These include word embedding models, which represent words in vector space, capturing semantic meanings and relationships; advanced models that enhance language understanding through contextual information, such as cross-lingual language models (XMLs), Transformer-XL, Parts-of-Speech (POS) Tagging, and Named Entity Recognition (NER), which identify grammatical structures and recognize entities in text; sentiment analysis algorithms, which determine the sentiment expressed in text, providing insights into opinions and emotions; topic modeling algorithms that extract hidden topics from text data, such as LDA and LSA; machine translation algorithms that facilitate the automatic translation of text between languages, such as Google Neural Machine Translation (GNMT), OpenNMT, and MarianMT; text summarization algorithms that condense lengthy documents into shorter documents; text generation models that produce human-like text based on input data; and question-answering algorithms that retrieve precise answers from a given context, such as RoBERTa and GPTs (Generative Pre-trained Transformers), which generates contextually relevant answers. This Special Issue presents six papers covering a wide range of applications, including convolutional networks for indoor localization using IoT and fingerprinting, demand prediction, transformer training in text-to-speech synthesis, ensemble learning using a Deep NLP, privacy preservation using wireless sensors, and sensor-based techniques for engagement. The list of contributions to this Special Issue is as follows:  
Contribution 1 discusses indoor localization techniques using Bluetooth Low Energy (BLE) and a Radio Signal Strength Indicator (RSSI) to address the limitations of GPS in indoor environments. The study evaluates the effectiveness of iBeacon transmitters for indoor positioning, comparing the Weighted Centroid Localization (WCL) and Positive Weighted Centroid Localization (PWCL) algorithms, along with fingerprinting methods enhanced with outlier detection and mapping filters. The methodology includes mapping a real environment onto a coordinate axis, collecting training data from a range of sampling points, and implementing four localization algorithms. Contribution 2 examines various spatiotemporal influencing factors associated with travel behavior and proposes a Local-Global Dynamic Multi-Graph Convolutional Network (LGDMGCN) model, driven by multi-source data, for the multi-step prediction of station-level bike-sharing demand. In dynamically modeling temporal dependencies by incorporating multiple sources of time semantic features, a time attention mechanism is integrated to better capture variations over time. The paper considers factors related to stations and utilizes spatial semantic features to construct dynamic multi-graphs, as well as a local-global structure to capture spatial dependencies among individual bike-sharing stations and all stations collectively for the optimization of intelligent transportation systems. Contribution 3 discusses text-to-speech (TTS) models and introduces an end-to-end TTS model for efficiently generating high-quality Kurdish audio. The paper proposes a method that leverages a Variational Autoencoder (VAE) pre-trained for audio waveform reconstruction and augmented through adversarial training. This involves aligning the prior distribution established by the pre-trained encoder with the posterior distribution of the text encoder within latent variables. Additionally, a stochastic duration predictor is incorporated to imbue synthesized Kurdish speech with diverse rhythms. By aligning latent distributions and integrating the stochastic duration predictor, the proposed method facilitates the real-time generation of natural Kurdish speech audio, offering flexibility in pitches and rhythms. Contribution 4 explores state-of-the-art pre-trained language models (PLMs) and transfer learning in natural language processing (NLP).