Background: Accurate classification of brain tumors in medical images is vital for effective diagnosis and treatment planning, which improves the patient’s survival rate. In this paper, we investigate the application of Convolutional Neural Networks (CNN) as a powerful tool for enhancing diagnostic accuracy using a Magnetic Resonance Imaging (MRI) dataset. Method: This study investigates the application of CNNs for brain tumor classification using a dataset of Magnetic Resonance Imaging (MRI) with a resolution of 200 × 200 × 1. The dataset is pre-processed and categorized into three types of tumors: Glioma, Meningioma, and Pituitary. The CNN models, including the Classic layer architecture and the ResNet50 architecture, are trained and evaluated using an 80:20 training-testing split. Results: The results reveal that both architectures accurately classify brain tumors. Classic layer architecture achieves an accuracy of 94.55%, while the ResNet50 architecture surpasses it with an accuracy of 99.88%. Compared to previous studies and 99.34%, our approach offers higher precision and reliability, demonstrating the effectiveness of ResNet50 in capturing complex features. Conclusions: The study concludes that CNNs, particularly the ResNet50 architecture, exhibit effectiveness in classifying brain tumors and hold significant potential in aiding medical professionals in accurate diagnosis and treatment planning. These advancements aim to further enhance the performance and practicality of CNN-based brain tumor classification systems, ultimately benefiting healthcare professionals and patients. For future research, exploring transfer learning techniques could be beneficial. By leveraging pre-trained models on large-scale datasets, researchers can utilize knowledge from other domains to improve brain tumor classification tasks, particularly in scenarios with limited annotated data.

Human facial feature extraction plays a critical role in various applications, including biorobotics, polygraph testing, and driver fatigue monitoring. However, many existing algorithms rely on end-to-end models that construct complex classifiers directly from face images, leading to poor interpretability. Additionally, these models often fail to capture dynamic information effectively due to insufficient consideration of respondents' personal characteristics. To address these limitations, this paper evaluates two prominent approaches: the constrained local model (CLM), which accurately extracts facial features depending on patch experts, and the active shape model (ASM), designed to simultaneously extract the appearance and shape of an object. We assess the performance of these models on the MORPH dataset using point to point error as evaluation metrics. Our experimental results demonstrate that the CLM achieves higher accuracy, while the ASM exhibits better efficiency. These findings provide valuable insights for selecting the appropriate model based on specific application requirements.

Background and Purpose: This study delves into the portrayal of gender equality and women's empowerment in Khaled Hosseini's novel, A Thousand Splendid Suns, within the context of Afghan society. Recognizing the significance of literature as a mirror reflecting societal realities, this research aims to explore the multifaceted experiences of female protagonists navigating oppressive patriarchal structures and uncover the complexities of gender inequality and women's agency depicted in the novel. Methodology: Employing thematic analysis, I conducted an in-depth examination of character dynamics, societal norms, and narrative elements present in A Thousand Splendid Suns. Through a systematic exploration of themes and patterns within the text, the study elucidates the nuanced complexities of gender discrimination and women's resilience in the face of adversity. Data collection involves close reading and interpretation of the novel, supplemented by theoretical engagement with relevant literature on gender studies and Afghan society. The qualitative approach allows for a rich exploration of the experiences and perspectives of female characters within the narrative context. Findings: The findings of the analysis illuminate the pervasive discrimination, violence, and systemic oppression faced by Afghan women as depicted in the novel. Characters like Mariam and Laila exemplify the intersectionality of gender with other axes of oppression, such as class and ethnicity, highlighting the interconnected nature of social hierarchies. Despite the challenges they face, female characters demonstrate resilience and determination in challenging societal norms and asserting their autonomy. The narrative underscores the transformative power of education as a catalyst for women's empowerment, inspiring others to advocate for systemic change and justice. Contributions: This study contributes to a deeper understanding of gender dynamics and societal challenges prevalent in Afghan culture by scrutinizing the complexities of gender portrayal in literature. By shedding light on the struggles and triumphs of female characters, the research underscores the importance of promoting gender equality and women's empowerment as fundamental principles of social justice and human rights. Ultimately, A Thousand Splendid Suns serves as a powerful testament to the enduring human spirit and the transformative potential of solidarity in the face of oppression and adversity. Keywords: Oppressive male dominance, Khaled Hosseini, gender equality, women’s empowerment, A Thousand Splendid Sun. Cite as: Alqaryouti, M. H. (2024). Gender equality and women’s empowerment in A Thousand Splendid Suns by Khaled Hosseini. Journal of Nusantara Studies, 9(2), 490-513. http://dx.doi.org/10.24200/jonus.vol9iss2pp490-513

Poetry is greatly influenced by the cultural background and personal experiences of the poets. Emily Dickson’s poems exemplify this because she draws a lot of her motivation from her heritage of New England and her life experience which had harsh incidents such as loss of friends and relatives. She lives a life of seclusion, where she rarely has face-to-face encounter with her friends as she prefers communicating through letters. Her limited interaction with the society gives her adequate space to reflect and write about different aspects of life. Emily’s poetry is also influenced by the doubts she holds about Christianity, especially in relation with survival of the soul after death. "Because I Could Not Stop for Death" and "I Heard a Fly Buzz- when I Died" are among her popular poems that indicate her religious doubt. She agrees with some of the Calvinist religious beliefs, but still has some doubts about the innate depravity of mankind and the concept of the afterlife.Dickinson’s spiritual background is indicated by her religious beliefs, which form the basis of her preoccupation with death. Although Dickinson is a religious person who believes in the inevitability of death and afterlife, she is a non-conformist as she is skeptical and curious about the nature of death. Transcendentalism is the other factor that contributes to Dickinson’s preoccupation with death as indicated in her poems. Dickinson’s preoccupation with death also results from her obsession, which is greatly contributed by the life experiences she has with death including loss of her family, mentors and close friends.