The entanglement classification of quantum states is nearly related to the positive homogeneous function that is invariant under stochastic local operations and classical communication (SLOCC) transformations. In this paper, first we give a general scheme to measure the N-tangle as an entanglement measure of an arbitrary odd-N qubit pure state. Then we develop a classification algorithm that is based on the integration between competitive learning and the computational power of quantum computing. Using this algorithm the entanglement measure we classify an input into one of two binary classes even if the input pattern is incomplete. The entanglement quantification and classification of quantum states of a large number of particles becomes quickly complex. This major challenge was raised within the quantum information science regarding the physical phenomenon of entanglement as a key resource. During the past years, the polynomial invariants such as concurrence for two-qubit states and three-tangle for pure three-qubit states was defined.

Background and Objectives: A predefined structure is usually employed for deep neural networks, which results in over- or underfitting, heavy processing load, and storage overhead. Training along with pruning can decrease redundancy in deep neural networks; however, it may lead to a decrease in accuracy.Methods: In this note, we provide a novel approach for structure optimization of deep neural networks based on competition of connections merged with brain-inspired synaptic pruning. The efficiency of each network connection is continuously assessed in the proposed scheme based on the global gradient magnitude criterion, which also considers positive scores for strong and more effective connections and negative scores for weak connections. But a connection with a weak score is not removed quickly; instead, it is eliminated when its net score reaches a predetermined threshold. Moreover, the pruning rate is obtained distinctly for each layer of the network.Results: Applying the suggested algorithm to a neural network model of a distillation column in a noisy environment demonstrates its effectiveness and applicability.Conclusion: The proposed method, which is inspired by connection competition and synaptic pruning in the human brain, enhances learning speed, preserves accuracy, and reduces costs due to its smaller network size. It also handles noisy data more efficiently by continuously assessing network connections

This systematic review article examines the role of competitive intelligence and machine learning in the insurance industry. Given the rapid advancements in information technology and the increasing need for accurate strategic decision-making in the face of intensifying competition, competitive intelligence has emerged as a vital tool for data analysis and predicting competitor behavior in this sector. This study analyzes various methods of applying machine learning to process both structured and unstructured data within the insurance industry and explores the associated challenges and opportunities. The article first analyzes the fundamental concepts of competitive intelligence and developments in information technology in this domain. It then investigates the challenges and requirements of the insurance industry in utilizing advanced data analytics techniques, including machine learning. The findings indicate that integrating machine learning with competitive intelligence can assist insurance companies in conducting large-scale data analysis, thereby enabling more accurate predictions of competitor behavior and market trends. Moreover, the article identifies existing research gaps and presents a conceptual model for the application of competitive intelligence in the insurance industry, offering recommendations for future research and practical applications for insurance industry managers.