A brain tumor occurs in humans when a normal cell turns into an aberrant cell inside the brain. Primarily, there are two types of brain tumors in Homo sapiens: benign tumors and malignant tumors. In brain tumor diagnosis, magnetic resonance imaging (MRI) plays a vital role that requires high precision and accuracy for diagnosis, otherwise, a minor error can result in severe consequences. In this study, we implemented various configured convolutional neural network (CNN) paradigms on brain tumor MRI scans that depict whether a person is a brain tumor patient or not. This paper emphasizes objective function values (OFV) achieved by various CNN paradigms with the least validation cross-entropy loss (LVCEL), maximum validation accuracy (MVA), and training time (TT) in seconds, which can be used as a feasible tool for clinicians and the medical community to recognize tumor patients precisely. Experimentation and evaluation were based on a total of 2189 brain MRI scans, and the best architecture shows the highest accuracy of 0.8275, maximum objective function value of 1.84, and an area under the ROC (AUC-ROC) curve of 0.737 to accurately recognize and classify whether or not a person has a brain tumor.

As the world is getting digitized the speed in which the amount of data is over owing from different sources in different format, it is not possible for the traditional system to compute and analysis this kind of big data for which big data tool like Hadoop is used which is an open source software. It stores and computes data in a distributed environment. In the last few years developing Big Data Applications has become increasingly important. In fact many organizations are depending upon knowledge extracted from huge amount of data. However traditional data technique shows a reduced performance, accuracy, slow responsiveness and lack of scalability. To solve the complicated Big Data problem, lots of work has been carried out. As a result various types of technologies have been developed. As the world is getting digitized the speed in which the amount of data is over owing from different sources in different format, it is not possible for the traditional system to compute and analysis this kind of big data for which big data tool like Hadoop is used which is an open source software. This research work is a survey about the survey of recent optimization technologies and their applications developed for Big Data. It aims to help to choose the right collaboration of various Big Data technologies according to requirements.

The convergence of automation, big data analytics (BDA), artificial intelligence (AI), and machine learning (ML) has ushered in a new era of technological advancement, reshaping industries, and societies worldwide. This review research work delves into the transformative impact of these technologies, focusing on their applications across various sectors. The study covers six key sectors: healthcare, banking, finance, retail, real estate, and agriculture, highlighting how these industries leverage automated systems and data analytics to enhance operations, manage risks, and improve decision-making processes. Drawing results from over 1,000 research papers and categorizing them into 100 key studies specifics, this survey-based review underscores the critical role of big data in enabling predictive analytics, improving outcomes, and driving innovation across sectors. The review research work explores how industries utilize vast data volumes from diverse sources to derive actionable insights, forecast trends, and optimize processes. Key applications included in the review are from the domains of disease prediction and electronic health record management in healthcare , fraud detection and credit risk assessment in banking and finance, consumer behavior analysis and inventory optimization in retail, market trend forecasting in real estate, and disaster risk management in agriculture. The paper also discusses the challenges including data quality, scalability, and privacy paving way towards future directions of big data analytics, emphasizing the need for machine-independent solutions, data security, and ethical considerations in the evolving landscape of data-driven decision-making.

Agriculture is the backbone of the human society as it is an essential need of every creature present on this planet. Rice cultivation plays a vital role in the life of humans, especially in the Indian Subcontinent. So, it is necessary for humans to protect the importance and productivity of agriculture. The IT industry is very significant for the agriculture and especially for the healthcare of agricultural industry. Machine Learning and Artificial Intelligence is a buzzword in the IT industry and now these buzzwords have helped a lot in the field of agriculture. These technologies give foundation to various aspects of decision making and can be productive for every industry. This paper presents a model that uses the CNN algorithm of Deep Learning for the prediction of paddy crop disease. This model also finds the probability of the occurrence of disease which can be helpful to take some vital decisions related to plant’s health.