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Style papers represent the most advanced research with the greatest potential for significant impact on the field. A Concept Paper is a large original document that includes certain methods or approaches, provides an overview of future research opportunities and describes possible research applications.
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Feature papers are submitted at the individual invitation or recommendation of the scientific editors and must receive positive feedback from the reviewers.
Editorials are based on the recommendations of scientific editors of journals from around the world. The editors select a small number of recently published articles in the journal that they believe will be of particular interest to readers or are important in each area of research. The aim is to provide a snapshot of some of the exciting work published in the various research sections of the journal.
By Abdulaziz Aldoseri Abdulaziz Aldoseri Scilit Preprints.org Google Scholar View Publications , Khalifa N. Al-Khalifa Khalifa N. Al-Khalifa Scilit Preprints.org Google Scholar View Publications and Abdel Magid Hamouda Abdel Magid Hamouda Scilit Preprints.org Google Scholar Viewings *
Received: 3 May 2023 / Revised: 30 May 2023 / Accepted: 7 June 2023 / Published: 13 June 2023
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Artificial intelligence (AI) is increasingly being used across industries such as healthcare, finance, and transportation. Artificial intelligence is based on the analysis of large datasets and the continuous delivery of high quality data. However, using data for AI is not a challenge. This paper provides a comprehensive review and critical evaluation of the challenges of using data for AI, including data quality, data volume, privacy and security, selectivity and accuracy, interpretation and explanation, ethical concerns, and technical expertise and skills. This paper examines these challenges and provides information on how companies and organizations can address them. By understanding and addressing these challenges, organizations can harness the power of AI to make decisions and gain competitive advantage in the digital age. Hopefully, as this review article provides and discusses various strategies for data challenges for AI over the past decade, it will help the scientific research community to formulate ideas. new and innovative to rethink our approach to data strategies for AI.
Artificial Intelligence (AI) refers to the ability of machines to imitate human intelligence and perform tasks that require human intelligence, such as learning, solving problems, and making decisions. , and natural language comprehension . Figure 1 describes AI technologies including machine learning, natural language processing, robotics, and computer vision. Machine learning is a subset of AI that involves training computer algorithms to learn patterns in data and make predictions or decisions based on the data . Deep learning is a type of machine learning that uses neural networks with multiple layers to process complex data such as images or information . Natural language processing is the ability of computers to understand, interpret, and reproduce human language, including speech and text . Computer vision is the ability of computers to analyze and interpret visual information such as images and videos .
AI is a rapidly growing field with the potential to change the way we live and work. From healthcare to finance and transportation, AI has the potential to transform many industries, creating new opportunities for businesses and organizations. AI has revolutionized various sectors, including healthcare, finance, and transportation, with significant advances in machine learning and deep learning techniques [6, 7]. At the heart of this revolution is data, which is essential for training and testing AI models. AI models rely on large datasets to identify patterns and trends that are difficult to detect using traditional data-analysis techniques. This allows them to learn and make predictions based on the data they were trained on.
However, using AI data is difficult. Data quality, quantity, diversity, and privacy are important aspects of data-driven AI applications, and each presents its own set of challenges. Poor data quality can lead to inaccurate or biased AI models, which can have a negative impact on areas such as healthcare and finance. Lack of data leads to overly simplistic models, which cannot accurately predict global outcomes. Lack of data diversity can also lead to biased samples that do not accurately represent the population they were designed to serve. Finally, data privacy is a major concern, as AI models may require access to sensitive data, raising concerns about data privacy and security.
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In this article, we outline the challenges of using data for AI and offer advice for companies seeking to address them. To meet these challenges, businesses and organizations must develop strategies and frameworks that promote data quality, quantity, diversity, and privacy. This may include implementing data cleaning and validation processes to ensure data quality, collecting and managing large amounts of diverse data, and implementing privacy policies and procedures. data to protect sensitive data. By focusing on these challenges, businesses and organizations can harness the power of data to create accurate, efficient and accurate AI applications that benefit society.
Data is very important for AI because they are the foundation on which machine learning algorithms learn, predict, and improve their performance over time. To train an AI model, a large amount of data is needed so that the model can recognize patterns, make predictions, and improve its performance over time.
AI algorithms require data to learn patterns and make predictions or decisions based on the data. AI machine learning techniques are algorithms that allow machines to learn patterns and make predictions from data without explicit programming. These techniques are widely used in a variety of applications, such as natural language processing, image and speech recognition, and recommendation systems. In general, the more data available for an AI algorithm to learn from, the more accurate its predictions or decisions will be. There are many data-learning approaches to building AI systems [8, 9]; for the generality of the text, we include the following, as shown in Figure 2.
Supervised Learning: In supervised learning, an AI system is trained on a labeled dataset and each data point is associated with a label or variable. The goal is to develop a model that can accurately predict the label or index for new data points. This approach is used for tasks such as image classification, speech recognition, and natural language processing .
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Unsupervised Learning: In unsupervised learning, an AI system is trained on an unlabeled dataset with no variables to predict. The goal is to identify patterns, relationships, and structures in the data. This approach is used for tasks such as clustering, anomaly detection, and size reduction .
Reinforcement Learning: In reinforcement learning, an AI system learns to make decisions based on feedback from the environment. The system receives rewards or punishments based on its actions and adjusts its behavior accordingly. This approach is used for applications such as gaming, robotics, and autonomous driving .
Transfer Learning: In transfer learning, an AI system uses the knowledge acquired in one task to improve performance in another related task. The system is trained on a large dataset and then fine-tuned on a smaller dataset for a specific task at hand. This approach can help reduce the amount of data needed to train an AI model and improve its accuracy and performance .
Deep Learning: Deep learning is a form of network-based machine learning that is particularly useful for tasks involving large amounts of data and complex relationships. Deep learning models consist of multiple layers of connected nodes that can learn more complex aspects of the data. This approach is used for tasks such as image and speech recognition, natural language processing, and computer vision .
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Ensemble Learning: Ensemble learning is a technique in which multiple models are trained and combined to make predictions or make decisions. Combining the predictions of multiple models can improve the accuracy and reliability of the final output .
Of course, the choice of data learning approach depends on the specific task, data and available resources. It is important to carefully evaluate the advantages and limitations of each approach and choose the one that best suits the requirements of the AI application being developed.
They are two related but distinct concepts in the world of data analysis and decision making. By using data, organizations can better understand their operations, customers, and markets and make decisions based on data-driven insights. Data-driven approaches are used in industries such as finance, healthcare, and marketing, where accurate and timely data is essential for decision-making.
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