Agriculture in Africa: the emerging role of artificial intelligence.

This chapter critically considers the application of artificial intelligence (AI) to agriculture in Africa. It contends that, while African countries can utilise AI to address agricultural challenges, realising the full potential of AI in agriculture requires the judicious adaptation of pervasive AI technologies to serve African interests. Africa's young, vibrant population along with the movement of people, goods and services around the continent, promoted under the African Union's (AU) Agenda 2063 provide a fecund platform for AI-driven agricultural transformation. This is pivotal because of the multilayered agricultural paradoxes on the continent. For instance, Africa is endowed with an abundance of uncultivated arable land and diverse agro-ecological zones, from rain-forest vegetation to dry and arid vegetation, which engender the growth of wide-ranging food and cash crops, yet it suffers an alarming increase in food insecurity. An AU, United Nations (UN) Economic Commission for Africa (UNECA) and Food and Agriculture Organisation of the UN (FAO) Report on Food Security and Nutrition in Africa confirmed that 281.6 million people on the continent, comprising one-fifth of the population, faced hunger in 2020; 346.4 million Africans suffered from severe food insecurity while 452 million suffered from moderate food insecurity in the same year

An orchestrator for networked control systems and its application to collision avoidance in multiple mobile robots.

Networked Control System (NCS) consists of controlled distributed nodes while an Orchestrator functions as a central coordinator for controlling the distributed tasks. The NCSs have challenges of coordination and right execution sequencing of operations. This paper proposes a framework named Controlled Orchestrator (COrch) for coordinating and sequencing the tasks of NCSs. An experiment was performed with three robotic vehicles that are considered as individual control system. Furthermore, the proposed orchestrator COrch decided the sequencing of operations of the robots while performing obstacle avoidance task for spatially distributed robots in parallel. COrch is used to control this task by utilizing the concept of Remote Method Invocation (RMI) and multithreading. RMI is used to prepare the software for controlling the robots at remote end while multithreading is used to perform parallel and synchronize execution of multiple robots. The remote end software generates signals for sequential, parallel and hybrid mode execution

Monitoring carbon emissions using deep learning and statistical process control: a strategy for impact assessment of governments' carbon reduction policies.

Across the globe, governments are developing policies and strategies to reduce carbon emissions to address climate change. Monitoring the impact of governments' carbon reduction policies can significantly enhance our ability to combat climate change and meet emissions reduction targets. One promising area in this regard is the role of artificial intelligence (AI) in carbon reduction policy and strategy monitoring. While researchers have explored applications of AI on data from various sources, including sensors, satellites, and social media, to identify areas for carbon emissions reduction, AI applications in tracking the effect of governments' carbon reduction plans have been limited. This study presents an AI framework based on long short-term memory (LSTM) and statistical process control (SPC) for the monitoring of variations in carbon emissions, using UK annual CO2 emission (per capita) data, covering a period between 1750 and 2021. This paper used LSTM to develop a surrogate model for the UK's carbon emissions characteristics and behaviours. As observed in our experiments, LSTM has better predictive abilities than ARIMA, Exponential Smoothing and feedforward artificial neural networks (ANN) in predicting CO2 emissions on a yearly prediction horizon. Using the deviation of the recorded emission data from the surrogate process, the variations and trends in these behaviours are then analysed using SPC, specifically Shewhart individual/moving range control charts. The result shows several assignable variations between the mid-1990s and 2021, which correlate with some notable UK government commitments to lower carbon emissions within this period. The framework presented in this paper can help identify periods of significant deviations from a country's normal CO2 emissions, which can potentially result from the government's carbon reduction policies or activities that can alter the amount of CO2 emissions

An artificial intelligence based quorum system for the improvement of the lifespan of sensor networks.

Artificial Intelligence-based Quorum systems are used to solve the energy crisis in real-time wireless sensor networks. They tend to improve the coverage, connectivity, latency, and lifespan of the networks where millions of sensor nodes need to be deployed in a smart grid system. The reality is that sensors may consume more power and reduce the lifetime of the network. This paper proposes a quorum-based grid system where the number of sensors in the quorum is increased without actually increasing quorums themselves, leading to improvements in throughput and latency by 14.23%. The proposed artificial intelligence scheme reduces the network latency due to an increase in time slots over conventional algorithms previously proposed. Secondly, energy consumption is reduced by weighted load balancing, improving the network’s actual lifespan. Our experimental results show that the coverage rate is increased on an average of 11% over the conventional Coverage Contribution Area (CCA), Partial Coverage with Learning Automata (PCLA), and Probabilistic Coverage Protocol (PCP) protocols respectively

Sentiment computation of UK-originated Covid-19 vaccine Tweets: a chronological analysis and news effect.

This study aimed to analyse public sentiments of UK-originated tweets about COVID-19 vaccines using six chronological data periods between January and December 2021. The dates are based on six BBC news reports about the most significant developments in the three main vaccines administered in the UK - Pfizer-BioNTech, Moderna, and Oxford-AstraZeneca. Each data period spans seven days, starting from the news report. The study employed the Bidirectional Encoder Representations from Transformers (BERT) model to analyse the sentiments in the 4,172 extracted tweets. The BERT model adopts the transformer architecture and uses the 'Masked Language Model' and 'Next Sentence Prediction'. The results show that the overall sentiments for all three vaccines were negative across all six periods, with Moderna having the least negative tweets and the highest percentage of positive tweets overall, while AstraZeneca attracted the most negative tweets. However, for all the considered periods, period 3 (23 -29 May 2021) received the least negative and the most positive tweets, following the BBC report – COVID - Pfizer and AstraZeneca jabs work against Indian variant, despite reports of blood clot cases associated with AstraZeneca in the same period. Periods 5 to 6, where there was no breaking news relating to COVID Vaccines, had no significant changes. We, therefore, conclude that the BBC News reports on COVID Vaccines significantly impacted public sentiments regarding the COVID-19 Vaccines

Maintaining privacy for a recommender system diagnosis using blockchain and deep learning.

The healthcare sector has been revolutionized by Blockchain and AI technologies. Artificial intelligence uses algorithms, recommender systems, decision-making abilities, and big data to display a patient's health records using blockchain. Healthcare professionals can make use of Blockchain to display a patient's medical records with a secured medical diagnostic process. Traditionally, data owners have been hesitant to share medical and personal information due to concerns about privacy and trustworthiness. Using Blockchain technology, this paper presents an innovative model for integrating healthcare data sharing into a recommender diagnostic computer system. Using the model, medical records can be secured, controlled, authenticated, and kept confidential. In this paper, researchers propose a framework for using the Ethereum Blockchain and x-rays as a mechanism for access control, establishing hierarchical identities, and using pre-processing and deep learning to diagnose COVID-19. Along with solving the challenges associated with centralized access control systems, this mechanism also ensures data transparency and traceability, which will allow for efficient diagnosis and secure data sharing

A Unified Latent Variable Model for Contrastive Opinion Mining

Ebuka IBEKE, Chenghua LIN , Adam WYNER, Mohamad Hardyman BARAW

Sentiment computation of UK-originated COVID-19 vaccine tweets: a chronological analysis and news effect

This study aimed to analyse public sentiments of UK-originated tweets related to COVID-19 vaccines, and it applied six chronological time periods, between January and December 2021. The dates were related to six BBC news reports about the most significant developments in the three main vaccines that were being administered in the UK at the time: Pfizer-BioNTech, Moderna, and Oxford-AstraZeneca. Each time period spanned seven days, starting from the day of the news report. The study employed the bidirectional encoder representations from transformers (BERT) model to analyse the sentiments in 4172 extracted tweets. The BERT model adopts the transformer architecture and uses masked language and next sentence prediction models. The results showed that the overall sentiments for all three vaccines were negative across all six periods, with Moderna having the least negative tweets and the highest percentage of positive tweets overall while AstraZeneca attracted the most negative tweets. However, for all the considered time periods, Period 3 (23–29 May 2021) received the least negative and the most positive tweets, following the related BBC report—’COVID: Pfizer and AstraZeneca jabs work against Indian variant’—despite reports of blood clots associated with AstraZeneca during the same time period. Time periods 5 and 6 had no breaking news related to COVID vaccines, and they reflected no significant changes. We, therefore, concluded that the BBC news reports on COVID vaccines significantly impacted public sentiments regarding the COVID-19 vaccines

Fintech applications on banking stability using big data of an emerging economy.

The rapid growth and development of financial technological advancement (Fintech) services and innovations have attracted the attention of scholars who are now on a quest to analyse their impact on the banking sector. This study conducts several kinds of analyses to measure the effect of the fintech era on the stability of the Chinese banking sector. It uses Big Data and performs Pearson correlation and regression analysis on the fintech era's transition period to measure the impact of several explanatory variables— institutional regulation, government stability, bank credit to deposit ratio, and economic growth— on the outcome variables, which includes Nonperforming loans (NPLs) and its numerical measurement in relation to the mean score of the Big Data (Z-score). This study uses yearly Big Data from 1995-2018 and revealed that compared to the first wave of the fintech era, the second wave helped in the reduction of NPLs and the enhancement of financial stability in China. This study concludes that in the second wave of the fintech era, the explanatory variables mentioned above had a positive impact on NPLs and banking stability. This work helps comprehend fintech development in modern society and the importance of its disruptive forces in developing and developed countries

An archetypal determination of mobile cloud computing for emergency applications using decision tree algorithm.

Numerous users are experiencing unsafe communications due to the growth of big network mediums, where no node communication is detected in emergency scenarios. Many people find it difficult to communicate in emergency situations as a result of such communications. In this paper, a mobile cloud computing procedure is implemented in the suggested technique in order to prevent such circumstances, and to make the data transmission process more effective. An analytical framework that addresses five significant minimization and maximization objective functions is used to develop the projected model. Additionally, all mobile cloud computing nodes are designed with strong security, ensuring that all the resources are allocated appropriately. In order to isolate all the active functions, the analytical framework is coupled with a machine learning method known as Decision Tree. The suggested approach benefits society because all cloud nodes can extend their assistance in times of need at an affordable operating and maintenance cost. The efficacy of the proposed approach is tested in five scenarios, and the results of each scenario show that it is significantly more effective than current case studies on an average of 86%