Advancing agricultural research using machine learning algorithms

BackgroundThe purpose of this scoping review was to explore the current applications of objective gait analysis using inertial measurement units, custom algorithms and artificial intelligence algorithms in detecting neurological and musculoskeletal gait altering pathologies from healthy gait patterns.MethodsLiterature searches were conducted of four electronic databases (Medline, PubMed, Embase and Web of Science) to identify studies that assessed the accuracy of these custom gait analysis models with inputs derived from wearable devices. Data was collected according to the preferred reporting items for systematic reviews and meta-analysis statement guidelines.ResultsA total of 23 eligible studies were identified for inclusion in the present review, including 10 custom algorithms articles and 13 artificial intelligence algorithms articles. Nine studies evaluated patients with Parkinson’s disease of varying severity and subtypes. Support vector machine was the commonest adopted artificial intelligence algorithm model, followed by random forest and neural networks. Overall classification accuracy was promising for articles that use artificial intelligence algorithms, with nine articles achieving more than 90% accuracy.ConclusionsCurrent applications of artificial intelligence algorithms are reasonably effective discrimination between pathological and non-pathological gait. Of these, machine learning algorithms demonstrate the additional capacity to handle complicated data input, when compared to other custom algorithms. Notably, there has been increasing application of machine learning algorithms for conducting gait analysis. More studies are needed with unsupervised methods and in non-clinical settings to better reflect the community and home-based usage.

Aim of nephrologists is to delay the outcome and reduce the number of patients undergoing renal failure (RF) by applying prevention protocols and accurately monitoring chronic kidney disease (CKD) patients. General practitioners and nephrologists are involved in the first and in the late stages of the disease, respectively. Early diagnosis of CKD is an important step in preventing the progression of kidney damage. Our aim was to review publications on machine learning algorithms (MLAs) that can predict early CKD and itsprogression. We conducted a systematic review and selected 55 articles on the application of MLAs in CKD.PubMed, Medline, Scopus, Web of Science and IEEE Xplore Digital Library of the Institute of Electrical and Electronics Engineers were searched. The search terms were chronic kidney disease, artificial intelligence, data mining and machine learning algorithms. MLAs use enormous numbers of predictors combining them in non-linear and highly interactive ways. This ability increases when new data is added. We observed some limitations in the publications: (i) databases were not accurately reviewed by physicians; (ii) databases did not report the ethnicity of the patients; (iii) some databases collected variables that were not important for the diagnosis and progression of CKD; (iv) no information was presented on the native kidney disease causing CKD; (v) no validation of the results in external independent cohorts was provided; and (vi) no insights were given on the MLAs that were used. Overall, there was limited collaboration among experts in electronics, computer science and physicians. The application of MLAs in kidney diseases may enhance the ability of clinicians to predict CKD and RF, thus improving diagnostic assistance and providing suitable therapeutic decisions. However, it is necessary to improve the development process of MLA tools.

Integrating artificial intelligence and high-throughput phenotyping for crop improvement

Key challenges for 5G and Beyond networks relate with the requirements for exceptionally low latency, high reliability, and extremely high data rates. The Ultra-Reliable Low Latency Communication (URLLC) use case is the trickiest to support and current research is focused on physical or MAC layer solutions, while proposals focused on the network layer using Machine Learning (ML) and Artificial Intelligence (AI) algorithms running on base stations and User Equipment (UE) or Internet of Things (IoT) devices are in early stages. In this paper, we describe the operation rationale of the most recent relevant ML algorithms and techniques, and we propose and validate ML algorithms running on both cells (base stations/gNBs) and UEs or IoT devices to handle URLLC service control. One ML algorithm runs on base stations to evaluate latency demands and offload traffic in case of need, while another lightweight algorithm runs on UEs and IoT devices to rank cells with the best URLLC service in real-time to indicate the best one cell for a UE or IoT device to camp. We show that the interplay of these algorithms leads to good service control and eventually optimal load allocation, under slow load mobility.

Overhead transmission lines are used to transmit electrical energy at a high voltage over long distances. The capacity of the transmission system is usually capped at a certain level to comply with the safety and reliability conditions of the system. The limit of the current capacity is estimated based on the worst weather conditions. However, during normal weather conditions, the conductor usually has more transition capacity than its limited level. Dynamic line ratings (DLR) provide an estimation of the line current capacity based on the actual weather conditions. Hence, the transmission line can be used to its full capacity when needed. Dynamic line ratings can be modelled physically, statistically, and using machine learning and artificial intelligence. This chapter aims to address the implementation of machine learning and artificial intelligence algorithms in predicting the DLR of transmission systems. Using artificial intelligence to forecast the measurements of DLR enables operators to estimate the current maximum capacity in real-time. The advantages and limitations of each approach are detailed in this chapter as well.KeywordsArtificial intelligenceMachine learningStatistical modellingDynamic line ratingOverhead lines

Artificial intelligence (AI) and machine learning (ML) algorithms are on the tremendous rise for being incorporated into the field of neurosurgery. AI and ML algorithms are different from other technological advances as giving the capability for the computer to learn, reason, and problem-solving skills that a human inherits. This review summarizes the current use of AI in neurosurgery, the challenges that need to be addressed, and what the future holds. A literature review was carried out with a focus on the use of AI in the field of neurosurgery and its future implication in neurosurgical research. The online literature on the use of AI in the field of neurosurgery shows the diversity of topics in terms of its current and future implications. The main areas that are being studied are diagnostic, outcomes, and treatment models. Wonders of AI in the field of medicine and neurosurgery hold true, yet there are a lot of challenges that need to be addressed before its implications can be seen in the field of neurosurgery from patient privacy, to access to high-quality data and overreliance on surgeons on AI. The future of AI in neurosurgery is pointed toward a patient-centric approach, managing clinical tasks, and helping in diagnosing and preoperative assessment of the patients.

In today's global online environment, automation is essential for establishing a competitive advantage. Conversational Artificial Intelligence Systems are an example of an automation technology that has been embraced by some of world's most famous companies. In the field, higher education, these handy gadgets come in handy for administrators.Students' responses and performances are highly important and desired areas to improve the teaching-learning environment in the education organism. Evaluate the feedback to aid in the identification of flaws and actions. Institutes and universities in the field of education collect both quantitative and qualitative responses in order to improve the teaching-learning environment. However, most educational institutions and universities lack a sufficient student feedback evaluation system for determining students' feelings. The grading technique is currently being utilized to collect input. However, the grading process does not reveal the students' genuine feelings about the educational system, whereas written feedback allows students to describe specific areas and situations. Sentiment analysis is a type of qualitative feedback analysis that is based on records. The Nave Bayes (NB) classifier, Support Vector Machine (SVM), and Random Forest were employed in the majority of recent machine learning-based Sentiment Analysis prototypes. When it comes to measuring performance, most people utilize the kids' average grades, however this is not an accurate way. Because students can benefit from the experience indefinitely.This proposal offered a method for analyzing the sentiment of students' responses by combining Natural Language Processing (NLP), Machine Learning (ML), and Artificial Intelligence algorithms (AI). The goal is to combine machine learning algorithms and natural language processing approaches to achieve high accuracy. In this proposal, I suggested a collective model that connects machine learning algorithms to improve accuracy and performance. At the end of the presentation, AI planned to collect student responses in real time. The proposed method analyzes student comments from course reviews to determine mood, emotions, and satisfaction vs. displeasure. The approach categorizes sentimentalities into two categories: positive and negative, and senders' feelings into eight categories (8 emotion groups). The Fuzzy logic technique will be used to assess student performance. We intend to make our program available for broad use in the education industry so that organizations can improve the teaching-learning environment's quality.

Machine learning algorithms are occupying a larger space in medical and urology applications. However, typical medical physicians are not trained on these technologies and do not master the possibilities offered by these tools, to imagine their applications in the medical field. This manuscript is indented to be a guide in the use of machine learning in different urology applications, and to demystify the available machine learning and artificial intelligence algorithms. This manuscript reviews some of their applications and potential applications to the medical and urology field. Multiple works are published on the use of machine learning in urology, with performance demonstrated to be noninferior to human experts on multiple occasions. However, the major part of the machine learning publications in urology applications are concentrated on diagnosis and/or prognosis. Advanced machine learning algorithms based on agentic artificial intelligence, able to perform decisions and causality-based treatment optimization, are rarely put to use in urology. The democratization of advanced machine learning technologies in the medical fields can accelerate the adoption of these techniques, and potentially improve the patient care through relevant suggestive decision making. This work aims to demystify the machine learning tools for medical applications, facilitate decision making and adoption of the correct tools for the correct applications, and places a roadmap for the future of machine learning in the enhancement of patient care in urology.

In this work we examine the largest 100 cryptocurrency return series ranging from 2015 to early 2018. We concentrate our analysis on daily returns and find several interesting stylized facts. First, principal components analysis reveals a complex return generating process. As we examine our data in the most recent year, we find that surprisingly more than one principal component appears to explain the cross-sectional variation in returns. Second, similar to hedge fund returns, cryptocurrency returns suffer from the “beta-in-the-tails” hidden risk. Third, we find that predicting cryptocurrency movements with machine learning and artificial intelligence algorithms is marginally attractive with variation in predictability power per cryptocurrency. Fourth, lower volatile cryptocurrencies are slightly more predictable than more volatile ones. Fifth, evidence exists that efficacy of distinct information sets varies across machine learning algorithms, showing that predictability may be much more complex given a set of machine learning algorithms. Finally, short-term predictability is very tenuous, which suggests that near-term cryptocurrency markets are semi-strong form efficient and therefore, day trading cryptocurrencies may be very challenging. Keywords: cryptocurrency, blockchain, machine learning, bitcoin, beta-in-the-tails, risks

Evaluation of multispectral imaging for freeze damage assessment in strawberries using AI-based computer vision technology

The principle behind artificial intelligence is mimicking human intelligence in the way that it can perform tasks, recognize patterns, or predict outcomes through learning from the acquired data of various sources. Artificial intelligence and machine learning algorithms have been widely used in autonomous driving, recommender systems in electronic commerce and social media, fintech, natural language understanding, and question answering systems. Artificial intelligence is also gradually changing the landscape of healthcare research (Yu et al. in Biomed Eng 2:719–731, 25). The rule-based approach that relied on the curation of medical knowledge and the construction of robust decision rules had drawn significant attention in diagnosing diseases and clinical decision support since half a century ago. In recent years, machine learning algorithms such as deep learning that can account for complex interactions between features is shown to be promising in predictive modeling in healthcare (Deo in Circulation 132:1920–1930, 26). Although many of these artificial intelligence and machine learning algorithms can achieve remarkably high performance, it is often difficult to be completely adopted in practical clinical environments due to the lack of explainability in some of these algorithms. Explainable artificial intelligence (XAI) is emerging to assist in the communication of internal decisions, behavior, and actions to health care professionals. Through explaining the prediction outcomes, XAI gains the trust of the clinicians as they may learn how to apply the predictive modeling in practical situations instead of blindly following the predictions. There are still many scenarios to explore how to make XAI effective in clinical settings due to the complexity of medical knowledge.

Purpose. Given the global technological trends, identify conceptual approaches to technical and technological modernization of the agricultural production as a key element of sustainable development of the real sector of the economy of Ukraine and strategic directions of agricultural science research. Methods. Monographic, system analysis, abstract and logical. Results. It has been established that in the conditions of global climatic changes, the basis of the formation of highly automated agriculture will be flexible ecologically oriented technological processes in agricultural production, built on fundamentally new technical and technological foundations, namely: intellectualization, digitalization, complex automation and robotization. The concept of development of high-tech agricultural production of Ukraine has been developed based on the principles of sustainable development and the innovative concept “Industry 4.0”, which will ensure high and sustainable economic growth of the agricultural sector, the development of domestic agricultural machinery and related sectors of the economy. Strategic tasks for agricultural science on the development of the technical and technological base of highly automated agricultural production are identified. Conclusions. The study of modern trends in the development of the economies of advanced countries and their effectiveness give grounds for asserting that the specified strategic directions of technical and technological modernization of the agricultural sector will be the basis for the formation of an effective model for the development of high-tech agricultural climate-oriented production in Ukraine. Keywords: climate change, high-tech production, agriculture, technology, intellectualization, automation, mechanical engineering.

Simple SummaryThe primary purpose of this review is to provide an in-depth analysis of existing Artificial Intelligence (AI) algorithms used in the field of prostate cancer (PC) for diagnosis and treatment. This review aims to show the research community that AI-enabled technologies have the potential for widespread growth and penetration of PC diagnostics and therapeutics to simplify and accelerate existing healthcare processes.As medical science and technology progress towards the era of “big data”, a multi-dimensional dataset pertaining to medical diagnosis and treatment is becoming accessible for mathematical modelling. However, these datasets are frequently inconsistent, noisy, and often characterized by a significant degree of redundancy. Thus, extensive data processing is widely advised to clean the dataset before feeding it into the mathematical model. In this context, Artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL) algorithms based on artificial neural networks (ANNs) and their types, are being used to produce a precise and cross-sectional illustration of clinical data. For prostate cancer patients, datasets derived from the prostate-specific antigen (PSA), MRI-guided biopsies, genetic biomarkers, and the Gleason grading are primarily used for diagnosis, risk stratification, and patient monitoring. However, recording diagnoses and further stratifying risks based on such diagnostic data frequently involves much subjectivity. Thus, implementing an AI algorithm on a PC’s diagnostic data can reduce the subjectivity of the process and assist in decision making. In addition, AI is used to cut down the processing time and help with early detection, which provides a superior outcome in critical cases of prostate cancer. Furthermore, this also facilitates offering the service at a lower cost by reducing the amount of human labor. Herein, the prime objective of this review is to provide a deep analysis encompassing the existing AI algorithms that are being deployed in the field of prostate cancer (PC) for diagnosis and treatment. Based on the available literature, AI-powered technology has the potential for extensive growth and penetration in PC diagnosis and treatment to ease and expedite the existing medical process.

Technical Skill Assessment using Machine Learning and Artificial Intelligence Algorithm - written by Mohammad Suhaib Ahmed , Nikhunj Saraf , Rohan Atale published on 2019/12/14 download full article with reference data and citations

The aim of this study was to design an expert system to predict the Non-Dipping or Dipping pattern by using several basic clinical and laboratory data through an artificial intelligence algorithm. Data Mining is a technique which extracts information from data sets by using a combination of both statistical analysis methods and artificial intelligence algorithms. Also in this study, the decision tree and naivebayes classification algorithms of this technique were used. Firstly, sixty-five patients (mean age 51±7 years, 40 females,) were included in the study. Systolic and diastolic dipping were found in 13 and 15 % of the patients, respectively. In the advancing process of the experiment, the number of instances were reduced, because of some missing data of the patients. The data sets were tested using the J48 decision tree algorithm. This classification algorithm was implemented on 56 instances, and also the number of attributes was reduced from 35 to 23. 66 % of the instances (37) were reserved for training and 44 % of the instances (19) were reserved for testing. When the algorithm was run, the Non-Dipper/Dipper pattern of the instances were correctly predicted in a rate of 73.6842 %. Model was built in 0.02 seconds. This pilot study shows that a machine learning algorithm can help in the prediction of diurnal blood pressure pattern relying on some basic demographic, clinical and laboratory data, with a reasonable accuracy.