In aspect-based sentiment analysis ABSA, implicit aspects extraction is a fine-grained task aim for extracting the hidden aspect in the in-context meaning of the online reviews. Previous methods have shown that handcrafted rules interpolated in neural network architecture are a promising method for this task. In this work, we reduced the needs for the crafted rules that wastefully must be articulated for the new training domains or text data, instead proposing a new architecture relied on the multi-label neural learning. The key idea is to attain the semantic regularities of the explicit and implicit aspects using vectors of word embeddings and interpolate that as a front layer in the Bidirectional Long Short-Term Memory Bi-LSTM. First, we

Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob

Banks represents financial institutions that influence the economy of any country and their evolution and development is the need to seek all states , communities, maintain and control a guarantee of the beneficiaries investors and to promote confidence, so the specialized organizations trying to set principles and rules must be adhered to and are Basel (II) of the necessary effects to be introduced and work on their application.  Banks exposed by various risks one of them operational risks that have multiple effects on the activity of the institution itself, as well as the national economy, so this study was to find out what those effects and to find solutions and appropriate proposals to avoid Iraqi banks from the effects of risk,

Abstract

      The target derivative are gentamicin linked with L-Val- L-Ala by an ester linkage. These were synthesized by esterification method, which included the reaction of -OH hydroxyl group on (carbon No.5) of gentamicin with the acid chloride of the corresponding dipeptide, The preparation of new derivative of gentamicin involved protected the primary & secondary amine groups of Gentamicin, by Ethylchloroformate (ECF) to give N-carbomethoxy Gentamicin which was used for further chemical synthesis involving the free hydroxyl groups.

Then prepared dipeptide (L-Val- L-Ala) by conventional solution method in present DCC & HoBt then reacted with thionyl

New derivatives of pyromellitamic diacids and pyromellitdiimides have been prepared by the reaction of one mole of pyromellitic dianhydride with two moles of aromatic amines, these derivatives were characterized by elemental analysis, FT-IR and melting point.

Modifying of HY/Zeolite is by loading nickel for applying catalyst in thermal catalytic cracking of furfural extract-40 from the lubricating base oil unit. The study involved the characterizing of HY-zeolite and promoted catalyst with nickel by X-ray diffraction analysis, Scanning electron microscopy (SEM), BET (Brunauer, Emmett, and Teller), and infrared ray analyses FTIR. The catalytic thermal cracking tubular reactor with a fixed bed with two type catalysts; HY/zeolite and Ni HY/zeolite, individually at a temperature of 580^oC with LHSV 5h^-1 was investigated. The results indicated that increase the conversion of catalytic cracking of furfural extract-40 also increases the yield of useful petroleum