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The article titled "A novel data privacy-preserving protocol for multi-data users by using genetic algorithm" has been retracted by the publisher due to concerns about compromised editorial handling and peer review processes, inappropriate references, and being out of scope for the journal. The retraction note indicates that the publisher no longer has confidence in the results and conclusions of the article. One of the authors, N Deepa, disagrees with the retraction, while the other author, P Pandiaraja, has not responded to correspondence regarding the retraction. [Extracted from the article]

The article "Hybrid Context Aware Recommendation System for E-Health Care by merkle hash tree from cloud using evolutionary algorithm" has been retracted by the publisher due to concerns about compromised editorial handling and peer review processes, inappropriate references, and being out of scope for the journal. The authors, N. Deepa and P. Pandiaraja, have differing responses to the retraction, with Deepa disagreeing and Pandiaraja not responding. The publisher, Springer Nature, has taken this action based on their investigation findings and no longer has confidence in the article's results and conclusions. [Extracted from the article]

Privacy preservation permits doctors to outsource the huge encrypted reports to the cloud and permits the authenticated patients to have a safe search over the reports without leaking the private information. The doctors in our proposed have used the merkle hash tree for storing the reports of all the patients in the hospital. The existing schemes have used many types of trees like binary tree, red–black tree, spanning tree, B+ tree, etc., for the index generation purpose. Since the security is less and the searching time is high for the above said trees, we have proposed the index generation phase based on the merkle hash tree based on the evolutionary algorithm and it takes less time for searching and highly secure for storing the patient reports. The evolutionary algorithm is used for breeding the new data's through crossover as well as mutation operations to give confinement to new children. When the patient submits the search request for specialized doctor, based on the patient disease our protocol will recommend the specialized doctors and send the recommended doctors information to the patients who have the highest rating in the online social networks. After receiving the recommended results, the patient can have the treatment via online booking appointment, video call or in person based on the appointment booked. After completely cured, the patients can rate the doctors based on the medicine satisfaction, doctors' fees and doctor's response over the call. In this mechanism, we have used the hybrid context aware recommendation system collaborative filtering for rating the doctors based on their performance. After rating the doctors, our protocol has measured the accuracy based on the predicted rating and the true rating. This kind of accuracy metrics is used for ranking the good doctors in the top rank for the patient use. Our proposed work Hybrid Context Aware Recommendation System for E-Health Care (HCARS-EHC) is implemented, and the implementation results of HCARS-EHC illustrate that our protocol is efficient based on the privacy preservation, recommendation and ranking with less computation and communication complexity. [ABSTRACT FROM AUTHOR]

In this paper, rough, fuzzy and soft set approaches have been integrated to develop a multi-class classification model to assist the farmers in taking decision on crop cultivation for a given agriculture land. The model is divided into three major sections, namely weight calculation of variables, conversion of continuous data to fuzzified values and classification rule generation. Dominance-based rough set approach is used for the calculation of relative weights of variables. Fuzzy proximity relation is applied to convert the continuous data into fuzzified values. Bijective soft set approach is used to generate classification rules for five agriculture crops, namely paddy, groundnut, sugarcane, cumbu and ragi. The developed model has been tested with agriculture dataset which showed 92% accuracy for the validation dataset and proved to be confident and robust for agriculture development. Further, the performance of the proposed model is compared with three popular classifiers such as naïve Bayes, support vector machine and J48. The obtained experimental results showed high predictive performance, and the potential of the proposed model is compared with the other classifiers. [ABSTRACT FROM AUTHOR]

In this research paper, the proposed work is to put forward a Novel Data Privacy-Preserving Protocol (NDPPP) for Multi-data Users by using genetic algorithm. The data owner outsources the files in the encrypted format to the cloud. The data users can efficiently download the encrypted files from the cloud service provider without any loss of data. To provide this facility, there are certain existing mechanisms in the literature. But the existing mechanisms will result high computation complexity. By means of minimizing the computation complexity, a NDPPP for multi-data users is proposed using genetic algorithm in this research work. Genetic algorithms are usually used to produce high-quality resolutions for optimization. A new trapdoor function is proposed to preserve the data privacy by using our Data Privacy-Preserving Protocol. With the aim of avoiding numerous attacks, a secure authentication protocol is moreover developed between the trusted third party and data user. Additionally, the security is improved. Provided the leakage of data, loss of data as well as the data modification can also be avoided. The proposed work is implemented, and the implementation results of NDPPP illustrate that our protocol is efficient through computation and communication complexity. Moreover, the proposed work NDPPP is secure against various attacks like impersonation attack, eavesdropping, man-in-the-middle attack as well as replay attack. [ABSTRACT FROM AUTHOR]

The multiresolution-based fusion techniques can be developed with innovative image preprocessing and fusion techniques. In this proposed work, the three-level decomposition in image preprocessing and optimized multi-feature fuzzy clustering (OMFC) for extracting features, fusing features and reconstructing image are done. Each source images are decomposed into equivalent smooth, structure and boundary levels with the help of spatial domain. Next, the large and small frequency bands are merged by applying enhanced parametric adaptive pulse coupled neural network technique for preserving significant information and data extraction. At last, reconstructing target image from the fused image by applying inverse OMFC is performed, and the validations are done in various categories. Demonstration of the proposed technique is given, and the results shows its performance better than the other existing techniques. [ABSTRACT FROM AUTHOR]