Fracture prediction poses a significant challenge in petroleum exploration and development. The unexpected intersection with fractures during drilling, particularly in horizontal drilling, can lead to substantial economic losses. Currently, there is no widely accepted or efficient method for accurately estimating fracture distribution in subsurface reservoirs. We propose a novel approach to evaluate fracture distribution in a shallow-buried, high-porosity sandstone reservoir. This method utilizes frequency analysis and seismic attenuation calculations while minimizing artificial influence. By analyzing the frequency spectrum, we can identify the effective high and low frequencies that reveal the impact of fractures on the seismic bandwidth. We generate seismic attenuation attributes related to the quality factor using the spectral ratio method. Finally, we calculate seismic attenuation based on the differences between high and low frequencies. Areas with notable disparities in high-frequency and low-frequency attenuation indicate the presence of fracture corridors. This proposed method has successfully interpreted the fracture distribution responsible for drilling failures in heavy oil production within the reservoir.

Speaker recognition systems are some kind of biometric systems used in security applications based on remote control phones. This paper presents an approach to balancing the increase in the number of speakers, runtime and error rate of the system. The efficiency of these systems often depends on a particular number of people.With a sudden increase in the number of speakers in terms of time, the recognition error rate experiences a sharp rise. This paper introduces a parameter to select the optimal number,noisy samples for training and testing the Support Vector Machine (SVM) classifier.This approach aims to significantly increase learning speed andto test speaker recognition systems by setting noisy parameters for learning the model and recognizing people. In the first stage,we use the ReliefF algorithm to select the optimal feature subset, then the SVM classifier to achieve better accuracy in recognition by changing existing SVM kernels and automatically setting the balance parameter during training. This method is evaluated based on the accuracy/(Total time ) ratio. The results show that by setting the balance parameter compared with non-setting, above ratio always shows a higher value.

A mobile ad hoc network (MANET) consists of a set of mobile hosts that can communicate with each other without the assistance of base stations. In MANETs, the dynamic nature of the network topology is a major reason for link failures. In this paper, we proposed the Ad hoc on-demand distance vector (AODV) routing (FLAODV) protocol for routing in mobile ad hoc networks to increase durability path. The performance of this routing protocol is studied using opnet10.5. The simulation results of the FLAODV show that the protocol is quite efficient and superior to AODV with the respects to the average Route Discovery Time, the average Packets overhead, average throughput and average end-to-end delay.

Ad hoc networks are dynamically configurable wireless networks that have no fixed infrastructures and do not require predefined configurations. \nIn large, distributed systems, like ad hoc networks, centralized learning of routing or movement policies may be impractical. We need to employ learning algorithms that can learn independently, without the need for extensive coordination. A search for alternative methods of routing packets has resulted in reinforcement learning (RL) as a good approach to adaptive routing. RL methods are able to learn and adapt to a unknown and changing environment. In This paper we propose a new intelligent routing protocol with RL approach for wireless ad hoc networks. The Intelligent Wireless Ad Hoc Routing protocol (IWARP) is an on-demand protocol and self configuring. It selects optimal routes based on local information and past experience.\nThe proposed scheme uses the distributed Q-Learning framework, to select a stable route in order to enhance system performance. Our study also compares the performance of the IWARP protocol with the well-known Ad hoc On-Demand Distance Vector (AODV) protocol, Results obtained by a simulation campaign show that IWARP increases the throughput and decreases the data dropped and number of hops per route.

In this article, a class of complex harmonic meromorphic functions with positive coefficients is introduced by making used of basic hypergeometric functions. We consider some properties such as coefficient inequality, growth theorems and extreme points.

Sentiment analysis or Opinion mining is the process of detecting the subjective information in given text. Text may include subjective information like opinions, attitudes and feelings. Sentiment analysis also has an important potential role as enabling technologies for other systems. This paper employed two semi supervised probabilistic approaches called JST model and Reverse JST model to detect sentimental topic. The system designed in this paper classifies positive and negative labels of an online review. In JST, the document level sentiment classification is based on topic detection and topic sentiment analysis. JST process, the sentiment labels are associated with documents, the topics are generated dependent on sentiment distribution and words are generated conditioned on the sentiment topic pair. In Reverse JST, the sentiment label is dependent on the topics. In this process, where the topics are associated with documents, the sentiment labels are associated with topics and words are associated with both topics and sentiment labels. In LDA, where topic are associated with documents and words are associated with topic distribution. JST and Reverse JST are evaluated on four different domains using the Gibbs Sampling Algorithm. The nature of JST makes it highly portable to other domains. It compares JST and Reverse JST with latent dirichlet allocation. In this paper observed topic and topic sentiment detected by JST are indeed coherent and informative.

This study aims to analyze the relationship between internal governance mechanisms and risk-taking by banks in\nTunisia. Empirical analyzes conducted from a sample of 10 Tunisian banks during an analysis period of 8 years\nfrom 2002 to 2009, also show significant results. Indeed, the concentration of capital and the size of banks have\nnegative impact and highly significant risk on banks. In addition, Tunisian banks are foreign owned and tend to\ntake less risk than other banks. As for the participation of the state in the capital, it has increased the risk in\nTunisian banks. In addition, the size of the board has a positive and highly significant impact on bank risks, while\nthe proportion of institutional administrators on the board is negatively and significantly related to the risk.

Transition to a market economy in Central and Eastern Europe countries was not successful for most of them, and to this situation have contributed a number of external and internal factors. After the collapse of communism the focus was on restructuring the industries through privatization process. Privatization in Central and Eastern Europe can not be seen only as a change of ownership but rather as a complex economic transformation. The transition to private ownership has not been fast, the privatization mechanisms differ from one state to another, which led to the establishment of a long period of decline. This article seeks to analyze the main aspects of the transformation from Central and Eastern European societies by approaching the main issues of privatization and its evolution in the most important central and eastern European countries.