The promoter is a regulatory DNA region and important for gene transcriptional regulation. It is located near the transcription start site (TSS) upstream of the corresponding gene. In the post-genomics era, the availability of data makes it possible to build computational models for robustly detecting the promoters as these models are expected to be helpful for academia and drug discovery. Until recently, developed models focused only on discriminating the sequences into promoter and non-promoter. However, promoter predictors can be further improved by considering weak and strong promoter classification. In this work, we introduce a hybrid model, named iPSW(PseDNC-DL), for identification of prokaryotic promoters and their strength. It combines a convolutional neural network with a pseudo-di-nucleotide composition (PseDNC). The proposed model iPSW(PseDNC-DL) has been evaluated on the benchmark datasets and outperformed the current state-of-the-art models in both tasks namely promoter identification and promoter strength identification. The developed tool iPSW(PseDNC-DL) has been constructed in a web server and made freely available at https://home.jbnu.ac.kr/NSCL/PseDNC-DL.htm.