Kidney disease is a major public health concern that has only recently emerged. Toxins are removed from the body by the kidneys through urine. In the early stages of the condition, the patient has no problems, but recovery is difficult in the later stages. Doctors must be able to recognize this condition early in order to save the lives of their patients. To detect this illness early on, researchers have used a variety of methods. Prediction analysis based on machine learning has been shown to be more accurate than other methodologies. This research can help us to better understand global disparities in kidney disease, as well as what we can do to address them and coordinate our efforts to achieve global kidney health equity. This study provides an excellent feature-based prediction model for detecting kidney disease. Various machine learning algorithms, including k-nearest neighbors algorithm (KNN), artificial neural networks (ANN), support vector machines (SVM), naive bayes (NB), and others, as well as Re-cursive Feature Elimination (RFE) and Chi-Square test feature-selection techniques, were used to build and analyze various prediction models on a publicly available dataset of healthy and kidney disease patients. The studies found that a logistic regression-based prediction model with optimal features chosen using the Chi-Square technique had the highest accuracy of 98.75 percent. White Blood Cell Count (Wbcc), Blood Glucose Random (bgr), Blood Urea (Bu), Serum Creatinine (Sc), Packed Cell Volume (Pcv), Albumin (Al), Hemoglobin (Hemo), Age, Sugar (Su), Hypertension (Htn), Diabetes Mellitus (Dm), and Blood Pressure (Bp) are examples of these traits.

Nowadays, there is a tremendous increase in the use of online secure payment systems. Even roadside shops all over India and other countries have access to online payment which made each and every person to go cash-free. But there is one important issue we need to consider which is the security during online transactions. There are several new techniques that are being developed nowadays but each has its own pros and cons. In order to enhance the security during online transaction, we have introduced a two-way authentication system which increases the security during online transaction. This system includes facial and proxy detection before entering the UPI pin. The algorithm used in this paper, will further improve the security and efficiency of facial recognition since it embeds 128 feature points of the user’s face thereby increasing the accuracy of the system. While the data is being transmitted for verification, we have included tripleDES encryption to further enhance the security of the work. After the verification is done by the system, the bank balance of the user is verified and the transaction is done. The proposed system has better efficiency when compared to the existing works and also its simple UI makes it easy to use.

Electronic documents contain tremendous amounts of information. A text summary system is needed to save time and rapidly know about the document, because the manual text summary requires time, effort, and costs, which automatically creates a summary from the source document. Hindi is India’s most spoken language. A Hindi document summarization system does not exist. The method of summary generation is either extractive or abstractive. The extractive text summary method extracts the most suitable phrases from input documents to be incorporated into the summary whereas the abstractive is the human-like summary method. To select a summary sentence, it must be the highest score. In this paper, we apply different mathematical combinations using nine textual features to score a sentence. We analyzed the results on 2000 documents taken from the Forum of Information Retrieval Evaluation (FIRE)¹ conference using ROUGE evaluation matrices at the retention rate of 40%.

We proposed the idea of editing a special issue that would compile the fruitful research that resulted from the stimulating discussions that occurred during the workshop that was held during the 5th International Conference on Intelligent Computing, Chennai on 25th & 26th March 2022. The objective of this special issue is to call for high-quality papers covering the latest data analytic concepts and technologies of big data and artificial intelligence. This special issue serves as a forum for researchers across the globe to discuss their work and recent advances in this field. The best papers from Artificial intelligence and Big Data Analytics (BAM) in the domains of Product, Finance, Health, and Environment were invited, peer-reviewed. The best high-quality papers were selected based on the innovativeness and relevance of the theme. The amount of data being generated and stored in various fields such as education, energy, environment, healthcare, fraud detection, and traffic is increasing exponentially in the modern era of Big Data. Simultaneously, there is a significant paradigm shift in business and society worldwide due to rapid advancements in fields such as artificial intelligence, machine learning, deep learning, and data analytics. This creates significant challenges for decision-making and the potential for transformation in areas such as the economy, government, and industry. Artificial Intelligence tools, techniques, and technologies, in conjunction with Big Data, improve the predictive power of the systems created and allow the government, public, and private sectors to discover new patterns and trends, as well as improve public values such as accountability, safety, security, and transparency to enable better decision-making, policies, and governance. They also have a wide range of capabilities to perform complex tasks that humans cannot. They could be used to collect, organize, and analyze large, diverse data sets to discover patterns and trends that address a variety of problems related to the development of the economy, such as identifying new sources of revenue, expanding the customer base for business, product reviews, and promotion, disease prediction and prevention, climatic variation prediction, and the provision of energy solutions. The wide variety of subject areas discussed at the 5th International Conference on Intelligent Computing is reflected in the seven accepted papers presented in the following section.

In order to accurately observe the globe, land use and land cover are crucial. Due to the proliferation of several global modifications associated with the existence of the planet, land use/land cover (LU/LC) classification is now regarded as a topic of highest significance in the natural environment and an important field to be researched by researchers. Google Earth provides satellite image dataset which contains high-resolution images; these images are used to analyze the land area. In order to address the dearth of review articles throughout the land use/land cover classification phase, we proposed a full evaluation, which might help researchers continue their work. Therefore, the purpose of this study is to investigate the methodical steps involved in classifying land use and land cover utilizing the Google Earth platform. The most widely used techniques researchers employ to achieve LU/LC classification using Google Earth Engine are examined in this work. The classification of land use and land cover for a specific region using time series was covered in this study, along with the many types of land use and land cover classes and the approach employed by Google Earth. The limits of the GEE tool and difficulties encountered during the process of classifying land use and cover have also been covered in this survey document. The importance of this review rests in inspiring future scholars to tackle the LU/LC analysis problem successfully, and this study offers researchers a road map for assessing land use/land cover classification.