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.

A multiband microstrip-fed wideband (WB) antenna with filtering notches is described for 3G/4G/5G applications. The proposed antenna comprises three notch bands by etching a modified inverted U-slot, a square ring slot, and an interdigital inductor slot on the patch element and feedline. The antenna resonates at 1.9, 2.3, and 3.5 GHz due to the resonant components’ mutual interaction, which eliminates interference at other frequencies. The antenna’s measured S ₁₁ are 18.79 dB at 1.9 GHz,–24.8 dB at 2.4 GHz, and –40.6 dB at 3.5 GHz, showing a multiband function with a band-reject level of 0.4 dB. The VSWR is less than two at all resonant frequencies. The effects of altering the notch dimensions on the S ₁₁ and VSWR were explored. The antenna was developed and tested using a Rogers RT/Duroid 5880 substrate. The agreement between measured and simulated results was satisfactory. The S ₁₁ result was validated using the ADS schematic and machine learning techniques. The proposed triband-notched antenna offers encouraging results, with radiation patterns exhibiting omnidirectional characteristics and effective performance within the required frequencies. Current distribution analysis reveals how notches disrupt surface current and lower radiation at specific frequencies. The antenna’s gain and efficiency performed satisfactorily in the stated frequency ranges.

We simulated the manufacturing production line in a micro, small, and medium enterprise (MSME) to assess the efficiency of a helmet product organization, using ARENA simulation modelling software version 15.10.00000. The process and standard time for each process in the production line were estimated from data provided by the enterprise’s management and direct observation. The enterprise line was engaged in six different processes to manufacture a singular product type. ARENA was used to analyse data. The simulation results showed an increase in workers’ utilization and reduced production duration for restructuring worker allocations, while maintaining a constant throughput rate.