The prediction of human diseases precisely is still an uphill battle task for better and timely treatment. A multidisciplinary diabetic disease is a life-threatening disease all over the world. It attacks different vital parts of the human body, like Neuropathy, Retinopathy, Nephropathy, and ultimately Heart. A smart healthcare recommendation system predicts and recommends the diabetic disease accurately using optimal machine learning models with the data fusion technique on healthcare datasets. Various machine learning models and methods have been proposed in the recent past to predict diabetes disease. Still, these systems cannot handle the massive number of multifeatures datasets on diabetes disease properly. A smart healthcare recommendation system is proposed for diabetes disease based on deep machine learning and data fusion perspectives. Using data fusion, we can eliminate the irrelevant burden of system computational capabilities and increase the proposed system’s performance to predict and recommend this life-threatening disease more accurately. Finally, the ensemble machine learning model is trained for diabetes prediction. This intelligent recommendation system is evaluated based on a well-known diabetes dataset, and its performance is compared with the most recent developments from the literature. The proposed system achieved 99.6% accuracy, which is higher compared to the existing deep machine learning methods. Therefore, our proposed system is better for multidisciplinary diabetes disease prediction and recommendation. Our proposed system’s improved disease diagnosis performance advocates for its employment in the automated diagnostic and recommendation systems for diabetic patients.

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(2011). A new splitting-based displacement prediction approach for location-based services. In: IEEE international conference on systems, man, and cybernetics 2011. IEEE Xplore, 392-397. Abstract-In location-based services (LBSs), the service is provided based on the users' locations through location determination and mobility realization. Several location prediction models have been proposed to enhance and increase the relevance of the information retrieved by users of mobile information systems, but none of them studied the relationship between accuracy rate of prediction and the performance of the model in terms of consuming resources and constraints of mobile devices. Most of the current location prediction research is focused on generalized location models, where the geographic extent is divided into regular-shape cells. These models are not suitable for certain LBSs where the objectives are to compute and present on-road services. One such technique is the Prediction Location Model (PLM), which deals with inner cell structure. The PLM technique suffers from memory usage and poor accuracy. The main goal of this paper is to propose a new path prediction technique for Location-Based Services. The new approach is competitive and more efficient compared to PLM regarding measurements such as accuracy rate of location prediction and memory usage. Copyright and re-use policy

Shill Bidding (SB) occurs when the fake bidders are introduced by the seller's side to increase the final price. SB is a crime committed during the e-Auction, and it is pretty difficult to detect because of its normal bidding behaviour. The bidder gets a lot of loss because he pays extra money, and the sellers benefit from shill bidding, so this article proposed a fusion base model. This proposed model is split into two parts training and validation, into 70 and 30 per cent. This model is divided into three sub-models, first two models are Support vector machine (SVM) and Artificial neural network (ANN) that are trained parallel on the same dataset and predict the bidding fraud. The prediction of these models becomes the input of the fuzzy-based fussed module, and fuzzy decide the actual output based on SVM and ANN predictions. On every bid, it predicts whether the fraud is committed or not. If the bidding behaviour is normal, then continue the bidding; otherwise, cancel the bid and block the user. The prediction accuracy of the proposed fussed machine learning approach is 99.63%. Simulation results have shown that the proposed fussed machine learning approach gives more attractive results than state-of-the-art published methods. INDEX TERMS ShillBidding, e-Auction fraud, online fraud detection, deep learning model.

In location-based services (LBSs), the service is provided based on the users' locations through location determination and mobility realization. Most of the current location prediction research is focused on generalized location models, where the geographic extent is divided into regular-shaped cells. These models are not suitable for certain LBSs where the objectives are to compute and present on-road services. Such techniques are the new Markov-based mobility prediction (NMMP) and prediction location model (PLM) that deal with inner cell structure and different levels of prediction, respectively. The NMMP and PLM techniques suffer from complex computation, accuracy rate regression, and insufficient accuracy. In this paper, a novel cell splitting algorithm is proposed. Also, a new prediction technique is introduced. The cell splitting is universal so it can be applied to all types of cells. Meanwhile, this algorithm is implemented to the Micro cell in parallel with the new prediction technique. The prediction technique, compared with two classic prediction techniques and the experimental results, show the effectiveness and robustness of the new splitting algorithm and prediction technique.

Production of high-quality software at lower cost has always been the main concern of developers. However, due to exponential increases in size and complexity, the development of qualitative software with lower costs is almost impossible. This issue can be resolved by identifying defects at the early stages of the development lifecycle. As a significant amount of resources are consumed in testing activities, if only those software modules are shortlisted for testing that is identified as defective, then the overall cost of development can be reduced with the assurance of high quality. An artificial neural network is considered as one of the extensively used machine-learning techniques for predicting defect-prone software modules. In this paper, a cloud-based framework for real-time softwaredefect prediction is presented. In the proposed framework, empirical analysis is performed to compare the performance of four training algorithms of the backpropagation technique on software-defect prediction: Bayesian regularization (BR), Scaled Conjugate Gradient, Broyden-Fletcher-Goldfarb-Shanno Quasi-Newton, and Levenberg-Marquardt algorithms. The proposed framework also includes a fuzzy layer to identify the best training function based on performance. Publicly available cleaned versions of NASA datasets are used in this study. Various measures are used for performance evaluation including specificity, precision, recall, F-measure, an area under the receiver operating characteristic curve, accuracy, R 2 , and mean-square error. Two graphical user interface tools are developed in MatLab software to implement the proposed framework. The first tool is developed for comparing training functions as well as for extracting the results; the second tool is developed for the selection of the best training function using fuzzy logic.

Hex-Cell is an interconnection network that has attractive features like the embedding capability of topological structures; such as; bus, ring, tree and mesh topologies. In this paper, we present two algorithms for embedding bus and ring topologies onto Hex-Cell interconnection network. We use three metrics to evaluate our proposed algorithms: dilation, congestion, and expansion. Our evaluation results show that the congestion of our two proposed algorithms is equal to one; and the dilation is equal to 2d-1 for the first algorithm and 1 for the second.