The evolution of the Internet of things (IoT) led to connect billions of heterogeneous physical devices together to improve the quality of human life by collecting data from their environment. However, there is a need to store huge data in big storage and high computational capabilities.   Cloud computing can be used to store big data.  The data of IoT devices is transferred using two types of protocols: Message Queuing Telemetry Transport (MQTT) and Hypertext Transfer Protocol (HTTP). This paper aims to make a high performance and more reliable system through efficient use of resources. Thus, load balancing in cloud computing is used to dynamically distribute the workload across nodes to avoid overloading any individual r

Secure information transmission over the internet is becoming an important requirement in data communication. These days, authenticity, secrecy, and confidentiality are the most important concerns in securing data communication. For that reason, information hiding methods are used, such as Cryptography, Steganography and Watermarking methods, to secure data transmission, where cryptography method is used to encrypt the information in an unreadable form. At the same time, steganography covers the information within images, audio or video. Finally, watermarking is used to protect information from intruders. This paper proposed a new cryptography method by using thre

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Many water supplies are now contaminated by anthropogenic sources such as domestic and agricultural waste, as well as manufacturing activities, the public's concern about the environmental effects of wastewater contamination has grown. Several traditional wastewater treatment methods, such as chemical coagulation, adsorption, and activated sludge, have been used to eliminate pollution; however, there are several drawbacks, most notably high operating costs, because of its low operating and repair costs, the usage of aerobic waste water treatment as a reductive medium is gaining popularity. Furthermore, it is simple to produce and has a high efficacy and potential to degrade pollu

Monaural source separation is a challenging issue due to the fact that there is only a single channel available; however, there is an unlimited range of possible solutions. In this paper, a monaural source separation model based hybrid deep learning model, which consists of convolution neural network (CNN), dense neural network (DNN) and recurrent neural network (RNN), will be presented. A trial and error method will be used to optimize the number of layers in the proposed model. Moreover, the effects of the learning rate, optimization algorithms, and the number of epochs on the separation performance will be explored. Our model was evaluated using the MIR-1K dataset for singing voice separation. Moreover, the proposed approach achi

This paper introduces a non-conventional approach with multi-dimensional random sampling to solve a cocaine abuse model with statistical probability. The mean Latin hypercube finite difference (MLHFD) method is proposed for the first time via hybrid integration of the classical numerical finite difference (FD) formula with Latin hypercube sampling (LHS) technique to create a random distribution for the model parameters which are dependent on time [Formula: see text]. The LHS technique gives advantage to MLHFD method to produce fast variation of the parameters’ values via number of multidimensional simulations (100, 1000 and 5000). The generated Latin hypercube sample which is random or non-deterministic in nature is further integ

Abstract

      In this study, we compare between the autoregressive approximations (Yule-Walker equations, Least Squares , Least Squares ( forward- backword ) and Burg’s (Geometric and Harmonic ) methods, to determine the optimal approximation to the time series generated from the first - order moving Average non-invertible process, and fractionally - integrated noise process, with several values for d (d=0.15,0.25,0.35,0.45) for different sample sizes (small,median,large)for two processes . We depend on figure of merit function which proposed by author Shibata in 1980, to determine the theoretical optimal order according to min

Sequencing technologies have reshaped the study of the subgingival microbiome, but selecting the appropriate method remains challenging because of differences in resolution, cost, host DNA contamination, and computational complexity. This review compares 16S rRNA sequencing, full-length 16S, shotgun metagenomics, and metatranscriptomics with respect to taxonomic resolution, functional output, sample requirements, and analytical limitations. Key practical issues, including low microbial biomass, contamination control, and the choice of appropriate bioinformatic tools, are emphasized to help researchers avoid common pitfalls. A decision-making framework is provided to link study goals to suitable sequencing methods while outlining rea