In healthcare industry, Neural Network has attained a milestone in solving many real-life classification problems varies from very simple to complex and from linear to non-linear. To improve the training process by reducing the training time, Adaptive Skipping Training algorithm named as Half of Threshold (HOT) has been proposed. To perform the fast classification and also to improve the computational efficiency such as accuracy, error rate, etc., the highlighted characteristics of proposed HOT algorithm has been integrated with Strassen's matrix multiplication algorithm and derived a novel, hybrid and computationally efficient algorithm for training and validating the neural network named as Strassen's Half of Threshold (SHoT) Training Algorithm. The experimental outcome based on the simulation demonstrated that the proposed SHOT algorithm outperforms both BPN and HOT algorithm in terms of training time and its efficiency on various dataset such as such as Hepatitis, SPeCT, Heart, Liver Disorders, Breast Cancer Wisconsin (Diagnostic), Drug Consumption, Cardiotocography, Splice-junction Gene Sequences and Thyroid Disease dataset that are extracted from Machine Learning Dataset Repository of UCI. It can be integrated with any type of supervised training algorithm.

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Multilayer Feed Forward Neural Network (MFNN) has been successfully administered architectures for solving a wide range of supervised pattern recognition tasks. The most problematic task of MFNN is training phase which consumes very long training time on very huge training datasets. An enhanced linear adaptive skipping training algorithm for MFNN called Half of Threshold (HOT) is proposed in this research paper. The core idea of this study is to reduce the training time through random presentation of training input samples without affecting the network's accuracy. The random presentation is done by partitioning the training dataset into two distinct classes, classified and misclassified class, based on the comparison result of the calculated error measure with half of threshold value. Only the input samples in the misclassified class are presented to the next epoch for training, whereas the correctly classified class is skipped linearly which dynamically reducing the number of input samples exhibited at every single epoch without affecting the network's accuracy. Thus decreasing the size of the training dataset linearly can reduce the total training time, thereby speeding up the training process. This HOT algorithm can be implemented with any training algorithm used for supervised pattern classification and its implementation is very simple and easy. Simulation study results proved that HOT training algorithm achieves faster training than the other standard training algorithm.

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The IT organization needs quality assurance for the management of software engineering technology in the region. To apply its quality, IT Company attributes a practical change approach that depends on the quality change of drivers that reduces operating costs of systems. ITIL (Information Technology Infrastructure Library) and six sigma for IT service management essentially provide a wholly defined structure and ground support. But ITIL highlights the need for service measurement and statements through service management software quality. The proposed system improves the quality of software, using advanced machine learning methods and cloud systems. A new short Kernel learning (NSKL) algorithm is used as a learning method for the classification task and select the function that contains the most relevant information about the quality of the software. The integration of machine learning in the cloud is called an intelligent cloud. Cloud computing is primarily used for networking and storage, but also significantly adds capabilities to cloud-based machine learning. Based on the test results, this can be effectively detected. It already aims to make changes and improve quality while focusing on control. Results show that IT streamlines the support management and event management process more efficiently and effectively.