Search results for: student centered learning

Authors: Ali Nouri

Abstract:

Neuroeducation is one of the most exciting research fields which is continually evolving. However, there is a need to develop its theoretical bases in connection to practice. The present paper is a starting attempt in this regard to provide a space from which to think about neuroeducational theory and invoke more investigation in this area. Accordingly, a comprehensive theory of neuroeducation could be defined as grouping or clustering of concepts and propositions that describe and explain the nature of human learning to provide valid interpretations and implications useful for educational practice in relation to philosophical aspects or values. Whereas it should be originated from the philosophical foundations of the field and explain its normative significance, it needs to be testable in terms of rigorous evidence to fundamentally advance contemporary educational policy and practice. There is thus pragmatically a need to include a course on neuroeducational theory into the curriculum of the field. In addition, there is a need to articulate and disseminate considerable discussion over the subject within professional journals and academic societies.

Keywords: Neuroeducation studies, neuroeducational theory, theory building, neuroeducation research.

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Authors: Shankar Kambhampaty, Harish Rohan Kambhampaty

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Data in IT systems in enterprises have been growing at phenomenal pace. This has provided opportunities to run analytics to gather intelligence on key business parameters that enable them to provide better products and services to customers. While there are several Artificial Intelligence/Machine Learning (AI/ML) and Business Intelligence (BI) tools and technologies available in marketplace to run analytics, there is a need for an integrated view when developing intelligent solutions in enterprises. This paper progressively elaborates a reference model for enterprise solutions, builds an integrated view of data, information and intelligence components and presents a reference architecture for intelligent enterprise solutions. Finally, it applies the reference architecture to an insurance organization. The reference architecture is the outcome of experience and insights gathered from developing intelligent solutions for several organizations.

Keywords: Architecture, model, intelligence, artificial intelligence, business intelligence, AI, BI, ML, analytics, enterprise.

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Authors: M.A. Lazim, M.T.Abu Osman

Abstract:

This paper deals with the application of a fuzzy set in measuring teachers- beliefs about mathematics. The vagueness of beliefs was transformed into standard mathematical values using a fuzzy preferences model. The study employed a fuzzy approach questionnaire which consists of six attributes for measuring mathematics teachers- beliefs about mathematics. The fuzzy conjoint analysis approach based on fuzzy set theory was used to analyze the data from twenty three mathematics teachers from four secondary schools in Terengganu, Malaysia. Teachers- beliefs were recorded in form of degrees of similarity and its levels of agreement. The attribute 'Drills and practice is one of the best ways of learning mathematics' scored the highest degree of similarity at 0. 79860 with level of 'strongly agree'. The results showed that the teachers- beliefs about mathematics were varied. This is shown by different levels of agreement and degrees of similarity of the measured attributes.

Keywords: belief, membership function, degree of similarity, conjoint analysis

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Authors: Ioannis Andrianakis, Vasileios Gkatas, Nikos Eleftheriadis, Alexios Ellinidis, Ermioni Avramidou

Abstract:

The paper discusses the main aspects involved in the development of a supply chain management system using the developed PredictionSCMS software as a basis for the discussion. The discussion is focused on three topics: the first is demand forecasting, where we present the predictive algorithms implemented and discuss related concepts such as the calculation of the safety stock, the effect of out-of-stock days etc. The second topic concerns the design of a supply chain, where the core parameters involved in the process are given, together with a methodology of incorporating these parameters in a meaningful order creation strategy. Finally, the paper discusses some critical events that can happen during the operation of a supply chain management system and how the developed software notifies the end user about their occurrence.

Keywords: Demand forecasting, machine learning, risk management, supply chain design.

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Authors: K. Ben Khalifa, M.H. Bédoui, M. Dogui, F. Alexandre

Abstract:

Several studies have been carried out, using various techniques, including neural networks, to discriminate vigilance states in humans from electroencephalographic (EEG) signals, but we are still far from results satisfactorily useable results. The work presented in this paper aims at improving this status with regards to 2 aspects. Firstly, we introduce an original procedure made of the association of two neural networks, a self organizing map (SOM) and a learning vector quantization (LVQ), that allows to automatically detect artefacted states and to separate the different levels of vigilance which is a major breakthrough in the field of vigilance. Lastly and more importantly, our study has been oriented toward real-worked situation and the resulting model can be easily implemented as a wearable device. It benefits from restricted computational and memory requirements and data access is very limited in time. Furthermore, some ongoing works demonstrate that this work should shortly results in the design and conception of a non invasive electronic wearable device.

Keywords: Electroencephalogram interpretation, artificialneural networks, vigilance states, hardware implementation

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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 over the past five years of 10 major tech companies: Adobe, Amazon, Apple, Facebook, Google, Microsoft, Netflix, Oracle, Salesforce, and Tesla. We implemented and tested three models – a linear regressor model, a k-nearest neighbor model (KNN), and a sequential neural network – and two 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.

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Authors: Manpreet Singh, Parvinder Singh, Suman

Abstract:

The data is available in abundance in any business organization. It includes the records for finance, maintenance, inventory, progress reports etc. As the time progresses, the data keep on accumulating and the challenge is to extract the information from this data bank. Knowledge discovery from these large and complex databases is the key problem of this era. Data mining and machine learning techniques are needed which can scale to the size of the problems and can be customized to the application of business. For the development of accurate and required information for particular problem, business analyst needs to develop multidimensional models which give the reliable information so that they can take right decision for particular problem. If the multidimensional model does not possess the advance features, the accuracy cannot be expected. The present work involves the development of a Multidimensional data model incorporating advance features. The criterion of computation is based on the data precision and to include slowly change time dimension. The final results are displayed in graphical form.

Keywords: Multidimensional, data precision.

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Authors: Honghao Ren, Mariko Kondo

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This study investigates the perceptual features of Japanese obstruent geminates among Chinese learners of Japanese, focusing on the dialectal effect of the checked-tone, a syllable that ends in a stop consonant or a glottal stop, which is similar to Japanese obstruent geminates phonetically. In this study, 41 native speakers of Cantonese are divided into two groups based on their proficiency as well as learning period of Japanese. All stimuli employed in this study are made into C[p,k,s]+V[a,e,i] structure such as /apa/, /eke/, /isi/. Both original sounds and synthesized sounds are used in three different parts of this study. The results of the present study show that the checked-tone does have the positive effect on the perception of Japanese gemination. Furthermore, the proportion of closure duration in the entire word would be a more reliable and appropriate criterion in testing this kind of task.

Keywords: Dialectal differences, Cantonese learners of Japanese, acoustic experiment, closure duration.

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Authors: César Camisón-Zornoza, Beatriz Forés-Julián, Alba Puig-Denia

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The literature has argued that firms based in industrial districts enjoy advantages for creating internal knowledge and absorbing external knowledge as a consequence of to the knowledge flows and spillovers that exist in the district. However, empirical evidence to show how belonging to an industrial district affects the business processes of creation and absorption of knowledge is scarce and, moreover, empirical research has not taken into account the influence of variations in the flows of knowledge circulating in each cluster. This study aims to extend empirical evidence on the effect that the stock of shared competencies in industrial districts has on the business processes of creation and absorption of knowledge, through data from an initial study on 952 firms and 35 industrial districts in Spain.

Keywords: Absorptive capacity, industrial district, knowledge creation, organisational learning

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Authors: P. Cermak

Abstract:

This paper shows possibility of extraction Social, Group and Individual Mind from Multiple Agents Rule Bases. Types those Rule bases are selected as two fuzzy systems, namely Mambdani and Takagi-Sugeno fuzzy system. Their rule bases are describing (modeling) agent behavior. Modifying of agent behavior in the time varying environment will be provided by learning fuzzyneural networks and optimization of their parameters with using genetic algorithms in development system FUZNET. Finally, extraction Social, Group and Individual Mind from Multiple Agents Rule Bases are provided by Cognitive analysis and Matching criterion.

Keywords: Mind, Multi-agent system, Cognitive analysis, Fuzzy system, Neural network, Genetic algorithm, Rule base.

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Authors: Quratulain N. Rajput, Sajjad Haider, Nasir Touheed

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The internet has become an attractive avenue for global e-business, e-learning, knowledge sharing, etc. Due to continuous increase in the volume of web content, it is not practically possible for a user to extract information by browsing and integrating data from a huge amount of web sources retrieved by the existing search engines. The semantic web technology enables advancement in information extraction by providing a suite of tools to integrate data from different sources. To take full advantage of semantic web, it is necessary to annotate existing web pages into semantic web pages. This research develops a tool, named OWIE (Ontology-based Web Information Extraction), for semantic web annotation using domain specific ontologies. The tool automatically extracts information from html pages with the help of pre-defined ontologies and gives them semantic representation. Two case studies have been conducted to analyze the accuracy of OWIE.

Keywords: Ontology, Semantic Annotation, Wrapper, Information Extraction.

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Authors: B. Alajmi, L. Marouf, A. S. Chaudhry

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Knowledge management is considered as an important factor in improving health care services. KM facilitates the transfer of existing knowledge and the development of new knowledge in hospitals. This paper reviews practices adopted by doctors in Kuwait for capturing, sharing, and generating knowledge. It also discusses the perceived impact of KM practices on performance of hospitals. Based on a survey of 277 doctors, the study found that KM practices among doctors in the sampled hospitals were not very effective. Little attention was paid to the main activities that support the transfer of expertise among doctors in hospitals. However, as predicted by previous studies, good km practices were perceived by doctors to have a positive impact on performance of hospitals. It was concluded that through effective KM practices hospitals could improve the services they provide. Documentation of best practices and capturing of lessons learnt for re-use of knowledge could help transform the hospitals into learning organizations.

Keywords: Health Sector, Hospitals, Knowledge Management, Kuwait, Tools and Practices.

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Authors: Qitao Xie, Qingquan Zhang, Xiaofei Zhang, Di Tian, Ruixuan Wen, Ting Zhu, Ping Yi, Xin Li

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Inspired by the recent movement of digital currency, we are building a question answering system concerning the subject of cryptocurrency using Bidirectional Encoder Representations from Transformers (BERT). The motivation behind this work is to properly assist digital currency investors by directing them to the corresponding knowledge bases that can offer them help and increase the querying speed. BERT, one of newest language models in natural language processing, was investigated to improve the quality of generated responses. We studied different combinations of hyperparameters of the BERT model to obtain the best fit responses. Further, we created an intelligent chatbot for cryptocurrency using BERT. A chatbot using BERT shows great potential for the further advancement of a cryptocurrency market tool. We show that the BERT neural networks generalize well to other tasks by applying it successfully to cryptocurrency.

Keywords: BERT, chatbot, cryptocurrency, deep learning.

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Authors: J. C. Moctezuma, V. Breña-Medina, Jose Luis Nunez-Yanez, Joseph P. McGeehan

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In this work we make a bifurcation analysis for a single compartment representation of Traub model, one of the most important conductance-based models. The analysis focus in two principal parameters: current and leakage conductance. Study of stable and unstable solutions are explored; also Hop-bifurcation and frequency interpretation when current varies is examined. This study allows having control of neuron dynamics and neuron response when these parameters change. Analysis like this is particularly important for several applications such as: tuning parameters in learning process, neuron excitability tests, measure bursting properties of the neuron, etc. Finally, a hardware implementation results were developed to corroborate these results.

Keywords: Traub model, Pinsky-Rinzel model, Hopf bifurcation, single-compartment models, Bifurcation analysis, neuron modeling.

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Authors: C. Y. Chen

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As the global climate changes, the threat from landslides and debris flows increases. Learning how a watershed initiates landslides under abnormal rainfall conditions and predicting landslide magnitude and frequency distribution is thus important. Landslides show a power-law distribution in the frequency-area distribution. The distribution curve shows an exponent gradient 1.0 in the Sandpile model test. Will the landslide frequency-area statistics show a distribution similar to the Sandpile model under extreme rainfall conditions? The purpose of the study is to identify the extreme rainfall-induced landslide frequency-area distribution in the Laonong River Basin in southern Taiwan. Results of the analysis show that a lower gradient of landslide frequency-area distribution could be attributed to the transportation and deposition of debris flow areas that are included in the landslide area.

Keywords: Landslide, power-law distribution, GIS.

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Authors: Fernando Miguel Campos, Fernando Jesus Aguiar, Pedro Campos

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A large amount of software products offer a wide range and number of features. This is called featuritis or creeping featurism and tends to rise with each release of the product. Feautiris often adds unnecessary complexity to software, leading to longer learning curves and overall confusing the users and degrading their experience. We take a look to a new design approach tendency that has been coming up, the so-called “What You Get is What You Need” concept that argues that products should be very focused, simple and with minimalistic interfaces in order to help users conduct their tasks in distraction-free ambiences. This isn’t as simple to implement as it might sound and the developers need to cut down features. Our contribution illustrates and evaluates this design method through a novel distraction-free diagramming tool named Delineato Pro for Mac OS X in which the user is confronted with an empty canvas when launching the software and where tools only show up when really needed.

Keywords: Diagramming, HCI, usability, user interface.

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Authors: Jaruphan Supprung

Abstract:

The aims of this research were to study the relationship between the Palaces (the Kings and the Royalty of the Chakri Dynasty) and the Buddhist temples including Wat Rajadhivas Vihara in Rattanakosin Period of Thailand with the purpose of creating knowledge for Thai lifelong learning, especially for Thai youth and children, and to create positive attitude on Nationalism, Buddhism and Monarchy of Thai people. The findings disclosed that the Palaces have had relationships with 33,902 temples, close relationship with 290 royal temples, and closer relationship with the 8 royal temples regarded as the “Temple of King Rama”. Moreover, there are only 16 Royal temples including Wat Rajadhivas Vihara where the Chakri Kings present the annual royal Kathin robes to the monks by themselves. Wat Rajadhivas Vihara has always been restored under royal patronage and served as royal shrine like the 8 Temples of King Rama.

Keywords: Palaces, Buddhists Temples, Wat Rajadhivas Vihara.

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Authors: Guilherme Ribeiro, Alexandre Oliveira, Antonio Ferreira, Shyam Visweswaran, Gregory Cooper

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Patient-specific models are instance-based learning algorithms that take advantage of the particular features of the patient case at hand to predict an outcome. We introduce two patient-specific algorithms based on decision tree paradigm that use AUC as a metric to select an attribute. We apply the patient specific algorithms to predict outcomes in several datasets, including medical datasets. Compared to the patient-specific decision path (PSDP) entropy-based and CART methods, the AUC-based patient-specific decision path models performed equivalently on area under the ROC curve (AUC). Our results provide support for patient-specific methods being a promising approach for making clinical predictions.

Keywords: Approach instance-based, area Under the ROC Curve, Patient-specific Decision Path, clinical predictions.

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Authors: Namal A. Senanayake, A. Beg, Withana C. Prasad

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This paper analyzes the patterns of the Monte Carlo data for a large number of variables and minterms, in order to characterize the circuit path length behavior. We propose models that are determined by training process of shortest path length derived from a wide range of binary decision diagram (BDD) simulations. The creation of the model was done use of feed forward neural network (NN) modeling methodology. Experimental results for ISCAS benchmark circuits show an RMS error of 0.102 for the shortest path length complexity estimation predicted by the NN model (NNM). Use of such a model can help reduce the time complexity of very large scale integrated (VLSI) circuitries and related computer-aided design (CAD) tools that use BDDs.

Keywords: Monte Carlo data, Binary decision diagrams, Neural network modeling, Shortest path length estimation.

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Authors: Haider Javed Uppal, Javed Yunas Uppal

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In today's globalized world, it is necessary to develop a form of music that is able to evoke equal emotional responses among people from diverse cultural backgrounds. Indigenous cultures throughout history have developed their own music cognition, specifically in terms of the connections between music and mood. With the advancements in artificial intelligence technologies, it has become possible to analyze and categorize music features such as timbre, harmony, melody, and rhythm, and relate them to the resulting mood effects experienced by listeners. This paper presents a model that utilizes a screenshot translator to convert music from different origins into waveforms, which are then analyzed using machine learning and information retrieval techniques. By connecting these waveforms with Thayer's matrix of moods, a mood classifier has been developed using fuzzy logic algorithms to determine the emotional impact of different types of music on listeners from various cultures.

Keywords: Cognition, world music, artificial intelligence, Thayer’s matrix.

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Authors: Andres Gonzalez, Adam Boies, Jacob Swanson, David Kittelson

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The concern of the indoor air quality (IAQ) has been increasing due to its risk to human health. The smoking, sweeping, and stove and stovetop use are the activities that have a major contribution to the indoor air pollution. Outdoor air pollution also affects IAQ. The most important factors over IAQ from cooking activities are the materials, fuels, foods, and ventilation. The low-cost, mobile air quality monitoring (LCMAQM) sensors, is reachable technology to assess the IAQ. This is because of the lower cost of LCMAQM compared to conventional instruments. The IAQ was assessed, using LCMAQM, during cooking activities in a University of Minnesota graduate-housing evaluating different ventilation systems. The gases measured are carbon monoxide (CO) and carbon dioxide (CO₂). The particles measured are particle matter (PM) _2.5 micrometer (µm) and lung deposited surface area (LDSA). The measurements are being conducted during April 2019 in Como Student Community Cooperative (CSCC) that is a graduate housing at the University of Minnesota. The measurements are conducted using an electric stove for cooking. The amount and type of food and oil using for cooking are the same for each measurement. There are six measurements: two experiments measure air quality without any ventilation, two using an extractor as mechanical ventilation, and two using the extractor and windows open as mechanical and natural ventilation. 3The results of experiments show that natural ventilation is most efficient system to control particles and CO₂. The natural ventilation reduces the concentration in 79% for LDSA and 55% for PM_2.5, compared to the no ventilation. In the same way, CO₂ reduces its concentration in 35%. A well-mixed vessel model was implemented to assess particle the formation and decay rates. Removal rates by the extractor were significantly higher for LDSA, which is dominated by smaller particles, than for PM_2.5, but in both cases much lower compared to the natural ventilation. There was significant day to day variation in particle concentrations under nominally identical conditions. This may be related to the fat content of the food. Further research is needed to assess the impact of the fat in food on particle generations.

Keywords: Cooking, indoor air quality, low-cost sensor, ventilation.

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Authors: Gung-Hun Jung, Jong-Hee Park

Abstract:

In this paper, we develop a Spatio-Temporal graph as of a key component of our knowledge representation Scheme. We design an integrated representation Scheme to depict not only present and past but future in parallel with the spaces in an effective and intuitive manner. The resulting multi-dimensional comprehensive knowledge structure accommodates multi-layered virtual world developing in the time to maximize the diversity of situations in the historical context. This knowledge representation Scheme is to be used as the basis for simulation of situations composing the virtual world and for implementation of virtual agents' knowledge used to judge and evaluate the situations in the virtual world. To provide natural contexts for situated learning or simulation games, the virtual stage set by this Spatio-Temporal graph is to be populated by agents and other objects interrelated and changing which are abstracted in the ontology.

Keywords: Ontology, Virtual Reality, Spatio-Temporal graph.

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Authors: O. O. Obe, V. Balanica, E. Neagoe

Abstract:

The effects of hypertension are often lethal thus its early detection and prevention is very important for everybody. In this paper, a neural network (NN) model was developed and trained based on a dataset of hypertension causative parameters in order to forecast the likelihood of occurrence of hypertension in patients. Our research goal was to analyze the potential of the presented NN to predict, for a period of time, the risk of hypertension or the risk of developing this disease for patients that are or not currently hypertensive. The results of the analysis for a given patient can support doctors in taking pro-active measures for averting the occurrence of hypertension such as recommendations regarding the patient behavior in order to lower his hypertension risk. Moreover, the paper envisages a set of three example scenarios in order to determine the age when the patient becomes hypertensive, i.e. determine the threshold for hypertensive age, to analyze what happens if the threshold hypertensive age is set to a certain age and the weight of the patient if being varied, and, to set the ideal weight for the patient and analyze what happens with the threshold of hypertensive age.

Keywords: Neural Network, hypertension, data set, training set, supervised learning.

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Authors: Ana Hol

Abstract:

Digital systems are in the Cognitive wave of the eTransformations and are now extensively aimed at meeting the individuals’ demands, both those of customers requiring services and those of service providers. It is also apparent that successful future systems will not just simply open doors to the traditional owners/users to offer and receive services such as Uber, for example, does today, but will in the future require more customized and cognitively enabled infrastructures that will be responsive to the system user’s needs. To be able to identify what is required for such systems this research reviews the historical and the current effects of the eTransformation process by studying: 1. eTransitions of company websites and mobile applications, 2. Emergence of new shared economy business models such as Uber, and 3. New requirements for demand driven, cognitive systems capable of learning and just-in-time decision-making. Based on the analysis, this study proposes a Cognitive eTransformation Framework capable of guiding implementations of new responsive and user aware systems.

Keywords: System implementations, AI supported systems, cognitive systems, eTransformation.

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Authors: Wilfred Fritz

Abstract:

The accreditation process of engineering degree programmes is based on various reports evaluated by the relevant governing bodies of the institution of higher education. One of the aforementioned reports for the accreditation process is a self-assessment report which is to be completed by the applying institution. This paper seeks to emphasise the importance of analysis of internal quality management systems and self-examination processes in the engineering accreditation processes. A description of how the programme fulfils the criteria should be given. Relevant stakeholders all need to contribute in the writing and structuring of the self-assessment report. The last step is to gather evidence in the form of supporting documentation. In conclusion, the paper also identifies learning outcomes in a case study in seeking accreditation from an international relevant professional body.

Keywords: Accreditation, governing bodies, self-assessment report, quality management.

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Authors: Kyu Chul Lee, Sung Hyun Yoo, Choon Ki Ahn, Myo Taeg Lim

Abstract:

Recent experimental evidences have shown that because of a fast convergence and a nice accuracy, neural networks training via extended kalman filter (EKF) method is widely applied. However, as to an uncertainty of the system dynamics or modeling error, the performance of the method is unreliable. In order to overcome this problem in this paper, a new finite impulse response (FIR) filter based learning algorithm is proposed to train radial basis function neural networks (RBFN) for nonlinear function approximation. Compared to the EKF training method, the proposed FIR filter training method is more robust to those environmental conditions. Furthermore , the number of centers will be considered since it affects the performance of approximation.

Keywords: Extended kalmin filter (EKF), classification problem, radial basis function networks (RBFN), finite impulse response (FIR)filter.

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Authors: Won-Ho Kim, Chang-Ho Hyun, Moon-Jung Kim

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In this paper, zigbee communication based wireless energy surveillance system is presented. The proposed system consists of multiple energy surveillance devices and an energy surveillance monitor. Each different standby power-off value of electric device is set automatically by using learning function of energy surveillance device. Thus adaptive standby power-off function provides user convenience and it maximizes the energy savings. Also, power consumption monitoring function is helpful to reduce inefficient energy consumption in home. The zigbee throughput simulator is designed to evaluate minimum transmission power and maximum allowable information quantity in the proposed system. The test result of prototype has been satisfied all the requirements. The proposed system has confirmed that can be used as an intelligent energy surveillance system for energy savings in home or office.

Keywords: Energy monitoring system, Energy surveillance system, Energy sensor network, Energy savings.

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Authors: Ahmed Abdolkhalig

Abstract:

This paper examines many mathematical methods for molding the hourly price forward curve (HPFC); the model will be constructed by numerous regression methods, like polynomial regression, radial basic function neural networks & a furrier series. Examination the models goodness of fit will be done by means of statistical & graphical tools. The criteria for choosing the model will depend on minimize the Root Mean Squared Error (RMSE), using the correlation analysis approach for the regression analysis the optimal model will be distinct, which are robust against model misspecification. Learning & supervision technique employed to determine the form of the optimal parameters corresponding to each measure of overall loss. By using all the numerical methods that mentioned previously; the explicit expressions for the optimal model derived and the optimal designs will be implemented.

Keywords: Forward curve, furrier series, regression, radial basic function neural networks.

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Authors: S. Sarumathi, N. Shanthi, S. Vidhya, M. Sharmila

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There have been a lot of efforts and researches undertaken in developing efficient tools for performing several tasks in data mining. Due to the massive amount of information embedded in huge data warehouses maintained in several domains, the extraction of meaningful pattern is no longer feasible. This issue turns to be more obligatory for developing several tools in data mining. Furthermore the major aspire of data mining software is to build a resourceful predictive or descriptive model for handling large amount of information more efficiently and user friendly. Data mining mainly contracts with excessive collection of data that inflicts huge rigorous computational constraints. These out coming challenges lead to the emergence of powerful data mining technologies. In this survey a diverse collection of data mining tools are exemplified and also contrasted with the salient features and performance behavior of each tool.

Keywords: Business Analytics, Data Mining, Data Analysis, Machine Learning, Text Mining, Predictive Analytics, Visualization.

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Authors: H. AbdelRahman, S. Rostom, Y. Lotfy, S. Salah Eldeen, R. Yassein, N. Awny

Abstract:

People are growing more concerned with their appearance in today's society. But they are terrified of what they will look like after a plastic surgery. People's mental health suffers when they have accidents, burns, or genetic issues that cause them to cleave certain body parts, which makes them feel uncomfortable and unappreciated. The method provides an innovative deep learning-based technique for image inpainting that analyzes different picture structures and fixes damaged images. This study proposes a model based on the Stable Diffusion Inpainting method for in-painting medical images. One significant advancement made possible by deep neural networks is image inpainting, which is the process of reconstructing damaged and missing portions of an image. The patient can see the outcome more easily since the system uses the user's input of an image to identify a problem. It then modifies the image and outputs a fixed image.

Keywords: Generative Adversarial Network, GAN, Large Mask Inpainting, LAMA, Stable Diffusion Inpainting.

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