In the past ten years, sustainable supply chain management (SSCM) attach great importance due to consumers, for-profit and profitless organizations, laws and regulations to the social and corporate responsibilities of consumers, so it has been recognized by practitioners and scholars. Supplier selection, environmental effect like a lockdown, and social cooperation and other SSCM programs can play an important part in realizing the triple bottom line (TBL) of economic, environmental, social assistances. In supply chain management (SCM), the sustainable supplier selection (SSS) and firm performance plays an important role. Traditionally, when evaluating SSS performance, organizations will consider a new framework to obtain the overall criteria/sub-criteria of the sustainability index by encapsulating sustainability. In this paper 12 sub-criteria for 3 pillars of sustainability as economic, environment and social performance is collected. Although there are many articles on SSS and evaluation, so far, research on sustainability issues is very limited. This study endeavours to propose a fuzzy multi-criteria approach to discuss SSCM planning, and studies the issue of determining a current model for SSS in the supply chain during COVID-19 based on the TBL method. For express the linguistic value of the subjective preference of experts we use triangular fuzzy numbers. By using fuzzy numbers to find standard weights for qualitative performance evaluation, then fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is proposed to find the ranking of SSS. However, COVID-19 has a negative role in SSS and in firm performance. The situation of lockdown due to COVID-19 has a negative effect on the performance of the organizations. An example is given of the proposed method.

It is increasingly difficult to identify complex cyberattacks in a wide range of industries, such as the Internet of Vehicles (IoV). The IoV is a network of vehicles that consists of sensors, actuators, network layers, and communication systems between vehicles. Communication plays an important role as an essential part of the IoV. Vehicles in a network share and deliver information based on several protocols. Due to wireless communication between vehicles, the whole network can be sensitive towards cyber-attacks.In these attacks, sensitive information can be shared with a malicious network or a bogus user, resulting in malicious attacks on the IoV. For the last few years, detecting attacks in the IoV has been a challenging task. It is becoming increasingly difficult for traditional Intrusion Detection Systems (IDS) to detect these newer, more sophisticated attacks, which employ unusual patterns. Attackers disguise themselves as typical users to evade detection. These problems can be solved using deep learning. Many machine-learning and deep-learning (DL) models have been implemented to detect malicious attacks; however, feature selection remains a core issue. Through the use of training empirical data, DL independently defines intrusion features. We built a DL-based intrusion model that focuses on Denial of Service (DoS) assaults in particular. We used K-Means clustering for feature scoring and ranking. After extracting the best features for anomaly detection, we applied a novel model, i.e., an Explainable Neural Network (xNN), to classify attacks in the CICIDS2019 dataset and UNSW-NB15 dataset separately. The model performed well regarding the precision, recall, F1 score, and accuracy. Comparatively, it can be seen that our proposed model xNN performed well after the feature-scoring technique. In dataset 1 (UNSW-NB15), xNN performed well, with the highest accuracy of 99.7%, while CNN scored 87%, LSTM scored 90%, and the Deep Neural Network (DNN) scored 92%. xNN achieved the highest accuracy of 99.3% while classifying attacks in the second dataset (CICIDS2019); the Convolutional Neural Network (CNN) achieved 87%, Long Short-Term Memory (LSTM) achieved 89%, and the DNN achieved 82%. The suggested solution outperformed the existing systems in terms of the detection and classification accuracy.

As global navigation satellite system (GNNS) signals are unable to enter indoor spaces, substitute methods such as indoor localization-based visible light communication (VLC) are gaining the attention of researchers. In this paper, the systematic investigation of a VLC channel is performed for both direct and indirect line of sight (LoS) by utilizing the impulse response of indoor optical wireless channels. In order to examine the localization scenario, two light-emitting diode (LED) grid patterns are used. The received signal strength (RSS) is observed based on the positional dilution of precision (PDoP), a subset of the dilution of precision (DoP) used in global navigation satellite system (GNSS) positioning. In total, 31 × 31 possible positional tags are set for a given PDoP configuration. The values for positional error in terms of root mean square error (RMSE) and the sum of squared errors (SSE) are taken into consideration. The performance of the proposed approach is validated by simulation results according to the selected indoor space. The results show that the position accuracy enhanced is at short range by 24% by utilizing the PDoP metric. As confirmation, the modeled accuracy is compared with perceived accuracy results. This study determines the application and design of future optical wireless systems specifically for indoor localization.

The data generated from non-Euclidean domains and its graphical representation (with complex-relationship object interdependence) applications has observed an exponential growth. The sophistication of graph data has posed consequential obstacles to the existing machine learning algorithms. In this study, we have considered a revamped version of a semi-supervised learning algorithm for graph-structured data to address the issue of expanding deep learning approaches to represent the graph data. Additionally, the quantum information theory has been applied through Graph Neural Networks (GNNs) to generate Riemannian metrics in closed-form of several graph layers. In further, to pre-process the adjacency matrix of graphs, a new formulation is established to incorporate high order proximities. The proposed scheme has shown outstanding improvements to overcome the deficiencies in Graph Convolutional Network (GCN), particularly, the information loss and imprecise information representation with acceptable computational overhead. Moreover, the proposed Quantum Graph Convolutional Network (QGCN) has significantly strengthened the GCN on semi-supervised node classification tasks. In parallel, it expands the generalization process with a significant difference by making small random perturbations of the graph during the training process. The evaluation results are provided on three benchmark datasets, including Citeseer, Cora, and PubMed, that distinctly delineate the superiority of the proposed model in terms of computational accuracy against state-of-the-art GCN and three other methods based on the same algorithms in the existing literature.