The Efficacy of Deep Learning-Based Mixed Model for Speech Emotion Recognition

Human speech indirectly represents the mental state or emotion of others. The use of Artificial Intelligence (AI)-based techniques may bring revolution in this modern era by recognizing emotion from speech. In this study, we introduced a robust method for emotion recognition from human speech using a well-performed preprocessing technique together with the deep learning-based mixed model consisting of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). About 2800 audio files were extracted from the Toronto emotional speech set (TESS) database for this study. A high pass and Savitzky Golay Filter have been used to obtain noise-free as well as smooth audio data. A total of seven types of emotions; Angry, Disgust, Fear, Happy, Neutral, Pleasant-surprise, and Sad were used in this study. Energy, Fundamental frequency, and Mel Frequency Cepstral Coefficient (MFCC) have been used to extract the emotion features, and these features resulted in 97.5% accuracy in the mixed LSTM+CNN model. This mixed model is found to be performed better than the usual state-of-the-art models in emotion recognition from speech. It also indicates that this mixed model could be effectively utilized in advanced research dealing with sound processing

Human Age Estimation Using Deep Learning from Gait Data

Identifying people’s ages and events by the use of gait information is a popular issue in our daily applications. The most popular application is health, security, entertainment and charging. A variety of algorithms for data mining and deep learning have been proposed. Many different technologies may be used to keep track of people’s ages and behaviors. Existing approaches and technologies are limited by their performance, as well as their privacy and deployment costs. For example CCTV or Kinect sensor technology constitutes a privacy offense and most people do not want to make pictures or videos when they are working every day. The inertial sensor-based gait data collection is a recent addition to the gait analysis field. We have identified the age of people in this paper from an inertial sensor-data. We obtained the gait data from the University of Osaka. Convolution Neural Network (CNN) and LSTM-Based Convolution Neural Network (LSTM-CNN) are two deep learning algorithms that have been used to predict people’s ages. The accuracy of age prediction via CNN is around 71.45%, while it is around 65.53% via CNN-LSTM, according to the experimental results. ISBN för värdpublikation: 978-3-030-82268-2; 978-3-030-82269-9</p

Gender Classification from Inertial Sensor-Based Gait Dataset

The identification of people’s gender and events in our everyday applications by means of gait knowledge is becoming important. Security, safety, entertainment, and billing are examples of such applications. Many technologies could also be used to monitor people’s gender and activities. Existing solutions and applications are subject to the privacy and the implementation costs and the accuracy they have achieved. For instance, CCTV or Kinect sensor technology for people is a violation of privacy, since most people don’t want to make their photos or videos during their daily work. A new addition to the gait analysis field is the inertial sensor-based gait dataset. Therefore, in this paper, we have classified people’s gender from an inertial sensor-based gait dataset, collected from Osaka University. Four machine learning algorithms: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Bagging, and Boosting have been applied to identify people’s gender. Further, we have extracted 104 useful features from the raw data. After feature selection, the experimental outcome exhibits the accuracy of gender identification via the Bagging stands at around 87.858%, while it is about 86.09% via SVM. This will in turn form the basis to support human wellbeing by using gait knowledge.ISBN för värdpublikation: 978-3-030-68153-1, 978-3-030-68154-8</p

IoT based Smart System to Support Agricultural Parameters : A Case Study

Now-a-days, the natural irrigation system is under pressure due to the growing water shortages, which are mainly caused by population growth and climate change. Therefore, the control of water resources to increase the allocation of retained water is very important. It has been observed in the last two decades, especially in the Indian sub-continent, the change of climate affects the agricultural crops production significantly. However, the prediction of good harvests before harvesting, enables the farmers as well as the government officials to take appropriate measures of marketing and storage of crops. Some strategies for predicting and modelling crop yields have been developed, although they do not take into account the characteristics of climate, and they are empirical in nature. In the proposed system, a Cuckoo Search Algorithm has been developed, allowing the allocation of water for farming under any conditions. The various parameters such as temperature, turbidity, pH., moisture have been collected by using Internet of Things (IoT) platform, equipped with related sensors and wireless communication systems. In this IoT platform the sensor data have been displayed in the cloud environment by using ThingSpeak. The data received in the ThingSpeak used in the proposed Cuckoo Search Algorithm, allowing the selection of appropriate crops for particular soi

Monkeypox genome mutation analysis using a timeseries model based on long short-term memory.

Monkeypox is a double-stranded DNA virus with an envelope and is a member of the Poxviridae family's Orthopoxvirus genus. This virus can transmit from human to human through direct contact with respiratory secretions, infected animals and humans, or contaminated objects and causing mutations in the human body. In May 2022, several monkeypox affected cases were found in many countries. Because of its transmitting characteristics, on July 23, 2022, a nationwide public health emergency was proclaimed by WHO due to the monkeypox virus. This study analyzed the gene mutation rate that is collected from the most recent NCBI monkeypox dataset. The collected data is prepared to independently identify the nucleotide and codon mutation. Additionally, depending on the size and availability of the gene dataset, the computed mutation rate is split into three categories: Canada, Germany, and the rest of the world. In this study, the genome mutation rate of the monkeypox virus is predicted using a deep learning-based Long Short-Term Memory (LSTM) model and compared with Gated Recurrent Unit (GRU) model. The LSTM model shows "Root Mean Square Error" (RMSE) values of 0.09 and 0.08 for testing and training, respectively. Using this time series analysis method, the prospective mutation rate of the 50th patient has been predicted. Note that this is a new report on the monkeypox gene mutation. It is found that the nucleotide mutation rates are decreasing, and the balance between bi-directional rates are maintained

Impact on mental health due to COVID-19 pandemic: A cross-sectional study in Bangladesh

The government of Bangladesh has implemented the “Stay Home” policy following the WHO recommendation to resist the community transmission of Covid-19. As a result, the routine activities of all government, semi-government establishments, including educational institutions, are severely affected, and the country's economic growth becomes slowed down. To overcome such a situation, the relevant authorities have introduced the “Work from Home” policy for the employees and “Remote Education” for students. However, due to the persistence of multi-dimensional socio-economic problems, many employees and students face big challenges in performing their regular jobs while adopting such a policy. Consequently, enormous psychological anxiety has been developed for all people, including students, parents, employees, etc., and concurrently created severe changes in their behavior. This study aims to analyze the reasons for the behavioral changes of the employees, students, academic staff, and family members of different ages due to psychological anxiety, stress, or physical issues. A comprehensive online-based survey has been carried out on people working in various sectors in Bangladesh. A modified Apriori Algorithm has been used to sort out the associations between the causes and types of behavioral changes. Analyzed data revealed a massive human behavioral change in most participants. This finding indicates that the negligence of those significant human behavioral changes may cause a higher risk of creating psychological imbalance. Therefore, there is a need to have a solid understanding of the reasons for the behavioral changes and set up standard guidelines to maintain “Work from Home” in this Covid-19 situation to avoid psychological imbalance. Based on this study, some suggestions have been given for implementation by the government on an urgent basis