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Breast cancer (BC) is the most widespread tumor in females worldwide and is a severe public health issue. BC is the leading reason of death affecting females between the ages of 20 to 59 around the world. Early detection and therapy can help women receive effective treatment and, as a result, decrease the rate of breast cancer disease. The cancer tumor develops when cells grow improperly and attack the healthy tissue in the human body. Tumors are classified as benign or malignant, and the absence of cancer in the breast is considered normal. Deep learning, machine learning, and transfer learning models are applied to detect and identify cancerous tissue like BC. This research assists in the identification and classification of BC.We implemented the pre-trained model AlexNet and proposed model Breast cancer identification and classification (BCIC), which are machine learning-based models, by evaluating them in the form of comparative research.We used 3 datasets, A, B, and C.We fuzzed these datasets and got 2 datasets, A2C and B3C. Dataset A2C is the fusion of A, B, and C with 2 classes categorized as benign and malignant. Dataset B3C is the fusion of datasets A, B, and C with 3 classes classified as benign, malignant, and normal. We used customized AlexNet according to our datasets and BCIC in our proposed model. We achieved an accuracy of 86.5% on Dataset B3C and 76.8% on Dataset A2C by using AlexNet, and we achieved the optimum accuracy of 94.5% on Dataset B3C and 94.9% on Dataset A2C by using proposed model BCIC at 40 epochs with 0.00008 learning rate. We proposed fuzzed dataset model using transfer learning. We fuzzed three datasets to get more accurate results and the proposed model achieved the highest prediction accuracy using fuzzed dataset transfer learning technique. [ABSTRACT FROM AUTHOR]

In current century, big number of people adopting social media very fastly because social media is very reliable, fast and easy to use. So, many big industries adopting social media also, like Facebook to analyze their customers' reviews, analyze customers' emotions and to understand their behavior, which is very helpful in providing facilities to customers. Nowadays bulk of data available is on social media. To increase competitive benefits and assess competitive environment of industries, companies need to analyze social media data as well as customer generated data. To guide large industries in understanding how they may use social media to understand customer emotion and use social media marketing approaches into their marketing strategies to get customer engagement. This paper describes in-depth analysis of pizza industry of United Kingdom to understand their customers' emotions and get data for analysis from Facebook. We are using January 2018–December 2018 data of five largest pizza chains in United Kingdom: Papa John, Prezzo, Pizza Express, Pizza Hut and Dominos. The results disclose the power of sentimental analysis and social media competitive analysis will put positive impact on business value. At the end of this paper we will provide some recommendations to enhance marketing strategies. [ABSTRACT FROM AUTHOR]

One of the most difficult jobs in the post-genomic age is identifying a genetic disease from a massive amount of genetic data. Furthermore, the complicated genetic disease has a very diverse genotype, making it challenging to find genetic markers. This is a challenging process since it must be completed effectively and efficiently. This research article focuses largely on which patients are more likely to have a genetic disorder based on numerous medical parameters. Using the patient's medical history, we used a genetic disease prediction algorithm that predicts if the patient is likely to be diagnosed with a genetic disorder. To predict and categorize the patient with a genetic disease, we utilize several deep and machine learning techniques such as Artificial neural network (ANN), K-nearest neighbors (KNN), and Support vector machine (SVM). To enhance the accuracy of predicting the genetic disease in any patient, a highly efficient approach was utilized to control how the model can be used. To predict genetic disease, deep and machine learning approaches are performed. The most productive tool model provides more precise efficiency. The simulation results demonstrate that by using the proposed model with the ANN, we achieve the highest model performance of 85.7%, 84.9%, 84.3% accuracy of training, testing and validation respectively. This approach will undoubtedly transform genetic disorder prediction and give a real competitive strategy to save patients' lives. [ABSTRACT FROM AUTHOR]