Neural Components Research Articles

Android Security: Malware Detection with Convolutional Neural Network and Feature Analysis

Android is a mobile operating system based on a modified version of the Linux kernel and other open-source tools. Due to its system efficiency and the multitude of features it offers to users, the Android operating system has taken a leading position in the technology market and often attracts the attention of cybercriminals. As malware continues to evolve, traditional methods for detecting Android malware, such as signature-based approaches, may not be sufficient to detect the latest malware threats. Therefore, this research proposes a deep learning algorithm, specifically Convolutional Neural Network (CNN) and Component Analysis (PCA), for feature extraction to enhance the accuracy of Android malware detection. The dataset used in this study is the CICAndMal2017 dataset. Testing results are evaluated using three parameters: accuracy, precision, and recall. Experimental results indicate that our deep learning approach outperforms many other methods with an accuracy of 91%.

Unveiling Hypothalamic Molecular Signatures via Retrograde Viral Tracing and Single-Cell Transcriptomics

Despite the importance of hypothalamic neurocircuits in regulating homeostatic and survival-related behaviors, our understanding of the intrinsic molecular identities of neural components involved in these complex multi-synaptic interactions remains limited. In this study, we constructed a Cre recombinase-dependent pseudorabies virus (PRVs) capable of crossing synapses, coupled with transcriptome analysis of single upstream neurons post-infection. By utilizing this retrograde nuclear Connect-seq (nuConnect-seq) approach, we generated a single nuclei RNA-seq (snRNA-seq) dataset of 1,533 cells derived from the hypothalamus of CRH-IRES-Cre (CRH-Cre) mice. To ensure the technical validity of our nuConnect-seq dataset, we employed a label transfer technique against an integrated reference dataset of postnatal mouse hypothalamus comprising 152,524 QC-passed cells. The uniqueness of our approach lies in the integration of diverse datasets for validation, providing a more nuanced diversity of hypothalamic cell types. The presented validated dataset may deepen our understanding of hypothalamic neurocircuits and underscore the essential role of comprehensive integrated transcriptomic data for technical validity.

Investigating the roles of reflexes and central pattern generators in the control and modulation of human locomotion using a physiologically plausible neuromechanical model

Objective. Studying the neural components regulating movement in human locomotion is obstructed by the inability to perform invasive experimental recording in the human neural circuits. Neuromechanical simulations can provide insights by modeling the locomotor circuits. Past neuromechanical models proposed control of locomotion either driven by central pattern generators (CPGs) with simple sensory commands or by a purely reflex-based network regulated by state-machine mechanisms, which activate and deactivate reflexes depending on the detected gait cycle phases. However, the physiological interpretation of these state machines remains unclear. Here, we present a physiologically plausible model to investigate spinal control and modulation of human locomotion. Approach. We propose a bio-inspired controller composed of two coupled CPGs that produce the rhythm and pattern, and a reflex-based network simulating low-level reflex pathways and Renshaw cells. This reflex network is based on leaky-integration neurons, and the whole system does not rely on changing reflex gains according to the gait cycle state. The musculoskeletal model is composed of a skeletal structure and nine muscles per leg generating movement in sagittal plane. Main results. Optimizing the open parameters for effort minimization and stability, human kinematics and muscle activation naturally emerged. Furthermore, when CPGs were not activated, periodic motion could not be achieved through optimization, suggesting the necessity of this component to generate rhythmic behavior without a state machine mechanism regulating reflex activation. The controller could reproduce ranges of speeds from 0.3 to 1.9 m s−1. The results showed that the net influence of feedback on motoneurons (MNs) during perturbed locomotion is predominantly inhibitory and that the CPGs provide the timing of MNs’ activation by exciting or inhibiting muscles in specific gait phases. Significance. The proposed bio-inspired controller could contribute to our understanding of locomotor circuits of the intact spinal cord and could be used to study neuromotor disorders.

Modeling personalized heart rate response to exercise and environmental factors with wearables data

Heart rate (HR) response to workout intensity reflects fitness and cardiorespiratory health. Physiological models have been developed to describe such heart rate dynamics and characterize cardiorespiratory fitness. However, these models have been limited to small studies in controlled lab environments and are challenging to apply to noisy—but ubiquitous—data from wearables. We propose a hybrid approach that combines a physiological model with flexible neural network components to learn a personalized, multidimensional representation of fitness. The physiological model describes the evolution of heart rate during exercise using ordinary differential equations (ODEs). ODE parameters are dynamically derived via a neural network connecting personalized representations to external environmental factors, from area topography to weather and instantaneous workout intensity. Our approach efficiently fits the hybrid model to a large set of 270,707 workouts collected from wearables of 7465 users from the Apple Heart and Movement Study. The resulting model produces fitness representations that accurately predict full HR response to exercise intensity in future workouts, with a per-workout median error of 6.1 BPM [4.4–8.8 IQR]. We further demonstrate that the learned representations correlate with traditional metrics of cardiorespiratory fitness, such as VO2 max (explained variance 0.81 ± 0.003). Lastly, we illustrate how our model is naturally interpretable and explicitly describes the effects of environmental factors such as temperature and humidity on heart rate, e.g., high temperatures can increase heart rate by 10%. Combining physiological ODEs with flexible neural networks can yield interpretable, robust, and expressive models for health applications.

The key role of depression and supramarginal gyrus in frailty: a cross-sectional study.

The age-related decrease in reserve and resistance to stressors is recognized as frailty, one of the most significant challenges identified in recent years. Despite a well-acknowledged association of frailty with cognitive impairment, depression, and gray matter morphology, no clear data are available regarding the nature of this relationship. This cross-sectional study aims to disentangle the role of the behavioral, neuropsychological, and neural components as predictors or moderators of frailty. Ninety-six older adults (mean age = 75.49 ± 6.62) were consecutively enrolled and underwent a clinical and MRI (3 T) evaluation to assess frailty, physical activity, global cognitive level, depression, wellbeing, autonomy in daily living, cortical thickness, and subcortical volumes. Results showed a full mediation of depression on the link between cortical thickness and frailty, while the cognitive level showed no significant mediating role. In particular, left supramarginal thickness had a predicting role on depression, that in turn impacted frailty occurrence. Finally, handgrip weakness was an early key indicator of frailty in this study's cohort. These data substantiate the role of depression in mediating the link between neural integrity of the supramarginal gyrus and frailty. In the complexity of frailty, handgrip weakness seems to be an early key indicator. These results are relevant for the design of rehabilitation interventions aimed at reversing the frail condition.

Phonological ability and neural congruency: Phonological loop or more?

ObjectiveWe explored neural components in Electroencephalography (EEG) signals during a phonological processing task to assess (a) the neural origins of Baddeley’s working-memory components contributing to phonological processing, (b) the unitary structure of phonological processing and (c) the neural differences between children with dyslexia (DYS) and controls (CAC). MethodsEEG data were collected from sixty children (half with dyslexia) while performing the initial- and final- phoneme elision task. We explored a novel machine-learning-based approach to identify the neural components in EEG elicited in response to the two conditions and capture differences between DYS and CAC. ResultsOur method identifies two sets of phoneme-related neural congruency components capturing neural activations distinguishing DYS and CAC across conditions. ConclusionsNeural congruency components capture the underlying neural mechanisms that drive the relationship between phonological deficits and dyslexia and provide insights into the phonological loop and visual-sketchpad dimensions in Baddeley’s model at the neural level. They also confirm the unitary structure of phonological awareness with EEG data. SignificanceOur findings provide novel insights into the neural origins of the phonological processing differences in children with dyslexia, the unitary structure of phonological awareness, and further verify Baddeley’s model as a theoretical framework for phonological processing and dyslexia.

The triple psychological and neural bases underlying procrastination: Evidence based on a two-year longitudinal study

The triple brain anatomical network model of procrastination theorized procrastination as the result of psychological and neural dysfunction implicated in self-control, emotion regulation and episodic prospection. However, no studies have provided empirical evidence for such model. To address this issue, we designed a two-wave longitudinal study where participants underwent the resting-state scanning and completed the questionnaires at two time-points that spanned 2-year apart (T1, n = 457; T2, n = 457). Using the cross-lagged panel network modeling (CLPN), we found that triple psychological components at T1, including self-control, emotion regulation (i.e., reappraisal) and episodic prospection, negatively predicted procrastination at T2 in the temporal network. Moreover, the CLPN modeling found that functional connectivity between networks accounting for episodic prospection (EP) and emotion regulation (ER) positively predicted future procrastination in the temporal network. The centrality analyzes further showed that procrastination was greatly affected by other nodes, whilst the psychological component (i.e., episodic prospection), and the functional network connectivity (FNC) of EP- ER exerted strongest impacts on other nodes in the networks, which indicated that treatments targeting episodic prospection might largely help reduce procrastination. Collectively, these findings firstly provide evidence for testifying the triple brain anatomical network model of procrastination, and highlights the contribution of triple psychological and neural components implicated in self-control, emotion regulation and episodic prospection to procrastination that enhances our understanding of causes of procrastination.

Observer-based safety monitoring of nonlinear dynamical systems with neural networks via quadratic constraint approach

The safety monitoring for nonlinear dynamical systems with embedded neural network components is addressed in this paper. The interval-observer-based safety monitor is developed consisting of two auxiliary neural networks derived from the neural network components of the dynamical system. Due to the presence of nonlinear activation functions in neural networks, we use quadratic constraints on the global sector to abstract the nonlinear activation functions in neural networks. By combining a quadratic constraint approach for the activation function with Lyapunov theory, the interval observer design problem is transformed into a series of quadratic and linear programming feasibility problems to make the interval observer operate with the ability to correctly estimate the system state with estimation errors within acceptable limits. The applicability of the proposed method is verified by simulation of the lateral vehicle control system.

VHXLA: A post-earthquake damage prediction method for high-speed railway track-bridge system using VMD and hybrid neural network

The timely and accurate prediction of post-earthquake damage is critically important for ensuring the safety of high-speed railway track-bridge systems. The study introduces a novel method known as the VHXLA model. This innovative model blends variational mode decomposition (VMD) with a hybrid neural network for predicting non-linear multi-component post-earthquake damage in high-speed railways. The VHXLA model comprises a signal processing module and a deep learning module. The signal processing module utilises VMD decomposition, and the Hilbert transform (HT) to transform two-dimensional seismic signals into four-dimensional complex time–frequency signals, thus simplifying the task of identifying seismic time–frequency characteristics via the neural network. The deep learning module integrates diverse types of neural network components, such as the Xception CNN feature extraction submodule, the LSTM RNN temporal learning submodule, and the multi-head attention mechanism submodule. A Bayesian self-optimisation method is implemented to determine the number of decomposition layers in VMD and select essential hyperparameters. The model's effectiveness is evaluated by predicting the damage of an experimentally validated finite element model, subjecting it to seismic loads, and thus gauging its performance. The results indicated that the signal processing module, based on VMD decomposition, significantly improves the neural network's signal processing capability. In addition, the synergistic integration of different modules in the VHXLA model provides superior prediction accuracy compared to pre-existing damage prediction models. Notably, the prediction accuracy is consistent across different positions of the same predicted component in the high-speed railway track-bridge system.

Distinct Neural Components of Visually Guided Grasping during Planning and Execution.

Selecting suitable grasps on three-dimensional objects is a challenging visuomotor computation, which involves combining information about an object (e.g., its shape, size, and mass) with information about the actor's body (e.g., the optimal grasp aperture and hand posture for comfortable manipulation). Here, we used functional magnetic resonance imaging to investigate brain networks associated with these distinct aspects during grasp planning and execution. Human participants of either sex viewed and then executed preselected grasps on L-shaped objects made of wood and/or brass. By leveraging a computational approach that accurately predicts human grasp locations, we selected grasp points that disentangled the role of multiple grasp-relevant factors, that is, grasp axis, grasp size, and object mass. Representational Similarity Analysis revealed that grasp axis was encoded along dorsal-stream regions during grasp planning. Grasp size was first encoded in ventral stream areas during grasp planning then in premotor regions during grasp execution. Object mass was encoded in ventral stream and (pre)motor regions only during grasp execution. Premotor regions further encoded visual predictions of grasp comfort, whereas the ventral stream encoded grasp comfort during execution, suggesting its involvement in haptic evaluation. These shifts in neural representations thus capture the sensorimotor transformations that allow humans to grasp objects.SIGNIFICANCE STATEMENT Grasping requires integrating object properties with constraints on hand and arm postures. Using a computational approach that accurately predicts human grasp locations by combining such constraints, we selected grasps on objects that disentangled the relative contributions of object mass, grasp size, and grasp axis during grasp planning and execution in a neuroimaging study. Our findings reveal a greater role of dorsal-stream visuomotor areas during grasp planning, and, surprisingly, increasing ventral stream engagement during execution. We propose that during planning, visuomotor representations initially encode grasp axis and size. Perceptual representations of object material properties become more relevant instead as the hand approaches the object and motor programs are refined with estimates of the grip forces required to successfully lift the object.

Integrating machine learning with pharmacokinetic models: Benefits of scientific machine learning in adding neural networks components to existing PK models.

Recently, the use of machine-learning (ML) models for pharmacokinetic (PK) modeling has grown significantly. Although most of the current approaches use ML techniques as black boxes, there are only a few that have proposed interpretable architectures which integrate mechanistic knowledge. In this work, we use as the test case a one-compartment PK model using a scientific machine learning (SciML) framework and consider learning an unknown absorption using neural networks, while simultaneously estimating other parameters of drug distribution and elimination. We generate simulated data with different sampling strategies to show that our model can accurately predict concentrations in extrapolation tasks, including new dosing regimens with different sparsity levels, and produce reliable forecasts even for new patients. By using a scenario of fitting PK data with complex absorption, we demonstrate that including known physiological structure into an SciML model allows us to obtain highly accurate predictions while preserving the interpretability of classical compartmental models.

Tail regeneration at different ontogenetic stages of the tiger salamander Ambystoma tigrinum suggests possible changes in regeneration between larval and metamorphic individuals

AbstractUrodele amphibians possess remarkable regenerative abilities, allowing them to rebuild lost body parts. Contrary to lizards, salamanders can fully restore their tails, including the neural spine and components of the vertebral column. The axolotl (Ambystoma mexicanum) is the vertebrate model organism for regeneration research due to its ease of breeding in captivity. However, axolotls are paedomorphic, retaining larval somatic features throughout adulthood and do not naturally undergo metamorphosis, a transition phase from aquatic larvae to terrestrial adults with profound morphological and physiological changes. We investigate the influence of metamorphosis on salamander tail regeneration after conspecific biting in the metamorphosing sister taxon Ambystoma tigrinum using histological analysis to answer two key questions: (1) Does regeneration continue during metamorphosis, or is it halted? (2) How does regeneration differ histologically among larval, metamorphosing and postmetamorphic individuals? Our findings demonstrate that regeneration continues even during metamorphic climax, indicating the simultaneous coordination of metamorphosis and regeneration. Additionally, notable distinctions were observed between developmental stages concerning the speed of regeneration and structural differences in the formation of an apical epithelial cap (AEC). While the approach taken in this study necessarily restricts sample size, it offers valuable insights into regeneration in a metamorphosing species under natural conditions.

Targeting the Cancer-Neuronal Crosstalk in the Pancreatic Cancer Microenvironment.

Pancreatic ductal adenocarcinoma (PDAC) represents one of the most aggressive solid tumors with a dismal prognosis and an increasing incidence. At the time of diagnosis, more than 85% of patients are in an unresectable stage. For these patients, chemotherapy can prolong survival by only a few months. Unfortunately, in recent decades, no groundbreaking therapies have emerged for PDAC, thus raising the question of how to identify novel therapeutic druggable targets to improve prognosis. Recently, the tumor microenvironment and especially its neural component has gained increasing interest in the pancreatic cancer field. A histological hallmark of PDAC is perineural invasion (PNI), whereby cancer cells invade surrounding nerves, providing an alternative route for metastatic spread. The extent of PNI has been positively correlated with early tumor recurrence and reduced overall survival. Multiple studies have shown that mechanisms involved in PNI are also involved in tumor spread and pain generation. Targeting these pathways has shown promising results in alleviating pain and reducing PNI in preclinical models. In this review, we will describe the mechanisms and future treatment strategies to target this mutually trophic interaction between cancer cells to open novel avenues for the treatment of patients diagnosed with PDAC.

FRI264 A Case Of Composite Pheochromocytoma And Ganglioneuroma

Abstract Disclosure: D. He: None. J. Pullman: None. S. Stefan: None. B.Y. Wong: None. Background: Pheochromocytomas are catecholamine-secreting tumors that arise from chromaffin cells in the adrenal medulla or paraganglia. Representing 1-9% of pheochromocytomas, composite pheochromocytomas are rare tumors composed of pheochromocytoma and neural tumor components such as neuroblastoma, ganglioneuroblastoma, ganglioneuroma or peripheral nerve sheath tumor1. We present a case of composite pheochromocytoma and ganglioneuroma. Clinical Case: A 69-year-old man presented with recurrent dyspnea at rest, diaphoresis and tachycardia. He was diagnosed with heart failure with reduced ejection fraction and management included metoprolol 50 mg daily. On work-up, he was incidentally found to have a 1.2 cm left upper lobe lung lesion and an indeterminate 2.4 cm right adrenal mass on CTA chest. PET/CT scan showed an intensely avid left upper lobe lung nodule and a right adrenal mass with mild FDG uptake (SUV max 3.8). MRI abdomen demonstrated a 3.1 cm right adrenal lesion with a mildly thickened enhancing rim and a central enhancing nodular component suspicious for pheochromocytoma, metastases, schwannoma or ganglioneuroma. He then had a stroke with mild residual right-sided weakness. Oncological work-up of his lung and adrenal lesions eventually directed tissue sampling to biopsy of the adrenal lesion and pathology was consistent with pheochromocytoma. Subsequent biochemical evaluation revealed elevated plasma metanephrines of 194 pg/mL (<= 57 pg/mL), plasma normetanephrines of 474 pg/mL (<= 148 pg/mL), 24-hour urine metanephrines of 389 mcg (90-315 mcg) and 24-hour urine normetanephrines of 428 mcg (122-676 mcg). Pre-operatively, he was placed on phenoxybenazmine 10 mg twice daily and metoprolol 62.5 mg every 8 hours. The patient then underwent right adrenalectomy. The tumor nodule was adherent to and dissected off the inferior vena cava. Pathology showed composite pheochromocytoma with a small component of ganglioneuroma. Conclusion: This rare case of a composite pheochromocytoma with ganglioneuroma was diagnosed through histopathology as composite pheochromocytomas are clinically and radiographically indistinguishable from pheochromocytomas1. Management of composite pheochromocytomas with ganglioneuroma is similar to the management of pheochromocytomas, and surgical resection remains the first line treatment. Combined alpha and beta-adrenergic blockade is recommended to prevent intraoperative hypertensive crisis. However, more studies are needed to further understand these rare composite pheochromocytomas.

Blue light exposure-dependent improvement in robustness of circadian rest-activity rhythm in aged rats.

The aging effects on circadian rhythms have diverse implications including changes in the pattern of rhythmic expressions, such as a wide fragmentation of the rhythm of rest-activity and decrease in amplitude of activity regulated by the suprachiasmatic nucleus (SCN). The study of blue light on biological aspects has received great current interest due, among some aspects, to its positive effects on psychiatric disorders in humans. This study aims to evaluate the effect of blue light therapy on the SCN functional aspects, through the evaluation of the rest-activity rhythm, in aging rats. For this, 33 sixteen-months-old male Wistar rats underwent continuous records of locomotor activity and were exposed to periods of 6 hours of blue light during the first half of the light phase (Zeitgeber times 0-6) for 14 days. After this, the rats were maintained at 12h:12h light:dark cycle to check the long-term effect of blue light for 14 days. Blue light repeated exposure showed positive effects on the rhythmic variables of locomotor activity in aged rats, particularly the increase in amplitude, elevation of rhythmic robustness, phase advance in acrophase, and greater consolidation of the resting phase. This effect depends on the presence of daily blue light exposure. In conclusion, our results indicate that blue light is a reliable therapy to reduce circadian dysfunctions in aged rats, but other studies assessing how blue light modulates the neural components to modulate this response are still needed.

MicroCam

The primary focus of this research is the discreet and subtle everyday contact interactions between mobile phones and their surrounding surfaces. Such interactions are anticipated to facilitate mobile context awareness, encompassing aspects such as dispensing medication updates, intelligently switching modes (e.g., silent mode), or initiating commands (e.g., deactivating an alarm). We introduce MicroCam, a contact-based sensing system that employs smartphone IMU data to detect the routine state of phone placement and utilizes a built-in microscope camera to capture intricate surface details. In particular, a natural dataset is collected to acquire authentic surface textures in situ for training and testing. Moreover, we optimize the deep neural network component of the algorithm, based on continual learning, to accurately discriminate between object categories (e.g., tables) and material constituents (e.g., wood). Experimental results highlight the superior accuracy, robustness and generalization of the proposed method. Lastly, we conducted a comprehensive discussion centered on our prototype, encompassing topics such as system performance and potential applications and scenarios.

Exploiting Information in Event-Related Brain Potentials from Average Temporal Waveform, Time-Frequency Representation, and Phase Dynamics.

Characterizing the brain's dynamic pattern of response to an input in electroencephalography (EEG) is not a trivial task due to the entanglement of the complex spontaneous brain activity. In this context, the brain's response can be defined as (1) the additional neural activity components generated after the input or (2) the changes in the ongoing spontaneous activities induced by the input. Moreover, the response can be manifested in multiple features. Three commonly studied examples of features are (1) transient temporal waveform, (2) time-frequency representation, and (3) phase dynamics. The most extensively used method of average event-related potentials (ERPs) captures the first one, while the latter two and other more complex features are attracting increasing attention. However, there has not been much work providing a systematic illustration and guidance for how to effectively exploit multifaceted features in neural cognitive research. Based on a visual oddball ERPs dataset with 200 participants, this work demonstrates how the information from the above-mentioned features are complementary to each other and how they can be integrated based on stereotypical neural-network-based machine learning approaches to better exploit neural dynamic information in basic and applied cognitive research.

A neural network approach to the environmental Kuznets curve

We investigate the relationship between per capita gross domestic product and per capita carbon dioxide emissions using national-level panel data for the period 1960–2018. We propose a novel semiparametric panel data methodology that combines country and time fixed effects with a nonparametric neural network regression component. Globally and for the regions OECD and Asia, we find evidence of an inverse U-shaped relationship, often referred to as an environmental Kuznets curve (EKC), in production-based emissions. For OECD, the EKC-shape disappears when using consumption-based emissions data, suggesting the EKC-shape observed for OECD is driven by emissions exports. For Asia, the EKC-shape becomes even more pronounced when using consumption-based emissions data and exhibits an earlier turning point.

Single-trial visually evoked potentials predict both individual choice and market outcomes

A central assumption in the behavioral sciences is that choice behavior generalizes enough across individuals that measurements from a sampled group can predict the behavior of the population. Following from this assumption, the unit of behavioral sampling or measurement for most neuroimaging studies is the individual; however, cognitive neuroscience is increasingly acknowledging a dissociation between neural activity that predicts individual behavior and that which predicts the average or aggregate behavior of the population suggesting a greater importance of individual differences than is typically acknowledged. For instance, past work has demonstrated that some, but not all, of the neural activity observed during value-based decision-making is able to predict not just individual subjects’ choices but also the success of products on large, online marketplaces—even when those two behavioral outcomes deviate from one another—suggesting that some neural component processes of decision-making generalize to aggregate market responses more readily across individuals than others do. While the bulk of such research has highlighted affect-related neural responses (i.e. in the nucleus accumbens) as a better predictor of group-level behavior than frontal cortical activity associated with the integration of more idiosyncratic choice components, more recent evidence has implicated responses in visual cortical regions as strong predictors of group preference. Taken together, these findings suggest a role of neural responses during early perception in reinforcing choice consistency across individuals and raise fundamental scientific questions about the role sensory systems in value-based decision-making processes. We use a multivariate pattern analysis approach to show that single-trial visually evoked electroencephalographic (EEG) activity can predict individual choice throughout the post-stimulus epoch; however, a nominally sparser set of activity predicts the aggregate behavior of the population. These findings support an account in which a subset of the neural activity underlying individual choice processes can scale to predict behavioral consistency across people, even when the choice behavior of the sample does not match the aggregate behavior of the population.

Hybrid Inception-embedded deep neural network ResNet for short and medium-term PV-Wind forecasting

Accurate and consistent forecasting of regional wind power is essential for efficient scheduling and maximizing the utilization of renewable energy in the power grid. Medium-term PV and wind power forecasting play a vital role in various aspects of renewable energy planning and management. It provides valuable insights into future wind power generation trends, enabling effective resource allocation, grid integration, and energy market operations. This paper proposed a hybrid inception embedded deep neural network with ResNet architecture, Proposed model utilizes ResNet blocks and Inception modules to enhance feature extraction and attention mechanisms. Furthermore, The bidirectional weighted LSTM and GRU layers are used to further increase the model's ability to capture temporal dependencies and provide accurate power generation. The Time2Vec method is integrated to capture important complex and nonlinear temporal information and patterns in the time series data. Our model utilizes both convolutional and recurrent neural network components to learn and predict power generation with improved accuracy. This study employs Chinese State Grid PV and wind datasets and Natal wind datasets to determine the performance of our proposed IEDN-RNET model. The results of our study show that the proposed IEDN-RNET model outperforms other algorithms with a 12% lower Mean Absolute Error, 13% lower Root Mean Square Error, 19% lower Normalized Mean Absolute Error, 20% lower Normalized Root Mean Square Error, and higher R-Square and correlation coefficients.