Glioblastoma is a common and fatal tumor presenting a poor survival rate. To choose the best course of treatment, patients and providers need to predict the survival rate of patients. Historically, statistical methods have helped analyze clinical features to forecast survival, while recently the same is being accomplished by applying artificial intelligence techniques. However, most of these works are limited to predicting 1-, 2-, or 10-year survivability with several of these works simulating data for balancing the dataset. Hence, there is a need for fine-grained prognosis without tempering the data. To achieve the same, we employ data from Surveillance, Epidemiology, and End Results (SEER) along with an ensemble of classification and regression models to develop a fine-grained model to predict the survival period of glioblastoma patients. The proposed framework titled 'Peshnaja' presents higher resolution in the prognosis of glioblastoma while showcasing an accuracy of 70% with an overall RMSE of 2.65. Moreover, a comparison of Peshnaja with other frameworks shows that we did not impute missing values nor employed synthetic data to force good results, thereby keeping Peshnaja true to the existing data.

As the survival rate of cancer patients is low, it is only natural for patients to know how long they will survive. The National Cancer Institute in MD, USA, has developed and maintained the Surveillance, Epidemiology, and End Results (SEER) program for the past four decades. The SEER program incorporates an extensive and growing repository of data related to different types of cancers. Even though the literature presents significant work on predicting the 5-year survivability of cancer patients, there is a need for a fine-grained prognosis. This paper employs the SEER dataset for developing a survivability model for thyroid cancer patients. The proposed framework titled ‘Praedico â\euro“ Salvos’ presents higher resolution in the prognosis of thyroid cancer patients while showcasing an accuracy of 88%.

Undergraduate program in Electrical and Computer Engineering (ECE) is a four years curriculum where students work on a number of software to complete their coursework. These tools are accessible to students in university where students spend several hours to complete their assignments, a process not doable under COVID-19 global lockdown. Hence, students either (i) access their university labs remotely via virtual private networks, or in the absence of remote access either (sadly) (ii) resort to using pirated software, or (regretfully) (iii) are deprived of hands-on knowledge of the course. In this (post) Covid-19 era, educators are required to adopt their teaching pedagogy to ensure maximum delivery of their course material. In an attempt to facilitate experimental learning, this humble effort provides a detailed survey of Linux-based tools coupled with their corresponding coursework within ECE with the hope that the content provided in this paper will be of some relief to students and teachers engaged in ECE education globally.

Kidney cancer is known as one of the deadliest cancers, with high morbidity and mortality rate. Survivability from cancers is always the patient’s first concern. Thus, methods need to be devised to find the survivability rate. To achieve this goal, machine learning models are effective at providing aid to clinicians because of their accuracy. In this research paper SEER (Surveillance, Epidemiology, and End Results) database was used to provide information on cancer statistics. Three different approaches were used in this paper. The first experiment used regression models, the second included multi-class classification models to predict the survivability rate, and the third used multi-tier classification. In the end, we achieved 71% accuracy through our models to predict the survivability of a patient.

Magnetic Resonance Imaging (MRI) assists in studying the nervous system. MRI scans undergo significant processing before presenting the final images to medical practitioners. These processes are executed with ease due to excellent software pipelines. However, establishing software workstations is non-trivial and requires researchers in life sciences to be comfortable in downloading, installing, and scripting software that is non-user-friendly and may lack basic GUI. As researchers struggle with these skills, there is a dire need to develop software packages that can automatically install software pipelines speeding up building software workstations and laboratories. Previous solutions include NeuroDebian and BIDS App. NeuroDebian covers neuroscience and is less focused, providing only 27 tools for MRI. Whereas, BIDS App is committed to neuroimaging, but is limited because it only accommodates the BIDS format, covering only 34 software related to MRI. This paper presents â\euro˜aXonica,â\euro™ an MRI-based neuroimaging support package that is unbiased towards any formatting standards and provides 130 applications, more than double that of NeuroDebian (27) and BIDS App (36) combined. Using a technology stack that employs GUI as the front-end and shell scripted back-end, aXonica provides (i) 130 tools that span the entire MRI-based neuroimaging analysis pipeline, and allow the user to (ii) select the software of their choice, (iii) automatically resolve individual dependencies and (iv) installs them. Hence, aXonica can serve as an important resource for researchers and teachers working in the field of MRI-based Neuroimaging to (a) develop software workstations, and/or (b) install newer tools in their existing workstations.