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3rd International conference on Information Technology, Data Science and Digital Health , will be organized around the theme “Data ethics in the era of AI: Balancing progress with responsibility”
DATA SCIENC 2023 is comprised of keynote and speakers sessions on latest cutting edge research designed to offer comprehensive global discussions that address current issues in DATA SCIENC 2023
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Statistical Analysis and Data Mining covers the broad area of â€‹â€‹data analysis, including data mining algorithms, statistical approaches, and practical operations. Subjects include problems involving massive and complex datasets, results exercising innovative data mining algorithms and/or new statistical approaches. Data mining is the process of rooting and discovering patterns in large data sets involving styles at the intersection of machine learning, statistics and database systems. Statistical analysis is the process of collecting and analyzing large volumes of data to identify trends and develop valuable insight. In the professional world, statistical judges collect raw data and find correlations between variables to reveal patterns and trends to applicable stakeholders.
Big data and artificial intelligence are giving the industry a tremendous boost. Smart software solutions can use the vast amounts of data generated by factories to identify trends and patterns, improving the efficiency of manufacturing processes and reducing energy consumption. The main categories where industrial AI can contribute include: product and service innovation, process improvement & insight discovery. The Cloud Foundry service platform extensively incorporates artificial intelligence technologies. AI can be applied to production data to improve failure prediction and planning. Thereby, reducing the maintenance cost of the production line. Many other applications and benefits of AI in production are also possible.
Big data analytics is the use of advanced analytical techniques on very large and diverse data sets consisting of structured, semi-structured and unstructured data from different sources, ranging in size from terabytes to zeta bytes. Here are some examples: Use analytics to understand customer behavior to optimize customer experience. Predict the future trends to make better business decisions. Improve your marketing campaigns by understanding what's working and what's not. In different phases of business analysis, large amounts of data are processed. According to the requirements of the type of analysis, there are five types of analysis: descriptive analysis, diagnostic analysis, predictive analysis, prescriptive analysis and cognitive analysis.
A blockchain technology is a distributed database or ledger (one of today's top technology trends), which means that the power to update the blockchain is distributed among nodes or participants in a public or private computer network. This is known as distributed ledger technology. Bitcoin purchases and sales are entered and transmitted to a network of powerful computers called nodes. The network of thousands of nodes around the world, compete to confirm transactions using computer algorithms. This is called Bitcoin mining. Blockchain helps to verify and trace multi-step transactions that need to be verified and traced. It provides secure transactions, reduces compliance costs & speed up the data transfer processing. Blockchain technology can help with contract management and auditing the provenance of products.
Information Technology (IT) refers to the use of any computer, storage, network and other physical devices, infrastructure and processes to create, process, store, protect and exchange electronic data in all its forms. Information Technology (IT) plays a vital role in today's personal, business and non-profit uses. Information Technology is the main driver of innovation without which businesses cannot survive because they are the wave of the future. Increased computer power has resulted in faster and cheaper computer processing & cheaper data storage. For example: optical storage media. Digitization of information texts, graphics, photos, speech, sound, video, etc
Edge computing is an emerging computing paradigm that refers to a series of networks and devices at or near the user. Edge refers to processing data closer to where it is generated, allowing for faster processing and greater capacity, resulting in better action-driven results in real time. Edge computing is the local processing and storage source for IoT device data and computing needs, reducing communication latency between IoT devices and the central IT network to which these devices are connected. Edge computing is a distributed computing framework that brings enterprise applications closer to data sources such as IoT devices or local edge servers etc. This proximity to the source of data can lead to powerful business benefits, including faster insights, improved response times & better bandwidth availability.
Health information systems (HIS) serve multiple users and broad purposes and can be summarized as generating information that enables decision makers at all levels of the health system to identify problems and needs, make evidence-based decisions on health policy and optimize the allocation of scarce resources. Health IT is also providing access through patient portals, giving patients greater control over their health data. Patient portals enable individuals to view medical test results, download patient data, communicate with physicians, schedule medical appointments, and more through a website application or mobile application. Most healthcare AI technologies using machine learning and precision medicine applications require medical images and clinical data for training, while the end result is known. This is called supervised learning.
The term "big data" refers to the volume of data that is beyond human comprehension and cannot be managed by standard computing systems. Big data is becoming more prevalent in nursing and healthcare settings. Data scientists continually develop knowledge and specific methods for managing this data. Nursing professionals who can leverage this data can use it to develop holistic treatment strategies for patients that more effectively meet their needs. Using big data in healthcare can help patients take a more proactive approach to care. Nurse researchers use secondary data analysis in epidemiological studies, risk assessments, skills assessments, and practice comparisons across geographic regions, and outcomes studies.
Big data technologies are software tools used to manage all types of data sets and turn them into business insights. In data science careers such as big data engineers, complex analytics evaluate and process large amounts of data. The most important and anticipated technology is now in sight. This is Apache Spark. It is an open-source analytics engine that supports big data processing. Big data analytics help organizations harness data and leverage it to discover new opportunities. The result is smarter business moves, more efficient operations, higher profits and happier customers. Companies using big data and advanced analytics can capture value in a number of ways, including: Reduced capital.
Trending Technologies in 2023
Data optimization is the practice of changing an organization's data strategy to improve the speed and efficiency of data extraction, analysis, and use. The goal of optimization is to find the best acceptable answer given some conditions of the problem. For a problem, there may be different solutions, in order to compare them and choose the optimal solution; a function called the objective function is defined. The goal of optimization is to achieve the "best" design according to a set of priority criteria or constraints. These include factors such as maximizing productivity, strength, reliability, longevity, efficiency and utilization. Data optimization alleviates this problem by reorganizing datasets and filtering out inaccuracies and noise. The result is often a dramatic increase in the speed at which actionable information is extracted, analyzed, and delivered to decision makers.
Big data can enhance the discovery, access, availability, utilization and supply of information in companies and supply chains. It can help discover new data sets that have not yet been used to drive value. Big data applications process and manage large amounts of data, often measured in terabytes or more. Processing such large data volumes can be time-consuming, taking months to complete. Big data challenges are real implementation barriers. These all require immediate attention and need to be dealt with, because if not dealt with, there may be technical glitches, which may also lead to some unpleasant results. Big data challenges include storing and analyzing extremely large and rapidly growing data volumes. Big data can reduce long-term costs, improve investment capabilities, and improve understanding of cost drivers and impacts.
Machine learning is an artificial intelligence technique that teaches computers to learn from experience. Machine learning algorithms use computational methods to learn information directly from data without relying on predetermined equations as models. There are four basic approaches: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Machine learning is used in internet search engines, email filters to classify spam, websites to provide personalized recommendations, banking software to detect unusual transactions, and many applications on our mobile phones (such as voice recognition).
A data warehouse integrates data and information collected from various sources into a comprehensive database. For example, a data warehouse might incorporate customer information from an organization's point-of-sale system, mailing lists, website, and comment cards. A data warehouse is a central repository of information that can be analyzed to make more informed decisions. Data typically flows into data warehouses regularly from transactional systems, relational databases, and other sources. SQL Data Warehouse is a cloud-based enterprise data warehouse (EDW) that leverages massively parallel processing (MPP) to quickly run complex queries across petabytes of data. Use a SQL data warehouse as a key component of a big data solution.
Cloud computing is the on-demand availability of computer system resources, especially data storage and computing power, without direct active management by users. The functions of a large cloud are often distributed across multiple locations, each of which is a data center. Simply put, cloud computing is the delivery of computing services (including servers, storage, databases, networking, software, analytics, and intelligence) over the Internet (“the cloud”) to provide faster innovation, flexible resources, and economies of scale. Cloud computing makes data backup, disaster recovery, and business continuity easier and less costly because data can be mirrored at multiple redundant sites on the cloud provider's network. They don't float in cyberspace. Cloud space exists on individual servers in data centers and server farms around the world. Data centers and hosting providers provide server space for cloud computing.
A cryptocurrency, crypto- currency, or crypto is a digital currency designed to be maintained or maintained over a computer network as a medium of exchange without relying on any central authority, such as a government or bank. It’s a decentralized system for verifying that parties to a sale have the funds they claim to have, eliminating the need for traditional intermediaries like banks when funds are transferred between two realities. A cryptocurrency is a type of digital currency, an integral form of payment created using encryption algorithms. The use of cryptography means that cryptocurrencies can be used both as money and as a virtual account system. Cryptocurrencies can be a great investment with astronomically high returns overnight; despite this, there is a considerable downside. Investors should analyze whether their time horizon, threat tolerance, and liquidity conditions fit their investor profile.
Cryptocurrency trading refers to taking a financial position on the price direction of an individual cryptocurrency against the U.S. dollar (in the crypto/dollar pair) or another cryptocurrency through crypto-to-crypto pairs. CFDs (Contracts for Difference) are a particularly popular way to trade cryptocurrencies due to their reduced inflexibility, leverage of leverage and the ability to take short and long positions. Cryptocurrency trading is becoming more and more popular. Cryptocurrencies are digital currencies created using Blockchain or peer-to-peer technology that uses cryptography to ensure security. They differ from fiat currencies issued by governments around the world in that they are untouchable; they consist of bits and bytes of data. Furthermore, cryptocurrencies do not have a central body or authority similar to a central bank to issue cryptocurrencies or regulate their circulation in the economy. Since cryptocurrencies are not issued by any government agency, they are not considered legal tender.