---

In this paper, a new method is presented using a combination of deep learning method, specifically recursive neural network, and Markov chain. The aim is to obtain more realistic results with lower cost in predicting COVID-19 patients. For this purpose, the BestFirst algorithm is used for the search section, and the Cfssubseteval algorithm is implemented for evaluating the features in the data preprocessing section. The proposed method is simulated using the real data of COVID-19 patients who were hospitalized in treatment centers of Tehran treatment management affiliated to the Social Security Organization of Iran in 2020. The obtained results were compared with three valid advanced methods. The results showed that the proposed method significantly reduces the amount of memory resource usage and CPU usage time compared to similar methods, and at the same time, the accuracy also increases significantly.
 

---

Data Envelopment Analysis is one of the most appropriate methods in Evaluation of decision-making units in the real world. That is why researchers have always tried to improve and develop existing methods and approaches in this field. Network Data Envelopment Analysis is used to evaluate the efficiency of network systems by considering processes within divisions. In the evaluation of network systems, one of the challenges is the presence of undesirable and non-discretionary data in the system. Not many conducted have been done about the simultaneous presence of these factors in general two-stage network systems. For this reason, by extending CCR model and combining some methods in this study, we presented a model that is able to evaluate two-stage systems with the mentioned conditions. One of the strengths of the proposed model in this study is the achievement of the efficiency of the system and divisions simultaneously. At the end of the article, we analyzed the results with a numerical example. The results show the ability of the presented model in evaluating the systems under investigation.

 

---

Cai et al.(2013) and  Cai and Han (2014) presented the polynomial time algorithms for two-pair and three-pair networks with common bottleneck links, respectively. Also, Chen and HaiBin(2012) proposed a non-polynomial time algorithms for $n$-pair networks with common bottleneck links, where $n$ is an arbitrary integer. This paper presents a new sufficient and necessary condition to determine the solvability of single rate $n$-pair networks with common bottleneck links, which concludes a polynomial time algorithm for $n$-pair networks with common bottleneck links, where $n$ is an arbitrary integer. Our algorithm runs in $O(|V||E|^{2})$ time, where $|V|$ and $|E|$ are the number of nodes and links, respectively.

---

Intelligence has long been an interesting and important topic in psychology and cognitive science. IQ is considered a basic measure of a person's cognitive abilities, which includes various aspects of reasoning, problem solving, memory, and overall intellectual ability. Considering the importance of IQ in cognitive and psychological evaluations, the main goal of this article was to provide a new and effective approach to improve the accuracy of estimating this measure through complex brain data processing. In this paper, we have analyzed and developed a hybrid model of GWO algorithm and CNN (GCNN) in order to estimate IQ using brain MRI images. The results of the experiments showed that the accuracy of the proposed model was significantly better than the traditional techniques, and this indicates the high capabilities of the model in interpreting complex medical data. By examining the results, we find that the accuracy of the proposed model with an estimation rate of 93.10% is better than other competing methods.

---

A sum-network is a directed acyclic network with multiple sources and multiple sinks where each sink  demands the sum of the independent information generated at the sources. The coding capacity of sum networks with independent sources has been investigated in Tripathy and Ramamoorthy(2015) and it was proven that the upper bound of the coding capacity of such networks is 1. In this paper,  it is shown that the upper bound of the coding capacity of a sum network with dependent sources is greater than 1 which is different from the obtained results in Tripathy and Ramamoorthy(2015).
 It is also shown that a non-solvable sum-network with independent sources can be converted to a solvable one when the sources have arbitrary dependencies

---

Evaluation of healthcare systems, as a key organization providing different health services, is essential. This issue becomes more crucial when occurring crises such as a pandemic. They need to keep track of their success in the face of the crisis to assess the effects of policy changes and their capability to respond to new challenges. The inverse data envelopment analysis (InvDEA) technique is an applicable method in order to estimate the input/output levels of decision-making units (DMUs) to preserve predetermined technical efficiency scores. In classic studies of InvDEA, decision-Making Units (DMUs) as black boxes, ignoring their internal structure. This paper estimates input levels and new intermediate products to achieve a predetermined efficiency score set by the decision maker. In traditional inverse data envelopment analysis models, precise data are required to determine the input and/or output levels of each decision-making unit. However, in many scenarios, such as system flexibility, social and cultural contexts information may be indeterminate. In these cases, experts’ opinions are used to model uncertainty. Uncertainty theory, a branch of mathematics, logically deals with degrees of belief. This paper aims to develop an inverse Network DEA model incorporating uncertainty theory. We assume that inputs and outputs of decision-making units are based on experts’ belief degrees. To demonstrate the model is performance, we explore efficiency of healthcare systems during COVID-19 pandemic.
 

---

Fraud is a phenomenon that involves deviations and manipulations in financial statements. These actions can lead to tax non-compliance and erode the trust of investors and other stakeholders. Given the intricate nature and vast amount of financial data within organizations, leveraging artificial intelligence as a sophisticated tool can greatly enhance fraud detection in financial statements and bolster confidence in the face of evolving fraudulent tactics. Fraud or deception in the financial information of individuals or organizations reduces the level of trust and confidence that people have in the reliability and integrity of this information. This can lead to serious negative impacts, including loss of trust from customers, investors, and other entities, negative financial and legal consequences, and the exposure of illegal or improper operations that may involve financial crimes. This paper introduces an intelligent method for detecting fraud in financial statements. Initially, the Apriori algorithm is utilized to select pertinent features in the financial data. Subsequently, the performance of the proposed method is enhanced by augmenting the dataset using the GAN-CNN network. Finally, fraud detection is executed with the assistance of XGBoost. The results demonstrate that the proposed method has achieved a fraud detection accuracy of 95.3%.