Modeling the Impact of Covid-19 on the Farm Produce Availability and Pricing in India

Aim/Purpose: This paper aims to analyze the availability and pricing of perishable farm produce before and during the lockdown restrictions imposed due to Covid-19. This paper also proposes machine learning and deep learning models to help the farmers decide on an appropriate market to sell their farm produce and get a fair price for their product. Background: Developing countries like India have regulated agricultural markets governed by country-specific protective laws like the Essential Commodities Act and the Agricultural Produce Market Committee (APMC) Act. These regulations restrict the sale of agricultural produce to a predefined set of local markets. Covid-19 pandemic led to a lockdown during the first half of 2020 which resulted in supply disruption and demand-supply mismatch of agricultural commodities at these local markets. These demand-supply dynamics led to disruptions in the pricing of the farm produce leading to a lower price realization for farmers. Hence it is essential to analyze the impact of this disruption on the pricing of farm produce at a granular level. Moreover, the farmers need a tool that guides them with the most suitable market/city/town to sell their farm produce to get a fair price. Methodology: One hundred and fifty thousand samples from the agricultural dataset, released by the Government of India, were used to perform statistical analysis and identify the supply disruptions as well as price disruptions of perishable agricultural produce. In addition, more than seventeen thousand samples were used to implement and train machine learning and deep learning models that can predict and guide the farmers about the appropriate market to sell their farm produce. In essence, the paper uses descriptive analytics to analyze the impact of COVID-19 on agricultural produce pricing. The paper explores the usage of prescriptive analytics to recommend an appropriate market to sell agricultural produce. Contribution: Five machine learning models based on Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Random Forest, and Gradient Boosting, and three deep learning models based on Artificial Neural Networks were implemented. The performance of these models was compared using metrics like Precision, Recall, Accuracy, and F1-Score. Findings: Among the five classification models, the Gradient Boosting classifier was the optimal classifier that achieved precision, recall, accuracy, and F1 score of 99%. Out of the three deep learning models, the Adam optimizer-based deep neural network achieved precision, recall, accuracy, and F1 score of 99%. Recommendations for Practitioners: Gradient boosting technique and Adam-based deep learning model should be the preferred choice for analyzing agricultural pricing-related problems. Recommendation for Researchers: Ensemble learning techniques like Random Forest and Gradient boosting perform better than non-Ensemble classification techniques. Hyperparameter tuning is an essential step in developing these models and it improves the performance of the model. Impact on Society: Statistical analysis of the data revealed the true nature of demand and supply and price disruption. This analysis helps to assess the revenue impact borne by the farmers due to Covid-19. The machine learning and deep learning models help the farmers to get a better price for their crops. Though the da-taset used in this paper is related to India, the outcome of this research work applies to many developing countries that have similar regulated markets. Hence farmers from developing countries across the world can benefit from the outcome of this research work. Future Research: The machine learning and deep learning models were implemented and tested for markets in and around Bangalore. The model can be expanded to cover other markets within India.

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Modelling on Car-Sharing Serial Prediction Based on Machine Learning and Deep Learning

Complexity ◽

10.1155/2022/8843000 ◽

2022 ◽

Vol 2022 ◽

pp. 1-20

Author(s):

Nihad Brahimi ◽

Huaping Zhang ◽

Lin Dai ◽

Jianzi Zhang

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Research Work ◽

Gradient Boosting ◽

Support Vector ◽

Learning Models ◽

Car Sharing ◽

K Nearest Neighbors ◽

Extreme Gradient Boosting ◽

On The Road

The car-sharing system is a popular rental model for cars in shared use. It has become particularly attractive due to its flexibility; that is, the car can be rented and returned anywhere within one of the authorized parking slots. The main objective of this research work is to predict the car usage in parking stations and to investigate the factors that help to improve the prediction. Thus, new strategies can be designed to make more cars on the road and fewer in the parking stations. To achieve that, various machine learning models, namely vector autoregression (VAR), support vector regression (SVR), eXtreme gradient boosting (XGBoost), k-nearest neighbors (kNN), and deep learning models specifically long short-time memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN), CNN-LSTM, and multilayer perceptron (MLP), were performed on different kinds of features. These features include the past usage levels, Chongqing’s environmental conditions, and temporal information. After comparing the obtained results using different metrics, we found that CNN-LSTM outperformed other methods to predict the future car usage. Meanwhile, the model using all the different feature categories results in the most precise prediction than any of the models using one feature category at a time

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Training confounder-free deep learning models for medical applications

Nature Communications ◽

10.1038/s41467-020-19784-9 ◽

2020 ◽

Vol 11 (1) ◽

Author(s):

Qingyu Zhao ◽

Ehsan Adeli ◽

Kilian M. Pohl

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Bone Age ◽

Magnetic Resonance Images ◽

Data Sets ◽

Large Set ◽

Learning Models ◽

End To End ◽

The Impact ◽

The Relationship

AbstractThe presence of confounding effects (or biases) is one of the most critical challenges in using deep learning to advance discovery in medical imaging studies. Confounders affect the relationship between input data (e.g., brain MRIs) and output variables (e.g., diagnosis). Improper modeling of those relationships often results in spurious and biased associations. Traditional machine learning and statistical models minimize the impact of confounders by, for example, matching data sets, stratifying data, or residualizing imaging measurements. Alternative strategies are needed for state-of-the-art deep learning models that use end-to-end training to automatically extract informative features from large set of images. In this article, we introduce an end-to-end approach for deriving features invariant to confounding factors while accounting for intrinsic correlations between the confounder(s) and prediction outcome. The method does so by exploiting concepts from traditional statistical methods and recent fair machine learning schemes. We evaluate the method on predicting the diagnosis of HIV solely from Magnetic Resonance Images (MRIs), identifying morphological sex differences in adolescence from those of the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA), and determining the bone age from X-ray images of children. The results show that our method can accurately predict while reducing biases associated with confounders. The code is available at https://github.com/qingyuzhao/br-net.

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A Comparative Analysis of Novel Deep Learning and Ensemble Learning Models to Predict the Allergenicity of Food Proteins

Foods ◽

10.3390/foods10040809 ◽

2021 ◽

Vol 10 (4) ◽

pp. 809

Author(s):

Liyang Wang ◽

Dantong Niu ◽

Xinjie Zhao ◽

Xiaoya Wang ◽

Mengzhen Hao ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Ensemble Learning ◽

Food Allergen ◽

Gradient Boosting ◽

Learning Models ◽

Food Proteins ◽

Deep Model ◽

Allergen Identification ◽

Machine Learning Models

Traditional food allergen identification mainly relies on in vivo and in vitro experiments, which often needs a long period and high cost. The artificial intelligence (AI)-driven rapid food allergen identification method has solved the above mentioned some drawbacks and is becoming an efficient auxiliary tool. Aiming to overcome the limitations of lower accuracy of traditional machine learning models in predicting the allergenicity of food proteins, this work proposed to introduce deep learning model—transformer with self-attention mechanism, ensemble learning models (representative as Light Gradient Boosting Machine (LightGBM) eXtreme Gradient Boosting (XGBoost)) to solve the problem. In order to highlight the superiority of the proposed novel method, the study also selected various commonly used machine learning models as the baseline classifiers. The results of 5-fold cross-validation showed that the area under the receiver operating characteristic curve (AUC) of the deep model was the highest (0.9578), which was better than the ensemble learning and baseline algorithms. But the deep model need to be pre-trained, and the training time is the longest. By comparing the characteristics of the transformer model and boosting models, it can be analyzed that, each model has its own advantage, which provides novel clues and inspiration for the rapid prediction of food allergens in the future.

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The Future of PHM Could be Tiny under Cloud: Exploring Potential Application Patterns of TinyML in PHM Scenarios

Annual Conference of the PHM Society ◽

10.36001/phmconf.2021.v13i1.3054 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Xingyu Zhou ◽

Zhuangwei Kang ◽

Robert Canady ◽

Shunxing Bao ◽

Daniel Allen Balasubramanian ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Remaining Useful Life ◽

Data Driven ◽

Learning Models ◽

Level Data ◽

Data Source ◽

Single Data ◽

The Impact ◽

Machine Learning Models

Deep learning has shown impressive performance acrosshealth management and prognostics applications. Nowadays, an emerging trend of machine learning deployment on resource constraint hardware devices like micro-controllers(MCU) has aroused much attention. Given the distributed andresource constraint nature of many PHM applications, using tiny machine learning models close to data source sensors for on-device inferences would be beneficial to save both time andadditional hardware resources. Even though there has beenpast works that bring TinyML on MCUs for some PHM ap-plications, they are mainly targeting single data source usage without higher-level data incorporation with cloud computing.We study the impact of potential cooperation patterns betweenTinyML on edge and more powerful computation resources oncloud and how this would make an impact on the application patterns in data-driven prognostics. We introduce potential ap-plications where sensor readings are utilized for system health status prediction including status classification and remaining useful life regression. We find that MCUs and cloud com-puting can be adaptive to different kinds of machine learning models and combined in flexible ways for diverse requirement.Our work also shows limitations of current MCU-based deep learning in data-driven prognostics And we hope our work can

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Image and structured data analysis for prognostication of health outcomes in patients presenting to the Emergency Department during the COVID-19 pandemic

10.1101/2021.07.07.21260097 ◽

2021 ◽

Author(s):

Liam Butler ◽

Ibrahim Karabayir ◽

Mohammad Samie Tootooni ◽

Majid Afshar ◽

Ari Goldberg ◽

...

Keyword(s):

Machine Learning ◽

Emergency Department ◽

Intensive Care ◽

Deep Learning ◽

Clinical Data ◽

Chest Radiographs ◽

Gradient Boosting ◽

Learning Models ◽

Icu Admission ◽

Light Gradient

Background: Patients admitted to the emergency department (ED) with COVID-19 symptoms are routinely required to have chest radiographs and computed tomography (CT) scans. COVID-19 infection has been directly related to development of acute respiratory distress syndrome (ARDS) and severe infections lead to admission to intensive care and can also lead to death. The use of clinical data in machine learning models available at time of admission to ED can be used to assess possible risk of ARDS, need for intensive care unit (ICU) admission as well as risk of mortality. In addition, chest radiographs can be inputted into a deep learning model to further assess these risks. Purpose: This research aimed to develop machine and deep learning models using both structured clinical data and image data from the electronic health record (EHR) to adverse outcomes following ED admission. Materials and Methods: Light Gradient Boosting Machines (LightGBM) was used as the main machine learning algorithm using all clinical data including 42 variables. Compact models were also developed using 15 the most important variables to increase applicability of the models in clinical settings. To predict risk of the aforementioned health outcome events, transfer learning from the CheXNet model was implemented on our data as well. This research utilized clinical data and chest radiographs of 3571 patients 18 years and older admitted to the emergency department between 9th March 2020 and 29th October 2020 at Loyola University Medical Center. Main Findings: Our research results show that we can detect COVID-19 infection (AUC = 0.790 (0.746-0.835)) and predict the risk of developing ARDS (AUC = 0.781 (0.690-0.872), ICU admission (AUC = 0.675 (0.620-0.713)), and mortality (AUC = 0.759 (0.678-0.840)) at moderate accuracy from both chest X-ray images and clinical data. Principal Conclusions: The results can help in clinical decision making, especially when addressing ARDS and mortality, during the assessment of patients admitted to the ED with or without COVID-19 symptoms.

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A Comparative Analysis of Novel Deep Learning Models and Ensemble Learning Models to Predict the Allergenicity of Food Allergens

10.1101/2021.03.10.434710 ◽

2021 ◽

Author(s):

Liyang Wang ◽

Dantong Niu ◽

Xinjie Zhao ◽

Xiaoya Wang ◽

Mengzhen Hao ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Ensemble Learning ◽

Food Allergen ◽

Learning Model ◽

Gradient Boosting ◽

Food Allergens ◽

Learning Models ◽

Allergen Identification ◽

Machine Learning Models

AbstractTraditional food allergen identification mainly relies on in vivo and in vitro experiments, which often needs a long period and high cost. The artificial intelligence (AI)-driven rapid food allergen identification method has solved the two drawbacks and is becoming an efficient auxiliary tool. Aiming to overcome the limitations of lower accuracy of traditional machine learning models in predicting the allergenicity of food allergens, this work proposed to introduce transformer deep learning model with self-attention mechanism and ensemble learning model (representative as Light Gradient Boosting Machine (LightGBM) and eXtreme Gradient Boosting (XGBoost)) to solve the problem. In order to highlight the superiority of the proposed novel method, the study also selected various commonly used machine learning models as the baseline classifiers. The results of 5-fold cross-validation found that the AUC of the deep model was the highest (0.9400), which was better than the ensemble learning and baseline algorithms. But it needed to be pre-trained, and the training cost was highest. By comparing the characteristics of transformer model and boosting models, it can be analyzed that the two types of models have their own advantages, which provides novel clues and inspiration for the rapid prediction of food allergens in the future.

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Short and Very Short Term Firm-level Load Forecasting for Warehouses: A Comparison of Machine Learning and Deep Learning Models

10.20944/preprints202201.0107.v1 ◽

2022 ◽

Author(s):

Andrea Maria N. C. Ribeiro ◽

Pedro Rafael X. do Carmo ◽

Patricia Takako Endo ◽

Pierangelo Rosati ◽

Theo Lynn

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Energy Consumption ◽

Load Forecasting ◽

Energy Performance ◽

Gradient Boosting ◽

Support Vector ◽

Learning Models ◽

Short Term ◽

Extreme Gradient Boosting

Commercial buildings are a significant consumer of energy worldwide. Logistics facilities, and specifically warehouses, are a common building type yet under-researched in the demand-side energy forecasting literature. Warehouses have an idiosyncratic profile when compared to other commercial and industrial buildings with a significant reliance on a small number of energy systems. As such, warehouse owners and operators are increasingly entering in to energy performance contracts with energy service companies (ESCOs) to minimise environmental impact, reduce costs, and improve competitiveness. ESCOs and warehouse owners and operators require accurate forecasts of their energy consumption so that precautionary and mitigation measures can be taken. This paper explores the performance of three machine learning models (Support Vector Regression (SVR), Random Forest, and Extreme Gradient Boosting (XGBoost)), three deep learning models (Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)), and a classical time series model, Autoregressive Integrated Moving Average (ARIMA) for predicting daily energy consumption. The dataset comprises 8,040 records generated over an 11-month period from January to November 2020 from a non-refrigerated logistics facility located in Ireland. The grid search method was used to identify the best configurations for each model. The proposed XGBoost models outperform other models for both very short load forecasting (VSTLF) and short term load forecasting (STLF); the ARIMA model performed the worst.

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Machine Learning and Deep Learning challenges for building 2′O site prediction

10.1101/2020.05.10.087189 ◽

2020 ◽

Author(s):

Milad Mostavi ◽

Yufei Huang

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Predictive Model ◽

Sequence Length ◽

Learning Models ◽

Rna Modifications ◽

Operating Characteristics ◽

Rna Detection ◽

Learning Challenges ◽

The Impact

Abstract2′-O-methylation (2′O) is one of the abundant post-transcriptional RNA modifications which can be found in all types of RNA. Detection and functional analysis of 2′O methylation have become challenging problems for biologists ever since its discovery. This paper addresses computational challenges for building Machine Learning and Deep Learning models for predicting 2′O sites. In particular, the impact of sequence length containing 2′O site, embedding method and the type of predictive model are each investigated separately. 30 different predictive models are built and each showed the impact of the mentioned parameters. The area under the precision-recall and receiving operating characteristics curves are utilized to test imbalanced case scenarios in the real world. By comparing the performance of these models, it is shown that embedding methods are crucial for Machine Learning models. However, they do not improve the performance of Deep Learning models. Furthermore, the best predictive model was further investigated to extract significant nucleotides surrounding 2′O sites. Interestingly, based on the significant score matrix achieved by all 2′O samples, it is depicted that model pays the highest attention at the location that the dominant 2′O motifs exist. Dataset and all of the codes are available at https://github.com/MMostavi/2_O_Me_sitePred

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Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease

PeerJ Computer Science ◽

10.7717/peerj-cs.369 ◽

2021 ◽

Vol 7 ◽

pp. e369

Author(s):

Arpan Srivastava ◽

Sonakshi Jain ◽

Ryan Miranda ◽

Shruti Patil ◽

Sharnil Pandya ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Respiratory Diseases ◽

Predictive Accuracy ◽

Research Work ◽

Respiratory Sounds ◽

Chronic Obstructive ◽

Learning Approach ◽

X Rays ◽

The Impact

In recent times, technologies such as machine learning and deep learning have played a vital role in providing assistive solutions to a medical domain’s challenges. They also improve predictive accuracy for early and timely disease detection using medical imaging and audio analysis. Due to the scarcity of trained human resources, medical practitioners are welcoming such technology assistance as it provides a helping hand to them in coping with more patients. Apart from critical health diseases such as cancer and diabetes, the impact of respiratory diseases is also gradually on the rise and is becoming life-threatening for society. The early diagnosis and immediate treatment are crucial in respiratory diseases, and hence the audio of the respiratory sounds is proving very beneficial along with chest X-rays. The presented research work aims to apply Convolutional Neural Network based deep learning methodologies to assist medical experts by providing a detailed and rigorous analysis of the medical respiratory audio data for Chronic Obstructive Pulmonary detection. In the conducted experiments, we have used a Librosa machine learning library features such as MFCC, Mel-Spectrogram, Chroma, Chroma (Constant-Q) and Chroma CENS. The presented system could also interpret the severity of the disease identified, such as mild, moderate, or acute. The investigation results validate the success of the proposed deep learning approach. The system classification accuracy has been enhanced to an ICBHI score of 93%. Furthermore, in the conducted experiments, we have applied K-fold Cross-Validation with ten splits to optimize the performance of the presented deep learning approach.

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Trust in Intrusion Detection Systems: An Investigation of Performance Analysis for Machine Learning and Deep Learning Models

Complexity ◽

10.1155/2021/5538896 ◽

2021 ◽

Vol 2021 ◽

pp. 1-23

Author(s):

Basim Mahbooba ◽

Radhya Sahal ◽

Wael Alosaimi ◽

Martin Serrano

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Intrusion Detection ◽

Detection System ◽

Learning Technologies ◽

Machine Learning Techniques ◽

Learning Models ◽

Network Intrusion ◽

Learning Techniques ◽

The Impact

To design and develop AI-based cybersecurity systems (e.g., intrusion detection system (IDS)), users can justifiably trust, one needs to evaluate the impact of trust using machine learning and deep learning technologies. To guide the design and implementation of trusted AI-based systems in IDS, this paper provides a comparison among machine learning and deep learning models to investigate the trust impact based on the accuracy of the trusted AI-based systems regarding the malicious data in IDs. The four machine learning techniques are decision tree (DT), K nearest neighbour (KNN), random forest (RF), and naïve Bayes (NB). The four deep learning techniques are LSTM (one and two layers) and GRU (one and two layers). Two datasets are used to classify the IDS attack type, including wireless sensor network detection system (WSN-DS) and KDD Cup network intrusion dataset. A detailed comparison of the eight techniques’ performance using all features and selected features is made by measuring the accuracy, precision, recall, and F1-score. Considering the findings related to the data, methodology, and expert accountability, interpretability for AI-based solutions also becomes demanded to enhance trust in the IDS.

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