In this paper, machine learning (ML) strategies have been utilized in predicting vehicles’ prices and good deals. Vehicle value prediction has been considered one of the most significant research topics with the rise of IoT for sustainability. This is because it requires observable exertion and massive field information. Towards generating a model that anticipates the vehicles’ price, we applied three ML methods (neural network, decision tree, support vector machine, and linear regression). However, the referenced methods have been applied to function together as a group in a hybrid model. The information utilized was gathered from an information and computer science school that houses different datasets. Separate exhibitions of several ML techniques were contrasted to reveal which one is suitable for the accessible information index. Various difficulties and challenges associated with this design have also been discussed. Moreover, the model was experimented, and a 90% precision was achieved. This potential result can help in providing precise vehicle deals in the emerging Internet of Things (IoT) for the sustainability paradigm.

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The resolution of complex medical diagnoses using pattern recognition requires an artificial neural network-based expert system to automate autoimmune disease diagnosis in blood samples. This process is done using image-based computer-aided diagnosis (CAD) to reduce errors in the diagnosis process. This paper describes a Multistage Classification Scheme (MSCS), which uses antinuclear antibody (ANA) tests to identify and classify the existence of autoantibodies in the blood serum that bind to antigens found in the nuclei of mammalian cells. The MSCS classified HEp-2 cells into three stages by using Binary Tree (BT), Artificial Neural Network (ANN), and Support Vector Machine (SVM) as basic blocks. The Indirect Immunofluorescence (IIF) technique is used in the ANA test with Human Epithelial type-2 (HEp-2) cells as substrates. The efficiency of the proposed methodology is assessed using the dataset of ICPR 2016. The intermediate cells (IMC) and positive cells (PC) were separated in Stage 1 prior to preprocessing based on their total strength, and special preprocessing is applied to intermediate cells for improved output, and positive cells are subjected to mild preprocessing. The mean class accuracy (MCA) was 84.9% for intermediate cells and 95.8% for positive cells, although the carefully picked 24 features and SVM classifier were applied. ANN showed better performance by adjusting the weights using the SCGBP algorithm. So, the MCA is 88.4% and 97.1% for intermediate and positive cells, respectively. BT had an MCA of 95.3% for intermediate and 98.6% for positive. In Stage 2, the meta learners BT2, ANN2, and SVM2 were trained for an augmented feature set (24 + 3 results from base learners). Therefore, the performance of BT2, ANN2, and SV M2 was increased by 1.8%, 4.5%, and 4.1% as compared to Stage 1. In Stage 3, the final prediction was performed by majority voting among the results of the three meta learners to achieve 99.1% MCA. The proposed algorithm can be embedded into a CAD framework built for the ANA examination. The proposed model will improve operational efficiency, decrease medical expenses, expand accessibility to healthcare, and improve patient safety in the sector, enabling enterprises to lower unplanned downtime, develop new products or services, increase operational effectiveness, and enhance risk management.

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