Template-Type: ReDIF-Article 1.0 Author-Name: Suzan Peters Author-Name: Shivanisinh Parmar Author-Name: Nitish Kumar Singh Author-Name: Premanshu Bhagat Title: Perceived Affordability and Demand Dynamics in Quick Commerce: Evidence from Generation Z and Millennials in Gujarat Publication-Status: Published Abstract: The fast growth of quick commerce apps has revolutionized the urban retail system of India. This paper will investigate the impact of perceived affordability on the demand of quick commerce services among the generation Z and Millennials in Gujarat. Primary data were collected using a descriptive quantitative research design and a structured questionnaire was used to collect data in 200 respondents in six cities; Gandhinagar, Ahmedabad, Surat, Vadodara, Rajkot and Dahod. Probability sampling was not accepted and analysis of data was done using SPSS. The results indicate that perceived affordability is a significant issue impacting on the purchase frequency and platform choice. Although speed and convenience are motivational to users, more importantly in demand formation are cost-efficiency, delivery fee and promotional offers. Affordability to young consumers is beyond the low prices to the general transaction convenience and to digital experience. Though the adoption is better in large urban centres, Tier 2 cities also have significant growth potential as long as the issue of pricing is well tackled. The research is based on the Consumer Value Theory [10] in placing affordability within a multidimensional construct multidimensional construct of functional, emotional, and conditional value. These conclusions have strategic implications to the operators of the platform who may want to gain competitive advantage based on pricing, special promotions and digitally enhanced customer interaction. Keywords: consumer demand; digital retail; generation z; millennials; perceived affordability; quick commerce Journal: Milky Way Scientific Journal Pages: 82-90 Volume: 1 Issue: 2 Year: 2025 Month: December DOI: 10.65021/mwsj.v1.i2.23 File-URL: https://mwsjournal.com/index.php/mws/article/view/23 File-Function: Abstract page File-Format: text/html File-URL: https://mwsjournal.com/index.php/mws/article/download/23/9 File-Function: Full text File-Format: application/pdf Handle: RePEc:gyr:mwsjou:v:1:y:2025:i:2:id:23 Template-Type: ReDIF-Article 1.0 Author-Name: Nitish Kumar Singh Author-Name: Suzan Peters Author-Name: Premanshu Bhagat Title: Digital Marketing Strategies and Consumer Engagement: A Systematic Review of Emerging Trends, Technologies, and Measurement Frameworks Publication-Status: Published Abstract: The fast growth of digital technologies has changed the environment of marketing radically, as the use of the conventional methods does not seem adequate to address and attract the modern consumer. In the given paper, an extensive systematic review of digital marketing, including social media marketing, search engine optimization (SEO), content marketing, influencer marketing, programmatic advertising, and data-driven personalization, is provided. Based on 87 peer-reviewed articles that were released within the 2018-2024 timeframe, this review brings together empirical evidence on the usefulness of every strategy in motivating consumer engagement, brand awareness, lead generation, and conversion rates. The results indicate that multichannel strategies always work better than one channel campaigns and personalization and artificial intelligence (AI)-driven products become essential instruments of marketing ROI. Also, the research reveals serious gaps in measurement systems, especially when it comes to long-term brand equity measures in the digital context. An updated Digital Marketing Effectiveness Framework (DMEF) is suggested to inform practitioners and a next-generation of study. Discussions on implications to marketing managers, policy makers as well as academics are observed with recommendations of ethical use of data and emerging measures of regulation. Keywords: digital marketing; consumer engagement; social media; content marketing; AI in marketing; marketing ROI; multichannel strategy; influencer marketing; programmatic advertising; SEO Journal: Milky Way Scientific Journal Pages: 91-98 Volume: 1 Issue: 2 Year: 2025 Month: December DOI: 10.65021/mwsj.v1.i2.26 File-URL: https://mwsjournal.com/index.php/mws/article/view/26 File-Function: Abstract page File-Format: text/html File-URL: https://mwsjournal.com/index.php/mws/article/download/26/10 File-Function: Full text File-Format: application/pdf Handle: RePEc:gyr:mwsjou:v:1:y:2025:i:2:id:26 Template-Type: ReDIF-Article 1.0 Author-Name: Waraz Alduhoki Author-Name: Seda Arslan Tuncer Title: Machine Learning in Medical Image Analysis: A Review of Techniques and Applications Publication-Status: Published Abstract: Medical image analysis is now an indispensable part of modern healthcare that not only provides an opportunity to conduct non-invasive diagnostics, plan treatment, and monitor disease progress but also makes this a possibility. The swiftly growing imaging technologies like Magnetic Resonance Imaging (MRI), Computed Tomography (CT), X-ray, and Ultrasound have generated huge quantities of multidimensional images that have put unnecessary pressure on manual interpretation. Deep learning (DL) and machine learning (ML) have come to be one of the most significant paradigms of automation and enhancement of the analysis of such images. In this review, the current application of ML to medical imaging is described. It explains basic ML methods, such as classical supervised and unsupervised techniques, and modern DL models (Convolutional Neural Networks [CNNs], Generative Adversarial Networks [GANs], and Recurrent Neural Networks [RNNs]) to discuss their application in clinical contexts, such as detecting, segmenting, registering, and planning the treatment of diseases. The benchmark datasets (LUNA16, BRATS, and CheXpert) and standard evaluation metrics (accuracy, precision, recall, F1-score, AUC-ROC, and Dice similarity coefficient) are also discussed. Some key barriers are distinguished, such as access and quality of data, model interpretability, computational requirements, and regulatory and ethical concerns. New areas like multimodal data fusion, explainable AI, federated learning to capture privacy-saving analytics, and real-time edge computing are also pointed out. Keywords: CNN; deep learning; image segmentation; machine learning; medical image analysis Journal: Milky Way Scientific Journal Pages: 99-110 Volume: 1 Issue: 2 Year: 2025 Month: December DOI: 10.65021/mwsj.v1.i2.28 File-URL: https://mwsjournal.com/index.php/mws/article/view/28 File-Function: Abstract page File-Format: text/html File-URL: https://mwsjournal.com/index.php/mws/article/download/28/11 File-Function: Full text File-Format: application/pdf Handle: RePEc:gyr:mwsjou:v:1:y:2025:i:2:id:28