This research presents an advanced paddy disease classification model utilizing the EfficientNet B4 deep learning architecture trained on the publicly available Paddy Doctor dataset, which includes 19,131 labeled images for training and 4,785 images for testing. The model aims to classify paddy leaf samples into nine disease categories or into normal specimens. EfficientNet B4, known for its structured scaling technique and Swish activation function, was trained using pretrained weights from ImageNet and optimized with the Adam optimizer. It achieved a peak accuracy of 99.09% during training and 96.91% during testing, highlighting its superior performance in image classification tasks. The model’s architecture, featuring mobile inverted bottleneck convolution (MBConv) and deep separable convolutions, facilitated efficient feature extraction, resulting in high accuracy, recall, and F1-scores, along with low loss values. Our findings demonstrate the model’s reliability and efficiency in diagnosing various paddy illnesses, underscoring the transformative potential of deep learning techniques such as EfficientNet B4 in enhancing agricultural disease management.

Problem: Oral squamous cell carcinoma (OSCC) is the eighth most prevalent cancer globally, leading to the loss of structural integrity within the oral cavity layers and membranes. Despite its high prevalence, early diagnosis is crucial for effective treatment.Aim: This study aimed to utilize recent advancements in deep learning for medical image classification to automate the early diagnosis of oral histopathology images, thereby facilitating prompt and accurate detection of oral cancer.Methods: A deep learning convolutional neural network (CNN) model categorizes benign and malignant oral biopsy histopathological images. By leveraging 17 pretrained DL-CNN models, a two-step statistical analysis identified the pretrained EfficientNetB0 model as the most superior. Further enhancement of EfficientNetB0 was achieved by incorporating a dual attention network (DAN) into the model architecture.Results: The improved EfficientNetB0 model demonstrated impressive performance metrics, including an accuracy of 91.1%, sensitivity of 92.2%, specificity of 91.0%, precision of 91.3%, false-positive rate (FPR) of 1.12%, F1 score of 92.3%, Matthews correlation coefficient (MCC) of 90.1%, kappa of 88.8%, and computational time of 66.41%. Notably, this model surpasses the performance of state-of-the-art approaches in the field.Conclusion: Integrating deep learning techniques, specifically the enhanced EfficientNetB0 model with DAN, shows promising results for the automated early diagnosis of oral cancer through oral histopathology image analysis. This advancement has significant potential for improving the efficacy of oral cancer treatment strategies.

Introduction: The advent of sixth-generation (6G) wireless communication technology promises to transform various sectors, with healthcare—particularly smart hospitals—standing to gain significantly. This study investigates the transformative potential of 6G in healthcare by exploring its architectural foundations and enabling technologies.Methods: A comprehensive review and analysis were conducted on current technological trends, frameworks, and integration strategies relevant to 6G-enabled healthcare systems. The proposed model integrates key technologies such as the Internet of Things (IoT), artificial intelligence (AI), blockchain, robotics, telemedicine, and advanced data analytics within the context of smart hospitals.Results: The findings suggest that 6G's ultralow latency, massive device connectivity, and high data throughput can dramatically enhance patient care, real-time monitoring, and hospital operational efficiency. The proposed 6G-based smart hospital model fosters seamless communication between medical devices and systems, enabling intelligent decision-making and optimized resource allocation.Discussion: Despite the promising benefits, several challenges were identified, including data privacy and security risks, system interoperability, and ethical implications. The study underscores the critical importance of robust regulatory frameworks and standardized protocols to ensure secure and ethical deployment of 6G technologies in healthcare settings.Conclusion: By providing a forward-looking analysis of the opportunities and challenges associated with 6G-powered smart hospitals, this research offers valuable insights into the evolving landscape of digital healthcare and its potential to redefine patient care and hospital management in the near future.

The deep learning method (DLM) is one way to fix issues in optical nonorthogonal multiple access (O-NOMA) systems that are caused by signals that overlap and interfere with each other. NOMA increases the optical framework's spectrum efficiency, allowing several users to share the same time-frequency resources. However, NOMA-DLM-based detection's complicated interference patterns and variable channel conditions are challenging for conventional detection methods to manage. By utilizing deep neural networks' advantages, these methods are able to overcome these challenges and improve detection performance. An overview of the main features and advantages of DLM detection in massive multiple input and output (M-MIMO) O-NOMA systems is given in this article. It describes the essential elements, such as the training procedure and the network design. In order to process the sent symbols or decode data streams, DLM networks are built to process the incoming signal, power allocation coefficients, and extra information. Gradient descent optimization is used to update the network parameters iteratively while training the network, and a diverse and representative dataset is created. Additionally, the challenges of detecting deep learning in O-NOMA systems are examined. It recognizes that in order to get the best results, significant computational resources, a large amount of training data, and careful model design are required. It looks at and compares the 16 × 16, 32 × 32, and 64 × 64 M-MIMO-NOMA models in terms of bit error rate (BER), complexity, and power spectral density (PSD). The suggested DLM algorithms have been demonstrated to perform better than traditional methods by achieving an excellent BER of 10-3 at 4.1 dB and PSD (−2500) performance with low complexity.

Smart hospitals are poised to greatly enhance life quality by offering persistent health monitoring capabilities. Remote healthcare and surgery, which are highly dependent on low latency, have seen a transformative improvement with the advent of 5G technology. This has facilitated a new breed of healthcare services, including monitoring and remote surgical procedures. The enhanced features of 5G, such as Enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communications (URLLC), have enabled the development of advanced healthcare systems. These systems reduce the need for direct patient contact in hospitals, which is especially pertinent as 5G becomes more widespread. This research presents novel hybrid detection algorithms, specifically QR decomposition with M-algorithm maximum likelihood-minimum mean square error (QRM-MLD-MMSE) and QRM-MLD-ZF (zero forcing), for use in Massive MIMO (M-MIMO) technology. These methods aim to decrease the latency in MIMO-based Non-Orthogonal Multiple Access (NOMA) waveforms while ensuring optimal bit error rate (BER) performance. We conducted simulations to evaluate parameters like BER and power spectral density (PSD) over Rician and Rayleigh channels using both the proposed hybrid and standard algorithms. The study concludes that our hybrid algorithms significantly enhance BER and PSD with lower complexity, marking a substantial improvement in 5G communication for smart healthcare applications.