Search results for: patronage network

Authors: Oluwaponmile David Alao

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Due to an increase in the death rate as a result of heart attack. There is need to develop a system that can be useful in the diagnosis of the disease at the medical centre. This system will help in preventing misdiagnosis that may occur from the medical practitioner or the physicians. In this research work, heart disease dataset obtained from UCI repository has been used to develop an intelligent prediction diagnosis system. The system is modeled on a feedforwad neural network and trained with back propagation neural network. A recognition rate of 86% is obtained from the testing of the network.

Keywords: heart disease, artificial neural network, diagnosis, prediction system

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Authors: Saulius Minkevicius, Edvinas Greicius

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The paper is devoted to the analysis of queueing systems in the context of the network and communications theory. We investigate the inequality in an open queueing network and its applications to the theorems in heavy traffic conditions (fluid approximation, functional limit theorem, and law of the iterated logarithm) for a queue of customers in an open queueing network.

Keywords: fluid approximation, heavy traffic, models of information systems, open queueing network, queue length of customers, queueing theory

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Authors: Manpreet Kaur, Amita Rani, Sanjay Kumar

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The Internet of Things (IoT), a cyber-physical paradigm, allows a large number of devices to connect and send the sensory data in the network simultaneously. This tremendous amount of data generated leads to very high network load consequently resulting in network congestion. It further amounts to frequent loss of useful information and depletion of significant amount of nodes’ energy. Therefore, there is a need to control congestion in IoT so as to prolong network lifetime and improve the quality of service (QoS). Hence, we propose a two-level clustering based routing algorithm considering congestion score and packet priority metrics that focus on minimizing the network congestion. In the proposed Priority based Congestion Control (PBCC) protocol the sensor nodes in IoT network form clusters that reduces the amount of traffic and the nodes are prioritized to emphasize important data. Simultaneously, a congestion score determines the occurrence of congestion at a particular node. The proposed protocol outperforms the existing Packet Discard Network Clustering (PDNC) protocol in terms of buffer size, packet transmission range, network region and number of nodes, under various simulation scenarios.

Keywords: internet of things, cyber-physical systems, congestion control, priority, transmission rate

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Authors: Che Z. Zulkifli, Nursyahida M. Noor, Siti N. Semunab, Shafawati A. Malek

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Wireless sensors, also known as wireless sensor nodes, have been making a significant impact on human daily life. The Radio Frequency Identification (RFID) and Wireless Sensor Network (WSN) are two complementary technologies; hence, an integrated implementation of these technologies expands the overall functionality in obtaining long-range and real-time information on the location and properties of objects and people. An approach for integrating ZigBee and RFID networks is proposed in this paper, to create an energy-efficient network improved by the benefits of combining ZigBee and RFID architecture. Furthermore, the compatibility and requirements of the ZigBee device and communication links in the typical RFID system which is presented with the real world experiment on the capabilities of the proposed RFID system.

Keywords: mesh network, RFID, wireless sensor network, zigbee

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Authors: Contimi Kenfack Mouafo, Sebastian Klick

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In the rural and remote areas of Cameroon, access to electricity is very limited since most of the population is not connected to the main utility grid. Throughout the country, efforts are underway to not only expand the utility grid to these regions but also to provide reliable off-grid access to electricity. The Cameroonian company Solahydrowatt is currently working on the design and planning of one of the first hybrid solar-hydropower plants of Cameroon in Fotetsa, in the western region of the country, to provide the population with reliable access to electricity. This paper models and proposes a design for the low-voltage network with a hybrid solar-hydropower plant in Fotetsa. The modelling takes into consideration the voltage compliance of the distribution network, the maximum load of operating equipment, and most importantly, the ability for the network to operate as an off-grid system. The resulting modelled distribution network does not only comply with the Cameroonian voltage deviation standard, but it is also capable of being operated as a stand-alone network independent of the main utility grid.

Keywords: Cameroon, rural electrification, hybrid solar-hydro, off-grid electricity supply, network simulation

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Authors: Noman Shabbir, Roheel Nawaz, Muhammad N. Iqbal, Junaid Zafar

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This paper focuses on the performance evaluation of routing protocols in WLAN based Wireless Sensor Networks (WSNs). A comparative analysis of routing protocols such as Ad-hoc On-demand Distance Vector Routing System (AODV), Dynamic Source Routing (DSR) and Optimized Link State Routing (OLSR) is been made against different network parameters like network load, end to end delay and throughput in small, medium and large-scale sensor network scenarios to identify the best performing protocol. Simulation results indicate that OLSR gives minimum network load in all three scenarios while AODV gives the best throughput in small scale network but in medium and large scale networks, DSR is better. In terms of delay, OLSR is more efficient in small and medium scale network while AODV is slightly better in large networks.

Keywords: WLAN, WSN, AODV, DSR, OLSR

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Authors: C. N. Vanitha, M. Usha

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In a real world scenario, the viability of the sensor networks has been proved by standardizing the technologies. Wireless sensor networks are vulnerable to both electronic and physical security breaches because of their deployment in remote, distributed, and inaccessible locations. The compromised sensor nodes send malicious data to the base station, and thus, the total network effectiveness will possibly be compromised. To detect and seclude the Route modifiers, a neural network based Pattern Learning predictor (PLP) is presented. This algorithm senses data at any node on present and previous patterns obtained from the en-route nodes. The eminence of any node is upgraded by their predicted and reported patterns. This paper propounds a solution not only to detect the route modifiers, but also to seclude the malevolent nodes from the network. The simulation result proves the effective performance of the network by the presented methodology in terms of energy level, routing and various network conditions.

Keywords: neural networks, pattern learning, security, wireless sensor networks

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Authors: Skiker Kaoutar, Mounir Maouene

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Neuroimaging studies indicate that a large fronto-parieto-temporal network support nouns and their features, with some areas store semantic knowledge (visual, auditory, olfactory, gustatory,…), other areas store lexical representation and other areas are implicated in general semantic processing. However, it is not well understood how this fronto-parieto-temporal network can be modulated by different semantic tasks and different semantic relations between nouns. In this study, we combine a behavioral semantic network, functional MRI studies involving object’s related nouns and brain network studies to explain how different semantic tasks and different semantic relations between nouns can modulate the activity within the brain network of nouns and their features. We first describe how nouns and their features form a large scale brain network. For this end, we examine the connectivities between areas recruited during the processing of nouns to know which configurations of interaction areas are possible. We can thus identify if, for example, brain areas that store semantic knowledge communicate via functional/structural links with areas that store lexical representations. Second, we examine how this network is modulated by different semantic tasks involving nouns and finally, we examine how category specific activation may result from the semantic relations among nouns. The results indicate that brain network of nouns and their features is highly modulated and flexible by different semantic tasks and semantic relations. At the end, this study can be used as a guide to help neurosientifics to interpret the pattern of fMRI activations detected in the semantic processing of nouns. Specifically; this study can help to interpret the category specific activations observed extensively in a large number of neuroimaging studies and clinical studies.

Keywords: nouns, features, network, category specificity

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Authors: Liu Zhiyuan, Sun Zongdi

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In this paper, the BP neural network model is established to predict the carbon trading price and carbon trading volume in Shanghai City. First of all, we find the data of carbon trading price and carbon trading volume in Shanghai City from September 30, 2015 to December 23, 2016. The carbon trading price and trading volume data were processed to get the average value of each 5, 10, 20, 30, and 60 carbon trading price and trading volume. Then, these data are used as input of BP neural network model. Finally, after the training of BP neural network, the prediction values of Shanghai carbon trading price and trading volume are obtained, and the model is tested.

Keywords: Carbon trading price, carbon trading volume, BP neural network model, Shanghai City

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Authors: Osama Elsarrar, Marjorie Darrah, Richard Devin

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This paper discusses the idea of capturing an expert’s knowledge in the form of human understandable rules and then inserting these rules into a dynamic cell structure (DCS) neural network. The DCS is a form of self-organizing map that can be used for many purposes, including classification and prediction. This particular neural network is considered to be a topology preserving network that starts with no pre-structure, but assumes a structure once trained. The DCS has been used in mission and safety-critical applications, including adaptive flight control and health-monitoring in aerial vehicles. The approach is to insert expert knowledge into the DCS before training. Rules are translated into a pre-structure and then training data are presented. This idea has been demonstrated using the well-known Iris data set and it has been shown that inserting the pre-structure results in better accuracy with the same training.

Keywords: neural network, self-organizing map, rule extraction, rule insertion

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Authors: Xiaohan Bookman, Xiaoyan Zhu

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Margin-Based Principle has been proposed for a long time, it has been proved that this principle could reduce the structural risk and improve the performance in both theoretical and practical aspects. Meanwhile, feed-forward neural network is a traditional classifier, which is very hot at present with a deeper architecture. However, the training algorithm of feed-forward neural network is developed and generated from Widrow-Hoff Principle that means to minimize the squared error. In this paper, we propose a new training algorithm for feed-forward neural networks based on Margin-Based Principle, which could effectively promote the accuracy and generalization ability of neural network classifiers with less labeled samples and flexible network. We have conducted experiments on four UCI open data sets and achieved good results as expected. In conclusion, our model could handle more sparse labeled and more high-dimension data set in a high accuracy while modification from old ANN method to our method is easy and almost free of work.

Keywords: Max-Margin Principle, Feed-Forward Neural Network, classifier, structural risk

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Authors: Subhash Reddy

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Mobile communication in today’s world means a lot to the human kind, through this many deals are made and others are broken, within seconds. That is because of our sophisticated methods of transporting the data at very high speeds and to very long distances, within no time. That is also because we kept on changing the method of serving the connections as the no of connections kept on increasing, that has led to many methods like TDMA, CDMA, and FDMA, etc. in wireless communications. And also the areas, where the connections are provided are also divided into CELLS, which are the basic blocks for cellular communications. Along with the wireless network, providing a wired network in mobile phones would serve as a very good alternative and would divert the extra traffic of a cell, so that a CELL which is providing wireless network can operate more efficiently.

Keywords: CDMA, FDMA, TDMA, CELL

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Authors: Tammam A. Benmusa, Mohamed A. Shlibek, Rawad M. Swesi

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The investigation of Buildings Penetration Loss (BPL) of radio signal is getting more and more important. It plays an important role in calculating the indoor coverage for wireless communication networks. In this paper, the theory behind BPL and its mechanisms have been reviewed. The operating frequency, coverage area type, climate condition, time of measurement, and other factors affecting the values of BPL have been discussed. The practical part of this work was conducting 4000 measurements of BPL in different areas in the Libyan capital, Tripoli, to get empirical model for this loss. The measurements were taken for 2 different types of wireless communication networks; mobile telephone network (for Almadar company), which operates at 900 MHz and WiMAX network (LTT company) which operates at 2500 MHz. The results for each network were summarized and presented in several graphs. The graphs are showing how the BPL affected by: time of measurement, morphology (type of area), and climatic environment.

Keywords: building penetration loss, wireless network, mobile network, link budget, indoor network performance

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Authors: Joseph Uzuazomaro Oju

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The ease at which manipulated media can be created, and the increasing difficulty in identifying fake media makes it a great threat. Most of the applications used for the creation of these high-quality fake videos and images are built with deep learning. Hence, the use of deep learning in creating a detection mechanism cannot be overemphasized. Any successful fake media that is being detected before it reached the populace will save people from the self-doubt of either a content is genuine or fake and will ensure the credibility of videos and images. The methodology introduced in this paper approaches the manipulated media detection challenge using a combo of VGG-19 and a deep capsule network. In the case of videos, they are converted into frames, which, in turn, are resized and cropped to the face region. These preprocessed images/videos are fed to the VGG-19 network to extract the latent features. The extracted latent features are inputted into a deep capsule network enhanced with a 3D -convolution dynamic routing agreement. The 3D –convolution dynamic routing agreement algorithm helps to reduce the linkages between capsules networks. Thereby limiting the poor learning shortcoming of multiple capsule network layers. The resultant output from the deep capsule network will indicate a media to be either genuine or fake.

Keywords: deep capsule network, dynamic routing, fake media detection, manipulated media

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Authors: Dweepabiswa Bagchi, D. V. Senthilkumar

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Numerous studies have typically demonstrated the devastation of invasions on an isolated ecosystem or, at most, a network of dispersively coupled similar ecosystem patches. Using such a simplistic 2-D network model, one can only consider dispersal coupling and inter-species trophic interactions. However, in a realistic ecosystem, numerous species co-exist and interact trophically and non-trophically in groups of 2 or more. Even different types of dispersal can introduce complexity in an ecological network. Therefore, a more accurate representation of actual ecosystems (or ecological networks) is a complex network consisting of motifs formed by two or more interacting species. Here, the apropos structure of the network should be multiplex or multi-layered. Motifs between different patches or species should be identical within the same layer and vary from one layer to another. This study investigates three distinct ecological multiplex networks facing invasion from one or more external species. This work determines and quantifies the criteria for the increased extinction risk of these networks. The dynamical states of the network with high extinction risk, i.e., the danger states, and those with low extinction risk, i.e., the resistive network states, are both subsequently identified. The analysis done in this study further quantifies the persistence of the entire network corresponding to simultaneous changes in the strength of invasive dispersal and higher-order trophic and non-trophic interactions. This study also demonstrates that the ecosystems enjoy an inherent advantage against invasions due to their multiplex network structure.

Keywords: increased ecosystem persistence, invasion on ecosystems, multiplex networks, non-trophic interactions

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Authors: John Kabuba

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The study investigated the implementation of the Neural Network (NN) techniques for prediction of the loading of Cu ions onto clinoptilolite. The experimental design using analysis of variance (ANOVA) was chosen for testing the adequacy of the Neural Network and for optimizing of the effective input parameters (pH, temperature and initial concentration). Feed forward, multi-layer perceptron (MLP) NN successfully tracked the non-linear behavior of the adsorption process versus the input parameters with mean squared error (MSE), correlation coefficient (R) and minimum squared error (MSRE) of 0.102, 0.998 and 0.004 respectively. The results showed that NN modeling techniques could effectively predict and simulate the highly complex system and non-linear process such as ion-exchange.

Keywords: clinoptilolite, loading, modeling, neural network

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Authors: Hamid R. Lashgarian Azad, Seyed N. Shetab Boushehri

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Wireless sensor networks (WSN) have become progressively popular due to their wide range of applications. Wireless Sensor Network is made of numerous tiny sensor nodes that are battery-powered. It is a very significant problem to maximize the lifetime of wireless sensor networks. In this paper, we propose a two-stage protocol based on an imperialist competitive algorithm (2S-ICA) to solve a sensor network optimization problem. The energy of the sensors can be greatly reduced and the lifetime of the network reduced by long communication distances between the sensors and the sink. We can minimize the overall communication distance considerably, thereby extending the lifetime of the network lifetime through connecting sensors into a series of independent clusters using 2SICA. Comparison results of the proposed protocol and LEACH protocol, which is common to solving WSN problems, show that our protocol has a better performance in terms of improving network life and increasing the number of transmitted data.

Keywords: wireless sensor network, imperialist competitive algorithm, LEACH protocol, k-means clustering

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Authors: Sathapath Kilaso

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Nowadays, Today, wireless sensor networks are an important technology that works with Internet of Things. It is receiving various data from many sensor. Then sent to processing or storing. By wireless network or through the Internet. The devices around us are intelligent, can receiving/transmitting and processing data and communicating through the system. There are many applications of wireless sensor networks, such as smart city, smart farm, environmental management, weather. This article will explore the use of wireless sensor networks in Thailand and collect data from Thai Thesis database in 2012-2017. How to Implementing Wireless Sensor Network Technology. Advantage from this study To know the usage wireless technology in many fields. This will be beneficial for future research. In this study was found the most widely used wireless sensor network in agriculture field. Especially for smart farms. And the second is the adoption of the environment. Such as weather stations and water inspection.

Keywords: wireless sensor network, smart city, survey, Adhoc Network

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Authors: Yinggang Guo, Zongchun Li

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In order to decrease the accumulated survey error of tunnel installation control network of particle accelerator, a sectional control method is proposed. Firstly, the accumulation rule of positional error with the length of the control network is obtained by simulation calculation according to the shape of the tunnel installation-control-network. Then, the RMS of horizontal positional precision of tunnel backbone control network is taken as the threshold. When the accumulated error is bigger than the threshold, the tunnel installation control network should be divided into subsections reasonably. On each segment, the middle survey station is taken as the datum for independent adjustment calculation. Finally, by taking the backbone control points as faint datums, the weighted partial parameters adjustment is performed with the adjustment results of each segment and the coordinates of backbone control points. The subsections are jointed and unified into the global coordinate system in the adjustment process. An installation control network of the linac with a length of 1.6 km is simulated. The RMS of positional deviation of the proposed method is 2.583 mm, and the RMS of the difference of positional deviation between adjacent points reaches 0.035 mm. Experimental results show that the proposed sectional control method can not only effectively decrease the accumulated survey error but also guarantee the relative positional precision of the installation control network. So it can be applied in the data processing of tunnel installation control networks, especially for large particle accelerators.

Keywords: alignment, tunnel installation control network, accumulated survey error, sectional control method, datum

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Authors: Teresa Azevedo PerdicoúLis, Paulo Lopes Dos Santos

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The conceptualisation of the problem of network optimisation as a noncooperative game sets up a holistic interactive approach that brings together different network features (e.g., com-pressor stations, sources, and pipelines, in the gas context) where the optimisation objectives are different, and a single optimisation procedure becomes possible without having to feed results from diverse software packages into each other. A mathematical model of this type, where independent entities take action, offers the ideal modularity and subsequent problem decomposition in view to design a decentralised algorithm to optimise the operation and management of the network. In a game framework, compressor stations and sources are under-stood as players which communicate through network connectivity constraints–the pipeline model. That is, in a scheme similar to tatonnementˆ, the players appoint their best settings and then interact to check for network feasibility. The devolved degree of network unfeasibility informs the players about the ’quality’ of their settings, and this two-phase iterative scheme is repeated until a global optimum is obtained. Due to network transients, its optimisation needs to be assessed at different points of the control interval. For this reason, the proposed approach to optimisation has two stages: (i) the first stage computes along the period of optimisation in order to fulfil the requirement just mentioned; (ii) the second stage is initialised with the solution found by the problem computed at the first stage, and computes in the end of the period of optimisation to rectify the solution found at the first stage. The liability of the proposed scheme is proven correct on an abstract prototype and three example networks.

Keywords: connectivity matrix, gas network optimisation, large-scale, noncooperative game, system decomposition

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Authors: Isaac Kofi Mensah, Jianing Mi, Cheng Feng

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The patronage of e-government services (demand side of e-government) is vital to the successful implementation of e-government initiatives. The purpose of this study is to explore the predictors determining the willingness of African students in China to adopt and use e-government services. The Unified Theory of Acceptance and Use of Technology (UTAUT) will be used as the theoretical foundation for this research. Research instrument will be developed and administered to 500 African students in China. Factors such as performance expectancy, effort expectancy, social influence, facilitating conditions and culture will be investigated to determine its significant impact on the willingness to use e-government services. This study is a research in progress. The outcome of this study will provide valuable recommendations to improve the provision of public services through e-government.

Keywords: e-government, e-government services, predictors, UTAUT

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Authors: Linh Nguyen Tung, Anh Truong Viet, Nghien Nguyen Ba, Chuong Trinh Trong

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This paper proposes a reconfiguration methodology based on a continuous genetic algorithm (CGA) and particle swarm optimization (PSO) for minimizing active power loss and minimizing voltage deviation. Both algorithms are adapted using graph theory to generate feasible individuals, and the modified crossover is used for continuous variable of CGA. To demonstrate the performance and effectiveness of the proposed methods, a comparative analysis of CGA with PSO for network reconfiguration, on 33-node and 119-bus radial distribution system is presented. The simulation results have shown that both CGA and PSO can be used in the distribution network reconfiguration and CGA outperformed PSO with significant success rate in finding optimal distribution network configuration.

Keywords: distribution network reconfiguration, particle swarm optimization, continuous genetic algorithm, power loss reduction, voltage deviation

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Authors: Tao Feng, Xiaomei Ma, Xian Guo, Jing Wang

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Named Data Network (NDN) is one of the future Internet architecture, all nodes (i.e., hosts, routers) are allowed to have a local cache, used to satisfy incoming requests for content. However, depending on caching allows an adversary to perform attacks that are very effective and relatively easy to implement, such as content pollution attack. In this paper, we use a method of secure network coding based on homomorphic signature system to solve this problem. Firstly ,we use a dynamic public key technique, our scheme for each generation authentication without updating the initial secret key used. Secondly, employing the homomorphism of hash function, intermediate node and destination node verify the signature of the received message. In addition, when the network topology of NDN is simple and fixed, the code coefficients in our scheme are generated in a pseudorandom number generator in each node, so the distribution of the coefficients is also avoided. In short, our scheme not only can efficiently prevent against Intra/Inter-GPAs, but also can against the content poisoning attack in NDN.

Keywords: named data networking, content polloution attack, network coding signature, internet architecture

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Authors: Sharad Shrivastava, Arun Jalan

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In this study tensile testing was performed on randomly oriented short glass fiber/epoxy resin composite specimens which were prepared using hand lay-up method. Samples were tested over a wide range of strain rate/loading rate from 2mm/min to 40mm/min to see the effect on ultimate tensile strength of the composite. A multi layered 'back propagation artificial neural network of supervised learning type' was used to analyze and predict the tensile properties with strain rate and temperature as given input and output as UTS to predict. Various network structures were designed and investigated with varying parameters and network sizes, and an optimized network structure was proposed to predict the UTS of short glass fiber/epoxy resin composite specimens with reasonably good accuracy.

Keywords: glass fiber composite, mechanical properties, strain rate, artificial neural network

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Authors: H. Zormati, J. Chebil, J. Bel Hadj Taher

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The goal of this paper is to present an addressing scheme that allows for assigning a unique IPv6 address to each node in the Internet of Things (IoT) network. This scheme guarantees uniqueness by extracting the clock skew of each communication device and converting it into an IPv6 address. Simulation analysis confirms that the presented scheme provides reductions in terms of energy consumption, communication overhead and response time as compared to four studied addressing schemes Strong DAD, LEADS, SIPA and CLOSA.

Keywords: addressing, IoT, IPv6, network, nodes

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Authors: Virender Ranga, Mayank Dave, Anil Kumar Verma

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Recently wireless sensor networks (WSNs) are used in many real life applications like environmental monitoring, habitat monitoring, health monitoring etc. Due to power constraint cheaper devices used in these applications, the energy consumption of each device should be kept as low as possible such that network operates for longer period of time. One of the techniques to prolong the network lifetime is an intelligent grouping of sensor nodes such that they can perform their operation in cooperative and energy efficient manner. With this motivation, we propose a novel approach by organize the sensor nodes in cooperative multihop mini-groups so that the total global energy consumption of the network can be reduced and network lifetime can be improved. Our proposed approach also reduces the number of transmitted messages inside the WSNs, which further minimizes the energy consumption of the whole network. The experimental simulations show that our proposed approach outperforms over the state-of-the-art approach in terms of stability period and aggregated data.

Keywords: clustering, cluster-head, mini-group, stability period

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Authors: Koray U. Erbas

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Neural networks are appealing for many applications since they are able to learn complex non-linear relationships between input and output data. As the number of neurons and layers in a neural network increase, it is possible to represent more complex relationships with automatically extracted features. Nowadays Deep Neural Networks (DNNs) are widely used in Computer Vision problems such as; classification, object detection, segmentation image editing etc. In this work, Facial Emotion Recognition task is performed by proposed Convolutional Neural Network (CNN)-based DNN architecture using FER2013 Dataset. Moreover, the effects of different hyperparameters (activation function, kernel size, initializer, batch size and network size) are investigated and ablation study results for Pooling Layer, Dropout and Batch Normalization are presented.

Keywords: convolutional neural network, deep learning, deep learning based FER, facial emotion recognition

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Authors: Rodrigo S. Moreira, Nelson F. F. Ebecken

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This paper proposes a maritime vessel tracker composed of an ensemble of WiSARD weightless neural network classifiers. A failure detector analyzes vessel movement with a Kalman filter and corrects the tracking, if necessary, using FFT matching. The use of the WiSARD neural network to track objects is uncommon. The additional contributions of the present study include a performance comparison with four state-of-art trackers, an experimental study of the features that improve maritime vessel tracking, the first use of an ensemble of classifiers to track maritime vessels and a new quantization algorithm that compares the values of pixel pairs.

Keywords: ram memory, WiSARD weightless neural network, object tracking, quantization

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Authors: Rosdyana Mangir Irawan Kusuma, Wei-Chun Kao, Ho-Thi Trang, Yu-Yen Ou, Kai-Lung Hua

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Stock market prediction is still a challenging problem because there are many factors that affect the stock market price such as company news and performance, industry performance, investor sentiment, social media sentiment, and economic factors. This work explores the predictability in the stock market using deep convolutional network and candlestick charts. The outcome is utilized to design a decision support framework that can be used by traders to provide suggested indications of future stock price direction. We perform this work using various types of neural networks like convolutional neural network, residual network and visual geometry group network. From stock market historical data, we converted it to candlestick charts. Finally, these candlestick charts will be feed as input for training a convolutional neural network model. This convolutional neural network model will help us to analyze the patterns inside the candlestick chart and predict the future movements of the stock market. The effectiveness of our method is evaluated in stock market prediction with promising results; 92.2% and 92.1 % accuracy for Taiwan and Indonesian stock market dataset respectively.

Keywords: candlestick chart, deep learning, neural network, stock market prediction

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Authors: Yalong Jiang, Zheru Chi

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In this paper, we study the factors which determine the capacity of a Convolutional Neural Network (CNN) model and propose the ways to evaluate and adjust the capacity of a CNN model for best matching to a specific pattern recognition task. Firstly, a scheme is proposed to adjust the number of independent functional units within a CNN model to make it be better fitted to a task. Secondly, the number of independent functional units in the capsule network is adjusted to fit it to the training dataset. Thirdly, a method based on Bayesian GAN is proposed to enrich the variances in the current dataset to increase its complexity. Experimental results on the PASCAL VOC 2010 Person Part dataset and the MNIST dataset show that, in both conventional CNN models and capsule networks, the number of independent functional units is an important factor that determines the capacity of a network model. By adjusting the number of functional units, the capacity of a model can better match the complexity of a dataset.

Keywords: CNN, convolutional neural network, capsule network, capacity optimization, character recognition, data augmentation, semantic segmentation

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