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This paper explains a new Adaptive Path Sensing Method (APSM) for indoor radio wave propagation prediction. Measurement campaigns, which cover indoor line-of-sight (LoS), non-line-of-sight (NLoS) and different room scenarios, are conducted at the new Wireless Communication Centre (WCC) block P15a) of Universiti Teknologi Malaysia (UTM), Johor, Malaysia. The proposed APSM is evaluated through a computerized modelling tool by comparing the Received Signal Strength Indicator (RSSI) with measurement data and the conventional Shooting-Bouncing Ray Tracing (SBRT) method. Simulations of the APSM and SBRT are performed with the same layout of the new WCC block P15a by using the exact building dimensions. The results demonstrate that the proposed method achieves a better agreement with measured data, compared to the conventional SBRT outputs. The reduced computational time and resources required are also important milestones to ray tracing technology. The proposed APSM method can assist engineers and researchers to reduce the time required in modelling and optimizing reliable radio propagation in an indoor environment.

Artificial intelligence (AI), a rousing advancement disrupting a wide spectrum of applications with remarkable betterment, has continued to gain momentum over the past decades. Within breast imaging, AI, especially machine learning and deep learning, honed with unlimited cross-data/case referencing, has found great utility encompassing four facets: screening and detection, diagnosis, disease monitoring, and data management as a whole. Over the years, breast cancer has been the apex of the cancer cumulative risk ranking for women across the six continents, existing in variegated forms and offering a complicated context in medical decisions. Realizing the ever-increasing demand for quality healthcare, contemporary AI has been envisioned to make great strides in clinical data management and perception, with the capability to detect indeterminate significance, predict prognostication, and correlate available data into a meaningful clinical endpoint. Here, the authors captured the review works over the past decades, focusing on AI in breast imaging, and systematized the included works into one usable document, which is termed an umbrella review. The present study aims to provide a panoramic view of how AI is poised to enhance breast imaging procedures. Evidence-based scientometric analysis was performed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guideline, resulting in 71 included review works. This study aims to synthesize, collate, and correlate the included review works, thereby identifying the patterns, trends, quality, and types of the included works, captured by the structured search strategy. The present study is intended to serve as a "one-stop center" synthesis and provide a holistic bird's eye view to readers, ranging from newcomers to existing researchers and relevant stakeholders, on the topic of interest. [ABSTRACT FROM AUTHOR]