J. B. Institute of Engineering and Technology

A novel series of 1,2,3-triazole-quinazoline-acetamide hybrids was synthesized and evaluated for anticancer activity against two breast cancer cell lines, MCF-7 and MDA-MB-231, using erlotinib as a standard drug. The results indicate that 2-[4-(4-methoxyphenyl)-1H-1,2,3-triazol-1-yl]-N-(quinazolin-4-yl)acetamide, 2-[4-(3,5-dimethoxyphenyl)-1H-1,2,3-triazol-1-yl]-N-(quinazolin-4-yl)acetamide, 2-[4-(3-methoxyphenyl)-1H-1,2,3-triazol-1-yl]-N-(quinazolin-4-yl)acetamide, and 2-[4-(4-cyanophenyl)-1H-1,2,3-triazol-1-yl]-N-(quinazolin-4-yl)acetamide exhibited higher activity compared to the standard drug. Additionally, the tyrosine kinase inhibitory activity of 2-[4-(4-methoxyphenyl)-1H-1,2,3-triazol-1-yl]-N-(quinazolin-4-yl)acetamide, 2-[4-(3,5-dimethoxyphenyl)-1H-1,2,3-triazol-1-yl]-N-(quinazolin-4-yl)acetamide, and 2-[4-(3-methoxyphenyl)-1H-1,2,3-triazol-1-yl]-N-(quinazolin-4-yl) acetamide was higher compared to erlotinib.

In this article, a novel, high-gain, compact, and self-isolated S-shaped dual-wideband MIMO antenna system is presented. In the proposed structure, the pentagonal shape of the antenna is modified to a novel S-shaped antenna by cutting two horizontal slits, which achieves a broader bandwidth, higher gain, improved isolation, and a lower envelope correlation coefficient. The proposed design is compact 56 × 37 × 1.6mm3 due to the absence of any additional isolation element. The proposed antenna operates on two wide bands between frequency ranges of 0.584—1 GHz, which finds its applications for 750, 850, and 900 GSM bands, while the other wideband covers the range of 3.24–4.32 GHz applicable for WLAN, 5G, and sub-6 GHz wireless applications. The isolation loss between both antenna elements is 31 dB in the first operating band and 20 dB in the second operating band. Several diversity parameters are also verified to analyze the MIMO performance of the proposed antenna, like envelope correlation coefficient (ECC < 0.015), diversity gain (DG > 9.85), TARC (< –15 dB), and CCL (< 0.35 bits/s/Hz) across both operating bands, indicating excellent impedance matching and high diversity performance. The proposed antenna is fabricated and successfully tested experimentally to verify the findings.

Every year, many people around the world are progressively affected by the devastating conditions of health problems such as heart disease, respiratory infections, neurological dysfunction, cognitive stress, cancer, stroke, diabetes, etc., which lead to severe health complications and associated abnormalities. Thus, early health analytics are crucial, as they enable timely intervention with targeted therapies, potentially providing immediate relief and sustained long-term benefits that may slow disease progression. Due to the complex pathophysiological processes and heterogeneous clinical trials in various health conditions, there is a need for highly sensitive, multimodal biomarkers and effective investigative approaches to accurately detect and monitor patient health outcomes. Therefore, machine learning algorithms with various categories and techniques are considered for predicting outcomes, including prognosis, risk assessment, patient stratification, and disease monitoring. The flow of the proposed work is divided into three stages, as the first stage defines the importance of healthcare with case studies, followed by the traditional Machine Learning (ML) algorithms, traditional Deep Learning (DL) approaches, and modern DL techniques (TabNet and AutoInt) in the second stage. Finally, the experiments are implemented to justify the results. This work highlights the grouping of modalities by integrating molecular protein, chemical, and genetic biomarkers with emerging ML features. The results indicate a significant improvement in predicting the accuracy using the proposed methodology.

A big problem for firms is employee turnover, which leads to lower organizational performance, higher recruiting expenses, and the loss of trained people. As a strategic human resource strategy, employee recognition efforts are gaining attention as a means for firms to retain valuable employees in today's competitive business climate. This research looks at how recognition programs may help keep employees from leaving by examining how workers feel about their own recognition practices and how that makes them feel about their jobs and whether or not they plan to leave. Primary data for the study came from a structured questionnaire given to workers; the research strategy was a combination of descriptive and analytical. The data is analyzed using statistical methods including percentage analysis, correlation, and regression analysis.

A malignant disorder known as breast cancer (BC) arises when cells in breast tissue grow out of control, frequently due to genetic, hormonal, or environmental reasons. Reducing death rates and increasing treatment outcomes depend on early and precise identification. However, a significant challenge lies in the variability of breast tissue among patients, dependence on high-resolution imaging, and difficulty interpreting subtle abnormalities, which delays accurate diagnosis and timely treatment in clinical settings. In this paper, an advanced AI-driven deep learning pipeline for highly accurate medical image analysis, enabling early detection and precision diagnosis of breast cancer (PDBC-KARN), is proposed. Initially, the input image is collected from the breast cancer detection dataset. Then, images were pre-processed by utilizing the Bilinear Double-Order Filter (BDOF) for image resizing and normalizing the image. Then the pre-processed images are given to the Kolmogorov-Arnold Recurrent Network (KARN) to detect breast cancer and are classified as benign and malignant. The Stellar Oscillation Optimizer (SOO) is utilized to optimize the weight parameters of KARN. The proposed PDBC-KARN approach is implemented in Python and establishes substantial improvements in accuracy, precision, recall, specificity, loss, training and validation accuracy, training and validation recall, confusion matrix, and F1-score. The proposed PDBC-KARN approach achieves an accuracy of 98.5% for benign and 97.0% for malignant, an f1-score of 98.9% for benign and 97.5% for malignant cases with existing methods, like dual-view deep learning (DL) techniques for accurate breast cancer (BC) detection in mammograms (BCDM- DNN), RI-ViT: a multi-scale hybrid technique depending upon vision transformer for detection of breast cancer in histopathological images(BCDH- CNN) PSO-optimized fractional order CNNs for enhanced detection of breast cancer (BCD- FOCNN) respectively.