The rapid expansion of the Internet of Things (IoT) and Machine Learning (ML) has significantly increased the demand for Location-Based Services (LBS) in today’s world. Among these services, indoor positioning and navigation have emerged as crucial components, driving the growth of indoor localization systems. However, using GPS in indoor environments is impractical, leading to a surge in interest in Received Signal Strength Indicator (RSSI) and machine learning-based algorithms for in-building localization and navigation in recent years. This paper aims to provide a comprehensive review of the technologies, applications, and future research directions of ML-based indoor localization for smart cities. Additionally, it examines the potential of ML algorithms in improving localization accuracy and performance in indoor environments.

The potential of indoor unmanned aerial vehicle (UAV) localization is paramount for diversified applications within large industrial sites, such as hangars, malls, warehouses, production lines, etc. In such real-time applications, autonomous UAV location is required constantly. This paper comprehensively reviews radio signal-based wireless technologies, machine learning (ML) algorithms and ranging techniques that are used for UAV indoor positioning systems. UAV indoor localization typically relies on vision-based techniques coupled with inertial sensing in indoor Global Positioning System (GPS)-denied situations, such as visual odometry or simultaneous localization and mapping employing 2D/3D cameras or laser rangefinders. This work critically reviews the research and systems related to mini-UAV localization in indoor environments. It also provides a guide and technical comparison perspective of different technologies, presenting their main advantages and disadvantages. Finally, it discusses various open issues and highlights future directions for UAV indoor localization.

The advent of sixth-generation (6G) networks promises revolutionary advancements in wireless communication, marked by unprecedented speeds, ultra-low latency, and ubiquitous connectivity. This research paper delves into the integration of Artificial Intelligence (AI) in 6G network applications, exploring the challenges and outlining future directions for this transformative synergy. The study investigates the key AI technologies for 6G: the potential of AI to optimize network performance, enhance user experience, and enable novel applications in diverse domains and AI-enabled applications. Analyzing the current landscape, the paper identifies key challenges such as scalability, security, and ethical considerations in deploying AI-enabled 6G networks. Moreover, it explores the dynamic interplay between AI and 6G technologies, shedding light on the intricate relationships that underpin their successful integration. The research contributes valuable insights to the ongoing discourse surrounding the convergence of AI and 6G networks, laying the groundwork for a robust and intelligent future communication infrastructure.

Indoor localization methods based on the Wi-Fireceived signal strength indicator (RSSI) ranging technology are sensitive to noise fluctuations and signal attenuations, which could lead to a significant localization error. Therefore, this study proposes an improved indoor localization algorithm using a deep randomized neural network (RandNN) with Wi-Fi-RSSI. We have conducted a real experiment testbed for Wi-Fi RSSI data collection from a complex indoor environment. An improved adaptive unscented Kalman filter (IAUKF) method is used to minimize noise fluctuations and signal attenuations in the raw Wi-Fi RSSI data collection. Moreover, we have investigated the deep RandNN in which the weights and biases of the input hyperparameter are initially randomized to obtain the best localization performance. For convenience, the presented localization model is known as RandNN-IAUKF. Furthermore, real experiments were conducted in a room surrounding working stations, walls, patriation separations, etc., to maximize the complexity of wireless signal propagation. The performance of the presented RandNN-IAUKF algorithm is assessed and compared with other well-known conventional localization approaches. Overall, the experimental results showed that the presented RandNN-IAUKF algorithm provides significant 95% and 67% location estimation errors only at 0.79 m and 1.31 m, respectively, outperforming conventional algorithms by approximately 30% under the same test environment

This study presents RetenNet, a comprehensive framework for managing customer churn in telecommunications, integrating predictive modelling, prescriptive optimization, and explainable artificial intelligence (XAI) incorporated with Large Language Models (LLMs). The process commences with the IBM Telco dataset, divided in an 80:20 ratio into training and testing sets. Categorical variables are converted by one-hot and label encoding, whilst class imbalance is mitigated using SMOTEENN. Min-max scaling and mutual information-based feature selection guarantee data appropriateness for machine learning models. Five classification algorithms, i.e., Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGB), Logistic Regression (LR), and Multi-Layer Perceptron (MLP) are assessed. The SVM model utilizing an RBF kernel exhibits optimal performance. In conjunction with nested cross-validation, Bayesian optimization guarantees excellent hyperparameter optimization and generalization. Performance is evaluated using the F1-score to highlight the implications of false negatives and false positives in churn situations. The methodology additionally incorporates fuzzy rule-based clustering, facilitating flexibility in customer segment identification for intervention priority. Prescriptive optimization uses linear integer programming to distribute retention budget according to model results and business constraints. SHAP waterfall plot employed to guarantee transparency and facilitate actionable insights. Furthermore, Gemini 1.5 Flash, a multimodal LLM, generates analysis and produces contextual recommendations derived from the SHAP waterfall plot. RetenNet offers a comprehensive and interpretable approach to the churn management pipeline, including classical machine learning, prescriptive optimization, and LLM-driven explainable artificial intelligence to enhance decision-making in customer retention efforts.

Today, large volumes of complex data are collected in many application domains such as health, finance and business. However, using traditional data visualization techniques, it is challenging to visualize abstract information to gain valuable insights into complex multidimensional datasets. One major challenge is the higher cognitive load in interpreting information. In this context, 3D metaphor-based information visualization has become a key research area in helping to gain useful insight into abstract data. Therefore, it has become critical to investigate the evolution of 3D metaphors with HCI techniques to minimize the cognitive load on the human brain. However, there are only a few recent reviews can be found for 3D metaphor-based data visualization. Therefore, this paper provides a comprehensive review of multidimensional data visualization by investigating the evolution of 3D metaphoric data visualization and interaction techniques to minimize the cognitive load on the human brain. Complying with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines this paper performs a systematic review of 3D metaphor-based data visualizations. This paper contributes to advancing the present state of knowledge in 3D metaphoric data visualization by critically analyzing the evolution of interactive 3D metaphors for information visualization. Further, this review identifies six main 3D metaphor categories and ten cognitive load minimizing techniques used in modern data visualization. In addition, this paper contributes three taxonomies by synthesizing the literature with a critical review of the strengths and weaknesses of metaphors. Finally, the paper discusses potential exploration paths for future research improvements.