## 1 Introduction  
Approaches of neural networks have gained significant importance in different tasks and applications. In speech recognition, [PERSON] et al., [1] proposed to exploit robust principle component analysis (RPCA) to extract the sparse partition of the FBANK/MFCC features of noise-corrupted utterances, which corresponds to the speech-dominant elements. The resulting RPCA-wise features show significantly improved recognition performance relative to the original FBANK/MFCC features under the AURORA-4 median-vocabulary recognition task with the renowned deep neural network (DNN) as the acoustic models. Furthermore, the newly proposed features reveal good addition to the popular normalization methods, mean normalization (MN) and relative spectral (RASTA), as the respective integration provides further improvement in recognition accuracy compared with the individual component method. In electronics, [PERSON] et al. [2] propose MB-CNN, a memristive accelerator for binary convolutional neural networks that perform XNOR convolution in-situ novel 2R memristive data blocks to improve power, performance and memory requirements of embedded mobile devices. In energy disaggregation, [PERSON] et al. [3] proposed to apply extreme learning machine (ELMs) to Non-intrusive appliance load monitoring (NILM). ELMs, both in their shallow and hierarchical configurations, act as a non-linear signal enhancement system allowing them to recover the power load of the target appliance from the aggregated load. The authors proposed to extend ELMs to the energy disaggregation problem and they have reported top performance on the UK-DALE dataset. Recent work has shown that exploiting a neural network can significantly improve the performance of Machine Translation (MT) and give a better translation quality. Neural Machine Translation (NMT), structured in a refined end-to-end architecture is incorporated of the capacity to raise the translation equality. One of the common issues prevalent in the NMT systems for Arabic dialects is the system's inability to translate parts of the source sentence and often, the parts of source sentences are translated twice as it was noticed in the translation models for the Arabic Dialects (AD) to the Modern Standard Arabic (MSA). NMT deals with the morphological, semantic, syntactic and different types of linguistic complexities that often pose a difficulty in capturing, without exploiting the linguistic resources such as part-of-speech (POS) tag. POS can be accorded as the process that assigns the part of speech to each word for a sequence of words. For the NMT system, the accuracy of POS tagging is an essential pre-processing step for high-quality translation. Similarly, employing POS information through POS tags as an additional feature, aids in modeling re-ordering between languages and improving the translation quality from AD to MSA. Consequently POS is considered to be advantageous because, it provides large amount of information on words and its neighbors. Knowing whether a word is a noun or a verb provides relevant information on the neighboring words (nouns are preceded by determiners and adjectives, verbs by nouns) as well as on the syntactic structure around the word (nouns are generally part of noun phrases), which makes part-of-speech tagging an essential component of machine translation task for Arabic dialects. Conventional approaches such as Statistical Machine Translation (SMT) requires high computational resources. SMT is not equipped to handle the word ordering problem, one of the major syntactic issues in Arabic dialects. Analysis of the word order involves figuring out the occurrence of subject, object and verb in the sentences. On the basis of the analysis, the different languages could be classed as SVO (English), SOV (Hindi) and VSO (Arabic) and some languages, such as Arabic Dialects allow a free word order. Videlicet, the word order does not convey any information about the subject and the object but possible different information (old & new).