Breast cancer continues to be one of the most prevalent and life-threatening diseases affecting women worldwide. Early and accurate diagnosis significantly improves survival rates, and in recent years, machine learning (ML) has emerged as a transformative tool in enhancing diagnostic precision. This paper presents a comprehensive state-of-the-art review of publicly available machine learning datasets specifically designed for breast cancer detection. The review categorizes and analyzes a wide range of datasets including tabular, imaging, and genomic types such as the Wisconsin Breast Cancer Dataset (WBCD/WDBC), BreakHis, Digital Database for Screening Mammography (DDSM), MIAS, CBIS-DDSM, and TCGA-BRCA. Each dataset is evaluated based on key parameters such as data type, feature richness, class balance, sample size, and its suitability for various ML tasks like classification, segmentation, and multimodal learning. Additionally, the paper outlines the strengths, limitations, and real-world applicability of each dataset, providing critical insights for researchers in selecting appropriate benchmarks for model development. The study also highlights current challenges and suggests future directions for constructing more diverse, annotated, and standardized datasets to support robust and generalizable breast cancer detection systems.

Brain tumor detection through machine learning has gained significant traction due to its potential for early diagnosis, accurate classification, and automated segmentation in clinical settings. The success of such models is closely tied to the quality and availability of annotated datasets. This survey presents a comprehensive review of major publicly available datasets—including BraTS, Figshare Brain MRI, TCGAGBM/LGG, REMBRANDT, CQ500, and IBSR—highlighting their imaging modalities, annotation protocols, dataset sizes, and clinical relevance. Special emphasis is placed on BraTS for segmentation and Figshare for multi-class classification. Genomics-integrated datasets like TCGA and REMBRANDT support multi-modal learning, while IXI and CQ500 are valuable for pretraining and emergency diagnostic models. The survey identifies key limitations, such as inter-observer variability, class imbalance, and inconsistent annotation formats. It also underscores the need for more diverse, standardized, and richly labeled datasets. By evaluating the strengths and weaknesses of existing resources, this work provides guidance for selecting suitable benchmarks and suggests future directions such as federated learning and synthetic data augmentation to improve clinical robustness.

This article asserts that Artificial Intelligence (AI) has been the focus of research in recent years, and the Internet of Things devices powered by AI are proven to perform better than general purpose, but has given rise to a new set of challenges in privacy and security. The authors agree that a potential solution to improve security is through Hardware Assisted Security (HAS).

Recently, harvesting natural energy is gaining more attention than other conventional approaches for sustainable IoT. System on chip power requirement for the internet of things (IoT) and generating higher voltages on chip is a massive challenge for on-chip peripherals and systems. In this article, an on-chip reliable energy-harvesting system (EHS) is designed for IoT with an inductor-free methodology. The control section monitors the computational load and the recharging of the battery/super-capacitor. An efficient maximum power point tracking algorithm is also used to avoid quiescent power consumption. The reliability of the proposed EHS is improved by using an aging tolerant ring oscillator. The effect of Trojan on the performance of energy-harvesting system is analyzed, and proper detection and mitigation mechanism is proposed. Finally, the proposed ripple mitigation techniques further improves the performance of the aging sensor. The proposed EHS is designed and simulated in CMOS 90-nm technology. The output voltage is in the range of 3–3.55 V with an input 1–1.5 V with a power throughput of 0–22 μW. The EHS consumes power under the ultra-low-power requirements of IoT smart nodes.

Security and energy-consumptiont are two conflicting challenges in the design and operation of the smart cities that use Internet-of-Things (IoT). Providing power to IoT things (i.e., sensors and their communications) is a challenge as battery have a limited lifetime, and their maintenance and disposal are costly and hazardous. System on chip (SoC) power requirements for IoT ultra-low-power realm is different and is a challenge for the design engineers to provide uninterrupted power. In this paper, a paradigm shift research that addresses a secure self-sustainable solar-energy harvesting system (EHS) with a security mechanism is proposed. This design incorporates Physically Unclonable Functions (PUFs) for the security of EHS along with an aging sensor for recycled IC detection. The control unit monitors the computational load, recharging of the battery, and security mechanism. Capacitor value modulation (CVM) is used for impedance matching between solar cell and converter during maximum power point tracking (MPPT) to avoid quiescent power consumption. The existing resources of EHS used for designing the PUFs and aging sensor. The secure EHS is designed and fabricated in CMOS 90nm technology. The resulting output is in the range of 3-3.55 V with an input 1-1.5 V. The proposed EHS is consuming 22 μW of power, that satisfies the ultra-low-power requirements of IoT smart nodes.