Integration of artificial intelligence (AI) techniques in wireless infrastructure, real-time collection, and processing of end-user devices is now in high demand. It is now superlative to use AI to detect and predict pandemics of a colossal nature. The Coronavirus disease 2019 (COVID-19) pandemic, which originated in Wuhan China, has had disastrous effects on the global community and has overburdened advanced healthcare systems throughout the world. Globally; over 4,063,525 confirmed cases and 282,244 deaths have been recorded as of 11th May 2020, according to the European Centre for Disease Prevention and Control agency. However, the current rapid and exponential rise in the number of patients has necessitated efficient and quick prediction of the possible outcome of an infected patient for appropriate treatment using AI techniques. This paper proposes a fine-tuned Random Forest model boosted by the AdaBoost algorithm. The model uses the COVID-19 patient's geographical, travel, health, and demographic data to predict the severity of the case and the possible outcome, recovery, or death. The model has an accuracy of 94% and a F1 Score of 0.86 on the dataset used. The data analysis reveals a positive correlation between patients' gender and deaths, and also indicates that the majority of patients are aged between 20 and 70 years.

/pdf/covid-19-patient-health-prediction-using-boosted-random-48ol7rni4n.pdf

COVID-19 Patient Health Prediction Using Boosted Random Forest Algorithm

A Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization.

Agriculture plays a vital role in the economic growth of any country. With the increase of population, frequent changes in climatic conditions and limited resources, it becomes a challenging task to fulfil the food requirement of the present population. Precision agriculture also known as smart farming have emerged as an innovative tool to address current challenges in agricultural sustainability. The mechanism that drives this cutting edge technology is machine learning (ML). It gives the machine ability to learn without being explicitly programmed. ML together with IoT (Internet of Things) enabled farm machinery are key components of the next agriculture revolution. In this article, authors present a systematic review of ML applications in the field of agriculture. The areas that are focused are prediction of soil parameters such as organic carbon and moisture content, crop yield prediction, disease and weed detection in crops and species detection. ML with computer vision are reviewed for the classification of a different set of crop images in order to monitor the crop quality and yield assessment. This approach can be integrated for enhanced livestock production by predicting fertility patterns, diagnosing eating disorders, cattle behaviour based on ML models using data collected by collar sensors, etc. Intelligent irrigation which includes drip irrigation and intelligent harvesting techniques are also reviewed that reduces human labour to a great extent. This article demonstrates how knowledge-based agriculture can improve the sustainable productivity and quality of the product.

/pdf/machine-learning-applications-for-precision-agriculture-a-595xsgydlw.pdf

Machine Learning Applications for Precision Agriculture: A Comprehensive Review

With the increase of COVID-19 cases worldwide, an effective way is required to diagnose COVID-19 patients. The primary problem in diagnosing COVID-19 patients is the shortage and reliability of testing kits, due to the quick spread of the virus, medical practitioners are facing difficulty identifying the positive cases. The second real-world problem is to share the data among the hospitals globally while keeping in view the privacy concerns of the organizations. Building a collaborative model and preserving privacy are major concerns for training a global deep learning model. This paper proposes a framework that collects a small amount of data from different sources (various hospitals) and trains a global deep learning model using blockchain based federated learning. Blockchain technology authenticates the data and federated learning trains the model globally while preserving the privacy of the organization. First, we propose a data normalization technique that deals with the heterogeneity of data as the data is gathered from different hospitals having different kinds of CT scanners. Secondly, we use Capsule Network-based segmentation and classification to detect COVID-19 patients. Thirdly, we design a method that can collaboratively train a global model using blockchain technology with federated learning while preserving privacy. Additionally, we collected real-life COVID-19 patients data, which is, open to the research community. The proposed framework can utilize up-to-date data which improves the recognition of computed tomography (CT) images. Finally, our results demonstrate a better performance to detect COVID-19 patients.

Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imaging

Novel coronavirus (COVID-19) outbreak, has raised a calamitous situation all over the world and has become one of the most acute and severe ailments in the past hundred years. The prevalence rate of COVID-19 is rapidly rising every day throughout the globe. Although no vaccines for this pandemic have been discovered yet, deep learning techniques proved themselves to be a powerful tool in the arsenal used by clinicians for the automatic diagnosis of COVID-19. This paper aims to overview the recently developed systems based on deep learning techniques using different medical imaging modalities like Computer Tomography (CT) and X-ray. This review specifically discusses the systems developed for COVID-19 diagnosis using deep learning techniques and provides insights on well-known data sets used to train these networks. It also highlights the data partitioning techniques and various performance measures developed by researchers in this field. A taxonomy is drawn to categorize the recent works for proper insight. Finally, we conclude by addressing the challenges associated with the use of deep learning methods for COVID-19 detection and probable future trends in this research area. The aim of this paper is to facilitate experts (medical or otherwise) and technicians in understanding the ways deep learning techniques are used in this regard and how they can be potentially further utilized to combat the outbreak of COVID-19.

/pdf/a-review-on-deep-learning-techniques-for-the-diagnosis-of-xgo92nemgm.pdf

A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)

Coronavirus (COVID-19) is a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The spread of COVID-19 seems to have a detrimental effect on the global economy and health. A positive chest X-ray of infected patients is a crucial step in the battle against COVID-19. Early results suggest that abnormalities exist in chest X-rays of patients suggestive of COVID-19. This has led to the introduction of a variety of deep learning systems and studies have shown that the accuracy of COVID-19 patient detection through the use of chest X-rays is strongly optimistic. Deep learning networks like convolutional neural networks (CNNs) need a substantial amount of training data. Because the outbreak is recent, it is difficult to gather a significant number of radiographic images in such a short time. Therefore, in this research, we present a method to generate synthetic chest X-ray (CXR) images by developing an Auxiliary Classifier Generative Adversarial Network (ACGAN) based model called CovidGAN. In addition, we demonstrate that the synthetic images produced from CovidGAN can be utilized to enhance the performance of CNN for COVID-19 detection. Classification using CNN alone yielded 85% accuracy. By adding synthetic images produced by CovidGAN,the accuracy increased to 95%. We hope this method will speed up COVID-19 detection and lead to more robust systems of radiology.

CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection

https://research.edgehill.ac.uk/ws/files/29279545/Final_Manuscript_Agr.pdf

An optimized dense convolutional neural network model for disease recognition and classification in corn leaf

The onset of the COVID-19 pandemic has brought the mental health of people under risk. Social counselling has gained remarkable significance in this environment. Unlike general goal-oriented dialogues, a conversation between a patient and a therapist is considerably implicit, though the objective of the conversation is quite apparent. In such a case, understanding the intent of the patient is imperative in providing effective counselling in therapy sessions, and the same applies to a dialogue system as well. In this work, we take forward a small but an important step in the development of an automated dialogue system for mental-health counselling. We develop a novel dataset, named HOPE, to provide a platform for the dialogue-act classification in counselling conversations. We identify the requirement of such conversation and propose twelve domain-specific dialogue-act (DAC) labels. We collect 12.9K utterances from publicly-available counselling session videos on YouTube, extract their transcripts, clean, and annotate them with DAC labels. Further, we propose SPARTA, a transformer-based architecture with a novel speaker- and time-aware contextual learning for the dialogue-act classification. Our evaluation shows convincing performance over several baselines, achieving state-of-the-art on HOPE. We also supplement our experiments with extensive empirical and qualitative analyses of SPARTA.

Speaker and Time-aware Joint Contextual Learning for Dialogue-act Classification in Counselling Conversations.

Bloom taxonomy is a common paradigm for categorizing educational learning objectives into three learning levels: cognitive, affective, and psychomotor. For the optimization of educational programs, it is crucial to design course learning outcomes (CLOs) according to the different cognitive levels of Bloom Taxonomy. Usually, administrators of the institutions manually complete the tedious work of mapping CLOs and examination questions to Bloom taxonomy levels. To address this issue, we propose a transformer-based model named BloomNet that captures linguistic as well semantic information to classify the course learning outcomes (CLOs). We compare BloomNet with a diverse set of basic as well as strong baselines and we observe that our model performs better than all the experimented baselines. Further, we also test the generalization capability of BloomNet by evaluating it on different distributions which our model does not encounter during training and we observe that our model is less susceptible to distribution shift compared to the other considered models. We support our findings by performing extensive result analysis. In ablation study we observe that on explicitly encapsulating the linguistic information along with semantic information improves the model on IID (independent and identically distributed) performance as well as OOD (out-of-distribution) generalization capability.

BloomNet: A Robust Transformer based model for Bloom's Learning Outcome Classification.

The recent emergence of ChatGPT has brought a revolutionary change in the landscape of NLP. Although ChatGPT has consistently shown impressive performance on English benchmarks, its exact capabilities on most other languages remain largely unknown. To better understand ChatGPT's capabilities on Arabic, we present a large-scale evaluation of the model on a broad range of Arabic NLP tasks. Namely, we evaluate ChatGPT on 32 diverse natural language understanding and generation tasks on over 60 different datasets. To the best of our knowledge, our work offers the first performance analysis of ChatGPT on Arabic NLP at such a massive scale. Our results show that, despite its success on English benchmarks, ChatGPT trained in-context (few-shot) is consistently outperformed by much smaller dedicated models finetuned on Arabic. These results suggest that there is significant place for improvement for instruction-tuned LLMs such as ChatGPT.

Abdul Waheed

Papers

CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection

An optimized dense convolutional neural network model for disease recognition and classification in corn leaf

Speaker and Time-aware Joint Contextual Learning for Dialogue-act Classification in Counselling Conversations.

BloomNet: A Robust Transformer based model for Bloom's Learning Outcome Classification.

GPTAraEval: A Comprehensive Evaluation of ChatGPT on Arabic NLP