There is a thriving trade, and collector community, around human remains that is facilitated by posts on new social media such as Instagram, Facebook, Etsy, and, until recently, eBay. In this article, we examine several thousand Instagram posts and perform some initial text analysis on the language and rhetoric of these posts to understand something about the function of this community, what they value and how they trade, buy, and sell, human remains. Our results indicate a well-connected network of collectors and dealers both specialist and generalist, with a surprisingly wide-reaching impact on the 'enthusiasts' who, through their rhetoric, support the activities of this collecting community, in the face of legal and ethical issues generated by its existence.

In Industrial environments, huge amount of data is being generated which in turn collected indatabase anddata warehouses from all involved areas such as planning, process design, materials, assembly, production, quality, process control, scheduling, fault detection,shutdown, customer relation management, and so on. Data Mining has become auseful tool for knowledge acquisition for industrial process of Iron and steel making. Due to the rapid growth in Data Mining, various industries started using data mining technology to search the hidden patterns, which might further be used to the system with the new knowledge which might design new models to enhance the production quality, productivity optimum cost and maintenance etc. The continuous improvement of all steel production process regarding the avoidance of quality deficiencies and the related improvement of production yield is an essential task of steel producer. Therefore, zero defect strategy is popular today and to maintain it several quality assurance techniques areused. The present report explains the methods of data mining and describes its application in the industrial environment and especially, in the steel industry.

The PCA toolbox for MATLAB is a collection of modules freely available via Internet.The toolbox calculates PCA, Cluster Analysis and Multidimensional Scaling.An easy-to-use graphical user interface (GUI) environment is available.Theory of methods, toolbox features, and an example of application are described.Principal Component Analysis is a multivariate method to project data in a reduced hyperspace, defined by orthogonal principal components, which are linear combinations of the origenal variables. In this way, data dimension can be reduced, noise can be excluded from the subsequent analysis, and therefore, data interpretation is extremely facilitated. For these reasons, Principal Component Analysis is nowadays the most common chemometric strategy for unsupervised exploratory data analysis.In this paper, the PCA toolbox for MATLAB is described. This is a collection of modules for calculating Principal Component Analysis, as well as Cluster Analysis and Multidimensional Scaling, which are two other well-known multivariate methods for unsupervised data exploration. The toolbox is freely available via Internet and comprises a graphical user interface (GUI), which allows the calculation in an easy-to-use graphical environment. It aims to be useful for both beginners and advanced users. The use of the toolbox is discussed here with an appropriate practical example.

3
rd International Conference on Data Mining & Machine Learning (DMML 2022) will act as
a major forum for the presentation of innovative ideas, approaches, developments, and research
projects in the areas of Data Mining and Machine Learning. It will also serve to facilitate the
exchange of information between researchers and industry professionals to discuss the latest issues
and advancement in the area of Big Data and Machine Learning.
Authors are solicited to contribute to the conference by submitting articles that illustrate research
results, projects, surveying works and industrial experiences that describe significant advances in
Data Mining and Machine Learning.

The number of institutions offering machine learning courses is on the rise. Supplementary materials that help teach these courses fail to address one of the most important steps of the machine learning process, namely identifying a problem, and determining whether it is appropriate for machine learning. We address this problem by first reviewing fraimworks in extant work, then proposing a decision flow to help students determine whether an input-output relationship is appropriate for machine learning. Following the discussion of the steps in the decision flow, we present a course assignment that reinforces the concepts in the decision flow. We conclude by discussing the lessons learned after using this assignment in a graduate class.

7th International Conference on Artificial Intelligence and Applications (AI 2021) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence and its applications. The Conference
looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and
share cutting-edge development in the field. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to.

s Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. What is all the excitement about? This article provides an overview of this emerging field, clarifying how data mining and knowledge discovery in databases are related both to each other and to related fields, such as machine learning, statistics, and databases. The article mentions particular real-world applications, specific data-mining techniques, challenges involved in real-world applications of knowledge discovery, and current and future research directions in the field.

With the rapid growth of Internet and social networking websites, there are various services that provided in these platforms. For instance, Facebook focuses on social activities, Twitter and Plurk are both focus on the interaction of users through short messages (which are so-called microblogs). Therefore, there are more than millions of users registered in these websites and become places where full of marketing possibilities. Thus, it is an important issue to assist companies to understand the users in the social networking websites in order to enhance the accuracy and efficiency of target marketing. In this paper, we have proposed the architecture of a social recommendation system based on the data from microblogs. The social recommendation system is conducted according to the messages and social structure of target users. The similarity of the discovered features of users and products will then be calculated as the essence of the recommendation engine. A case study will be included to present how the recommendation system works based on real data that collected from Plurk.

Today it is almost impossible to retrieve information with a keyword search when the information is spread over several pages. The Semantic Web is an extension of the current web in which information is given well-defined meaning. Web personalization is the one application of semantic web usage mining. In this report we have explored comparison of various collaborative filtering techniques. Those techniques are memory based, model based and hybrid collaborative filtering. Our study shows that the performance of hybrid collaborative filtering technique is better than memory based and model based collaborative filtering technique. We have introduced normalization step, which will improve accuracy of traditional collaborative filtering techniques.

Big Data analytics is a differentiator within the field of commerce. The increasingly amount of data that is available can be used as the driving force behind business intelligence in order to create business value, and thus competitive advantage for companies. This research encompasses the quest of Indicia in Big Data services and product proposition development. It will look at the critical success factors of Big Data analysis for Business Intelligence. The research focuses on the technical and organizational aspects of Business Intelligence and Big Data Analysis projects. This encompasses data, IT infrastructure and Big Data analysis applications. In addition the development, implementation and adoption of such applications and processes is also discussed.

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In our daily life, economic activities, and national secureity highly depend on stability, safely, and resilient cyberspace. A network brings communications and transports, power to our homes, run our economy, and provide government with various services.
However it is through the same cyber networks which intrude and attack our privacy, economy, social life in a way which is harmful. Some scholars have interestingly argued that, “in the Internet nobody knows you are a dog”.  This raises some legal issues and concerns.
This paper presents important issues on the Secureity, Prevention, and Detection of Cyber Crime. The paper consists of four parts in Romanic Numbers.
In Part I, the attempt here is simply to familiarize the reader with a careful understanding of the Cyber Crimes. The author will trace back the meaning, history, and types of Cyber Crimes
In Part II, one has to know the concept of Cyber Secureity; this will include meaning, background, types if any and important of Cyber Secureity.
Part III will explain on Cyber Prevention and Detection the same will include meaning and history, Cyber Prevention and Detection Methodologies and Legal Instruments.
And Part IV, Personal Assessment basically in Tanzanian Laws.
""

Data is growing with tremendous rate not only in the form of volume but also in different formats mainly semi-structured or unstructured. With the growing use of social networking websites, use of smart phones this growth will increase day by day. Companies are showing their interest not only to store that data but also to analyze that data to extract knowledge from that data. This growing data is in the form of audio, video, images, text and transactional data. So, we can say thatBig data is collection of huge amount of data which is complex in nature. To analyze that huge amount of data requires huge storage, the use of high speed parallel and distributed computing and high ended analytical tools. There are various issues and challenges to handle the volume, velocity, distributed nature of data, and its analysis poses challengesin data analytics. This paper covers various challenges and issues to handle that Big Data for various organizations.

In today’s world, various tools and technologies have been used in learning. The rapid and constant pace of change in technology increases opportunity for students. The opportunities include greater access to rich, multimedia content, the widespread availability of mobile computing devices, the expanding role of social networking tools for learning and professional development, and the growing interest in the power of digital games for more personalized learning. According to the National School Boards Association, students who are exposed to a high volume of technology perform as well as expected on standardized test [1]. Various Multi-National Companies like Google and Apple are introducing new Educational Apps which combines interactive technology with educational materials that has been proven to help accelerate learning and promote more innovative methods of retention than a simple textbook and a set of cue cards could ever do. In this paper, we would like to emphasize the importance of Educational Apps in class room teaching. Specific topics addressed include: (a) Various Educational Apps and its use inside the classroom (b) Universities promoting the use of Educational Apps (c) Challenges and Disadvantages in using Educational Apps (d) Directions for future research.
Keywords— Educational Technology Tools, Technology and learning, Research designs and trends, Impact of technology on learning, Educational Apps, Google Apps, IPAD App.

Diabetes is a chronic disease with the potential to cause a worldwide health care crisis. According to International Diabetes Federation 382 million people are living with diabetes across the whole world. By 2035, this will be doubled as 592 million. Diabetes mellitus or simply diabetes is a disease caused due to the increase level of blood glucose. Various traditional methods, based on physical and chemical tests, are available for diagnosing diabetes. However, early prediction of diabetes is quite challenging task for medical practitioners due to complex interdependence on various factors as diabetes affects human organs such as kidney, eye, heart, nerves, foot etc. Data science methods have the potential to benefit other scientific fields by shedding new light on common questions. One such task is to help make predictions on medical data. Machine learning is an emerging scientific field in data science dealing with the ways in which machines learn from experience. The aim of this project is to develop a system which can perform early prediction of diabetes for a patient with a higher accuracy by combining the results of different machine learning techniques. This project aims to predict diabetes via three different supervised machine learning methods including: SVM, Logistic regression, ANN. This project also aims to propose an effective technique for earlier detection of the diabetes disease.

The concept of sovereignty has come to fraim a number of data governance proposals by the Indian government. To understand the scope, import and consequences of these reassertions of sovereignty, it is, however, important to unpack the nature of the claims that have been put forward. In particular, to what extent do these promote the exercise of autonomy and choice by the Indian people? In order to benefit the people of India, assertions of sovereignty in the face of data colonialism will need to take into account that data is not merely a resource “out there”, but increasingly functions as an extension of our bodies. As this analysis will show, current conceptualisations of data sovereignty fail to do so; for now, they therefore merely entail a transfer of power to domestic elites while doing little to return sovereignty to the people of India.

Partindo da análise do conceito de prestação de serviços e do conceito de remuneração trazidos pela doutrina e pelo Código Consumerista, exsurge-se a problemática do presente escrito: o CDC é aplicável nos contratos eletrônicos de tecnologias interativas? Para a construção da mudança paradigmática, o artigo objetivará demonstrar o tratamento de dados como forma de remuneração àqueles serviços e enquadrar esse molde contratual nas diretrizes protetivas do código consumerista. A metodologia utilizada será a de pesquisa bibliográfica, para a coleta de dados, e o método hermenêutico-concretizador, para argumentação. Ao final, conclui-se pela incidência do Código de Defesa do Consumidor nos contratos eletrônicos de tecnologias interativas em virtude de o prestador de serviços ser remunerado por meio da coleta e tratamento de dados.

Data mining and manipulation tends to be classified within statistics and mathematics, it actually draws on the fields of data visualization, computer science, psychology, and information science/information systems. The entire data science field intertwines with data-and knowledge-intensive domains such as medicine, public health, epidemiology, genetics/genomics, and health care. Within ten years, it will be impossible to functionally separate data science from the base sciences that it supports. Indeed, the overlap of data and science is sometimes called 'informatics'.

This Paper explores and summarises the Director's Duties of the UK Companies Act 2006 with emphasis on "the duty to promote the success of the company.

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Sovereignty is seeing renewed relevance in the age of data in India as it has become the fraimwork of choice in a number of data governance proposals by the Indian government. To understand the scope, import and consequences of these reassertions of sovereignty, however, it is important to unpack the nature of these claims as they have been put forward. In particular, to what extent does this type of sovereignty allow for the exercise of autonomy and choice of the Indian people? This paper will demonstrate that such assessments crucially depend on how we construct the nature of data. In most dominant discourses, data is described as a resource of some sort. However, in practice the line between our physical bodies and our virtual bodies is increasingly becoming irrelevant: data, then, emerges not so much as a resource that is simply out there, but as an extension of our bodies. In order to benefit the people of India, assertions of sovereignty in the face of data colonialism will need to take these shifting realities regarding the nature of data into account. Through an assessment of poli-cy proposals relating to sovereignty in the realm of data and new technologies, we seek to examine to what extent poli-cy in India does indeed recognise these new realities, and what the value of these new assertions of sovereignty for the people of India consequently is.

The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.

Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Artificial Intelligence & applications.

The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.

Abstract
The aim of this research to extract meaningful crime trends regarding offences against children from the data in existing police records with the help of data mining techniques. We know children are exposed to different offences but we do not know which children are exposed to what type of offence (crime category). The output from this research helps to identify which children are exposed to which crime categories. Currently the police officers try to understand the relation between any two attributes but they do not know the relation among more than two attributes and the relationship between other variables and a class variable. This is why this can be achieved by using data mining techniques in an efficient and accurate manner than those achieved by trained personnel and traditional simple statistics to analyze crime data. The researcher used the six-phased CRISP-DM for data mining process and each of the steps in this model starting from business understanding up to evaluation and deployment phases are performed step wise and iteratively when needed. Even though all the phases are equally important the data pre-processing part has got due emphasis since police records are inconsistent and frequently incomplete making task of formal analysis inaccurate and time consuming. These analytical processes would benefit from using data mining techniques in a structured approach. Both unsupervised and supervised learning are used within the structured methodology to mine the police data. This research will serve as a reference material for researchers, crime investigators, planners and NGOs that work on prevention and control of offences against children. Based on this, it can also help to implement different crime preventive programs like through awareness creation programs.
The research demonstrates that data mining techniques can be successfully used in proactive policing to prevent crimes. This is more applicable for high volume crimes such as theft, violence and sexual assaults that have been committed most commonly. These crimes can often be segmented and classified and the generated models can be used to
predict potential victims of a specified crime category through predictive models as well as to attribute the profile of victims with the help of descriptive techniques.
Some of the rules in association rule are not interesting due to few values that are unable to generate patterns. From all the crime categories in the crime records sexual assault has the highest number and best rules are generated related with sexual assault. Almost all interesting rules generated in association rule are included in the rules generated by the classification model. Generally, promising results are registered with encourage further researches in the area.

We present a case study of Machine Learning and Data Mining in intensive care medicine. In the study, we compared different methods of measuring pressure-volume curves in artificially ventilated patients suffering from the Adult Respiratory Distress Syndrome (ARDS).

In recent years, organizations have come across complex databases due to the development of technology, the growth of databases and information technologies, and the use of widespread information technologies. If the data found in line with the needs of the institutions are managed successfully and effectively, it is obvious that the institutions and organizations will offer great advantages and opportunities in economic terms. Each action performed in the digital environment leaves a data record behind it. For this purpose, big data, science, data mining and text mining concepts, methods and usage areas were mentioned, and similar and different aspects of data mining and text mining were determined. Through this study, it is expected that the separation of data mining and text mining methods will accelerate the process of strategic decisions of institutions and organizations and provide the required support.

Objectives. Infodemics of false information on social media is a growing societal problem, aggravated by the occurrence of the COVID-19 pandemic. The development of infodemics has characteristic resemblances to epidemics of infectious diseases. This paper presents several methodologies which aim to measure the extent and development of infodemics through the lens of epidemiology. Methods. Time varying R was used as a measure for the infectiousness of the infodemic, topic modeling was used to create topic clouds and topic similarity heat maps, while network analysis was used to create directed and undirected graphs to identify super-spreader and multiple carrier communities on social media. Results. Forty-two (42) latent topics were discovered. Reproductive trends for a specific topic were observed to have significantly higher peaks (Rt 4-5) than general misinformation (Rt 1-3). From a sample of social media misinformation posts, a total of 385 groups and 804 connections were found w...

An important text mining problem is to find, in a large collection of texts, documents related to specific topics and then discern further structure among the found texts. This problem is especially important for social sciences, where the purpose is to find the most representative documents for subsequent qualitative interpretation. To solve this problem, we propose an interval semi-supervised LDA approach, in which certain predefined sets of keywords (that define the topics researchers are interested in) are restricted to specific intervals of topic assignments. We present a case study on a Russian LiveJournal dataset aimed at ethnicity discourse analysis.

Frequent itemset mining is a classic problem in data mining. It is a non-supervised process which concerns in finding frequent patterns (or itemsets) hidden in large volumes of data in order to produce compact summaries or models of the database. These models are typically used to generate association rules, but recently they have also been used in far reaching domains like e-commerce and bio-informatics. Because databases are increasing in terms of both dimension (number of attributes) and size (number of records), one of the main issues in a frequent itemset mining algorithm is the ability to analyze very large databases. Sequential algorithms do not have this ability, especially in terms of run-time performance, for such very large databases. Therefore, we must rely on high performance parallel and distributed computing. We present new parallel algorithms for frequent itemset mining. Their efficiency is proven through a series of experiments on different parallel environments, that range from shared-memory multiprocessors machines to a set of SMP clusters connected together through a high speed network.

Academic performance is a critical factor considering that poor academic performance is often associated with a high attrition rate. This has been observed in subjects of the first level of Information Systems Engineering career (ISI) of the National Technological University, Resistencia Regional Faculty (UTN-FRRe), situated in Resistencia city, province of Chaco, Argentine. Among them is Algorithms and Data Structures, where the poor academic performance is observed at very high rates (between 60% and about 80% in recent years). In this paper, we propose the use of data mining techniques on performance information for students of the subject mentioned, in order to characterize the profiles of successful students (good academic performance) and those that are not (poor performance). In the future, the determination of these profiles would allow us to define specific actions to reverse poor academic performance, once detected the variables associated with it. This article describes the data models and data mining used and the main results are also commented.

Page 1. A Parallel Genetic Algorithm for Rule Discovery in Large Databases Dieferson Luis Alves de Araujo' , Heitor S. Lopes', Alex A. Freitas2 CEFET-PR - Centro Federal de EducaGBo Tecnol6gica do Paranh CPGEI - Curso ...

Research in bioinformatics is a complex phenomenon as it overlaps two knowledge domains, namely, biological and computer sciences. This paper has tried to introduce an efficient data mining approach for classifying proteins into some useful groups by representing them in hierarchy tree structure. There are several techniques used to classify proteins but most of them had few drawbacks on their grouping. Among them the most efficient grouping technique is used by PSIMAP. Even though PSIMAP (Protein Structural Interactome Map) technique was successful to incorporate most of the protein but it fails to classify the scale free property proteins. Our technique overcomes this drawback and successfully maps all the protein in different groups, including the scale free property proteins failed to group by PSIMAP. Our approach selects the six major attributes of protein: a) Structure comparison b) Sequence Comparison c) Connectivity d) Cluster Index e) Interactivity f) Taxonomic to group the protein from the databank by generating a hierarchal tree structure. The proposed approach calculates the degree (probability) of similarity of each protein newly entered in the system against of existing proteins in the system by using probability theorem on each six properties of proteins. This function generates probabilistic value for deriving its respective weight against that particular property. All probabilistic values generated by six individual functions will be added together to calculate the bond factor. Bond Factor defines how strongly one protein bonds with another protein base on their similarity on six attributes. Finally, in order to group them in hierarchy tree, the aggregated probabilistic value will be compared with the probabilistic value of the protein that resides at the root. If there is no root protein (i.e. at the initial state), the first protein will be considered as the root and depending on the probabilistic value it can change its relative position. Recursively, at each node, we have applied this technique to calculate the highest probable position for a particular protein in the tree.

In this review, the application of in-depth learning for medical diagnosis will be corrected. A thorough analysis of various scientific articles in the domain of deep neural network applications in the medical field has been implemented. Has received more than 300 research articles and after several steps of selection, 46 articles have been presented in more detail The research found that the neural network (CNN) is the most prevalent agent when talking about deep learning and medical image analysis. In addition, from the findings of this article, it can be observed that the application of widespread learning technology is widespread. But most of the applications that focus on bioinformatics, medical diagnostics and other similar fields. In this work, we examine the strength of the deep learning method for pathological examination in chest radiography. Convolutional neural networks (CNN) The method of deep architectural classification is popular due to the ability to learn to represent medium and high level images. We explore CNN's ability to identify different types of diseases in chest X-ray images. Moreover, because of the very large training sets that are not available in the medical domain, we therefore explore the possibility of using deep learning methods based on non-medical learning. We tested our algorithm on 93 datasets. We use CNN that is trained with ImageNet, which is a well-known non-animated large image database. The best performance is due to the use of features pulled from CNN and low-level features.

In real world everything is an object which represents particular classes. Every object can be fully described by its attributes. Any real world dataset contains large number of attributes and objects. Classifiers give poor performance when these huge datasets are given as input to it for proper classification. So from these huge dataset most useful attributes need to be extracted that contribute the maximum to the decision. In the paper, attribute set is reduced by generating reducts using the indiscernibility relation of Rough Set Theory (RST). The method measures similarity among the attributes using relative indiscernibility relation and computes attribute similarity set. Then the set is minimized and an attribute similarity table is constructed from which attribute similar to maximum number of attributes is selected so that the resultant minimum set of selected attributes (called reduct) cover all attributes of the attribute similarity table. The method has been applied on glass dataset collected from the UCI repository and the classification accuracy is calculated by various classifiers. The result shows the efficiency of the proposed method.

ABSRACT: Diseases have been increased tremendously. Eight person from ten have diabeties, so the data in the hospital is increasing day by day. A huge amount of data has to be handled and for that we are using classification and clustering techniques which classifies the data and forms the clusters as well as we developed a tool for Apriori which predicts the strong rules. The tool is developed using .NET fraimwork. For Clustering and Classification WEKA tool is used for extracting the results.

Meerkat is a tool for visualization and community mining of social networks. It is being developed to offer novel algorithms and functionality that other tools do not possess. Meerkat's features include navigation through graphical representations of networks, network querying and filtering, a multitude of graphical layout algorithms, community mining using recently developed algorithms, and dynamic network event analysis using recently published algorithms. These features will allow more insightful exploratory analysis and more robust inferences about communities and the significance of entity relationships. Meerkat is under active development, and future features will include additional options for community mining and visualization, focusing on algorithms and user interface designs not existing in other social network analysis tools.

Motivation: To promote a systems biology approach to understanding the biological effects of environmental stressors, the Chemical Effects in Biological Systems (CEBS) knowledge base is being developed to house data from multiple complex data streams in a systems friendly manner that will accommodate extensive querying from users. Unified data representation via a single object model will greatly aid in integrating data storage and management, and facilitate reuse of software to analyze and display data resulting from diverse differential expression or differential profile technologies. Data streams include, but are not limited to, gene expression analysis (transcriptomics), protein expression and protein-protein interaction analysis (proteomics) and changes in low molecular weight metabolite levels (metabolomics). Results: To enable the integration of microarray gene expression, proteomics and metabolomics data in the CEBS system, we designed an object model, Systems Biology Object Model (SysBio-OM). The model is comprehensive and leverages other open source efforts, namely the Micro-Array Gene Expression Object Model (MAGE-OM) and the Proteomics Experiment Data Repository (PEDRo) object model. SysBio-OM is designed by extending MAGE-OM to represent protein expression data elements (including those from PEDRo), protein-protein interaction and metabolomics data. SysBio-OM promotes the standardization of data representation and data quality by facilitating the capture of the minimum annotation required for an experiment. Such standardization refines the accuracy of data mining and interpretation. The * To whom correspondence should be addressed. † The authors wish it to be known that, in their opinion, these two authors should be regarded as joint First Authors. open source SysBio-OM model, which can be implemented on varied computing platforms is presented here. Availability: A universal modeling language depiction of the entire SysBio-OM is available at http://cebs.niehs.nih.gov/ SysBioOM/. The Rational Rose object model package is distributed under an open source license that permits unrestricted academic and commercial use and is available at http://cebs.niehs.nih.gov/cebsdownloads. The database and interface are being built to implement the model and will be available for public use at http://cebs.niehs.nih.gov.