Internet of Things (IoT) performs a vital role in providing connectivity between computing devices, processes, and things. It significantly increases the communication facilities and giving up-to-date information to distributed networks. On the other hand, the techniques of artificial intelligence offer numerous and valuable services in emerging fields. An IoT-based healthcare solution facilitates patients, hospitals, and professionals to observe real-time and critical data. In the literature, most of the solution suffers from data intermission, high ethical standards, and trustworthiness communication. Moreover, network interruption with recurrent expose of sensitive and personal health data decreases the reliance on network systems. Therefore, this paper intends to propose an IoT solution for AI-enabled privacy-preserving with big data transferring using blockchain. Firstly, the proposed algorithm uses a graph-modeling to develop a scalable and reliable system for gathering and transmitting data. In addition, it extracts the subset of nodes using the artificial intelligence approach and achieves efficient services for the healthcare system. Secondly, symmetric-based digital certificates are utilized to offer authentic and confidential transmission with communication resources using blockchain. The proposed algorithm is explored with existing solutions through multiple simulations and proved improvement in terms of realistic parameters.

In recent times, health applications have been gaining rapid popularity in smart cities using the Internet of Medical Things (IoMT). Many real-time solutions are giving benefits to both patients and professionals for remote data accessibility and suitable actions. However, timely medical decisions and efficient management of big data using IoT-based resources are the burning research challenges. Additionally, the distributed nature of data processing in many proposed solutions explicitly increases the threats of information leakages and damages the network integrity. Such solutions impose overhead on medical sensors and decrease the stability of the real-time transmission systems. Therefore, this paper presents a machine-learning model with SDN-enabled security to predict the consumption of network resources and improve the delivery of sensors data. Additionally, it offers centralized-based software define network (SDN) architecture to overcome the network threats among deployed sensors with nominal management cost. Firstly, it offers an unsupervised machine learning technique and decreases the communication overheads for IoT networks. Secondly, it predicts the link status using dynamic metrics and refines its strategies using SDN architecture. In the end, a security algorithm is utilized by the SDN controller that efficiently manages the consumption of the IoT nodes and protects it from unidentified occurrences. The proposed model is verified using simulations and improves system performance in terms of network throughput by 13%, data drop ratio by 39%, data delay by 11%, and faulty packets by 46% compared to HUNA and CMMA schemes.

Background: Medical imaging is to assume greater and greater significance in an efficient and precise diagnosis process. Discussion: It is a set of various methodologies which are used to capture internal or external images of the human body and organs for clinical and diagnosis needs to examine human form for various kind of ailments. Computationally intelligent machine learning techniques and their application in medical imaging can play a significant role in expediting the diagnosis process and making it more precise. Conclusion: This review presents an up-to-date coverage about research topics which include recent literature in the areas of MRI imaging, comparison with other modalities, noise in MRI and machine learning techniques to remove the noise.

In recent years, deep learning (DL) has become indispensable in medical image segmentation (MIS), proven by numerous studies showcasing its effectiveness. This paper presents two significant original contributions and conducts a comprehensive thematic evaluation of DL approaches in MIS. Diverging from traditional surveys that categorize DL literature into multiple groups and analyze individual works within each category, we adopt a multi-level classification approach. This method organizes existing literature from a broad overview to finer details. Furthermore, to address the inherent challenges of this field, we introduce an innovative method called L-UNet. The L-UNet method strategically utilizes filters with reduced parameters to construct a U-shaped convolutional neural network. This not only demonstrates impressive performance but also alleviates complexities associated with architectural parameters. Extensive evaluation across multiple datasets, including lung image segmentation, PH2 skin cancer segmentation, liver image segmentation, Chest X-ray segmentation, and COVID-19 chest X-ray segmentation, highlights the exceptional performance of L-UNet. Notably, experimental results showcase competitive accuracies, achieving figures such as 99.15% for lung image segmentation, 99.53% for liver image segmentation, 95.45% for PH2 skin cancer segmentation, and 98.99% for Chest X-ray segmentation. Moreover, for COVID-19 chest X-ray segmentation, a commendable accuracy of 93.45% is observed. The simplification of L-UNet renders it a highly practical choice for deployment in resource-limited environments or real-world scenarios necessitating faster performance and enhanced efficiency.

Weeds cause harm to crops by competing for water, light, nutrients and space, reducing crop yields and inhibiting the efficiency of machinery. Although there is a large volume of work on this topic, previous studies have lacked accuracy and efficiency. In this paper an algorithm has been developed and analyzed for real-time specific weeds discrimination that is employed Haar Wavelet Transform (HWT). There are three stages of this paper, segmentation stage, feature extraction stage, and classification stage. Based on the proposed algorithm, 200 highest and informative coefficients are extracted at feature extraction stage, then at the classification stage, a well known classifier k-NN (k-Nearest Neighbor, for which k = 2) has been applied. The proposed method was tested on the database of 200 samples of each category. Furthermore, the result of our proposed method improves the performance by 7--10% as compared with some of the existing techniques that used histogram maxima with threshold, and angular cross sectional intensities. In conclusion, the proposed technique is more accurate at weed leaf classification than the existing techniques when images are captured by a CCD camera. The overall accuracy and efficiency utilizing the proposed method using haar wavelet transform is 94% and 40 ms respectively.

The examination of Alzheimer’s disease (AD) using adaptive machine learning algorithms has unveiled promising findings. However, achieving substantial credibility in medical contexts necessitates a combination of notable accuracy, minimal processing time, and universality across diverse populations. Therefore, we have formulated a hybrid methodology in this study to classify AD by employing a brain MRI image dataset. We incorporated an averaging filter during preprocessing in the initial stage to reduce extraneous details. Subsequently, a combined strategy was utilized, involving principal component analysis (PCA) in conjunction with stepwise linear discriminant analysis (SWLDA), followed by an artificial neural network (ANN). SWLDA employs a combination of forward and backward recursion methods to choose a restricted set of features. The forward recursion identifies the most interconnected features based on partial Z-test values. Conversely, the backward recursion method eliminates the least correlated features from the same feature space. After the extraction and selection of features, an optimized artificial neural network (ANN) was utilized to differentiate the various classes of AD. To demonstrate the significance of this hybrid approach, we utilized publicly available brain MRI datasets using a 10-fold cross-validation strategy. The proposed method excelled over existing state-of-the-art systems, attaining weighted average recognition rates of 99.35% and 96.66%, respectively, across all the datasets.