Search or ask a question

What are the moderen Intelligent Data Analytics Techniques?

Unsupervised learning

Artificial neural network

Naive Bayes classifier

Reinforcement learning

Supervised learning

Best insight from top research papers

Modern Intelligent Data Analytics Techniques encompass a variety of approaches drawing from machine learning, computational intelligence, and statistical methods. These techniques include supervised, unsupervised, semi-supervised, active, and reinforcement learning, as well as algorithms like naive Bayes classifiers, decision trees, support vector machines, neural networks, artificial neural networks (ANNs), genetic programming, fuzzy logic, and nature-inspired techniques such as gravitational search algorithms. Intelligent Data Analysis (IDA) involves the interpretation and understanding of datasets through linguistic methods and cognitive systems, aiding in complex data analysis processes and decision-making. These advanced techniques are crucial for tasks like ground resistance estimation, flashover voltage prediction, load forecasting, and optimization in electric power engineering domains.

Answers from top 5 papers

PDF

Open Access

More filters

Papers (5)	Insight
Book Chapter•DOI Intelligent Data Analysis Xiaohui Liu 01 Jan 2005 13 Citations	Modern Intelligent Data Analytics Techniques include artificial intelligence, databases, high-performance computing, pattern recognition, and statistics, which complement each other for effective data analysis (Berthold & Hand, 2003; Liu, 1999).
Book Chapter•DOI Intelligent Data Analysis in Electric Power Engineering Applications Vasilios P. Androvitsaneas, Konstantinos Boulas, Georgios Dounias - Show less +2 more 01 Jan 2019 1 Citations	Modern Intelligent Data Analytics Techniques in electric power engineering include ANNs, inductive ML, genetic programming, wavelet NNs, fuzzy logic, and nature-inspired algorithms like gravitational search algorithm for various applications.
Book Chapter•DOI Intelligent Data Analysis Techniques—Machine Learning and Data Mining Dan A. Simovici 01 Jan 2015 6 Citations	Modern Intelligent Data Analytics Techniques include supervised, unsupervised, semi-supervised, active, and reinforcement learning, utilizing algorithms like naive Bayes, decision trees, support vector machines, and neural networks for evaluation.
Book Chapter•DOI Intelligent Computer Data Analysis Techniques Lidia Ogiela 01 Jan 2017 2 Citations	Intelligent data analysis techniques involve semantic analysis, linguistic methods, and cognitive systems for interpreting datasets, showcasing modern approaches in computerized information analysis.
Book Chapter•DOI Intelligent Data Analysis with Classical Machine Learning Sanjeev Kumar Punia, Manoj Kumar, Amit Sharma - Show less +2 more 01 Jan 2021 7 Citations	Intelligent Data Analysis Techniques in modern times primarily rely on Classical Machine Learning algorithms for advanced reasoning systems, as highlighted in the paper.

My columns

Related Questions

What are the most common visualization techniques used in data analytics?4 answersThe most common visualization techniques used in data analytics include bar graphs, heat maps, world maps, line graphs, scatterplots, slopegraphs, combination charts, waterfall charts, waffle charts, Sankey diagrams, and pie charts. Data visualization aids in data verification, exploration, discovery, and communication of business outcomes to stakeholders. It simplifies the understanding of complex data relationships and patterns, making it easier for viewers to grasp difficult concepts or identify new insights. Despite the increasing interest in data visualization, there is still a gap between new visualization research and the techniques commonly applied in data analytics workflows. This highlights the need for integrating more advanced visualization techniques, especially for interactive exploratory data analysis, into existing data science tools.

How many data analytics methods?5 answersThere are numerous data analytics methods discussed in the provided contexts. These methods include elementary T-scores, nonlinear principal component analysis (PCA), data normalization, quantification of categorical attributes, categorical principal component analysis (CatPCA), sparse PCA, k-nearest neighbors, decision trees, linear discriminant analysis, Gaussian mixture models, probability density function estimation, logistic regression, naive Bayes approach, random forest, and data visualization techniques. Additionally, the methods cover analyzing data using analysis programs, creating learning parameter sets, associating reference data groups, determining analysis parameters, and analyzing unanalyzed data. Furthermore, the data analytics systems involve parsing reference data, storing study data, matching analyte namesets, generating links between study and reference data, utilizing library overlays, and manipulating data for presentation to researchers.

What data mining and techniques?5 answersData mining involves extracting valuable patterns and information from large datasets using machine learning algorithms, aiding in solving complex problems like customer churn in telecommunication companies. Various techniques such as Support Vector Machine, Logistic Regression, and Decision Tree algorithms are applied to predict customer churn and improve business sustainability. Data mining plays a crucial role in diverse fields like healthcare, education, and business by extracting useful data and applying algorithms to enhance performance and make predictions for the future. Additionally, social media data mining helps companies attract more customers, predict trends, reduce costs, and increase revenue, although initial investment costs may hinder smaller organizations from utilizing this process effectively. Spectral classification techniques are also vital in data-driven mining, with different algorithms being applied successfully to analyze astronomical data.

What is Data Analysis Methods?5 answersData analysis methods encompass various techniques for extracting valuable insights from data. These methods evolve to address complex applications, increasing data volumes, and technological advancements. They play crucial roles in identifying and quantifying analytes, pattern recognition, compound quantification, and optimizing separation parameters in chromatographic data. Additionally, data analysis methods involve segmenting learning content, searching for keywords, and determining similarities to enhance learning processes. Teaching research methods and data analysis skills is vital for boosting research output and ensuring accurate conclusions. Furthermore, innovative data analysis methods enable secure data analysis without the need for intermediaries, enhancing applicability and avoiding potential drawbacks. In essence, data analysis methods are essential tools for deriving meaningful insights and making informed decisions across various domains.

How can AI be used in data analytics?5 answersAI can be used in data analytics to extract insights and knowledge from large and complex datasets. It can be applied in various domains, including healthcare, where it can assist doctors in establishing improved approaches and treatment strategies for patients. AI algorithms can be used for tasks such as diagnosis, prediction, and therapy in the medical field. Additionally, AI can be utilized in the analysis and processing of data, combining the learning characteristics of neural networks and the fault-tolerant advantages of fuzzy systems. Open-source machine learning tools can also be explored for performing analysis tasks, as they are renowned, straightforward, and often provide better results compared to paid versions. Overall, AI in data analytics enables the utilization of advanced techniques to process and analyze large datasets, leading to valuable insights and improved decision-making.

What are the best practices for conducting data analysis in data science?5 answersData analysis in data science follows a set of best practices to ensure accurate and meaningful insights. These practices include data acquisition, collection, visualization, quantification, and interpretation. Additionally, the use of methodological frameworks for analyzing data is important. It is crucial to consider the ethical and governance questions that arise when integrating privacy-preserving techniques like differential privacy into the data science workflow. A six-phase framework can be used to guide the investigation of data, incorporating key practices, processes, and dispositions. Implementing end-to-end data management best practices early in the research design phase can have a positive impact on data analysis. Understanding common patterns and solutions in data mining processes can also enhance data analysis tasks.

See what other people are reading

What is the most citated paper about Computational Neuroscience?

The most cited paper about Computational Neuroscience is the one that discusses the development of a methodology for modeling and analyzing neural excitability based on the Hodgkin-Huxley model. This paper lays the foundation for deeper studies and higher-order network models that could aid in the therapeutic treatment of brain diseases like Alzheimer and Parkinson. Computational Neuroscience, as a field, emerged to complement experimental neurophysiology, as mathematical and computational techniques were deemed essential for a comprehensive understanding of neural system function. Additionally, the paper emphasizes the importance of simulating candidate models in cognitive neuroscience to support specific claims about behavioral function, highlighting the necessity of model simulations in interpreting model comparison results.How does the AI clinician decision support system compare in terms of prediction accuracy to traditional methods?

The AI clinician decision support systems (CDSS) showcased varying prediction accuracies compared to traditional methods across different medical scenarios. For instance, the CDSS system "C the Signs" demonstrated a high sensitivity of 96.7% in identifying patients at risk of cancer, with an 85.6% accuracy in predicting cancer types. On the other hand, an AI-based algorithm for detecting colorectal neoplasms achieved an accuracy of 83.2% and a sensitivity of 77.2% in detecting neoplasms during colonoscopy recordings. Additionally, an AI-assisted CDSS model for holistic health care significantly improved sensitivity from 26.44% to 80.84% compared to a rule-based expert system, showcasing the potential of AI in predicting patients in need of holistic health care. These findings highlight the promising advancements of AI in enhancing prediction accuracy in clinical decision support systems.What factors have a significant impact on academic performance in different educational levels?

Various factors significantly impact academic performance across different educational levels. Factors such as trajectory movements, perceived usefulness, difficulty, behavior, attitude, diverse backgrounds, behaviors, spatial features, semester-wise behaviors, past performance, social status, mental issues, working status, time spent on gadgets, study duration, family support, student-specific attributes, well-defined academic goals, student focus, and self-confidence play crucial roles in determining academic success. Studies emphasize the importance of understanding these factors to predict and improve student performance, highlighting the need for tailored interventions and support mechanisms to enhance academic outcomes at various educational levels.How is the reassessment for returning to work coal worker's pneumoconiosis (buatkan dalam bahasa indonesia)?

Reassesment untuk kembali bekerja setelah terkena coal worker's pneumoconiosis (CWP) merupakan langkah penting dalam memastikan kesehatan pekerja tambang batubara. Studi menunjukkan bahwa durasi kerja yang lebih lama meningkatkan insiden CWP pada pekerja tambang batubara. Penyakit ini dapat terjadi akibat paparan debu batubara, konsentrasi debu yang terhirup, dan kandungan silika bebas. Reidentifikasi kasus CWP saat ini lebih akurat daripada menggambarkannya sebagai "keluarnya kembali" penyakit ini, karena faktor-faktor seperti diagnosis yang salah dan kurangnya pengumpulan data sebelumnya dapat menyembunyikan keberadaannya. Oleh karena itu, penilaian ulang sebelum kembali bekerja harus mempertimbangkan paparan debu, kandungan silika, dan faktor risiko lainnya untuk mencegah insiden CWP yang lebih lanjut.What is the state-of-the-art of Computational Neuroscience?

The state-of-the-art in Computational Neuroscience involves the integration of advanced mathematical modeling paradigms, artificial intelligence techniques like machine learning and deep learning, and the utilization of neuroinformatics tools. These tools aid in analyzing the complex operations of neural systems, understanding brain functions, and simulating neural activities. Computational neuroscience also focuses on developing innovative applications such as predicting brain network organization, diagnosing brain disorders, and simulating the brain in 3D. Additionally, in silico modeling plays a crucial role in neuroinformatics by enabling the prediction of neurotoxicity, identifying pharmacological targets, and facilitating the development of safer chemicals and therapeutic strategies. The field is evolving towards more generalized algorithms with broader applications, drawing insights from computational neuroscience literature for future advancements.How does AGI aim to mimic human cognitive abilities?

Artificial General Intelligence (AGI) aims to mimic human cognitive abilities by adopting a multifaceted approach that draws inspiration from various aspects of human cognition and intelligence. One foundational aspect is the development of systems that can perform equitably effective information processing, similar to human capabilities, through the analysis of human information processing for designing intelligent systems. AGI endeavors to capture the salient high-level features of human intelligence, such as goal-oriented behavior, sophisticated learning, self-reflection, and more, with architectures and algorithms designed for modern computing hardware. To achieve human-like cognition, AGI research also focuses on creating cognitive models that can negotiate shared mental models with humans, enabling co-creation and understanding in dynamic settings. This involves leveraging cognitive science principles to address hard AI problems, such as continual, quick, and efficient learning mechanisms that are characteristic of human learning. Moreover, AGI seeks to incorporate metacognitive capabilities, allowing systems to self-monitor and adapt their performance in real-time, akin to human cognitive flexibility. In the quest for human-like AGI, researchers are exploring cognitively-plausible, pattern-based systems that mimic the human way of playing games or solving problems, emphasizing the importance of context-based knowledge and generalization techniques. Additionally, the integration of cognitive computing models inspired by human cognition aims to reconcile the differences between human and computer cognition, particularly in handling uncertain concepts. The interaction between humans and AGI systems is also a critical area of focus, with research into human-artificial agents interaction highlighting the need for AGI to provide accurate mental models of their behavior to facilitate effective cooperation. Furthermore, the development of AI-mediated frameworks that augment human cognition, such as providing adaptive feedback based on deep reinforcement learning, demonstrates the potential of AGI to enhance human performance in specific tasks. Overall, AGI's pursuit to mimic human cognitive abilities is a complex, interdisciplinary endeavor that spans cognitive science, computer science, and engineering, requiring iterative feedback loops and meticulous validity tests to progress towards more human-like artificial intelligence.How can big data help in identifying Cyber Risks?

Big data plays a crucial role in identifying cyber risks by enabling advanced cyber attack detection models, analyzing network traffic for potential threats, and aiding in risk management for organizations. By utilizing techniques like feature extraction, selection, and hybrid classifiers with LSTM and DMO, big data mining can uncover hidden patterns in large datasets to detect cyber threats effectively. Organizations can leverage big data analytics to identify fraud, financial risks, and potential risk factors, enhancing risk management strategies and decision-making processes. Additionally, the use of artificial intelligence algorithms like SVMs can further enhance cyber protection by optimizing configurations for effective threat detection. In essence, big data empowers entities to proactively address cyber risks through comprehensive analysis and strategic decision-making.What is definition of vsm method in lean?

Value Stream Mapping (VSM) is a lean manufacturing tool designed to visualize the entire production process, representing both material and information flows, with the primary goal of identifying waste and opportunities for improvement. It serves as a critical analytical tool for transforming the production environment into a lean operational state by highlighting inefficiencies and facilitating the elimination of waste. VSM is not only applicable in manufacturing but extends its utility to designing sustainable supply chains by assessing performance through realistic indicators, thereby supporting the lean-driven sustainability approach. This method's adaptability allows it to address the dynamic nature of manufacturing processes by incorporating simulation and multiple-attribute decision-making (MADM) to prioritize improvement initiatives and evaluate multiple performance metrics. The application of VSM has evolved to include an extended version (X-VSM), which increases the level of detail in process representation and supports process simulation for strategic management decisions regarding waste reduction. Moreover, VSM, combined with simulation models, has proven effective in designing lean service systems, validating the lean concepts' implementation in various sectors beyond traditional manufacturing. Despite its proven efficacy, the implementation of VSM in complex production environments poses challenges, necessitating a tailored approach to effectively leverage its benefits. Recent advancements have seen the integration of VSM with simulation approaches, such as ARENA, to optimize manufacturing processes, significantly reducing cycle times and improving ecological parameters. This integration underscores VSM's capability as a diagnostic and planning tool, providing a lean perspective to identify opportunities for implementing various lean projects or tools. However, the method's future sustainability is contingent upon its adaptation to the ongoing digitalization, addressing its current limitations and enhancing its flexibility. Lastly, VSM's role in lean manufacturing philosophy is underscored by its effectiveness in identifying wastes at each production stage, facilitating the selection and prioritization of lean tools for waste elimination and process flow enhancement.What is the main difference between SSPs and RCPs?

The main difference between SSPs (Shared Socioeconomic Pathways) and RCPs (Representative Concentration Pathways) lies in their focus and purpose. SSPs are scenarios that describe potential future socioeconomic developments, including population growth, economic trends, and technological advancements, influencing greenhouse gas emissions and land use. On the other hand, RCPs are pathways that represent different levels of radiative forcing based on greenhouse gas concentrations in the atmosphere. While SSPs provide a framework for exploring how different societal choices can lead to varying emissions, RCPs offer a set of scenarios for modeling the resulting climate impacts. In essence, SSPs focus on socioeconomic factors shaping emissions, while RCPs concentrate on the resulting atmospheric greenhouse gas concentrations and their impact on climate change modeling.What are new trends in federated learning?

New trends in federated learning include the integration with other learning algorithms to enhance the vanilla federated averaging algorithm, termed as federated x learning, which encompasses multitask learning, meta-learning, transfer learning, unsupervised learning, and reinforcement learning. Additionally, there is a shift towards adapting federated learning to real-world scenarios with dynamic task sequences and strict storage constraints through the introduction of new generation federated learning (NGFL). NGFL focuses on continuous or incremental learning, where each client not only trains the server but also learns private tasks independently of server communication, addressing challenges like aggregation of heterogeneous output layers and task transformation mutual knowledge problem. These advancements aim to make federated learning more adaptable and efficient in practical applications.Can machine learning algorithms be trained to identify more sophisticated phishing attacks that use deep learning techniques?

Machine learning algorithms, including deep learning techniques, can indeed be trained to identify sophisticated phishing attacks. Researchers have developed models utilizing various algorithms such as Support Vector Machines, Gradient Boosting, Random Forests, and Convolutional Neural Networks to detect phishing attempts with high accuracy rates ranging up to 97%. These models analyze URL properties, metrics, and other external services to extract features and identify malicious URLs. By leveraging deep learning methods, such as CNNs, researchers have achieved improved detection capabilities for phishing assaults, enhancing accuracy in identifying fraudulent emails and websites. Therefore, the integration of machine learning and deep learning algorithms presents a promising approach to combatting evolving and sophisticated phishing attacks.