Search results for: neural network

Authors: Michael R. Phangtriastu, Herriyandi Herriyandi, Diaz D. Santika

Abstract:

This paper presents a comparison of the implementation of metaheuristic algorithms to train the antecedent parameters and consequence parameters in the adaptive network-based fuzzy inference system (ANFIS). The algorithms compared are genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC). The objective of this paper is to benchmark well-known metaheuristic algorithms. The algorithms are applied to several data set with different nature. The combinations of the algorithms' parameters are tested. In all algorithms, a different number of populations are tested. In PSO, combinations of velocity are tested. In ABC, a different number of limit abandonment are tested. Experiments find out that ABC is more reliable than other algorithms, ABC manages to get better mean square error (MSE) than other algorithms in all data set.

Keywords: ANFIS, artificial bee colony, genetic algorithm, metaheuristic algorithm, particle swarm optimization

Procedia PDF Downloads 324

Authors: Moh'd Anwer Al-Shboul

Abstract:

Jordan Customs (JC) has been established to achieve objectives that must be consistent with the guidance of the wise leadership and its aspirations toward tomorrow. Therefore, it has developed several needed tools to provide a distinguished service to simplify work procedures and used modern technologies. A supply chain (SC) consists of all parties that are involved directly or indirectly in order to fulfill a customer request, which includes manufacturers, suppliers, shippers, retailers and even customer brokers. Within each firm, the SC includes all functions involved in receiving a filling a customers’ requests; one of the main functions include customer service. JC and global SCs are evolving into dynamic environment, which requires flexibility, effective communication, and team management. Thus, human resources (HRs) insight in these areas are critical for the effective development of global process network. The importance of HRs has increased significantly due to the role of employees depends on their knowledge, competencies, abilities, skills, and motivations. Strategic planning in JC began at the end of the 1990’s including operational strategy for Human Resource Management and Development (HRM&D). However, a huge transformation in human resources happened at the end of 2006; new employees’ regulation for customs were prepared, approved and applied at the end of 2007. Therefore, many employees lost their positions, while others were selected based on professorial recruitment and selection process (enter new blood). One of several policies that were applied by human resources in JC department is job rotation. From the researcher’s point of view, it was not based on scientific basis to achieve its goals and objectives, which at the end leads to having a significant negative impact on the Organizational Performance (OP) and weak job rotation approach. The purpose of this study is to call attention to re-review the applying process and procedure of job rotation that HRM directorate is currently applied at JC. Furthermore, it presents an overview of managing the HRs in the SC network that affects their success. The research methodology employed in this study was described as qualitative by conducting few interviews with managers, internal employee, external clients and reviewing the related literature to collect some qualitative data from secondary sources. Thus, conducting frequently and unstructured job rotation policy (i.e. monthly) will have a significant negative impact on JC performance as a whole. The results of this study show that the main impacts will affect on three main elements in JC: (1) internal employees' performance; (2) external clients, who are dealing with customs services; and finally, JC performance as a whole. In order to implement a successful and perfect job rotation technique at JC in a scientific way and to achieve its goals and objectives; JCs should be taken into consideration the proposed solutions and recommendations that will be presented in this study.

Keywords: efficiency, supply chain, human resources, job rotation, organizational performance, Jordan customs

Procedia PDF Downloads 194

Authors: Maryam Tabrizi, Shahrzad Aarup

Abstract:

Objectives- Understanding the close association between oral health and overall health for older adults at the right time and right place, a person, focus treatment through Project ECHO telementoring. Methodology- Data from monthly ECHO telementoring sessions were provided for three years. Sessions including case presentations, overall health conditions, considering medications, organ functions limitations, including the level of cognition. Contributions- Providing the specialist level of providing care to all elderly regardless of their location and other health conditions and decreasing oral health inequity by increasing workforce via Project ECHO telementoring program worldwide. By 2030, the number of adults in the USA over the age of 65 will increase more than 60% (approx.46 million) and over 22 million (30%) of 74 million older Americans will need specialized geriatrician care. In 2025, a national shortage of medical geriatricians will be close to 27,000. Most individuals 65 and older do not receive oral health care due to lack of access, availability, or affordability. One of the main reasons is a significant shortage of Oral Health (OH) education and resources for the elderly, particularly in rural areas. Poor OH is a social stigma, a thread to quality and safety of overall health of the elderly with physical and cognitive decline. Poor OH conditions may be costly and sometimes life-threatening. Non-traumatic dental-related emergency department use in Texas alone was over $250 M in 2016. Most elderly over the age of 65 present with at least one or multiple chronic diseases such as arthritis, diabetes, heart diseases, and chronic obstructive pulmonary disease (COPD) are at higher risk to develop gum (periodontal) disease, yet they are less likely to get dental care. In addition, most older adults take both prescription and over-the-counter drugs; according to scientific studies, many of these medications cause dry mouth. Reduced saliva flow due to aging and medications may increase the risk of cavities and other oral conditions. Most dental schools have already increased geriatrics OH in their educational curriculums, but the aging population growth worldwide is faster than growing geriatrics dentists. However, without the use of advanced technology and creating a network between specialists and primary care providers, it is impossible to increase the workforce, provide equitable oral health to the elderly. Project ECHO is a guided practice model that revolutionizes health education and increases the workforce to provide best-practice specialty care and reduce health disparities. Training oral health providers for utilizing the Project ECHO model is a logical response to the shortage and increases oral health access to the elderly. Project ECHO trains general dentists & hygienists to provide specialty care services. This means more elderly can get the care they need, in the right place, at the right time, with better treatment outcomes and reduces costs.

Keywords: geriatric, oral health, project echo, chronic disease, oral health

Procedia PDF Downloads 152

Authors: Irene M. Arora

Abstract:

Hypothesis: Attention Deficit Hyperactivity Disorder is the result of an underdeveloped prefrontal cortex which is the primary cause for the signs and symptoms seen as defining features of ADHD. Methods: Through ‘PubMed’, ‘Wiley’ and ‘Google Scholar’ studies published between 2011-2018 were evaluated, determining if a dysfunctional prefrontal cortex caused the characteristic symptoms associated with ADHD. The search terms "prefrontal cortex", "Attention-Deficit/Hyperactivity Disorder", "cognitive control", "frontostriatal tract" among others, were used to maximize the assortment of relevant studies. Excluded papers were systematic reviews, meta-analyses and publications published before 2010 to ensure clinical relevance. Results: Nine publications were analyzed in this review, all of which were non-randomized matched control studies. Three studies found a decrease in the functional integrity of the frontostriatal tract fibers in conjunction with four studies finding impaired frontal cortex stimulation. Prefrontal dysfunction, specifically medial and orbitofrontal areas, displayed abnormal functionality of reward processing in ADHD patients when compared to their normal counterparts. A total of 807 subjects were studied in this review, yielding that a little over half (54%) presented with remission of symptoms in adulthood. Conclusion: While the prefrontal cortex shows the highest consistency of impaired activity and thinner volumes in patients with ADHD, this is a heterogenous disorder implicating its pathophysiology to the dysfunction of other neural structures as well. However, remission of ADHD symptomatology in adulthood was found to be attributable to increased prefrontal functional connectivity and integration, suggesting a key role for the prefrontal cortex in the development of ADHD.

Keywords: prefrontal cortex, ADHD, inattentive, impulsivity, reward processing

Procedia PDF Downloads 91

Authors: Getaneh Berie Tarekegn, Li-Chia Tai

Abstract:

With the rapid advancement of artificial intelligence, low-power built-in sensors on Internet of Things devices, and communication technologies, location-aware services have become increasingly popular and have permeated every aspect of people’s lives. Global navigation satellite systems (GNSSs) are the default method of providing continuous positioning services for ground and aerial vehicles, as well as consumer devices (smartphones, watches, notepads, etc.). However, the environment affects satellite positioning systems, particularly indoors, in dense urban and suburban cities enclosed by skyscrapers, or when deep shadows obscure satellite signals. This is because (1) indoor environments are more complicated due to the presence of many objects surrounding them; (2) reflection within the building is highly dependent on the surrounding environment, including the positions of objects and human activity; and (3) satellite signals cannot be reached in an indoor environment, and GNSS doesn't have enough power to penetrate building walls. GPS is also highly power-hungry, which poses a severe challenge for battery-powered IoT devices. Due to these challenges, IoT applications are limited. Consequently, precise, seamless, and ubiquitous Positioning, Navigation and Timing (PNT) systems are crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarms, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

Procedia PDF Downloads 11

Authors: Hamid Havasi, Mohamad Reza Gholami Dehbalaei, Hamed Khorami, Shahram Karimi, Hamdi Abdi

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Doubly-fed induction generator (DFIG) equipped with a power converter is an efficient tool for converting mechanical energy of a variable speed system to a fixed-frequency electrical grid. Since electrical energy sources faces with production problems such as harmonics caused by nonlinear loads, so in this paper, compensation performance of DPC and FOC method on harmonics reduction of a DFIG wind turbine connected to a nonlinear load in MATLAB Simulink model has been simulated and effect of each method on nonlinear load harmonic elimination has been compared. Results of the two mentioned control methods shows the advantage of the FOC method on DPC method for harmonic compensation. Also, the fifth and seventh harmonic components of the network and THD greatly reduced.

Keywords: DFIG machine, energy conversion, nonlinear load, THD, DPC, FOC

Procedia PDF Downloads 563

Authors: Marc Solé, Francesc Giné, Magda Valls, Nina Bijedic

Abstract:

What people say on social media has turned into a rich source of information to understand social behavior. Specifically, the growing use of Twitter social media for political communication has arisen high opportunities to know the opinion of large numbers of politically active individuals in real time and predict the global political tendencies of a specific country. It has led to an increasing body of research on this topic. The majority of these studies have been focused on polarized political contexts characterized by only two alternatives. Unlike them, this paper tackles the challenge of forecasting Spanish political trends, characterized by multiple political parties, by means of analyzing the Twitters Users political tendency. According to this, a new strategy, named Tweets Analysis Strategy (TAS), is proposed. This is based on analyzing the users tweets by means of discovering its sentiment (positive, negative or neutral) and classifying them according to the political party they support. From this individual political tendency, the global political prediction for each political party is calculated. In order to do this, two different strategies for analyzing the sentiment analysis are proposed: one is based on Positive and Negative words Matching (PNM) and the second one is based on a Neural Networks Strategy (NNS). The complete TAS strategy has been performed in a Big-Data environment. The experimental results presented in this paper reveal that NNS strategy performs much better than PNM strategy to analyze the tweet sentiment. In addition, this research analyzes the viability of the TAS strategy to obtain the global trend in a political context make up by multiple parties with an error lower than 23%.

Keywords: political tendency, prediction, sentiment analysis, Twitter

Procedia PDF Downloads 211

Authors: Andrea Pietrelli, Vincenzo Ferrara, Bruno Allard, Francois Buret, Irene Bavasso, Nicola Lovecchio, Francesca Costantini, Firas Khaled

Abstract:

This paper aims to show some applications of microbial fuel cells (MFCs), an energy harvesting technique, as clean power source to supply low power device for application like wireless sensor network (WSN) for environmental monitoring. Furthermore, MFC can be used directly as biosensor to analyse parameters like pH and temperature or arranged in form of cluster devices in order to use as small power plant. An MFC is a bioreactor that converts energy stored in chemical bonds of organic matter into electrical energy, through a series of reactions catalysed by microorganisms. We have developed a lab-scale terrestrial microbial fuel cell (TMFC), based on soil that acts as source of bacteria and flow of nutrient and a lab-scale waste water microbial fuel cell (WWMFC), where waste water acts as flow of nutrient and bacteria. We performed large series of tests to exploit the capability as biosensor. The pH value has strong influence on the open circuit voltage (OCV) delivered from TMFCs. We analyzed three condition: test A and B were filled with same soil but changing pH from 6 to 6.63, test C was prepared using a different soil with a pH value of 6.3. Experimental results clearly show how with higher pH value a higher OCV was produced; indeed reactors are influenced by different values of pH which increases the voltage in case of a higher pH value until the best pH value of 7 is achieved. The influence of pH on OCV of lab-scales WWMFC was analyzed at pH value of 6.5, 7, 7.2, 7.5 and 8. WWMFCs are influenced from temperature more than TMFCs. We tested the power performance of WWMFCs considering four imposed values of ambient temperature. Results show how power performance increase proportionally with higher temperature values, doubling the output power from 20° to 40°. The best value of power produced from our lab-scale TMFC was equal to 310 μW using peaty soil, at 1KΩ, corresponding to a current of 0.5 mA. A TMFC can supply proper energy to low power devices of a WSN by means of the design of three stages scheme of an energy management system, which adapts voltage level of TMFC to those required by a WSN node, as 3.3V. Using a commercial DC/DC boost converter, that needs an input voltage of 700 mV, the current source of 0.5 mA, charges a capacitor of 6.8 mF until it will have accumulated an amount of charge equal to 700 mV in a time of 10 s. The output stage includes an output switch that close the circuit after a time of 10s + 1.5ms because the converter can boost the voltage from 0.7V to 3.3V in 1.5 ms. Furthermore, we tested in form of clusters connected in series up to 20 WWMFCs, we have obtained a high voltage value as output, around 10V, but low current value. MFC can be considered a suitable clean energy source to be used to supply low power devices as a WSN node or to be used directly as biosensor.

Keywords: energy harvesting, low power electronics, microbial fuel cell, terrestrial microbial fuel cell, waste-water microbial fuel cell, wireless sensor network

Procedia PDF Downloads 186

Authors: H. Mahdjoub, B. Hamaidi, B. Zerouali, S. Rouabhia

Abstract:

The issue of fault detection and diagnosis (FDD) has gained widespread industrial interest in process condition monitoring applications. Accordingly, the use of neuro-fuzzy technic seems very promising. This paper treats a diagnosis modeling a strategic equipment of an industrial installation. We propose a diagnostic tool based on adaptive neuro-fuzzy inference system (ANFIS). The neuro-fuzzy network provides an abductive diagnosis. Moreover, it takes into account the uncertainties on the maintenance knowledge by giving a fuzzy characterization of each cause. This work was carried out with real data of a lubrication circuit from the gas turbine. The machine of interest is a gas turbine placed in a gas compressor station at South Industrial Centre (SIC Hassi Messaoud Ouargla, Algeria). We have defined the zones of good and bad functioning, and the results are presented to demonstrate the advantages of the proposed method.

Keywords: fault detection and diagnosis, lubrication system, turbine, ANFIS, training, pattern recognition

Procedia PDF Downloads 460

Authors: Dileep G., Akash Singh, Dalchand Ahirwar, Arkadeep Ghosh, Ashutosh Purohit, Gaurav Guleria, Kshatriya Om Prashant, Pushkar Patel, Saksham Kumar, Vanshaj Nathani, Vikas Dangi, Shubhajit Roy Chowdhury, Varun Dutt

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Transcranial direct current stimulation (tDCS) has shown promise as a novel approach to enhance cognitive performance and provide therapeutic benefits for various brain disorders. However, the exact underlying brain mechanisms are not fully understood. We conducted a study to examine the brain's functional changes when subjected to simultaneous tDCS and music (Indian classical raga). During the study, participants in the experimental group underwent a 20-minute session of tDCS at two mA while listening to music (raga) for a duration of seven days. In contrast, the control group received a sham stimulation for two minutes at two mA over the same seven-day period. The objective was to examine whether repetitive tDCS could lead to the formation of additional functional connections between the medial prefrontal cortex (the stimulated area) and the auditory cortex in comparison to a sham stimulation group. In this study, 26 participants (5 female) underwent pre- and post-intervention scans, where changes were compared after one week of either tDCS or sham stimulation in conjunction with music. The study revealed significant effects of tDCS on functional connectivity between the stimulated area and the auditory cortex. The combination of tDCS applied over the mPFC and music resulted in newly formed connections. Based on our findings, it can be inferred that applying anodal tDCS over the mPFC enhances functional connectivity between the stimulated area and the auditory cortex when compared to the effects observed with sham stimulation.

Keywords: fNIRS, tDCS, neuroplasticity, music

Procedia PDF Downloads 46

Authors: Masoud Charkhabi

Abstract:

We examine trade theory with this motivation. The full set of World Trade Organization data are organized into country-year pairs, each treated as a different entity. Topological Data Analysis reveals that among the 16 region and 240 region-year pairs there exists in fact a distinguishable group of region-period pairs. The generally accepted periods of shifts from dissimilar-dissimilar to similar-similar trade in goods among regions are examined from this new perspective. The period breaks are treated as cumulative and are flexible. This type of all-subsets analysis is motivated from computer science and is made possible with Lossy Compression and Graph Theory. The results question many patterns in similar-similar to dissimilar-dissimilar trade. They also show indications of economic shifts that only later become evident in other economic metrics.

Keywords: econometrics, globalization, network science, topological data, analysis, trade theory, visualization, world trade

Procedia PDF Downloads 343

Authors: Düzgün Akmaz, Hüseyin Erişti

Abstract:

In the last few years, harmonics have been occurred with the increasing use of nonlinear loads, and these harmonics have been an ever increasing problem for the line systems. This situation importantly affects the quality of power and gives large losses to the network. An efficient way to solve these problems is providing harmonic compensation through parallel active power filters. Many methods can be used in the control systems of the parallel active power filters which provide the compensation. These methods efficiently affect the performance of the active power filters. For this reason, the chosen control method is significant. In this study, Fourier analysis (FA) control method and synchronous reference frame (SRF) control method are discussed. These control methods are designed for both eliminate harmonics and perform reactive power compensation in MATLAB/Simulink pack program and are tested. The results have been compared for each two methods.

Keywords: parallel active power filters, harmonic compensation, power quality, harmonics

Procedia PDF Downloads 425

Authors: Mehmet Hakan Karaata, Ali Hamdan, Omer Yusuf Adam Mohamed

Abstract:

Consider a distributed system that delivers messages from a process to another. Such a system is often required to deliver each message to its destination regardless of whether or not the system components experience arbitrary forms of faults. In addition, each message received by the destination must be a message sent by a system process. In this paper, we first identify the necessary and sufficient conditions to detect some restricted form of Byzantine faults referred to as modifying Byzantine faults. An observable form of a Byzantine fault whose effect is limited to the modification of a message metadata or content, timing and omission faults, and message replay is referred to as a modifying Byzantine fault. We then present a distributed protocol to detect modifying Byzantine faults using optimal number of messages over node-disjoint paths.

Keywords: Byzantine faults, distributed systems, fault detection, network pro- tocols, node-disjoint paths

Procedia PDF Downloads 537

Authors: Lakshya Bhat, Anubhav Shrivastava, Shivarudraswamy

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There has been a formidable interest in the area of Distributed Generation in recent times. A wide number of loads are addressed by Distributed Generators and have better efficiency too. The major disadvantage in Distributed Generation is voltage control- is highlighted in this paper. The paper addresses voltage control at buses in IEEE 14 Bus system by regulating reactive power. An analysis is carried out by selecting the most optimum location in placing the Distributed Generators through load flow analysis and seeing where the voltage profile rises. Matlab programming is used for simulation of voltage profile in the respective buses after introduction of DG’s. A tolerance limit of +/-5% of the base value has to be maintained.To maintain the tolerance limit , 3 methods are used. Sensitivity analysis of 3 methods for voltage control is carried out to determine the priority among the methods.

Keywords: distributed generators, distributed system, reactive power, voltage control, sensitivity analysis

Procedia PDF Downloads 565

Authors: Caterina Muzzi, Sergio Albertini, Davide Giacomini

Abstract:

The research is aimed to investigate the role of young entrepreneurs’ personality traits and their contextual background on the success of entrepreneurial initiatives. In the literature, the debate is still open about the main drivers in predicting entrepreneurial success. Classical theories are focused on looking at specific personality traits that could lead to successful start-ups initiatives, while emerging approaches are more interested in young entrepreneurs’ contextual background (such as the family of origin, the previous experience and their professional network). An online survey was submitted to the participants of an entrepreneurial training initiative organised by the Italian Young Entrepreneurs Association (Confindustria) in Brescia headquarter (AIB). At the time the authors started data collection for this research, the third edition of the initiative was just concluded and involved a total amount of 37 young future entrepreneurs. In the literature General self-efficacy (GSE) and, more specifically, entrepreneurial self-efficacy (ESE) have often been associated to positive performances, as they allow future entrepreneurs to effectively cope with entrepreneurial activities, both at an early stage and in new venture management. In a counter-intuitive manner, optimism is not always associated with entrepreneurial positive results. Too optimistic people risk taking hazardous risks and some authors suggest that moderately optimistic entrepreneurs achieve more positive results than over-optimistic ones. Indeed highly optimistic individuals often hold unrealistic expectations, discount negative information, and mentally reconstruct experiences so as to avoid contradictions The importance of context has been increasingly considered in entrepreneurship literature and its role strongly emerges starting from the earliest entrepreneurial stage and it is crucial to transform the “intention of entrepreneurship” into the actual start-up. Furthermore, coherently with the “network approach to entrepreneurship”, context embeddedness allow future entrepreneurs to leverage relationships built through previous experiences and/or thanks to the fact of belonging to families of entrepreneurs. For the purpose of this research, entrepreneurial success was measured by the fact of having or not founded a new venture after the training initiative. In this research, the authors measured GSE, ESE and optimism using already tested items that showed to be reliable also in this case. They collected 36 completed questionnaires. The t-test for independent samples run to measure significant differences in means between those that already funded the new venture and those that did not. No significant differences emerged with respect to all the tested personality traits, but a logistic regression analysis, run with contextual variables as independent ones, showed that personal and professional networking, made both before and during the master, is the most relevant variable in determining new venture success. These findings shed more light on the process of new venture foundation and could encourage national and local policy makers to invest on networking as one of the main drivers that could support the creation of new ventures.

Keywords: entrepreneurship, networking, new ventures, personality traits

Procedia PDF Downloads 119

Authors: R. F. Louh, Cathy Chou, Victor Wang, Howard Yan

Abstract:

The aim of this study is to manufacture titania and reduced graphene oxide (TiO2/rGO) composite nanofibers via electrospinning (ESP) of precursor fluid consisted of titania sol containing polyvinylpyrrolidone (PVP) and titanium isopropoxide (TTIP) and GO solution. The GO nanoparticles were derived from Hummers’ method. A metal grid ring was used to provide the bias voltage to reach higher ESP yield and nonwoven fabric with dense network of TiO2/GO composite nanofibers. The ESP product was heat treated at 500°C for 2 h in nitrogen atmosphere to acquire TiO2/rGO nanofibers by thermal reduction of GO and phase transformation into anatase TiO2. The TiO2/rGO nanofibers made from various volume fractions of GO solution by ESP were analyzed by FE-SEM, TEM, XRD, EDS, BET and FTIR. Such TiO2/rGO fibers having photocatalytic property, high specific surface area and electrical conductivity can be used for photovoltaics and chemical sensing applications.

Keywords: electrospinning process, titanium oxide, thermally reduced graphene oxide, composite nanofibers

Procedia PDF Downloads 432

Authors: Getaneh Berie Tarekegn, Li-Chia Tai

Abstract:

With the rapid advancement of artificial intelligence, low-power built-in sensors on Internet of Things devices, and communication technologies, location-aware services have become increasingly popular and have permeated every aspect of people’s lives. Global navigation satellite systems (GNSSs) are the default method of providing continuous positioning services for ground and aerial vehicles, as well as consumer devices (smartphones, watches, notepads, etc.). However, the environment affects satellite positioning systems, particularly indoors, in dense urban and suburban cities enclosed by skyscrapers, or when deep shadows obscure satellite signals. This is because (1) indoor environments are more complicated due to the presence of many objects surrounding them; (2) reflection within the building is highly dependent on the surrounding environment, including the positions of objects and human activity; and (3) satellite signals cannot be reached in an indoor environment, and GNSS doesn't have enough power to penetrate building walls. GPS is also highly power-hungry, which poses a severe challenge for battery-powered IoT devices. Due to these challenges, IoT applications are limited. Consequently, precise, seamless, and ubiquitous Positioning, Navigation and Timing (PNT) systems are crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

Procedia PDF Downloads 13

Authors: G. Agrawal, R. Agrawal, A. Pich

Abstract:

The pH and temperature responsive biodegradable poly(N-vinylcaprolactam) (PVCL) based microgels functionalized with itaconic acid (IA) units are prepared via precipitation polymerization for drug delivery applications. Volume phase transition temperature (VPTT) of the obtained microgels is influenced by both IA content and pH of the surrounding medium. The developed microgels can be degraded under acidic conditions due to the presence of hydrazone based crosslinking points inside the microgel network. The microgel particles are able to effectively encapsulate doxorubicin (DOX) drug and exhibit low drug leakage under physiological conditions. At low pH, rapid DOX release is observed due to the changes in electrostatic interactions along with the degradation of particles. The results of the cytotoxicity assay further display that the DOX-loaded microgel exhibit effective antitumor activity against HeLa cells demonstrating their great potential as drug delivery carriers for cancer therapy.

Keywords: degradable, drug delivery, hydrazone linkages, microgels, responsive

Procedia PDF Downloads 283

Authors: R. Nagarani

Abstract:

An intrinsic property of software in a real-world environment is its need to evolve, which is usually accompanied by the increase of software complexity and deterioration of software quality, making software maintenance a tough problem. Refactoring is regarded as an effective way to address this problem. Many refactoring approaches at the method and class level have been proposed. But the extent of research on software refactoring at the package level is less. This work presents a novel approach to refactor the package structures of object oriented software using genetic algorithm based community detection. It uses software networks to represent classes and their dependencies. It uses a constrained community detection algorithm to obtain the optimized community structures in software networks, which also correspond to the optimized package structures. It finally provides a list of classes as refactoring candidates by comparing the optimized package structures with the real package structures.

Keywords: community detection, complex network, genetic algorithm, package, refactoring

Procedia PDF Downloads 395

Authors: N. F. F. Ebecken, G. C. Pereira

Abstract:

Ecological systems are exposed and are influenced by various natural and anthropogenic disturbances. They produce various effects and states seeking response symmetry to a state of global phase coherence or stability and balance of their food webs. This research project addresses the development of a computational methodology for modeling plankton food webs. The use of algorithms to establish connections, the generation of representative fuzzy multigraphs and application of technical analysis of complex networks provide a set of tools for defining, analyzing and evaluating community structure of coastal aquatic ecosystems, beyond the estimate of possible external impacts to the networks. Thus, this study aims to develop computational systems and data models to assess how these ecological networks are structurally and functionally organized, to analyze the types and degree of compartmentalization and synchronization between oscillatory and interconnected elements network and the influence of disturbances on the overall pattern of rhythmicity of the system.

Keywords: ecological networks, plankton food webs, fuzzy multigraphs, dynamic of networks

Procedia PDF Downloads 279

Authors: Anna Strąkowska, Marian Zaborski

Abstract:

The studies were focused on POSS application with methylvinylsilicone rubber (MVQ). The obtained results indicate that they can be successfully incorporated into silica-filled rubbers as modifying agents since they enhance cross-link density and improve most properties of the resulting network. It is also worth noting that the incorporation of POSS molecules resulted in stabilizing effect against adverse changes induced by the climatic, ozone or UV ageing of the rubbers. Furthermore, we obtained interesting results of rubbers surface modification using POSS functionalised with halogen groups (Cl, F, and Br). As the results, surface energy of the elastomeric composites and their hydrophobicity increased, barrier properties improved and thermal stability increased as well. Additionally, the studies with silicone rubber and POSS containing acidic and alkaline groups revealed composites with self-healing properties. The observed effects strictly depend on a kind and quantity of functional groups present in angles of POSS cages.

Keywords: elastomeric composites, POSS, properties modyfication, silicone rubber

Procedia PDF Downloads 332

Authors: Abigail Qian Zhou

Abstract:

In today's China, the well-educated middle class, with stable jobs and above-average income, are the driving force behind its Internet society. Through the analysis of data from the 2015 Chinese General Social Survey and 50 interviewees, this study investigates the current situation of this group’s specific internet usage. The findings of this study demonstrate that daily life among the members of this socioeconomic group is closely tied to the Internet. For Chinese middle class, the Internet is used to socialize and entertain self and others. It is also used to search for and share information as well as to build their identities. The empirical results of this study will provide a reference, supported by factual data, for enterprises seeking to target the Chinese middle class through online marketing efforts.

Keywords: middle class, Internet use, network behaviour, online marketing, China

Procedia PDF Downloads 93

Authors: Yağmur Can Gündoğan, Kübra Gürcan Bayrak, Ece Özerdem, Buse Katipoğlu, Erhan Ayas, Rifat Yılmaz

Abstract:

Eutectic oxide based ceramic matrix composites have a unique microstructure that does not include grain boundary in the form of a continuous network. Because of this, these materials have the properties of perfect high-temperature strength, creep strength, and high oxidation strength. Mechanical properties of them are much related to occurring solidification structures during eutectic reactions. One of the most important production methods of this kind of material is the process of vacuum arc melting. Within scope of this studying, it is aimed to investigate the production of Al₂O₃-YAG-based eutectic ceramics by Arc melting and Spark Plasma Sintering methods for use in aerospace and defense industries where high-temperature environments play an important role and to examine the effects of ZrO₂ and LiF additions on microstructure development and mechanical properties.

Keywords: alumina, composites, eutectic, YAG

Procedia PDF Downloads 91

Authors: Vinayaka Gude Divya Sampath

Abstract:

The research introduces an integrated prediction model to assess the susceptibility of roads in a future flooding event. The model consists of deep learning algorithm for forecasting gauge height data and Flood Inundation Mapper (FIM) for spatial flooding. An optimal architecture for Long short-term memory network (LSTM) was identified for the gauge located on Tangipahoa River at Robert, LA. Dropout was applied to the model to evaluate the uncertainty associated with the predictions. The estimates are then used along with FIM to identify the spatial flooding. Further geoprocessing in ArcGIS provides the susceptibility values for different roads. The model was validated based on the devastating flood of August 2016. The paper discusses the challenges for generalization the methodology for other locations and also for various types of flooding. The developed model can be used by the transportation department and other emergency response organizations for effective disaster management.

Keywords: deep learning, disaster management, flood prediction, urban flooding

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Authors: Jie Lin, Yiwen Pan, Li Zhang, Zhangyong Xia

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Atherosclerosis is a chronic inflammatory disease characterized by the accumulation of lipids, immune cells, and extracellular matrix in the arterial walls. This pathological process can lead to the formation of plaques that can obstruct blood flow and trigger various cardiovascular diseases such as heart attack and stroke. The underlying molecular mechanisms still remain unclear, although many studies revealed the dysfunction of endothelial cells, recruitment and activation of monocytes and macrophages, and the production of pro-inflammatory cytokines and chemokines in atherosclerosis. This study aimed to identify hub genes involved in the progression of atherosclerosis and to analyze their biological function in silico, thereby enhancing our understanding of the disease’s molecular mechanisms. Through the analysis of microarray data, we examined the gene expression in media and neo-intima from plaques, as well as distant macroscopically intact tissue, across a cohort of 32 hypertensive patients. Initially, 112 differentially expressed genes (DEGs) were identified. Subsequent immune infiltration analysis indicated a predominant presence of 27 immune cell types in the atherosclerosis group, particularly noting an increase in monocytes and macrophages. In the Weighted gene co-expression network analysis (WGCNA), 10 modules with a minimum of 30 genes were defined as key modules, with blue, dark, Oliver green and sky-blue modules being the most significant. These modules corresponded respectively to monocyte, activated B cell, and activated CD4 T cell gene patterns, revealing a strong morphological-genetic correlation. From these three gene patterns (modules morphology), a total of 2509 key genes (Gene Significance >0.2, module membership>0.8) were extracted. Six hub genes (CD36, DPP4, HMOX1, PLA2G7, PLN2, and ACADL) were then identified by intersecting 2509 key genes, 102 DEGs with lipid-related genes from the Genecard database. The bio-functional analysis of six hub genes was estimated by a robust classifier with an area under the curve (AUC) of 0.873 in the ROC plot, indicating excellent efficacy in differentiating between the disease and control group. Moreover, PCA visualization demonstrated clear separation between the groups based on these six hub genes, suggesting their potential utility as classification features in predictive models. Protein-protein interaction (PPI) analysis highlighted DPP4 as the most interconnected gene. Within the constructed key gene-drug network, 462 drugs were predicted, with ursodeoxycholic acid (UDCA) being identified as a potential therapeutic agent for modulating DPP4 expression. In summary, our study identified critical hub genes implicated in the progression of atherosclerosis through comprehensive bioinformatic analyses. These findings not only advance our understanding of the disease but also pave the way for applying similar analytical frameworks and predictive models to other diseases, thereby broadening the potential for clinical applications and therapeutic discoveries.

Keywords: atherosclerosis, hub genes, drug prediction, bioinformatics

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Authors: Vladimir Simovic, Matija Varga, Predrag Oreski

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XHTML and XBRL are the standard languages for creating a database for the purpose of displaying financial statements on web applications. Today, XBRL is one of the most popular languages for business reporting. A large number of countries in the world recognize the role of XBRL language for financial reporting and the benefits that the reporting format provides in the collection, analysis, preparation, publication and the exchange of data (information) which is the positive side of this language. Here we present all advantages and opportunities that a company may have by using the XBRL format for business reporting. Also, this paper presents XBRL and other languages that are used for creating the database, such XML, XHTML, etc. The role of the AJAX complex model and technology will be explained in detail, and during the exchange of financial data between the web client and web server. Here will be mentioned basic layers of the network for data exchange via the web.

Keywords: XHTML, XBRL, XML, JavaScript, AJAX technology, data exchange

Procedia PDF Downloads 372

Authors: Adrià Arbués-Sangüesa, Coloma Ballester, Gloria Haro

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Tracking sports players is a widely challenging scenario, specially in single-feed videos recorded in tight courts, where cluttering and occlusions cannot be avoided. This paper presents an analysis of several geometric and semantic visual features to detect and track basketball players. An ablation study is carried out and then used to remark that a robust tracker can be built with Deep Learning features, without the need of extracting contextual ones, such as proximity or color similarity, nor applying camera stabilization techniques. The presented tracker consists of: (1) a detection step, which uses a pretrained deep learning model to estimate the players pose, followed by (2) a tracking step, which leverages pose and semantic information from the output of a convolutional layer in a VGG network. Its performance is analyzed in terms of MOTA over a basketball dataset with more than 10k instances.

Keywords: basketball, deep learning, feature extraction, single-camera, tracking

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Authors: Sallama Resen, Celal Öztürk

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Distance estimation between tow nodes has wide scope of surveillance and tracking applications. This paper suggests a Bluetooth Low Energy (BLE) technology as a media for transceiver and receiver signal in small indoor areas. As an example, BLE communication technologies used in child safety domains. Local network is designed to detect child position in indoor school area consisting Mobile Beacons (MB), Access Points (AP) and Smart Phones (SP) where MBs stuck in children’s shoes as wearable sensors. This paper presents a technique that can detect mobile beacons’ position and help finding children’s location within dynamic environment. By means of bluetooth beacons that are attached to child’s shoes, the distance between the MB and teachers SP is estimated with an accuracy of less than one meter. From the simulation results, it is shown that high accuracy of position coordinates are achieved for multi-mobile beacons in different environments.

Keywords: bluetooth low energy, child safety, mobile beacons, received signal strength

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Authors: Meet Patel, Darshan Patel, Nilay Shah

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Excess growth of wind-based renewable energy sources is required to identify the optimal location and damping capacity of doubly fed induction-generator-based (DFIG) wind farms while it penetrates into the transmission network. In this analysis, various ratings of DFIG wind farms are penetrated into the Single Machine Infinite Bus (SMIB ) at a different distance of the transmission line. On the basis of detailed examinations, a prime position is evaluated to maximize the stability of overall systems. A damping controller is designed at an optimum location to mitigate the small oscillations. The proposed model was validated using eigenvalue analysis, calculation of the participation factor, and time-domain simulation.

Keywords: DFIG, small signal stability, eigenvalues, time domain simulation

Procedia PDF Downloads 80

Authors: Chowduru Ramachandra Sharma, Shatunjay Rawat

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A false positive state is when the IDS/IPS identifies an activity as an attack, but the activity is acceptable behavior in the system. False positives in a Network Intrusion Detection System ( NIDS ) is an issue because they desensitize the administrator. It wastes computational power and valuable resources when rules are not tuned properly, which is the main issue with anomaly NIDS. Furthermore, most false positives reduction techniques are not performed during the real-time of attempted intrusions; instead, they have applied afterward on collected traffic data and generate alerts. Of course, false positives detection in ‘offline mode’ is tremendously valuable. Nevertheless, there is room for improvement here; automated techniques still need to reduce False Positives in real-time. This paper uses the Snort signature detection model to redirect the alerted attacks to Honeypots and verify attacks.

Keywords: honeypot, TPOT, snort, NIDS, honeybird, iptables, netfilter, redirection, attack detection, docker, snare, tanner

Procedia PDF Downloads 136