Search results for: credit network

Authors: Ana L. Martins, Rita S. Saraiva, João C. Ferreira

Abstract:

Much effort has been placed on the assessment of perceived service quality. Several models can be found in literature, but these are mainly focused on business-to-consumer (B2C) relationships. Literature on how to assess perceived quality in business-to-business (B2B) contexts is scarce both conceptually and in terms of its application. This research aims at filling this gap in literature by applying INDSERV to a case study situation. Under this scope, this research aims at analyzing the adequacy of the proposed assessment tool to other context besides the one where it was developed and by doing so analyzing the perceive quality of the advertisement service provided by a specific television network to its B2B customers. The INDSERV scale was adopted and applied to a sample of 33 clients, via questionnaires adapted to interviews. Data was collected in person or phone. Both quantitative and qualitative data collection was performed. Qualitative data analysis followed content analysis protocol. Quantitative analysis used hypotheses testing. Findings allowed to conclude that the perceived quality of the television service provided by television network is very positive, being the Soft Process Quality the parameter that reveals the highest perceived quality of the service as opposed to Potential Quality. To this end, some comments and suggestions were made by the clients regarding each one of these service quality parameters. Based on the hypotheses testing, it was noticed that only advertisement clients that maintain a connection to the television network from 5 to 10 years do show a significant different perception of the TV advertisement service provided by the company in what the Hard Process Quality parameter is concerned. Through the collected data content analysis, it was possible to obtain the percentage of clients which share the same opinions and suggestions for improvement. Finally, based on one of the four service quality parameter in a B2B context, managerial suggestions were developed aiming at improving the television network advertisement perceived quality service.

Keywords: B2B, case study, INDSERV, perceived service quality

Procedia PDF Downloads 206

Authors: Navdeep Kaur

Abstract:

Corporate Social Responsibility is the process through which the organizations execute their philanthropic visions for social welfare. This paper considers the data of one Public Sector Bank–State Bank of India (SBI) and one Private Sector Bank-Industrial Credit and Investment Corporation of India (ICICI) from the year 2008 to 2016. The study is based on descriptive research design, and secondary data collected from the annual report of respective bank from website and different literature are reviewed. Least Square Method is used for estimating CSR spending for the financial year 2017-18. The analysis shows that these banks are making efforts for the implementation of CSR, but are not spending their 2% share of profits on CSR. There is a need for better CSR activities by the banks, which is possible by concentrating more on the prevailing social issues. The finding reveals that the percentage of profit after tax spends for CSR by SBI is more compare to ICICI. The estimated Spending for CSR for 2017-18 is also more in SBI as compared to ICICI.

Keywords: banking sector, corporate social responsibility in India, financial institution, public sector banks, SBI, ICICI

Procedia PDF Downloads 192

Authors: Angeliki Peponi, Paulo Morgado, Jorge Trindade

Abstract:

Artificial Neural Networks (ANNs) and Geographic Information Systems (GIS) are applied as a robust tool for modeling and forecasting the erosion changes in Costa Caparica, Lisbon, Portugal, for 2021. ANNs present noteworthy advantages compared with other methods used for prediction and decision making in urban coastal areas. Multilayer perceptron type of ANNs was used. Sensitivity analysis was conducted on natural and social forces and dynamic relations in the dune-beach system of the study area. Variations in network’s parameters were performed in order to select the optimum topology of the network. The developed methodology appears fitted to reality; however further steps would make it better suited.

Keywords: artificial neural networks, backpropagation, coastal urban zones, erosion prediction

Procedia PDF Downloads 391

Authors: H. Aoudia, Y. Touati, E. H. Teguig, A. Ali Cherif

Abstract:

Conceiving and developing routing protocols for wireless sensor networks requires considerations on constraints such as network lifetime and energy consumption. In this paper, we propose a hybrid hierarchical routing protocol named HHRP combining both clustering mechanism and multipath optimization taking into account residual energy and RSSI measures. HHRP consists of classifying dynamically nodes into clusters where coordinators nodes with extra privileges are able to manipulate messages, aggregate data and ensure transmission between nodes according to TDMA and CDMA schedules. The reconfiguration of the network is carried out dynamically based on a threshold value which is associated with the number of nodes belonging to the smallest cluster. To show the effectiveness of the proposed approach HHRP, a comparative study with LEACH protocol is illustrated in simulations.

Keywords: routing protocol, optimization, clustering, WSN

Procedia PDF Downloads 468

Authors: Akshit Singhal, Xuan Wang, Zhijun Wang, Hao Che, Hong Jiang

Abstract:

Multiple Path Transmission Control Protocols (MPTCPs) allow flows to explore path diversity to improve the throughput, reliability and network resource utilization. However, the existing solutions may discourage users to adopt the solutions in the face of multipath scenario where different paths are charged based on different pricing structures, e.g., WiFi vs cellular connections, widely available for mobile phones. In this paper, we propose a Hybrid MPTCP (H-MPTCP) with a built-in mechanism to incentivize users to use multiple paths with different pricing structures. In the meantime, H-MPTCP preserves the nice properties enjoyed by the state-of-the-art MPTCP solutions. Extensive real Linux implementation results verify that H-MPTCP can indeed achieve the design objectives.

Keywords: network, TCP, WiFi, cellular, congestion control

Procedia PDF Downloads 712

Authors: Shunzuo Wu, Xudong Luo, Yuanxiu Liao

Abstract:

Recently, the rapid development of deep learning makes artificial intelligence (AI) penetrate into many fields, replacing manual work there. In particular, AI systems also become a research focus in the field of automatic office. To meet real needs in automatic officiating, in this paper we develop an automatic form filling system. Specifically, it uses two classical neural network models and several word embedding models to classify various relevant information elicited from the Internet. When training the neural network models, we use less noisy and balanced data for training. We conduct a series of experiments to test my systems and the results show that our system can achieve better classification results.

Keywords: artificial intelligence and office, NLP, deep learning, text classification

Procedia PDF Downloads 198

Authors: Jing Zhao, Dayong Liu, Shihao Wang, Xinghua Zhu, Delong Li

Abstract:

Uhumanned Underwater Vehicles generally operate in the deep sea, which has its own unique working conditions. Lithium-ion power batteries should have the necessary stability and endurance for use as an underwater vehicle’s power source. Therefore, it is essential to accurately forecast how long lithium-ion batteries will last in order to maintain the system’s reliability and safety. In order to model and forecast lithium battery Remaining Useful Life (RUL), this research suggests a model based on Complete Ensemble Empirical Mode Decomposition with Adaptive noise-Temporal Convolutional Net (CEEMDAN-TCN). In this study, two datasets, NASA and CALCE, which have a specific gap in capacity data fluctuation, are used to verify the model and examine the experimental results in order to demonstrate the generalizability of the concept. The experiments demonstrate the network structure’s strong universality and ability to achieve good fitting outcomes on the test set for various battery dataset types. The evaluation metrics reveal that the CEEMDAN-TCN prediction performance of TCN is 25% to 35% better than that of a single neural network, proving that feature expansion and modal decomposition can both enhance the model’s generalizability and be extremely useful in industrial settings.

Keywords: lithium-ion battery, remaining useful life, complete EEMD with adaptive noise, temporal convolutional net

Procedia PDF Downloads 150

Authors: Meriem Mossaddek, El Mehdi Laadissi, El Mehdi Loualid, Chouaib Ennawaoui, Sohaib Bouzaid, Abdelowahed Hajjaji

Abstract:

An electrochemical system is a subset of mechatronic systems that includes a wide variety of batteries and nickel-cadmium, lead-acid batteries, and lithium-ion. Those structures have several non-linear behaviors and uncertainties in their running range. This paper studies an effective technique for modeling Lithium-Ion (Li-Ion) batteries using a Nonlinear Auto-Regressive model with exogenous input (NARX). The Artificial Neural Network (ANN) is trained to employ the data collected from the battery testing process. The proposed model is implemented on a Li-Ion battery cell. Simulation of this model in MATLAB shows good accuracy of the proposed model.

Keywords: lithium-ion battery, neural network, energy storage, battery model, nonlinear models

Procedia PDF Downloads 112

Authors: Wei Li, Tuo Yang

Abstract:

In order to satisfy the real-time and accurate requirements of cigarette smoke detection in complex scenes, a cigarette smoke detection technology based on the combination of deep learning and color features was proposed. Firstly, based on the color features of cigarette smoke, the suspicious cigarette smoke area in the image is extracted. Secondly, combined with the efficiency of cigarette smoke detection and the problem of network overfitting, a network model for cigarette smoke detection was designed according to YOLOV3 algorithm to reduce the false detection rate. The experimental results show that the method is feasible and effective, and the accuracy of cigarette smoke detection is up to 99.13%, which satisfies the requirements of real-time cigarette smoke detection in complex scenes.

Keywords: deep learning, computer vision, cigarette smoke detection, YOLOV3, color feature extraction

Procedia PDF Downloads 84

Authors: Rafia Afroz

Abstract:

This article examines the perceptions, consciousness and attitudes of farmers on climate change in Kedah Malaysia. To reach the objectives of this study, a survey was conducted on 350 farmers and the collected data were used to perform multiple regression analysis. The results show that factors such as age, farm income, farm experience, limited agricultural prevalence, inadequacies in credit availability and farm size are factors influencing adjustment practices. Moreover, farmers have a positive attitude through adaptation strategies that are appropriate for climate change issues. However, the study found that various adaptation barriers prevented farmers from implementing climate change adjustment policies. At present, Malaysia does not seem to have a specific policy aimed at counteracting the impacts and productivity of climate change on individual sectors. For this reason, the findings of this study will help policymakers to comply with an appropriate policy framework that takes into account the best fit alignment strategies that can come across all relevant obstacles.

Keywords: climate change, adaptation, awareness, perception, attitude

Procedia PDF Downloads 246

Authors: Jie Zhang, Chaomin Zhang

Abstract:

Based on the network theory, this paper selects three Emerging-Market Multinationals Enterprises (EMNEs) as the research object and takes the typical overseas market opportunities constructed by them as the analysis unit to research the internal mechanism of overseas market opportunity construction of the EMNEs. The results show that: (1) EMNEs overseas market opportunity construction is a complex process, through the continuous interaction between enterprises and entities in the internal and external networks to achieve opportunity prototype, opportunity creation, and opportunity optimization in overseas markets. (2) Governments, foreign institutions and industry associations in the institutional network and competitors, partners, and customers in the commercial networks are the important entities in the construction of overseas market opportunities. Through the interaction of entity perception, relationship construction, and utilization, enterprises can obtain the necessary information, resources, and political asylum in the process of opportunity construction. (3) Organizations, project teams, and organizational sub-units within the enterprise are important internal entities for the construction of overseas market opportunities. Through the connection between different entities, they can achieve the circulation of resources within the organization and promote the opportunity construction of overseas markets. The research conclusions expand the relevant research on international opportunities and have inspiring and guiding significance for the expansion of EMNEs overseas markets.

Keywords: international (overseas) opportunities, opportunity construction, network entities, interaction, resource circulation

Procedia PDF Downloads 15

Authors: Muhammad Akbar Hussain, Muhammad Mehdi Hussain, Waqar Haider

Abstract:

There are tens of thousands of undetected and uncatalogued pieces of space debris in the Low Earth Orbit (LEO). They are not only difficult to be detected and tracked, their sheer number puts active satellites and humans in orbit around Earth into danger. With the entry of more governments and private companies into harnessing the Earth’s orbit for communication, research and military purposes, there is an ever-increasing need for not only the detection and cataloguing of these pieces of space debris, it is time to take measures to take them out and clean up the space around Earth. Current optical and radar-based Space Situational Awareness initiatives are useful mostly in detecting and cataloguing larger pieces of debris mainly for avoidance measures. Smaller than 10 cm pieces are in a relatively dark zone, yet these are deadly and capable of destroying satellites and human missions. A network of mobile observatories, connected to each other in real time and working in unison as a single instrument, may be able to detect small pieces of debris and achieve effective triangulation to help create a comprehensive database of their trajectories and parameters to the highest level of precision. This data may enable ground-based laser systems to help deorbit individual debris. Such a network of observatories can join current efforts in detection and removal of space debris in Earth’s orbit.

Keywords: space debris, low earth orbit, mobile observatories, triangulation, seamless operability

Procedia PDF Downloads 164

Authors: Doyin Afolabi, Phillip Adewole, Oladipupo Sennaike

Abstract:

Most well-known classifiers, including the decision tree algorithm, can make predictions on balanced datasets efficiently. However, the decision tree algorithm tends to be biased towards imbalanced datasets because of the skewness of the distribution of such datasets. To overcome this problem, this study proposes a weighted decision tree algorithm that aims to remove the bias toward the majority class and prevents the reduction of majority observations in imbalance datasets classification. The proposed weighted decision tree algorithm was tested on three imbalanced datasets- cancer dataset, german credit dataset, and banknote dataset. The specificity, sensitivity, and accuracy metrics were used to evaluate the performance of the proposed decision tree algorithm on the datasets. The evaluation results show that for some of the weights of our proposed decision tree, the specificity, sensitivity, and accuracy metrics gave better results compared to that of the ID3 decision tree and decision tree induced with minority entropy for all three datasets.

Keywords: data mining, decision tree, classification, imbalance dataset

Procedia PDF Downloads 133

Authors: Colin Smith, Linsey S Passarella

Abstract:

Due to the proliferation and decentralized nature of academic publication, no widely accepted scheme exists for organizing papers by their scientific field of study (FoS) to the author’s best knowledge. While many academic journals require author provided keywords for papers, these keywords range wildly in scope and are not consistent across papers, journals, or field domains, necessitating alternative approaches to paper classification. Past attempts to perform field-of-study (FoS) classification on scientific texts have largely used a-hierarchical FoS schemas or ignored the schema’s inherently hierarchical structure, e.g. by compressing the structure into a single layer for multi-label classification. In this paper, we introduce an application of a Layered Neural Network (LNN) to the problem of performing supervised hierarchical classification of scientific fields of study (FoS) on research papers. In this approach, paper embeddings from a pretrained language model are fed into a top-down LNN. Beginning with a single neural network (NN) for the highest layer of the class hierarchy, each node uses a separate local NN to classify the subsequent subfield child node(s) for an input embedding of concatenated paper titles and abstracts. We compare our LNN-FOS method to other recent machine learning methods using the Microsoft Academic Graph (MAG) FoS hierarchy and find that the LNN-FOS offers increased classification accuracy at each FoS hierarchical level.

Keywords: hierarchical classification, layer neural network, scientific field of study, scientific taxonomy

Procedia PDF Downloads 132

Authors: Mayada Mazher, Ahmed Moustafa, Ahmed Abdellatif

Abstract:

Background: Autophagy is a eukaryotic, highly conserved catabolic process implicated in many pathophysiologies such as wound healing. Autophagy-associated genes serve as a scaffolding platform for signal transduction of macrophage polarization during the inflammatory phase of wound healing and tissue repair process. In the current study, we report a model for the interplay between autophagy-associated genes and macrophages polarization associated genes. Methods: In silico analysis was performed on 249 autophagy-related genes retrieved from the public autophagy database and gene expression data retrieved from Gene Expression Omnibus (GEO); GSE81922 and GSE69607 microarray data macrophages polarization 199 DEGS. An integrated protein-protein interaction network was constructed for autophagy and macrophage gene sets. The gene sets were then used for GO terms pathway enrichment analysis. Common transcription factors for autophagy and macrophages' polarization were identified. Finally, microRNAs enriched in both autophagy and macrophages were predicated. Results: In silico prediction of common transcription factors in DEGs macrophages and autophagy gene sets revealed a new role for the transcription factors, HOMEZ, GABPA, ELK1 and REL, that commonly regulate macrophages associated genes: IL6,IL1M, IL1B, NOS1, SOC3 and autophagy-related genes: Atg12, Rictor, Rb1cc1, Gaparab1, Atg16l1. Conclusions: Autophagy and macrophages' polarization are interdependent cellular processes, and both autophagy-related proteins and macrophages' polarization related proteins coordinate in tissue remodelling via transcription factors and microRNAs regulatory network. The current work highlights a potential new role for transcription factors HOMEZ, GABPA, ELK1 and REL in wound healing.

Keywords: autophagy related proteins, integrated network analysis, macrophages polarization M1 and M2, tissue remodelling

Procedia PDF Downloads 150

Authors: K. Hołda, D. Śliwa, K. Daniec, A. Nawrat

Abstract:

This article was created as part of the developed master's thesis. It attempts to present a newly developed device, which will support the work of specialists dealing with broadly understood cybersecurity terms. The device is contrived to automate security tests. In addition, it simulates potential cyberattacks in the most realistic way possible, without causing permanent damage to the network, in order to maximize the quality of the subsequent corrections to the tested network systems. The proposed solution is a fully operational prototype created from commonly available electronic components and a single board computer. The focus of the following article is not only put on the hardware part of the device but also on the theoretical and applicatory way in which implemented cybersecurity tests operate and examples of their results.

Keywords: Raspberry Pi, ethernet, automated cybersecurity tests, ARP, DNS, backdoor, TCP, password sniffing

Procedia PDF Downloads 123

Authors: Aulia Dwi Nastiti

Abstract:

A number of studies and reports on terrorism have continuously addressed the role of internet and online activism to support terrorist and extremist groups. In particular, they stress on social media’s usage that generally serves for the terrorist’s propaganda as well as justification of their causes. While those analyses are important to understand how social media is a vital tool for global network terrorism, they are inadequate to explain the online communication patterns that enable terrorism acts. Beyond apparent online narratives, there is a deep cyber sphere where the very vein of terrorism movement lies. That is a hidden space in the internet called ‘darknet’. Recent investigations, particularly in Middle Eastern context, have shed some lights that this invisible space in the internet is fundamental to maintain the terrorist activities. Despite that, limited number of research examines darknet within the issue of terrorist movements in Indonesian context—where the country is considered quite a hotbed for extremist groups. Therefore, this paper attempts to provide an insight of darknet operation in Indonesian cases. By exploring how the darknet is used by the Indonesian-based extremist groups, this paper maps out communication patterns of terrorist groups on the internet which appear as an intermingled network. It shows the usage of internet is differentiated in different level of anonymity for distinctive purposes. This paper further argues that the emerging terrorist communication network calls for a more comprehensive counterterrorism strategy on the Internet.

Keywords: communication pattern, counterterrorism, darknet, extremist groups, terrorism

Procedia PDF Downloads 292

Authors: Deborah Weidner, Melissa Morgera

Abstract:

The model of care delivery in the Behavioral Health Network (BHN) is integrated across all five regions of Hartford Healthcare and thus spans the entirety of the state of Connecticut, with care provided in seven inpatient settings and over 30 ambulatory outpatient locations. While safety has been a core priority of the BHN in alignment with High Reliability practices, safety initiatives have historically been facilitated locally in each region or within each entity, with interventions implemented locally as opposed to throughout the network. To address this, the BHN introduced a network wide Safety Huddle during 2022. Launched in January, the BHN Safety Huddle brought together internal stakeholders, including medical and administrative leaders, along with executive institute leadership, quality, and risk management. By bringing leaders together and introducing a network-wide safety huddle into the way we work, the benefit has been an increase in awareness of safety events occurring in behavioral health areas as well as increased systemization of countermeasures to prevent future events. One significant discussion topic presented in huddles has pertained to environmental design and patient access to potentially dangerous items, addressing some of the most relevant factors resulting in harm to patients in inpatient and emergency settings for behavioral health patients. The safety huddle has improved visibility of potential environmental safety risks through the generation of over 15 safety alerts cascaded throughout the BHN and also spurred a rapid improvement project focused on standardization of patient belonging searches to reduce patient access to potentially dangerous items on inpatient units. Safety events pertaining to potentially dangerous items decreased by 31% as a result of standardized interventions implemented across the network and as a result of increased awareness. A second positive outcome originating from the BHN Safety Huddle was implementation of a recommendation to increase the emergency Narcan®(naloxone) supply on hand in ambulatory settings of the BHN after incidents involving accidental overdose resulted in higher doses of naloxone administration. By increasing the emergency supply of naloxone on hand in all ambulatory and residential settings, colleagues are better prepared to respond in an emergency situation should a patient experience an overdose while on site. Lastly, discussions in safety huddle spurred a new initiative within the BHN to improve responsiveness to assaultive incidents through a consultation service. This consult service, aligned with one of the network’s improvement priorities to reduce harm events related to assaultive incidents, was borne out of discussion in huddle in which it was identified that additional interventions may be needed in providing clinical care to patients who are experiencing multiple and/ or frequent safety events.

Keywords: quality, safety, behavioral health, risk management

Procedia PDF Downloads 82

Authors: H. El-Kamchouchi, Heba Gaber, Fatma Ahmed, Dalia H. El-Kamchouchi

Abstract:

A digital signature is an electronic signature form used by an original signer to sign a specific document. When the original signer is not in his office or when he/she travels outside, he/she delegates his signing capability to a proxy signer and then the proxy signer generates a signing message on behalf of the original signer. The two parties must be able to authenticate one another and agree on a secret encryption key, in order to communicate securely over an unreliable public network. Authenticated key agreement protocols have an important role in building a secure communications network between the two parties. In this paper, we present a secure proxy signature scheme over an efficient and secure authenticated key agreement protocol based on factoring and discrete logarithm problem.

Keywords: discrete logarithm, factoring, proxy signature, key agreement

Procedia PDF Downloads 307

Authors: Salim Ouchtati, Jean Sequeira, Mouldi Bedda

Abstract:

In this work we present an off line system for the recognition of the Arabic handwritten words of the Algerian departments. The study is based mainly on the evaluation of neural network performances, trained with the gradient back propagation algorithm. The used parameters to form the input vector of the neural network are extracted on the binary images of the handwritten word by several methods: the parameters of distribution, the moments centered of the different projections and the Barr features. It should be noted that these methods are applied on segments gotten after the division of the binary image of the word in six segments. The classification is achieved by a multi layers perceptron. Detailed experiments are carried and satisfactory recognition results are reported.

Keywords: handwritten word recognition, neural networks, image processing, pattern recognition, features extraction

Procedia PDF Downloads 513

Authors: S. K. Gupta, Richa Bansal

Abstract:

In a deregulated power system structure, power producers, and customers share a common transmission network for wheeling power from the point of generation to the point of consumption. All parties in this open access environment may try to purchase the energy from the cheaper source for greater profit margins, which may lead to overloading and congestion of certain corridors of the transmission network. This may result in violation of line flow, voltage and stability limits and thereby undermine the system security. Utilities therefore need to determine adequately their Available Transfer Capability (ATC) to ensure that system reliability is maintained while serving a wide range of bilateral and multilateral transactions. This paper presents power transfer distribution factor based on AC load flow for the determination and enhancement of ATC. The study has been carried out for IEEE 24 bus Reliability Test System.

Keywords: available transfer capability, FACTS devices, power transfer distribution factors, electric

Procedia PDF Downloads 495

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

Procedia PDF Downloads 122

Authors: Muhamad Aris Burhanudin, Angga Firmansyas, Bagus Jaya Santosa

Abstract:

Earthquakes are natural hazard that can trigger the most dangerous hazard, tsunami. 26 December 2004, a giant earthquake occurred in north-west Andalas Island. It made giant tsunami which crushed Sumatra, Bangladesh, India, Sri Lanka, Malaysia and Singapore. More than twenty thousand people dead. The occurrence of earthquake and tsunami can not be avoided. But this hazard can be mitigated by earthquake forecasting. Early preparation is the key factor to reduce its damages and consequences. We aim to investigate quantitatively on pattern of earthquake. Then, we can know the trend. We study about earthquake which has happened in Andalas island, Indonesia one last decade. Andalas is island which has high seismicity, more than a thousand event occur in a year. It is because Andalas island is in tectonic subduction zone of Hindia sea plate and Eurasia plate. A tsunami forecasting is needed to mitigation action. Thus, a Tsunami Forecasting Method is presented in this work. Neutral Network has used widely in many research to estimate earthquake and it is convinced that by using Backpropagation Method, earthquake can be predicted. At first, ANN is trained to predict Tsunami 26 December 2004 by using earthquake data before it. Then after we get trained ANN, we apply to predict the next earthquake. Not all earthquake will trigger Tsunami, there are some characteristics of earthquake that can cause Tsunami. Wrong decision can cause other problem in the society. Then, we need a method to reduce possibility of wrong decision. Fuzzy TOPSIS is a statistical method that is widely used to be decision seconder referring to given parameters. Fuzzy TOPSIS method can make the best decision whether it cause Tsunami or not. This work combines earthquake prediction using neural network method and using Fuzzy TOPSIS to determine the decision that the earthquake triggers Tsunami wave or not. Neural Network model is capable to capture non-linear relationship and Fuzzy TOPSIS is capable to determine the best decision better than other statistical method in tsunami prediction.

Keywords: earthquake, fuzzy TOPSIS, neural network, tsunami

Procedia PDF Downloads 489

Authors: Zhihao Zheng, Zhilin Wang, Linxin Liu

Abstract:

In the research of automated knowledge question-answering systems, accuracy and efficiency are critical challenges. This paper proposes a knowledge graph-enhanced Retrieval-Augmented Generation (RAG) method, combined with a Graph Neural Network (GNN) structure, to automatically determine the correctness of knowledge competition questions. First, a domain-specific knowledge graph was constructed from a large corpus of academic journal literature, with key entities and relationships extracted using Natural Language Processing (NLP) techniques. Then, the RAG method's retrieval module was expanded to simultaneously query both text databases and the knowledge graph, leveraging the GNN to further extract structured information from the knowledge graph. During answer generation, contextual information provided by the knowledge graph and GNN is incorporated to improve the accuracy and consistency of the answers. Experimental results demonstrate that the knowledge graph and GNN-enhanced RAG method perform excellently in determining the correctness of questions, achieving an accuracy rate of 95%. Particularly in cases involving ambiguity or requiring contextual information, the structured knowledge provided by the knowledge graph and GNN significantly enhances the RAG method's performance. This approach not only demonstrates significant advantages in improving the accuracy and efficiency of automated knowledge question-answering systems but also offers new directions and ideas for future research and practical applications.

Keywords: knowledge graph, graph neural network, retrieval-augmented generation, NLP

Procedia PDF Downloads 38

Authors: Farnaz Fotovvatikhah, Javad Akbari

Abstract:

Test time of a test architecture is an important factor which depends on the architecture's delay and test patterns. Here a new architecture to store the test results based on network on chip is presented. In addition, simple analytical model is proposed to calculate link test time for built in self-tester (BIST) and external tester (Ext) in multiprocessor systems. The results extracted from the model are verified using FPGA implementation and experimental measurements. Systems consisting 16, 25, and 36 processors are implemented and simulated and test time is calculated. In addition, BIST and Ext are compared in terms of test time at different conditions such as at different number of test patterns and nodes. Using the model the maximum frequency of testing could be calculated and the test structure could be optimized for high speed testing.

Keywords: test, nano on-chip network, JTAG, modelling

Procedia PDF Downloads 486

Authors: Adebowale Olukayode Efunnowo

Abstract:

This study was carried out to assess the awareness and utilization of Social Network Tools (SNTs) among agricultural science students in Colleges of Education in Ogun State, Nigeria. Simple random sampling techniques were used to select 280 respondents from the study area. Descriptive statistics was used to describe the objectives while Pearson Product Moment Correlation was used to test the hypothesis. The result showed that the majority (71.8%) of the respondents were single, with a mean age of 20 years. Almost all (95.7%) the respondents were aware of Facebook and 2go as a Social Network Tools (SNTs) while 85.0% of the respondents were not aware of Blackplanet, LinkedIn, MyHeritage and Bebo. Many (41.1%) of the respondents had views that using SNTs can enhance extensive literature survey, increase internet browsing potential, promote teaching proficiency, and update on outcomes of researches. However, 51.4% of the respondents perceived that SNTs usage as what is meant for the lecturers/adults only while 16.1% considered it as mainly used by internet fraudsters. Findings revealed that about 50.0% of the respondents browsed Facebook and 2go daily while more than 80% of the respondents used Blackplanet, MyHeritage, Skyrock, Bebo, LinkedIn and My YearBook as the need arise. Major constraints to the awareness and utilization of SNTs were high cost and poor quality of ICTs facilities (77.1%), epileptic power supply (75.0%), inadequate telecommunication infrastructure (71.1%), low technical know-how (62.9%) and inadequate computer knowledge (61.1%). The result of PPMC analysis showed that there was an inverse relationship between constraints and utilization of SNTs at p < 0.05. It can be concluded that constraints affect efficient and effective utilization of SNTs in the study area. It is hereby recommended that management of colleges of education and agricultural institutes should provide good internet connectivity, computer facilities, and alternative power supply in order to increase the awareness and utilization of SNTs among students.

Keywords: awareness, utilization, social network tools, constraints, students

Procedia PDF Downloads 350

Authors: Sofia Stoica

Abstract:

In this paper, we use historical data to predict the stock price of a tech company. To this end, we use a dataset consisting of the stock prices in the past five years of ten major tech companies – Adobe, Amazon, Apple, Facebook, Google, Microsoft, Netflix, Oracle, Salesforce, and Tesla. We experimented with a variety of models– a linear regressor model, K nearest Neighbors (KNN), a sequential neural network – and algorithms - Multiplicative Weight Update, and AdaBoost. We found that the sequential neural network performed the best, with a testing error of 0.18%. Interestingly, the linear model performed the second best with a testing error of 0.73%. These results show that using historical data is enough to obtain high accuracies, and a simple algorithm like linear regression has a performance similar to more sophisticated models while taking less time and resources to implement.

Keywords: finance, machine learning, opening price, stock market

Procedia PDF Downloads 187

Authors: David P. Tantua, Abdulkader Helwan

Abstract:

Biometrics has been recently used for the human identification systems using the biological traits such as the fingerprints and iris scanning. Identification systems based biometrics show great efficiency and accuracy in such human identification applications. However, these types of systems are so far based on some image processing techniques only, which may decrease the efficiency of such applications. Thus, this paper aims to develop a human palmprint identification system using multi-layer perceptron neural network which has the capability to learn using a backpropagation learning algorithms. The developed system uses images obtained from a public database available on the internet (CASIA). The processing system is as follows: image filtering using median filter, image adjustment, image skeletonizing, edge detection using canny operator to extract features, clear unwanted components of the image. The second phase is to feed those processed images into a neural network classifier which will adaptively learn and create a class for each different image. 100 different images are used for training the system. Since this is an identification system, it should be tested with the same images. Therefore, the same 100 images are used for testing it, and any image out of the training set should be unrecognized. The experimental results shows that this developed system has a great accuracy 100% and it can be implemented in real life applications.

Keywords: biometrics, biological traits, multi-layer perceptron neural network, image skeletonizing, edge detection using canny operator

Procedia PDF Downloads 371

Authors: Aleksei Samarin, Artyom Nazarenko, Valentin Malykh

Abstract:

Image enhancement is becoming more and more popular, especially on mobile devices. Nowadays, it is a common approach to enhance an image using a convolutional neural network (CNN). Such a network should be of significant size; otherwise, a possibility for the artifacts to occur is overgrowing. The existing large CNNs are computationally expensive, which could be crucial for mobile devices. Another important flaw of such models is they are poorly interpretable. There is another approach to image enhancement, namely, the usage of predefined filters in combination with the prediction of their applicability. We present an approach following this paradigm, which outperforms both existing CNN-based and filter-based approaches in the image enhancement task. It is easily adaptable for mobile devices since it has only 47 thousand parameters. It shows the best SSIM 0.919 on RANDOM250 (MIT Adobe FiveK) among small models and is thrice faster than previous models.

Keywords: universal filter, image enhancement, neural networks, computer vision

Procedia PDF Downloads 101

Authors: Katharina Eberhardt, Florian Klaus Kaiser, Frank Schultmann

Abstract:

Last-mile distribution is one of the most critical parts of a disaster relief operation. Various uncertainties, such as infrastructure conditions, resource availability, and fluctuating beneficiary demand, render last-mile distribution challenging in disaster relief operations. The need to balance critical performance criteria like response time, meeting demand and cost-effectiveness further complicates the task. The occurrence of disasters cannot be controlled, and the magnitude is often challenging to assess. In summary, these uncertainties create a need for additional flexibility, agility, and preparedness in logistics operations. As a result, strategic planning and efficient network design are critical for an effective and efficient response. Furthermore, the increasing frequency of disasters and the rising cost of logistical operations amplify the need to provide robust and resilient solutions in this area. Therefore, we formulate a scenario-based bi-objective optimization model that integrates pre-positioning, allocation, and distribution of relief supplies extending the general form of a covering location problem. The proposed model aims to minimize underlying logistics costs while maximizing demand coverage. Using a set of disruption scenarios, the model allows decision-makers to identify optimal network solutions to address the risk of disruptions. We provide an empirical case study of the public authorities’ emergency food storage strategy in Germany to illustrate the potential applicability of the model and provide implications for decision-makers in a real-world setting. Also, we conduct a sensitivity analysis focusing on the impact of varying stockpile capacities, single-site outages, and limited transportation capacities on the objective value. The results show that the stockpiling strategy needs to be consistent with the optimal number of depots and inventory based on minimizing costs and maximizing demand satisfaction. The strategy has the potential for optimization, as network coverage is insufficient and relies on very high transportation and personnel capacity levels. As such, the model provides decision support for public authorities to determine an efficient stockpiling strategy and distribution network and provides recommendations for increased resilience. However, certain factors have yet to be considered in this study and should be addressed in future works, such as additional network constraints and heuristic algorithms.

Keywords: humanitarian logistics, bi-objective optimization, pre-positioning, last mile distribution, decision support, disaster relief networks

Procedia PDF Downloads 79