The sudden increase in adoption of the Internet of Things (IoT) has revolutionized modern living but also brought unprecedented security challenges due to its distributed, heterogeneous, and resource-constrained nature. This review paper offers a comprehensive examination of machine learning (ML) and deep learning (DL) approaches tailored for intrusion detection and threat mitigation in IoT ecosystems. It explores the landscape of anomaly detection and classification techniques while analyzing their suitability, limitations, and deployment feasibility across IoT layers. The study also investigates the significance of feature engineering, model selection, and system scalability. A novel addition to this review is the integration of emerging trends such as explainable AI (XAI), which enhances transparency and trust in black-box ML/DL models, and federated learning (FL), a privacy-preserving paradigm that allows decentralized model training without raw data sharing. The synergy between FL and Edge AI is discussed to highlight real-time, low-latency security analytics at the network's edge. Comparative tables, domain-specific applications (e.g., smart homes, healthcare, and industrial IoT), and architectural illustrations support the discourse, providing readers with an up-to-date understanding of current capabilities and ongoing research challenges. This paper concludes with practical implications, research gaps, and future directions for building intelligent, secure, and explainable IoT security frameworks that respect user privacy and enable scalable deployment. This article is categorized under: Fundamental Concepts of Data and Knowledge > Explainable AI Technologies > Internet of Things Technologies > Machine Learning
Crowdsourcing has recently evolved as a distributed human problem-solving method and has received considerable interest from academics and practitioners in various domains. The proliferation of crowdsourcing has made it much simpler to utilize the intelligence and adaptability of many people to learn new knowledge to solve the problem of acquiring new knowledge. In the past, numerous crowdsourcing works have highlighted multiple aspects; however, no surveys have been conducted that focus on the entire crowdsourcing process. This concentrated survey provides a comprehensive review of the technical advances from a systematic perspective. This survey systematically reviews technical advances for a crowdsourcing process that contains four dimensions: task modeling, crowdsourcing data acquisition, the learning process, and predictive model learning, and proposes a comprehensive and scalable framework from CROWD4AI (Crowdsourcing Framework with 4 Dimensions for Artificial Intelligence). In addition, this paper focuses on each dimension's potential challenges and future direction, encouraging researchers to participate in crowdsourcing. To bridge theory with practice, we also include a detailed case study that demonstrates the real-world application of our proposed framework in the context of annotating cultural heritage damages using crowdsourced input. The case study illustrates how the framework supports effective task design, label collection, robust learning strategies, and accurate predictive modeling in a practical setting.This article is categorized under: Technologies > Crowdsourcing Technologies > Machine Learning
Managing complex disaster risks requires interdisciplinary efforts. Breaking down silos between law, social sciences, and natural sciences is critical for all processes of disaster risk reduction. It is essential to explore how AI enhances understanding of legal frameworks and environmental management, while also examining how legal and environmental factors may limit AI's role in society. From a co-production review perspective, drawing on insights from lawyers, social scientists, and environmental scientists, principles for responsible data mining are proposed based on safety, transparency, fairness, accountability, and contestability. This discussion offers a blueprint for interdisciplinary collaboration to create adaptive law systems based on AI integration of knowledge from environmental and social sciences. When social networks are useful for mitigating disaster risks based on AI, the legal implications related to privacy and liability of the outcomes of disaster management must be considered. Fair and accountable principles emphasize environmental considerations and foster socioeconomic discussions related to public engagement. AI also has an important role to play in education, bringing together the next generations of law, social sciences, and natural sciences to work on interdisciplinary solutions in harmony. Although emerging AI approaches can be powerful tools for disaster management, they must be implemented with ethical considerations and safeguards to address concerns about bias, transparency, and privacy. The responsible execution of AI approaches, based on the dynamic interplay between AI, law, and environmental risk, promotes sustainable and equitable practices in data mining.
Early diagnosis of abnormal cervical cells enhances the chance of prompt treatment for cervical cancer (CrC). Artificial intelligence (AI)-assisted decision support systems for detecting abnormal cervical cells are developed because manual identification needs trained healthcare professionals, and can be difficult, time-consuming, and error-prone. The purpose of this study is to present a comprehensive review of AI technologies used for detecting cervical pre-cancerous lesions and cancer. The review study includes studies where AI was applied to Pap Smear test (cytological test), colposcopy, sociodemographic data and other risk factors, histopathological analyses, magnetic resonance imaging-, computed tomography-, and positron emission tomography-scan-based imaging modalities. We performed searches on Web of Science, Medline, Scopus, and Inspec. The preferred reporting items for systematic reviews and meta-analysis guidelines were used to search, screen, and analyze the articles. The primary search resulted in identifying 9745 articles. We followed strict inclusion and exclusion criteria, which include search windows of the last decade, journal articles, and machine/deep learning-based methods. A total of 58 studies have been included in the review for further analysis after identification, screening, and eligibility evaluation. Our review analysis shows that deep learning models are preferred for imaging techniques, whereas machine learning-based models are preferred for sociodemographic data. The analysis shows that convolutional neural network-based features yielded representative characteristics for detecting pre-cancerous lesions and CrC. The review analysis also highlights the need for generating new and easily accessible diverse datasets to develop versatile models for CrC detection. Our review study shows the need for model explainability and uncertainty quantification to increase the trust of clinicians and stakeholders in the decision-making of automated CrC detection models. Our review suggests that data privacy concerns and adaptability are crucial for deployment hence, federated learning and meta-learning should also be explored. This article is categorized under: Fundamental Concepts of Data and Knowledge > Explainable AI Technologies > Machine Learning Technologies > Classification
Pests pose a major danger to a variety of industries, including agriculture, public health, and ecosystems. Fast and precise pest detection, as well as the ability to predict infestations, are required for effective pest management tactics. This paper provides a comprehensive literature review on this subject to provide an overview of the state of research on pest detection and infestation prediction. The paper investigates and presents background information on the necessity of pest control as well as the difficulty in recognizing pests and forecasting. Several strategies, including approaches to data collection, modeling, and assessment of models, are reviewed in the research described. The authors examine various pest detection methods involving the utilization of convolutional neural networks and several object detection architectures categorized broadly into one-stage and two-stage object detection algorithms. Methods for predicting pest infestations that involve regression, classification, and time series forecasting are also thoroughly investigated. The challenges of recognizing pests and predicting infestations are underlined, as are issues with data quality, feature selection, and model interpretability. The report also indicates the limitations to pest detection and infestation prediction as well as intriguing topics for further research on the same. The findings of the literature research demonstrate how Artificial Intelligence, Computer Vision, and the Internet of Things have been applied for Pest Detection and Infestation Prediction. The research serves as a base for surveying and summarizing the approaches utilized for the task of pest detection (an object detection problem) and pest infestation prediction (a forecasting problem) and its findings and recommendations serve as a platform for future study and the development of effective pest management solutions.
