Big data, fueled by the influx of information from sources like the internet, cloud platforms, and biosensors, is revolutionizing the modern information landscape. Global initiatives are maximizing its strategic potential. Companies like Wal-Mart and Facebook, for instance, regularly process petabytes of data. However, the challenge goes beyond storage to the extraction of meaningful insights. Technologies, such as Apache Spark built on Hadoop, are emerging to cater to the escalating data demands. Eric Topol emphasized how the future of medicine will see AI intertwined with human intellect. In the healthcare sector, especially in the U.S., the priority is on managing massive medical data sets while simultaneously confronting economic hurdles.
Artificial intelligence (AI), boosted by technological progress and rapid processing, has evolved from a concept to a functional tool. Machine learning (ML), a subset of AI, emphasizes automating data analysis to identify patterns in large datasets, with recent advancements such as GraphCL-DTA leveraging graph contrastive learning to enhance representation quality in complex domains like drug-target interaction prediction. This is impactful in areas like consumer analytics. This article explores ML's significance in big data analytics. We present both empirical and experimental assessments of different methods. Through discussions, analyses, and evaluations, we give an in-depth understanding of ML techniques in big data. This guides researchers for better decisions. The article also discusses potential ML advancements in Big Data and future exploration avenues.
In this paper, we examine and assess algorithms that utilize ML to explore IoT, Social Media, NLP, and Information Security. In the "Big Data" age, extracting online insights has grown. Emotions and opinions shared online shape netizen perceptions. Growing food safety concerns have spurred discussions on adulteration and agricultural pollution. These sentiment evaluations support risk communication and decisions. The Internet is the primary source for food safety, yet it propagates misinformation, intensifying concerns.
Thus, analyzing online food safety sentiments is vital for risk control. In Big Data, real-time categorization and prediction of vast data are daunting due to fluctuating attributes. For instance, recent advances such as the AE-BiLSTM model with heuristic tuning proposed by Bhasha and Panda demonstrate robust privacy-preserving authentication for IoMT applications using deep learning in real-time streaming environments. Real-time tasks like recommendation engines and spam filters necessitate such classification, focusing on efficiency. With digital data growing quickly, there's a push for advanced computational techniques. To address this, robust platforms are needed, and recent works such as Subramanian et al. propose advanced adversarial architectures like modified BiGAN-AH for securing cloud environments through AI-based authentication mechanisms. Big Data systems, delivers fast and scalable solutions marked by elasticity.
This survey offers a comprehensive review of Big Data Analytics in IoT, Social Media, NLP, and Information Security, using a consistent methodological framework to explore trends, challenges, and applications within these domains. A key contribution of this work is the introduction of a taxonomic framework that categorizes algorithms based on their specific applications, providing researchers with a clearer understanding of the relationships between techniques. The study integrates empirical evaluations, focusing on scalability, interpretability, accuracy, and efficiency, with experimental analyses that show the relative accuracy and computational time of various techniques within the same Big Data application domain.
By bridging empirical insights with experimental results, this paper not only highlights the strengths and weaknesses of various algorithms but also provides actionable guidance for selecting appropriate techniques. Ultimately, the survey lays the groundwork for future advancements in Big Data Analytics, offering researchers valuable tools and perspectives to address emerging challenges and drive innovation in this field.
Code: >> Click Here <<
Enregistrer un commentaire