Network-Computation in Neural Systems最新文献

Interpretable machine learning models are instrumental in disease diagnosis and clinical decision-making, shedding light on relevant features. Notably, Boruta, SHAP (SHapley Additive exPlanations), and BorutaShap were employed for feature selection, each contributing to the identification of crucial features. These selected features were then utilized to train six machine learning algorithms, including LR, SVM, ETC, AdaBoost, RF, and LR, using diverse medical datasets obtained from public sources after rigorous preprocessing. The performance of each feature selection technique was evaluated across multiple ML models, assessing accuracy, precision, recall, and F1-score metrics. Among these, SHAP showcased superior performance, achieving average accuracies of 80.17%, 85.13%, 90.00%, and 99.55% across diabetes, cardiovascular, statlog, and thyroid disease datasets, respectively. Notably, the LGBM emerged as the most effective algorithm, boasting an average accuracy of 91.00% for most disease states. Moreover, SHAP enhanced the interpretability of the models, providing valuable insights into the underlying mechanisms driving disease diagnosis. This comprehensive study contributes significant insights into feature selection techniques and machine learning algorithms for disease diagnosis, benefiting researchers and practitioners in the medical field. Further exploration of feature selection methods and algorithms holds promise for advancing disease diagnosis methodologies, paving the way for more accurate and interpretable diagnostic models.

An Adaptive activation Functions with Deep Kronecker Neural Network optimized with Bear Smell Search Algorithm (BSSA) (ADKNN-BSSA-CSMANET) is proposed for preventing MANET Cyber security attacks. The mobile users are enrolled with Trusted Authority Using a Crypto Hash Signature (SHA-256). Every mobile user uploads their finger vein biometric, user ID, latitude and longitude for confirmation. The packet analyser checks if any attack patterns are identified. It is implemented using adaptive density-based spatial clustering (ADSC) that deems information from packet header. Geodesic filtering (GF) is used as a pre-processing method for eradicating the unsolicited content and filtering pertinent data. Group Teaching Algorithm (GTA)-based feature selection is utilized for ideal collection of features and Adaptive Activation Functions along Deep Kronecker Neural Network (ADKNN) is used to categorizing normal and attack packets (DoS, Probe, U2R, and R2L). Then BSSA is utilized for optimizing the weight parameters of ADKNN classifier for optimal classification. The proposed technique is executed in python and its efficiency is evaluated by several performances metrics, such as Accuracy, Attack Detection Rate, Detection Delay, Packet Delivery Ratio, Throughput, and Energy Consumption. The proposed technique provides 36.64%, 33.06%, and 33.98% lower Detection Delay on NSL-KDD dataset compared with the existing methods.

As the number of patients increases, physicians are dealing with more and more cases of degenerative spine pathologies on a daily basis. To reduce the workload of healthcare professionals, we propose a modified Swin-UNet network model. Firstly, the Swin Transformer Blocks are improved using a residual post-normalization and scaling cosine attention mechanism, which makes the training process of the model more stable and improves the accuracy. Secondly, we use the log-space continuous position biasing method instead of the bicubic interpolation position biasing method. This method solves the problem of performance loss caused by the large difference between the resolution of the pretraining image and the resolution of the spine image. Finally, we introduce a segmentation smooth module (SSM) at the decoder stage. The SSM effectively reduces redundancy, and enhances the segmentation edge processing to improve the model's segmentation accuracy. To validate the proposed method, we conducted experiments on a real dataset provided by hospitals. The average segmentation accuracy is no less than 95%. The experimental results demonstrate the superiority of the proposed method over the original model and other models of the same type in segmenting the spinous processes of the vertebrae and the posterior arch of the spine.

Plant diseases are rising nowadays. Plant diseases lead to high economic losses. Internet of Things (IoT) technology has found its application in various sectors. This led to the introduction of smart farming, in which IoT has been utilized to help identify the exact spot of the diseased affected region on the leaf from the vast farmland in a well-organized and automated manner. Thus, the main focus of this task is the introduction of a novel plant disease detection model that relies on IoT technology. The collected images are given to the Image Transmission phase. Here, the encryption task is performed by employing the Advanced Encryption Standard (AES) and also the decrypted plant images are fed to the pre-processing stage. The Mask Regions with Convolutional Neural Networks (R-CNN) are used to segment the pre-processed images. Then, the segmented images are given to the detection phase in which the Adaptive Dense Hybrid Convolution Network with Attention Mechanism (ADHCN-AM) approach is utilized to perform the detection of plant disease. From the ADHCN-AM, the final detected plant disease outcomes are obtained. Throughout the entire validation, the offered model shows 95% enhancement in terms of MCC showcasing its effectiveness over the existing approaches.