In the era of automated and digitalized information, advanced computer applications deal with a major part of the data that comprises audio-related information. Advancements in technology have ushered in a new era where cutting-edge devices can deliver comprehensive insights into audio content, leveraging sophisticated algorithms such such as Mel Frequency Cepstral Coefficients (MFCCs) and Short-Time Fourier Transform (STFT) to extract and provide pertinent information. Our study helps in not only efficient audio file management and audio file retrievals but also plays a vital role in security, the robotics industry, and investigations. Beyond its industrial applications, our model exhibits remarkable versatility in the corporate sector, particularly in tasks like siren sound detection and more. Embracing this capability holds the promise of catalyzing the development of advanced automated systems, paving the way for increased efficiency and safety across various corporate domains. The primary aim of our experiment is to focus on creating highly efficient audio classification models that can be seamlessly automated and deployed within the industrial sector, addressing critical needs for enhanced productivity and performance. Despite the dynamic nature of environmental sounds and the presence of noises, our presented audio classification model comes out to be efficient and accurate. The novelty of our research work reclines to compare two different audio datasets having similar characteristics and revolves around classifying the audio signals into several categories using various machine learning techniques and extracting MFCCs and STFTs features from the audio signals. We have also tested the results after and before the noise removal for analyzing the effect of the noise on the results including the precision, recall, specificity, and F1-score. Our experiment shows that the ANN model outperforms the other six audio models with the accuracy of 91.41% and 91.27% on respective datasets.

Liver diseases like fatty liver disease, chronic active hepatitis, and cirrhosis are the major cause of mortality in India. Alcohol consumption, inhalation of harmful toxic gases, improper consumption of contaminated pickles, drugs, and foods are the major cause of diseases in the liver. Diagnosis of liver disease needs high accuracy and precise results for predicting whether a person is suffering from liver disease or not. Major disastrous repercussions can be the result of minor errors in the diagnosis of liver diseases. The major goal of this paper is for the detection of liver disease at right time and helping the doctors and combating the increasing number of cases. In this paper, we implemented various machine learning techniques like logistic regression, KNN, XG-Boost, SVM, Gaussian NB, Random forest, Decision tree, Gradient Boosting, CatBoost, AdaBoost, and LightGBM on selected features from the dataset for predicting liver disease and it was found that Random Forest performed best among all the technique and gained high accuracy and performed outstandingly in all metric evaluations.

A program’s bug, fault, or mistake that results in unintended results is known as a software defect or fault. Software flaws are programming errors due to mistakes in the requirements, architecture, or source code. Finding and fixing bugs as soon as they arise is a crucial goal of software development that can be achieved in various ways. So, selecting a handful of optimal subsets of features from any dataset is a prime approach. Indirectly, the classification performance can be improved through the selection of features. A novel approach to feature selection (FS) has been developed, which incorporates the Golden Jackal Optimization (GJO) algorithm, a meta-heuristic optimization technique that draws on the hunting tactics of golden jackals. Combining this algorithm with four classifiers, namely K-Nearest Neighbor, Decision Tree, Quadrative Discriminant Analysis, and Naive Bayes, will aid in selecting a subset of relevant features from software fault prediction datasets. To evaluate the accuracy of this algorithm, we will compare its performance with other feature selection methods such as FSDE (Differential Evolution), FSPSO (Particle Swarm Optimization), FSGA (Genetic Algorithm), and FSACO (Ant Colony Optimization). The result that we got from FSGJO is great for almost all the cases. For many of the results, FSGJO has given higher classification accuracy. By utilizing the Friedman and Holm tests, to determine statistical significance, the suggested strategy has been verified and found to be superior to prior methods in selecting an optimal set of attributes.

These days immense abstraction of data yields and collected in every instance of time. So to analyze them is the toughest task to do. This immense volume of data has been generated from unlike sources like health care, social media, business applications, manufacturing industries and many more. HealthCare plays a pivotal role in Big Data. Spotting and safeguarding of the diseases at a primitive stage are very much crucial. Heart disease specifically implies the condition of the heart that contracts or obstructs blood vessels which result in pain in chest and heart attack. This paper emphasizes on the diagnosis of heart diseases at a primitive stage so that it will lead to a successful cure of the diseases. In this paper, frequent item mining is used for filtering the attributes and diverse data mining classification method like Decision tree classification, Naive Bayes classification, Support Vector Machine classification, and k-NN classification are used for determination and safeguard of the diseases at an early stage so that it can be treated and preventable.