Abstract Vehicular Sensor Networks (VSN) introduced a new paradigm for modern transportation systems by improving traffic management and comfort. However, the increasing adoption of smart sensing technologies with the Internet of Things (IoT) made VSN a high-value target for cybercriminals. In recent years, Machine Learning (ML) and Deep Learning (DL) techniques attracted the research community to develop security solutions for IoT networks. Traditional ML and DL approaches that operate with data stored on a centralized server raise major privacy problems for user data. On the other hand, the resource-constrained nature of a smart sensing network demands lightweight security solutions. To address these issues, this article proposes a Federated Learning (FL)-based attack detection framework for VSN. The proposed scheme utilizes a group of Gated Recurrent Units (GRU) with a Random Forest (RF)-based ensembler unit. The effectiveness of the suggested framework is investigated through multiple performance metrics. Experimental findings indicate that the proposed FL approach successfully detected the cyberattacks in VSN with the highest accuracy of 99.52%. The other performance scores, precision, recall, and F1 are attained as 99.77%, 99.54%, and 99.65%, respectively. 

/pdf/a-federated-learning-framework-for-cyberattack-detection-in-2mbp23w4.pdf

A federated learning framework for cyberattack detection in vehicular sensor networks

This paper aims to provide a comprehensive review of the trends and challenges of deep neural networks for protein secondary structure prediction (PSSP). In recent years, deep neural networks have become the primary method for protein secondary structure prediction. Previous studies showed that deep neural networks had uplifted the accuracy of three-state secondary structure prediction to more than 80%. Favored deep learning methods, such as convolutional neural networks, recurrent neural networks, inception networks, and graph neural networks, have been implemented in protein secondary structure prediction. Methods adapted from natural language processing (NLP) and computer vision are also employed, including attention mechanism, ResNet, and U-shape networks. In the post-AlphaFold era, PSSP studies focus on different objectives, such as enhancing the quality of evolutionary information and exploiting protein language models as the PSSP input. The recent trend to utilize pre-trained language models as input features for secondary structure prediction provides a new direction for PSSP studies. Moreover, the state-of-the-art accuracy achieved by previous PSSP models is still below its theoretical limit. There are still rooms for improvement to be made in the field. 

https://doi.org/10.1016/j.csbj.2022.11.012

Deep learning for protein secondary structure prediction: Pre and post-AlphaFold

Proteins may be defined as one of the most prominent structural and functional units of life. The structure of a protein is as diverse as the function it sustains. Knowledge about the structural folding of protein serves to ascertain its function. Experimental determination of protein function via its structure is a tedious and gradual process. Protein structure prediction thus; seeks to cultivate adequate ways aimed at providing plausible models for proteins whose structures remain unexplored. Hence, there is a dire need of computational tools to predict, evaluate and visualize the structures of unknown proteins from their amino acid sequences. However, the existing computational tools suffer from various drawbacks that affect their performance substantially like—low prediction accuracy, inefficient modeling of sequence–structure relationship, local methods lacking global exploration, and inability to suffice to the dynamic and exponentially growing data. Accordingly, swarm Intelligence based particle swarm optimization in combination with fuzzy sets has been introduced to propose a neural network based model for secondary structure prediction of protein. The data from six standard datasets namely- RS126, EVA6, CB396, CB513, Manesh and PSS504 has been utilized for the training and testing of the neural network. The model is evaluated using performance measures like- sensitivity, fallout, false discovery rate, miss-rate, specificity, false omission rate, precision, negative predictive value, Q3 accuracy and Matthews correlation coefficient. Sensitivity analysis and 10, 20, 30 and 40 fold cross validation has been performed for further verification of results. The proposed model resolves most of the issues addressed above, thus achieving an average Q3 accuracy of above 90% which is better than the existing tools for secondary structure prediction.

PSO Based Neuro-fuzzy Model for Secondary Structure Prediction of Protein

Proteins form the basis of all major life processes that sustain life. The functionality of a protein is a direct consequence of its underlying structure. Protein structure prediction thus serves to ascertain the function of similar or dissimilar proteins, accordingly. Secondary structure prediction paves way for 3D structures that eventually decides protein properties. It also aims to facilitate probable structures for proteins whose structures remain undiscovered. Although experimental approaches have been quite efficient in extracting protein secondary structure from its amino acid sequence, yet it is often cumbersome and time intensive to achieve it in vitro. Hence, computational approaches are required to predict secondary structures for the diverse amino acids constituting these proteins. However, the available computational models fail to register good prediction accuracy due to inadequate modelling of sequence-structure relationship. Also, the dearth of global exploration-based methods further makes them ineffective in catering to the evolving proteomic data. Accordingly, PSO (Particle swarm optimization) has been explored to propose a neural network model for protein secondary structure prediction (PSSP). Six standard datasets namely- PSS504, RS126, EVA6, CB396, Manesh and CB513 have been utilized for the training and testing of the neural network. The proposed model is evaluated on the basis of its Q3 accuracy, precision, and recall. The 10, 20, 30 and 40 fold cross validation in combination with sensitivity analysis and has been carried out for verification of results. The proposed model is found to outperform most of the existing models by demonstrating a better average Q3 accuracy lying above 81% for PSSP.

Swarm optimization-based neural network model for secondary structure prediction of proteins

Predicting a protein’s secondary structure is crucial for understanding the working of proteins. Despite advancements over the years, the top predictors have achieved only 80% Q8 accuracy when sequence profile information is the sole input. An ensemble approach is proposed using convolutional neural network (CNN) and a classifier known as support vector machine (SVM) on both the partial and the whole CullPDB datasets. The protein secondary structure (PSS) has a complex hierarchical structure, as well as the ability to take into account the reliance between neighbouring labels. A detailed experiment yielding high levels of Q8 accuracy with scores of 97.91%, 85.13%, and 78.02% using 20%, 80%, and 100% respectively of the protein residues on the new predicted dataset CullPDB6133 which is better than the accuracies predicted by similar models. The proposed methodology highlights the use of CNN as a general framework, for efficiently predicting eight-state (Q8) accuracy of secondary protein structures with a low time and space complexity.

pdf/ensemble-model-for-accuracy-prediction-of-protein-secondary-2yw2jag5ap.pdf

Ensemble model for accuracy prediction of protein secondary structure

A special class of neurons in the hippocampal formation broadly known as the spatial cells, whose subcategories include place cells, grid cells, and head direction cells, are considered to be the building blocks of the brain's map of the spatial world. We present a general, deep learning‐based modeling framework that describes the emergence of the spatial‐cell responses and can also explain responses that involve a combination of path integration and vision. The first layer of the model consists of head direction (HD) cells that code for the preferred direction of the agent. The second layer is the path integration (PI) layer with oscillatory neurons: displacement of the agent in a given direction modulates the frequency of these oscillators. Principal component analysis (PCA) of the PI‐cell responses showed the emergence of cells with grid‐like spatial periodicity. We show that the Bessel functions could describe the response of these cells. The output of the PI layer is used to train a stack of autoencoders. Neurons of both the layers exhibit responses resembling grid cells and place cells. The paper concludes by suggesting the wider applicability of the proposed modeling framework beyond the two simulated studies.

An integrated deep learning‐based model of spatial cells that combines self‐motion with sensory information

Studies on the neural correlates of navigation in 3D environments are plagued by several unresolved issues. For example, experimental studies show markedly different place cell responses in rats and bats, both navigating in 3D environments. In an effort to understand this divergence, we propose a deep autoencoder network to model the place cells and grid cells in a simulated agent navigating in a 3D environment. We also explore the possibility of a vital role that Head Direction (HD) tuning plays in determining the isotropic or anisotropic nature of the observed place fields in different species. The input layer to the autoencoder network model is the HD layer which encodes the agent’s HD in terms of azimuth (θ) and pitch angles (ϕ). The output of this layer is given as input to the Path Integration (PI) layer, which integrates velocity information into the phase of oscillating neural activity. The output of the PI layer is modulated and passed through a low pass filter to make it purely a function of space before passing it to an autoencoder. The bottleneck layer of the autoencoder model encodes the spatial cell like responses. Both grid cell and place cell like responses are observed. The proposed model is verified using two experimental studies with two 3D environments in each. This model paves the way for a holistic approach of using deep networks to model spatial cells in 3D navigation.

Modeling Hippocampal Spatial Cells in Rodents navigating in 3D environments

Protein secondary structure is the local conformation assigned to protein sequences with the help of its three-dimensional structure. Assigning the local conformation to protein sequences requires much computational work. There exists a vast literature on the protein secondary structure prediction approaches (more than 20 techniques), but to date, none of the existing techniques is entirely accurate. Thus, there is an excellent room for developing new models of protein secondary structure prediction to address the issues of prediction accuracy. In the present study, ensemble techniques such as AdaBoost- and Bagging-based deep learning models are proposed to predict the protein secondary structure. The data from standard datasets, namely CB513, RS126, PTOP742, PSA472, and MANESH, have been used for training and testing purposes. These standard datasets possess less than 25% redundancy. The model is evaluated using performance measures: Q8 and Q3 cross-validation accuracy, class precision, class recall, kappa factor, and testing on a dataset that is not used for training purposes, i.e., blind test. The ensembling technique used along with variability in datasets can remove the bias of each dataset by balancing it and making the features more distinguishable, leading to the improvement in accuracy as compared to the conventional and existing techniques. The proposed model shows an average improvement of ~ 2% and ~ 3% accuracy over the existing methods in a blind test for Q8 and Q3 accuracy.

Deep learning model with ensemble techniques to compute the secondary structure of proteins

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An integrated deep learning‐based model of spatial cells that combines self‐motion with sensory information

Modeling Hippocampal Spatial Cells in Rodents navigating in 3D environments

Deep learning model with ensemble techniques to compute the secondary structure of proteins