HRMARS is well established journal publisher based in Pakistan. It is publishing prominent journals in social sciences since 2011.

This paper aims to develop a new recommended human-centric framework for the smart remanufacturing of End-Life-Vehicle (ELV)s stabilizer bars to enhance worker experiences. The human-centered method has been applied in various applications since its introduction. Still, there has been little consideration of worker experience challenges in developing the smart remanufacturing of ELV’s stabilizer bar concept. Data were analyzed using a regression model to evaluate the six hypotheses to generate the human-centric model. According to the research, five aspects of the human-centric model strongly correlate with worker experience: emotional, cognitive, intellectual, confidence and trust. However, only one aspect, behaviour, did not significantly correlate with worker experience. As a result, the conceptual framework excludes the minor aspect of worker experience in human-centric design. Recognizing the importance of smart remanufacturing of ELV’s stabilizer bar, this model is expected to be used as guidance to build a smart remanufacturing of ELV’s stabilizer bar system that specifically responds to human needs.

Due to the rapid progression of smart applications and the Internet in our daily life, we have become more dependent on technology in solving issues. The recent white flag movement highlighted inequality in food distribution. This project was developed to solve these issues by providing a centralized platform for food donors and food recipients throughout the state of Selangor. This project also gives emphasis to cybersecurity features to ensure the safety of its users. The literature reviews of this project illustrate the factors affecting food distribution and how implementing a proper security system can help improve user experience. This project will be developed using programming languages and databases on an Android platform, with the use of common gridles and packages to ensure compatibility with most Android devices. The research behind this project is conducted through questionnaires directed to a sample that are residents in Selangor regarding the idea of having a food donation application. It is found that most of the residents agree with the idea behind this project as well as the security aspect of the application. Taking all this into consideration, the application called “FeedBack” was developed to tackle this issue.

HRMARS is well established journal publisher based in Pakistan. It is publishing prominent journals in social sciences since 2011.

Background and Objective: Cervical cancer remains one of the leading causes of death among women worldwide, particularly in regions with limited access to early screening. Pap smear screening is the primary tool for early detection, but manual interpretation is labor-intensive, subjective, and prone to inconsistency and misdiagnosis. Accurate segmentation of cervical cell nuclei is essential for automated analysis but is often hampered by overlapping cells, poor contrast, and staining variability. This research aims to develop an improved algorithm for accurate cervical nucleus segmentation to support automated Pap smear analysis. Method: The proposed method involves a combination of adaptive gamma correction for contrast enhancement, followed by Otsu thresholding for segmentation. Post-processing is performed using adaptive morphological operations to refine the results. The system is evaluated using standard image quality assessment metrics and validated against ground truth annotations. Result: The results show a significant improvement in segmentation performance over conventional methods. The proposed algorithm achieved a Precision of 0.9965, an F-measure of 97.29%, and an Accuracy of 98.39%. The PSNR value of 16.62 indicates enhanced image clarity after preprocessing. The method also improved sensitivity, leading to better identification of nuclei boundaries. Advanced preprocessing techniques, including edge-preserving filters and multi-Otsu thresholding, contributed to more accurate cell separation. The segmentation method proved effective across varying cell overlaps and staining conditions. Comparative evaluations with traditional clustering methods confirmed its superior performance. Conclusions: The proposed algorithm delivers robust and accurate segmentation of cervical cell nuclei, addressing common challenges in Pap smear image analysis. It provides a consistent framework for automated screening tools. This work enhances diagnostic reliability in cervical cancer screening and offers a foundation for broader applications in medical image analysis.