Predicting stock markets remains a critical and challenging task due to many factors, such as the enormous volume of generated price data, instant price data changes, and sensitivity to human sentiments, wars, and natural disasters. Since the previous three years of the COVID-19 pandemic, forecasting stock markets is more difficult, complex, and problematic for stock market analysts. However, technical analysts of the stock market and academic researchers are continuously trying to develop innovative and modern methods for forecasting stock market prices, using statistical techniques, machine learning, and deep learning-based algorithms. This study investigated a Transformer sequential-based approach to forecast the closing price for the next day. Ten sliding window timesteps were used to forecast next-day stock closing prices. This study aimed to investigate reliable techniques based on stock input features. The proposed Transformer-based method was compared with ARIMA, Long-Short Term Memory (LSTM), and Random Forest (RF) algorithms, showing its outstanding results on Yahoo Finance data, Facebook Intra data, and JPMorgan's Intra data. Each model was evaluated using Mean Absolute Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE).

Parkinson’s disease (PD) is the second most common neurodegenerative disorder after Alzheimer’s disease. It has a slow progressing neurodegenerative disorder rate. PD patients have multiple motor and non-motor symptoms, including vocal impairment, which is one of the main symptoms. The identification of PD based on vocal disorders is at the forefront of research. In this paper, an experimental study is performed on an open source Kaggle PD speech dataset and novel comparative techniques were employed to identify PD. We proposed an unsupervised autoencoder feature selection technique, and passed the compressed features to supervised machine-learning (ML) algorithms. We also investigated the state-of-the-art deep learning 1D convolutional neural network (CNN-1D) for PD classification. In this study, the proposed algorithms are support vector machine, logistic regression, random forest, naïve Bayes, and CNN-1D. The classifier performance is evaluated in terms of accuracy score, precision, recall, and F1 score measure. The proposed 1D-CNN model shows the highest result of 0.927%, and logistic regression shows 0.922% on the benchmark dataset in terms of F1 measure. The major contribution of the proposed approach is that unsupervised neural network feature selection has not previously been investigated in Parkinson’s detection. Clinicians can use these techniques to analyze the symptoms presented by patients and, based on the results of the above algorithms, can diagnose the disease at an early stage, which will allow for improved future treatment and care.

Purpose: In 2016, Kingdom of Saudi Arabia (KSA) initiated Saudi Vision 2030, an ambitious plan to lessen the country's dependency on fossil fuels and increase economic diversification. The Vision 2030 framework strives to establish a thriving economy, a vibrant society and an ambitious nation. This study aims to investigate the role of green service innovation (SI) and green work engagement (WE) in mediating the nexus between green human resource management (HRM) and green creativity (GC) under conditional role of spiritual leadership (SL).Design/methodology/approach: A survey was done of 300 female intrapreneurs working in the organization within Saudi Arabia. This study has collected data via stratified random sampling technique. The framework was tested using PLS-SEM software.Findings: The findings reveal that WE fully intervenes the nexus between green HRM and GC. Moreover, SL positively moderates the nexus between green HRM and SI.Originality/value: Thus, based on findings, it is recommended that female intrapreneurs prioritize environmentally responsible operations to gain and sustain competitive edge over rivals in Saudi competitive market.

This study explores entrepreneurial leadership as a moderator between employee work engagement and perceptions of high-performance human resource management (HRM) practices. Additionally, it delves into the mediating role of employee work engagement between employee creativity and these perceptions. Utilizing a quantitative research strategy, the study includes a sample of 396 members from the banking sector in Pakistan. The study employs PLS-SEM to test hypotheses. Findings indicate that employee work engagement serves as a mediator between employees’ perceptions of high-performance HRM practices and creativity, while entrepreneurial leadership moderates this association. Entrepreneurial leadership enhances the employee–organization relationship and strengthens the impact of high-performance HRM practices on positive work-related behaviours. The study contributes to the body of knowledge by examining the strong role of employee work engagement in the relationship between employee perceptions of high-performance HRM practices and creativity as mediator, as well as the significance of entrepreneurial leadership as a moderator in that relationship to optimize the working of organization stimulating through the demand resource theory.

This paper aims to examine the mediating role of green service innovation between green entrepreneurship intention, green knowledge sharing and green knowledge management and green competitive advantage. A survey was administered to 300 respondents. For the collection of data, this study used judgemental sampling. Structural equation modelling technique implemented to examine the framework. The findings of the study reveal that green service innovation mediates the relationship between green entrepreneurship intention, green knowledge sharing and green knowledge management and green competitive advantage. These findings also support the proposed direct hypothesis. The findings of the study proposed that marketers should focus on environmentally friendly activities to gain a competitive advantage in the KSA market.