Search results for: monitoring networks

Authors: İkbal Eski

Abstract:

This investigation is researched on design of robust neural network predictors for analyzing vibration effects on moving parts of a drilling machine. Moreover, the research is divided two parts; first part is experimental investigation, second part is simulation analysis with neural networks. Therefore, a real time the drilling machine is used to vibrations during working conditions. The measured real vibration parameters are analyzed with proposed neural network. As results: Simulation approaches show that Radial Basis Neural Network has good performance to adapt real time parameters of the drilling machine.

Keywords: artificial neural network, vibration analyses, drilling machine, robust

Procedia PDF Downloads 394

Authors: Fahad Alamoudi, Yaser Miaji

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The growing popularity of swimming pools and other activities in the water for sport, fitness, therapy or just enjoyable relaxation have led to the increased use of swimming pools and the establishment of a variety of specific-use pools such as spa pools, water slides, and more recently, hydrotherapy and wave pools. In this research, a few simple equipment is used for test, detect and alert for detection of water cleanness and pollution. YSI Photometer Systems, TDSTestr High model, Rio 12HF and Electrode A1. The researchers used electrolysis as a method of separating bonded elements and compounds by passing an electric current through them. The results which use 41 experiments show the higher the salt concentration, the more efficient the electrode and the smaller the gap between the plates, the lower the electrode voltage. Furthermore, it is proved that the larger the surface area, the lower the cell voltage and the higher current used the more chlorine produced.

Keywords: photometer, electrode, electrolysis, swimming pool chlorination

Procedia PDF Downloads 363

Authors: Adeniran Adetunji, Babalola M. Florence, Akande Ademola

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In this paper, we made an overview on the concept of adaptive e-Learning system, enumerates the elements of adaptive learning concepts e.g. A pedagogical framework, multiple learning strategies and pathways, continuous monitoring and feedback on student performance, statistical inference to reach final learning strategy that works for an individual learner by “mass-customization”. Briefly highlights the motivation of this new system proposed for effective learning teaching. E-Review literature on the concept of adaptive e-learning system and emphasises on the Item Response Calibration, which is an important approach to developing an adaptive e-Learning system. This paper write-up is concluded on the justification of item response calibration/estimation towards designing a successful and effective adaptive e-Learning system.

Keywords: adaptive e-learning system, pedagogical framework, item response, computer applications

Procedia PDF Downloads 596

Authors: Sukhpreet Singh, Kishan Bhojrath, Vijay, Avinash Kumar, Mandlesh Mishra

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The project uses an Arduino and ESP8266 to construct an alcohol detection system with an engine locking mechanism, offering a distinct way to fight drunk driving. An alcohol sensor module is used by the system to determine the amount of alcohol present in the ambient air. When the system detects alcohol levels beyond a certain threshold that is deemed hazardous for driving, it activates a relay module that is linked to the engine of the car, so rendering it inoperable. By preventing people from operating a vehicle while intoxicated, this preventive measure seeks to improve road safety. Adding an ESP8266 module also allows for remote monitoring and notifications, giving users access to real-time status updates on their system. By using an integrated strategy, the initiative provides a workable and efficient way to lessen the dangers related to driving while intoxicated.

Keywords: MQ3 sensor, ESP 8266, arduino, IoT

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Authors: Kwaku Damoah

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This study presents a comprehensive data-driven analysis to understand the evolution of adverse events (AEs) in pharmacovigilance. Utilizing data from the FDA Adverse Event Reporting System (FAERS), we employed three analytical methods: rank-based, frequency-based, and percentage change analyses. These methods assessed temporal trends and patterns in AE reporting, focusing on various drug-active ingredients and patient demographics. Our findings reveal significant trends in AE occurrences, with both increasing and decreasing patterns from 2000 to 2023. This research highlights the importance of continuous monitoring and advanced analysis in pharmacovigilance, offering valuable insights for healthcare professionals and policymakers to enhance drug safety.

Keywords: event analysis, FDA adverse event reporting system, pharmacovigilance, temporal trend analysis

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Authors: Belkacem Benadda, Adil Benabdellah, Boutheyna Souna

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The Internet of Things (IoT) is an unprecedented creation. Electronics objects are now able to interact, share, respond and adapt to their environment on a much larger basis. Actual spread of these modern means of connectivity and solutions with high data volume exchange are affecting our ways of life. Accommodation is becoming an intelligent living space, not only suited to the people circumstances and desires, but also to systems constraints to make daily life simpler, cheaper, increase possibilities and achieve a higher level of services and luxury. In this paper we are as Internet access, teleworking, consumption monitoring, information search, etc.). This paper addresses the design and integration of a smart home, it also purposes an IoT solution that allows smart power consumption based on measurements from power-grid and deep learning analysis.

Keywords: array sensors, IoT, power grid, FPGA, embedded

Procedia PDF Downloads 116

Authors: Farina Riaz, Shahab Abdulla, Srinjoy Ganguly, Hajime Suzuki, Ravinesh C. Deo, Susan Hopkins

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Recently, quantum machine learning has received significant attention. For various types of data, including text and images, numerous quantum machine learning (QML) models have been created and are being tested. Images are exceedingly complex data components that demand more processing power. Despite being mature, classical machine learning still has difficulties with big data applications. Furthermore, quantum technology has revolutionized how machine learning is thought of, by employing quantum features to address optimization issues. Since quantum hardware is currently extremely noisy, it is not practicable to run machine learning algorithms on it without risking the production of inaccurate results. To discover the advantages of quantum versus classical approaches, this research has concentrated on colored image data. Deep learning classification models are currently being created on Quantum platforms, but they are still in a very early stage. Black and white benchmark image datasets like MNIST and Fashion MINIST have been used in recent research. MNIST and CIFAR-10 were compared for binary classification, but the comparison showed that MNIST performed more accurately than colored CIFAR-10. This research will evaluate the performance of the QML algorithm on the colored benchmark dataset CIFAR-10 to advance QML's real-time applicability. However, deep learning classification models have not been developed to compare colored images like Quantum Convolutional Neural Network (QCNN) to determine how much it is better to classical. Only a few models, such as quantum variational circuits, take colored images. The methodology adopted in this research is a hybrid approach by using penny lane as a simulator. To process the 10 classes of CIFAR-10, the image data has been translated into grey scale and the 28 × 28-pixel image containing 10,000 test and 50,000 training images were used. The objective of this work is to determine how much the quantum approach can outperform a classical approach for a comprehensive dataset of color images. After pre-processing 50,000 images from a classical computer, the QCNN model adopted a hybrid method and encoded the images into a quantum simulator for feature extraction using quantum gate rotations. The measurements were carried out on the classical computer after the rotations were applied. According to the results, we note that the QCNN approach is ~12% more effective than the traditional classical CNN approaches and it is possible that applying data augmentation may increase the accuracy. This study has demonstrated that quantum machine and deep learning models can be relatively superior to the classical machine learning approaches in terms of their processing speed and accuracy when used to perform classification on colored classes.

Keywords: CIFAR-10, quantum convolutional neural networks, quantum deep learning, quantum machine learning

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Authors: Wesley H. Brewer, John C. Sanford

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SciPaaS is a prototype development of an execution platform/middleware designed to make it easy for scientists to rapidly deploy their scientific applications (apps) to the cloud. It provides all the necessary infrastructure for running typical IXP (Input-eXecute-Plot) style apps, including: a web interface, post-processing and plotting capabilities, job scheduling, real-time monitoring of running jobs, and even a file/case manager. In this paper, first the system architecture is described and then is demonstrated for a two scientific applications: (1) a simple finite-difference solver of the inviscid Burger’s equation, and (2) Mendel’s Accountant—a forward-time population genetics simulation model. The implications of the prototype are discussed in terms of ease-of-use and deployment options, especially in cloud environments.

Keywords: web-based simulation, cloud computing, Platform-as-a-Service (PaaS), rapid application development (RAD), population genetics

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Authors: Kacharava Tamar, Korakhashvili Avtandil, Epitashvili Tinatin

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The article deals with the database, which was created by the authors, related to biodiversity of some families of useful plants (medicinal, aromatic, spices, dye and poisonous) existing in Georgia considering important taxonomy. Our country is also rich with endemic genera. The results of monitoring of the phytogenetic resources to reveal perspective species and situation of endemic species and resources are also discussed in this paper. To get some new medicinal and preventive treatments using plant raw material in the phytomedicine, phytocosmetics and phytoculinary, the unique phytogenetic resources should be protected because the application of useful plants is becoming irreversible. This can be observed along with intensification and sustainable use of ethnobotanical traditions and promotion of phytoproduction based on the international requirements on biodiversity (Convention on Biological Diversity - CBD). Though Georgian phytopharmacy has the centuries-old traditions, today it is becoming the main concern.

Keywords: aromatic, medicinal, poisonous, spicy, dye plants, endemic biodiversity, endemic, ELISA, GIS

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Authors: Raja Omman Zafar, Yves Rybarczyk, Johan Borg

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This article proposes a conceptual framework for the application of digital twin technology in home care. The main goal is to bridge the gap between advanced digital twin concepts and their practical implementation in home care. This study uses a literature review and thematic analysis approach to synthesize existing knowledge and proposes a structured framework suitable for homecare applications. The proposed framework integrates key components such as IoT sensors, data-driven models, cloud computing, and user interface design, highlighting the importance of personalized and predictive homecare solutions. This framework can significantly improve the efficiency, accuracy, and reliability of homecare services. It paves the way for the implementation of digital twins in home care, promoting real-time monitoring, early intervention, and better outcomes.

Keywords: digital twin, homecare, older adults, healthcare, IoT, artificial intelligence

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Authors: Anwar Jarndal

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In this paper, a genetic-neural-network (GNN) based large-signal model for GaN HEMTs is presented along with its parameters extraction procedure. The model is easy to construct and implement in CAD software and requires only DC and S-parameter measurements. An improved decomposition technique is used to model self-heating effect. Two GNN models are constructed to simulate isothermal drain current and power dissipation, respectively. The two model are then composed to simulate the drain current. The modeling procedure was applied to a packaged GaN-on-Si HEMT and the developed model is validated by comparing its large-signal simulation with measured data. A very good agreement between the simulation and measurement is obtained.

Keywords: GaN HEMT, computer-aided design and modeling, neural networks, genetic optimization

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Authors: Muhammad Asghar Saqib, Saad Saleem Khan, Syed Abdul Rahman Kashif

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In order to have higher efficiency, performance and reliability the regular monitoring of an electrical motor is required. This article presents a novel view of the air-gap magnetic field analysis of a switched reluctance motor under rotor cracks and rotor tilt along its shaft axis. The fault diagnosis is illustrated on the basis of a 3-D model of the motor using finite element analysis (FEA). The analytical equations of flux linkages have been used to determine the inductance. The results of the 3-D finite element analysis on a 6/4 switched reluctance motor (SRM) shows the variation of mutual inductance with the tilting of the rotor shaft and cracked rotor conditions. These results present useful information regarding the detection of shaft tilting and cracked rotors.

Keywords: switched reluctance motor, finite element analysis, cracked rotor, 3-D modelling of a srm

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Authors: Manish Kumar Thakur, Subrata Kumar Ghosh

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Various types of particles found in lubricant may be characterized by their fractal dimension. Some of the available methods are: yard-stick method or structured walk method, box-counting method. This paper presents a review of the developments and progress in fractal dimension computing methods as applied to characteristics the surface of wear particles. An overview of these methods, their implementation, their advantages and their limits is also present here. It has been accepted that wear particles contain major information about wear and friction of materials. Morphological analysis of wear particles from a lubricant is a very effective way for machine condition monitoring. Fractal dimension methods are used to characterize the morphology of the found particles. It is very useful in the analysis of complexity of irregular substance. The aim of this review is to bring together the fractal methods applicable for wear particles.

Keywords: fractal dimension, morphological analysis, wear, wear particles

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Authors: Amir H. Alavi, Kaveh Barri, Qianyun Zhang

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This study presents a new generation of lightweight and mechanically tunable structural composites with sensing and energy harvesting functionalities. This goal is achieved by integrating metamaterial and triboelectric energy harvesting concepts. Proof-of-concept polymeric beam prototypes are fabricated using 3D printing methods based on the proposed concept. Experiments and theoretical analyses are conducted to quantitatively investigate the mechanical and electrical properties of the designed multifunctional beams. The results show that these integrated structural elements can serve as nanogenerators and distributed sensing mediums without a need to incorporating any external sensing modules and electronics. The feasibility of design self-sensing and self-powering structural elements at multiscale for next generation infrastructure systems is further discussed.

Keywords: multifunctional structures, composites, metamaterial, triboelectric nanogenerator, sensors, structural health monitoring, energy harvesting

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Authors: Younghyun Jeon, Seungjoo Maeng

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In the Ad-hoc network, the great interests regarding MIMO scheme leads to their combination, which is also utilized into its applicable network. We manage the field of the problem into Reverse-link MIMO Ad-hoc Network (RMAN) and propose the methodology to maximize the data rate with its power consumption using Node-Cooperative beamforming technique. Based on the result of mathematical optimization formulation, we design the algorithm to construct optimal orthogonal weight vector according to channel feedback and control its transmission power according to QoS-pricing value level. In simulation results, we show the validity of the proposed mathematical optimization result and algorithm which mean that the sum-rate of each link is converged into some point.

Keywords: ad-hoc network, MIMO, cooperative beamforming, transmit power

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Authors: Bharti Ahirwar

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The main object of this study was to evaluate the effect of kankasava on OVA-induced asthma in mouse model. Present study has demonstrated that kankasava exhibited an antiasthmatic effect by attenuated AHR and reducing level of IgE, IL-5, and IL-13, in both serum and BALF in OVA induced asthmatic mice. Effect of kankasav on airway responsiveness was obtained by monitoring the enhanced pen value . Kankasava significantly reduced AHR can be explained, in part, by reduction in both IgE overexoression and cytokine levels. Kankasava significantly decreased IL-4, IL-5, and IL-13 in BALF indicate that it may suppress the excess activity of T-cells and Th2 cytokines, which are implicated in the pathogenesis of allergic asthma, and consequently restore the Th1/Th2 imbalance of the immune system. In summary, we hypothesize that kankasava effectively suppressed elevations in IgE and cytokines levels, AHR, and mucus overproduction in mice with OVA-induced asthma suggested kankasava could be effective in immunological and pharmacological modulation of allergic asthma.

Keywords: asthma, ayurveda, kankasava, cytokine

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Authors: Lucia Antonela Mitidieri

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This research analyzes the application of a model of settlements management based on networks of territorial integration that advocates planning as a cyclical and participatory process that engages early on with civic society, the private sector and the state. Through qualitative methods such as participant observation, interviews with snowball technique and an active research on territories, concrete results of community empowerment are obtained from the promotion of productive enterprises and community spaces of encounter and exchange. Studying the cultural and organizational dimensions of empowerment allows building indicators such as increase of capacities or community cohesion that can lead to support local governments in achieving sustainable urban development for a reduction of urban poverty.

Keywords: community spaces, empowerment, network urbanism, participatory process

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Authors: Harche Rima, Laoufi Nadia Aicha

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The present work consists of studying the behavior of steel, copper, and aluminum vis-à-vis the corrosion in an aqueous medium in the presence of the antifreeze COOLELF MDX -26°C. For this, we have studied the influence of the temperature and the different concentrations of the antifreeze on the corrosion of these three metals, this will last for two months by the polarization method and weight loss. In the end, we investigated the samples with the optic microscope to know their surface state. The aim of this work is the protection of contraptions. The use of antifreeze in ordinary water has a high efficiency against steel corrosion, as demonstrated by electrochemical tests (potential monitoring as a function of time and tracing polarization curves). The inhibition rate is greater than 99% for different volume concentrations, ranging from 40% to 60%. The speeds are in turn low in the order of 10-4 mm/year. On the other hand, the addition of antifreeze to ordinary water increases the corrosion potential of steel by more than 400 mV.

Keywords: corrosion and prevention, steel, copper, aluminum, corrosion inhibitor, anti-cooling

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Authors: Tahira Zaman

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The paper intends to discover the utility of hybrid teaching methods to aid undergraduate students to improve their English academic writing skills. A total of 45 undergraduate students were selected randomly from three classes from varying language abilities, with the research design of monitoring and rubrics evaluation as a means of measure. Language skills of the students were upgraded with the help of experiential learning methods using reflective writing technique, guided method in which students were merely directed to correct form of writing techniques along with self-guided method for the students to produce a library research-based article measured through a standardized rubrics provided. The progress of the students was monitored and checked through rubrics and self-evaluation and concluded that a change was observed in the students’ writing abilities.

Keywords: self evaluation, hybrid, self evaluation, reflective writing

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Authors: Xiao-Mei Zhang, Sian-Hong Tseng, Ming-Yen Lu

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Two-dimensional (2D) transition metal dichalcogenides (TMDCs), such as MoS₂, have attracted considerable attention owing to the unique optical and electronic properties related to its 2D ultrathin atomic layer structure. MoS₂ is becoming prevalent in post-silicon digital electronics and in highly efficient optoelectronics due to its extremely low thickness and its tunable band gap (Eg = 1-2 eV). For low-power, high-performance complementary logic applications, both p- and n-type MoS₂ FETs (NFETs and PFETs) must be developed. NFETs with an electron accumulation channel can be obtained using unintentionally doped n-type MoS₂. However, the fabrication of MoS₂ FETs with complementary p-type characteristics is challenging due to the significant difficulty of injecting holes into its inversion channel. Plasma treatments with different species (including CF₄, SF₆, O₂, and CHF₃) have also been found to achieve the desired property modifications of MoS₂. In this work, we demonstrated a p-type multilayer MoS₂ enabled by selective-area doping using CHF₃ plasma treatment. Compared with single layer MoS₂, multilayer MoS₂ can carry a higher drive current due to its lower bandgap and multiple conduction channels. Moreover, it has three times the density of states at its minimum conduction band. Large-area growth of MoS₂ films on 300 nm thick SiO₂/Si substrate is carried out by thermal decomposition of ammonium tetrathiomolybdate, (NH₄)₂MoS₄, in a tube furnace. A two-step annealing process is conducted to synthesize MoS₂ films. For the first step, the temperature is set to 280 °C for 30 min in an N₂ rich environment at 1.8 Torr. This is done to transform (NH₄)₂MoS₄ into MoS₃. To further reduce MoS₃ into MoS₂, the second step of annealing is performed. For the second step, the temperature is set to 750 °C for 30 min in a reducing atmosphere consisting of 90% Ar and 10% H₂ at 1.8 Torr. The grown MoS₂ films are subjected to out-of-plane doping by CHF₃ plasma treatment using a Dry-etching system (ULVAC original NLD-570). The radiofrequency power of this dry-etching system is set to 100 W and the pressure is set to 7.5 mTorr. The final thickness of the treated samples is obtained by etching for 30 s. Back-gated MoS₂ PFETs were presented with an on/off current ratio in the order of 10³ and a field-effect mobility of 65.2 cm²V⁻¹s⁻¹. The MoS₂ PFETs photodetector exhibited ultraviolet (UV) photodetection capability with a rapid response time of 37 ms and exhibited modulation of the generated photocurrent by back-gate voltage. This work suggests the potential application of the mild plasma-doped p-type multilayer MoS₂ in UV photodetectors for environmental monitoring, human health monitoring, and biological analysis.

Keywords: photodetection, p-type doping, multilayers, MoS₂

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Authors: Shuo Liu

Abstract:

In Chinese text classification tasks, redundant words and phrases can interfere with the formation of extracted and analyzed text information, leading to a decrease in the accuracy of the classification model. To reduce irrelevant elements, extract and utilize text content information more efficiently and improve the accuracy of text classification models. In this paper, the text in the corpus is first extracted using the TextRank algorithm for abstraction, the words in the abstract are used as nodes to construct a text graph, and then the graph attention network (GAT) is used to complete the task of classifying the text. Testing on a Chinese dataset from the network, the classification accuracy was improved over the direct method of generating graph structures using text.

Keywords: Chinese natural language processing, text classification, abstract extraction, graph attention network

Procedia PDF Downloads 100

Authors: Jorge Maestre Vidal, Ana Lucila Sandoval Orozco, Luis Javier García Villalba

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With the increase in popularity of mobile devices, new and varied forms of malware have emerged. Consequently, the organizations for cyberdefense have echoed the need to deploy more effective defensive schemes adapted to the challenges posed by these recent monitoring environments. In order to contribute to their development, this paper presents a malware detection strategy for mobile devices based on sequence alignment algorithms. Unlike the previous proposals, only the system calls performed during the startup of applications are studied. In this way, it is possible to efficiently study in depth, the sequences of system calls executed by the applications just downloaded from app stores, and initialize them in a secure and isolated environment. As demonstrated in the performed experimentation, most of the analyzed malicious activities were successfully identified in their boot processes.

Keywords: android, information security, intrusion detection systems, malware, mobile devices

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Authors: Firas Gerges, Frank Y. Shih

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Malignant melanoma, known simply as melanoma, is a type of skin cancer that appears as a mole on the skin. It is critical to detect this cancer at an early stage because it can spread across the body and may lead to the patient's death. When detected early, melanoma is curable. In this paper, we propose a deep learning model (convolutional neural networks) in order to automatically classify skin lesion images as malignant or benign. Images underwent certain pre-processing steps to diminish the effect of the normal skin region on the model. The result of the proposed model showed a significant improvement over previous work, achieving an accuracy of 97%.

Keywords: deep learning, skin cancer, image processing, melanoma

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Authors: Qiaoyun Yang

Abstract:

Organizational scholars have paid enormous attention on how local governments manage their knowledge during the past two decades. Government knowledge management (KM) research recognizes that the management of knowledge flows and networks is critical to reforms on government service efficiency and the effect of administration. When dealing with complex affairs, all the limitations resulting from a lack of KM concept, processes and technologies among all the involved organizations begin to be exposed and further compound the processing difficulty of the affair. As a result, the challenges for individual or group knowledge sharing, knowledge digging and organizations’ collaboration in government's activities are diverse and immense. This analysis presents recent situation of government KM in China drawing from a total of more than 300 questionnaires and highlights important challenges that remain. The causes of the lapses in KM processes within and across the government agencies are discussed.

Keywords: KM processes, KM technologies, government, KM situation

Procedia PDF Downloads 362

Authors: Gunasekaran Raja, Rajakumar Arul, Kottilingam Kottursamy, Ramkumar Jayaraman, Sathya Pavithra, Swaminathan Venkatraman

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Wireless Interoperability for Microwave Access (WiMAX) utilizes the IEEE 802.1X mechanism for authentication. However, this mechanism incurs considerable delay during handoffs. This delay during handoffs results in service disruption which becomes a severe bottleneck. To overcome this delay, our article proposes a key caching mechanism based on user path prediction. If the user mobility follows that path, the user bypasses the normal IEEE 802.1X mechanism and establishes the necessary authentication keys directly. Through analytical and simulation modeling, we have proved that our mechanism effectively decreases the handoff delay thereby achieving fast authentication.

Keywords: authentication, authorization, and accounting (AAA), handoff, mobile, user path prediction (UPP) and user pattern

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Authors: Dev Pratap Singh, Harshika Hasija, Ashwini S.

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The project aims to provide a touchless, intuitive interface for human-computer interaction, enabling users to control their computers using hand gestures and voice commands. The system leverages advanced computer vision techniques using the Media Pipe framework and OpenCV to detect and interpret real-time hand gestures, transforming them into mouse actions such as clicking, dragging, and scrolling. Additionally, the integration of a voice assistant powered by the speech recognition library allows for seamless execution of tasks like web searches, location navigation, and gesture control in the system through voice commands.

Keywords: gesture recognition, hand tracking, machine learning, convolutional neural networks, natural language processing, voice assistant

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Authors: Liaquat Ahmad, Muhammad Aslam, Muhammad Azam

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In this article, we propose a control chart based on repetitive group sampling scheme for the location parameter. This charting scheme is based on the regression estimator; an estimator that capitalize the relationship between the variables of interest to provide more sensitive control than the commonly used individual variables. The control limit coefficients have been estimated for different sample sizes for less and highly correlated variables. The monitoring of the production process is constructed by adopting the procedure of the Shewhart’s x-bar control chart. Its performance is verified by the average run length calculations when the shift occurs in the average value of the estimator. It has been observed that the less correlated variables have rapid false alarm rate.

Keywords: average run length, control charts, process shift, regression estimators, repetitive group sampling

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Authors: Sadiq J. Abou-Loukh

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The present work deals with implementation of Petri nets, which own the perfect ability of modeling, are used to establish a fault diagnosis model. Fault diagnosis of a control system received considerable attention in the last decades. The formalism of representing neural networks based on Petri nets has been presented. Neural Petri Net (NPN) reasoning model is investigated and developed for the fault diagnosis process of electric control system. The proposed NPN has the characteristics of easy establishment and high efficiency, and fault status within the system can be described clearly when compared with traditional testing methods. The proposed system is tested and the simulation results are given. The implementation explains the advantages of using NPN method and can be used as a guide for different online applications.

Keywords: petri net, neural petri net, electric control system, fault diagnosis

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Authors: L. Ramirez, E. Guillén, J. Sánchez

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Some of the main goals about telecare such as monitoring, treatment, telediagnostic are deployed with the integration of applications with specific appliances. In order to achieve a coherent model to integrate software, hardware, and healthcare systems, different telehealth models with Internet of Things (IoT), cloud computing, artificial intelligence, etc. have been implemented, and their advantages are still under analysis. In this paper, we propose an integrated model based on IoT architecture and cloud computing telehealth center. Analytics module is presented as a solution to control an ideal diagnostic about some diseases. Specific features are then compared with the recently deployed conventional models in telemedicine. The main advantage of this model is the availability of controlling the security and privacy about patient information and the optimization on processing and acquiring clinical parameters according to technical characteristics.

Keywords: analytics, telemedicine, internet of things, cloud computing

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Authors: Nuwan Kuruwitaarachchi, M. K. M. Peiris, C. N. Madawala, K. M. A. R. Perera, V. U. N Perera

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Dealing with businesses with the foreign market always took a special place in a country’s economy. Political and social factors came into play making currency rate changes fluctuate rapidly. Currency rate prediction has become an important factor for larger international businesses since large amounts of money exchanged between countries. This research focuses on comparing the accuracy of mainly three models; Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Networks(ANN) and Support Vector Machines(SVM). series of data import, export, USD currency exchange rate respect to LKR has been selected for training using above mentioned algorithms. After training the data set and comparing each algorithm, it was able to see that prediction in SVM performed better than other models. It was improved more by combining SVM and SVR models together.

Keywords: ARIMA, ANN, FFNN, RMSE, SVM, SVR

Procedia PDF Downloads 212