The agricultural sector plays a critical role in Bangladesh's rapid economic growth, as 50% of the population directly depends on the industry for their livelihood. This article provides an overview of the global state of IoT-based smart agriculture today, with a focus on how control activities and powering the entire cultivation and irrigation process are carried out. We also looked at the various tools and technology employed in IoT-based smart agriculture and their potential to advance Bangladesh's agricultural industry. More than 70% of the country's land is used for agricultural production. Bangladesh is regarded as a nation particularly vulnerable to climate change, with its agricultural sector frequently being devastated by natural calamities. IoT-based smart agriculture has the potential to drastically alter Bangladesh's agricultural industry by lowering risks, increasing productivity, and boosting resistance to climate change. The bulk of people working in the agricultural industry should raise their level of life by embracing the era of Internet of Things (IoT)-based smart agriculture. To do this, the electricity needed for the monitoring and management of IoT-based smart agriculture will need to be supplied by renewable energy sources. Given that agriculture plays a significant part in Bangladesh's economy and that this technology is being embraced globally in this sector, it is imperative that Bangladesh address its issues and make the necessary preparations to ensure that it is going forward in a sustainable manner.

Accurate prediction of cancer type and primary tumor site is critical for effective diagnosis, personalized treatment, and improved outcomes. Traditional models struggle with the complexity of genomic and clinical data, but quantum computing offers enhanced computational capabilities. This study develops a hybrid quantum-classical transformer model, incorporating quantum attention mechanisms via variational quantum circuits (VQCs) to improve prediction accuracy. Using 30,000 anonymized cancer samples from the Genome Warehouse (GWH), data preprocessing included cleaning, encoding, and feature selection. Classical self-attention modules were replaced with quantum attention layers, with classical data encoded into quantum states via amplitude encoding. The model, trained using hybrid backpropagation and quantum gradient calculations, outperformed the classical transformer model, achieving 92.8% accuracy and an AUC of 0.96 compared to 87.5% accuracy and an AUC of 0.89. It also demonstrated 35% faster training and 25% fewer parameters, highlighting computational efficiency. These findings showcase the potential of quantum-enhanced transformers to advance biomedical data analysis, enabling more accurate diagnostics and personalized medicine.

The study explores the integration of quantum-enhanced feature mapping and optimization within Graph Neural Networks (GNNs) for molecular classification tasks. Utilizing IBM's Qiskit platform, we implement Variational Quantum Circuits (VQCs) to transform classical molecular features into quantum states, capturing complex correlations through quantum entanglement. Additionally, the Quantum Approximate Optimization Algorithm (QAOA) is employed for hyperparameter tuning to enhance model convergence and performance. Comparative analyses against traditional machine learning models demonstrate the superiority of the quantum-enhanced GNN, highlighting the potential of quantum computing in advancing molecular property prediction.

Background: The integration of artificial intelligence (AI) into healthcare represents a transformative shift in medical procedures, offering substantial benefits across various domains. With advancements in AI technologies such as machine learning (ML), deep learning (DL), and natural language processing (NLP), healthcare systems are witnessing improvements in early detection, patient treatment, and overall administration. This article traces the evolution of AI, from foundational contributions by Alan Turing during World War II to contemporary applications like ChatGPT, and examines the impact of AI in enhancing diagnostic accuracy and treatment outcomes. Methods: This comprehensive review analyzes the existing literature on AI applications in healthcare, focusing on various AI methodologies and their integration into clinical settings. It evaluates the effectiveness of AI in processing large datasets, improving diagnostic precision, and facilitating data-driven decision-making. The study also explores the ethical, legal, and technical challenges associated with AI deployment in medical environments. Results: AI technologies have demonstrated significant improvements in healthcare, particularly in early disease detection, personalized treatment plans, and resource management. The use of AI in analyzing vast medical datasets has enhanced diagnostic accuracy, reduced costs, and optimized patient care. However, challenges related to ethical considerations, patient privacy, and system reliability remain critical barriers to full-scale AI adoption. Conclusion: Despite the challenges, AI is positioned as an indispensable tool in modern medicine, capable of enhancing preventive care, personalizing treatments, and improving healthcare delivery. This review proposes a framework for evaluating the benefits, challenges, and strategies of AI integration in healthcare. Further research is essential to maximize AI's potential while addressing ethical and practical concerns, ensuring safe and effective implementation in clinical settings.

Background: The Internet of Things (IoT) is revolutionizing healthcare by introducing intelligent systems that improve patient monitoring, diagnosis, and treatment while addressing challenges like inefficiencies, high costs, and limited access to quality care. By leveraging IoT-enabled devices, healthcare can enhance accessibility, efficiency, and outcomes, especially in chronic disease management, pediatric care, and surgical procedures. Methods: This study employs a comprehensive review methodology, synthesizing peer-reviewed articles, industry reports, and case studies to assess IoT's impact in healthcare. The focus areas include the role of wearable sensors, remote patient monitoring systems, and automated medical equipment. Key challenges, such as data security, privacy concerns, and interoperability, are analyzed alongside proposed solutions for seamless integration. Results: IoT applications in healthcare demonstrate transformative potential. Devices such as insulin pumps, pacemakers, and smart baby monitors provide real-time data to enhance medical decision-making and patient care. IoT technologies improve chronic disease management, enable precise surgical assistance, and enhance pediatric care. However, issues like data breaches and compatibility between devices remain critical hurdles. Conclusion: The integration of IoT in healthcare marks a paradigm shift toward patient-centered, efficient, and accessible services. Addressing challenges related to security, privacy, and system compatibility through collaborative efforts among policymakers, healthcare providers, and technology developers will be crucial. IoT stands as a cornerstone for future innovations in healthcare, promising improved health outcomes and operational efficiencies.

Multimodal artificial intelligence (AI) integrated with augmented reality (AR) can advance healthcare education by accommodating diverse input modalities and complex data. This work enhances the PGxKnow platform for pharmacogenomics instruction by combining voice-based navigation, eye-tracking analytics, gesture recognition, and adaptive spatial mapping on the Microsoft HoloLens 2. A domain-adapted Bidirectional Long Short-Term Memory (BiLSTM) model processes specialized pharmacogenomics voice commands, while gaze data are interpreted by an attention-based CNN and refined via a Spatial Transformer Network to highlight contextually relevant visuals. Gesture inputs are recognized by a CNN optimized for pinch, rotate, and zoom actions, allowing direct manipulation of molecular models. Liquid Neural Networks dynamically adjust hologram placement in response to environmental changes, ensuring stability and coherence. A transformer-based fusion model integrates all modalities, preserving temporal context and leveraging self-attention for adaptive, context-aware responses. This multimodal AI-AR integration demonstrates how advanced computing techniques can deliver cohesive, real-time, and domain-specific educational experiences in pharmacogenomics.