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A Systematic Review of Challenges, Tools, and Myths of Big Data Ingestion

Each sector of the digital world generates enormous data as human life continues to transform. Areas like data analytics, data science, knowledge discovery in databases (KDD), machine learning, and artificial intelligence depend on highly distributed data which requires appropriate storage in a data lake. Collecting the data from different heterogeneous sources and creating a single lake of data is called data ingestion. Ironically, data ingestion has been treated as a less important stage in data analysis because it is considered a minor first step. There are several misconceptions in the data and analytics domain about data ingestion. The survey employed in this research presents a list of significant challenges faced by information technology (IT) industries during data ingestion. The available frameworks are compared in terms of standard parameters that are set against the existing challenges and myths. The findings from the comparison are compiled in a tabular format for easy reference. The paper places emphasis on the significance of data ingestion and attempts to present it as a major activity on the big data platform. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Conference Paper

A Systematic Review of Challenges and Techniques of Privacy-Preserving Machine Learning

Machine learning (ML) techniques are the backbone of Prediction and Recommendation systems, widely used across banking, medicine, and finance domains. ML techniques effectiveness depends mainly on the amount, distribution, and variety of training data that requires varied participants to contribute data. However, its challenging to combine data from multiple sources due to privacy and security concerns, competitive advantages, and data sovereignty. Therefore, ML techniques must preserve privacy when they aggregate, train, and eventually serve inferences. This survey establishes the meaning of privacy in ML, classifies current privacy threats, and describes state-of-the-art mitigation techniques named Privacy-Preserving Machine Learning (PPML) techniques. The paper compares existing PPML techniques based on relevant parameters, thereby presenting gaps in the existing literature and proposing probable future research drifts. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Conference Paper

A Systematic Review of AI Privileges to Combat Widen Threat of Flavivirus

In order to prevent the extraordinary spread of sickness caused by Flavivirus, the healthcare business as well as public health are working tirelessly. Individual lives have been affected, but mosquito-infested public locations have made a considerable influence on the general publics health. Site adaptability, climate change, and inadequate healthcare services and surveillance all contribute to the spread of the virus. The potential dangers of this virus, on the other hand, have been uncovered through extensive and ongoing research in the healthcare business. Modern healthcare facilities may benefit from the reasoning capabilities and ever-evolving analysis techniques provided by artificial intelligence. More conclusive findings have been demonstrated in the realm of AI applications in healthcare domains such as cancer, neurology, and cardiology. A number of research works have justified the use of AI-oriented algorithms for intelligently handling unstructured and huge healthcare data. When it comes to using artificial intelligence (AI) to identify, forecast, diagnose, and treat disease using data from public health and biological databases, the current effort aims to undertake an extensive examination. There may be issues in integrating assistive technology into the current healthcare system, as well. Because of this review, we hope that by merging AI research with clinical and public health specialists, critical knowledge may be extracted from data in order to unchain the relevant information of Flavivirus disease from its chains. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Book Chapter

A Systematic Literature Review on Image Preprocessing and Feature Extraction Techniques in Precision Agriculture

Revolutions in information technology have been helping agriculturists to increase the productivity of the cultivation. Many techniques exist for farming, but precision agriculture (PAg) is one technique that has gained popularity and has become a valuable tool for agriculture. Nowadays, farmers find it difficult to get expert advice regarding crops on time. As a solution, image processing techniques (IPTs) embedded PAg applications are developed to support farmers for the benefit of agriculture. In recent years, IPT has contributed a lot to provide a significant solution in PAg. This systematic review provides an understanding on preprocessing and feature extraction in PAg applications along with limitations. Preprocessing and feature extraction are the major steps of any application using IPTs. This study gives an overall view of the different preprocessing, feature extraction, and classification methods proposed by the researchers for PAg. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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