A convolutional neural network to classify American Sign Language fingerspelling from depth and colour images

Sign language is used by approximately 70 million1 people throughout the world, and an automatic tool for interpreting it could make a major impact on communication between those who use it and those who may not understand it. However, computer interpretation of sign language is very difficult given the variability in size, shape and position of the fingers or hands in an image. Hence, this paper explores the applicability of deep learning for interpreting sign language. The paper develops a convolutional neural network aimed at classifying fingerspelling images using both image intensity and depth data. The developed convolutional network is evaluated by applying it to the problem of finger spelling recognition for American Sign Language. The evaluation shows that the developed convolutional network performs better than previous studies and has precision of 82% and recall of 80%. Analysis of the confusion matrix from the evaluation reveals the underlying difficulties of classifying some particular signs which is discussed in the paper

Pruning neural networks using multi-armed bandits

The successful application of deep learning has led to increasing expectations of their use in embedded systems. This in turn, has created the need to find ways of reducing the size of neural networks. Decreasing the size of a neural network requires deciding which weights should be removed without compromising accuracy, which is analogous to the kind of problems addressed by multi-arm bandits. Hence, this paper explores the use of multi-armed bandits for reducing the number of parameters of a neural network. Different multi-armed bandit algorithms, namely e-greedy, win-stay, lose-shift, UCB1, KL-UCB, BayesUCB, UGapEb, Successive Rejects and Thompson sampling are evaluated and their performance compared to existing approaches. The results show that multi- armed bandit pruning methods, especially those based on UCB, outperform other pruning methods

Studies on Influence of Seasonality on Clinical Conditions of Small Ruminants in Ogbomoso Areas of Oyo State

Information on influence of seasonality on clinical conditions of small ruminants in Ogbomoso area of Oyo State has not been extensively documented. 10 years' worth of data on clinical cases in sheep and goats were analysed from records kept at the Veterinary Clinics from 1995- 2005 using simple descriptive, frequency and percentage distribution. The results showed that a total of eight hundred and eighty seven different cases were reported at the clinic between 1995-2005. 758 (85.46%) of Caprine and 129 (14.54%) of Ovine species cases were reported. Helminthosis occurred most (26.99%) in the early wet season while wounds occurred most on early dry season (20.87%). Non-infectious conditions such as dystocia (13.92%) and fractures (9.71%) were reported most on late wet season . Mange (5.83%), mastitis (7.77%), placenta retention (2.91%), sprain (5.34%) as well as prophylactic treatment (7.77%) were reported most in early dry season respectively. PPR were reported most in late dry season (9.59%). Other cases reported with values lower than 2.0% all year round were considered and these include ascitis, amputation, foot rot, milk fever, paralysis, pneumonia, rumen impaction, salmonellosis, skin burn, vulvitis, and tetanus. Information generated can be useful to Government agencies in strategic planning and guidelines for prevention and control of ruminant diseases.Key words: Seasonality, Clinical conditions, Small ruminant, Ogbomos

Optimizing deep learning networks using multi-armed bandits

Deep learning has gained significant attention recently following their successful use for applications such as computer vision, speech recognition, and natural language processing. These deep learning models are based on very large neural networks, which can require a significant amount of memory and hence limit the range of applications. Hence, this study explores methods for pruning deep learning models as a way of reducing their size, and computational time, but without sacrificing their accuracy. A literature review was carried out, revealing existing approaches for pruning, their strengths, and weaknesses. A key issue emerging from this review is that there is a trade-off between removing a weight or neuron and the potential reduction in accuracy. Thus, this study develops new algorithms for pruning that utilize a framework, known as a multi-armed bandit, which has been successfully applied in applications where there is a need to learn which option to select given the outcome of trials. There are several different multi-arm bandit methods, and these have been used to develop new algorithms including those based on the following types of multi-arm bandits: (i) Epsilon-Greedy (ii) Upper Confidence Bounds (UCB) (iii) Thompson Sampling and (iv) Exponential Weight Algorithm for Exploration and Exploitation (EXP3). The algorithms were implemented in Python and a comprehensive empirical evaluation of their performance was carried out in comparison to both the original neural network models and existing algorithms for pruning. The existing methods that are compared include: Random Pruning, Greedy Pruning, Optimal Brain Damage (OBD) and Optimal Brain Surgeon (OBS). The thesis also includes an empirical comparison with a number of other learning methods such as KNN, decision trees, SVM, Naïve Bayes, LDA, QDA, logistic regression, Gaussian process classifier, kernel ridge regression, LASSO regression, linear regression, Bayesian Ridge regression, boosting, bagging and random forests. The results on the data sets show that some of the new methods (i) generalize better than the original model and most of the other methods such as KNN and decision trees (ii) outperform OBS and OBD in terms of reduction in size, generalization, and computational time (iii) outperform the greedy algorithm in terms of accuracy

The extraction of proteins from the neem seed (Indica azadirachta A. Juss)

Techniques for maximizing the extraction of protein from the neem seed (Indica azadirachta A. Juss) were investigated. Extractants used were sodium chloride and sodium sulphate solutions of varying concentration and pH. Maximum extractions of 17.86 g of extractable protein was obtained from 1 kg of crude protein, using 0.5 M NaCl solution at pH of 7.5. All the extracts were devoid of the usual neem smell and its bitter taste. As the pH increased from 7.0 to 7.5 there was steady increase in the quantity of extractable protein by sodium chloride solutions. However a decrease in the quantities of extractableproteins was observed at pH of 8.0 to 10 with sodium chloride solution. As the pH increased from 7.0 to 7.5 on the other hand, the quantities of the extract with sodium sulphate solutions decreased. While at pH of 8.0 to 9.5 the quantity of extractable protein increased, and the least quantity was obtained at pH of 10. 0.5 M NaCl at pH of 7.5 was found to be a better extractant for neem seed protein

The micro-minerals composition in serum of rabbits (Oryctolagus cuniculus) infected with Trypanosoma congolense

Sixteen (16) rabbits aged between 6 to 12 months were infected with fresh stock of Trypanosoma congolense (Gboko strain) intravenously at the rate of 1.0 × 106ml. Animals were classified into two groups; groups A were infected, while group B served as uninfected controls. Samples from the infected and the uninfected controls showed a significant increase in the levels of sodium (Na+), calcium (Ca2+), phosphate (PO4 2-) and blood urea nitrogen (BUN) (P < 0.05) and a significant decline in the levels of potassium (K+) and bicarbonate (HCO3 2-) (P < 0.05). Therefore, the alterations in the compositions of these micro-minerals in the serum of rabbits may suggest that, they could have a role in the pathogenesis of trypanosomosis due to T. congolense infection.Key words: Rabbits, micro-minerals, Trypanosoma congolense, pathogenesis

Anthelmintic efficacy of pawpaw (Carica papaya) seeds in commercial layers

The anthelmintic efficacy of the aqueous and crude extract of Carica papaya seeds was studied in 40 Isa Brown commercial layers infected naturally with nematodes. They were randomly divided into 4 groups: A, B, C and D with 10 birds per group. Group A birds were untreated, while groups B, C and D were treated orally with proprietary anthelmintic (piperazine 322 mg/kg body weight/day), powdery (300 mg/day/bird) and aqueous (1:10 ml water required/day) extracts of C. papaya, respectively. Two weeks after treatment, blood and faecal samples were collected to evaluate for hematological values and faecal egg counts respectively. The results of this study showed that the powdered and aqueous extract of C. papaya after its administration, produced a significant increase (P < 0.001) in packed cell volume, red blood cells, haemoglobin concentration, lymphocyte counts and significant decrease in eosinophil counts. The faecal egg counts also showed a remarkable and significant reduction in  the levels of the identified helminths. The reduction in faecal egg counts was more pronounced with the aqueous extract than crude extract administered. The effects of the C. papaya seed extracts in this study therefore showed that C. papaya extracts can serve as a source of chemical substance for use in the development of effective anthelmintic agents.Key words: Carica papaya, anthelmintic, piperazine, helminths, haematology

Vaccine Storage and Handling Practices among routine immunization service providers in a metropolitan city of North-Central Nigeria

Background: The efficacy of vaccines can be compromised by faulty transport, storage, and handling. This study was conducted to assess the knowledge and practice of vaccine storage and handling among Primary Health Care Workers (PHCWs) offering routine immunization (RI) services in static health facilities in Ilorin metropolis, North-central Nigeria.Methodology: It was a descriptive cross-sectional study carried out among 457 Primary Health Care Workers (PHCWs) in 2 Local Government Authorities in Kwara State, north central Nigeria, using multi stage sampling technique. The research instruments were pretested self-administered questionnaire and observational checklist. The data generated were analyzed using EPI-INFO version 3.5.1 software package. Level of significance was predetermined at p-value of less than 0.05 at 95% confidence interval.Results: About half of the respondents (52.1%) knew the optimal vaccine storage temperature, 35.4% knew that freezing is harmful to certain vaccines. Although, 67.8% were aware of the 'shake test', only 48.4% of them knew how to conduct it. Up to 367 (80.3%) acknowledged that heat is harmful to vaccines. Even though, 267 (58.4%) knew the vaccine vial monitor (VVM) stages, only 248 (45.3%) could interpret the VVM correctly. About 30% of the health facilities (HFs) had adequate vaccine storage equipments while less than one third (28.6%) refrigerators were used exclusively for vaccine storage. However, functioning thermometers were present in all the refrigerators devoted to vaccine storage.Conclusions: Vaccine storage and handling practices among PHCWs providing routine immunization (RI) services in the study area was still sub-optimal. There is need for periodic on the job training and supportive supervision of health workers by middle cadre immunization officers in the local government to improve on the vaccine storage and handling practices of RI service providers.Keywords: Routine immunization, knowledge, vaccine handling, Nigeria, practice

Recent Advances and Developments in Phase Change Materials in High-Temperature Building Envelopes: A Review of Solutions and Challenges

The use of phase change materials (PCMs) has become an increasingly common way to reduce a building’s energy usage when added to the building envelope. This developing technology has demonstrated improvements in thermal comfort and energy efficiency, making it a viable building energy solution. The current study intends to provide a comprehensive review of the published studies on the utilization of PCMs in various constructions of energy-efficient roofs, walls, and ceilings. The research question holds massive potential to unlock pioneering solutions for maximizing the usefulness of PCMs in reducing cooling demands, especially in challenging high-temperature environments. Several issues with PCMs have been revealed, the most significant of which is their reduced effectiveness during the day due to high summer temperatures, preventing them from crystallizing at night. However, this review investigates how PCMs can delay the peak temperature time, reducing the number of hours during which the indoor temperature exceeds the thermal comfort range. Additionally, the utilization of PCMs can improve the building’s energy efficiency by mitigating the need for cooling systems during peak hours. Thus, selecting the right PCM for high temperatures is both critical and challenging. Insulation density, specific heat, and thermal conductivity all play a role in heat transfer under extreme conditions. This study introduces several quantification techniques and paves the way for future advancements to accommodate practical and technical solutions related to PCM usage in building materials