Insights Into Movie Ratings Through Data Analytics and Machine Learning

Movie rating is a measure of viewer's reaction to movie performance at the box office and also a key feature to garner publicity. Movie ratings are feedback measures given by a subset of the audience voluntarily. If the degree of effect on the human mindset can be measured through real-time behavior analyzing and rated, the results can help film houses to understand the secret of generating a commercial success movie. Prediction of movie ratings is a complex problem. Viewers, producers, directors, and production houses are curious about how a given movie will perform in theatres with different customer segments. Research works have been carried out relating to movie rating prediction using social networking, blogs articles, but much less has been explored by the consumer behavioral data and attributes while watching a movie continuously and using emotions and body movement dimensions [1 – 4], [7],[12]. We created an audience footage data set and transformed it into numerical feature data representing the audience's behavior. Prepossessing and machine learning approaches were applied to build an efficient model that can predict the movies' popularity.

Analytics of machine learning-based algorithms for text classification

The research of this article aims to achieve the classification of positive and negative emotions in film review texts. The dataset used is the IMDB dataset from the Kaggle website, which contains approximately 50,000 movie reviews that are classified as positive or negative emotions. This is a dataset for binary-sensitive classification containing substantively more data than previous benchmark datasets. This research will use two different models, SVM and CNN, to classify the dataset and then compare and analyze them. After completing the experiment, the SVM model achieved an accuracy of nearly 90%, while the CNN model only had an accuracy of about 82%. This research found that for binary classification problems, as long as the input data is processed well, SVM can also achieve results that are no less than or even better than CNN. SVM is simple and efficient but difficult to implement for large-scale training samples; CNN is suitable for solving problems with complex internal mechanisms, but its learning speed is relatively slow. Finally, we proposed our prospects for future research.

<span lang="EN-US">Nowadays, predicting the success of a new movie is a crucial task. In this work, the hybrid approach considers the movie features as well as sentiment expressed in the movie review to predict the success rate of a movie. Multiple movie features such as title, director, star cast, and writer. Are considered for prediction. The related raw data is collected from the internet movie database (IMDb) website and after pre-processing, the collected data is used to generate the supervised machine learning model. Different supervised learning models are compared and the one with the best results is used further. The mean squared error, root mean squared error and r2 score of the models generated are comparable with existing models. Further, sentiment analysis of the movie-related tweets is performed. The accuracy of best sentiment analysis model is 88.47%. Finally, the two models are combined to give the success prediction rating of new movies and the results of the hybrid model are encouraging. The proposed model may be used to find the top-rated movies of a particular calendar year.</span>

In the contemporary world, people share their thoughts rapidly in social media. Mining and extracting knowledge from this information for performing sentiment analysis is a complex task. Even though automated machine learning algorithms and techniques are available, and extraction of semantic and relevant key terms from a sparse representation of the review is difficult. Word embedding improves the text classification by solving the problem of sparse matrix and semantics of the word. In this paper, a novel architecture is proposed by combining long short-term memory (LSTM) with word embedding to extract the semantic relationship between the neighboring words and also a weighted self-attention is applied to extract the key terms from the reviews. Based on the experimental analysis on the IMDB dataset, the authors have shown that the proposed architecture word-embedding self-attention LSTM architecture achieved an F1 score of 88.67%, while LSTM and word embedding LSTM-based models resulted in an F1 score of 84.42% and 85.69%, respectively.

Film Industry is not only a industry or a centre of entertainment, rather it is now a centre of global business. All over the world is now excited about a movie's box office success, popularity etc. A huge data is available online about these movies success or popularity. We have used hollywood movie list from Wikipedia and their rating from IMDb movie rating website to create our data set. Then machine learning classification algorithms are applied of the data set. Lastly an efficient model is developed to predict a movie's IMDb rating. The model gives good classification measures with the data set.

This century has witnessed the emergence of new branch of science—data science that facilitates the analysis of large amounts of data which in turn helps in taking model-based data-driven decisions. The prelude to any successful analytical model building and implementation phase is a properly conducted initial data analysis stage. IDA encompasses laborious tasks of data cleansing: missing value treatment, outlier detection, checking the veracity of data, data transformation, and thus preparing data for model building. A systematic, disciplined, and non-personalized approach to IDA reduces the probability of incorrect and inaccurate results from the model. The amount of data presented for model building today makes the IDA stage a very crucial task which cannot be manually conducted. Machine learning can be applied to analyze complex and bigger data, find patterns accurately, etc. Hence, it could also be used for data preparation prior to model building. This paper tries to reduce the ad hoc nature of IDA by providing a conceptual framework using machine learning.

Models of water resources systems are conceived to capture the underlying environmental dynamics occurring within watersheds. All such models can be regarded as working hypotheses, differing in the aspects of process representation and conceptualization. Most of the associated efforts in the water resources research community is dedicated to development of new models that perform well under specific atmospheric conditions and catchment properties. In this context, flexible modeling frameworks are gaining importance as they facilitate the model building process by providing the model building blocks, whereby the hydrologist is free to assemble the model for task at hand. Such flexible models have high degree of transferability, which in turn aid in progressing toward a unified hydrological theory at catchment scale. However, in cases without sufficient insights regarding a catchment characteristics and/or lack of expert's knowledge, one may have to try a large number of model configurations based on available model building blocks to construct an appropriate model for the catchment of interest. Undoubtedly, this may be time consuming and computationally intensive. This paper proposes a novel model building algorithm, which uses the full potential of flexible modeling frameworks by searching the model space and inferring suitable model configurations relying on machine learning. Proposed machine learning algorithm is based on evolutionary computation approach using genetic programming (GP). State‐of‐art GP applications in rainfall‐runoff modeling so far used the algorithm as a short‐term forecasting tool that generates an expected future time series very similar to neural networks application. In this case, the proposed algorithm develops a physically meaningful rainfall‐runoff model. Although at the moment we learn models using two flexible modeling frameworks (SUPERFLEX and FUSE), the model induction toolkit can be armed with any internal coherence building blocks. The model induction capabilities of the proposed framework have been evaluated on the Blackwater River basin, Alabama, United States. The model configurations evolved through the model induction toolkit are consistent with the fieldwork investigations and previously reported research findings.

The exponential growth in the number of complex datasets every year requires\nmore enhancement in machine learning methods to provide robust and accurate\ndata classification. Lately, deep learning approaches have achieved surpassing\nresults in comparison to previous machine learning algorithms. However, finding\nthe suitable structure for these models has been a challenge for researchers.\nThis paper introduces Random Multimodel Deep Learning (RMDL): a new ensemble,\ndeep learning approach for classification. RMDL solves the problem of finding\nthe best deep learning structure and architecture while simultaneously\nimproving robustness and accuracy through ensembles of deep learning\narchitectures. In short, RMDL trains multiple randomly generated models of Deep\nNeural Network (DNN), Convolutional Neural Network (CNN) and Recurrent Neural\nNetwork (RNN) in parallel and combines their results to produce better result\nof any of those models individually. In this paper, we describe RMDL model and\ncompare the results for image and text classification as well as face\nrecognition. We used MNIST and CIFAR-10 datasets as ground truth datasets for\nimage classification and WOS, Reuters, IMDB, and 20newsgroup datasets for text\nclassification. Lastly, we used ORL dataset to compare the model performance on\nface recognition task.\n

The 2010-2011 Canterbury Earthquake sequence (CES) led to unprecedented building damage in the Canterbury region, New Zealand. Commercial and residential buildings were significantly affected. Due to New Zealand’s unique insurance setting, around 80% of the losses were covered by insurance (Bevere & Balz, 2012; King et al., 2014). The Insurance Council of New Zealand (ICNZ) estimated the total economic losses to be more than NZ$40 billion, with the Earthquake Commission (EQC) and private insurers covering NZ$10 billion and NZ$21 billion of the losses, respectively (ICNZ, 2021). As a result of the CES and the 2016 Kaikoura earthquake, EQC’s Natural Disaster Fund was depleted (EQC, 2022). This highlighted the need for improved tools enabling damage and loss analysis for natural hazards.This research project used residential building claims collected by EQC following the CES to develop a rapid seismic loss prediction model for residential buildings in Christchurch. Geographic information systems (GIS) tools, data science techniques, and machine learning (ML) were used for the model development. Before the training of the ML model, the claims data was enriched with additional information from external data sources. The seismic demand, building characteristics, soil conditions, and information about the liquefaction occurrence were added to the claims data. Once merged and pre-processed, the aggregated data was used to train ML models based on the main events in the CES. Emphasis was put on the interpretability and explainability of the model. The ML model delivered valuable insights related to the most important features contributing to losses. Those insights are aligned with engineering knowledge and observations from previous studies, confirming the potential of using ML for disaster loss prediction and management. Care was also put into the retrainability of the model to ensure that any new data from future earthquake events can rapidly be added to the model. 

Machine learning-assisted design guidelines and performance prediction of CMOS-compatible metal oxide-based resistive switching memory devices

Enhancing forensic investigations: Identifying bloodstains on various substrates through ATR-FTIR spectroscopy combined with machine learning algorithms

Background Most prognostic stratification tools in acute myocardial infarction (AMI) have been derived from populations including both women and men and including only a small proportion of women. Purpose Using supervised machine learning (ML), we examined the performance of 2 models assessing 5-year mortality after AMI, one derived from a population of women only and one from a population of men only, to determine whether sex-specific models improved outcome prediction. Methods This cohort study used the French registry on acute ST-elevation and non-ST-elevation myocardial infarction (FAST-MI) 2010 and 2015 surveys. This multicentric registry led in more than 200 French hospitals, enrolled all consecutive patients with acute myocardial infarction during a 1-month recruitment period. Our analysis included all men and women presenting STEMI who underwent invasive coronary angiography (ICA). To build and validate the models, the data set were split with a 70%/30% ratio into training and testing sets. The primary outcome was 5-year all-cause mortality. Then, 52 clinical, laboratory, ECG, echocardiographic, and ICA parameters were evaluated for feature selection using Boruta algorithm. Different supervised machine learning algorithms, including random forest (RF), were assessed for model building, and their performance were compared in women and men. To compare prediction accuracy according to sex, we evaluate the performance of the women-based ML model in men from the same cohort. Results 1,189 consecutive women and 3,685 men with STEMI (mean age 61±13 and 69±15 years, respectively) were recruited; 12% of men and 20% of women experienced 5-year all-cause mortality. Using Boruta algorithm, the 10 most important variables for prediction were selected (Figure 1). For women-based ML model building, the RF algorithm exhibited the best performance to predict mortality with an area under the receiver-operating characteristic curve (ROC-AUC) of 0.82 (95% CI: 0.77 – 0.88), an area under the precision-recall curve (PR-AUC) of 0.59 (95% CI: 0.54 – 0.64); and a F1-score of 0.58. The women-based ML model exhibited lower performance in men (ROC-AUC: 0.78; PR-AUC: 0.43; Figure 2). Conversely, the men-based model exhibited better accuracy in men than in women. Conclusion In a large multicentric cohort of STEMI patients, a women-based ML-model exhibited a good accuracy to predict 5-year all-cause mortality in women, but a drop of accuracy when applied to men. In mirror, the accuracy of the men-based model was lower in women. This suggests that sex-specific models might be superior to general models to predict mortality after AMI.Features selection and ML model buildingML model performance according to sex

When bioprocess engineering meets machine learning: A survey from the perspective of automated bioprocess development

Cardiovascular disease is a major global health problem affecting people around the globe. Although modern medicine has contributed in providing significant data to help mitigate cardiovascular disease, however, there is still needs to provide an ideal solution that will help in detecting early cardiovascular diseases. As a result, existing researchers have proposed several hybrid cardiovascular disease detection techniques using both machine learning and deep learning approaches. However, most solution requires improvement especially in term of accuracy detection. In this paper, a tri-hybrid Naïve Bayes classification algorithm for accuracy prediction of early cardiovascular disease detection is developed. In the developed algorithm, the strength of the conventional Naïve Bayes classification algorithm is improved with that of decision tree approach to achieve better accuracy. To further enhance its accuracy, Neural Network (NN) procedure is later incorporated into the improved Naïve Bayes classification algorithm. Implementation of the developed tri-hybrid algorithm is carried out on Waikato Environment for Knowledge Analysis (WEKA) and evaluated using dataset from the kaggle website that contain instances of 1025 and 14 attributes. Experimental results based on confusion matrix for performance evaluation indicates the developed tri-hybrid Naïve Bayes algorithm has achieved high classification accuracy and recall rates of 98.54 and 99% respectively when compared with that of the benchmarked schemes. The performance of the proposed solution deployed can help cardiologist make better prediction in the diagnosis of the heart disease. Although several machine learning algorithms as seen in the literatures are trained on historical data, these algorithms may not be able to accurately predict the risk of heart disease in patients who are exposed to new or emerging risk factors. Further research is to experiments a wider range of algorithms with a build in model that can detect early cardiovascular diseases as well improve accuracy.