Prediction of the Age and Gender Based on Human Face Images Based on Deep Learning Algorithm.

In recent times, nutrition recommendation system has gained increasing attention due to their need for healthy living. Current studies on the food domain deal with a recommendation system that focuses on independent users and their health problems but lack nutritional advice to individual users. The proposed system is developed to suggest nutritional food to people based on age and gender predicted from their face image. The designed methodology preprocesses the input image before performing feature extraction using the deep convolution neural network (DCNN) strategy. This network extracts D-dimensional characteristics from the source face image, followed by the feature selection strategy. The face's distinctive and identifiable traits are chosen utilizing a hybrid particle swarm optimization (HPSO) technique. Support vector machine (SVM) is used to classify a person's age and gender. The nutrition recommendation system relies on the age and gender classes. The proposed system is evaluated using classification rate, precision, and recall using Adience dataset and UTKface dataset, and real-world images exhibit excellent performance by achieving good prediction results and computation time.

Moth-Flame Optimization for Early Prediction of Heart Diseases.

Heart disease is among the leading causes of mortality globally. Predicting cardiovascular disease is a major difficulty in clinical data analysis. AI has been demonstrated to be powerful in deciding and anticipating an enormous measure of information created by the health domain. We provide a unique method for finding essential traits employing machine learning approaches in this paper, which enhances the effectiveness of identifying heart diseases. Decision tree (DT), support vector machine (SVM), artificial neural network (ANN), and K-nearest neighbor (KNN) are the classification techniques used to create the proposed system. Ensemble stacking integrates the four classification models to create a single best-fit predictive model using logistic regression. Many explorations have been directed at the identification of cardiac infection; however, the exactness of the outcomes is poor. Accordingly, to further enhance the efficiency, Moth-Flame Optimization (MFO) algorithm is proposed. The feature selection strategies are used to improve the classification accuracy while shortening the execution time of the classification system. Medical data are used to assess the probability of heart disease based on BP, age, gender, chest ache, cholesterol, blood sugar, and other variables. Results revealed that the proposed system excelled other existing models, obtaining 99% accuracy in the Cleveland dataset.

Human Centered Decision-Making for COVID-19 Testing Center Location Selection: Tamil Nadu-A Case Study.

This paper proposes a blend of three techniques to select COVID-19 testing centers. The objective of the paper is to identify a suitable location to establish new COVID-19 testing centers. Establishment of the testing center in the needy locations will be beneficial to both public and government officials. Selection of the wrong location may lead to lose both health and wealth. In this paper, location selection is modelled as a decision-making problem. The paper uses fuzzy analytic hierarchy process (AHP) technique to generate the criteria weights, monkey search algorithm to optimize the weights, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method to rank the different locations. To illustrate the applicability of the proposed technique, a state named Tamil Nadu, located in India, is taken for a case study. The proposed structured algorithmic steps were applied for the input data obtained from the government of India website, and the results were analyzed and validated using the government of India website. The ranks assigned by the proposed technique to different locations are in aligning with the number of patients and death rate.

Primary Squamous Cell Carcinoma of Thyroid - A Rare Malignant Goitre.

Primary squamous cell carcinoma of thyroid is a very rare malignant disease because thyroid gland lacks squamous cells. The disease is almost fatal. Only around 60 cases have been reported in literature. Its incidence is less than 1 % of all thyroid malignancies. The overall survival rate is less than 1 year after the diagnosis. Here we report a rare case of a 70 year old lady who presented with sudden increase in the size of swelling associated with pain and obstructive symptoms in long standing thyroid swelling. Considering patient's age and inoperable nature of disease we treated the patient with concurrent chemoradiation.

A giant vagal schwannoma with unusual extension from skull base to the mediastinum.

Cervical vagal schwannoma is an extremely rare neoplasm. Middle aged people are usually affected. These tumors usually present as asymptomatic masses. These tumors are almost always benign. Preoperative diagnosis of these lesions is important due to the morbidity associated with its excision. Preoperative tissue diagnosis is not accurate. The imaging modality can be done to assess the extent and for planning the treatment. Surgical excision with preservation of neural origin is the treatment option. Giant vagal schwannomas are extremely rare. Only one case has been reported in the literature till date. There has no reported case of extensive vagal schwannoma from skull base to the mediastinum. Here, we describe the asymptomatic presentation of an unusual appearing giant cervical vagal schwannoma with an extension from skull base to the mediastinum.