Improving Routing Performance in Mobile Ad Hoc Networks Using Artificial Neural Networks for Mobility Prediction using deep learning

Purpose: Flat-panel radiography detectors employ thin-film transistor (TFT) panels to acquire high-quality x-ray images. Pixel defects occur due to circuit shorts or opens in the TFT panel. The defects may degrade the image quality, as well as lower the production yield, and eventually raise the production cost. Hence, it is important to develop an appropriate defect correction algorithm for acquired images. Traditional correction algorithms are based on a complicated adaptive filtering technique, which exploits neighbor pixels, to faithfully preserve the edge components. Because of the complexity of the traditional sophisticated approaches, optimizing their correction performances is difficult. Approach: We considered various pixel-defect correction algorithms based on different deep learning models, such as the artificial neural network (ANN), convolutional neural network (CNN), concatenate CNN, and generative adversarial networks (GAN). We considered two cases of maximal defect sizes, and , and conducted extensive learning experiments to find the best structures of the learning models using the mean square error (MSE) as the loss function. Results: To conduct experiments, practical chest x-ray images were acquired from a general radiography detector. The MSE values of the correction results from ANN, CNN, concatenate CNN, and GAN were 69.40, 75.13, 68.21, and 73.77, respectively, and were much smaller than that of the conventional template match correction method. Conclusions: A concatenate CNN showed the best defect-correction performance. However, ANN could achieve a similar correction performance with much smaller encoding complexity. Therefore, the single-layer ANN can efficiently conduct defect corrections in terms of both correction and complexity.

1 - Congruence of deep learning in biomedical engineering: future prospects and challenges

In this paper, we present a comprehensive statistical experiment and identify the key factors and interactions that impact network performance over mobile ad hoc networks. We use the design of experiments software named Design-Ease to establish the mathematical model for analyzing the set of simulation results from ns2. A variety of important parameters for network performance including routing protocol, node density, node speed, traffic load and pause time are considered to capture the vital factors and interactions for network performance by measuring the packet delivery ratio, average delay, discovery time and recovery time in which latter two are the crucial metrics for ad hoc self-organising performance measurement. With the simulation and analysis results, we conclude the key factors and interactions which influence the specific network performance and suggest which aspects the designers should focus on for improving the given network performance

Daily scale streamflow forecasting in multiple stream orders of Cauvery River, India: Application of advanced ensemble and deep learning models

There have been many studies in recent years reporting the performance and survivability of mobile ad-hoc networks (MANETs). These studies invariably consider the network as covering a relatively small geographic area. However, in many crises, such as may occur during disaster relief and military operations, a wide geographic area may require network coverage. In these circumstances, the infrastructure free mobile wireless communications features of MANETs may be critical in forming a satisfactory network. Given transmission range limitations within current MANET proposals, it is likely that providing network coverage to a wide geographic area would be problematic. This paper presents the results of a study on clustering in the spatial distribution of communication nodes. A realistic environment (e.g. topography and organisational limitations) and operating procedures are introduced through the use of the results of war games. The spatial distribution of nodes is used as the basis for MANET internetwork topology generation.

The World Health Organization (WHO) grading system of pancreatic neuroendocrine tumor (PNET) plays an important role in the clinical decision. The rarity of PNET often negatively affects the radiological application of deep learning algorithms due to the low availability of radiological images. We tried to investigate the feasibility of predicting WHO grades of PNET on contrast-enhanced magnetic resonance (MR) images by deep learning algorithms. Ninety-six patients with PNET underwent preoperative contrast-enhanced MR imaging. Fivefold cross-validation was used in which five iterations of training and validation were performed. Within every iteration, on the training set augmented by synthetic images generated from generative adversarial network (GAN), a convolutional neural network (CNN) was trained and its performance was evaluated on the paired internal validation set. Finally, the trained CNNs from cross-validation and their averaged counterpart were separately assessed on another ten patients from a different external validation set. Averaging the results across the five iterations in the cross-validation, for the CNN model, the average accuracy was 85.13% ± 0.44% and micro-average AUC was 0.9117 ± 0.0053. Evaluated on the external validation set, the average accuracy of the five trained CNNs ranges between 79.08 and 82.35%, and the range of micro-average AUC was between 0.8825 and 0.8932. The average accuracy and micro-average AUC of the averaged CNN were 81.05% and 0.8847, respectively. Synthetic images generated from GAN could be used to alleviate the difficulty of radiological image collection for uncommon disease like PNET. With the help of GAN, the CNN showed the potential to predict the WHO grades of PNET on contrast-enhanced MR images.

In recent years, Deep Learning (DL), such as the algorithms of Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Generative Adversarial Networks (GAN), has been widely studied and applied in various fields including agriculture. Researchers in the fields of agriculture often use software frameworks without sufficiently examining the ideas and mechanisms of a technique. This article provides a concise summary of major DL algorithms, including concepts, limitations, implementation, training processes, and example codes, to help researchers in agriculture to gain a holistic picture of major DL techniques quickly. Research on DL applications in agriculture is summarized and analyzed, and future opportunities are discussed in this paper, which is expected to help researchers in agriculture to better understand DL algorithms and learn major DL techniques quickly, and further to facilitate data analysis, enhance related research in agriculture, and thus promote DL applications effectively. Keywords: deep learning, smart agriculture, neural network, convolutional neural networks, recurrent neural networks, generative adversarial networks, artificial intelligence, image processing, pattern recognition DOI: 10.25165/j.ijabe.20181104.4475 Citation: Zhu N Y, Liu X, Liu Z Q, Hu K, Wang Y K, Tan J L, et al. Deep learning for smart agriculture: Concepts, tools, applications, and opportunities. Int J Agric & Biol Eng, 2018; 11(4): 32-44.

Mobility modelling plays a pivotal role in evaluating the performance of Mobile Ad Hoc Networks (MANETs), as the movement patterns of nodes strongly influence routing efficiency, connectivity, and network stability. Existing mobility models, particularly entity and group-based frameworks, have proven useful in simulating various MANET applications but remain limited in capturing the complex movement behaviour characteristic of Search-and-Rescue (SAR) operations in disaster scenarios. This study proposes a realistic hybrid mobility model that integrates the essential features of both entity and group mobility paradigms to better represent the coordinated yet flexible dynamics of SAR teams. Three sets of simulations were performed using the Network Simulator 2 (NS2). The first and second simulations focused on generating and combining existing entity and group mobility patterns using the BonnMotion tool, resulting in two composite models (Real1 and Real2). The third simulation compared these models using AODV and DSDV routing protocols under varying node mobility levels and traffic conditions. Key performance metrics, including relative mobility, node degree, partition count, link duration, packet delivery ratio (PDR), throughput, and average end-to-end delay, were analysed. Results demonstrate that it is feasible to concatenate existing mobility models into a coherent hybrid framework. Among the developed models, Real2 exhibited superior performance in most test conditions, yielding higher PDR, lower delay, and more stable connectivity than its constituent models. These findings confirm that the performance of MANETs is highly dependent on mobility realism. Consequently, selecting a model that accurately reflects the behavioural characteristics of the target scenario is essential for credible MANET performance evaluation in emergency response contexts.

SummarySoftware Defined‐DCN (SD‐DCN) is a layered topology with logically centralized control which provides intelligence thro‐ugh programmability. The controller in SD‐DCN provides different network management skills by decoupling of control plane and data plane. However, the controller applies traditional artificial intelligence approaches and neural network approaches for routing strategies in recent systems. To manage dynamic changes in the network, deep learning approaches provide deep analysis and prediction to enhance the existing network performance. In convolutional neural network (CNN) deep learning model, the learning process executes based on present traffic data and forgets the inputs for the future learning phase. To overcome CNN limitation, in this article, different recurrent neural network (RNN) deep learning models are used to improve the routing computation for SD‐DCN topology. The long short‐term memory (LSTM)‐ RNN and bi‐directional long short‐term memory (BiLSTM)‐RNN deep learning models provide forget gate to analyze periodic network traffic datasets and improve routing by delivering dynamic and intelligent routing paths in the network. The proposed system is implemented in a simulation scenario, and the proposed work improves the network performance in hot‐spot traffic as compared to the existing mechanisms. Moreover, error analysis of different deep learning models is presented in the evaluation results.

Though large amounts of work with artificial intelligence are used in typhoon rainfall forecasting, the predictive skills of existing models are unsatisfactory. To address this problem, this study aims to propose an improved cluster‐wise typhoon rainfall forecasting model that integrates the grid‐search cross‐validation method with machine learning and deep learning (DL) models including support vector machine (SVM), random forest (RF), adaptive boosting (AdaBoost), convolutional neural network (CNN), and long short‐term memory (LSTM). Grid‐search cross‐validation is a modified parameterization technique that helps to find the best parameters for machine/DL models. In the first stage, a second‐order polynomial regression model was used to cluster the typhoon track; and in the second stage, cluster‐wise typhoon rainfall was recognized within a 500 km radius from each typhoon center. After that, a modified cluster‐wise typhoon rainfall forecasting model was proposed using cluster‐wise antecedent hourly typhoon rainfall within this distance for 1–6 hr lead time. Results show that the proposed model based on the SVM, RF, AdaBoost, CNN, and LSTM is capable of providing more accurate forecasts (the efficiency of the forecast is increased by 45%–90%) than the existing typhoon rainfall forecasting models that are based on SVM with a genetic algorithm, RF, artificial neural network, multilayer perceptron network, and deep neural network. Therefore, the current study recommends using cluster‐wise typhoon rainfall forecasting model with a grid‐search cross‐validation method for disaster prevention and mitigation.

Identify suitable artificial groundwater recharge zones using hybrid deep learning models

COVID-19 is highly infectious and causes acute respiratory disease. Machine learning (ML) and deep learning (DL) models are vital in detecting disease from computerized chest tomography (CT) scans. The DL models outperformed the ML models. For COVID-19 detection from CT scan images, DL models are used as end-to-end models. Thus, the performance of the model is evaluated for the quality of the extracted feature and classification accuracy. There are four contributions included in this work. First, this research is motivated by studying the quality of the extracted feature from the DL by feeding these extracted to an ML model. In other words, we proposed comparing the end-to-end DL model performance against the approach of using DL for feature extraction and ML for the classification of COVID-19 CT scan images. Second, we proposed studying the effect of fusing extracted features from image descriptors, e.g., Scale-Invariant Feature Transform (SIFT), with extracted features from DL models. Third, we proposed a new Convolutional Neural Network (CNN) to be trained from scratch and then compared to the deep transfer learning on the same classification problem. Finally, we studied the performance gap between classic ML models against ensemble learning models. The proposed framework is evaluated using a CT dataset, where the obtained results are evaluated using five different metrics The obtained results revealed that using the proposed CNN model is better than using the well-known DL model for the purpose of feature extraction. Moreover, using a DL model for feature extraction and an ML model for the classification task achieved better results in comparison to using an end-to-end DL model for detecting COVID-19 CT scan images. Of note, the accuracy rate of the former method improved by using ensemble learning models instead of the classic ML models. The proposed method achieved the best accuracy rate of 99.39%.

Personalized medicine represents a paradigm shift in healthcare, aiming to tailor treatment strategies to the unique genetic, environmental, and lifestyle characteristics of individual patients. This approach holds immense potential for improving therapeutic efficacy and minimizing adverse drug reactions. With the rapid advancement of artificial intelligence, deep learning has emerged as a transformative tool in pharmacology, enabling precise modeling of complex biological data and uncovering hidden patterns in patient-specific information. This study investigates the application of deep learning techniques - such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformer architectures, and Generative Adversarial Networks (GANs) - in optimizing personalized treatment strategies. Using a diverse dataset comprising electronic health records (EHRs), genomic sequences, and clinical indicators, we developed and trained deep learning models for tasks including drug response prediction, biomarker identification, and adverse drug reaction (ADR) forecasting. Among the models evaluated, Transformer-based architectures demonstrated superior performance, achieving an accuracy of 91.2% and an AUC-ROC of 0.92 in drug response prediction tasks. Moreover, the integration of deep learning models into the treatment pipeline resulted in a 20-30% improvement in drug-patient matching efficiency compared to traditional statistical methods. The findings underscore the potential of AI-powered systems to enhance clinical decision-making and enable precision pharmacotherapy. However, challenges such as data privacy, model interpretability, and regulatory compliance remain critical barriers to widespread adoption. The study also explores future directions, including the implementation of explainable AI (XAI) and federated learning, to address these limitations and facilitate the integration of deep learning into routine clinical practice.

paper, we focus on exploring the relationship between weave patterns and their mechanical properties in woven fiber composites through Machine Learning. Specifically, we explore the interactions between woven architectures and in-plane stiffness properties through Deep Convolutional Neural Network (DCNN) and Generative Adversarial Network (GAN). Our research is important for exploring how woven composite’s pattern is related to its mechanical properties and accelerating woven composite design as well as optimization. We focus on two tasks: (1) Stiffness prediction: Predicting in-plane stiffness properties for given weave patterns. Our DCNN extracts high-level features through several convolutional and fully connected layers to determine the final predictions. (2) Weave pattern prediction: Predicting weave patterns for target stiffness properties, which can be treated as the reverse task of the first one. Due to many-to-one mapping between weave patterns and the composite properties, we utilize a Decoder Neural Network as our baseline model and compare its performance with GAN and Genetic Algorithm. We represent the weave patterns as 2D checkerboard models and use finite element analysis (FEA) to determine in-plane stiffness properties, which serve as input data for our ML framework. We show that: (1) for stiffness prediction, DCNN can predict stiffness values for a given weave pattern with relatively high accuracy (above 93%); (2) for weave pattern prediction, the GAN model gives the best prediction accuracy (above 92%) while Decoder Neural Network has the best time efficiency. HAOTIAN FENG

PurposeTo develop generative adversarial networks (GANs) that synthesize realistic anterior segment optical coherence tomography (AS-OCT) images and evaluate deep learning (DL) models that are trained on real and synthetic datasets for detecting angle closure.MethodsThe GAN architecture was adopted and trained on the dataset with AS-OCT images collected from the Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, synthesizing open- and closed-angle AS-OCT images. A visual Turing test with two glaucoma specialists was performed to assess the image quality of real and synthetic images. DL models, trained on either real or synthetic datasets, were developed. Using the clinicians’ grading of the AS-OCT images as the reference standard, we compared the diagnostic performance of open-angle vs. closed-angle detection of DL models and the AS-OCT parameter, defined as a trabecular-iris space area 750 µm anterior to the scleral spur (TISA750), in a small independent validation dataset.ResultsThe GAN training included 28,643 AS-OCT anterior chamber angle (ACA) images. The real and synthetic datasets for DL model training have an equal distribution of open- and closed-angle images (all with 10,000 images each). The independent validation dataset included 238 open-angle and 243 closed-angle AS-OCT ACA images. The image quality of real versus synthetic AS-OCT images was similar, as assessed by the two glaucoma specialists, except for the scleral spur visibility. For the independent validation dataset, both DL models achieved higher areas under the curve compared with TISA750. Two DL models had areas under the curve of 0.97 (95% confidence interval, 0.96–0.99) and 0.94 (95% confidence interval, 0.92–0.96).ConclusionsThe GAN synthetic AS-OCT images appeared to be of good quality, according to the glaucoma specialists. The DL models, trained on all-synthetic AS-OCT images, can achieve high diagnostic performance.Translational RelevanceThe GANs can generate realistic AS-OCT images, which can also be used to train DL models.