Due to its superlative physical qualities and its beauty, the diamond is a renowned structure. While the green-colored perimantanes diamondoid is one of a higher diamond structure. Motivated by the structure's applications and usage, we look into the metric-based parameters of this structure. In this draft, we have discussed metric dimension and their generalizations for the generalized perimantanes diamondoid structure and proved that each parameter depends on the copies of original or base perimantanes diamondoid structure and changes with the parameter n or its number of copies.INDEX TERMS Vertex metric dimension, generalized perimantanes diamondoids, diamond structure, resolving set.

The market of Saudi Arabia offers considerable potential for the success of mobile commerce (m-commerce) technology. In this context, this study aims to investigate the factors that influence the intentions of Saudi citizens and firms to use m-commerce technology. This study paper advances the literature by proposing a novel model to investigate the motivations behind adopting m-commerce in Saudi Arabia. The model defines factors that influence both customer and provider intentions of adopting m-commerce. The paper also outlines the methodology to be followed to evaluate the proposed model.

The use of an automatic histopathological image identification system is essential for expediting diagnoses and lowering mistake rates. Although it is of enormous clinical importance, computerized breast cancer multiclassification using histological pictures has rarely been investigated. A deep learning-based classification strategy is suggested to solve the challenge of automated categorization of breast cancer pathology pictures. The attention model that acts on the feature channel is the channel refinement model. The learned channel weight may be used to reduce superfluous features when implementing the feature channel. To increase classification accuracy, calibration is necessary. To increase the accuracy of channel recalibration findings, a multiscale channel recalibration model is provided, and the msSE-ResNet convolutional neural network is built. The multiscale properties flow through the network’s highest pooling layer. The channel weights obtained at different scales are delivered into line fusion and used as input to the next channel recalibration model, which may improve the results of channel recalibration. The experimental findings reveal that the spatial recalibration model fares poorly on the job of classifying breast cancer pathology pictures when applied to the semantic segmentation of brain MRI images. The public BreakHis dataset is used to conduct the experiment. The network performs benign/malignant breast pathology picture classification collected at various magnifications with a classification accuracy of 88.87 percent, according to experimental data. The diseased images are also more resilient. Experiments on pathological pictures at various magnifications show that msSE-ResNet34 is capable of performing well when used to classify pathological images at various magnifications.

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Eye tracking technology has been adopted in numerous studies in the field of human-computer interaction (HCI) to understand visual and display-based information processing as well as the underlying cognitive processes employed by users when navigating a computer interface. Analyzing eye tracking data can also help identify interaction patterns with regard to salient regions of an information display. Deep learning technology is increasingly being used in the analysis of eye tracking data by allowing for the classification of large amounts of eye tracking results. In this paper, eye tracking data and convolutional neural networks (CNNs) were used to perform a classification task to predict three types of information presentation methods. As a first step, a number of data preprocessing and feature engineering approaches were applied to eye tracking data collected through a controlled visual information processing experiment. The resulting data were used as input for the comparison of four CNN models with different architectures. In this experiment, two CNN models were effective in classifying the information presentations with overall accuracy greater than 80%.

The Internet of Medical Things (IoMT) is a network of healthcare devices such as wearables, diagnostic equipment, and implantable devices, which are linked to the internet and can communicate with one another. Blockchain (BC) technology can design a secure, decentralized system to store and share medical data in an IoMT-based intelligent healthcare system. Patient records were stored in a tamper-proof and decentralized way using BC, which provides high privacy and security for the patients. Furthermore, BC enables efficient and secure sharing of healthcare data between patients and health professionals, enhancing healthcare quality. Therefore, in this paper, we develop an IoMT with a blockchain-based smart healthcare system using encryption with an optimal deep learning (BSHS-EODL) model. The presented BSHS-EODL method allows BC-assisted secured image transmission and diagnoses models for the IoMT environment. The proposed method includes data classification, data collection, and image encryption. Initially, the IoMT devices enable data collection processes, and the gathered images are stored in BC for security. Then, image encryption is applied for data encryption, and its key generation method can be performed via the dingo optimization algorithm (DOA). Finally, the BSHS-EODL technique performs disease diagnosis comprising SqueezeNet, Bayesian optimization (BO) based parameter tuning, and voting extreme learning machine (VELM). A comprehensive set of simulation analyses on medical datasets highlights the betterment of the BSHS-EODL method over existing techniques with a maximum accuracy of 98.51%, whereas the existing methods such as DBN, YOLO-GC, ResNet, VGG-19, and CDNN models have lower accuracies of 94.15%, 94.24%, 96.19%, 91.19%, and 95.29% respectively.

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