Abstract Information seeking/access is an inherent practice through which humans cope with their environment. Advancements in technology have dramatically changed information-seeking, making it technologically driven and dependent. Due to its importance, the various aspects of information-access behaviour have been explored across populations, contexts, and geographies. A comprehensive analysis that contextualises and coherently integrates the findings of studies conducted in the Kashmir... Information seeking/access is an inherent practice through which humans cope with their environment. Advancements in technology have dramatically changed information-seeking, making it technologically driven and dependent. Due to its importance, the various aspects of information-access behaviour have been explored across populations, contexts, and geographies. A comprehensive analysis that contextualises and coherently integrates the findings of studies conducted in the Kashmir region of India is lacking. A search was conducted in the Google Scholar database to find the existing studies on information-seeking behaviour in Kashmir. Of 389 search results, 37 research studies met the inclusion criteria. This study uses a systematic review to explore the general information-seeking behaviour in Kashmir. Findings reveal that the Internet and libraries are popular sources of information. Research is the primary information needed by most users. Ease of access is the most cited criterion for source selection. Internet connectivity, infrastructure, and political climate are key factors affecting information-seeking behaviour. The Apriori system and content knowledge result in high recall and precision. Read More Read Less

Abstract Scalp and hair diseases, affecting millions worldwide, pose significant challenges regarding accurate diagnosis and effective treatment. Traditionally reliant on expert evaluation, these conditions can often be misdiagnosed due to their complex and overlapping symptoms. In recent times, especially in information technology, convolutional neural networks (CNNS) have become more prominent thanks to their ability to analyse and process image data for... Scalp and hair diseases, affecting millions worldwide, pose significant challenges regarding accurate diagnosis and effective treatment. Traditionally reliant on expert evaluation, these conditions can often be misdiagnosed due to their complex and overlapping symptoms. In recent times, especially in information technology, convolutional neural networks (CNNS) have become more prominent thanks to their ability to analyse and process image data for classification and recognition tasks. CNNs learn to recognize patterns from images through convolutional layers to detect characteristic features in image sand have revolutionized the field of image recognition, offering promising applications in medical diagnostics. Despite their potential, few studies have thoroughly explored the capabilities of multiple CNN architecturesin the context of dermatology. This study aims to bridge this gap by evaluating the effectiveness of several CNN models—VGG16, VGG19 , Inception-V3, ResNet50, and ResNet152--in detecting scalp and hair diseases. The findings indicate that VGG16 and VGG19 consistently outper for mother models in accuracy across all disease categories, demon strating their robustness and reliability for this application. By providing a comparative analysis of these architectures with a user interface (UI), we seek to advance automated diagnostic methods, ultimately enhancing clinical decision-making and patient care. Read More Read Less

Abstract This review discusses the evolution, approaches, methodologies, features, and outcomes of emotion-induced music recommendation systems (MRS) in light of the growing demand for personalised music experiences. Traditional MRS often overlook the emotional context of users, making the integration of emotion recognition a promising enhancement for user satisfaction. The paper examines 32 studies published between 2011 and 2025, detailing how various... This review discusses the evolution, approaches, methodologies, features, and outcomes of emotion-induced music recommendation systems (MRS) in light of the growing demand for personalised music experiences. Traditional MRS often overlook the emotional context of users, making the integration of emotion recognition a promising enhancement for user satisfaction. The paper examines 32 studies published between 2011 and 2025, detailing how various inputs, such as facial expressions and physiological signals, can inform personalised music recommendations. It highlights the application of advanced machine learning techniques and the challenges that arise, including the cold-start problem and the need for real-time processing capabilities. The review categorises existing systems into content-based filtering, sequential recommendations, and emotion detection using physiological signals. Additionally, it emphasises the importance of context-aware recommen der systems that factor in user environments. Future research is encouraged to address limitations in accuracy, scalability, and ethical considerations while exploring multimodal approaches for more robust MRS. Ultimately, the review highlights the transformative potential of emotion-based music recommendation systems (MRS) in enhancing user interaction and personalisation with digital music platforms. Read More Read Less