Melanoma is among the most lethal forms of skin cancer, where early and accurate diagnosis significantly improves patient survival. Traditional diagnostic pathways, including clinical inspection and dermoscopy, are constrained by interobserver variability and limited access to expertise. Between 2020 and 2025, advances in artificial intelligence (AI) and medical imaging technologies have substantially redefined melanoma diagnostics. This narrative review synthesizes key developments in AI-based approaches, emphasizing the progression from convolutional neural networks to vision transformers and multimodal architectures that incorporate both clinical and imaging data. We examine the integration of AI with non-invasive imaging techniques such as reflectance confocal microscopy, high-frequency ultrasound, optical coherence tomography, and three-dimensional total body photography. The role of AI in teledermatology and mobile applications is also addressed, with a focus on expanding diagnostic accessibility. Persistent challenges include data bias, limited generalizability across diverse skin types, and a lack of prospective clinical validation. Recent regulatory frameworks, including the European Union Artificial Intelligence Act and the United States Food and Drug Administration’s guidance on adaptive systems, are discussed in the context of clinical deployment. The review concludes with perspectives on explainable AI, federated learning, and strategies for equitable implementation in dermatological oncology.

Low-power acoustic telemetry remains a significant challenge in Internet of Things monitoring systems deployed in remote environments. This paper presents an embedded fast Fourier transform framework for communication-efficient acoustic monitoring of honey bee colonies. Instead of transmitting raw audio streams, the proposed approach extracts compact spectral descriptors directly on an embedded sensing node and transmits only a small feature vector using a low-power cellular network. The framework is evaluated using labeled queenright and queenless colony recordings. The proposed solution targets resource-constrained ESP32 class IoT nodes operating over LTE-M and NB-IoT networks. The analysis covered such parameters as dominant frequency, peak amplitude, mean spectral amplitude, spectral centroid, spectral entropy, and band energy extracted from the 200 - 400 Hz band. The results showed that dominant frequency alone did not significantly differentiate colony states at the file level, while mean spectral amplitude remained statistically significant. Queenless recordings also exhibited higher dominant frequency variability. The proposed approach reduces the transmitted payload by more than three orders of magnitude while remaining compatible with resource-constrained ESP32 class IoT devices. The results demonstrate that the extraction of embedded acoustic features is a practical method for scalable smart beehive monitoring under strict memory, power, and bandwidth constraints.

This publication underscores the vital role of bees, particularly their contribution to products like honey, pollen, and propolis, which offer significant health benefits. Notably, 79% of the world’s food supply relies on bees; thus, their declining numbers pose a threat to pollination and food security. The study explores an ecological thermal insulation for hives using animal biomass and a monitoring system to detect anomalies in bee habitats early. This approach aims to protect bee populations and enhance global food security through improved data-driven practices.

Honeybee colonies are essential for biodiversity and food production but face increasing environmental pressures. This study presents a bio-based needle-punched nonwoven insulation composed of recycled chicken feathers and sheep wool, with a mass per unit area of approximately 433 g/m², integrated with an telemetry system for beehive monitoring. The multilayer composite was validated under real apiary conditions during a 1-year pilot study. Insulated hives exhibited improved thermal stability and enhanced overwintering vitality. Monitoring temperature, humidity, weight, and acoustic activity revealed seasonal colony dynamics. Correlations with Normalized Difference Vegetation Index and PM2.5 data suggest that intelligent hives can serve as bioindicators of environmental conditions.

Energy management in battery-powered embedded Artificial Intelligence of Things (AIoT) nodes requires coordinated control of sensing, computation, and wireless communication. This structured review combines a documented search of Scopus, Web of Science Core Collection, and IEEE Xplore with screening of 548 unique records and full-text source-to-construct coding of 56 database-derived sources. The synthesis shows that acquisition, Tiny Machine Learning (TinyML) inference, communication, protocol-state overhead, adaptive sensing, and energy harvesting are commonly optimized separately, while field-validated joint control remains limited. The review develops a full-cycle energy model and cross-layer framework linking sensing, processing, payload representation, radio policy, and energy availability. Dimensionless break-even analysis shows that the admissible local-processing budget is bounded by fixed communication overhead and non-machine-learning edge costs. A targeted Long-Term Evolution for Machines (LTE-M) experiment comprising 40 complete transactions showed that payload length alone was insufficient to characterize communication energy; a state-aware model incorporating measured signaling duration achieved $R^2=0.980$ and predicted a temporally separated second session with 1.66% relative root mean square error (RMSE). Precision agriculture and precision apiculture illustrate modality- and network-dependent trade-offs. The findings support full-cycle benchmarking and context-aware orchestration rather than isolated optimization of inference or communication.