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Increasing spectrum efficiency in new-generation communication networks is possible by raising the operating frequencies or increasing the number of serving cells. Both of the solutions indirectly lead to a decrease in the cell size. By shrinking the coverage area of the cells, the mobility management becomes a critical issue in order to provide seamless connectivity. The main tool for mobility management in connected mode is the handover procedure, which conventionally is triggered based on the user equipment's (UE's) measurements of serving and neighboring cells' signal quality. The performance of the methods provided in the 3GPP standard for setting up the handover is based on a comparison between the quality of the received signal from the serving cell and the neighbor cells, which is severely sensitive to the appropriate setting of threshold values and waiting time for setting up the handover. The remarkable methods in the literature to improve the handover performance in the 3GPP standard either utilize positional information or are based on complex algorithms. Positional information parameters such as speed, location, direction of movement, etc., need extra measurement modules. Also, complex algorithms like deep learning are not suitable for a wide range of active devices in 5 G networks with limited resources. In this paper, a novel fuzzy logic-based method is proposed to trigger the handover procedure based on estimated serving cell's and neighbor cells' radio link quality (RLQ) values. The proposed system consists of two stages. A second-order regressor beside a simple fuzzy logic system is introduced to predict the serving cell's and neighbor cells' RLQ. The final handover trigger decision is made with the help of another cascade fuzzy logic system, which is responsible for eliminating too-early, too-late, and ping-pong handovers. Considering the uncertainty handling feature of the interval type II fuzzy logic systems versus type I, we implement both methods and compare their results. Finally, we simulate the proposed algorithm using the ns-3 LTE module with a very tight setting for detecting radio link failure to meet 5 G strict standards. Proposed method succeeded in improving the performance of the handover process at high-speed scenarios by 50%, only with the help of radio link quality information. The main advantage of the proposed method is the proper management of the handover procedure, independent of UE's velocity, as well as its simple structure with few rules, which makes it suitable for use in UAV and IoT devices. [ABSTRACT FROM AUTHOR]

Objectives: Development of automated segmentation models enabling standardized volumetric quantification of fibroglandular tissue (FGT) from native volumes and background parenchymal enhancement (BPE) from subtraction volumes of dynamic contrast-enhanced breast MRI. Subsequent assessment of the developed models in the context of FGT and BPE Breast Imaging Reporting and Data System (BI-RADS)-compliant classification. Methods: For the training and validation of attention U-Net models, data coming from a single 3.0-T scanner was used. For testing, additional data from 1.5-T scanner and data acquired in a different institution with a 3.0-T scanner was utilized. The developed models were used to quantify the amount of FGT and BPE in 80 DCE-MRI examinations, and a correlation between these volumetric measures and the classes assigned by radiologists was performed. Results: To assess the model performance using application-relevant metrics, the correlation between the volumes of breast, FGT, and BPE calculated from ground truth masks and predicted masks was checked. Pearson correlation coefficients ranging from 0.963 ± 0.004 to 0.999 ± 0.001 were achieved. The Spearman correlation coefficient for the quantitative and qualitative assessment, i.e., classification by radiologist, of FGT amounted to 0.70 (p < 0.0001), whereas BPE amounted to 0.37 (p = 0.0006). Conclusions: Generalizable algorithms for FGT and BPE segmentation were developed and tested. Our results suggest that when assessing FGT, it is sufficient to use volumetric measures alone. However, for the evaluation of BPE, additional models considering voxels' intensity distribution and morphology are required. Critical relevance statement: A standardized assessment of FGT density can rely on volumetric measures, whereas in the case of BPE, the volumetric measures constitute, along with voxels' intensity distribution and morphology, an important factor. Key points: • Our work contributes to the standardization of FGT and BPE assessment. • Attention U-Net can reliably segment intricately shaped FGT and BPE structures. • The developed models were robust to domain shift. [ABSTRACT FROM AUTHOR]

The ability to sequentially learn multiple tasks without forgetting is a key skill of biological brains, whereas it represents a major challenge to the field of deep learning. To avoid catastrophic forgetting, various continual learning (CL) approaches have been devised. However, these usually require discrete task boundaries. This requirement seems biologically implausible and often limits the application of CL methods in the real world where tasks are not always well defined. Here, we take inspiration from neuroscience, where sparse, non-overlapping neuronal representations have been suggested to prevent catastrophic forgetting. As in the brain, we argue that these sparse representations should be chosen on the basis of feed forward (stimulus-specific) as well as top-down (context-specific) information. To implement such selective sparsity, we use a bio-plausible form of hierarchical credit assignment known as Deep Feedback Control (DFC) and combine it with a winner-take-all sparsity mechanism. In addition to sparsity, we introduce lateral recurrent connections within each layer to further protect previously learned representations. We evaluate the new sparse-recurrent version of DFC on the split-MNIST computer vision benchmark and show that only the combination of sparsity and intra-layer recurrent connections improves CL performance with respect to standard backpropagation. Our method achieves similar performance to well-known CL methods, such as Elastic Weight Consolidation and Synaptic Intelligence, without requiring information about task boundaries. Overall, we showcase the idea of adopting computational principles from the brain to derive new, task-free learning algorithms for CL. [ABSTRACT FROM AUTHOR]

Parkinson's disease is one of the most common degenerative conditions that affect people aged 60 and older. The illness is normally diagnosed by clinical indicators that develop as a variety of movement symptoms and medical observations. Conventional diagnostic methods, on the other hand, rely on the detection of small motions, which are notoriously difficult to pin down with absolute precision. This makes it possible for these methods to lend themselves to subjective interpretations. This is because traditional diagnostic methods rely on the interpretation of motions. Image categorization is performed using an altered version of the ResNeXt model in the proposed model. The proposed ResNeXt extends version of ResNet architecture by introducing a new block called the "cardinality block." The cardinality block consists of multiple parallel branches, each with its own set of convolutional layers. These branches are then combined by concatenation before being passed to the next layer. The key idea behind the cardinality block is to increase the capacity of the network without significantly increasing the number of parameters. By using parallel branches with different filter sizes, ResNeXt is able to capture a wider range of features in the input image, leading to better performance on image classification tasks. In order to enhance the performance of the standard ResNeXt model, certain extra dense and dropout layers have been included. The size of the final model is reduced by pruning the model in order to improve the efficiency of the network connections and minimize the overall size of the model. The proposed method is compared to a number of deep learning models that are already in use, and it is shown that the acquired results are superior to those of the deep learning models that are already in use. [ABSTRACT FROM AUTHOR]

Deep convolutional networks are data hungry learners and, to compensate for the limited amount of available data, various augmentation methods have been proposed. While the initial approaches aimed to fill the space around existing data points, more recent methods use the "mixing" procedure, which is a convex combination between points, being both holistic and local (from a spatial point of view). Although the mixing techniques improved the performance for standard benchmarks, we do notice that they are less effective for problems that exhibit poor class separation. For these scenarios, we propose a soft labeling technique based on distances to class prototypes, as extracted on an intermediate CNN layer. The mixing is performed between relative positions in the clustering space. The method, named SoftClusterMix, is shown to be competitive on standard image classification benchmarks and leads to significantly improved accuracy for problems where there is a poor class separation. We report better performance in three such categories: face expression recognition, aesthetic image classification and painting style classification. Ablation tests allow deep insights of the method. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) is an emerging paradigm that decreases the computational burden of mobiles by task offloading. MEC is regarded as an effective method to offer computing capacities in close proximities to mobile users. The major issue in MEC is how to offload the heterogeneous task of mobile apps effectively from the user equipment to the MEC host. Some of the existing techniques contain feeble adaptability to new circumstance due to minimum sample effectiveness and require complete retraining. The purpose of MEC is to efficiently solve offloading issues such as network load and latency. In this paper, a deep belief network is proposed for solving the offloading issue in the clusters of numerous service node and several dependencies for a mobile task in huge-scale heterogeneous mobile edge computing. The deep belief network's weight is tuned optimally using Adaptive Harris Hawks optimization (AHHO) algorithm for solving the offloading issue optimally in the MEC environment. An AHHO algorithm is employed to improve the search performances of the standard algorithm. The limitations of the standard algorithm are poor stability among exploitation as well as exploration. Hence, two schemes such as the Gaussian mutation scheme and the cuckoo search are combined so as to form an adaptive Harris hawks optimization to enhance this stability. The task offloading issues are implemented using Google cluster trace and iFogSim. Furthermore, the simulation results depict that the offloading scheme based on the deep belief network-based adaptive Harris Hawks optimization approach has better results with respect to measures such as load balancing, energy consumption, average execution time, and latency than any other approach. [ABSTRACT FROM AUTHOR]

Detection of lane region under the road boundary is an imperative module for intelligent vehicle system. Lane markings provide separate regions on the road for the vehicles to avoid the possibility of accidents. Existing methods in lane detection have limited performance using various sensor-based approaches such as Radar and LiDAR and have high operational costs. To achieve a steady and optimal lane detection, the vision-based lane region detection scheme VLDNet is proposed which utilizes a encoder-decoder network using semantic segmentation architecture. In this direction, a hybrid model using UNet and ResNet has been adopted, where UNet is used as a segmentation model and ResNet-50 is used for down-sampling the image and identifying the required features. These identified features have been then applied into UNet for up-sampling and decoding the segments of the images. The publicly available KITTI dataset have been accessed for experiments and validation of the proposed network. The method outperforms the existing state-of-the-art methods in lane region detection. The network achieves better performance using standard evaluation measures such as accuracy of 98.87%, the precision of 98.24%, recall of 96.55%, frequency weighted IoU of 97.78%, and MaxF score of 97.77%. [ABSTRACT FROM AUTHOR]