This article presents a compact size and high isolation 2 × 2, Multi-Input Multi-Output (MIMO) antenna for Industrial Scientific and Medical (ISM) band and 5G lower frequency band of 5G applications. Mutual coupling has been a great challenge in these applications. To improve isolation between elements of 2 × 2 MIMO antennas, a mushroom-shaped electromagnetic bandgap (EBG) and a fractal shaped EBG have been investigated. The overall size of the proposed antenna is 38.2 × 95.94 × 1.6 mm 3 with inter-element spacing (edge to edge) of 0.140λ. The proposed antenna has been designed, simulated, fabricated, and tested. The resulting outcome shows that the antenna operates in the band of 2.43-2.50 GHz and radiates in TM 10 mode. By using fractal shaped EBG, isolation of-24.67 dB is achieved. Apart from isolation, other performance parameters of the MIMO antenna are verified. The proposed antenna is suitable for weather radar, surface ship radar, satellite communication, and wireless local area network (WLAN) applications.

This paper introduces a compact, triband four-port Multiple Input Multiple Output (MIMO) antenna optimized for mmWave 5G and navigation services. The antenna is built on a Rogers RT Duroid 5880 substrate, with dimensions of 31 × 42 mm² and a thickness of 0.4 mm. It utilizes a 50 Ω microstrip line to feed a stub-type radiating patch, creating a dipole-loop type structure on the substrate’s top side, with a full ground plane for narrowband. The antenna is initially designed for 38 GHz but was subsequently modified for triband performance by tapering the edges of the stub shape, enabling it to function across multiple frequency ranges. The tapered edges of the radiating patch enhance resonance across the three bands. To improve isolation and bandwidth, a parasitic element is strategically placed between the MIMO elements, results isolation greater than 30 dB at the 32 GHz and 38 GHz bands. The MIMO elements are mirror images placed adjacent to one another, while the other two elements are arranged 180º apart, ensuring compactness. The proposed antenna operates across three frequency bands: 27.76–28.15 GHz (n261), 32.02–32.46 GHz (part of n260 and n261), and 37.39–38.586 GHz (part of n260), offering enhanced resonance and improved isolation. The parasitic element reduces mutual coupling between adjacent elements, improving diversity parameters such as Envelope Correlation Coefficient (ECC) < 0.0010, Diversity Gain (DG) = 10 dB, Channel Capacity Loss (CCL) = 0.15 bits/sec/Hz, Total Active Reflection Coefficient (TARC) < -10 dB, and Mean Effective Gain (MEG) between − 3 and − 12 dB across all ports. Specific Absorption Rate (SAR) analysis for on-body applications confirms safe levels, with values below 1.6 W/kg at the resonating frequencies. Bending tests also show favourable results within the application bandwidth, further validating the antenna’s robustness. These technical improvements make the antenna highly suitable for integration into smart devices, defence navigation systems, mobile phones, and future 5G applications.

In this endeavor, miniaturized quad-port MIMO _MB (multiple-input-multiple-output multi-band) antenna with an overall area of 400 mm ² offering wider-impedance-bandwidth of 8.31GHz-36.14GHz is reported which covers multiple bands including X-band (8.00-12.0 GHz), Ku-band (12.0-18.0 GHz), K-band (18.0-27.0 GHz), partial Ka-band (27.0-40.0 GHz), FR2: n257 (26.50-29.50 GHz), n258 (24.25-27.50) and n261 (27.50-28.35 GHz) is reported for wideband infrastructure. The proposed antenna radiating EM energy is printed on very thin Rogers RTDuroid5880 substrate with thickness 0.254mm. The radiating EM wave patch consists of hexagonal geometry which is etched with a circular-rectangular slot and partial-ground etched by a beveled shape patch for matching of impedance. The conformal capability of the proposed antenna is verified by S-for single-port, dual-port, and proposed four-port antenna. The time-domain analysis confirms the faithful reception of transmitted signals in far-field regions. The diversity performance including ECC _MB , DG _MB , TARC _MB , CCL _MB , and MEG _MB is below permissible standard values with a maximum peak gain of 7.17dBi with stable 2-D radiation patterns in principal planes. The Specific-Absorption-Rate _MB (SAR) analysis is carried out at different operating frequencies in Microwave-Millimeter wave bands with SAR ≤1.60W/Kg in human phantom tissue and makes it suitable for on-body wireless applications.

Safeguarding Personally Identifiable Information (PII) in financial documents is essential to prevent data breaches and maintain regulatory compliance. This research presents a scalable hybrid approach that integrates rule-based Natural Language Processing (NLP), Machine Learning (ML) approaches, and a custom Named Entity Recognition (NER) model for the accurate detection and anonymization of Personally Identifiable Information (PII). A varied and accurate synthetic dataset was created to replicate genuine financial document formats, enhancing model training and assessment. The model has attained a precision of 94.7%, a recall of 89.4%, an F1-score of 91.1%, and an overall accuracy of 89.4% on synthetic datasets. Additional validation on actual financial documents, such as audit reports and vendor bills, revealed a consistent performance with an accuracy of 93%. The study utilizes confusion matrices, ROC curves, and precision-recall curves to evaluate the model which further validates the model’s capabilities and generalization ability. The suggested approach provides a robust and efficient solution for protecting sensitive information in operational financial contexts, markedly enhancing current methods for PII protection.

This paper presents how machine learning techniques may be applied in the process of designing a compact dual-band H-shaped rectangular microstrip antenna (RMSA) operating in 0.75–2.20 GHz and 3.0–3.44 GHz frequency ranges. In the design process, the same dimensions of upper and lower notches are incorporated, with the centered position right in the middle. Notch length and width are verified for investigating the antenna. An artificial neural network (ANN) model is developed from the simulated dataset, and is used for shape prediction. The same dataset is used to create a mathematical model as well. The predicted outcome is compared and it is determined that the model relying on ANN offers better results