Background: Alzheimer’s, the predominant formof dementia, is a neurodegenerative brain disorder with no known cure. With the lack of innovative findings to diagnose and treat Alzheimer’s, the number of middle-aged people with dementia is estimated to hike nearly to 13 million by the end of 2050. The estimated cost of Alzheimer’s and other related ailments is USD321 billion in 2022 and can rise above USD1 trillion by the end of 2050. Therefore, the early prediction of such diseases using computer-aided systems is a topic of considerable interest and substantial study among scholars. The major objective is to develop a comprehensive framework for the earliest onset and categorization of different phases of Alzheimer’s. Methods: Experimental work of this novel approach is performed by implementing neural networks (CNN) on MRI image datasets. Five classes of Alzheimer’s disease subjects are multi-classified. We used the transfer learning determinant to reap the benefits of pre-trained health data classification models such as the MobileNet. Results: For the evaluation and comparison of the proposed model, various performance metrics are used. The test results reveal that the CNN architectures method has the following characteristics: appropriate simple structures that mitigate computational burden, memory usage, and overfitting, as well as offering maintainable time. The MobileNet pre-trained model has been fine-tuned and has achieved 96.6 percent accuracy for multi-class AD stage classifications. Other models, such as VGG16 and ResNet50 models, are applied tothe same dataset whileconducting this research, and it is revealed that this model yields better results than other models. Conclusion: The study develops a novel framework for the identification of different AD stages. The main advantage of this novel approach is the creation of lightweight neural networks. MobileNet model is mostly used for mobile applications and was rarely used for medical image analysis; hence, we implemented this model for disease detection andyieldedbetter results than existing models.

The recommended tool for assessing knee injury is magnetic resonance imaging (MRI). However, knee MRI interpretation takes time and is vulnerable to clinical errors and inconsistency. A deep learning automated technique for reading knee MRI might help physicians identify high-risk patients and make diagnosis easier. In this study, we have proposed a deep learning-based model to detect ACL and meniscus tears and other knee abnormalities. At its core, this model is based on the ResNet50 transfer learning technique. In this paper, we have focused to present a ResNet50-based model for detecting different knee problems using MRIs. The best models for every option achieved the objectives that were probably similar. The models were developed using 18, 3, and 1 slice. These models’ outcomes were rather startling. The AUC findings obtained with 1 slice per MRI exam were equivalent to those obtained with 18 and 3 slices and, in some cases, were significantly better. The dataset used in this model is from Stanford University. We trained this model in three different settings of MRI slices (18, 3, and 1). The best results that our models were able to achieve were when trained using 3 slices of each MRI sample. The area under the receiver operating characteristic curve, or AUC curve, values that our best models were able to achieve for detecting ACL, meniscus, and other knee abnormalities are 0.87, 0.82, and 0.90, respectively. The results of our models are comparable to some state-of-the-art models. These models are very fast and efficient to train and hence will be helpful to doctors for making an effective and fast diagnosis based on knee MRIs.

Researchers have developed a range of methods and strategies to decrease wireless sensor network energy consumption. Mote clustering is one of the competent topological control approaches to boost the networks’ energy efficiency, scalability, and performance. Energy is dissipated during the cluster creation, cluster head selection, routing from the head and base stations of the cluster, and data aggregation—clustering and routing emphasis on the stability and the longevity of the network. This research work provides the optimization technique for the wireless sensor network to optimize the energy through NN‐LEACH. The main goal is to extend network life and reduce power consumption by clustering and routing sensor nodes using the two‐step NN‐LEACH protocol, which is suggested. An additional goal might be to establish the appropriate course of action for the suggested approach for this network.