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Big data is the collection of large datasets from traditional and digital sources to identify trends and patterns. The quantity and variety of computer data are growing exponentially for many reasons. For example, retailers are building vast databases of customer sales activity. Organizations are working on logistics financial services, and public...
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... view companies' profiles, historical trading data, news, analysts' opinions, financial statements, and messages to identify which company share values have increased or decreased. Machine learning techniques are applied to these datasets to predict the movements of stock prices. Analysis of messages helps investors to plan their trading. Fig. 2 shows a Yahoo! Finance message panel with different investor analyses of company data. Fig. 2 shows an idea of how investors analyzed market data to predict market movements. ...
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... statements, and messages to identify which company share values have increased or decreased. Machine learning techniques are applied to these datasets to predict the movements of stock prices. Analysis of messages helps investors to plan their trading. Fig. 2 shows a Yahoo! Finance message panel with different investor analyses of company data. Fig. 2 shows an idea of how investors analyzed market data to predict market movements. ...
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... the naive Bayes and logistic regression classifier models to news and messages to predict the movement of stock prices. Naive Bayes gives approximately 60%-70% accuracy, and so both models give close accuracy. Nevertheless, the logistics classifier model is better to predict the value of stock price movements, as it provides 60% to 80% accuracy. Fig. 12 shows the results of the text classifier model (Naive Bayes and logistic regression), when applied to news and messages about APPL stock. Tab. 5 presents the results for the naïve Bayes and logistic models. Tab. 4 presents the results of the NB and Logistical LR models when applied to different datasets. The data are collected from the ...
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This transformation of food delivery businesses to online platforms has gained high attention in recent years. This due to the availability of customizing ordering experiences, easy payment methods, fast delivery, and others. The competition between online food delivery providers has intensified to attain a wider range of customers. Hence, they sho...
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... The CryptoPTD dataset extends the study to the realm of cryptocurrency markets, encompassing time series data for the prices and volatility of multiple cryptocurrencies, including Bitcoin and Ethereum. By incorporating cryptocurrency data, the research acknowledges the evolving landscape of financial markets, allowing the ISSA-BiLSTM-TPA model to adapt to the unique characteristics of the cryptocurrency market (Javed Awan et al., 2021). This dataset provides a foundation for exploring the dynamics of cryptocurrency prices and volatility. ...
This paper introduces the ISSA-BiLSTM-TPA model to improve financial investment decision-making. Traditional deep learning models face limitations in handling the complexity and uncertainty of financial markets. Our approach incorporates attention mechanisms, Bidirectional Long Short-Term Memory (BiLSTM), and Temporal Pattern Attention (TPA) to enhance accuracy in modeling and forecasting financial time series. The attention mechanism focuses on crucial information, BiLSTM captures bidirectional dependencies, and TPA identifies optimal solutions. Experimental results show higher prediction accuracy compared to traditional models, offering more reliable decision support for financial practitioners. Continuous optimization aims to provide innovative decision-making tools for the finance industry, advancing deep learning technology in finance.
... The marketing mix is a strategic approach utilized in marketing to effectively distribute information, introduce a range of products and services, and encourage consumers to build personal preferences for a product's image. Consequently, the marketing mix is regarded as one of the most promising strategic components in the realm of product marketing (Awan et al., 2020;Awan et al., 2021). ...
... The development of marketing strategies for the heritage attractions of the Manuaba Royal Palace is carried out by increasing research and understanding related to the target audience expected to visit the Manuaba Royal Palace. This includes understanding travellers' preferences and needs, which will provide recommendations to other travelers on social media (Javed Awan et al., 2021;Lamberton & Stephen, 2016;Xiang & Gretzel, 2010). In addition, it is important to improve the way branding is stronger, especially developing a brand identity that is able to reflect the historical and cultural values of this tourist destination to attract the attention of tourists. ...
The Manuaba Royal Palace is one of the heritage tourist
attractions that has cultural heritage, building architecture, and artifact
collections with historical value; however, to date, this tourist destination
has not been optimal in presenting tourists to be able to visit this region,
so a comprehensive and measurable marketing strategy is needed to
accelerate its development. This study aimed to analyze marketing
strategies for the heritage tourist attraction of the Manuaba Royal Palace
in the Kenderan Tourism Village, Bali. A mixed method research design
was used by conducting interviews with the managers of as many as 30
Manuaba Royal Palace tourist destinations who met the inclusion and
exclusion criteria. The data obtained were analyzed using IFAS, EFAS, and
SWOT matrices and are presented descriptively. The results show that the
Manuaba Royal Palace has implemented the 7P marketing mix strategy,
currently in Quadrant I (a very favorable situation because it has great
opportunities to demonstrate internal strength) and S<O (the direction of
Manuaba Royal Pae's policy in growth strategy conditions). The current
focus and marketing strategy includes developing technology-based
historical courses accompanied by the use of social media, presenting
information related to intangible cultural heritage, and guiding tourists
more massively by providing exclusive packages for booking heritage
tourism online. In the future, it is important to maximize the marketing
strategies used to increase the number of visits and to be more
competitive in the tourism industry
... While this research study provides valuable insights, there are opportunities for future exploration and refinement of predictive modeling in women's premier league cricket (Awan et al., 2021). The inclusion of additional features, such as player fitness, team strategies, and external factors like weather conditions, could enhance predictive models. ...
Background. The interest in women’s premier league cricket has caused the need for advanced analytics to understand the multifaceted dynamics of the sport. Study Purpose. This study aimed to contribute to sports analytics by assessing the efficacy of Support Vector Regression (SVR) kernel models in predicting the most valuable player. Such research methods as ANOVA, Bessel function, and Inverse MultiQuadratic kernel application have been deliberately chosen for their diverse mathematical approaches, aligning with the nuanced intricacies of women's premier league cricket. Materials and methods. Player performance was analyzed by using the following study methods: ANOVA, Bessel function and Inverse MultiQuadratic kernel application. The data, sourced from espncricinfo.com and the International Cricket Council, includes essential metrics for five teams. Rigorous preprocessing techniques, such as imputation and outlier removal, enhance data reliability, ensuring robust predictive models. Results. The application of the Inverse MultiQuadratic kernel exhibits exceptional predictive performance, surpassing ANOVA and Bessel function models. The kernels radial basis function proves effective in capturing the intricate dynamics of women’s premier league cricket. The findings underscore the suitability of kernel method for predicting standout performers in the Womenʼs Premier League 2024 season. Conclusions. The study revealed the dynamic interplay between sports analytics and machine learning in women's premier league cricket. The application of the Inverse MultiQuadratic kernel stands out as the most effective model, providing key insights into player predictions. This emphasizes the continual integration of advanced analytical techniques to enhance our understanding of the evolving landscape of women’s premier league cricket. As the sport gains prominence on the global stage, such analytical endeavors become imperative for strategic decision-making and sustained growth.
... Many restaurants buy products from local farmers and fair-trade, organic, and sustainably produced products. These restaurants help promote a more sustainable food system, supporting local businesses and the environment (Javed et al., 2021). As a result, CSR projects in restaurants are complicated and balancing financial feasibility and sustainability can be difficult. ...
As well as contributing to society, corporate social responsibility (CSR) initiatives have frequently been utilized by businesses to gain a competitive edge. Nevertheless, despite companies’ endeavors to leverage their CSR initiatives, limited stakeholder awareness of these efforts poses challenges to fully realize the strategic value of CSR. Hence, the primary objective of this study was to examine the distinct impact of various components of CSR on satisfaction, trust, and loyalty within the restaurant industry. Data were collected (n = 239) from restaurant customers in Tehran, and the results found no significant relationship between the economic dimension of CSR and customer satisfaction and customer trust, while there was a positive and significant relationship between the other aspects of CSR (e.g. legal, ethical, philanthropic and environmental) and customer trust and satisfaction. Finally, there was also a positive and strong relationship between customer trust and customer loyalty in the studied restaurants. This study offers an enhanced comprehension of CSR strategy in the restaurant sector, providing valuable insights by highlighting how specific CSR activities influence customer satisfaction and loyalty.
... Similarly, [14] states that there are limitations in predicting stock prices using the integration of Big Data, AI and ML technology. External factors such as market volatility, policy changes, and unexpected events can affect stock price movements that are difficult to fully predict [15] Therefore, more in-depth research and mature technical exploration is needed to overcome this challenge and apply this integration effectively in investment decision making. This article attempts to build a conceptual framework through investigation and exploration to integrate Big Data Engineering, AI, and ML to produce more accurate and effective predictions. ...
... This study delves deep into the multifaceted process of customer personality analysis, a technique that scrutinizes the ideal customer profile through a rich dataset encompassing variables such as age, educational background, marital status, parental status, income brackets, and expenditure patterns across various products. By harnessing this datadriven approach, businesses can foster strategies that resonate with the needs and aspirations of their clientele, facilitating informed decision-making and fostering a competitive edge in the social media market [6]. Central to this study is the exploration of synthetic data generation to pinpoint customer personality traits, a venture that encompasses a series of meticulous steps including data preparation and cleansing. ...
Today’s businesses rely heavily on focused marketing to improve their chances of growing and keeping their consumer base. Internet behemoths like Google and Facebook have expanded their business models around targeted advertisements that support business growth. Customer personality identification helps for churn prediction for companies. This problem arises in several companies where customer leaves companies for many reasons. This gap leads to conduct study for customer personality analysis. The collected dataset was highly imbalanced in nature. Two class balancing approaches CTGAN (Conditional tabular Generative adversarial networks) and SMOTE (Synthetic minority oversampling technique) has been utilized to equalize the both classes. There are three ensemble approaches such as bagging, boosting and stacking have been utilized for modeling purpose bagging approach uses Random Forest (RF) boosting utilizes XGBoost (XGB), Light Gradient Boosting Machine (LGBM) and ADA Boost (ADA B). The proposed Hybrid Model HSLR comprises of RF, XGB, ADA Boost, LGBM approaches as base classifiers and LR as a Meta classifier. Three testing independent set, k-fold with 5 and 10 folds have been utilized. To evaluate the performance of classifiers evaluation metrics such as Accuracy score, Precision, Recall, F1 score, MCC and ROC score have been utilized. The SMOTE generated data has shown results as compare with CTGAN generated data. The SMOTE approach has shown the highest results of 94.06, 94.23, 94.28, 94.05, 88.13 and 0.984 as accuracy score, Precision, recall, F1, MCC and Roc score respectively.
... Finally, AM can be used to capture the impact of the feature changes of the time series data at varying moments on the prediction outputs. In addition, the authors of [21] use numerous ML models to forecast stock market movements using Spark MLlib and PySpark. Linear regression (LR), decision tree (DT), random forest (RF), and generalized LR are some of the models available. ...
As the economy has grown rapidly in recent years, more and more people have begun putting their money into the stock market. Thus, predicting trends in the stock market is regarded as a crucial endeavor, and one that has proven to be more fruitful than others. Profitable investments will result in rising stock prices. Investors face significant difficulties making stock market-related predictions due to the lack of movement and noise in the data. In this paper, a new system for predicting stock market prices is introduced, namely stock market prediction based on deep leaning (SMP-DL). SMP-DL splits into two stages, which are (i) data preprocessing (DP) and (ii) stock price’s prediction (SP²). In the first stage, data are preprocessed to obtain cleaned ones through several stages which are detect and reject missing value, feature selection, and data normalization. Then, in the second stage (e.g., SP²), the cleaned data will pass through the used predicted model. In SP², long short-term memory (LSTM) combined with bidirectional gated recurrent unit (BiGRU) to predict the closing price of stock market. The obtained results showed that the proposed system perform well when compared to other existing methods. As RMSE, MSE, MAE, and R² values are 0.2883, 0.0831, 0.2099, and 0.9948. Moreover, the proposed method was applied using different datasets and it performs well.
... We decide to use LSTM networks and TCNs since both have shown remarkable success in a variety of forecasting tasks [14,34,53,65,66], making them a very common choice for models with high complexity. Moreover, we decide to use the D-Linear method and linear regression since both have shown potential in making accurate forecasts while being comparatively simple [35,62,67,68]. For forecasting, all our baselines rely solely on the price and do not require any training. ...
Recently, various methods to predict the future price of financial assets have emerged. One promising approach is to combine the historic price with sentiment scores derived via sentiment analysis techniques. In this article, we focus on predicting the future price of Bitcoin, which is currently the most popular cryptocurrency. More precisely, we propose a hybrid approach, combining time series forecasting and sentiment prediction from microblogs, to predict the intraday price of Bitcoin. Moreover, in addition to standard sentiment analysis methods, we are the first to employ a fine-tuned BERT model for this task. We also introduce a novel weighting scheme in which the weight of the sentiment of each tweet depends on the number of its creator’s followers. For evaluation, we consider periods with strongly varying ranges of Bitcoin prices. This enables us to assess the models w.r.t. robustness and generalization to varied market conditions. Our experiments demonstrate that BERT-based sentiment analysis and the proposed weighting scheme improve upon previous methods. Specifically, our hybrid models that use linear regression as the underlying forecasting algorithm perform best in terms of the mean absolute error (MAE of 2.67) and root mean squared error (RMSE of 3.28). However, more complicated models, particularly long short-term memory networks and temporal convolutional networks, tend to have generalization and overfitting issues, resulting in considerably higher MAE and RMSE scores.
... Segundo Awan et al. (2021), uma previsão com maior acurácia dos preços de ações no mercado de capitais, que normalmente envolve muitos fatores e uma enorme quantidade de dados, requer um sistema construído com o uso de algoritmos de aprendizado de máquina e de outras técnicas de mineração de dados, como análise de séries temporais. Dentre os algoritmos de aprendizado supervisionado de máquina geralmente usados em previsões de preços, mas principalmente de estimação de movimentos de preços de ativos em mercados de capitais, destaca-se o Naive Bayes (DUARTE; GONZALEZ;CRUZ Jr., 2021;AVELAR et al., 2022). ...
O estudo apresentado neste trabalho teve como objetivo analisar o desempenho da utilização do algoritmo de aprendizado de máquina Naive Bayes para previsão do movimento dos preços das ações que compõem o Índice Ibovespa do mercado de capitais brasileiro (B3 – Brasil, Bolsa, Balcão). Para alcançar o objetivo proposto, foram coletados dados diários dos preços das ações, com participação superior a 1% na carteira teórica do Índice Ibovespa, e calculados indicadores técnicos no período de janeiro de 2012 a dezembro de 2021. Os resultados evidenciaram que os modelos desenvolvidos a partir do algoritmo Naive Bayes obtiveram um desempenho estatisticamente superior à média de mercado. Desse modo, o emprego desse algoritmo de aprendizado de máquina supera o retorno médio esperado com base em dados passados, questionando-se a eficiência desses mercados na forma fraca da hipótese de mercados eficientes (HME). A pesquisa realizada contribui para a literatura das finanças e a prática no mercado de capitais sobre o uso de algoritmos de aprendizado de máquina (especialmente, o Naive Bayes) para previsão do movimento dos preços de ativos listados no mercado brasileiro sob diferentes perspectivas: (i) o estudo acerca da predição dos movimentos diários dos principais ativos do Ibovespa; (ii) a evidenciação de que os desempenhos dos diferentes grupos de indicadores técnicos utilizados não apresentaram diferenças significantes; e (iii) o questionamento da eficiência dos mercados estudados em sua forma fraca em um contexto de ampla automatização por algoritmos de aprendizagem de máquina.
... A huge amount of structured and unstructured information is enclosed in big data (Awan et al., 2021). Its analytics aid to manage enormous volumes of data produced by several businesses and could be wielded for forecasting trends (Gupta & Sharma, 2020). ...
