Speech has long been recognized as the main form of communication between people and computers. Technology made it possible for humans and computers to interact through the development of human-computer interfaces. Although speech emotion recognition systems have advanced quickly in recent years, many difficulties have also arisen during this development, such as the inability to recognize emotions that lead to depression and mood swings, which can be used by therapists to track their patients' moods. It is necessary to create a model that detects the many emotions that contribute to depression to improve doctor-patient relationships and increase the effectiveness of spoken emotion recognition models. In this paper, over 2000 audio files were compiled. We curated a local dataset that accounts for 60% of the total dataset acquired, 40% of the dataset used was obtained from RAVDESS. To extract the proper vocal features, we employed the Mel-Frequency Cepstral Coefficients (MFCC), Zero Crossing Rate (ZCR), and Root Mean Square (RMS). The Tensorflow CONV1D with Relu activation function and several sequential layers was used to build the model. The batch size was 64 and the epoch size was 50. Seven emotional states, including anger, disgust, sadness, happiness, surprise, fear, and neutrality were extracted. The accuracy of the confusion matrix, which served as the performance metrics, is 96%.

Big data analytics is a significant way of improving telecommunications operators’ Quality of Service (QoS) and improve their businesses. This study was aimed at determining the impact of big data analysis in telecommunication industry and also to arouse their interest towards big data. The study used four domestic telecommunication companies as case study, namely MTN, GLO, 9Mobile and AIRTEL. The study uses survey method of data collection by using structured questionnaire. Sixteen network nodes were used in the study from four clusters. Analysis of Variance (ANOVA), Cronbach’s Alpha Coefficient using Smart – PLS, DBSCAN Algorithm was employed using google python as data analysis method. The study revealed that there is high traffic data from the telecoms and this is as a result of various user activities like internet usage and highly dense network clusters, which happened at a specific time or within a particular duration at a specified location. The findings led to a conclusion that the big data analytics can be considered as a solution to directional small cells in millimeter wave cellular networks to improve QoS. The study recommends that big data should be given due consideration and be used in making national budget and policy formulation. The study further recommends that tertiary institutions should create big data analytics training to create a platform for training and development of manpower mainly on big data analytics as is obtainable in developing and IT-driven countries.