Loughborough University Spontaneous Expression Database and baseline results for automatic emotion recognition

The study of facial expressions in humans dates back to the 19th century and the study of the emotions that these facial expressions portray dates back even further. It is a natural part of non-verbal communication for humans to pass across messages using facial expressions either consciously or subconsciously, it is also routine for other humans to recognize these facial expressions and understand or deduce the underlying emotions which they represent. Over two decades ago and following technological advances, particularly in the area of image processing, research began into the use of machines for the recognition of facial expressions from images with the aim of inferring the corresponding emotion. Given a previously unknown test sample, the supervised learning problem is to accurately determine the facial expression class to which the test sample belongs using the knowledge of the known class memberships of each image from a set of training images. The solution to this problem building an effective classifier to recognize the facial expression is hinged on the availability of representative training data. To date, much of the research in the area of Facial Expression Recognition (FER) is still based on posed (acted) facial expression databases, which are often exaggerated and therefore not representative of real life affective displays, as such there is a need for more publically accessible spontaneous databases that are well labelled. This thesis therefore reports on the development of the newly collected Loughborough University Spontaneous Expression Database (LUSED); designed to bolster the development of new recognition systems and to provide a benchmark for researchers to compare results with more natural expression classes than most existing databases. To collect the database, an experiment was set up where volunteers were discretely videotaped while they watched a selection of emotion inducing video clips. The utility of the new LUSED dataset is validated using both traditional and more recent pattern recognition techniques; (1) baseline results are presented using the combination of Principal Component Analysis (PCA), Fisher Linear Discriminant Analysis (FLDA) and their kernel variants Kernel Principal Component Analysis (KPCA), Kernel Fisher Discriminant Analysis (KFDA) with a Nearest Neighbour-based classifier. These results are compared to the performance of an existing natural expression database Natural Visible and Infrared Expression (NVIE) database. A scheme for the recognition of encrypted facial expression images is also presented. (2) Benchmark results are presented by combining PCA, FLDA, KPCA and KFDA with a Sparse Representation-based Classifier (SRC). A maximum accuracy of 68% was obtained recognizing five expression classes, which is comparatively better than the known maximum for a natural database; around 70% (from recognizing only three classes) obtained from NVIE

A DEEP LEARNING MODEL FOR ELECTRICITY DEMAND FORECASTING BASED ON A TROPICAL DATA

Electricity demand forecasting is a term used for prediction of users’ consumption on the grid ahead of actual demand. It is very important to all power stakeholders across levels. The power players employ electricity demand forecasting for sundry purposes. Moreover, the government’s policy on its market deregulation has greatly amplified its essence. Despite numerous studies on the subject using certain classical approaches, there exists an opportunity for exploration of more sophisticated methods such as the deep learning (DL) techniques. Successful researches about DL applications to com¬puter vision, speech recognition, and acoustic computing problems are motivation. However, such researches are not sufficiently exploited for electricity demand forecasting using DL methods. In this paper, we considered specific DL techniques (LSTM, CNN, and MLP) to short-term load fore¬casting problems, using tropical institutional data obtained from a Transmission Company. We also test how accurate are predictions across the techniques. Our results relatively revealed models appropriateness for the problem

AN OVERVIEW OF DEEP LEARNING TECHNIQUES FOR SHORT-TERM ELECTRICITY LOAD FORECASTING

This paper presents an overview of some Deep Learning (DL) techniques applicable to forecasting electricity consumptions, especially in the short-term horizon. The paper introduced key parts of four DL architectures including the RNN, LSTM, CNN and SAE, which are recently adopted in implementing Short-term (electricity) Load Forecasting problems. It further presented a model approach for solving such problems. The eventual implication of the study is to present an insightful direction about concepts of the DL methods for forecasting electricity loads in the short-term period, especially to a potential researcher in quest of solving similar problems

A Framework For A Blood Seeker - Donor Matching System

The work designed, implemented and evaluated the blood seeker-donor (BSD) matching system. The system comprises of a backend server and a mobile application that serves as the main interaction interface between the users and the system. This is with the view to providing a computerized system that enables blood seekers and blood donation centers in the Nigerian geographic space to find and communicate with prospective blood donors, in a fast and efficient manner. The system connects the users using the blood type compatibility, relative location between the users, and other blood donation related attributes specified by the users. The backend service of the system was implemented using the Firebase platform. The mobile application was implemented using Java programming language on the Android platform. The results of the evaluation showed that; on a scale of 1 – 10, the system has a mean score of 7.02 for its effectiveness. The mean score for ease-of-use is 7.17 and the score for learnability is 7.39. The work concluded that geo-location technologies, through the use of mobile applications, can provide an easier, faster and efficient means of finding and communicating with prospective blood donors

TEXTURE MODELING AND SIMULATION FOR SYNTHETIC PALM VEIN IMAGE GENERATION SYSTEM

Unavailability of large-scale palm vein databases due to their intrusiveness have posed challenges in exploring this technology for large-scale applications. Hence, this research modelled and generated synthetic palm vein images from only a couple of initial samples using statistical features. Variations were introduced to the three optimized statistical features (S5; the original images were employed as training images and the best variation in the first experiment  as training images, S4; the best variation in the first experiment  as training images while the original images were used as testing images, S3; mean vectors, covariance matrices and correlation coefficient, S2; mean vectors and covariance matrices, S1; mean vectors, Non-Synthetic; acquired image) which were used to generate synthetic palm vein images employing statistical and Genetic Algorithm (GA) approaches and were evaluated based on Equal Error Rate (EER), Average Recognition Accuracy (ARA) and Average Recognition Time (ART). The results obtained from the experiment showed that EERs were 0.22, 0.51, 0.58 and 4.36 for S3, S2, S1 and NS respectively. S3 had superior ARA (99.83%) compared with S2 (99.77 %), S1 (99.70 %) and NS (98.33 %). The ARTs obtained were 84.97s, 75.55s, 84.04s and 681.74s for S1, S2, S3 and NS respectively with S2 (75.55s) having significantly least value. Furthermore, EER, ARA and ART for S4 were 0.43, 99.00%, and 12.13s, respectively while the corresponding values for S5 were 1.43, 97.50%, and 680.13s, respectively. The research outcome justifies the extraction of mean vectors, covariance matrices and correlation coefficient

Recognition of West African Indigenous Fruits using a Convolutional Neural Network Model

The. Fruit recognition involves the extraction and processing of relevant features from fruit images in order to deduce the categories of that fruit. Due to its importance to human health and sustainability, various systems exist for recognition of fruits, although none exist for recognition of west Africa's indigenous fruits. This research developed a fruit recognition system using a convolutional neural network (CNN) based model. Five west Africa indigenous fruits were selected, while “images were directly used as input to CNN based model of (3 convolutional layers, 3 max pooling layers and 1 fully connected layer) for training and recognition without features extraction process. The study further presents a transfer learning on visual geometry group 16 and ResNet models for result comparison. Using the optimal training set, the proposed CNN based model produced a recognition rate of 96%

Sustainable generation of bioethanol from sugarcane wastes by Streptomyces coelicolor strain COB KF977550 isolated from a tropical estuary

The damaging effect and challenges associated with the use of fossil fuel is enormous and very costly. Biofuels could be obtained from plant biomass wastes which are known to be sources of environmental pollution and breeding grounds for vectors of diseases. Sugarcane bagasse was exploited as a renewable substrate for obtaining bioethanol using Streptomyces strain COB KF977550 as inoculum. Submerged aerobic batch fermentation was performed in flasks containing mineral salts medium supplemented with 5.0 g (w/v) sugarcane bagasse. Incubation was done in a shaker (150 rpm) at 30 oC for 21 days. Microbial growth was assessed by measurement of the optical density (O.D 600nm) at 3-day intervals. Fractional distillation was carried out in batch mode using a simple fractional distillation setup. Metabolic products were determined using GC-FID. Further analyses were performed using FTIR and GC-MS. The optical density of S.coelicolor strain COB KF977550 increased from 0.9 to 1.41. The GC-FID showed that 43.08 g/L ethanol was generated. Interestingly, the results showed the presence of diverse biochemicals released into the medium in addition to the main product ethanol. Ten carboxylic acids including formic acid, glycolic acid, tartaric acid, acetic acid, citric acid, oxalic acid, malic acid, lactic acid, n-valeric acid, and 3-hydroxybutyric acid were identified as biochemical organic acids by-products

Implementing decision support tool for low-back pain diagnosis and prediction based on the range of motions

Low-back pain (LBP) is a complex health problem requiring accurate diagnosis and effective treatment. However, the current decision support system (DSS) for LBP only considers the patient’s pain intensity and treatment suitability, which may not lead to optimal outcomes. This paper proposes a novel DSS that combines machine learning (ML) and expert input to classify LBP types and provide more reliable and personalized recommendations. We used an open-source dataset to train and test various ML models, including an ensemble model that combines multiple classifiers. We also performed data analysis and feature extraction to enhance the model’s predictive power. We developed a prototype tool to demonstrate the model’s performance and usability. Our results show that the ensemble model achieved the highest accuracy of 92.02%, followed by random forest (RF) (91.01%), multilayer perceptron (MP) (91.01%), and support vector machine (SVM) (87.88%). Our findings suggest that ML can help LBP specialists diagnose and treat LBP more effectively by learning from historical data and predicting LBP categories. Our DSS can potentially improve the quality of life for LBP patients and reduce the burden on the healthcare system