Alzheimer’s disease is incurable at the moment. If it can be appropriately diagnosed, the correct treatment can postpone the patient’s illness. To aid in the diagnosis of Alzheimer’s disease and to minimize the time and expense associated with manual diagnosis, a machine learning technique is employed, and a transfer learning method based on 3D MRI data is proposed. Machine learning algorithms can dramatically reduce the time and effort required for human treatment of Alzheimer’s disease. This approach extracts bottleneck features from the M-Net migration network and then adds a top layer to supervised training to further decrease the dimensionality and delete portions. As a consequence, the transfer network presented in this study has several advantages in terms of computational efficiency and training time savings when used as a machine learning approach for AD-assisted diagnosis. Finally, the properties of all subject slices are combined and trained in the classification layer, completing the categorization of Alzheimer’s disease symptoms and standard control. The results show that this strategy has a 1.5 percentage point better classification accuracy than the one that relies exclusively on VGG16 to extract bottleneck features. This strategy could cut the time it takes for the network to learn and improve its ability to classify things. The experiment shows that the method works by using data from OASIS. A typical transfer learning network’s classification accuracy is about 8% better with this method than with a typical network, and it takes about 1/60 of the time with this method.

Banana cultivation is one of the main agricultural elements in India, while the common problem of cultivation is that the crop has been influenced by several diseases, while the pest indications have been needed for discovering the infections initially for avoiding the financial loss to the farmers. This problem will affect the entire banana productivity and directly affects the economy of the country. A hybrid convolution neural network (CNN) enabled banana disease detection, and the classification is proposed to overcome these issues guide the farmers through enabling fertilizers that have to be utilized for avoiding the disease in the initial stages, and the proposed technique shows 99% of accuracy that is compared with the related deep learning techniques.

Big data is one of the impacts of information revolution due to technological advancements such as communication, mobile and cloud services. The uncontrolled accumulation of structured and unstructured enormous volumes of data creates challenges in storing and manipulating data and obtaining valuable insights from these data. Big Data Analytics is progressively becoming popular and the organizations are in forefront to devise and adopt diversified approaches including machine learning for Big Data Analytics. Business organizations are using data learning as a scientific method for dealing with big data. The use of appropriate data analytics tools is crucial for the organizations to withstand in their business, to face the challenges in the market and gain out of competitive advantage. By considering the overwhelming demand on the data analytics tools, this review paper presents the comprehensive view on various Big Data Analytics methods in place and the state-of-the-art approaches towards Big Data Analytics. This paper also presents upcoming challenges towards big data and suggests certain mechanisms to thwart those challenges.

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Fuzzy Class Association Rules (FCARs) play an important role in decision support systems and have thus been extensively studied. Mining the important rules in FCARs becomes very difficult task, so Enhanced Equivalence Fuzzy Class Rule tree (EEFCR-tree) algorithm is proposed in this work. However, a major weakness of FCARs Miner is that when the number of constrained rules in a given class dominates the total constrained rules; its performance becomes slower than the normal method. To solve this problem this paper proposes a Proportion of Constraint Class Estimation (PPCE) algorithm for mining Enhanced Proportion Equivalence Fuzzy Constraint Class Association Rules (EPEFCARs) in order to save memory usage, run time and accuracy. Then, Proportion Frequency Occurrence count with Bat Algorithm (PFOCBA) is proposed for pruning rules which much satisfying the class constraints. Finally, an efficient algorithm is proposed for mining PEFCARs rules. Experimental results show that the proposed EPEFCR-tree algorithm is more efficient than Enhanced Equivalence Fuzzy Class Rule tree (EEFCRtree), Novel Equivalence Fuzzy Class Rule tree (NECR-tree) Miner results are measured in terms of run time, accuracy and memory usage. Experiments show that the proposed method is faster than existing methods.