Cancer’s staggering molecular heterogeneity demands innovative approaches beyond traditional single-omics methods. The integration of multi-omics data, spanning genomics, transcriptomics, proteomics, metabolomics and radiomics, can improve diagnostic and prognostic accuracy when accompanied by rigorous preprocessing and external validation; for example, recent integrated classifiers report AUCs around 0.81–0.87 for difficult early-detection tasks. This review synthesizes how artificial intelligence (AI), particularly deep learning and machine learning, bridges this gap by enabling scalable, non-linear integration of disparate omics layers into clinically actionable insights. We explore cutting-edge AI methodologies, including graph neural networks for biological network modeling, transformers for cross-modal fusion, and explainable AI (XAI) for transparent clinical decision support. Critical applications are highlighted, such as AI-driven therapy selection (e.g., predicting targeted therapy resistance), proteogenomic early detection, and radiogenomic non-invasive diagnostics. We further address translational challenges: data harmonization, batch correction, missing data imputation, and computational scalability. Emerging trends, federated learning for privacy-preserving collaboration, spatial/single-cell omics for microenvironment decoding, quantum computing, and patient-centric “N-of-1” models, signal a paradigm shift toward dynamic, personalized cancer management. Despite persistent hurdles in model generalizability, ethical equity, and regulatory alignment, AI-powered multi-omics integration promises to transform precision oncology from reactive population-based approaches to proactive, individualized care.

This study investigates the impact of artificial intelligence (AI)-assisted assessment on young L2 learners’ vocabulary knowledge, immunity, and resilience, considering parental and teacher support roles. Sixty junior high school students in Afghanistan, aged 13 to 14, participated in the study. They were divided into an experimental group receiving AI-assisted assessment and a control group with traditional instruction. The research employed a pretest–posttest control group design, using teacher-made vocabulary tests validated for reliability and instruments measuring immunity and resilience. The findings revealed that AI-assisted assessment significantly improved vocabulary knowledge and emotional resilience compared to the control group. While parental support showed a positive trend toward vocabulary enhancement, teacher support did not significantly impact the outcomes. The study highlights the potential of AI in language education, emphasizing the need for collaborative efforts among educators, parents, materials developers, syllabus designers, and policymakers to maximize the benefits of AI tools. These findings underscore the importance of integrating advanced technologies into educational frameworks to support cognitive and emotional development in learners.

India, facing rapid urbanization and a burgeoning population, is embracing smart city initiatives to enhance urban infrastructure and sustainability. The Internet of Things (IoT) plays a pivotal role in this transformation by providing real-time data and connectivity to optimize urban services. This paper investigates the impact of IoT on India's smart city development, focusing on energy management, waste disposal, traffic systems, and environmental monitoring. Through a combination of qualitative and quantitative research methods, including case studies and data analysis, this study highlights IoT's significant contributions to urban sustainability and resilience. Findings reveal that IoT-enabled smart grids, intelligent transportation systems, and waste management solutions are substantially improving urban efficiency and quality of life. The research underscores the need for recognizing and harnessing IoT's full potential to achieve sustainable urban development and address the challenges posed by rapid urbanization.

Skin cancer is widely known as the most frequent cancer type worldwide. Early cancer detection is critical as it might cause death if not diagnosed in the early stages. It can be identified through open eyes, but due to great inter-class similarities and intra-class variances, it is difficult to notice. Because of the global prevalence of skin cancers, deep learning is used to create various automated methods to assist physicians in early skin lesion diagnosis. The primary goal of this study is to propose a new tested and calibrated model of deep learning for multiple class categorization of skin lesions. The research community developed numerous Computer-Aided Diagnostic systems (CADs) to support lesion detection. However, this study proposes an automated system of CAD designed for multi-class classification of skin lesions. The dataset used by the authors is HAM1000, and a comprehensive study was performed on three pre-trained Convolutional Neural Network (CNN) models and three hybrid CNN models. To increase efficacy and performance, the Model is suggested and it is multilayered that is designed with great care having multiple with varied sizes of filter, but fewer parameters too. The models’ effectiveness was tested on parameters like precision, the phenomenon of recall, and the score of F1. Maximum accuracy is 91.63% which is reported through a model which is hybrid, and it is proposed in this paper.