Conventional Learning Approaches Research Articles

Small water body extraction in remote sensing with enhanced CNN architecture

In climate change, the precise acquisition of data about natural resources is essential and poses a significant difficulty in remote sensing. Identifying water resources has become a key focus in addressing the global issue of water scarcity. Using remote sensing techniques to extract water bodies from satellite images is complex. While Convolutional Neural Networks (CNNs) have shown potential in image segmentation, challenges such as unclear boundaries, the requirement for extensive training data, and a high number of trainable parameters persist. To address these challenges, a CNN architecture called ”WaterNet” has been developed by integrating two residual refinement capsules to improve predicted results and ensure continuity in boundary pixels. Incorporating dense layers within the network reduces the total count of trainable parameters, thus alleviating the computational burden. To assess the effectiveness of the proposed method, a comparison was made with several conventional and deep learning approaches. These included the use of NDWI, Unet, Deepunet, Segnet, DeepwaterMap, and Multi-layered Feature Fusion Approach (MFFA). The proposed solution demonstrated superior performance, surpassing all existing approaches, with an outstanding accuracy of 0.97% in extracting water resources from high-resolution satellite imagery. The model is accurate in distinguishing water from other features such as land, ice, clouds, and shadows using sentinel data as input. The uniqueness of this work lies in the development of the ”WaterNet” system tailored for water body extraction, showcasing improved accuracy, efficient context transmission, incorporation of dense layers, utilization of residual refinement modules, and addressing the limitations of conventional approaches. These contributions collectively advance remote sensing and deep learning used in the detection of small bodies of water from imagery captured by satellites.

Creating An Immersive Business Simulation: The Virtual Reality (Vr) Coffee Shop

ABSTRACT Virtual reality (VR) learning is believed to increase focus, engagement, and emotional connection. To that end, we developed an immersive business case study in VR to teach essential business concepts. This paper outlines the process we followed to develop and launch the case in the hope that other institutions can use our process as a blueprint for launching their own similar VR case simulations. In an immersive virtual space using head-mounted display (HMD) headsets, students navigate various business challenges, make cross-disciplinary decisions, and witness real-time outcomes across four increasingly complex scenarios. This immersive learning approach allows students to safely practice making challenging decisions and learn from mistakes, gaining insights into the consequences of their choices in a uniquely compelling way and developing a comprehensive understanding of various business aspects, including finance, operations, marketing, management, and social responsibility. User testing included an in-headset test to assess usability, engagement, and educational value of the VR simulation in a controlled environment. The VR case simulation was found to be more engaging than more conventional learning approaches, helped to reduce distractions to a greater extent, and offered greater tangibility around decisions and consequences.

Enhancing misogyny detection in bilingual texts using explainable AI and multilingual fine-tuned transformers

Gendered disinformation undermines women’s rights, democratic principles, and national security by worsening societal divisions through authoritarian regimes’ intentional weaponization of social media. Online misogyny represents a harmful societal issue, threatening to transform digital platforms into environments that are hostile and inhospitable to women. Despite the severity of this issue, efforts to persuade digital platforms to strengthen their protections against gendered disinformation are frequently ignored, highlighting the difficult task of countering online misogyny in the face of commercial interests. This growing concern underscores the need for effective measures to create safer online spaces, where respect and equality prevail, ensuring that women can participate fully and freely without the fear of harassment or discrimination. This study addresses the challenge of detecting misogynous content in bilingual (English and Italian) online communications. Utilizing FastText word embeddings and explainable artificial intelligence techniques, we introduce a model that enhances both the interpretability and accuracy in detecting misogynistic language. To conduct an in-depth analysis, we implemented a range of experiments encompassing classic machine learning methodologies and conventional deep learning approaches to the recent transformer-based models incorporating both language-specific and multilingual capabilities. This paper enhances the methodologies for detecting misogyny by incorporating incremental learning for cutting-edge datasets containing tweets and posts from different sources like Facebook, Twitter, and Reddit, with our proposed approach outperforming these datasets in metrics such as accuracy, F1-score, precision, and recall. This process involved refining hyperparameters, employing optimization techniques, and utilizing generative configurations. By implementing Local Interpretable Model-agnostic Explanations (LIME), we further elucidate the rationale behind the model’s predictions, enhancing understanding of its decision-making process.

An ensemble deep learning model for medical image fusion with Siamese neural networks and VGG-19.

Multimodal medical image fusion methods, which combine complementary information from many multi-modality medical images, are among the most important and practical approaches in numerous clinical applications. Various conventional image fusion techniques have been developed for multimodality image fusion. Complex procedures for weight map computing, fixed fusion strategy and lack of contextual understanding remain difficult in conventional and machine learning approaches, usually resulting in artefacts that degrade the image quality. This work proposes an efficient hybrid learning model for medical image fusion using pre-trained and non-pre-trained networks i.e. VGG-19 and SNN with stacking ensemble method. The model leveraging the unique capabilities of each architecture, can effectively preserve the detailed information with high visual quality, for numerous combinations of image modalities in image fusion challenges, notably improved contrast, increased resolution, and lower artefacts. Additionally, this ensemble model can be more robust in the fusion of various combinations of source images that are publicly available from Havard-Medical-Image-Fusion Datasets, GitHub. and Kaggle. Our proposed model performance is superior in terms of visual quality and performance metrics to that of the existing fusion methods in literature like PCA+DTCWT, NSCT, DWT, DTCWT+NSCT, GADCT, CNN and VGG-19.

Reinforcement learning-based portfolio optimization with deterministic state transition

Portfolio optimization has attracted substantial interest within the artificial intelligence community due to its significant impact on financial decision-making, risk management, and market analysis. Reinforcement learning fits well with portfolio optimization because their goal is to maximize cumulative returns. In reinforcement learning, state transition probabilities are often unknown and must be estimated. However, in portfolio backtesting experiments, these probabilities are deterministic, making the conventional reinforcement learning approach to estimating state transitions suboptimal for portfolio optimization. Addressing this issue, this study decomposes the portfolio optimization into two core tasks: prediction and profit policy optimization and proposes a novel reinforcement learning framework that assumes deterministic state transition probabilities, comprised of three main modules: feature extraction, prediction, and profit strategy optimization. To model assets more effectively and comprehensively, we capture their temporal features, relational features, and market state. We introduce a patch-wise correlation method and attribute based gate to enhance feature extraction. In the profit policy module, we utilize a deterministic strategy, employing a recursive reinforcement learning method based on Monte Carlo sampling to train the policy network. This enables dynamic adjustments of asset investment weights, ensuring the maximization of cumulative returns. Extensive experiments conducted on cryptocurrency datasets demonstrate the superior performance of our approach, and achieving 36.6%-75.6% improvements in main measurements on cryptocurrency datasets.

Ischemic Stroke Segmentation by Transformer and Convolutional Neural Network Using Few-Shot Learning

Stroke is a major factor in causing disability and fatalities. Doctors use computerized tomography (CT) and magnetic resonance imaging (MRI) scans to assess the severity of a stroke. Automatic image segmentation can help doctors diagnose strokes more quickly and accurately, but it is challenging due to the variability of stroke lesions and the limited availability of labeled data. Deep learning is the cutting-edge technique of machine learning and artificial intelligence, which needs an extensive labeled dataset for effective training. Unfortunately, in the medical domain, the availability of labeled data is severely limited, posing a challenge for conventional deep learning approaches. In this paper, we introduce a system that utilizes deep learning in the form of fusing transformer-based and convolutional neural network (CNN)-based features and few-shot learning techniques to segment ischemic strokes in multimedia MRIs. To accomplish this, we employ two different methods. The first method involves parallel fusion, where we combine CNN-based and transformer-based features. The second method utilizes serial fusion, combining CNN-based and transformer models using few-shot learning. Through the integration of transformer and CNN models, we can extract both global and local features and enhance the system's performance. Moreover, we tackle the issue of limited labeled data by integrating few-shot learning techniques. Additionally, our system optimizes efficiency by selecting only the slices with lesions, disregarding unlesioned slices. The system under consideration is trained with the BraTS2020 dataset, evaluated on the ISLES 2015 dataset, and contrasted the performance with cutting-edge systems. The suggested system attains a dice coefficient score of 0.76, surpassing the scores of previous cutting-edge systems by a substantial margin.

THE IMPACT OF GAMIFIED EDUCATIONAL APPLICATIONS ON SECONDARY SCHOOL STUDENTS' ACHIEVEMENT IN LEARNING ENGLISH AS A FOREIGN LANGUAGE

This study investigates the effectiveness of gamified educational applications in improving students' involvement in the process of learning the English language among secondary school students. The study employs a comparative methodology to evaluate the influence of gamification on students' motivation, language acquisition, and overall learning experience. The study uses a sample of students with different language backgrounds and applies both quantitative and qualitative methodologies to assess the efficacy of gamified educational apps in comparison to conventional language learning approaches. The results demonstrate a notable and favorable association between the use of gamified language learning and heightened student involvement. The use of gamification components, such as interactive challenges, prizes, and competition, has been proven to create a dynamic and interesting learning environment. This, in turn, motivates students to actively engage in language acquisition activities. The comparative analysis emphasizes the benefits of gamified applications in enhancing language proficiency, memory, and interpersonal abilities. Furthermore, the study examines how technology facilitates language learning by including gamification. It assesses the accessibility, usefulness, and adaptability of these programs. The findings of this research add to the continuing discussion on creative teaching methods, providing valuable information on the effectiveness of gamified educational apps as instruments for teaching the English language. The results endorse the incorporation of gamified educational apps into language classes, providing a more engaging and participatory learning experience for learners.

Increasing Science Literacy through Make a Match Learning Media on Human Growth Materials in Class V of Elementary School

This study aims to evaluate the influence of the Make a Match learning method on the science literacy ability of grade V elementary school students. This method is compared to conventional learning approaches in improving students' understanding of science materials. The research method used is an experiment using the Rasch Model to analyze the difficulty level of pretest and posttest questions. The results showed that there was a significant improvement in the science literacy ability of students in the experimental class who used the Make a Match method compared to the control class. However, there are several questions that are still considered difficult by students in both groups. This finding highlights the need to adjust learning strategies to overcome these difficulties to improve students' understanding of science literacy.

Global information aware network with global interaction graph attention for infrared small target detection

AbstractDetecting small targets in infrared images is crucial for ground surveillance and air traffic control. However, distinguishing small infrared targets from similar backgrounds is challenging due to their lack of structural and textural characteristics. To address these challenges, this study proposes a novel global information‐aware network with global interaction graph attention (GIGA) for infrared small target detection. The GIGA consists of a global interaction layer (GILayer), graph attention weights (GAW), and a global relational learning (GRL) module. Specifically, the GILayer dynamically learns global inter‐pixel relationships of small target images by enhancing the dependencies between feature dimensions. The GAW component calculates pixel‐by‐pixel similarity across the entire feature map using graph attention mechanisms, while the GRL module retains critical similarity features in the feature extraction network, thereby facilitating small target detection. Additionally, the multi‐scale context fusion module utilises self‐attention and dilation convolution to complement richer feature details at different scales. Experimental results on both natural and synthetic datasets demonstrate the proposed method's superiority over other state‐of‐the‐art conventional and deep learning approaches in infrared small target detection.

Ranking factors affecting the decontamination efficacy of non-thermal plasma: The approach of dissipated power per plasma volume through machine learning modeling

Non-thermal plasma treatment can preserve food, but a meta-analysis assessing its efficacy has not been performed. This study retrieved the inactivation kinetics parameter log10D (n = 519), which varied largely among different plasma setups. Atmospheric pressure plasma jet, corona discharge, surface barrier discharge, dielectric barrier discharge, and inductively-coupled plasma had the highest efficacy with median D-values under 2 min. Dielectric barrier discharge, the most frequent setup (n = 160), was analyzed using dissipated power per plasma volume (W/cm3) as an integrated predictor of decontamination efficacy. Using conventional and machine learning approaches the most correlated parameters to the log10D were: dissipated power per plasma volume and matrix category, followed by microbial genus and pH. This study uses active learning to improve literature screening for data collection and various data analysis techniques for data treatment and ranking of the factors affecting non-thermal plasma decontamination. Industrial relevanceNon-thermal plasma decontamination shows potential but is also highly affected by the setup, and technical, biological, and food parameters. This study gives an overview of the decontamination performance that is to be expected for different matrix categories and setups, which could guide possible industrial applications. The ranking of the most important parameters to affect decontamination efficacy could be used for the optimization of these applications.

Advancements in nanobody generation: Integrating conventional, in silico, and machine learning approaches.

Nanobodies, derived from camelids and sharks, offer compact, single-variable heavy-chain antibodies with diverse biomedical potential. This review explores their generation methods, including display techniques on phages, yeast, or bacteria, and computational methodologies. Integrating experimental and computational approaches enhances understanding of nanobody structure and function. Future trends involve leveraging next-generation sequencing, machine learning, and artificial intelligence for efficient candidate selection and predictive modeling. The convergence of traditional and computational methods promises revolutionary advancements in precision biomedical applications such as targeted drug delivery and diagnostics. Embracing these technologies accelerates nanobody development, driving transformative breakthroughs in biomedicine and paving the way for precision medicine and biomedical innovation.

Transformers for colorectal cancer segmentation in CT imaging.

Most recently transformer models became the state of the art in various medical image segmentation tasks and challenges, outperforming most of the conventional deep learning approaches. Picking up on that trend, this study aims at applying various transformer models to the highly challenging task of colorectal cancer (CRC) segmentation in CT imaging and assessing how they hold up to the current state-of-the-art convolutional neural network (CNN), the nnUnet. Furthermore, we wanted to investigate the impact of the network size on the resulting accuracies, since transformer models tend to be significantly larger than conventional network architectures. For this purpose, six different transformer models, with specific architectural advancements and network sizes were implemented alongside the aforementioned nnUnet and were applied to the CRC segmentation task of the medical segmentation decathlon. The best results were achieved with the Swin-UNETR, D-Former, and VT-Unet, each transformer models, with a Dice similarity coefficient (DSC) of 0.60, 0.59 and 0.59, respectively. Therefore, the current state-of-the-art CNN, the nnUnet could be outperformed by transformer architectures regarding this task. Furthermore, a comparison with the inter-observer variability (IOV) of approx. 0.64 DSC indicates almost expert-level accuracy. The comparatively low IOV emphasizes the complexity and challenge of CRC segmentation, as well as indicating limitations regarding the achievable segmentation accuracy. As a result of this study, transformer models underline their current upward trend in producing state-of-the-art results also for the challenging task of CRC segmentation. However, with ever smaller advances in total accuracies, as demonstrated in this study by the on par performances of multiple network variants, other advantages like efficiency, low computation demands, or ease of adaption to new tasks become more and more relevant.

Autonomous design of noise-mitigating structures using deep reinforcement learning.

This paper explores the application of deep reinforcement learning for autonomously designing noise-mitigating structures. Specifically, deep Q- and double deep Q-networks are employed to find material distributions that result in broadband noise mitigation for reflection and transmission problems. Unlike conventional deep learning approaches which require prior knowledge for data labeling, the double deep Q-network algorithm learns configurations that result in broadband noise mitigations without prior knowledge by utilizing pixel-based inputs. By employing unified hyperparameters and network architectures for transmission and reflection problems, the capability of the algorithms to generalize over different environments is demonstrated. In addition, a comparison with a genetic algorithm highlights the potential for generalized design in complex environments, despite the algorithms tending to predict local maxima. Furthermore, we examine the impact of hyperparameters and environment types on agent performance. The autonomous design approach offers generalized learning while avoiding restrictions to specific shapes or prior knowledge of the task.

Effectiveness of error-based active learning compared to conventional lecture-based method among undergraduate dental students: A randomized controlled trial.

Dental education assumes an indispensable role in adequately equipping students for a successful career in dentistry. Error-based active learning, an innovative approach in dental education, is a combination of mistake-driven learning (MDL) and case-based learning (CBL) to provide a transformative learning atmosphere for students. While active learning has gained popularity in dental education, the effectiveness of error-based active learning remains unexplored. This study aims to evaluate the effectiveness of error-based active learning among final-year dental undergraduate students in comparison with the conventional lecture-based approach. A parallel-arm single-blind randomized controlled trial was conducted in a dental institute in India from November 2022 to December 2022. A total of 74 students were randomly allocated to two groups: Group A (n = 37) received error-based active learning and group B (n = 37) received conventional lecture-based learning approach. Atraumatic restorative treatment (ART) was chosen as the topic for the study. The knowledge of students was assessed at three intervals: baseline, post-intervention, and 4 weeks after the intervention, using a self-designed and validated questionnaire with Cronbach's alpha of 0.87 and a content validity ratio of 0.84. A standard survey questionnaire was employed to evaluate students' perceptions of the teaching methods. Error-based active learning group outperformed the lecture-based group significantly in the post-intervention test (20.92 ± 1.42 vs 16.97 ± 3.06), with better knowledge retention (18.30 ± 2.02 vs 14.05 ± 4.26) and positive feedback from the students. The error-based active learning approach proved superior to the conventional lecture-based method in enhancing and retaining knowledge regarding ART.

Eye Strain Expression Classification using Attention Capsule Network for Adapting Screen Vision

Beside the conventional facial expression recognition methods, the research focuses on developing a system for recognizing various eye expressions under different screen conditions. This research deals with the use of Capsule Network (a recent Deep Learning algorithm) to enhance facial expression recognition capabilities and to develop adaptive screen technologies aimed at mitigating digital eye strain. The main objective of this research is to engineer a sophisticated system that employs the capabilities of Capsule Nets to recognize the various expressions that user makes and based on the recognized expression, dynamically modify screen settings, ensuring optimal user visual comfort. The research primarily concentrates on the exploration and application of various Capsule Net architectures designed for the recognition of expressions related to eye strain. The baseline model utilized elementary convolutional layers which feed into subsequent fully connected layers for the task of classification. The model has since been refined by incorporating advanced techniques such as attention mechanisms and more sophisticated network architectures where the classification is done by Capsule Network. Results have demonstrated a modest enhancement in the Capsule Net’s predictive performance, attributed to its superior spatial and hierarchical processing of facial features, in comparison to conventional deep learning approaches. The final model has an accuracy of 82.27%. As a final system the model has been deployed to an application to process frames from video camera in the device and make prediction to prompt the notifications or recommendations.

Replacing Face-to-Face Classes with Collegium: Ed-tech platform (Online Platform)

Abstract: The incorporation of technology has sparked a revolutionary change in the way that students interact with instructional materials in today's quickly changing educational environment. This paradigm shift is demonstrated by the creation of collegium, which bridges the gap between cutting-edge technological solutions and conventional learning approaches. Collegium reimagines the educational experience by utilizing cutting-edge frameworks like ReactJS, NodeJS, ExpressJS, and MongoDB, providing a dynamic platform that overcomes time and space constraints.

Surrogate-assisted multi-objective evolutionary feature selection of generation-based fixed evolution control for time series forecasting with LSTM networks

Surrogate-assisted multi-objective evolutionary algorithms are powerful techniques to solve computationally expensive multi-objective optimization problems. In this paper, we propose a direct fitness replacement method with generation-based fixed evolution control to implement a multi-objective evolutionary algorithm that uses a surrogate model for wrapper-type feature selection, where long short-term memory is established as the learning algorithm. The importance of the work and its benefits lie in the need to reduce the excessive computational time required by conventional wrapper-type feature selection methods based on multi-objective evolutionary algorithms and LSTM networks, maintaining or improving the predictive capacity of the models. We analyze the use of incremental learning to update the surrogate model, in comparison with the conventional non-incremental learning approach. We applied these methods in real-life time series forecasting of air quality, indoor temperature in a smart building and oil temperature in electricity transformers. Multi-step ahead predictions of the forecast models obtained with different meta-learners of the surrogate model were compared by using the Diebold–Mariano statistical test on a multi-criteria performance metric. The proposed method outperformed other approaches for feature selection including, among others, methods based on surrogate-assisted multi-objective evolutionary algorithms developed by the authors in previous research, other surrogate-assisted deterministic methods for feature selection and the conventional wrapper-type feature selection method based on LSTM, improving the prediction on test dataset by 23.98%, 34.61% and 13.77%, respectively.

Deep multiple instance learning versus conventional deep single instance learning for interpretable oral cancer detection.

The current medical standard for setting an oral cancer (OC) diagnosis is histological examination of a tissue sample taken from the oral cavity. This process is time-consuming and more invasive than an alternative approach of acquiring a brush sample followed by cytological analysis. Using a microscope, skilled cytotechnologists are able to detect changes due to malignancy; however, introducing this approach into clinical routine is associated with challenges such as a lack of resources and experts. To design a trustworthy OC detection system that can assist cytotechnologists, we are interested in deep learning based methods that can reliably detect cancer, given only per-patient labels (thereby minimizing annotation bias), and also provide information regarding which cells are most relevant for the diagnosis (thereby enabling supervision and understanding). In this study, we perform a comparison of two approaches suitable for OC detection and interpretation: (i) conventional single instance learning (SIL) approach and (ii) a modern multiple instance learning (MIL) method. To facilitate systematic evaluation of the considered approaches, we, in addition to a real OC dataset with patient-level ground truth annotations, also introduce a synthetic dataset-PAP-QMNIST. This dataset shares several properties of OC data, such as image size and large and varied number of instances per bag, and may therefore act as a proxy model of a real OC dataset, while, in contrast to OC data, it offers reliable per-instance ground truth, as defined by design. PAP-QMNIST has the additional advantage of being visually interpretable for non-experts, which simplifies analysis of the behavior of methods. For both OC and PAP-QMNIST data, we evaluate performance of the methods utilizing three different neural network architectures. Our study indicates, somewhat surprisingly, that on both synthetic and real data, the performance of the SIL approach is better or equal to the performance of the MIL approach. Visual examination by cytotechnologist indicates that the methods manage to identify cells which deviate from normality, including malignant cells as well as those suspicious for dysplasia. We share the code as open source.

Hybrid Random Forest Regression and Artificial Neural Networks for Modelling and Monitoring the State of Health of Li-Ion Battery

Lithium-ion batteries play a significant role in modern energy storage systems for a number of applications, including renewable energy installations, electric cars, and portable devices. In order to guarantee the long-term dependability and safety of these battery packs, comprehensive modelling and monitoring approaches are required for accurate State of Health (SoH) evaluation. This research suggests an innovative strategy that combines the strengths of Random Forest Regression (RFR) and Artificial Neural Networks (ANN) for reliable Li-ion battery SoH modelling and monitoring. The research starts with the collection of thorough Li-ion battery data, which includes voltage, current, temperature, and cycling characteristics. The battery dataset's nonlinear relationships are captured utilising RFR as the main modelling method. In terms of feature selection and prediction precision, RFR's ensemble of decision trees shines, making it a strong contender for modelling complex battery behaviour. The SoH prediction is then enhanced by the use of ANN, a deep learning framework renowned for its ability to extract detailed patterns from data. The characteristics of RFR are complemented by ANN's capacity to discover hidden features and nonlinear correlations, improving the overall prediction performance. The suggested hybrid approach, that combines RFR and ANN, is trained and verified utilising a diverse database acquired from Li-ion battery sources under operational circumstances. To assess the prediction accuracy of a suggested framework measures like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R2) values are employed. The outcomes show that the hybrid RFR-ANN model works better than the individual RFR and ANN models, producing better SoH predictions. A viable solution for real-time Li-ion battery health monitoring is provided by this combination of conventional ML and deep learning approaches, which demonstrates the synergy among complexity and interpretability. This research advances battery SoH assessment and paves the way for the practical incorporation of precise monitoring systems into applications like electric vehicles, where timely and accurate SoH data is essential for maximising battery performance and extending operational lifespan.

A Stochastic Regression and Sentiment Analysis-Based Approach for Forecasting Trends in the Stock Market

Stochastic regression problems especially applied to time series forecasting problems often encounter the challenge of volatility and unpredictable seasonality in datasets. One such application happens to forecasting the movement of stock markets globally. Stock data across the world exhibits an intrinsic baseline noise, which often can’t be correlated to previous temporal patterns. Rather, such divergences are an effect of multiple non-numeric variables which govern stock movement such as political scenarios, trade wars, imminent economic waxing and waning trends to name a few. However, it is essential to incorporate these factors while training a model so as to design a strongly sentient and anticipatory regression algorithm which is both pervasive and robust, encompassing as many numeric and non-numeric factors as possible. While conventional machine learning and deep learning approaches tend to leverage tested model architectures, they often miss out on the optimization of both training data and training method to yield potentially high forecasting accuracy. The paper combines stochastic regression in terms of momentum based training and data optimization which is shown to exhibit considerable lower forecasting error compared to existing approaches. To encompass a wider feature space, sentiment analysis has been incorporated to correlate stock movements with public opinions. The proposed approach has been tested on a multitude of benchmark datasets, to validate the performance of the approach. The mean absolute percentage error (MAPE) and regression values have been selected as primary metrics for the performance evaluation. The proposed approach attains a mean MAPE value of 3.22%. The analysis of the MAPE and forecasting accuracy w.r.t. existing benchmark approaches proves the improved forecasting performance of the proposed approach over a period of 300 days.