Search results for: Neural Networks Model

Authors: D. Pugazhenthi, B. Sree Vidya

Abstract:

Cloud computing is one of the emerging technologies that enables end users to use the services of cloud on ‘pay per usage’ strategy. This technology grows in a fast pace and so is its security threat. One among the various services provided by cloud is storage. In this service, security plays a vital factor for both authenticating legitimate users and protection of information. This paper brings in efficient ways of authenticating users as well as securing information on the cloud. Initial phase proposed in this paper deals with an authentication technique using multi-factor and multi-dimensional authentication system with multi-level security. Unique identification and slow intrusive formulates an advanced reliability on user-behaviour based biometrics than conventional means of password authentication. By biometric systems, the accounts are accessed only by a legitimate user and not by a nonentity. The biometric templates employed here do not include single trait but multiple, viz., iris and finger prints. The coordinating stage of the authentication system functions on Ensemble Support Vector Machine (SVM) and optimization by assembling weights of base SVMs for SVM ensemble after individual SVM of ensemble is trained by the Artificial Fish Swarm Algorithm (AFSA). Thus it helps in generating a user-specific secure cryptographic key of the multimodal biometric template by fusion process. Data security problem is averted and enhanced security architecture is proposed using encryption and decryption system with double key cryptography based on Fuzzy Neural Network (FNN) for data storing and retrieval in cloud computing . The proposing scheme aims to protect the records from hackers by arresting the breaking of cipher text to original text. This improves the authentication performance that the proposed double cryptographic key scheme is capable of providing better user authentication and better security which distinguish between the genuine and fake users. Thus, there are three important modules in this proposed work such as 1) Feature extraction, 2) Multimodal biometric template generation and 3) Cryptographic key generation. The extraction of the feature and texture properties from the respective fingerprint and iris images has been done initially. Finally, with the help of fuzzy neural network and symmetric cryptography algorithm, the technique of double key encryption technique has been developed. As the proposed approach is based on neural networks, it has the advantage of not being decrypted by the hacker even though the data were hacked already. The results prove that authentication process is optimal and stored information is secured.

Keywords: artificial fish swarm algorithm (AFSA), biometric authentication, decryption, encryption, fingerprint, fusion, fuzzy neural network (FNN), iris, multi-modal, support vector machine classification

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Authors: Kalyani Kulkarni, Bharat Chaudhari

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This paper addresses the issue of resource allocation in the emerging cognitive technology. Focusing the quality of service (QoS) of primary users (PU), a novel method is proposed for the resource allocation of secondary users (SU). In this paper, we propose the unique utility function in the game theoretic model of Cognitive Radio which can be maximized to increase the capacity of the cognitive radio network (CRN) and to minimize the interference scenario. The utility function is formulated to cater the need of PUs by observing Signal to Noise ratio. The existence of Nash equilibrium is for the postulated game is established.

Keywords: cognitive networks, game theory, Nash equilibrium, resource allocation

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Authors: Maulik Parmar, Sumeet Sandhu

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The online fashion industry experiences high product return rates. Many returns are because of size/fit mismatches -the size scale on labels can vary across brands, the size parameters may not capture all fit measurements, or the product may have manufacturing defects. Warehouse quality check of garment sizes can be semi-automated to improve speed and accuracy. This paper presents an approach for automatically measuring garment sizes from a single image of the garment -using Deep Learning to learn garment keypoints. The paper focuses on the waist size measurement of jeans and can be easily extended to other garment types and measurements. Experimental results show that this approach can greatly improve the speed and accuracy of today’s manual measurement process.

Keywords: convolutional neural networks, deep learning, distortion, garment measurements, image warping, keypoints

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Authors: Nouha Khediri, Mohammed Ben Ammar, Monji Kherallah

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Recently, facial emotion recognition (FER) has become increasingly essential to understand the state of the human mind. Accurately classifying emotion from the face is a challenging task. In this paper, we present a facial emotion recognition approach named CV-FER, benefiting from deep learning, especially CNN and VGG16. First, the data is pre-processed with data cleaning and data rotation. Then, we augment the data and proceed to our FER model, which contains five convolutions layers and five pooling layers. Finally, a softmax classifier is used in the output layer to recognize emotions. Based on the above contents, this paper reviews the works of facial emotion recognition based on deep learning. Experiments show that our model outperforms the other methods using the same FER2013 database and yields a recognition rate of 92%. We also put forward some suggestions for future work.

Keywords: CNN, deep-learning, facial emotion recognition, machine learning

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Authors: Ahmed O. Babaleye, Rafet E. Kurt

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The global oil and gas industry is beginning to witness an increase in the number of installations moving towards decommissioning. Decommissioning of offshore installations is a complex, costly and hazardous activity, making safety one of the major concerns. Among existing removal options, complete and partial removal options pose the highest risks. Therefore, a dynamic risk model of the accidents from the two options is important to assess the risks on an overall basis. In this study, a risk-based safety model is developed to conduct quantitative risk analysis (QRA) for jacket structure systems failure. Firstly, bow-tie (BT) technique is utilised to model the causal relationship between the system failure and potential accident scenarios. Subsequently, to relax the shortcomings of BT, Bayesian Belief Networks (BBNs) were established to dynamically assess associated uncertainties and conditional dependencies. The BBN is developed through a similitude mapping of the developed bow-tie. The BBN is used to update the failure probabilities of the contributing elements through diagnostic analysis, thus, providing a case-specific and realistic safety analysis method when compared to a bow-tie. This paper presents the application of dynamic safety analysis to guide the allocation of risk control measures and consequently, drive down the avoidable cost of remediation.

Keywords: Bayesian belief network, offshore decommissioning, dynamic safety model, quantitative risk analysis

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Authors: Danielle Shackley, Yetunde Folajimi

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As more people turn to the internet seeking health-related information, there is more risk of finding false, inaccurate, or dangerous information. Sentiment analysis is a natural language processing technique that assigns polarity scores to text, ranging from positive, neutral, and negative. In this research, we evaluate the weight of a sentiment analysis feature added to fake health news classification models. The dataset consists of existing reliably labeled health article headlines that were supplemented with health information collected about COVID-19 from social media sources. We started with data preprocessing and tested out various vectorization methods such as Count and TFIDF vectorization. We implemented 3 Naive Bayes classifier models, including Bernoulli, Multinomial, and Complement. To test the weight of the sentiment analysis feature on the dataset, we created benchmark Naive Bayes classification models without sentiment analysis, and those same models were reproduced, and the feature was added. We evaluated using the precision and accuracy scores. The Bernoulli initial model performed with 90% precision and 75.2% accuracy, while the model supplemented with sentiment labels performed with 90.4% precision and stayed constant at 75.2% accuracy. Our results show that the addition of sentiment analysis did not improve model precision by a wide margin; while there was no evidence of improvement in accuracy, we had a 1.9% improvement margin of the precision score with the Complement model. Future expansion of this work could include replicating the experiment process and substituting the Naive Bayes for a deep learning neural network model.

Keywords: sentiment analysis, Naive Bayes model, natural language processing, topic analysis, fake health news classification model

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Authors: Xun Sun, Le Li, Ce Mo, Lei Mo, Ruiming Wang, Guosheng Ding

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Cognitive control plays a central role in information processing, which is comprised of various components including response suppression and inhibitory control. Response suppression is considered to inhibit the irrelevant response during the cognitive process; while inhibitory control to inhibit the irrelevant stimulus in the process of cognition. Both of them undertake distinct functions for the cognitive control, so as to enhance the performances in behavior. Among numerous factors on cognitive control, bilingual experience is a substantial and indispensible factor. It has been reported that bilingual experience can influence the neural activity of cognitive control as whole. However, it still remains unknown how the neural influences specifically present on the components of cognitive control imposed by bilingualism. In order to explore the further issue, the study applied fMRI, used anti-saccade paradigm and compared the cerebral activations between high and low proficient Chinese-English bilinguals. Meanwhile, the study provided experimental evidence for the brain plasticity of language, and offered necessary bases on the interplay between language and cognitive control. The results showed that response suppression recruited the middle frontal gyrus (MFG) in low proficient Chinese-English bilinguals, but the inferior patrietal lobe in high proficient Chinese-English bilinguals. Inhibitory control engaged the superior temporal gyrus (STG) and middle temporal gyrus (MTG) in low proficient Chinese-English bilinguals, yet the right insula cortex was more active in high proficient Chinese-English bilinguals during the process. These findings illustrate insights that bilingual experience has neural influences on different components of cognitive control. Compared with low proficient bilinguals, high proficient bilinguals turn to activate advanced neural areas for the processing of cognitive control. In addition, with the acquisition and accumulation of language, language experience takes effect on the brain plasticity and changes the neural basis of cognitive control.

Keywords: bilingual experience, cognitive control, inhibition control, response suppression

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Authors: Asma Ben Yaghlane, Mohamed Naceur Azaiez

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We consider the problem of attacks on networks. We define the concept of system survivability in networks in the presence of intelligent threats. Our setting of the problem assumes a flow to be sent from one source node to a destination node. The attacker attempts to disable the network by preventing the flow to reach its destination while the defender attempts to identify the best path-set to use to maximize the chance of arrival of the flow to the destination node. Our concept is shown to be different from the classical concept of network reliability. We distinguish two types of network survivability related to the defender and to the attacker of the network, respectively. We prove that the defender-based-network survivability plays the role of a lower bound while the attacker-based-network survivability plays the role of an upper bound of network reliability. We also prove that both concepts almost never agree nor coincide with network reliability. Moreover, we use the shortest-path problem to determine the defender-based-network survivability and the min-cut problem to determine the attacker-based-network survivability. We extend the problem to a variety of models including the minimum-spanning-tree problem and the multiple source-/destination-network problems.

Keywords: defense/attack strategies, information, networks, reliability, survivability

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Authors: Danilo López, Edwin Rivas, Fernando Pedraza

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Currently, one of the major challenges in wireless networks is the optimal use of radio spectrum, which is managed inefficiently. One of the solutions to existing problem converges in the use of Cognitive Radio (CR), as an essential parameter so that the use of the available licensed spectrum is possible (by secondary users), well above the usage values that are currently detected; thus allowing the opportunistic use of the channel in the absence of primary users (PU). This article presents the results found when estimating or predicting the future use of a spectral transmission band (from the perspective of the PU) for a chaotic type channel arrival behavior. The time series prediction method (which the PU represents) used is ANFIS (Adaptive Neuro Fuzzy Inference System). The results obtained were compared to those delivered by the RNA (Artificial Neural Network) algorithm. The results show better performance in the characterization (modeling and prediction) with the ANFIS methodology.

Keywords: ANFIS, cognitive radio, prediction primary user, RNA

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Authors: Ammar Alali, Mahmoud Abughaban, William Contreras Otalvora

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Optimizing the drilling process for cost and efficiency requires the optimization of the rate of penetration (ROP). ROP is the measurement of the speed at which the wellbore is created, in units of feet per hour. It is the primary indicator of measuring drilling efficiency. Maximization of the ROP can indicate fast and cost-efficient drilling operations; however, high ROPs may induce unintended events, which may lead to nonproductive time (NPT) and higher net costs. The proposed ROP optimization solution is a hybrid, data-driven system that aims to improve the drilling process, maximize the ROP, and minimize NPT. The system consists of two phases: (1) utilizing existing geological and drilling data to train the model prior, and (2) real-time adjustments of the controllable dynamic drilling parameters [weight on bit (WOB), rotary speed (RPM), and pump flow rate (GPM)] that direct influence on the ROP. During the first phase of the system, geological and historical drilling data are aggregated. After, the top-rated wells, as a function of high instance ROP, are distinguished. Those wells are filtered based on NPT incidents, and a cross-plot is generated for the controllable dynamic drilling parameters per ROP value. Subsequently, the parameter values (WOB, GPM, RPM) are calculated as a conditioned mean based on physical distance, following Inverse Distance Weighting (IDW) interpolation methodology. The first phase is concluded by producing a model of drilling best practices from the offset wells, prioritizing the optimum ROP value. This phase is performed before the commencing of drilling. Starting with the model produced in phase one, the second phase runs an automated drill-off test, delivering live adjustments in real-time. Those adjustments are made by directing the driller to deviate two of the controllable parameters (WOB and RPM) by a small percentage (0-5%), following the Constrained Random Search (CRS) methodology. These minor incremental variations will reveal new drilling conditions, not explored before through offset wells. The data is then consolidated into a heat-map, as a function of ROP. A more optimum ROP performance is identified through the heat-map and amended in the model. The validation process involved the selection of a planned well in an onshore oil field with hundreds of offset wells. The first phase model was built by utilizing the data points from the top-performing historical wells (20 wells). The model allows drillers to enhance decision-making by leveraging existing data and blending it with live data in real-time. An empirical relationship between controllable dynamic parameters and ROP was derived using Artificial Neural Networks (ANN). The adjustments resulted in improved ROP efficiency by over 20%, translating to at least 10% saving in drilling costs. The novelty of the proposed system lays is its ability to integrate historical data, calibrate based geological formations, and run real-time global optimization through CRS. Those factors position the system to work for any newly drilled well in a developing field event.

Keywords: drilling optimization, geological formations, machine learning, rate of penetration

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Authors: Fathi Abid, Bilel Kaffel

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The olive tree, the olive harvest during winter season and the production of olive oil better known by professionals under the name of the crushing operation have interested institutional traders such as olive-oil offices and private companies such as food industry refining and extracting pomace olive oil as well as export-import public and private companies specializing in olive oil. The major problem facing producers of olive oil each winter campaign, contrary to what is expected, it is not whether the harvest will be good or not but whether the sale price will allow them to cover production costs and achieve a reasonable margin of profit or not. These questions are entirely legitimate if we judge by the importance of the issue and the heavy complexity of the uncertainty and competition made tougher by a high level of indebtedness and the experience and expertise of speculators and producers whose objectives are sometimes conflicting. The aim of this paper is to study the formation mechanism of olive oil prices in order to learn about speculators’ behavior and expectations in the market, how they contribute by their industry knowledge and their financial alliances and the size the financial challenge that may be involved for them to build private information hoses globally to take advantage. The methodology used in this paper is based on two stages, in the first stage we study econometrically the formation mechanisms of olive oil price in order to understand the market participant behavior by implementing ARMA, SARMA, GARCH and stochastic diffusion processes models, the second stage is devoted to prediction purposes, we use a combined wavelet- ANN approach. Our main findings indicate that olive oil market participants interact with each other in a way that they promote stylized facts formation. The unstable participant’s behaviors create the volatility clustering, non-linearity dependent and cyclicity phenomena. By imitating each other in some periods of the campaign, different participants contribute to the fat tails observed in the olive oil price distribution. The best prediction model for the olive oil price is based on a back propagation artificial neural network approach with input information based on wavelet decomposition and recent past history.

Keywords: olive oil price, stylized facts, ARMA model, SARMA model, GARCH model, combined wavelet-artificial neural network, continuous-time stochastic volatility mode

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Authors: Ravneil Nand

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Cooperative coevolution uses problem decomposition methods to solve a larger problem. The problem decomposition deals with breaking down the larger problem into a number of smaller sub-problems depending on their method. Different problem decomposition methods have their own strengths and limitations depending on the neural network used and application problem. In this paper we are introducing a new problem decomposition method known as Hidden-Neuron Level Decomposition (HNL). The HNL method is competing with established problem decomposition method in time series prediction. The results show that the proposed approach has improved the results in some benchmark data sets when compared to the standalone method and has competitive results when compared to methods from literature.

Keywords: cooperative coevaluation, feed forward network, problem decomposition, neuron, synapse

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Authors: R. Schweighoffer, N. Nagy, E. Reeves, B. Liebig

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Due to aging populations, the need for seamless palliative care provision is of central interest for western societies. An essential aspect of palliative care delivery is the quality of collaboration amongst palliative care providers. Therefore, the current research is based on Bainbridge’s conceptual framework, which provides an outline for the evaluation of palliative care provision. This study is the first one to investigate the predictive validity of spatial distribution on the quantity of interaction amongst various palliative care providers. Furthermore, based on the familiarity principle, we examine whether the extent of collaboration influences the perceived quality of collaboration among palliative care providers in urban versus rural areas of Switzerland. Based on a population-representative survey of Swiss palliative care providers, the results of the current study show that professionals in densely populated areas report higher absolute numbers of interactions and are more satisfied with their collaborative practice. This indicates that palliative care providers who work in urban areas are better embedded into networks than their counterparts in more rural areas. The findings are especially important, considering that efficient collaboration is a prerequisite to achieve satisfactory patient outcomes. Conclusively, measures should be taken to foster collaboration in weakly interconnected palliative care networks.

Keywords: collaboration, healthcare networks, palliative care, Switzerland

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Authors: Heewon Jeong, Seongpyo Kim, Joon Ha Kim

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Over the last few tens of years, the coastal regions of Korea have experienced red tide algal blooms, which are harmful and toxic to both humans and marine organisms due to their potential threat. It was accelerated owing to eutrophication by human activities, certain oceanic processes, and climate change. Previous studies have tried to monitoring and predicting the algae concentration of the ocean with the bio-optical algorithms applied to color images of the satellite. However, the accurate estimation of algal blooms remains problems to challenges because of the complexity of coastal waters. Therefore, this study suggests a new method to identify the concentration of red tide algal bloom from images of geostationary ocean color imager (GOCI) which are representing the water environment of the sea in Korea. The method employed GOCI images, which took the water leaving radiances centered at 443nm, 490nm and 660nm respectively, as well as observed weather data (i.e., humidity, temperature and atmospheric pressure) for the database to apply optical characteristics of algae and train deep learning algorithm. Convolution neural network (CNN) was used to extract the significant features from the images. And then artificial neural network (ANN) was used to estimate the concentration of algae from the extracted features. For training of the deep learning model, backpropagation learning strategy is developed. The established methods were tested and compared with the performances of GOCI data processing system (GDPS), which is based on standard image processing algorithms and optical algorithms. The model had better performance to estimate algae concentration than the GDPS which is impossible to estimate greater than 5mg/m³. Thus, deep learning model trained successfully to assess algae concentration in spite of the complexity of water environment. Furthermore, the results of this system and methodology can be used to improve the performances of remote sensing. Acknowledgement: This work was supported by the 'Climate Technology Development and Application' research project (#K07731) through a grant provided by GIST in 2017.

Keywords: deep learning, algae concentration, remote sensing, satellite

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Authors: T. Damak, O. Kriaa, A. Baccar, M. A. Ben Ayed, N. Masmoudi

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In the last few years, Automatic Number Plate Recognition (ANPR) systems have become widely used in the safety, the security, and the commercial aspects. Forethought, several methods and techniques are computing to achieve the better levels in terms of accuracy and real time execution. This paper proposed a computer vision algorithm of Number Plate Localization (NPL) and Characters Segmentation (CS). In addition, it proposed an improved method in Optical Character Recognition (OCR) based on Deep Learning (DL) techniques. In order to identify the number of detected plate after NPL and CS steps, the Convolutional Neural Network (CNN) algorithm is proposed. A DL model is developed using four convolution layers, two layers of Maxpooling, and six layers of fully connected. The model was trained by number image database on the Jetson TX2 NVIDIA target. The accuracy result has achieved 95.84%.

Keywords: ANPR, CS, CNN, deep learning, NPL

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Authors: Himanshu Sharma, Karthik Kumar, Harish Kumar

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The major problem in crop cultivation is the occurrence of multiple crop diseases. During the growth stage, timely identification of crop diseases is paramount to ensure the high yield of crops, lower production costs, and minimize pesticide usage. In most cases, crop diseases produce observable characteristics and symptoms. The Surveyors usually diagnose crop diseases when they walk through the fields. However, surveyor inspections tend to be biased and error-prone due to the nature of the monotonous task and the subjectivity of individuals. In addition, visual inspection of each leaf or plant is costly, time-consuming, and labour-intensive. Furthermore, the plant pathologists and experts who can often identify the disease within the plant according to their symptoms in early stages are not readily available in remote regions. Therefore, this study specifically addressed early detection of leaf scald, red rot, and eyespot types of diseases within sugarcane plants. The study proposes a computer vision-based approach using a convolutional neural network (CNN) for automatic identification of crop diseases. To facilitate this, firstly, images of sugarcane diseases were taken from google without modifying the scene, background, or controlling the illumination to build the training dataset. Then, the testing dataset was developed based on the real-time collected images from the sugarcane field from India. Then, the image dataset is pre-processed for feature extraction and selection. Finally, the CNN-based Visual Geometry Group (VGG) model was deployed on the training and testing dataset to classify the images into diseased and healthy sugarcane plants and measure the model's performance using various parameters, i.e., accuracy, sensitivity, specificity, and F1-score. The promising result of the proposed model lays the groundwork for the automatic early detection of sugarcane disease. The proposed research directly sustains an increase in crop yield.

Keywords: automatic classification, computer vision, convolutional neural network, image processing, sugarcane disease, visual geometry group

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Authors: Asmau Mukhtar Ahmed, Olga Duran

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Hydrocarbon (HC) spills constitute a significant problem that causes great concern to the environment. With the latest technology (hyperspectral images) and state of the earth techniques (image processing tools), hydrocarbon spills can easily be detected at an early stage to mitigate the effects caused by such menace. In this study; a controlled laboratory experiment was used, and clay soil was mixed and homogenized with different hydrocarbon types (diesel, bio-diesel, and petrol). The different mixtures were scanned with HYSPEX hyperspectral camera under constant illumination to generate the hypersectral datasets used for this experiment. So far, the Short Wave Infrared Region (SWIR) has been exploited in detecting HC spills with excellent accuracy. However, the Near-Infrared Region (NIR) is somewhat unexplored with regards to HC contamination and how it affects the spectrum of soils. In this study, Deep Neural Network (DNN) was applied to the controlled datasets to detect and quantify the amount of HC spills in soils in the Near-Infrared Region. The initial results are extremely encouraging because it indicates that the DNN was able to identify features of HC in the Near-Infrared Region with a good level of accuracy.

Keywords: hydrocarbon, Deep Neural Network, short wave infrared region, near-infrared region, hyperspectral image

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Authors: Mechlouch Ridha Fethi

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This paper presents a recent mathematical model in political sciences concerning democracy. The model is represented by a logarithmic equation linking the Relative Index of Democracy (RID) to Participation Ratio (PR). Firstly the meanings of the different parameters of the model were presented; and the variation curve of the RID according to PR with different critical areas was discussed. Secondly, the model was applied to a virtual group where we show that the model can be applied depending on the gender. Thirdly, it was observed that the model can be extended to different language models of democracy and that little use to assess the state of democracy for some International organizations like UNO.

Keywords: democracy, mathematic, modelization, quantification

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Authors: Ethan Nikookar, Yalda Boroushaki, Larissa Statsenko, Jorge Ochoa Paniagua

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Given the serious impact that supply chain disruptions can have on a firm's bottom-line performance, both industry and academia are interested in supply chain resilience, a capability of the supply chain that enables it to cope with disruptions. To date, much of the research has focused on the antecedents of supply chain resilience. This line of research has suggested various firm-level capabilities that are associated with greater supply chain resilience. A consensus has emerged among researchers that supply chain flexibility holds the greatest potential to create resilience. Supply chain flexibility achieves resilience by creating readiness to respond to disruptions with little cost and time by means of reconfiguring supply chain resources to mitigate the impacts of the disruption. Decisions related to supply chain disruptions are made by supply chain managers; however, the role played by supply chain managers' reference networks has been overlooked in the supply chain resilience literature. This study aims to understand the impact of supply chain managers on their firms' supply chain resilience. Drawing on social capital theory and social network theory, this paper proposes a conceptual model to explore the role of supply chain managers in developing the resilience of supply chains. Our model posits that higher level of supply chain managers' embeddedness in their reference network is associated with increased resilience of their firms' supply chain. A reference network includes individuals from whom supply chain managers seek advice on supply chain related matters. The relationships between supply chain managers' embeddedness in reference network and supply chain resilience are mediated by supply chain flexibility.

Keywords: supply chain resilience, embeddedness, reference networks, social capitals

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Authors: Ritesh Kumar, Gitanjali Chhetri, Mandira Bhatia, Mohit Mishra, Abhijith Bailur, Abhinav

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The prevalence of the internet and technology in our day to day lives is creating more security issues than ever. The need for protecting and providing a secure access to private and business data has led to the development of many security systems. One of the potential solutions is to employ the bio-metric authentication technique. In this paper we present an innovative biometric authentication method that utilizes a person’s EEG signal, which is acquired in response to an auditory stimulus,and transferred wirelessly to a computer that has the necessary ANN algorithm-Multi layer perceptrol neural network because of is its ability to differentiate between information which is not linearly separable.In order to determine the weights of the hidden layer we use Gaussian random weight initialization. MLP utilizes a supervised learning technique called Back propagation for training the network. The complex algorithm used for EEG classification reduces the chances of intrusion into the protected public or private data.

Keywords: EEG signal, auditory evoked potential, biometrics, multilayer perceptron neural network, back propagation rule, Gaussian random weight initialization

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Authors: Mikel Alonso López

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This paper explores, in depth, the idea that emotions are present in all consumer decision making processes, meaning that purchase decisions have never been purely cognitive or as they traditionally have been defined, rational. Human beings, in all kinds of decisions, has "always" used neural systems related to emotions along with neural systems related to cognition, regardless of the type of purchase or the product or service in question. Therefore, all purchase decisions are, at the same time, cognitive and emotional. This paper presents an analysis of the main contributions of researchers in this regard.

Keywords: emotions, decision making, consumer behaviour, emotional behaviour

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Authors: Shahed Vahedi, Mohd Salmi Md Noorani

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This paper investigates cluster synchronization phenomena between community networks. We focus on the situation where a variety of dynamics occur in the clusters. In particular, we show that different synchronization states simultaneously occur between the networks. The controller is designed having an adaptive control gain, and theoretical results are derived via Lyapunov stability. Simulations on well-known dynamical systems are provided to elucidate our results.

Keywords: cluster synchronization, adaptive control, community network, simulation

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Authors: Nada Souissi, Mourad Mroua

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The main purpose of this study is to examine the interaction between financial asset volatility, economic factors and investor's behavioral indicators related to both the company's and the markets stocks for the period from January 2000 to January2020. Using multiple linear regression and Bayesian Networks modeling, results show a positive and negative relationship between investor's psychology index, economic factors and predicted stock market return. We reveal that the application of the Bayesian Discrete Network contributes to identify the different cause and effect relationships between all economic, financial variables and psychology index.

Keywords: Financial asset return predictability, Economic factors, Investor's psychology index, Bayesian approach, Probabilistic networks, Parametric learning

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Authors: Nicola Rubino

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This paper presents an analysis of a group of commodity exporting countries' nominal exchange rate movements in relationship to the US dollar. Using a series of Unrestricted Self-exciting Threshold Autoregressive models (SETAR), we model and evaluate sixteen national CPI price differentials relative to the US dollar CPI. Out-of-sample forecast accuracy is evaluated through calculation of mean absolute error measures on the basis of two-hundred and fifty-three months rolling window forecasts and extended to three additional models, namely a logistic smooth transition regression (LSTAR), an additive non linear autoregressive model (AAR) and a simple linear Neural Network model (NNET). Our preliminary results confirm presence of some form of TAR non linearity in the majority of the countries analyzed, with a relatively higher goodness of fit, with respect to the linear AR(1) benchmark, in five countries out of sixteen considered. Although no model appears to statistically prevail over the other, our final out-of-sample forecast exercise shows that SETAR models tend to have quite poor relative forecasting performance, especially when compared to alternative non-linear specifications. Finally, by analyzing the implied half-lives of the > coefficients, our results confirms the presence, in the spirit of arbitrage band adjustment, of band convergence with an inner unit root behaviour in five of the sixteen countries analyzed.

Keywords: transition regression model, real exchange rate, nonlinearities, price differentials, PPP, commodity points

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Authors: Wen-Bin Liau, Wan-Ting Wang, Chiang-Jen Hsiao, Sheng-Mao Tseng

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In this paper, an interesting and easy method to prepare one-dimensional nanostructured polyaniline/dendritic silver composites is reported. It is well known that the morphology of metal particle is a very important factor to influence the properties of polymer-metal composites. Usually, the dendritic silver is prepared by kinetic control in reduction reaction. It is not a thermodynamically stable structure. It is the goal to reduce silver ion to dendritic silver by polyaniline polymer via kinetic control and form one-dimensional nanostructured polyaniline/dendritic silver composites. The preparation is a two steps sequential reaction. First step, the polyaniline networks composed of nano fibrillar polyaniline are synthesized from aniline monomers aqueous with ammonium persulfate as the initiator at room temperature. In second step, the silver nitrate is added into polyaniline networks dispersed in deionized water. The dendritic silver is formed via reduction by polyaniline networks under the kinetic control. The formation of polyaniline is discussed via transmission electron microscopy (TEM). Nanosheets, nanotubes, nanospheres, nanosticks, and networks are observed via TEM. Then, the mechanism of formation of one-dimensional nanostructured polyaniline/dendritic silver composites is discussed. The formation of dendritic silver is observed by TEM and X-ray diffraction.

Keywords: 1D nanostructured polyaniline, dendritic silver, synthesis

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Authors: Foteini Zagklavara, Peter Jimack, Nikil Kapur, Ozz Querin, Harvey Thompson

Abstract:

The invention and development of the Polymerase Chain Reaction (PCR) technology have revolutionised molecular biology and molecular diagnostics. There is an urgent need to optimise their performance of those devices while reducing the total construction and operation costs. The present study proposes a CFD-enabled optimisation methodology for continuous flow (CF) PCR devices with serpentine-channel structure, which enables the trade-offs between competing objectives of DNA amplification efficiency and pressure drop to be explored. This is achieved by using a surrogate-enabled optimisation approach accounting for the geometrical features of a CF μPCR device by performing a series of simulations at a relatively small number of Design of Experiments (DoE) points, with the use of COMSOL Multiphysics 5.4. The values of the objectives are extracted from the CFD solutions, and response surfaces created using the polyharmonic splines and neural networks. After creating the respective response surfaces, genetic algorithm, and a multi-level coordinate search optimisation function are used to locate the optimum design parameters. Both optimisation methods produced similar results for both the neural network and the polyharmonic spline response surfaces. The results indicate that there is the possibility of improving the DNA efficiency by ∼2% in one PCR cycle when doubling the width of the microchannel to 400 μm while maintaining the height at the value of the original design (50μm). Moreover, the increase in the width of the serpentine microchannel is combined with a decrease in its total length in order to obtain the same residence times in all the simulations, resulting in a smaller total substrate volume (32.94% decrease). A multi-objective optimisation is also performed with the use of a Pareto Front plot. Such knowledge will enable designers to maximise the amount of DNA amplified or to minimise the time taken throughout thermal cycling in such devices.

Keywords: PCR, optimisation, microfluidics, COMSOL

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Authors: Le Kang

Abstract:

On the research question of 'how to achieve USR', this contribution reflects the concept of university social responsibility, identify three achievement models of USR as the society - diversified model, the university-cooperation model, the government - compound model, also conduct a case study to explore characteristics of Chinese achievement model of USR. The contribution concludes with discussion of how the university, government and society balance demands and roles, make necessarily strategic adjustment and innovative approach to repair the shortcomings of each achievement model.

Keywords: modern university, USR, achievement model, compound model

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Authors: André Smith, Amr Abdel-Dayem

Abstract:

The stereo matching problem, while having been present for several decades, continues to be an active area of research. The goal of this research is to find correspondences between elements found in a set of stereoscopic images. With these pairings, it is possible to infer the distance of objects within a scene, relative to the observer. Advancements in this field have led to experimentations with various techniques, from graph-cut energy minimization to artificial neural networks. At the basis of these techniques is a cost function, which is used to evaluate the likelihood of a particular match between points in each image. While at its core, the cost is based on comparing the image pixel data; there is a general lack of consistency as to what image data representation to use. This paper presents an experimental analysis to compare the effectiveness of more common image data representations. The goal is to determine the effectiveness of these data representations to reduce the cost for the correct correspondence relative to other possible matches.

Keywords: colour data, local stereo matching, stereo correspondence, disparity map

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Authors: Ahmedou Moulaye Idriss, Tfeil Yahya, Tamas Ungi, Gabor Fichtinger

Abstract:

Image-guided therapy (IGT) plays a crucial role in minimally invasive procedures for liver interventions. Accurate segmentation of the liver and precise needle placement is essential for successful interventions such as needle aspiration. In this study, we propose a deep learning-based liver 3D slicer designed to enhance segmentation accuracy and facilitate needle aspiration procedures. The developed 3D slicer leverages state-of-the-art convolutional neural networks (CNNs) for automatic liver segmentation in medical images. The CNN model is trained on a diverse dataset of liver images obtained from various imaging modalities, including computed tomography (CT) and magnetic resonance imaging (MRI). The trained model demonstrates robust performance in accurately delineating liver boundaries, even in cases with anatomical variations and pathological conditions. Furthermore, the 3D slicer integrates advanced image registration techniques to ensure accurate alignment of preoperative images with real-time interventional imaging. This alignment enhances the precision of needle placement during aspiration procedures, minimizing the risk of complications and improving overall intervention outcomes. To validate the efficacy of the proposed deep learning-based 3D slicer, a comprehensive evaluation is conducted using a dataset of clinical cases. Quantitative metrics, including the Dice similarity coefficient and Hausdorff distance, are employed to assess the accuracy of liver segmentation. Additionally, the performance of the 3D slicer in guiding needle aspiration procedures is evaluated through simulated and clinical interventions. Preliminary results demonstrate the effectiveness of the developed 3D slicer in achieving accurate liver segmentation and guiding needle aspiration procedures with high precision. The integration of deep learning techniques into the IGT workflow shows great promise for enhancing the efficiency and safety of liver interventions, ultimately contributing to improved patient outcomes.

Keywords: deep learning, liver segmentation, 3D slicer, image guided therapy, needle aspiration

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Authors: Farouk Shehu Abdulwahab

Abstract:

The concept of farmer-farmer knowledge sharing was introduced to bridge the extension worker-farmer ratio gap in developing countries. However, the idea was poorly accepted, especially in typical agrarian communities. Therefore, the study explores the concept of a farmer-to-farmer knowledge-sharing network to enhance extension service delivery. The study collected data from 80 farmers randomly selected through a series of multiple stages. The Data was analysed using a 5-point Likert scale and descriptive statistics. The Likert scale results revealed that 62.5% of the farmers are satisfied with farmer-to-farmer knowledge-sharing networks. Moreover, descriptive statistics show that lack of capacity building and low level of education are the most significant problems affecting farmer-farmer sharing networks. The major implication of these findings is that the concept of farmer-farmer knowledge-sharing networks can work better for farmers in developing countries as it was perceived by them as a reliable alternative for information sharing. Therefore, the study recommends introducing incentives into the concept of farmer-farmer knowledge-sharing networks and enhancing the capabilities of farmers who are opinion leaders in the farmer-farmer concept of knowledge-sharing to make it more sustainable.

Keywords: agricultural productivity, extension, farmer-to-farmer, livelihood, technology transfer

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