This paper proposes a novel method for speech emotion recognition. Empirical mode decomposition (EMD) is applied in this paper for the extraction of emotional features from speeches, and a deep neural network (DNN) is used to classify speech emotions. This paper enhances the emotional components in speech signals by using EMD with acoustic feature Mel-Scale Frequency Cepstral Coefficients (MFCCs) to improve the recognition rates of emotions from speeches using the classifier DNN. In this paper, EMD is first used to decompose the speech signals, which contain emotional components into multiple intrinsic mode functions (IMFs), and then emotional features are derived from the IMFs and are calculated using MFCC. Then, the emotional features are used to train the DNN model. Finally, a trained model that could recognize the emotional signals is then used to identify emotions in speeches. Experimental results reveal that the proposed method is effective.

Drug recommendation systems are systems that have the capability to recommend drugs. On a daily basis, a huge amount of data is being generated by the patients. All this valuable data can be properly utilized to create a reliable drug recommendation system. In this paper, we recommend a system for drug recommendations. The main scope of our system is to predict the correct medication based on reviews and ratings. Our proposed system uses natural language processing techniques (NLP), recurrent neural networks (RNN), and cellular automata (CA). We also considered various metrics like precision, recall, accuracy, F1 score, and ROC curve as measures of our system’s performance. NLP techniques are being used for gathering useful information from patient data, and RNN is a machine learning methodology that works really well in analyzing textual data. The system considers various patient data attributes like age, gender, dosage, medical history, and symptoms in order to make appropriate predictions. The proposed system has the potential to help medical professionals make informed drug recommendations.

Placement is the process of connecting the selected candidate with the employer. Every student might have a dream of having a job offer when he or she is about to complete her course. All educational institutions aim at having their students well placed in good organizations. The reputation of any institution depends on the placement of its students. Hence, many institutions try hard to have a good placement cell. Classification using machine learning may be utilized to retrieve data from the student-databases. A prediction model that can foretell the eligibility of the students based on their academic and extracurricular achievements is proposed. Related data was collected from many institutions for which the placement-prediction is made. This paradigm is being weighed up with the existing algorithms, and findings have been made regarding the accuracy of predictions. It was found that the proposed algorithm performed significantly better and yielded good results.

E-learning (EL) has emerged as one of the most valuable means for continuing education around the world, especially in the aftermath of the global pandemic that included a variety of obstacles. Real-time online assessments have become a significant concern for educational organizations. Instances of fraudulent behavior during online exams (OEs) have created considerable challenges for exam invigilators, who are unable to identify and remove such dishonest behavior. In response to this significant issue, educational institutions have used a variety of manual procedures to alleviate the situation, but none of these measures have shown to be particularly innovative or effective. The current study presents a novel strategy for detecting fraudulent actions in real time during OEs that uses convolutional neural network (CNN) algorithms and image processing. The development model will be trained using the CK and CK++ datasets. The training procedure will use 80% of the selected dataset, with the remaining 20% used for model testing to confirm the model’s efficacy and generalization capacity. This project intends to revolutionize the monitoring and prevention of fraudulent actions during online tests by integrating CNN techniques and image processing. The use of CK and CK++ datasets, as well as an 80–20 split for training and testing, contributes to the study’s thorough and rigorous approach. Educational institutions can improve their assessment procedures and maintain the credibility of EL as a credible and equitable way of continuing education by successfully using this unique technique.

The need for reliable automated object detection and classification in the maritime sphere has increased significantly over the last decade. The increased use of unmanned marine vehicles necessitates the development and use of an autonomous object detection and classification systems that utilize onboard cameras and operate reliably, efficiently, and accurately without the need for human intervention. As the autonomous vehicle realm moves further away from active human supervision, the requirements for a detection and classification suite call for a higher level of accuracy in the classification models. This paper presents a comparative study using different classification models on a large maritime vessel image dataset. For this study, additional images were annotated using Roboflow, focusing on the types of subclasses that had the lowest detection performance, a previous work by Brown et al. (1). The present study uses a dataset of over 5,000 images. Using the enhanced set of image annotations, models were created in the cloud using Google Colab using YOLOv5, YOLOv7, and YOLOv8 as well as in AmazonWeb Services using Amazon Rekognition. The models were tuned and run for five runs of 150 epochs each to collect efficiency and performance metrics. Comparison of these metrics from the YOLO models yielded interesting improvements in classification accuracies for the subclasses that previously had lower scores, but at the expense of the overall model accuracy. Furthermore, training time efficiency was improved drastically using the newer YOLO APIs. In contrast, using Amazon Rekognition yielded superior accuracy results across the board, but at the expense of the lowest training efficiency.

Deforestation, illegal hunting, and forest fires are a few current issues that have an impact on the diversity and ecosystem of forests. To increase the biodiversity of species and ecosystems, it becomes imperative to preserve the forest. The conventional techniques employed to prevent these issues are costly, less effective, and insecure. The current systems are unreliable and use more energy. By utilizing an Internet of Things (IOT) system, this technology offers a more practical and economical method of continuously maintaining and monitoring the status of the forest. To guarantee excellent security, this system combines a number of sensors, alarms, cameras, lights, and microphones. It aids in reducing forest loss, animal trafficking, and forest fires. In the suggested system, sensors are used for monitoring, and cloud storage is used for data storage. Through the use of machine learning, the raspberry pi camera module significantly aids in the prevention of unlawful wildlife hunting as well as the detection and prevention of forest fires.

Air pollution is now one of the biggest environmental risks, which causes more than 6 million premature deaths each year from heart diseases, stroke, diabetes, respiratory disease, and so on. Protecting humans from the damage which is caused by air pollution is one of the major issues for the global community. The prediction of air pollution can be done by machine learning (ML) algorithms. ML combines statistics and computer science to maximize the prediction power. ML can be also used to predict the air quality index (AQI). The aim of this research is to develop a convolutional neural network (CNN) model to predict air quality from the unseen data set, which includes concentration of nitrogen dioxide (NO2), carbon monoxide (CO), and sulfur dioxide (SO2). The proposed system will be implemented in two steps; the first step will focus on data analysis and pre-processing, including filtering, feature extraction, constructing convolutional neural network layers, and optimizing the parameters of each layer, while the second step is used to evaluate its model accuracy. The output is predicted as AQI for the developed CNN model. The developed CNN model achieves a root-mean-square error of 13.4150 and a high accuracy of 86.585%. The overall model is implemented using MATLAB software.

Air pollution is now one of the biggest environmental risks, which causes more than 6 million premature deaths each year from heart diseases, stroke, diabetes, respiratory disease, and so on. Protecting humans from the damage which is caused by air pollution is one of the major issues for the global community. The prediction of air pollution can be done by machine learning (ML) algorithms. ML combines statistics and computer science to maximize the prediction power. ML can be also used to predict the air quality index (AQI). The aim of this research is to develop a convolutional neural network (CNN) model to predict air quality from the unseen data set, which includes concentration of nitrogen dioxide (NO2), carbon monoxide (CO), and sulfur dioxide (SO2). The proposed system will be implemented in two steps; the first step will focus on data analysis and pre-processing, including filtering, feature extraction, constructing convolutional neural network layers, and optimizing the parameters of each layer, while the second step is used to evaluate its model accuracy. The output is predicted as AQI for the developed CNN model. The developed CNN model achieves a root-mean-square error of 13.4150 and a high accuracy of 86.585%. The overall model is implemented using MATLAB software.