The recommender system is the most developing application in the web environment. A good Recommendation system needs to make choices and decisions to provide recommendations to the user. The Recommender system filters the information and provides accurate recommendations of products or services for a concerned individual. In this research, we propose a model, which employs three approaches; clustering, nature-inspired optimizer, and deep learning. The fuzzy c-means clustering technique is combined with the particle swarm optimization algorithm. The particle swarm optimizer works as a feature extractor. The particle swarm optimizer is applied to the Movielens dataset to get the initial cluster positions. The fuzzy c-means is used to classify the users in the dataset and to reduce data redundancy. The optimized and clustered data is provided as input to the autoencoder to get the final recommendations of the movies to the users. We have proposed our recommender system in two parts; 1) Using a single-layer autoencoder, and 2) Using a deep autoencoder. We analyzed our proposed system over a publicly available dataset- Movielens. The proposed system is compared with the existing recommender systems. The experimental results of our system revealed that the performance and efficiency are improved, and offered better recommendations.

As computer systems become more pervasive and complex, security is increasingly important. Secure Transmission refers to the transfer of data such as confidential or proprietary information over a secure channel. Many secure transmission methods require a type of encryption. Secure transmissions are put in place to prevent attacks such as ARP spoofing and general data loss. Hence, in order to provide a better security mechanism, in this paper we propose Enhanced Tiny Encryption Algorithm with Embedding (ETEA), a data hiding technique called steganography along with the technique of encryption (Cryptography). The advantage of ETEA is that it incorporates cryptography and steganography. The advantage proposed algorithm is that it hides the messages.

Background Eosinophils are bone marrow-derived granulocytes known to have an imperative role in tissue inflammation. The mechanism of tumour-associated tissue eosinophilia (TATE) in head and neck cancers is however not well understood, and its role as a prognosticator is under evaluation. The aim of this study was to evaluate the association of TATE with factors associated with head and neck cancer and to assess its role as a prognostic marker in such patients. Results 102 males and 24 females comprised the study population, and 34.9% of which were in the age group of 41 to 50 years. Amongst these 126 patients, most (37.3%) presented in stage III followed by stage IV (28.6%). 29.4% had well-differentiated SCC, 55.6% had moderately differentiated SCC, and 15% were diagnosed with poorly differentiated SCC. 42.8% had TATE grade II, followed by grade III (29.4%) and grade I (27.8%). Correlation studies showed that factors significantly associated with TATE were age, site and tumour differentiation. While 45.7% poorly differentiated tumours showed grade I eosinophilia, 51.4% of well-differentiated tumours had grade III TATE. Conclusions TATE showed a highly significant association with tumour differentiation, suggestive of eosinophils partaking a tumouricidal role. This association may be utilised as a convenient early prognosticator for head and neck cancers and should be made a regular feature of biopsy reports. Furthermore, it may be utilised in planning and adopting appropriate treatment modalities in malignancies predicted to have an aggressive course.

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