Lexicon-based bot-aware public emotion mining and sentiment analysis of the Nigerian 2019 presidential election on Twitter

Online social networks have been widely engaged as rich potential platforms to predict election outcomes' in several countries of the world. The vast amount of readily-available data on such platforms, coupled with the emerging power of natural language processing algorithms and tools, have made it possible to mine and generate foresight into the possible directions of elections' outcome. In this paper, lexicon-based public emotion mining and sentiment analysis were conducted to predict win in the 2019 presidential election in Nigeria. 224,500 tweets, associated with the two most prominent political parties in Nigeria, People's Democratic Party (PDP) and All Progressive Congress (APC), and the two most prominent presidential candidates that represented these parties in the 2019 elections, Atiku Abubakar and Muhammadu Buhari, were collected between 9th October 2018 and 17th December 2018 via the Twitter's streaming API. tm and NRC libraries, defined in the 'R' integrated development environment, were used for data cleaning and preprocessing purposes. Botometer was introduced to detect the presence of automated bots in the preprocessed data while NRC Word Emotion Association Lexicon (EmoLex) was used to generate distributions of subjective public sentiments and emotions that surround the Nigerian 2019 presidential election. Emotions were grouped into eight categories (sadness, trust, anger, fear, joy, anticipation, disgust, surprise) while sentiments were grouped into two (negative and positive) based on Plutchik's emotion wheel. Results obtained indicate a higher positive and a lower negative sentiment for APC than was observed with PDP. Similarly, for the presidential aspirants, Atiku has a slightly higher positive and a slightly lower negative sentiment than was observed with Buhari. These results show that APC is the predicted winning party and Atiku as the most preferred winner of the 2019 presidential election. These predictions were corroborated by the actual election results as APC emerged as the winning party while Buhari and Atiku shared very close vote margin in the election. Hence, this research is an indication that twitter data can be appropriately used to predict election outcomes and other offline future events. Future research could investigate spatiotemporal dimensions of the prediction

Breathogenomics: A Computational Architecture for Screening, Early Diagnosis and Genotyping of Lung Cancer

The genome sequences of some genes have been implicated to carry various mutations that lead to the initiation and advancement of lung cancer. In addition, it has been scientifically established that anytime we breathe out, chemicals called Volatile Organic Compounds (VOCs) are released from the breath. Hundreds of such VOCs have been uniquely identified from samples of breathe collected from lung cancer patients, which make them viable as chemical biomarkers for lung cancer. Based on the foregoing scientific breakthroughs, we developed breathogenomics, a computational architecture for screening, early diagnosis and genotyping of lung cancer victims anchored on the analysis of exhaled breath and mutational profiles of genomic biomarkers. The architecture contains two important sub-modules. At the first sub-module, the exhaled breadths of smokers or persons that are at risk of lung cancer are collected and appropriate computational algorithms are employed to determine the presence of any of the VOC biomarkers. Next, a patient with any VOC biomarker in the exhaled breath proceeds to the second sub-module, which contains appropriate computational models for the detection of mutated genes. Once mutations are detected in any of the biomarker genes found in a given patient, such patient is recommended for targeted therapy to promptly curtail the progression of the mutations to advanced stages. The breathogenomics architecture serves as a generic template for the development of clinical equipment for breath and genomic based screening, early diagnosis and genotyping of lung cancer. In this paper, we report the preliminary result obtained from the prototype that we are currently developing based on the architecture. Constructing a lung cancer early diagnosis/screening system based on the prototype when fully developed will hopefully minimize the current spate of deaths as a result of late diagnosis of the disease

Rab-KAMS: A reproducible knowledge management system with visualization for preserving Rabbit Farming and Production Knowledge

The sudden rise in rural-to-urban migration has been a key challenge threatening food security and most especially the survival of Rabbit Farming and Production (RFP) in Sub-Saharan Africa. Currently, significant knowledge of RFP is going into extinction as evident by the drastic fall in commercial rabbit farming and production indices. Hence, the need for a system to proactively preserve RFP knowledge for future potential farmers cannot be overemphasized. To this end, knowledge archiving and management are key concepts of ensuring long-term digital storage of conceptual blueprints and specifications of systems, methods and frameworks with capacity for future updates while making such information readily accessible to relevant stakeholders on demand. Therefore, a reproducible Rabbit production' Knowledge Archiving and Management System (Rab-KAMS) is developed in this paper. A 3-staged approach was adopted to develop the Rab-KAMS. This include a knowledge gathering and conceptualization stage; a knowledge revision stage to validate the authenticity and relevance of the gathered knowledge for its intended purpose and a prototype design stage adopting the use of unified modelling language conceptual workflows, ontology graphs and frame system. For seamless accessibility and ubiquitous purposes, the design was implemented into a mobile application having interactive end-users' interfaces developed using XML and Java in Android 3.0.2 Studio development environment while adopting the V-shaped software development model. The qualitative evaluation results obtained for Rab-KAMS based on users' rating and reviews indicate a high level of acceptability and reliability by the users. It also indicates that relevant RFP knowledge were correctly captured and provided in a user-friendly manner. The developed Rab-KAMS could offer seamless acquisition, representation, organization and mining of new and existing verified knowledge about RFP and in turn contributing to food security

Algorithm for solutions of nonlinear equations of strongly monotone type and applications to convex minimization and variational inequality problems

Real-life problems are governed by equations which are nonlinear in nature. Nonlinear equations occur in modeling problems, such as minimizing costs in industries and minimizing risks in businesses. A technique which does not involve the assumption of existence of a real constant whose calculation is unclear is used to obtain a strong convergence result for nonlinear equations of (p, {\eta})-strongly monotone type, where {\eta} > 0, p > 1. An example is presented for the nonlinear equations of (p, {\eta})-strongly monotone type. As a consequence of the main result, the solutions of convex minimization and variational inequality problems are obtained. This solution has applications in other fields such as engineering, physics, biology, chemistry, economics, and game theory.Comment: 11 page

Formulation and Evaluation of Protein Bound Paclitaxel Nanoparticles for Injectable Suspension

Aims: The aim of present study is to develop Paclitaxel nanoparticles for injectable suspension, an anti-neoplastic drug formulation. Study design:  Mention the design of the study here. Place and Duration of Study: Sagar Institute of Research & Technology- Pharmacy, Ayodhya Bypass Road, Bhopal, between June 2017 and June2018. Methodology: The Human serum albumin (HSA) is the most abundant plasma protein in the human blood with a half-life of 19 days. It can reversibly bind hydrophobic drug substances, transport them in the body and release drugs at cell surface. The formulation is prepared by homogenization at high-pressure of paclitaxel in the presence of human serum albumin into a nanoparticle colloidal suspension. Paclitaxel nano particles have been stabilized by human albumin and maintain the average size of 100 nm Results: The particle size of the reconstituted solution is checked using the laser diffraction Technique the particle is White to yellow lyophilized after reconstitution the particle become homogeneous milky suspension without visible particulates. Reconstitution time NMT 25 minutes 20.45 sec. The assay was performed by HPLC and found to be 90.0% to 110.0% of label. The retention time of Paclitaxel peak obtained in sample corresponds to the respective standards obtained from standards the pH NLT 6.00 to NMT 8.00 Particle size NLT 100 nm – NMT 200 nm. Sterility test was comply as per USP <71> Conclusion: The nano-delivery systems could have the potential to be free of Cremophor EL and ethanol, enhance Paclitaxel solubility, improve Paclitaxel pharmacokinetic profiles in vivo, decrease its side effects, passively or actively target to tumor sites due to the EPR (Enhanced Permeability and Retention) effect and the use of targeting ligands, respectively, nanotechnology is a very active research area in both academic and industrial settings. Keywords: paclitaxel, Anti-cancer, nanotechnology, Injectable, Suspensio

A study of comparison of efficacy and side effects of intravenous paracetamol and intravenous diclofenac as a postoperative analgesic

BBackground: A postoperative patient suffers from pain the best relief of which is a clinician’s duty. Till date very few studies have been conducted for comparison of paracetamol and diclofenac as analgesics. As a result a comparative study between Paracetamol and Diclofenac was carried out. The aim of the study was to compare the efficacy and side effects of intravenous Paracetamol and intravenous Diclofenac in patients undergoing major abdominal open surgeries in obstetrics and gynaecology. The study was conducted to assess the postoperative visual analogue pain scores (VAS) and total analgesic requirement in the first 24 hours and also to study the total requirement of additional analgesics despite administration of either Paracetamol or Diclofenac in postoperative period.Methods: 100 patients satisfying the inclusion/exclusion criteria were recruited for the study. They were divided into two groups of 50 each. Group A was given IV Paracetamol 6 hourly for 48 hours starting 2 hours after surgery. Group B was given IV Diclofenac 8 hourly for 48 hours starting 2 hours after surgery. Patients were assessed for pain relief by visual analogue scale (VAS) of zero to ten after 6 hours, 12 hours, 24 hours and 48 hours of surgery by asking the patient to point the position on the 100 mm scale.Results: The results revealed that when we compared the VAS scores between the 2 Groups at different time intervals, it showed that at 24 hours and 48 hours VAS score in the Diclofenac Group was significantly less than the Paracetamol Group. The main side effects were nausea and vomiting in both the groups. There was more nausea and vomiting in Diclofenac group compared to Paracetamol group.Conclusions: It was concluded that at 24 hour and 48 hour pain reduction was more in the Diclofenac group as compared to Paracetamol group, but the side effects were more in the Diclofenac group compared to Paracetamol group

The Benefits and Challenges of the Gig Economy: Perspective of Gig Workers and Small Medium and Micro Enterprises (SMMEs) in South Africa

Digital work, otherwise referred to as 'gig' work, is heralded as a useful strategy that could help bridge the unemployment rate in South Africa by connecting job seekers and business organisations across the global spectrum. The purpose of this paper was to explore the benefits and challenges of the gig economy on SMMEs in South Africa. In this study, an interpretive research paradigm was followed to explore the benefits and challenges of the gig economy in the SMMEs in South Africa. Semi-structured focus group interviews were conducted with 20 participants, consisting of thirteen gig workers (n=13) and seven business organization employees (n=7). The data collected were thematically analysed with the aid of NVivo v12 software (QSR International Pty Ltd, 2015). The participants held the view that the gig economy can promote business growth and economic inclusion, and help organisations better manage their resources. While gig work offers some advantages, the participants highlighted concerns surrounding the lack of clear policy, occupational vulnerability, precarity, platform-based work, and the risks of gig work. The study suggests that gig work is critical to advancing the growth of Small, Medium, and Micro Enterprises (SMMEs) in South Africa

The Benefits and Challenges of the Gig Economy: Perspective of Gig Workers and Small Medium and Micro Enterprises (SMMEs) in South Africa

Digital work, otherwise referred to as 'gig' work, is heralded as a useful strategy that could help bridge the unemployment rate in South Africa by connecting job seekers and business organisations across the global spectrum. The purpose of this paper was to explore the benefits and challenges of the gig economy on SMMEs in South Africa. In this study, an interpretive research paradigm was followed to explore the benefits and challenges of the gig economy in the SMMEs in South Africa. Semi-structured focus group interviews were conducted with 20 participants, consisting of thirteen gig workers (n=13) and seven business organization employees (n=7). The data collected were thematically analysed with the aid of NVivo v12 software (QSR International Pty Ltd, 2015). The participants held the view that the gig economy can promote business growth and economic inclusion, and help organisations better manage their resources. While gig work offers some advantages, the participants highlighted concerns surrounding the lack of clear policy, occupational vulnerability, precarity, platform-based work, and the risks of gig work. The study suggests that gig work is critical to advancing the growth of Small, Medium, and Micro Enterprises (SMMEs) in South Africa

The Benefits and Challenges of the Gig Economy: Perspective of Gig Workers and Small Medium and Micro Enterprises (SMMEs) in South Africa

Digital work, otherwise referred to as 'gig' work, is heralded as a useful strategy that could help bridge the unemployment rate in South Africa by connecting job seekers and business organisations across the global spectrum. The purpose of this paper was to explore the benefits and challenges of the gig economy on SMMEs in South Africa. In this study, an interpretive research paradigm was followed to explore the benefits and challenges of the gig economy in the SMMEs in South Africa. Semi-structured focus group interviews were conducted with 20 participants, consisting of thirteen gig workers (n=13) and seven business organization employees (n=7). The data collected were thematically analysed with the aid of NVivo v12 software (QSR International Pty Ltd, 2015). The participants held the view that the gig economy can promote business growth and economic inclusion, and help organisations better manage their resources. While gig work offers some advantages, the participants highlighted concerns surrounding the lack of clear policy, occupational vulnerability, precarity, platform-based work, and the risks of gig work. The study suggests that gig work is critical to advancing the growth of Small, Medium, and Micro Enterprises (SMMEs) in South Africa

News Article Classification using Kolmogorov Complexity Distance Measure and Artificial Neural Network

News article classification is a recently growing area of interest in text classification because of its associated multiple matching categories. However, the weak reliability indices and ambiguities associated with state-of-the-art classifiers often employed make success in this domain very limited. Also, the high sensitivity and large disparity in performance results of classifiers to the varying nature of real-world datasets make the need for comparative evaluation inevitable. In this paper, the accuracy and computational time efficiency of the Kolmogorov Complexity Distance Measure (KCDM) and Artificial Neural Network (ANN) were experimentally evaluated for a prototype large dimensional news article classification problem. 2000 News articles from a dataset of 2225 British Broadcasting Corporation (BBC) news documents (including examples from sport, politics, entertainment, education and technology, and business) were used for categorical testing purposes. Porter’s algorithm was used for word stemming after tokenization and stop-words removal, and a Normalized Term Frequency–Inverse Document Frequency (NTF-IDF) technique was adopted for feature extraction. Experimental results revealed that ANN performs better in terms of accuracy while the KCDM produced better results than ANN in terms of computational time efficiency