Short-term Memory Performance Research Articles

Selectively Blocking Small Conductance Ca2+-Activated K+ Channels Improves Cognition in Aged Mice

Aging is associated with decreased neuronal sensitivity and activity that creates deficits in cognitive processes, including learning, memory, motivation, general activity, and other behaviors. These effects are due in part to decreased intracellular Ca2+ homeostasis, increasing hyperpolarization of the resting potential in aged neurons and therefore decreasing their excitability. To reduce hyperpolarization in aged mice, we used apamin, a selective small conductance Ca2+-activated K+ (sKCa) channel blocker. By blocking the sKCa channels, apamin decreases the egress of the K+ out of the cell, reducing its hyperpolarization and causing it to be closer to threshold potential. As a result, neurons should be more sensitive to excitatory stimuli and more active. We evaluated the performance of aged mice in a selection of cognitive and behavioral tests prior to and after systemic applications of apamin or the vehicle saline. Apamin improved performance in short-term memory, increased attention to tasks, and decreased anhedonia. Apamin had no significant effect on long-term spatial and recognition memory, risk-taking behavior, sociability, and anxiety. Our results are compatible with the known effects of sKCa channel blockade on neuronal sensitivity and activity; however, these short-term effects were not reflected in longer-term alterations of neural plasticity responsible for long-term spatial and recognition memory or other more complex cognitive processes we evaluated.

Enhancing LSTM performance in sentiment analysis through advanced data preprocessing and model optimization techniques

This study explores enhancing the performance of Long Short-Term Memory (LSTM) networks in sentiment analysis by integrating advanced data preprocessing techniques and hybrid model architectures. A robust preprocessing pipeline was implemented, involving tokenization, normalization, slang handling, and dataset balancing to improve data quality. A CNN-GloVe-LSTM hybrid model was developed, leveraging GloVe embeddings for semantic representation, CNN for local feature extraction, and LSTM for sequential dependency learning. The study also examined an ensemble of LSTM and Random Forest models. Performance metrics, including accuracy, precision, recall, F1-score, and AUC-ROC, were used for evaluation. Results indicate that the CNN-GloVe-LSTM model achieved the highest accuracy (92.05%) and computational efficiency, outperforming both the standalone LSTM and ensemble approaches. The hybrid model demonstrated a significant reduction in training time while maintaining robust classification capabilities, making it a superior choice for sentiment analysis tasks on social media data.

The CD74 inhibitor DRhQ improves short-term memory and mitochondrial function in 5xFAD mouse model of Aβ accumulation.

Neuroinflammation and mitochondrial dysfunction are early events in Alzheimer's disease (AD) and contribute to neurodegeneration and cognitive impairment. Evidence suggests that the inflammatory axis mediated by macrophage migration inhibitory factor (MIF) binding to its receptor, CD74, plays an important role in many central nervous system (CNS) disorders such as AD. Our group has developed DRhQ, a novel CD74 binding construct which competitively inhibits MIF binding, blocks macrophage activation and migration into the CNS, enhances anti-inflammatory microglia cell numbers and reduces pro-inflammatory gene expression. Here, we evaluate its effects in amyloid-β (Aβ) overexpressing mice. 5xFAD mice and their wild type littermates were treated with DRhQ (100µg) or vehicle for 4 weeks. DRhQ improved cognition and cortical mitochondrial function in both male and female 5xFAD mice. Aβ plaque burden in 5xFAD animals was not robustly impacted by DRhQ treatment in either the hippocampus or the cortex. Cortical microglial activation was similarly not apparently affected by DRhQ treatment, although in the hippocampus there was evidence of a reduction in activated microglia for female 5xFAD mice. Future studies are needed to confirm this possible sex-dependent response on microglial activation, as well as to optimize the dose and timing of DRhQ treatment and gain a better understanding of its mechanism of action in AD.

Obesity-associated memory impairment and neuroinflammation precede widespread peripheral perturbations in aged rats

BackgroundObesity and metabolic syndrome are major public health concerns linked to cognitive decline with aging. Prior work from our lab has demonstrated that short-term high fat diet (HFD) rapidly impairs memory function via a neuroinflammatory mechanism. However, the degree to which these rapid inflammatory changes are unique to the brain is unknown. Moreover, deviations in gut microbiome composition have been associated with obesity and cognitive impairment, but how diet and aging interact to impact the gut microbiome, or how rapidly these changes occur, is less clear. Thus, our study investigated the impact of HFD after two distinct consumption durations: 3 months (to model diet-induced obesity) or 3 days (to detect the rapid changes occurring with HFD) on memory function, anxiety-like behavior, central and peripheral inflammation, and gut microbiome profile in young and aged rats.ResultsOur data indicated that both short-term and long-term HFD consumption impaired memory function and increased anxiety-like behavior in aged, but not young adult, rats. These behavioral changes were accompanied by pro- and anti-inflammatory cytokine dysregulation in the hippocampus and amygdala of aged HFD-fed rats at both time points. However, changes to fasting glucose, insulin, and inflammation in peripheral tissues such as the distal colon and visceral adipose tissue were increased in young and aged rats only after long-term, but not short-term, HFD consumption. Furthermore, while subtle HFD-induced changes to the gut microbiome did occur rapidly, robust age-specific effects were only present following long-term HFD consumption.ConclusionsOverall, these data suggest that HFD-evoked neuroinflammation, memory impairment, and anxiety-like behavior in aging develop quicker than, and separately from the peripheral hallmarks of diet-induced obesity.

Wheel flat detection using long short-term memory and transformer models with a 1:10 scale railway test rig

In railway systems, the detection of wheel flats is essential for ensuring safety and reducing maintenance costs. This study compares the performance of Long Short-Term Memory and Transformer models in detecting wheel flats using data from a 1:10 scale railway test rig. The findings indicate that the Transformer model significantly outperforms the Long Short-Term Memory model, especially when feature-level sensor fusion is employed, achieving an average error as low as 0.0069 mm with percentage of error at 5.30%, minimizing the maximum error to 0.0985 mm. The study emphasizes the potential of Transformer models in railway diagnostics, particularly for applications requiring high accuracy and reliability. The insights gained from this research have practical implications for improving the precision of wheel flat detection in real-world railway operations, enhancing both safety and efficiency.

Bi-Level Interturn Short-Circuit Fault Monitoring for Wind Turbine Generators With Benchmark Dataset Development

Abstract Flourished wind energy market pushes the latest wind turbines (WTs) to further and harsher inland and offshore environment. Increased operation and maintenance cost calls for more reliable and cost effective condition monitoring systems. In this article, a bi-level condition monitoring framework for interturn short-circuit faults (ITSCFs) in WT generators is proposed. A benchmark dataset, consisting of 75 ITSCF scenarios and generator current signals of a specific WT, has been created and made publicly available on Zenodo. The data are simulated at a rate of 4 kHz. Based on the time and frequency features extracted from data processing, machine learning-based severity estimation and faulty phase identification modules can provide valuable diagnostic information for wind farm operators. Specifically, the performance of long short-term memory (LSTM) networks, gated recurrent unit (GRU) networks, and convolutional neural networks (CNNs) are analyzed and compared for severity estimation and faulty phase identification. For test-bed experimental reference, various numbers of scenarios for training the models are analyzed. Numerical experiments demonstrate the computational efficiency and robust denoising capability of the CNN algorithm. The GRU network, however, achieves the highest accuracy. The overall system performance improves significantly, from 87.76% with 16 training scenarios to 99.95% with 52 training scenarios, when tested on a set containing all 76 scenarios from an unforeseen period.

OP03 Redesigning the patient management pathway through prediction of flares in axial spondyloarthritis using a machine learning approach to improve care

Abstract Background and aims Treatment of axial spondyloarthritis (axSpA) involves the management of flares which can be unpredictable. ASAS developed a data-driven definition of flares/disease worsening as an increase in ASDAS-CRP of at least 0.9 points, for use in axSpA clinical trials (1). With the current pressures on clinic capacity, there is a challenge to see flare patients in a timely manner. Artificial intelligence (AI) and natural language processing (NLP) methods provide algorithms for learning systems to recognise disease-related terms and classify clinical phenotypes using large datasets, which may support early identification of patients with a risk of having a flare. In previous work, we presented an approach using NLP to identify patients who may have rheumatic disease with predictors to discriminate patients with diagnosed inflammatory arthritis from controls with non-inflammatory arthritis. With the same aim, we extended the panel of predictors by combining clinical and laboratory data to predict flares in axSpA. Methods 282 axSpA patients seen between January 2016 to May 2024 were included in the study. There are 100 flare and 182 non-flare patients studied. We utilised patient blood test results, demographic information, electronic patient-reported outcomes scores, comorbidity, weight and height. We developed a time series dataset and applied Long Short-Term Memory (LSTM) networks and neural networks (NN) to forecast the risk of flare before their future clinics. Results We conducted predictive modelling to forecast flare and non-flare events using data collected prior to patients upcoming clinic appointments. The dataset was split into training and testing sets based on observations taken 2 months before the clinic visit. Our LSTM model achieved 90.25% accuracy, 0.89 sensitivity, 0.96 specificity, 0.93 precision and 0.98 Area Under the Receiver Operating Characteristic Curve (AUROC) predicting flare and non-flare events 2 months before the clinics. The results are summarized in Table 1. OP03 Table 1.Performance of Long Short-Term Memory (LSTM) and neural networks (NN) on data from 2 months before the clinic visit.AVG ACCURACYAVG SENSITIVITYAVG SPECIFICITYAVG PRECISIONAVG AUROCLSTM (2 months before the next clinics)90.25%0.890.960.930.98NEURAL NETWORK (2 months before the next clinics)71.90%0.910.010.610.5 Conclusions The presented combination of clinical and laboratory data shows promising results for the prediction of flare in axSpA and may in the future assist clinicians in seeing the right patient in the right clinic. A study is planned not only to confirm the results but also to refine the machine learning (ML) models developed and specify disease attributes. In the service redesign, patients identified as at risk of flares will be booked and seen in routine clinics at the right time to prevent flares. This process in the long-term will reduce the reliance on flare and urgent clinics.

The Sexual Dimorphism of the Neuroimmune Response in the Brains of Taenia crassiceps-Infected Mice.

Helminth infections are associated with cognitive deficits, especially in school-age children. Deworming treatment in heavily infected children improves their short- and long-term memory recall. In mice, intraperitoneal helminth infection with Taenia crassiceps (T. crassiceps) shows sexual dimorphism in terms of the parasite load, immune response, hormone levels, and behavioral changes. We have previously shown poorer short-term memory performance and changes in the concentrations of cytokines and neurotransmitters in the hippocampus, which were replicated in this study. The molecular changes in other brain structures, such as those related to reproduction, are unknown. Male and female Balb/cAnN mice were chronically infected with T. crassiceps larvae. We determined the peritoneal parasite load and established the presence of cytokines and neurotransmitters in the hippocampus, olfactory bulb, and hypothalamus. The parasite load was higher in female than male infected mice, as expected. In the hippocampus, the neurotransmitters norepinephrine and serotonin increased in males but decreased in females. In contrast, in the olfactory bulb and hypothalamus, the neurotransmitters assessed showed no statistical differences. The cytokine profiles were different in each brain structure. The TNF-α levels in the olfactory bulb and the IL-4 levels in the hippocampus of infected mice were dimorphic; IFN-γ was augmented in both male and female infected animals, although the increase was higher in infected males. The brain responds to peripheral infection with cytokine levels that vary from structure to structure. This could be a partial explanation for the dimorphic behavioral alterations associated with infection, it also demonstrates the synergic interaction between the immune, the endocrine, and the nervous systems.

Optimizing Currency Circulation Forecasts in Indonesia: A Hybrid Prophet- Long Short Term Memory Model with Hyperparameter Tuning

The core problem for decision-makers lies in selecting an effective forecasting method, particularly when faced with the challenges of nonlinearity and nonstationarity in time series data. To address this, hybrid models are increasingly employed to enhance forecasting accuracy. In Indonesia and other Muslim countries, monthly economic and business time series data often include trends, seasonality, and calendar variations. This study compares the performance of the hybrid Prophet-Long Short-Term Memory (LSTM) model with their individual counterparts to forecast such patterned time series. The aim is to identify the best model through a hybrid approach for forecasting time series data exhibitingtrend, seasonality, and calendar variations, using the real-life case of currency circulation in South Sulawesi. The goodness of the models is evaluated using the smallest Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) values. The results indicate that the hybrid Prophet- LSTM model demonstrates superior accuracy, especially for predicting currency outflow, with lower MAPE and RMSE values than standalone models. The LSTM model shows excellent performance for currency inflow, while the Prophet model lags in inflow and outflow accuracy. This insight is valuable for Bank Indonesia’s strategic planning, aiding in better cash flow prediction and currency stock management.

Advanced Monthly Rainfall Forecasting in Southern Lazio: Integrating Climatic Indices, Classical or Deep Neural Networks, and a Feature Selection Algorithm

Accurately predicting monthly precipitation is crucial for managing water resources, particularly in regions with complex climates like Southern Lazio, Italy. This study addresses the challenges of selecting relevant climate parameters and optimizing model complexity for precise forecasting. Four models, based on Bi-directional Long Short-Term Memory (BI-LSTM) and Radial Basis Function (RBF) neural networks, were compared, focusing on their ability to predict monthly precipitation using diverse inputs such as mean temperature, relative humidity, sea level pressure, radiation, precipitation change, and 12 climate indices. One significant challenge was determining the most impactful subset of input variables from a large dataset to enhance model performance without overfitting. To address this, the Particle Swarm Optimization (PSO) feature selection algorithm was employed, significantly improving model accuracy by identifying the optimal subset of input variables. The comparative analysis revealed that PSO-based models consistently outperformed those using all input variables, with the PSO-RBF model achieving a Kling-Gupta Efficiency (KGE) value of up to 0.80 during testing. Notably, both PSO-BI-LSTM and PSO-RBF models demonstrated strong performance in predicting precipitation peaks, achieving KGE values of 0.74. The study highlights the efficacy of the RBF model combined with PSO, which optimizes fewer parameters compared to the more complex BI-LSTM network while reducing input variables. This approach is not only less computationally intensive but also easier to implement, particularly in regions with limited instrumentation. These findings suggest that the PSO-RBF model is a reliable and efficient tool for monthly precipitation prediction in challenging environments.

Forecasting Bitcoin volatility using machine learning techniques

This paper studies the Bitcoin volatility forecasting performance between popular traditional econometric models and machine learning techniques. We compare the 1-day to 2-month ahead forecasting performance of the Long Short-Term Memory (LSTM) and a hybrid Convolutional Neural Network-LSTM (CNN-LSTM) model to the traditional models. We find that neural networks outperform Generalised Autoregressive Conditional Heteroskedasticity (GARCH) models for all forecasting horizons. Furthermore, the LSTM model outperforms the Heterogeneous Autoregressive (HAR) model and by integrating the Markov Transition Field (MTF) into the CNN-LSTM model, we achieve superior forecasting results in the short-term, particularly for the 7-day forecasts.

Hippocampal rejuvenation by a single intracerebral injection of one-carbon metabolites in C57BL6 old wild-type mice.

The Izpisua-Belmonte group identified a cocktail of metabolites that promote partial reprogramming in cultured muscle cells. We tested the effect of brain injection of these metabolites in the dentate gyrus of aged wild-type mice. The dentate gyrus is a brain region essential for memory function and is extremely vulnerable to aging. A single injection of the cocktail containing four compounds (putrescine, glycine, methionine and threonine) partially reversed brain aging phenotypes and epigenetic alterations in age-associated genes. Our analysis revealed three levels: chromatin methylation, RNA sequencing, and protein expression. Functional studies complemented the previous ones, showing cognitive improvement. In summary, we report the reversal of various age-associated epigenetic changes, such as the transcription factor Zic4, and several changes related to cellular rejuvenation in the dentate gyrus (DG). These changes include increased expression of the Sox2 protein. Finally, the increases in the survival of newly generated neurons and the levels of the NMDA receptor subunit GluN2B were accompanied by improvements in both short-term and long-term memory performance. Based on these results, we propose the use of these metabolites to explore new strategies for the development of potential treatments for age-related brain diseases.

An embedded computational framework of memory: Accounting for the influence of semantic information in verbal short-term memory

We introduce the Embedded Computational Framework of Memory (eCFM), a model that integrates structured semantic word representations with an instance-based memory model to account for the influence of semantic information in verbal short-term memory. The eCFM combines principles from the episodic MINERVA 2 model and the semantic Latent Semantic Analysis model. After reviewing how semantic information impacts verbal short-term memory performance, we demonstrate eCFM’s ability to reconcile various phenomena within a common computational framework. Our model captures key findings, such as the influence of semantic information in serial recall, its reduction in serial reconstruction, and the impact of task difficulty on semantic information. In five experiments, we tested predictions derived from the eCFM. Experiments 1 and 2 manipulated list organization, with Experiment 1 using alternating lists of related or unrelated words and Experiment 2 using blocked lists. Experiment 3 varied presentation rates, Experiment 4 revisited the detrimental effect of semantic information on order information, and Experiment 5 explored false recall. We found that semantic information interacts with list composition, presentation rate affects the magnitude of its influence, and semantic information impacts order information contrary to the dominant view. Additionally, increasing the number of related study words to a non-studied semantic lure boosts false recall. The eCFM captured these findings as well as memory at the item level. Our demonstration provides insight into the cognitive mechanisms underlying verbal short-term memory and the interplay of semantic and episodic memory processes in recall.

Unveiling the Significance of Individual Level Predictions: A Comparative Analysis of GRU and LSTM Models for Enhanced Digital Behavior Prediction

The widespread use of technology has led to a transformation of human behaviors and habits into the digital space; and generating extensive data plays a crucial role when coupled with forecasting techniques in guiding marketing decision-makers and shaping strategic choices. Traditional methods like autoregressive moving average (ARMA) can-not be used at predicting individual behaviors because we can-not create models for each individual and buy till you die (BTYD) models have limitations in capturing the trends accurately. Recognizing the paramount importance of individual-level predictions, this study proposes a deep learning framework, specifically uses gated recurrent unit (GRU), for enhanced behavior analysis. This article discusses the performance of GRU and long short-term memory (LSTM) models in this framework for forecasting future individual behaviors and presenting a comparative analysis against benchmark BTYD models. GRU and LSTM yielded the best results in capturing the trends, with GRU demonstrating a slightly superior performance compared to LSTM. However, there is still significant room for improvement at the individual level. The findings not only demonstrate the performance of GRU and LSTM models but also provide valuable insights into the potential of new techniques or approaches for understanding and predicting individual behaviors.

Effects of natural reduced water on cognitive functions in older adults: A RCT study

Oxidative stress and diabetes increase the risk of cognitive decline and dementia. Natural reduced water contains active hydrogen (hydrogen radicals), eliminates reactive oxygen species, and has antidiabetic effects. However, whether natural reduced water affects human cognitive function is unknown. Therefore, we implemented a double-blind intervention experiment in which participants consumed 1 L of natural reduced water or tap water daily for 6 months. The participants were healthy older adults living in Japan. The intervention group showed significant improvements in cognitive functions of attention function (p < 0.01) and short-term memory (p < 0.05). These results indicate that the continuous intake of natural reduced water improves several cognitive functions.

Optimal Sensor Placement and Multimodal Fusion for Human Activity Recognition in Agricultural Tasks

This study examines the impact of sensor placement and multimodal sensor fusion on the performance of a Long Short-Term Memory (LSTM)-based model for human activity classification taking place in an agricultural harvesting scenario involving human-robot collaboration. Data were collected from twenty participants performing six distinct activities using five wearable inertial measurement units placed at various anatomical locations. The signals collected from the sensors were first processed to eliminate noise and then input into an LSTM neural network for recognizing features in sequential time-dependent data. Results indicated that the chest-mounted sensor provided the highest F1-score of 0.939, representing superior performance over other placements and combinations of them. Moreover, the magnetometer surpassed the accelerometer and gyroscope, highlighting its superior ability to capture crucial orientation and motion data related to the investigated activities. However, multimodal fusion of accelerometer, gyroscope, and magnetometer data showed the benefit of integrating data from different sensor types to improve classification accuracy. The study emphasizes the effectiveness of strategic sensor placement and fusion in optimizing human activity recognition, thus minimizing data requirements and computational expenses, and resulting in a cost-optimal system configuration. Overall, this research contributes to the development of more intelligent, safe, cost-effective adaptive synergistic systems that can be integrated into a variety of applications.

The presence of cognitive impairments in the acute phase of traumatic upper limb injuries: A cross-sectional observational study

ObjectivesThe purpose of this study was to investigate the association between cognitive impairments and traumatic upper limb injuries of the acute phase. Material and methodsA cross-sectional observational study was conducted with three groups: a nerve-injury group, a without nerve injury group, and a control group (uninjured participants). Demographic characteristics (e.g. age, sex, body mass index, and education) and traumatic characteristics (duration since injury, injury side, pain, light touch sensation, hand motor function) were recorded. Short-term memory and executive functions were assessed using Rey Auditory and Verbal Learning Test (RAVLT) and Stroop Color and Word Test (SCWT, including SIECT and SIECN), respectively. ResultsThe study comprised 43 participants in the nerve-injury group, 30 participants in the group without nerve injury, and 104 participants in the control group. Generalized linear model was applied to explore the difference of cognitive functions among three groups with impactors. Significantly poorer performance on the RAVLT was observed in the nerve-injury group compared to the other two groups, and lower score of SIECT in nerve-injury group was lower compared to the control group. However, there was no significant difference of SIECN among three groups. In addition, traumatic characteristics did not significantly impact RAVLT and SIECT (p > 0.05) in all injured participants. ConclusionTraumatic nerve injury to the upper limb appears to be associated with both short-term memory and executive function impairment, whereas musculoskeletal injuries without nerve damage showed no cognitive impairment. Therefore, it is important to monitor cognitive function following upper limb nerve injuries.

An aircraft engine remaining useful life prediction method based on predictive vector angle minimization and feature fusion gate improved transformer model

Remaining useful life (RUL) prediction is crucial for achieving intelligent and predictive maintenance of aircraft engines. In practical applications, advance prediction values smaller than the true values can prevent serious deferred maintenance accidents. Using asymmetric loss functions directly leads to noticeable accuracy degradation, and existing methods often fail to satisfy accuracy and advance prediction requirements. To address this problem, this paper proposes a novel RUL prediction method based on the Prediction Vector Angle (PVA) minimization and Feature Fusion Gate (FFG) improved Transformer network. Specifically, the FFG is proposed to enhance Transformer prediction accuracy by dynamically fusing global and local features. The concept of PVA is first introduced based on the tilting properties of the RUL descent process. The target of the prediction model is cleverly transformed from error minimization to PVA minimization through the cosine similarity loss function. Various experiments on the CMAPSS dataset demonstrate the effectiveness of the proposed method in achieving high accuracy and advanced prediction. Compared to the state-of-the-art method, RMSE is reduced by at least 2.94 % and Score by 7.00 %. Finally, the PVA minimization mechanism significantly improves long short-term memory and convolutional neural network performance. The proposed method is noteworthy for its superiority and applicability.

Dissociable effects of mild COVID-19 on short- and long-term memories.

Recent studies have highlighted the presence of cognitive deficits following COVID-19 that persist beyond acute infection, regardless of the initial disease severity. Impairments in short- and long-term memory are among the core deficits reported by patients and observed in objective tests of memory performance. We aimed to extend previous studies by examining performance in a task that allows us to directly compare and contrast memories at different timescales. More specifically, we assessed both short- and long-term memories for contextual-spatial associations encoded during a common session and probed at different durations using an equivalent task in non-hospitalized individuals recovering from mild COVID-19 compared to healthy controls. The approach equated all aspects of memory materials and response demands, isolating performance changes resulting only from memory timescales and thus allowing us to quantify the impact of COVID-19 on cognition. In addition to providing measures of accuracy and response times, the task also provided a sensitive continuous readout of the precision of memory representations, specifically by examining the resolution with which spatial locations were retained in memory. The results demonstrated selective impairment of long-term memory performance in individuals recovering from mild COVID-19 infection. Short-term memory performance remained comparable to healthy controls. Specifically, poor precision of long-term memory representations was demonstrated, which improved with days since diagnosis. No such relationship was observed for short-term memory performance. Our findings reveal a specific impairment to the precision of spatial-contextual long-term memory representations in individuals recovering from mild COVID-19 and demonstrate evidence of recovery in long-term memory over time. Further, the experimental design provides a carefully controlled and sensitive framework to assess memory across different durations with the potential to provide more detailed phenotyping of memory deficits associated with COVID-19 in general.

Seasonal solar irradiance forecasting using artificial intelligence techniques with uncertainty analysis

Renewable integration in utility grid is crucial in the current energy scenario. Optimized utilization of renewable energy can minimize the energy consumption from the grid. This demands accurate forecasting of renewable contribution and planning. Most of the researches aim to find a suitable forecasting model in terms of accuracy and error metrics. However, the uncertainty and variability in these forecasts are also significant. This work combines point forecast with interval forecast to provide comprehensive information about the forecast uncertainty. In this work, solar irradiance forecasting is carried out using artificial intelligence (AI) techniques. Forecasting is done using seasonal auto-regressive moving average with exogenous factors (SARIMAX), support vector regression (SVR), long short term memory (LSTM) techniques and performance is evaluated. SVR model exhibited the best performance with R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^2$$\\end{document} values of 0.97 and 0.96 for winter and summer respectively and 0.85 for monsoon and post-monsoon seasons. This is followed by forecast error distribution studies and uncertainty analysis. For this, SVR forecast error data is fitted using laplace distribution. Uncertainty study is carried out using confidence intervals and coverage rates. Excellent coverage rates are obtained for various confidence levels for all seasons, indicating the appropriate fitting of error distribution. For the narrow 85% confidence band, coverage rates of 89%, 95%, 90%, and 88% are obtained for winter, summer, monsoon and post-monsoon respectively. The work emphasizes the need for error-distribution studies, modeling of forecast errors and their application in providing reliable forecast intervals with the perspective of enhancing system reliability.