the recently reported approaches are based on neural network. The attraction of the methods lies in the assumption that neural networks are able to learn properties of the load. However, the development of the methods is not finished, and the lack of comparative results on different model variations is a problem. This paper presents a new approach in order to predict the Tunisia daily peak load. The proposed method employs a computational intelligence scheme based on the Fuzzy neural network (FNN) and support vector regression (SVR). Experimental results obtained indicate that our proposed FNN-SVR technique gives significantly good prediction accuracy compared to some classical techniques.

Keywords: neural network, load forecasting, fuzzy inference, machine learning, fuzzy modeling and rule extraction, support vector regression

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Authors: Jameson McFarlane, Thorne J. McFarlane, Leon Bernard

Abstract:

Social network sites (SNS) is an emerging phenomenon that is here to stay. The popularity and the ubiquity of the SNS technology are undeniable. Because most SNS are free and easy to use people from all walks of life and from almost any age are attracted to that technology. College age students are by far the largest segment of the population using SNS. Since most SNS have been adapted for mobile devices, not only do you find students using this technology in their study, while working on labs or on projects, a substantial number of students have been found to use SNS even while listening to lectures. This study found that SNS use has a significant negative impact on the grade point average of college students particularly in the first semester. However, this negative impact is greatly diminished by the end of the third semester partly because the students have adjusted satisfactorily to the challenges of college or because they have learned how to adequately manage their time. It was established that the kinds of activities the students are engaged in during the SNS use are the leading factor affecting academic performance. Of those activities, using SNS during a lecture or while studying is the foremost contributing factor to lower academic performance. This is due to “cognitive” or “information” bottleneck, a condition in which the students find it very difficult to multitask or to switch between resources leading to inefficiency in information retention and thus, educational performance.

Keywords: social network sites, social network analysis, regression coefficient, psychological engagement

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