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This paper aims to find the effective 3D printing process parameters based on mechanical characteristics such as tensile strength and hardness of poly lactic acid (PLA)/carbon fibre composites (CF-PLA) by implementing intelligent frameworks.

The experiment trials are conducted based on design of experiments (DoE) using Taguchi L9 orthogonal array with three factors (speed, infill % and pattern type) and three levels. The factors have been optimized by solving the regression equation which is obtained from analysis of variance (ANOVA). The contour plots are generated by response surface methodology (RSM). The influencing parameters are found by using Box–Behnken design. The second order response surface model demonstrated the optimal combination of input parameters for higher tensile strength and hardness.

The influencing parameters are found by using Box–Behnken design. The second order response surface model demonstrated the optimal combination of input parameters for higher tensile strength and hardness. The results obtained from RSM are also confirmed by implementing the machine learning classifiers, such as logistic regression, ridge classifier, random forest, K nearest neighbour and support vector classifier (SVC). The results show that the SVC can predict the optimized process parameters with an accuracy of 95.65%.

3D printing parameters which are considered in this work such as pattern types for PLA/CF-PLA composites based on intelligent frameworks has not been attempted previously.

Aspect-based sentiment analysis (ASA) is a task of sentiment analysis that requires predicting aspect sentiment polarity for a given sentence. Many traditional techniques use graph-based mechanisms, which reduce prediction accuracy and introduce large amounts of noise. The other problem with graph-based mechanisms is that for some context words, the feelings change depending on the aspect, and therefore it is impossible to draw conclusions on their own. ASA is challenging because a given sentence can reveal complicated feelings about multiple aspects.

This research proposed an optimized attention-based DL model known as optimized aspect and self-attention aware long short-term memory for target-based semantic analysis (OAS-LSTM-TSA). The proposed model goes through three phases: preprocessing, aspect extraction and classification. Aspect extraction is done using a double-layered convolutional neural network (DL-CNN). The optimized aspect and self-attention embedded LSTM (OAS-LSTM) is used to classify aspect sentiment into three classes: positive, neutral and negative.

To detect and classify sentiment polarity of the aspect using the optimized aspect and self-attention embedded LSTM (OAS-LSTM) model. The results of the proposed method revealed that it achieves a high accuracy of 95.3 per cent for the restaurant dataset and 96.7 per cent for the laptop dataset.

The novelty of the research work is the addition of two effective attention layers in the network model, loss function reduction and accuracy enhancement, using a recent efficient optimization algorithm. The loss function in OAS-LSTM is minimized using the adaptive pelican optimization algorithm, thus increasing the accuracy rate. The performance of the proposed method is validated on four real-time datasets, Rest14, Lap14, Rest15 and Rest16, for various performance metrics.

This paper aims to explore the dimensions of spiritual tourism development, examine its current status, effectiveness and scope and analyze the knowledge landscape in terms of theories, contexts and research methodologies. The study also seeks to guide future research on spiritual tourism development.

A systematic literature review (SLR) and bibliometric analysis were used using a framework-based approach. The theories, constructs, characteristics and methods (TCCM) framework guided the SLR, whereas VOS-Viewer facilitated comprehensive bibliometric analysis.

The study conducted a quantitative SLR, analyzing 80 research articles published between 2003 and 2023. Using the TCCM framework, the research identified crucial factors influencing the growth of spiritual tourist destinations, such as intrinsic motivation, destination physicality, tourist experience, spiritual activities and host community support.

This study contributes to theoretical advancement in spiritual tourism, provides insights into the current research landscape, offers practical guidance for stakeholders and serves as a roadmap for future research endeavors.

This research enhances knowledge by thoroughly assessing prior research, addressing gaps and offering practical managerial insights for spiritual tourism development. The managerial implications outlined in the study offer practical insights for destination planning and promotion in the context of spiritual tourism.

Amidst the rising awareness of sustainable consumption, this study aims to delve into the dimensions shaping individuals' preferences for traditional gastronomic delicacies taking an emerging economy’s perspective, India.

A qualitative research methodology in the form of a Grounded Theory Approach is used to develop theories. Important dimensions that drive attitude and intention towards experiencing traditional gastronomic delicacies are explored. Based on literary inputs and qualitative study, a research framework is developed and empirically validated thereon with SEM analysis using SPSS-AMOS.

Drawing on the Theory of Consumption Values and Stakeholder Theory, key influencers (consumption values) of traditional gastronomic delicacies were identified as Travel Motivation (Functional Values), Tourist Expectations (Emotional Values), Socio-economic Perspectives (Socio-economic Values), Mindful Consumption Practice (Epistemic Values), Community Awareness (Epistemic Values) and Sustainable Marketing Stimuli (Conditional Values).

This research has a multifaceted impact. At the macro-level, it supports stakeholders in Gastronomic Tourism (GT) – marketers, regional tourism bodies, policymakers and tour operators with distinct consumer values – in crafting regional culinary tourism, influencing economic policies and advocating for cultural conservation. At the micro-level, it aids scholars in initiating future research to elevate dining experiences, promote consumer education and tackle health and nutritional aspects within the evolving gastronomic industry.

This study makes a novel attempt to explore important drivers, categorizing the drivers into distinct consumer values that influence tourists and food connoisseurs towards traditional gastronomic delicacies by blending an innovative qualitative research methodology like grounded theory approach supported by the empirical validation process (quantitative). Additionally, it proposes a theoretical framework for future advancement of gastronomic literature.

Globally, postnatal women endure a prominent issue caused by midline separation of abdominal recti muscles, characterized by a sagging and pouch-like appearance of the belly termed as Diastasis Recti Abdominis (DRA). The necessity of ensuring the efficacy of rehabilitative workouts for individuals with DRA cannot be overstated, as inaccurate exercises can exacerbate the condition and deteriorate the health of affected women. The purpose of these exercises is to specifically focus on the rectus abdominis muscles to facilitate the reapproximation of the linea alba. The primary aim of this research work is to assess the effectiveness of rehabilitation exercises for DRA women obtained from Inertial Measurement Unit (IMU) and Electromyography (EMG) sensors.

Convolutional neural networks (CNN) employs convolutional activation functions and pooling layers. Recently, 1D CNNs have emerged as a promising approach used in various applications, including personalized biomedical data classification and early diagnosis, structural health monitoring and anomaly detection. Yet another significant benefit is the feasibility of a real-time and cost-effective implementation of 1D CNN. The EMG and IMU signals serve as inputs for the 1D CNN. Features are then extracted from the fully connected layer of the CNN and fed into a boosting machine learning algorithm for classification.

The findings demonstrate that a combination of sensors provides more details about the exercises, thereby contributing to the classification accuracy.

In real time, collecting data from postnatal women was incredibly challenging. The process of examining these women was time-consuming, and they were often preoccupied with their newborns, leading to a reluctance to focus on their own health. Additionally, postnatal women might not be fully aware of the implications of DRA and the importance of rehabilitation exercises. Many might not realize that neglecting DRA can lead to long-term issues such as back pain, pelvic floor dysfunction, and compromised core strength.

During our data collection camps, there were educational sessions to raise awareness about the DRA problem and the benefits of rehabilitation exercises. This dual approach helped in building trust and encouraging participation. Moreover, the use of wearable sensors in this study provided a non-invasive and convenient way for new mothers to engage in rehabilitation exercises without needing frequent visits to a clinic, which is often impractical for them.

The utilization of discriminating features retrieved from the output layer of 1D CNN is a significant contribution to this work. The responses of this study indicate that 1D convolutional neural network (1D CNN) and Boosting algorithms used in a transfer learning strategy produce successful discrimination between accurate and inaccurate performance of exercises by achieving an accuracy of 96%.