The purpose of this paper is to show concisely how we can promote chatbots in the medical sector and cure infectious diseases. We can create awareness through the users and the users can get proper medical solutions to prevent disease. We created a preliminary training model and a study report to improve human interaction in databases in 2021. Through natural language processing, we describe the human behaviors and characteristics of the chatbot. In this paper, we propose an AI Chatbot interaction and prediction model using a deep feedforward multilayer perceptron. Our analysis discovered a gap in knowledge about theoretical guidelines and practical recommendations for creating AI chatbots for lifestyle improvement programs. A brief comparison of our proposed model concerning the time complexity and accuracy of testing is also discussed in this paper. In our work, the loss is a minimum of 0.1232 and the highest accuracy is 94.32%. This study describes the functionalities and possible applications of medical chatbots and explores the accompanying challenges posed by the use of these emerging technologies during such health crises mainly posed by pandemics. We believe that our findings will help researchers get a better understanding of the layout and applications of these revolutionary technologies, which will be required for continuous improvement in medical chatbot functionality and will be useful in avoiding COVID-19.

Because of the metaphysical shift in the technological era, the world is witnessing machine to machine (M2M) communication. Sensors are omnipresent, owing to which wireless sensor networks (WSNs) are developing as a key enabling technology for the Internet of Things (IoT). M2M interaction poses a severe threat to the lifetime of the network. Clustering plays an essential role in elevating the energy efficiency of the WSN. Particularly, uniform distribution of cluster heads (CHs) is crucial for achieving better network lifetime and uninterrupted sensing from the area under observation. For this, we propose an efficient two-fold clustering algorithm, namely second-fold clustering (SFC). In the first phase, the selection of CH is made based on zonal residual energy and zonal degree of connectivity. In the second phase, the leftover nodes known as isolated nodes are clumped. CH is selected based on the zonal degree of connectivity to ensure that energy is disbursed (almost) uniformly by all the nodes. This uniformity makes it suitable for integration with the IoT. To find out the efficacy of SFC, carried rigorous experimental study out where it is compared to two existing clustering algorithms. The simulation results show that SFC outperforms its counterparts. The results showed that SFC improved network lifetime by approximately 9.6% and 10.2% compared to regional energy-aware clustering scheme and Residual-Low Energy Adaptive Clustering Hierarchy (R-LEACH), respectively.

Breast tumors are from the common infections among women around the world. Classifying the various types of breast tumors contribute to treating breast tumors more efficiently. However, this classification task is often hindered by dense tissue patterns captured in mammograms. The present study has been proposed a dense tissue pattern characterization framework using deep neural network. A total of 322 mammograms belonging to the mini-MIAS dataset and 4880 mammograms from DDSM dataset have been taken, and an ROI of fixed size 224 × 224 pixels from each mammogram has been extracted. In this work, tedious experimentation has been executed using different combinations of training and testing sets using different activation function withAlexNet,ResNet-18model. Data augmentation has been used to create a similar type of virtual image for proper training of the DL model. After that, the testing set is applied on the trained model to validate the proposed model. During experiments, four different activation functions ‘sigmoid’, ‘tanh’, ‘ReLu’, and ‘leakyReLu’ are used, and the outcome for each function has been reported. It has been found that activation function ‘ReLu’ perform always outstanding with respect to others. For each experiment, classification accuracy and kappa coefficient have been computed. The obtained accuracy and kappa value for MIAS dataset usingResNet-18model is 91.3% and 0.803, respectively. For DDSM dataset, the accuracy of 92.3% and kappa coefficient value of 0.846 are achieved. After the combination of both dataset images, the achieved accuracy is 91.9%, and kappa coefficient value is 0.839 usingResNet-18model. Finally, it has been concluded that theResNet-18model andReLuactivation function yield outstanding performance for the task.

The correct diagnosis of heart disease can save lives, while the incorrect diagnosis can be lethal. The UCI machine learning heart disease dataset compares the results and analyses of various machine learning approaches, including deep learning. We used a dataset with 13 primary characteristics to carry out the research. Support vector machine and logistic regression algorithms are used to process the datasets, and the latter displays the highest accuracy in predicting coronary disease. Python programming is used to process the datasets. Multiple research initiatives have used machine learning to speed up the healthcare sector. We also used conventional machine learning approaches in our investigation to uncover the links between the numerous features available in the dataset and then used them effectively in anticipation of heart infection risks. Using the accuracy and confusion matrix has resulted in some favorable outcomes. To get the best results, the dataset contains certain unnecessary features that are dealt with using isolation logistic regression and Support Vector Machine (SVM) classification.