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Machine Learning is an intelligent methodology used for prediction and has shown promising results in predictive classifications. One of the critical areas in which machine learning can save lives is diabetes prediction. Diabetes is a chronic disease and one of the 10 causes of death worldwide. It is expected that the total number of diabetes will be 700 million in 2045; a 51.18% increase compared to 2019. These are alarming figures, and therefore, it becomes an emergency to provide an accurate diabetes prediction.

Health professionals and stakeholders are striving for classification models to support prognosis of diabetes and formulate strategies for prevention. The authors conduct literature review of machine models and propose an intelligent framework for diabetes prediction.

The authors provide critical analysis of machine learning models, propose and evaluate an intelligent machine learning-based architecture for diabetes prediction. The authors implement and evaluate the decision tree (DT)-based random forest (RF) and support vector machine (SVM) learning models for diabetes prediction as the mostly used approaches in the literature using our framework.

This paper provides novel intelligent diabetes mellitus prediction framework (IDMPF) using machine learning. The framework is the result of a critical examination of prediction models in the literature and their application to diabetes. The authors identify the training methodologies, models evaluation strategies, the challenges in diabetes prediction and propose solutions within the framework. The research results can be used by health professionals, stakeholders, students and researchers working in the diabetes prediction area.

In this study, response surface methodology (RSM) is applied to a three-point bending stiffness analysis of low-cost material (PLA) specimens printed using FDM technology to analyze the performance of different internal lattice structures (Octet and IsoTruss principally). The purpose of this study is to extend the definition from a discrete (lattice) model to an analytical one for its use in subsequent design phases, capable of optimizing the type of cell to be used and its defining parameters to find the best stiffness-to-weight ratio.

The representative function of their mechanical behavior is extrapolated through a two-variable polynomial model based on the cell size and the thickness of the beam elements characterizing it. The polynomial is obtained thanks to several tests performed according to the scheme of RSM. An analysis on the estimation errors due to discontinuities in the physical specimens is also conducted. Physical tests applied to the specimens showed some divergences from the virtual (ideal) behavior of the specimens.

The study allowed to validate the RSM models proposed to predict the behavior of the system as the size, thickness and type of cells vary. Changes in stiffness and weight of specimens follow linear and quadratic models, respectively. This generally allows to find optimal design points where the stiffness-to-weight ratio is at its highest.

Although the literature provides numerous references to studies characterizing and parameterizing lattice structures, the industrial/practical applications concerning lattice structures are often still detached from theoretical research and limited to achieving functioning models rather than optimal ones. The approach here described is also aimed at overcoming this limitation. The software used for the design is nTop. Subsequent three-point bending tests have validated the reliability of the model derived from the method’s application.

This study aims to discuss what prompted this organization to embark on the journey of transformational change, challenges faced strategies adopted to overcome challenges, leadership role and outcomes.

The descriptive approach is used to comprehend the transformational change process in this gigantic public sector undertaking (PSU). To have an idea of the effectiveness of the change process, the pre- and post-change performance of the company was taken into account through collection and analysis of physical and financial parameters. However, focus of this paper is concentrated on the transformation process and its chronological sequence only. Human resource productivity trend and organization development interventions adopted over the years were also observed along with conducting a sentiment analysis of the employees who lived through this entire change process in the organization.

The case study describes how this Indian PSU went through the process of transformational change management and leaves the reader to assess the degree and extent of success of the approach and strategy of the company in this regard. There may be many what-if situations and contingencies in this case for readers to explore for suggestions and solutions and finding new possibilities.

Change management is not a new exercise for the Indian corporate sector. What makes this case unique is the pro-active action initiated by a traditional high-performing and well-protected PSU to anticipate the future challenges and initiate action to overcome these. Change agents must “rewire” the plane while it is flying if the organization hopes to survive and perhaps prosper in the future. This case study is a first-hand account of the change process happening in a gigantic Indian PSU with Maharatna status.