The complexity and variety of language included in policy and academic documents make the automatic classification of research papers based on the United Nations Sustainable Development Goals (SDGs) somewhat difficult. Using both pre-trained and contextual word embeddings to increase semantic understanding, this study presents a complete deep learning pipeline combining Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures which aims primarily to improve the comprehensibility and accuracy of SDG text classification, thereby enabling more effective policy monitoring and research evaluation. Successful document representation via Global Vector (GloVe), Bidirectional Encoder Representations from Tra

Image compression plays an important role in reducing the size and storage of data while increasing the speed of its transmission through the Internet significantly. Image compression is an important research topic for several decades and recently, with the great successes achieved by deep learning in many areas of image processing, especially image compression, and its use is increasing Gradually in the field of image compression. The deep learning neural network has also achieved great success in the field of processing and compressing various images of different sizes. In this paper, we present a structure for image compression based on the use of a Convolutional AutoEncoder (CAE) for deep learning, inspired by the diversity of human eye

The study of properties of space of entire functions of several complex variables was initiated by Kamthan [4] using the topological properties of the space. We have introduced in this paper the sub-space of space of entire functions of several complex variables which is studied by Kamthan.

Thin films of (Cu2S)100-x( SnS2 )x at X=[ 30,40, &50)]% with thickness (0.9±0.03)µm , had been prepared by chemical spray pyrolysis method on glass substrates at 573 K. These films were then annealed under low pressure of(10-2) mbar ,373)423&473)K for one hour . This research includes , studying the the optical properties of (Cu2S)100-x-(SnS2)x at X=[ 30,40, &50)]% .Moreover studying the effect of annealing on their optical properties , in order to fabricate films with high stability and transmittance that can be used in solar cells. The transmittance and absorbance spectra had been recorded in the wavelength range (310 - 1100) nm in order to study the optical properties . It was found that these films had direct optical band

A polycrystalline PbxS1-x alloys with various Pb content ( 0.54 and 0.55) has been prepared successfully. The structure and composition of alloys are determined by X-ray diffraction (XRD), atomic absorption spectroscopy (AAS) and X-ray fluorescence (XRF) respectively. The X-ray diffraction results shows that the structure is polycrystalline with cubic structure, and there are strong peaks at the direction (200) and (111), the grain size varies between 20 and 82 nm. From AAS and XRF result, the concentrations of Pb content for these alloys were determined. The results show high accuracy and very close to the theoretical values. A photoconductive detector as a bulk has been fabricated by taking pieces of prepared alloys and polished chemic

Cloud computing provides huge amount of area for storage of the data, but with an increase of number of users and size of their data, cloud storage environment faces earnest problem such as saving storage space, managing this large data, security and privacy of data. To save space in cloud storage one of the important methods is data deduplication, it is one of the compression technique that allows only one copy of the data to be saved and eliminate the extra copies. To offer security and privacy of the sensitive data while supporting the deduplication, In this work attacks that exploit the hybrid cloud deduplication have been identified, allowing an attacker to gain access to the files of other users based on very small hash signatures of

 In this paper, the human robotic leg which can be represented mathematically by single input-single output (SISO) nonlinear differential model with one degree of freedom, is analyzed and then a simple hybrid neural fuzzy controller is designed to improve the performance of this human robotic leg model. This controller consists from SISO fuzzy proportional derivative (FPD) controller with nine rules summing with single node neural integral derivative (NID) controller with nonlinear function. The Matlab simulation results for nonlinear robotic leg model with the suggested controller showed that the efficiency of this controller when compared with the results of the leg model that is controlled by PI+2D, PD+NID, and F

The application of the test case prioritization method is a key part of system testing intended to think it through and sort out the issues early in the development stage. Traditional prioritization techniques frequently fail to take into account the complexities of big-scale test suites, growing systems and time constraints, therefore cannot fully fix this problem. The proposed study here will deal with a meta-heuristic hybrid method that focuses on addressing the challenges of the modern time. The strategy utilizes genetic algorithms alongside a black hole as a means to create a smooth tradeoff between exploring numerous possibilities and exploiting the best one. The proposed hybrid algorithm of genetic black hole (HGBH) uses the