Content is a user-designed form of information, for example, observation, perception, or review. This type of information is more relevant to users, as they can relate it to their experience. The research problem is to identify the credibility and the percentage of credibility as well. Assessment of such content is important to convey the right understanding of the information. Different techniques are used for content analysis, such as voting the content, Machine Learning Techniques, and manual assessment to evaluate the content and the quality of information. In this research article, content analysis is performed by collecting the Movie Review dataset from Kaggle. Features are extracted and the most relevant features are shortlisted for experimentation. The effect of these features is analyzed by using base regression algorithms, such as Linear Regression, Lasso Regression, Ridge Regression, and Decision Tree. The contribution of the research is designing a heterogeneous ensemble regression algorithm for content credibility score assessment, which combines the above baseline methods. Moreover, these factors are also toned down to obtain the values closer to Gradient Descent minimum. Different forms of Error Loss, such as Mean Absolute Error, Mean Squared Error, LogCosh, Huber, and Jacobian, and the performance is optimized by introducing the balancing bias. The accuracy of the algorithm is compared with induvial regression algorithms and ensemble regression separately; this accuracy is 96.29%.

Awareness about the features of the internet, easy access to data using mobile, and affordable data facilities have caused a lot of traffic on the internet. Digitization came with a lot of opportunities and challenges as well. One of the important advantages of digitization is paperless transactions, and transparency in payment, while data privacy, fake news, and cyber-attacks are the evolving challenges. The extensive use of social media networks and e-commerce websites has caused a lot of user-generated information, misinformation, and disinformation on the Internet. The quality of information depends upon various stages (of information) like generation of information, medium of propagation, and consumption of information. Content being user-generated, information needs a quality assessment before consumption. The loss of information is also necessary to be examined by applying the machine learning approach as the volume of content is extremely huge. This research work focuses on novel multinomial classification (based on multinoulli distribution) techniques to determine the quality of the information in the given content. To evaluate the information content a single algorithm with some processing is not sufficient and various approaches are necessary to evaluate the quality of content. We propose a novel approach to calculate the bias, for which the Machine Learning model will be fitted appropriately to classify the content correctly. As an empirical study, rotten tomatoes’ movie review data set is used to apply the classification techniques. The accuracy of the system is evaluated using the ROC curve, confusion matrix, and MAP.

Indian bay leaf is a crucial spice in Indian cuisine. However, its quality and authenticity are often compromised. To address this, we introduce The Digital Indian Bay leaf dataset, a comprehensive collection of high-resolution 5696 images capturing diverse bay leaf samples under controlled conditions. The dataset encompasses variations in leaf conditions such as fresh leaf, dried leaf and diseased prone leaf. The dataset is meticulously curated to support research in condition analysis and machine learning applications for leaf quality assessment. To ensure data diversity, each category includes a wide range of images captured under controlled conditions with varying lighting, background, and leaf orientation. By providing a standardized and accessible resource, this dataset aims to accelerate research in this domain and contribute to the improvement of the Indian spice industry.

Dryingfish is a simple and economical way to process the catch. It creates a profitable business for coastal communities by providing a market for their catches, even during periods of abundance. It's a traditional method to preserve fish, especially valuable in regions where fresh fish isn't readily available or affordable throughout the year. This dataset provides a rich resource of 8290 images specifically designed for machine learning applications. It focuses on the five most popular types of dried seafood in India: prawns (shrimp), small anchovies (tingali), golden anchovies (mandeli), mackerel (bangada), and Bombay duck (bombil). To ensure high-quality data for machine learning applications for Identification and classification of different dried fish varieties, the dataset features a diverse set of images in singles and in bulk for each category. The dataset utilizes standardized lighting, background, and object pose for optimal machine learning performance. This rich dataset empowers researchers and data scientists to leverage machine learning for various applications in the Indian dried fish industry.Overall, the Dried Fish Dataset for Indian Seafood aims to leverage machine learning to improve the standardization, quality control, safety, and efficiency of the Indian dried fish industry.

In today's world, stress has become an undoubtedly severe problem that affects people's health. Stress can modify a person's behavior, ideas, and feelings in addition to having an impact on mental health. Unchecked stress can contribute to chronic illnesses including high blood pressure, diabetes, and obesity. Early stress detection promotes a healthy lifestyle in society. This work demonstrates a deep learning-based method for identifying stress from facial expressions and speech signals.An image dataset formed by collecting images from the web is used to construct and train the model Convolution Neural Network (CNN), which then divides the images into two categories: stressed and normal. Recurrent Neural Network (RNN), which is used to categorize speech signals into stressed and normal categories based on the features extracted by the MFCC (Mel Frequency Cepstral Coefficient), is thought to perform better on sequential data since it maintains the past results to determine the final output.

One of the global issues that matters most right now is healthcare. The primary cause of death globally is brain stroke. A valuable contribution to medicine is the early prediction and identification of brain stroke events. Numerous factors, including BMI (body mass index), age, sex, family history, gender, smoking status, hypertension, and so on, are linked to brain stroke deaths. While forecasting heart illness has received a great deal of interest in the medical community, predicting a brain stroke has received less attention. This study's primary goal is to evaluate various previously published research publications and select the most effective machine learning methods for brain stroke prediction for our next projects. It was shown that mortality rate and functional outcomes are the expected outcomes for the majority of the study work done after analyzing the various machine learning techniques used for stroke predictions and after accounting for the previously published studies. The techniques that were used most commonly were LR, DTC, RFC, SVM, KNN.