## 1 Introduction  
In the field of transcriptomics, enhancers [1] are unique non-coding DNA fragments that target the control of gene expression for the generation of proteins (activators) and RNA [2]. The proteins are conventionally referred to as transcription factors, which are proteins involved in transcribing DNA into RNA, and they can be located and mapped up to 1 Mbp from the gene or sometimes even to divergent chromosomes [3]. Due to their vigorous nature, the occurrence of enhancers in perceptible genomic sections makes them difficult to identify. Despite some of these challenges in pinning down enhancers at a genomic scale, some useful methods have been initiated over the last few years. The presence of enhancers has been revealed in different species of mammals and vertebrates [4]. For instance, inflammatory bowel disease (IBD)-unregulated promoters and enhancers are highly enriched with IBD-associated SNPs and are bound by the same transcription factors [5]. Much remains to be completed to identify enhancers and connect them to human biology and disease. The human genome consists of a large number of enhancers. Furthermore, enhancers are found in both prokaryotes and eukaryotes. A promoter, a slightly different fragment of DNA, is a sequence where gene transcription initiates [3]. Unlike promoters, enhancers are typically found 20 kb upstream/downstream of genes, or even of chromosomes that may not be carrying genes. Due to the nature of this location disparity, it is very challenging to identify new enhancers. Certain contemporary studies of alterations suggest that enhancers are a large class of functional elements that may have many subgroups, whose targets experience different types of biological activities and regulatory effects [6]. Moreover, genetic variety in enhancers has been demonstrated to be associated with human diseases such as different types of cancer and inflammatory bowel disease [5]. Some computational work has recently been carried out to identify regulatory enhancers because experimental methods are time-consuming and costly. In contrast to the past, computational researchers are now well-appointed with high-profile computing assets and sophisticated strategies to master the outgrowth of biological data. With the improvement of machine learning, numerous computational prediction models that can rapidly recognize enhancers in genes have been developed. These approaches include CSI-ANN [7], RFECS [8], EnhancerFinder [9], ChromeGenSVM [10], GKM-SVM [11], DEEP [12], EnhancerDBN [13], and BiRen [14]. However, these methods merely classify identified enhancers. Meanwhile, enhancers are wide-ranging and comprise many diverse subgroups such as strong/weak enhancers, inactive enhancers, and pent-up enhancers. iEnhancer-2L [3] is the primary predictive model that solely relies on sequence data to assist predictors in differentiating enhancers and their quality. Recently, two efficient predictors, iEnhancer-2L [3] and iEnhancer-EL [15], have been proposed; these do not only identify enhancers but also identify their strength and classify them as strong and weak enhancers. In 2016, iEnhancer-2L by [PERSON] et al. [3] presented an operational methodology using a support vector machine (SVM) learning algorithm. Pseudo k-tuple nucleotide composition (PseKNC) was used for the sequence-encoding scheme in iEnhancer-2L [3]. In 2018, iEnhancer-EL [15] was presented as an improved description of iEnhancer-2L [3]. This model is complex as it requires an ensemble of six classifiers for the first stage and ten classifiers for the second stage. The crucial classifiers were assembled from many SVM-based elementary classifiers [15], which are composed of three diverse feature categories: the PseKNC, k-mers, and subsequence profiles. Although the aforementioned machine learning-based methods can deliver good results, deep learning models perform better without the need for manual feature extraction [16; 17].