Search results for: network data mining

Authors: Mehdi Destani, Asghar Heydari

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Objective: The present study aimed to investigate the association between pathological personality dimensions and emotion dysregulation through the mediating role of Cyberchondria behaviors among users of virtual networks. Materials and methods: A descriptive–correlational research method was used in this study, and the statistical population consisted of all people active on social network sites in 2020. The sample size was 300 people who were selected through Convenience Sampling. Data collection was carried out in a survey method using online questionnaires, including the "Difficulties in Emotion Regulation Scale" (DERS), Personality Inventory for DSM-5 Brief Form (PID-5-BF), and Cyberchondria Severity Scale Brief Form (CSS-12). Data analysis was conducted using Pearson's Correlation Coefficient and Structural Equation Modeling (SEM). Findings: Findings suggested that pathological personality dimensions and Cyberchondria behaviors have a positive and significant association with emotion dysregulation (p<0.001). The presented model had a good fit with the data. The variable “pathological personality dimensions” with an overall effect (p<0.001, β=0.658), a direct effect (p<0.001, β=0.528), and an indirect mediating effect through Cyberchondria Behaviors (p<.001), β=0.130), accounted for emotion dysregulation among virtual network users. Conclusion: The research findings showed a necessity to pay attention to the pathological personality dimensions as a determining variable and Cyberchondria behaviors as a mediator in the vulnerability of users of social network sites to emotion dysregulation.

Keywords: cyberchondria, emotion dysregulation, pathological personality dimensions, social networks

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Authors: Duncan Wallace, M-Tahar Kechadi

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In recent years, Machine Learning (ML) approaches have been successfully applied to an analysis of patient symptom data in the context of disease diagnosis, at least where such data is well codified. However, much of the data present in Electronic Health Records (EHR) are unlikely to prove suitable for classic ML approaches. Furthermore, as scores of data are widely spread across both hospitals and individuals, a decentralized, computationally scalable methodology is a priority. The focus of this paper is to develop a method to predict outliers in an out-of-hours healthcare provision center (OOHC). In particular, our research is based upon the early identification of patients who have underlying conditions which will cause them to repeatedly require medical attention. OOHC act as an ad-hoc delivery of triage and treatment, where interactions occur without recourse to a full medical history of the patient in question. Medical histories, relating to patients contacting an OOHC, may reside in several distinct EHR systems in multiple hospitals or surgeries, which are unavailable to the OOHC in question. As such, although a local solution is optimal for this problem, it follows that the data under investigation is incomplete, heterogeneous, and comprised mostly of noisy textual notes compiled during routine OOHC activities. Through the use of Deep Learning methodologies, the aim of this paper is to provide the means to identify patient cases, upon initial contact, which are likely to relate to such outliers. To this end, we compare the performance of Long Short-Term Memory, Gated Recurrent Units, and combinations of both with Convolutional Neural Networks. A further aim of this paper is to elucidate the discovery of such outliers by examining the exact terms which provide a strong indication of positive and negative case entries. While free-text is the principal data extracted from EHRs for classification, EHRs also contain normalized features. Although the specific demographical features treated within our corpus are relatively limited in scope, we examine whether it is beneficial to include such features among the inputs to our neural network, or whether these features are more successfully exploited in conjunction with a different form of a classifier. In this section, we compare the performance of randomly generated regression trees and support vector machines and determine the extent to which our classification program can be improved upon by using either of these machine learning approaches in conjunction with the output of our Recurrent Neural Network application. The output of our neural network is also used to help determine the most significant lexemes present within the corpus for determining high-risk patients. By combining the confidence of our classification program in relation to lexemes within true positive and true negative cases, with an inverse document frequency of the lexemes related to these cases, we can determine what features act as the primary indicators of frequent-attender and non-frequent-attender cases, providing a human interpretable appreciation of how our program classifies cases.

Keywords: artificial neural networks, data-mining, machine learning, medical informatics

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Authors: Navdeep Singh Randhawa, Shally Sharma

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Wireless Sensor Network is an autonomous technology emanating in the current scenario at a fast pace. This technology faces a number of defiance’s and energy management is one of them, which has a huge impact on the network lifetime. To sustain energy the different types of routing protocols have been flourished. The classical routing protocols are no more compatible to perform in complicated environments. Hence, in the field of routing the intelligent algorithms based on nature systems is a turning point in Wireless Sensor Network. These nature-based algorithms are quite efficient to handle the challenges of the WSN as they are capable of achieving local and global best optimization solutions for the complex environments. So, the main attention of this paper is to develop a routing algorithm based on some swarm intelligent technique to enhance the performance of Wireless Sensor Network.

Keywords: wireless sensor network, routing, swarm intelligence, MPRSO

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Authors: Svitov David, Alyamkin Sergey

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The usage of convolutional neural networks (CNNs) in conjunction with the margin-based softmax approach demonstrates the state-of-the-art performance for the face recognition problem. Recently, lightweight neural network models trained with the margin-based softmax have been introduced for the face identification task for edge devices. In this paper, we propose a distillation method for lightweight neural network architectures that outperforms other known methods for the face recognition task on LFW, AgeDB-30 and Megaface datasets. The idea of the proposed method is to use class centers from the teacher network for the student network. Then the student network is trained to get the same angles between the class centers and face embeddings predicted by the teacher network.

Keywords: ArcFace, distillation, face recognition, margin-based softmax

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Authors: Emad A. Mohammed

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Utilizing the capabilities of Data Mining and Artificial Intelligence in the prediction of the minimum miscibility pressure (MMP) required for multi-contact miscible (MCM) displacement of reservoir petroleum by hydrocarbon gas flooding using Fuzzy Logic models and Artificial Neural Network models will help a lot in giving accurate results. The factors affecting the (MMP) as it is proved from the literature and from the dataset are as follows: XC2-6: Intermediate composition in the oil-containing C2-6, CO2 and H2S, in mole %, XC1: Amount of methane in the oil (%),T: Temperature (°C), MwC7+: Molecular weight of C7+ (g/mol), YC2+: Mole percent of C2+ composition in injected gas (%), MwC2+: Molecular weight of C2+ in injected gas. Fuzzy Logic and Neural Networks have been used widely in prediction and classification, with relatively high accuracy, in different fields of study. It is well known that the Fuzzy Inference system can handle uncertainty within the inputs such as in our case. The results of this work showed that our proposed models perform better with higher performance indices than other emprical correlations.

Keywords: MMP, gas flooding, artificial intelligence, correlation

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Authors: Anurag Sharma, Manoj Kumar, Ashima, Sooraj Parkash

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The work in this paper presents simulative comparison for different modulation formats such as NRZ, Manchester and CRZ in a 100 subscribers at 5 Gbps bit rate Gigabit Ethernet Passive Optical Network (GEPON) FTTH network. It is observed from the simulation results that the CRZ modulation format is best suited for the designed system. A link design for 1:100 splitter is used as Passive Optical Network (PON) element which creates communication between central offices to different users. The Bit Error Rate (BER) is found to be 2.8535e-10 at 5 Gbit/s systems for CRZ modulation format.

Keywords: PON , FTTH, OLT, ONU, CO, GEPON

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Authors: S. McLean, J. A. Scott

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The research topic is focusing on improving sustainable operation through recovery and reuse of waste heat in process water streams, an area in the mining industry that is often overlooked. There are significant advantages to the application of this topic, including economic and environmental benefits. The smelting process in the mining industry presents an opportunity to recover waste heat and apply it to alternative uses, thereby enhancing the overall process. This applied research has been conducted at the Sudbury Integrated Nickel Operations smelter site, in particular on the water cooling towers. The aim was to determine and optimize methods for appropriate recovery and subsequent upgrading of thermally low-grade heat lost from the water cooling towers in a manner that makes it useful for repurposing in applications, such as within an acid plant. This would be valuable to mining companies as it would be an opportunity to reduce the cost of the process, as well as decrease environmental impact and primary fuel usage. The waste heat from the cooling towers needs to be upgraded before it can be beneficially applied, as lower temperatures result in a decrease of the number of potential applications. Temperature and flow rate data were collected from the water cooling towers at an acid plant over two years. The research includes process control strategies and the development of a model capable of determining if the proposed heat recovery technique is economically viable, as well as assessing any environmental impact with the reduction in net energy consumption by the process. Therefore, comprehensive cost and impact analyses are carried out to determine the best area of application for the recovered waste heat. This method will allow engineers to easily identify the value of thermal resources available to them and determine if a full feasibility study should be carried out. The rapid scoping model developed will be applicable to any site that generates large amounts of waste heat. Results show that heat pumps are an economically viable solution for this application, allowing for reduced cost and CO₂ emissions.

Keywords: environment, heat recovery, mining engineering, sustainability

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Authors: Yu Song, Yuefei Jin

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Urban public transportation can be integrated when there is an efficient connection between urban rail lines, however, there are currently no effective or quick solutions being investigated for this connection. This paper analyzes the space-time distribution and travel demand of passenger connection travel based on taxi track data and data from the road network, excavates potential bus connection stations based on potential connection demand data, and introduces the link growth probability model in the complex network to solve the basic connection bus lines in order to ascertain the direction of the bus lines that are the most connected given the demand characteristics. Then, a tree view exhaustive approach based on constraints is suggested based on graph theory, which can hasten the convergence of findings while doing chain calculations. This study uses WEI QU NAN Station, the Xi'an Metro Line 2 terminal station in Shaanxi Province, as an illustration, to evaluate the model's and the solution method's efficacy. According to the findings, 153 prospective stations have been dug up in total, the feeder bus network for the entire line has been laid out, and the best route adjustment strategy has been found.

Keywords: feeder bus, route optimization, link growth probability, the graph theory

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Authors: Tushar Bhardwaj

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Internet of Things (IoT) possesses a dynamic network where the network nodes (mobile devices) are added and removed constantly and randomly, hence the traffic distribution in the network is quite variable and irregular. The basic but very important part in any network is route searching. We have many conventional route searching algorithms like link-state, and distance vector algorithms but they are restricted to the static point to point network topology. In this paper we propose a model that uses the Ant Colony Algorithm for route searching. It is dynamic in nature and has positive feedback mechanism that conforms to the route searching. We have also embedded the concept of Non-Deterministic Finite Automata [NDFA] minimization to reduce the network to increase the performance. Results show that Ant Colony Algorithm gives the shortest path from the source to destination node and NDFA minimization reduces the broadcasting storm effectively.

Keywords: routing, ant colony algorithm, NDFA, IoT

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Authors: Said Rabah Azzam, Andrew Graham

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Fragmentation of packets caused by encryption applied on the network layer of the IOS model in Internet Protocol version 4 (IPv4) networks as well as the possibility of using fragmentation and Access Control Lists (ACLs) as a method of restricting network access to certain hosts or areas of a network.Using default settings, fragmentation is expected to occur and each fragment to be reassembled at the other end. If this does not occur then a high number of ICMP messages should be generated back towards the source host indicating that the packet is too large and that it needs to be made smaller. This result is also expected when the MTU is changed for certain links between devices.When using ACLs and packet fragments to restrict access to hosts or network segments it is possible that ACLs cannot be set up in this way. If ACLs cannot be setup to allow only fragments then it is a limitation of the hardware’s firmware holding back this particular method. If the ACL on the restricted switch can be set up in such a way to allow only fragments then a connection that forces packets to fragment should be allowed to pass through the ACL. This should then make a network connection to the destination machine allowing data to be sent to and from the destination machine. ICMP messages from the restricted access switch and host should also be blocked from being sent back across the link which will be shown in an SSH session into the switch.

Keywords: fragmentation, encryption, security, switch

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Authors: F. Massri, M. Shkarneh, B. Almassri

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Water scarcity and increasing water demand especially for residential use are major challenges facing Palestine. The need to accurately forecast water consumption is useful for the planning and management of this natural resource. The main objective of this paper is to (i) study the major factors influencing the water consumption in Palestine, (ii) understand the general pattern of Household water consumption, (iii) assess the possible changes in household water consumption and suggest appropriate remedies and (iv) develop prediction model based on the Artificial Neural Network to the water consumption in Palestinian cities. The paper is organized in four parts. The first part includes literature review of household water consumption studies. The second part concerns data collection methodology, conceptual frame work for the household water consumption surveys, survey descriptions and data processing methods. The third part presents descriptive statistics, multiple regression and analysis of the water consumption in the two Palestinian cities. The final part develops the use of Artificial Neural Network for modeling the water consumption in Palestinian cities.

Keywords: water management, demand forecasting, consumption, ANN, Ramallah

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Authors: Zeta Dooly, Aidan Duane

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The collaborative European funded research and development landscape provides prime environmental conditions for multi-disciplinary teams to learn and enhance their knowledge beyond the capability of training and learning within their own organisation cocoons. Whilst the emergence of the academic entrepreneur has changed the focus of educational institutions to that of quasi-businesses, the training and professional development of lecturers and academic staff are often not formalised to the same level as industry. This research focuses on industry and academic collaborative research funded by the European Commission. The impact of research is scalable if an optimum research network is created and managed effectively. This paper investigates network embeddedness, the nature of relationships, links, and nodes within a research network, and the enhancement of the network’s knowledge. The contribution of this paper extends our understanding of establishing and maintaining effective collaborative research networks. The effects of network embeddedness are recognized in the literature as pertinent to innovation and the economy. Network theory literature claims that networks are essential to innovative clusters such as Silicon valley and innovation in high tech industries. This research provides evidence to support the impact collaborative research has on the disparate individuals toward their innovative contributions to their organisations and their own professional development. This study adopts a qualitative approach and uncovers some of the challenges of multi-disciplinary research through case study insights. The contribution of this paper recommends the establishment of scaffolding to accommodate cooperation in research networks, role appointment, and addressing contextual complexities early to avoid problem cultivation. Furthermore, it suggests recommendations in relation to network formation, intra-network challenges in relation to open data, competition, friendships, and competency enhancement. The network capability is enhanced by the adoption of the relevant theories; network theory, open innovation, and social exchange, with the understanding that the network structure has an impact on innovation and social exchange in research networks. The research concludes that there is an opportunity to deepen our understanding of the impact of network reuse and network hoping that provides scaffolding for the network members to enhance and build upon their knowledge using a progressive approach.

Keywords: research networks, competency building, network theory, case study

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Authors: Moataz Ammar, Ahdab Elmorshedy

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The distributed generation hosting capacity of a distribution network is typically limited at a given connection point by the upper voltage limit that can be violated due to the injection of active power into the distribution network. The upper voltage limit violation concern becomes more important as the network equivalent resistance increases with respect to its equivalent reactance. This paper investigates the impact of modifying the distribution network equivalent reactance at the point of connection such that the upper voltage limit is violated at a higher distributed generation penetration, than it would without the addition of series reactive compensation. The results show that series reactive compensation proves efficient in certain situations (based on the ratio of equivalent network reactance to equivalent network resistance at the point of connection). As opposed to the conventional case of capacitive compensation of a distribution network to reduce voltage drop, inductive compensation is seen to be more appropriate for alleviation of distributed-generation-induced voltage rise.

Keywords: distributed generation, distribution networks, series compensation, voltage rise

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Authors: Dasom Kim, William Xiu Shun Wong, Yoonjin Hyun, Donghoon Lee, Minji Paek, Sungho Byun, Namgyu Kim

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There have been many researches on document classification for classifying voluminous documents automatically. Through document classification, we can assign a specific category to each unlabeled document on the basis of various machine learning algorithms. However, providing labeled documents manually requires considerable time and effort. To overcome the limitations, the semi-supervised learning which uses unlabeled document as well as labeled documents has been invented. However, traditional document classifiers, regardless of supervised or semi-supervised ones, cannot sufficiently explain the reason or the process of the classification. Thus, in this paper, we proposed a methodology to visualize major topics and class components of each document. We believe that our methodology for visualizing topics and classes of each document can enhance the reliability and explanatory power of document classifiers.

Keywords: data mining, document classifier, text mining, topic modeling

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Authors: Faiza Hussain, Usman Qamar

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Synonym-based searching is recognized to be a complicated problem as text mining from unstructured data of web is challenging. Finding useful information which matches user need from bulk of web pages is a cumbersome task. In this paper, a novel and practical synonym retrieval technique is proposed for addressing this problem. For replacement of semantics, user intent is taken into consideration to realize the technique. Parts-of-Speech tagging is applied for pattern generation of the query and a thesaurus for this experiment was formed and used. Comparison with Non-Context Based Searching, Context Based searching proved to be a more efficient approach while dealing with linguistic semantics. This approach is very beneficial in doing intent based searching. Finally, results and future dimensions are presented.

Keywords: natural language processing, text mining, information retrieval, parts-of-speech tagging, grammar, semantics

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Authors: Jegede Oluseye Oladayo

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The study mapped innovation activities in the formal and informal mining sector in Nigeria. Data were collected through primary and secondary sources. Primary data were collected through guided questionnaire administration, guided interviews and personal observation. A purposive sampling method was adopted to select firms that are micro, small and medium enterprises. The study covered 100 (50 in the formal sector and 50 in the informal sector) purposively selected companies in south-western Nigeria. Secondary data were collected from different published sources. Data were analysed using descriptive and inferential statistics. Of the four types of technological innovations sampled, organisational innovation was found to be highest both in the formal (100%) and informal (100%) sectors, followed by process innovation: 60% in the formal sector and 28% in the informal sector, marketing innovation and diffusion based innovation were implemented by 64% and 4% respectively in the formal sector. There were no R&D activities (intramural or extramural) in both sectors, however, innovation activities occur at moderate levels in the formal sector. This is characterised by acquisition of machinery, equipment, hardware (100%), software (56), training (82%) and acquisition of external knowledge (60%) in the formal sector. In the informal sector, innovation activities were characterised by acquisition of external knowledge (100%), training/learning by experience (100%) and acquisition of tools (68%). The impact of innovation on firm’s performance in the formal sector was expressed mainly as increased capacity of production (100%), reduced production cost per unit of labour (88%), compliance with governmental regulatory requirements (72%) and entry on new markets (60%). In the informal sector, the impact of innovation was mainly expressed in improved flexibility of production (70%) and machinery/energy efficiency (70%). The important technological driver of process innovation in the mining sector was acquisition of machinery which accounts for the prevalence of 100% both in the formal and informal sectors. Next to this is training and re-training of technical staff, 74% in both the formal and the informal sector. Other factors influencing organisational innovation are skill of workforce with a prevalence of 80% in both the formal and informal sector. The important technological drivers include educational background of the manager/head of technical department (54%) for organisational innovation and (50%) for process innovation in the formal sector. The study concluded that innovation competence of the firms was mostly organisational changes.

Keywords: innovation prevalence, innovation activities, innovation performance, innovation drivers

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Authors: Waleed Basuliman

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Anthropometric dimensions are considered one of the important factors when designing human-machine systems. In this study, the estimation of anthropometric dimensions has been improved by using Artificial Neural Network (ANN) model that is able to predict the anthropometric measurements of Saudi males in Riyadh City. A total of 1427 Saudi males aged 6 to 60 years participated in measuring 20 anthropometric dimensions. These anthropometric measurements are considered important for designing the work and life applications in Saudi Arabia. The data were collected during eight months from different locations in Riyadh City. Five of these dimensions were used as predictors variables (inputs) of the model, and the remaining 15 dimensions were set to be the measured variables (Model’s outcomes). The hidden layers varied during the structuring stage, and the best performance was achieved with the network structure 6-25-15. The results showed that the developed Neural Network model was able to estimate the body dimensions of Saudi male population in Riyadh City. The network's mean absolute percentage error (MAPE) and the root mean squared error (RMSE) were found to be 0.0348 and 3.225, respectively. These results were found less, and then better, than the errors found in the literature. Finally, the accuracy of the developed neural network was evaluated by comparing the predicted outcomes with regression model. The ANN model showed higher coefficient of determination (R2) between the predicted and actual dimensions than the regression model.

Keywords: artificial neural network, anthropometric measurements, back-propagation

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Authors: Muhammad R. Ahmed, Mohammed Aseeri

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Wireless Sensor Network (WSN) consists of low-cost and multi functional resources constrain nodes that communicate at short distances through wireless links. It is open media and underpinned by an application driven technology for information gathering and processing. It can be used for many different applications range from military implementation in the battlefield, environmental monitoring, health sector as well as emergency response of surveillance. With its nature and application scenario, security of WSN had drawn a great attention. It is known to be valuable to variety of attacks for the construction of nodes and distributed network infrastructure. In order to ensure its functionality especially in malicious environments, security mechanisms are essential. Malicious or internal attacker has gained prominence and poses the most challenging attacks to WSN. Many works have been done to secure WSN from internal attacks but most of it relay on either training data set or predefined threshold. Without a fixed security infrastructure a WSN needs to find the internal attacks is a challenge. In this paper we present an internal attack detection method based on maximum entropy model. The final experimental works showed that the proposed algorithm does work well at the designed level.

Keywords: internal attack, wireless sensor network, network security, entropy

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Authors: Deokho Kim, Wonwoo Lee, Jaewoong Lee, Teresa Ng, Gun-Ill Lee, Jiwon Jeong

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This paper presents a method for improving object search accuracy using a deep learning model. A major limitation to provide accurate similarity with deep learning is the requirement of huge amount of data for training pairwise similarity scores (metrics), which is impractical to collect. Thus, similarity scores are usually trained with a relatively small dataset, which comes from a different domain, causing limited accuracy on measuring similarity. For this reason, this paper proposes a deep learning model that can be trained with a significantly small amount of data, a clustered data which of each cluster contains a set of visually similar images. In order to measure similarity distance with the proposed method, visual features of two images are extracted from intermediate layers of a convolutional neural network with various pooling methods, and the network is trained with pairwise similarity scores which is defined zero for images in identical cluster. The proposed method outperforms the state-of-the-art object similarity scoring techniques on evaluation for finding exact items. The proposed method achieves 86.5% of accuracy compared to the accuracy of the state-of-the-art technique, which is 59.9%. That is, an exact item can be found among four retrieved images with an accuracy of 86.5%, and the rest can possibly be similar products more than the accuracy. Therefore, the proposed method can greatly reduce the amount of training data with an order of magnitude as well as providing a reliable similarity metric.

Keywords: visual search, deep learning, convolutional neural network, machine learning

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Authors: Dalin Si, Azizan Aziz, Bertrand Lasternas

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To encourage building owners to purchase electricity at the wholesale market and reduce building peak demand, this study aims to develop models that predict day-ahead hourly electricity consumption and demand using artificial neural network (ANN) and support vector machine (SVM). All prediction models are built in Python, with tool Scikit-learn and Pybrain. The input data for both consumption and demand prediction are time stamp, outdoor dry bulb temperature, relative humidity, air handling unit (AHU), supply air temperature and solar radiation. Solar radiation, which is unavailable a day-ahead, is predicted at first, and then this estimation is used as an input to predict consumption and demand. Models to predict consumption and demand are trained in both SVM and ANN, and depend on cooling or heating, weekdays or weekends. The results show that ANN is the better option for both consumption and demand prediction. It can achieve 15.50% to 20.03% coefficient of variance of root mean square error (CVRMSE) for consumption prediction and 22.89% to 32.42% CVRMSE for demand prediction, respectively. To conclude, the presented models have potential to help building owners to purchase electricity at the wholesale market, but they are not robust when used in demand response control.

Keywords: building energy prediction, data mining, demand response, electricity market

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Authors: Wang Xue, Fan Liwei

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The trust and use of social network platforms by users are instrumental factors that contribute to the platform’s sustainable development. Studying the influential factors of the use of social network platforms is beneficial for developing and maintaining a large user base. This study constructed an extended unified theory of acceptance and use of technology (UTAUT2) moderating model with perceived privacy risks to analyze the factors affecting the trust and use of social network platforms. 444 participants completed our 35 surveys, and we verified the survey results by structural equation model. Empirical results reveal the influencing factors that affect the trust and use of social network platforms, and the extended UTAUT2 model with perceived privacy risks increases the applicability of UTAUT2 in social network scenarios. Social networking platforms can increase their use rate by increasing the economics, functionality, entertainment, and privacy security of the platform.

Keywords: perceived privacy risk, social network, trust, use, UTAUT2 model

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Authors: Hamid Rostami Jaz, Kamran Ameri Siahooei

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Up to now different methods have been used to forecast the index returns and the index rate. Artificial intelligence and artificial neural networks have been one of the methods of index returns forecasting. This study attempts to carry out a comparative study on the performance of different Radial Base Neural Network and Feed-Forward Perceptron Neural Network to forecast investment returns on the index. To achieve this goal, the return on investment in Tehran Stock Exchange index is evaluated and the performance of Radial Base Neural Network and Feed-Forward Perceptron Neural Network are compared. Neural networks performance test is applied based on the least square error in two approaches of in-sample and out-of-sample. The research results show the superiority of the radial base neural network in the in-sample approach and the superiority of perceptron neural network in the out-of-sample approach.

Keywords: exchange index, forecasting, perceptron neural network, Tehran stock exchange

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Authors: Rose A. Yemson, Ping Jiang, Oyedeji L. Inumoh

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The deployment of wireless sensor network has rapidly increased, however with the increased capacity and diversity of sensors, and applications ranging from biological, environmental, military etc. generates tremendous volume of data’s where more attention is placed on the distributed sensing and little on how to manage, analyze, retrieve and understand the data generated. This makes it more quite difficult to process live sensor data, run concurrent control and update because sensor data are either heavyweight, complex, and slow. This work will focus on developing a web service platform for automatic detection of sensors, acquisition of sensor data, storage of sensor data into a database, processing of sensor data using reconfigurable software components. This work will also create a web service based sensor data management system to monitor physical movement of an individual wearing wireless network sensor technology (SunSPOT). The sensor will detect movement of that individual by sensing the acceleration in the direction of X, Y and Z axes accordingly and then send the sensed reading to a database that will be interfaced with an internet platform. The collected sensed data will determine the posture of the person such as standing, sitting and lying down. The system is designed using the Unified Modeling Language (UML) and implemented using Java, JavaScript, html and MySQL. This system allows real time monitoring an individual closely and obtain their physical activity details without been physically presence for in-situ measurement which enables you to work remotely instead of the time consuming check of an individual. These details can help in evaluating an individual’s physical activity and generate feedback on medication. It can also help in keeping track of any mandatory physical activities required to be done by the individuals. These evaluations and feedback can help in maintaining a better health status of the individual and providing improved health care.

Keywords: HTML, java, javascript, MySQL, sunspot, UML, web-based, wireless network sensor

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Authors: Masood Ahmad, Ataul Aziz Ikram, Ishtiaq Wahid

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Wireless sensor network (WSN) is the interconnection of mobile wireless nodes with limited energy and memory. These networks can be deployed formany critical applications like military operations, rescue management, fire detection and so on. In flat routing structure, every node plays an equal role of sensor and router. The topology may change very frequently due to the mobile nature of nodes in WSNs. The topology maintenance may produce more overhead messages. To avoid topology maintenance overhead messages, an optimized cluster based mobile wireless sensor network using memetic algorithm is proposed in this paper. The nodes in this network are first divided into clusters. The cluster leaders then transmit data to that base station. The network is validated through extensive simulation study. The results show that the proposed technique has superior results compared to existing techniques.

Keywords: WSN, routing, cluster based, meme, memetic algorithm

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Authors: Yichao Lv, Minmin Cai, Yanqiang Tao, Xinyuan Zou, Chao Zhang, Xiangping Liu

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Objective: This study aims to explore the network structure of personality traits and mental health and identify key factors for effective intervention strategies. Methods: All participants (N = 6,067; 3,368 females) underwent the Eysenck Personality Scale (EPQ) to measure personality traits and the Symptom Self-rating Scale (SCL-90) to measure psychiatric symptoms. Using the mean value of the SCL-90 total score plus one standard deviation as the cutoff, 854 participants (14.08%; 528 females) were categorized as individuals exhibiting potential psychological symptoms and were included in the follow-up network analysis. The structure and bridge centrality of the network for dimensions of EPQ and SCL-90 were estimated. Results: Between the EPQ and SCL-90, psychoticism (P), extraversion (E), and neuroticism (N) showed the strongest positive correlations with somatization (Som), interpersonal sensitivity (IS), and hostility (Hos), respectively. Extraversion (E), somatization (Som), and anxiety (Anx) were identified as the most important bridge factors influencing the overall network. Conclusions: This study explored the network structure and complex connections between mental health and personality traits from a network perspective, providing potential targets for intervening in adolescent personality traits and mental health.

Keywords: EPQ, SCL-90, Chinese adolescents, network analysis

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Authors: K. Vrinda

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Coastal ecosystems across the world are facing a lot of challenges due to natural phenomena as well as from uncontrolled human interventions. Here, Alappad, a coastal island situated in Kerala, India is undergoing significant damage and is gradually losing its environmental and social sustainability. The area is blessed with very rare and precious black mineral sand deposits. Sand mining for these minerals started in 1911 and is still continuing. But, unfortunately all the problems that Alappad faces now, have its root on mining of this mineral sand. The land area is continuously diminishing due to sea erosion. The mining has also caused displacement of people and environmental degradation. Marine life also is getting affected by mining on beach and pollution. The inhabitants are fishermen who are largely dependent on the eco-system for a living. So loss of environmental sustainability subsequently affects social sustainability too. Now the damage has reached a point beyond which our actions may not be able to make any impact. This was one of the most affected areas of the 2004 tsunami and the environmental degradation has further increased the vulnerability. So this study focuses on understanding the concerns related to the resource utilization, environment and the indigenous community staying there, and on formulating suitable strategies to restore the sustainability of the area. An extensive study was conducted on site, to find out the physical, social, and economical characteristics of the area. A focus group discussion with the inhabitants shed light on different issues they face in their day-to-day life. The analysis of all these data, led to the formation of a new development vision for the area which focuses on environmental restoration and socio-economic development while allowing controlled exploitation of resources. A participatory approach is formulated which enables these three aspects through community based programs.

Keywords: Community development, Disaster resilience, Ecological restoration, Environmental sustainability, Social-environmental planning, Social Sustainability

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Authors: Muhammad Ali

Abstract:

Cyber-attacks and anomaly detection on the Internet of Things (IoT) infrastructure is emerging concern in the domain of data-driven intrusion. Rapidly increasing IoT risk is now making headlines around the world. denial of service, malicious control, data type probing, malicious operation, DDos, scan, spying, and wrong setup are attacks and anomalies that can affect an IoT system failure. Everyone talks about cyber security, connectivity, smart devices, and real-time data extraction. IoT devices expose a wide variety of new cyber security attack vectors in network traffic. For further than IoT development, and mainly for smart and IoT applications, there is a necessity for intelligent processing and analysis of data. So, our approach is too secure. We train several machine learning models that have been compared to accurately predicting attacks and anomalies on IoT systems, considering IoT applications, with ANOVA-based feature selection with fewer prediction models to evaluate network traffic to help prevent IoT devices. The machine learning (ML) algorithms that have been used here are KNN, SVM, NB, D.T., and R.F., with the most satisfactory test accuracy with fast detection. The evaluation of ML metrics includes precision, recall, F1 score, FPR, NPV, G.M., MCC, and AUC & ROC. The Random Forest algorithm achieved the best results with less prediction time, with an accuracy of 99.98%.

Keywords: machine learning, analysis of variance, Internet of Thing, network security, intrusion detection

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Authors: Bowei Yuan, Shi Li, Liuyang Song, Huaqing Wang, Lingli Cui

Abstract:

Convolutional neural networks (CNN) have received extensive attention in the field of fault diagnosis. Many fault diagnosis methods use CNN for fault type identification. However, when the amount of raw data collected by sensors is massive, the neural network needs to perform a time-consuming classification task. In this paper, a mechanical fault diagnosis method based on vibration signal down-sampling and the improved one-dimensional convolutional neural network is proposed. Through the robust principal component analysis, the low-rank feature matrix of a large amount of raw data can be separated, and then down-sampling is realized to reduce the subsequent calculation amount. In the improved one-dimensional CNN, a smaller convolution kernel is used to reduce the number of parameters and computational complexity, and regularization is introduced before the fully connected layer to prevent overfitting. In addition, the multi-connected layers can better generalize classification results without cumbersome parameter adjustments. The effectiveness of the method is verified by monitoring the signal of the centrifugal pump test bench, and the average test accuracy is above 98%. When compared with the traditional deep belief network (DBN) and support vector machine (SVM) methods, this method has better performance.

Keywords: fault diagnosis, vibration signal down-sampling, 1D-CNN

Procedia PDF Downloads 125

Authors: A. Basu, A. A. Purohit, M. M. Vaidya, M. D. Kudale

Abstract:

Mumbai, being traditionally the epicenter of India's trade and commerce, the existing major ports such as Mumbai and Jawaharlal Nehru Ports (JN) situated in Thane estuary are also developing its waterfront facilities. Various developments over the passage of decades in this region have changed the tidal flux entering/leaving the estuary. The intake at Pir-Pau is facing the problem of shortage of water in view of advancement of shoreline, while jetty near Ulwe faces the problem of ship scheduling due to existence of shallower depths between JN Port and Ulwe Bunder. In order to solve these problems, it is inevitable to have information about tide levels over a long duration by field measurements. However, field measurement is a tedious and costly affair; application of artificial intelligence was used to predict water levels by training the network for the measured tide data for one lunar tidal cycle. The application of two layered feed forward Artificial Neural Network (ANN) with back-propagation training algorithms such as Gradient Descent (GD) and Levenberg-Marquardt (LM) was used to predict the yearly tide levels at waterfront structures namely at Ulwe Bunder and Pir-Pau. The tide data collected at Apollo Bunder, Ulwe, and Vashi for a period of lunar tidal cycle (2013) was used to train, validate and test the neural networks. These trained networks having high co-relation coefficients (R= 0.998) were used to predict the tide at Ulwe, and Vashi for its verification with the measured tide for the year 2000 & 2013. The results indicate that the predicted tide levels by ANN give reasonably accurate estimation of tide. Hence, the trained network is used to predict the yearly tide data (2015) for Ulwe. Subsequently, the yearly tide data (2015) at Pir-Pau was predicted by using the neural network which was trained with the help of measured tide data (2000) of Apollo and Pir-Pau. The analysis of measured data and study reveals that: The measured tidal data at Pir-Pau, Vashi and Ulwe indicate that there is maximum amplification of tide by about 10-20 cm with a phase lag of 10-20 minutes with reference to the tide at Apollo Bunder (Mumbai). LM training algorithm is faster than GD and with increase in number of neurons in hidden layer and the performance of the network increases. The predicted tide levels by ANN at Pir-Pau and Ulwe provides valuable information about the occurrence of high and low water levels to plan the operation of pumping at Pir-Pau and improve ship schedule at Ulwe.

Keywords: artificial neural network, back-propagation, tide data, training algorithm

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Authors: Ebenezer Obaloluwa Olaniyi

Abstract:

In this paper, an intelligent system has been designed to grade apple based on either its defective or healthy for production in food processing. This paper is segmented into two different phase. In the first phase, the image processing techniques were employed to extract the necessary features required in the apple. These techniques include grayscale conversion, segmentation where a threshold value is chosen to separate the foreground of the images from the background. Then edge detection was also employed to bring out the features in the images. These extracted features were then fed into the neural network in the second phase of the paper. The second phase is a classification phase where neural network employed to classify the defective apple from the healthy apple. In this phase, the network was trained with back propagation and tested with feed forward network. The recognition rate obtained from our system shows that our system is more accurate and faster as compared with previous work.

Keywords: image processing, neural network, apple, intelligent system

Procedia PDF Downloads 392