As urbanization and industrial activities accelerate globally, air quality has become a pressing concern, particularly due to the harmful effects of particulate matter (PM), notably PM_2.5 and PM₁₀. This review paper presents a comprehensive systematic assessment of machine learning (ML) techniques for estimating PM concentrations, drawing on studies published from 2018 to 2024. Traditional statistical methods often fail to account for the complex dynamics of air pollution, leading to inaccurate predictions, especially during peak pollution events. In contrast, ML approaches have emerged as powerful tools that leverage large datasets to capture nonlinear, intricate relationships among various environmental, meteorological, and anthropogenic factors. This review synthesizes findings from 32 studies, demonstrating that ML techniques, particularly ensemble learning models, significantly enhance estimation accuracy. However, challenges remain, including data quality, the need for diverse and balanced datasets, issues related to feature selection, and spatial discontinuity. This paper identifies critical research gaps and proposes future directions to improve model robustness and applicability. By advancing the understanding of ML applications in air quality monitoring, this review seeks to contribute to developing effective strategies for mitigating air pollution and protecting public health.

Nowadays, the Internet of Things (IoT) has become immensely popular in various fields like healthcare, smart cities, and industrial automation. IoT networks are expanding rapidly, including different IoT devices with limited capabilities in terms of power and storage which make the IoT security a crucial issue. IoT Network Intrusion Detection System is one of the most famous solutions that used to identify different types of attack and extract their features (e.g. IP addresses of attackers). The IP address is a valuable feature that can identify malicious traffic of an attacker who attempts to access the IoT network. However, IoT Network Intrusion Detection Systems has different limitations: centralization and scalability which easily allow attackers to access the IoT network. Accordingly, this paper aims to address these issues by proposing a novel collaborative framework called Blockchain-based Collaborative Intrusion Detection Systems (BC-IDSs) that utilizes Blockchain technology to connect several IDSs. The BC-IDSs framework (1) creates a list of malicious IP addresses using IDSs; (2) utilizes Blockchain to share and store the Blacklist; (3) creates a function for duplication check in the Blockchain layer. Further, the implementation of a proof of concept for BC-IDSs framework is presented by using Ethereum Blockchain simulators. Compared to previous works, this paper discusses several types of performance metrics that prove BC-IDSs is able to secure IoT networks. BC-IDSs also increases the scalability by 50% when compared to one of the previous defence work.

Artificial intelligence (AI) transforms multiple businesses, including medical research, where AI-driven developments bring significant advantages. The application of machine learning algorithms enables medical researchers to examine large amounts of data accurately, which leads to the development of precise and effective treatment approaches. Lung cancer leads the list of critical healthcare issues because it remains the world’s most lethal form of cancer thus demanding innovative diagnostic tools for faster and accurate identification. The proposed study introduces an innovative method called CTGAN-RF which uses conditional tabular generative adversarial networks (CTGAN) and random forest (RF) classifier to detect lung cancer through synthetic data generation. The proposed model demonstrated superior performance by achieving a 0.9893 score of accuracy and 0.99 value for precision, F1 score, and recall. Extensive experimental evaluation for this method included testing nine classification algorithms. The implementation of different classifiers employed data balancing methods including SMOTE and borderline-SMOTE along with SMOTE ENN and unbalanced data configurations. Comparative analysis showed that CTGAN-RF consistently performed significantly better than traditional classifiers in dealing with class imbalance and improving prediction accuracy. After testing with fivefold cross-validation, the reliability of the model was further validated. In comparison to cutting-edge approaches for lung cancer diagnosis, the proposed methodology outperformed in terms of classification metrics. This in-depth evaluation of synthetic data augmentation with machine learning in lung cancer detection has helped in the development of personalized treatment strategies in the fight against such a life-threatening disease.

Locating available on-street parking in busy urban areas is often frustrating and time-consuming, particularly during peak hours. Inefficient parking searches contribute to traffic congestion, increased fuel consumption, and higher carbon emissions, making real-time parking availability information essential for smart urban mobility. This paper presents FedPark, a privacy-preserving crowdsensing system that leverages drivers’ smartphone sensors to monitor parking space occupancy. FedPark employs a Federated Learning (FL) algorithm, enabling the creation of a global model that aggregates insights from multiple local models maintained on individual drivers’ devices. Each local model learns from a user’s driving patterns without sharing personal data, ensuring enhanced privacy protection. Instead of transmitting raw data, only model updates are shared, reducing communication overhead and security risks. FedPark is evaluated under different urban conditions such as different numbers of drivers and destinations. The evaluation demonstrates that FedPark achieves a high level of accuracy at 98.31%, recall of 98.31%, precision of 98.34%, and an F1-score of 98.31% comparable to centralized machine learning models, including XGBoost, Random Forest, CatBoost, CNN, and ensemble classifiers while preserving driver privacy.

Cruising for vacant curbside parking spaces causes waste of time, frustration, waste of fuel, and pollution. This problem has been addressed by centralized solutions that perform parking assignments and communicate them to drivers’ smart phones. These solutions suffer, however, from two intrinsic problems: scalability, as the server has to perform intensive computation and communication with the drivers; and privacy, as the drivers have to disclose their destinations to the server. This article proposes DFPS, a distributed mobile system for free parking assignment. DFPS solves the scalability problem by using the drivers’ smart phones to cooperatively compute the parking assignments, and a centralized dispatcher to receive and distribute parking requests to the network of smart phones. The phones of the parked drivers in DFPS are structured in a K-D tree to serve parking requests in a distributed fashion. DFPS removes the computation from the dispatcher and substantially reduces its communication load. DFPS solves the privacy problem through an entropy-based cloaking technique that runs on drivers’ smart phones and conceals drivers’ destinations from the dispatcher. The evaluation demonstrates that DFPS is scalable and obtains better travel time than a centralized system, while protecting the privacy of drivers’ destinations.