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Direct marketing involves telephone calls, personalized emails, and messages, newsletters, which catch the eye of the customer, and in turn, attract them towards the company. In this paper data of a Portuguese bank is considered. The bank makes a call to its potential customers regarding its term deposit schemes and is interested in customers that will invest in term deposits. With the advent of machine learning algorithms in prediction, comparative study of various algorithms such as Support Vector Machine (SVM), Gaussian Naive Bayes, Random Forest, Light Gradient Boosting (GBM), and Extreme Gradient Boosting is performed for the said data set. Light GBM gives the best results among these in very less processing time.

Oropharyngeal carcinoma is a type of head and neck cancer caused by division of cells present in the throat, which can affect a single region or multiple regions of the throat. A commonly found disease can be fatal if not diagnosed at an early stage. Various medical methodologies have been developed with time; however, the prediction of survival is still uncertain. In this research, the medical treatment offered to patients along with detailed information about them such as their drinking habits, age, time of diagnosis, and type of cancer was taken into consideration. In this paper, comparative analysis of neural network, three bagging, and three boosting algorithms was performed for prediction of oropharyngeal cancer. The bagging algorithms considered are support vector classifier (SVC), decision tree, and random forest. Boosting algorithms are AdaBoost, XGBoost, and CatBoost. Based on accuracy, time of processing and various performance parameters CatBoost was determined as the best algorithm to predict the survival chances of a cancer patient.

Facial expressions are the translation of the emotions such as anger, sadness, happiness, disgust felt by a person. Facial expression recognition, classification of expressions which has application in various industries such as hospitality, medical to name a few. There are various datasets available for facial expression recognition, we used FER 2013 dataset to build a classification algorithm. This algorithm classifies the emotions into seven categories namely, angry, disgust, happy, sad, fear, surprise and neutral. In traditional convolutional neural network algorithm the computing time is very large, ensemble learning significantly reduced the computing time and offered a promising accuracy. Features of images were extracted using the convolutional neural network, further these features were implemented using XGBoost and Random Forest to build classification algorithms and an accuracy of 77% and 74% was obtained. This was comparable to the accuracy obtained by traditional convolutional neural network which was 75% also with very less computing time.

Prohibitin (PHB), an evolutionary conserved gene, is mapped at the chromosomal location 17q21–q22 and is present with two isoforms, namely, PHB1 and PHB2. Both of these isoforms have their individual roles in tumor suppression and cell proliferation. The presence of these isoforms is not restricted to single cellular organelle but can be located in the nucleus, mitochondria, and cytosol. Importantly, loss of heterozygosity in the prohibitin gene has been noted in a significant number of different cancer types. Along with this, there are other mutations that contribute to tumorigenesis and cancer cell proliferation. Sporadic breast cancer, lung cancer, and prostate cancer are a few examples in which regulation of PHB plays a major role. PHB has shown to be both up-regulated and down-regulated depending on the type of cancer or disease. PHB acts as the biomarker for several types of cancers and is also seen as a potential therapeutic target. Along with cancer, PHB has also been seen to play an important role in other diseases such as neurological diseases, cardiac diseases, and renal diseases. The link between PHB and a plethora of diseases opens a new window in which PHB can be actually targeted in treatment as well as disease management. Here, we review the regulation of PHB in different types of cancers along with its significant interaction in other disorders.