The first paper, "Biometrics Classification Using Audio Data and Machine Learning" by Pranit Kotkar, deals with the signal processing that is penetrating into the latest technological developments and emerging as a leading element of research. It is gaining immense traction in the virtual assistant/BPO industry for designing AI solutions. The paper focuses on the development of classification strategies based on the information pulled from voice data. The linear Support Vector Machine (SVM) and Random Forest (RF) classifiers render the best results for gender identification. The methodology that suited the requirements for age classification was Ridge Regression with costs, wherein the age groups were distributed among three classes. It was observed that the data imbalance fetched disappointing results, and it was concluded from the literature review that the Synthetic Minority Oversampling Technique (SMOTE) could potentially improve the output. Identity recognition involved open and closed classification, and the closed set delivered high returns. It was found that the size of the dataset was a limiting factor and stood in the way of securing higher accuracy. In the second paper, "Internet of Things and Machine Learning for Weather Prediction" the authors, Neethu Behanan, Rakshita Ramesh and Vijayalakshmi A, have performed complex models with satellite data and readings from weather stations. In recent times, modern technology has replaced these conventional techniques. However, obtaining accurate and precise weather predictions continues to remain extremely challenging. This work proposes an architecture to enable weather forecasting using a combination of two modern technologies - Machine Learning (ML) and Internet of Things (IoT). By making use of the concept of crowd sensing and Collaborative Internet of Things (C-IoT), the authors have proposed how large amount of useful information can be collected and then processed using ML algorithms to arrive at meaningful results that aid in weather prediction. This can help the IoT devices become more efficient in terms of energy consumption.

The third paper, "Weighing Prototype Simulation for Hogs with Web-Based Monitoring System Using Radio Frequency Identification" by Xena S Alcazaren, April Jay C Diolata and Noel P Sobejana, is a study about weighing the hogs in order to determine their growth and identifying them individually with the use of Radio Frequency Identification (RFID) sensor. The system would read the data from the prototype simulation of the project and would be saved in the database. The data gathered in the database would be viewed on the website as graphical information of all the hogs and also their profile information would be viewed on the website. The objectives of the study are focused on four aspects, namely, the development of the prototype that can identify the weight of the hogs using the RFID and capture the weight of the hogs using a weighing scale, development of the system that can monitor the hog's weight and record it in the database automatically, provide graphical information of the hog's weight on the website and capture the effectiveness of the weighing prototype simulation for hogs with web-based monitoring system using RFID in terms of functionality, reliability and usefulness from the evaluators.

The fourth paper, "Predicting Students' Performance in a Nigerian Polytechnic Institution Using Data Mining Techniques of Neural Network, Support Vector Machine and K-Nearest Neighbor" by Ogwueleka Francisca Nonyelum, Lydia Mohammed and Irhebhude Martins Ekata, investigates the performance of students in a Polytechnic, Department of Nigeria education. Different factors that may affect the performance of students were identified and classified. The factors considered include National Diploma (ND) grade, time elapsed before admission for Higher National Diploma (HND), age, marital status, gender, region of origin and Polytechnic attended for ND. The seven factors were categorized and used to develop the models for the analysis. The data was collected directly from the examination and record office of the department and analyzed using the WEKA data mining tool in order to determine which classification algorithm model performs best on the dataset. Predicting student's performance using data mining techniques is a vital angle which helps instructors in the learning and educating process, based on the performance of students in their ND program and based on certain factors from the student's registration forms and examination result. The learning management system generates huge sum of information that when processed could be used in improving the academic performance of the students. With the percentage accuracy of 95.8 for neural network, 97.7 for SVM and 83.1 for K-Nearest Neighbor (KNN), it goes to show that the factors used have a profound effect on the performance of the students.

The last paper, "Radio Frequency Identification-Based Short Message Service Patient Alert Healthcare System" is authored by Abraham Eseoghene Evwiekpaefe and Abimbola Hannah Ayangbayi. The present state of hospitals in Nigeria at large is worrisome. Normally, patients go to the hospital and fill out registration forms and face long queues before meeting a medical doctor for consultation. Patient registration system in most healthcare facilities is carried out using the traditional paper and pen which are kept in files assigned to patient's name. Also, the number of missed appointments in healthcare institutions in Nigeria causes more health challenges and hence the need for an IoT-based healthcare system to provide adequate and seamless care to patients. Hence, this paper developed a prototype RFID-based Short Message Service (SMS) system for prompt medical response. The programming language used in creating the system was Python version 3.7.0 with SQLite as the database. An SMS alert system was integrated into the system to remind patients of their appointments with the doctors at the hospital within a specified time and day through the mobile phone numbers provided during registration. A delivery report is received by the system administrator and the message is delivered almost immediately to the patient's mobile phone number.