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 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

Flexible pavements are considered an essential element of transportation infrastructure. So, evaluations of flexible pavement performance are necessary for the proper management of transportation infrastructure. Pavement condition index (PCI) and international roughness index (IRI) are common indices applied to evaluate pavement surface conditions. However, the pavement condition surveys to calculate PCI are costly and time-consuming as compared to IRI. This article focuses on developing regression models that predict PCI from IRI. Eighty-three flexible pavement sections, with section length equal to 250 m, were selected in Al-Diwaniyah, Iraq, to develop PCI-IRI relationships. In terms of the quantity and severity of eac

A batch adsorption system was applied to study the adsorption of methylene blue from aqueous solution by Iraqi bentonite and treated bentonite with different amount of zinc oxide (ZnO). The adsorption capacities of methylene blue onto bentonite were evaluated. The equilibrium between liquid and solid phase was described by Langmuir model better than the Freundlich model. Langmuir and Freundlich constants have been determined. The separation factor or equilibrium parameter, RL which is used to predict if an adsorption system is favourable or unfavourable was calculated for all cases.

This paper proposes a neuro-fuzzy system to model β-glucosidase activity based on the reaction’s pH level and temperature. The developed fuzzy inference system includes two input variables (pH level and temperature) and one output (enzyme activity). The multi-input fuzzy inference system was developed in two stages: first, developing a single input-single output fuzzy inference system for each input variable (pH, temperature) separately, using the robust adaptive network-based fuzzy inference system (ANFIS) approach. The neural network learning techniques were used to tune the membership functions based on previously published experimental data for β-glucosidase. Second, each input’s optimized membership functions from the ANF

The national educational systems both in the Russian Federation and Iraq Republic have to adjust the training programs to duly prepare the pedagogical university students for the modern challenges and situations on the market of educational services. For success of the service, the education specialists have to fully mobilize their physical, mental and emotional resources and persistently advance their skills and knowledge using the relevant online education courses; practical research conferences; persistent self-education to master theoretical fundamentals of the modern physical education and sport service; and be active in trainings and competitions in their vocational individual game sports including badminton, table tennis and tennis.

Anaerobic digestion is a technology widely used for treatment of organic waste for biogas production as a source for clean energy. In this study, poultry house wastes (PHW) material was examined as a source for biogas production. The effects of inoculum addition, pretreatment of the substrate, and temperature on the biogas production were taken into full consideration. Results revealed that the effect of inoculum addition was more significant than the alkaline pretreatment of raw waste materials. The biogas recovery from inoculated waste materials exceeds its production from wastes without inoculation by approximately 70% at mesophilic conditions. Whereby, the increase of biogas recovery from pretreated wastes was by 20% higher than its