The evolution in the field of Artificial Intelligent (AI) with its training algorithms make AI very important in different aspect of the life. The prediction problem of behavior of dynamical control system is one of the most important issue that the AI can be employed to solve it. In this paper, a Convolutional Multi-Spike Neural Network (CMSNN) is proposed as smart system to predict the response of nonlinear dynamical systems. The proposed structure mixed the advantages of Convolutional Neural Network (CNN) with Multi -Spike Neural Network (MSNN) to generate the smart structure. The CMSNN has the capability of training weights based on a proposed training algorithm. The simulation results demonstrated that the proposed

Wireless Multimedia Sensor Networks (WMSNs) are a type of sensor network that contains sensor nodes equipped with cameras, microphones; therefore the WMSNS are able to   produce multimedia data such as video and audio streams, still images, and scalar data from the surrounding environment. Most multimedia applications typically produce huge volumes of data, this leads to congestion. To address this challenge, This paper proposes Modify Spike Neural Network control for Traffic Load Parameter with Exponential Weight of Priority Based Rate Control algorithm (MSNTLP with EWBPRC). The Modify Spike Neural Network controller (MSNC) can calculate the appropriate traffi

Humanity's relationship with the environment is a delicate balance. Since the industrial revolution, the world's population has grown at an exponential rate, and this has a major environmental effect. Deforestation, pollution, and global climate change are just a few of the negative consequences of population and technological growth. Particulates, Sulphur dioxide (SO2), and nitrogen oxides (NOx) are the primary pollutants that harm our health. These contaminants may be directly emitted into the atmosphere (primary pollutants) or formed in the atmosphere from primary pollutants reacting (secondary pollutants. Tropospheric ozone is created When water reacts with volatile organic compounds (VOC) and nitrogen oxides (NOx) in the presen

Priority of road maintenance can be viewed as a process influenced by decision-makers with varying decision-making power. Each decision-maker may have their view and judgment depending on their function and responsibilities. Therefore, determining the priority of road maintenance can be thought of as a process of MCDM. Regarding the priority of road maintenance, this is a difficult MCDM problem involving uncertainty, qualitative criteria, and possible causal relationships between choice criteria. This paper aims to examine the applicability of multiple MCDM techniques, which are used for assessing the priority of road maintenance, by adapting them to this sector. Priority of road maintenance problems subject to internal

Objective: The purpose of this study was to assess the effectiveness of Vibriophage Universiti Sains Malaysia 8 (VPUSM 8), a bacteriophage that destroys bacteria, in managing the proliferation of Vibrio cholerae, specifically the El Tor serotype, as an alternate therapeutic strategy. Methods: The study entailed subjecting water samples from Kelantan, Malaysia, to reproduce the natural circumstances that promote the growth of V. cholerae. Subsequently, the samples were contaminated with the V. cholerae O1 El Tor Inaba strain and treated using VPUSM 8. The study employed a controlled experimental design, wherein the samples were divided into three groups, each experiencing different treatment methods. Quantifying the number of colony-

The study aimed at clarifying the contradictions of the general industrial companies despite the investment allocations and the government investment expenditure on manufacturing activities under the so- called rehabilitation programs. However, this did not contribute to a certain extent in the growth and industrial leap in the direction of developing the activities of the sector Industrial sector in Iraq because of the lack of adoption of a number of basic principles towards the need to take priority of investment in the field of manufacturing and industrial decision-making in the restructuring of industry according to the priorities of investment in light of the international industrial trend, Tosmarah available to the manufact

Here we determined the structure of a cold active family IV esterase (EstN7) cloned from Bacillus cohnii strain N1. EstN7 is a dimer with a classical α/β hydrolase fold. It has an acidic surface that is thought to play a role in cold-adaption by retaining solvation under changed water solvent entropy at lower temperatures. The conformation of the functionally important cap region is significantly different to EstN7's closest relatives, forming a bridge-like structure with reduced helical content providing greater access to the active site through more than one substrate access tunnel. However, dynamics do not appear to play a major role in cold adaption. Molecular dyn

The tourism industry has become, currently, an art, an industry and a science. It is also one of the components that make up touristic regions. Tourist attractions are no longer the exclusive visits of museums and archeological sites, but also involve other service facilities. It is, therefore, imperative that the authorities should become aware of the degradation of tourist resorts and prevent them from getting worse. Moreover, the authorities should take a set of decisions concerning the protection of the urban aspect with its historical, social, and environmental dimensions, as well as, adapting it to the modern requirements that can bring comfort to the citizens and tourists at physical and psychological levels.

Software-defined networking (SDN) presents novel security and privacy risks, including distributed denial-of-service (DDoS) attacks. In response to these threats, machine learning (ML) and deep learning (DL) have emerged as effective approaches for quickly identifying and mitigating anomalies. To this end, this research employs various classification methods, including support vector machines (SVMs), K-nearest neighbors (KNNs), decision trees (DTs), multiple layer perceptron (MLP), and convolutional neural networks (CNNs), and compares their performance. CNN exhibits the highest train accuracy at 97.808%, yet the lowest prediction accuracy at 90.08%. In contrast, SVM demonstrates the highest prediction accuracy of 95.5%. As such, an