Variations in the size and texture of melanoma make the classification procedure more complex in a computer-aided diagnostic (CAD) system. The research proposes an innovative hybrid deep learning-based layer-fusion and neutrosophic-set technique for identifying skin lesions. The off-the-shelf networks are examined to categorize eight types of skin lesions using transfer learning on International Skin Imaging Collaboration (ISIC) 2019 skin lesion datasets. The top two networks, which are GoogleNet and DarkNet, achieved an accuracy of 77.41 and 82.42%, respectively. The proposed method works in two successive stages: first, boosting the classification accuracy of the trained networks individually. A suggested feature fusion methodology is applied to enrich the extracted features’ descriptive power, which promotes the accuracy to 79.2 and 84.5%, respectively. The second stage explores how to combine these networks for further improvement. The error-correcting output codes (ECOC) paradigm is utilized for constructing a set of well-trained true and false support vector machine (SVM) classifiers via fused DarkNet and GoogleNet feature maps, respectively. The ECOC’s coding matrices are designed to train each true classifier and its opponent in a one-versus-other fashion. Consequently, contradictions between true and false classifiers in terms of their classification scores create an ambiguity zone quantified by the indeterminacy set. Recent neutrosophic techniques resolve this ambiguity to tilt the balance toward the correct skin cancer class. As a result, the classification score is increased to 85.74%, outperforming the recent proposals by an obvious step. The trained models alongside the implementation of the proposed single-valued neutrosophic sets (SVNSs) will be publicly available for aiding relevant research fields.

Human activity recognition (HAR) from inertial sensor data plays a pivotal role in various domains, such as healthcare, sports, and smart environments. In this paper, we present a groundbreaking approach, DeepHAR-Net, for enhancing the accuracy and robustness of human activity recognition using inertial sensor data. Traditional methods in this field often rely on handcrafted features and shallow models, which may struggle to capture the intricate patterns and nuances within complex activities. DeepHAR-Net overcomes these limitations by leveraging the power of deep learning to automatically learn hierarchical representations from raw sensor data. The proposed DeepHAR-Net architecture employs a novel combination of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. This fusion enables the model to effectively capture both spatial and temporal dependencies present in multi-dimensional sensor sequences. Additionally, we introduce a data augmentation strategy tailored to inertial sensor data, further enhancing the model's ability to generalize across variations in sensor placement and orientation. We rigorously evaluate DeepHAR-Net on benchmark datasets, comparing its performance against state-of-the-art methods. The experimental results demonstrate significant improvements in accuracy, outperforming existing techniques in various activity recognition scenarios. Notably, DeepHAR-Net showcases remarkable adaptability to different sensor configurations, showcasing its potential for real-world deployment in diverse applications.

Assessing blockchain technology strategies is crucial for organizations seeking to leverage the potential benefits of blockchain in their operations. We explore the importance of evaluating blockchain technology strategies and emphasize critical considerations in the assessment process. These considerations encompass aligning strategy with business objectives, evaluating technology capabilities, analyzing implementation challenges, assessing security and privacy implications, considering regulatory compliance, and measuring the impact of the strategy. The study outlines the significance of a comprehensive assessment in ensuring the effectiveness and success of blockchain technology strategies. By conducting thorough assessments, organizations can make informed decisions, mitigate risks, and maximize the value of implementing blockchain technology. This study proposed utilizing the fuzzy set to handle and evaluate the linguistic data. We integrated the fuzzy set with the TOPSIS method to show the rank of strategies. We collect ten criteria and ten strategies in this study. We conducted a sensitivity analysis to show the stability of the results.

The global significant rise in the number of sick individuals and fatalities has made the ongoing struggle against the severe and lethal COVID-19 pandemic a global effort. There are several ongoing therapies for COVID-19, and more are being developed. However, selecting the best therapy option for COVID-19 patients is still needed. Patients may easily choose from the available COVID-19 therapies using the multi-criteria decision-making method. As a result, the present study provides an MCDM method that is created to determine COVID-19 therapies. Probabilistic Hesitant Fuzzy Set numbers, values, and ambiguity are introduced. Theorems and characteristics of PHFS numbers are also investigated in depth. The Complex Proportional Assessment technique is used, based on the PHFS, for dealing with ambiguity issues. This study uses ten criteria and three treatment methods: antibacterial medication and plasma treatment, vaccinations, as well as quarantine and in-house isolation. The study results reveal that quarantine and isolation at home mark the most effective treatment, followed by vaccinations with antibiotics and plasma therapy.

Managing healthcare waste (HCWTT) from healthcare facilities is difficult. It's high up on the list of health concerns. This growth in HCWTT has been especially visible in recent years, as the quantity of medical services available has increased. Because of the potential danger, this garbage poses to people and the planet, it must be properly disposed of. Because of ineffective waste management practices, inadequate financial resources, and a lack of adequate facilities, HCWT administration is especially crucial in developing nations. Reducing HCWT via appropriate treatment is important for the area's financial and environmental health. In order to solve single-valued neutrosophic (SVN) group decision-making issues with missing weight details, this research creates a unique multi-criterion decision-making (MCDM) approach. First, it's important to remember that various decision-makers (DMs) have varying levels of expertise. To get over the problem that the standards cannot compensate for each other, an improved version of ELECTRE is studied. The ELECTRE method is used under neutrosophic environment to rank solutions of HCWT.

Data Mining is a field that interconnects areas from computer science, trying to discover knowledge from databases in order to simplify the decision making. Classification is a Data Mining chore that learns from a set of instances in order to precisely classify the target class for new instances. Open source Data Mining tools can be used to make classification. This paper compares four tools: KNIME, Orange, Tanagra and Weka. Our goal is to discover the most precise tool and technique for breast cancer classifications. The experimental results show that some tools achieve better results more than others. Also, using fusion classification task verified to be better than the single classification task over the four datasets have been used. Also, we present a comparison between using complete datasets by substituting missing feature values and incomplete ones. The experimental results show that some datasets have better accuracy when using complete datasets.