scholarly journals Detecting and Classifying Pests in Crops Using Proximal Images and Machine Learning: A Review

AI ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 312-328
Author(s):  
Jayme Garcia Arnal Barbedo
Keyword(s):  
Machine Learning ◽  
Digital Images ◽  
Research Effort ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Actual State ◽  
Considerable Research ◽  
Learning Techniques ◽  
Pest Detection ◽  
The Subject

Pest management is among the most important activities in a farm. Monitoring all different species visually may not be effective, especially in large properties. Accordingly, considerable research effort has been spent towards the development of effective ways to remotely monitor potential infestations. A growing number of solutions combine proximal digital images with machine learning techniques, but since species and conditions associated to each study vary considerably, it is difficult to draw a realistic picture of the actual state of the art on the subject. In this context, the objectives of this article are (1) to briefly describe some of the most relevant investigations on the subject of automatic pest detection using proximal digital images and machine learning; (2) to provide a unified overview of the research carried out so far, with special emphasis to research gaps that still linger; (3) to propose some possible targets for future research.

Machine Learning

2012 ◽  
pp. 13-22 ◽  
Author(s):  
João Gama ◽  
André C.P.L.F. de Carvalho
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Text Processing ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Background Information ◽  
Future Research ◽  
Personal View ◽  
Learning Techniques ◽  
Future Research Directions

Machine learning techniques have been successfully applied to several real world problems in areas as diverse as image analysis, Semantic Web, bioinformatics, text processing, natural language processing,telecommunications, finance, medical diagnosis, and so forth. A particular application where machine learning plays a key role is data mining, where machine learning techniques have been extensively used for the extraction of association, clustering, prediction, diagnosis, and regression models. This text presents our personal view of the main aspects, major tasks, frequently used algorithms, current research, and future directions of machine learning research. For such, it is organized as follows: Background information concerning machine learning is presented in the second section. The third section discusses different definitions for Machine Learning. Common tasks faced by Machine Learning Systems are described in the fourth section. Popular Machine Learning algorithms and the importance of the loss function are commented on in the fifth section. The sixth and seventh sections present the current trends and future research directions, respectively.

Machine Learning

2011 ◽  
pp. 2462-2468 ◽  
Author(s):  
João Gama ◽  
André C.P.L.F. de Carvalho
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Text Processing ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Background Information ◽  
Future Research ◽  
Personal View ◽  
Learning Techniques ◽  
Future Research Directions

Machine learning techniques have been successfully applied to several real world problems in areas as diverse as image analysis, Semantic Web, bioinformatics, text processing, natural language processing,telecommunications, finance, medical diagnosis, and so forth. A particular application where machine learning plays a key role is data mining, where machine learning techniques have been extensively used for the extraction of association, clustering, prediction, diagnosis, and regression models. This text presents our personal view of the main aspects, major tasks, frequently used algorithms, current research, and future directions of machine learning research. For such, it is organized as follows: Background information concerning machine learning is presented in the second section. The third section discusses different definitions for Machine Learning. Common tasks faced by Machine Learning Systems are described in the fourth section. Popular Machine Learning algorithms and the importance of the loss function are commented on in the fifth section. The sixth and seventh sections present the current trends and future research directions, respectively.

Machine Learning Techniques to Identify and Characterize Sleep Disorders Using Biosignals

2021 ◽  
pp. 136-160
Author(s):  
Mercedes Barrachina ◽  
Laura Valenzuela López
Keyword(s):  
Machine Learning ◽  
Sleep Disorders ◽  
Adult Population ◽  
The United States ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Research Directions ◽  
Learning Techniques ◽  
Control And Prevention ◽  
Future Research Directions

Sleep disorders are related to many different diseases, and they could have a significant impact in patients' health, causing an economic impact to the society and to the national health systems. In the United States, according to information from the Center for Disease Control and Prevention, those disorders are affecting 50-70 million in the adult population. Sleep disorders are causing annually around 40,000 deaths due to cardiovascular problems, and they cost the health system more than 16 billion. In other countries, such as in Spain, those disorders affect up to 48% of the adult population. The main objective of this chapter is to review and evaluate the different machine learning techniques utilized by researchers and medical professionals to identify, assess, and characterize sleep disorders. Moreover, some future research directions are proposed considering the evaluated area.

scholarly journals Machine learning for energy-water nexus: challenges and opportunities

2018 ◽  
Vol 2 (3) ◽  
pp. 228-267 ◽  
Author(s):  
Zaidi ◽  
Chandola ◽  
Allen ◽  
Sanyal ◽  
Stewart ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Systems ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Technological Advancement ◽  
Research Directions ◽  
Learning Techniques ◽  
Challenges And Opportunities ◽  
Future Research Directions ◽  
System Sustainability

Modeling the interactions of water and energy systems is important to the enforcement of infrastructure security and system sustainability. To this end, recent technological advancement has allowed the production of large volumes of data associated with functioning of these sectors. We are beginning to see that statistical and machine learning techniques can help elucidate characteristic patterns across these systems from water availability, transport, and use to energy generation, fuel supply, and customer demand, and in the interdependencies among these systems that can leave these systems vulnerable to cascading impacts from single disruptions. In this paper, we discuss ways in which data and machine learning can be applied to the challenges facing the energy-water nexus along with the potential issues associated with the machine learning techniques themselves. We then survey machine learning techniques that have found application to date in energy-water nexus problems. We conclude by outlining future research directions and opportunities for collaboration among the energy-water nexus and machine learning communities that can lead to mutual synergistic advantage.

Extraction of drug–drug interaction using neural embedding

2018 ◽  
Vol 16 (06) ◽  
pp. 1840027 ◽  
Author(s):  
Wen Juan Hou ◽  
Bamfa Ceesay
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Drug Interaction ◽  
Relevant Information ◽  
Learning System ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Learning Approach ◽  
Learning Techniques ◽  
Drug Drug Interaction

Information on changes in a drug’s effect when taken in combination with a second drug, known as drug–drug interaction (DDI), is relevant in the pharmaceutical industry. DDIs can delay, decrease, or enhance absorption of either drug and thus decrease or increase their action or cause adverse effects. Information Extraction (IE) can be of great benefit in allowing identification and extraction of relevant information on DDIs. We here propose an approach for the extraction of DDI from text using neural word embedding to train a machine learning system. Results show that our system is competitive against other systems for the task of extracting DDIs, and that significant improvements can be achieved by learning from word features and using a deep-learning approach. Our study demonstrates that machine learning techniques such as neural networks and deep learning methods can efficiently aid in IE from text. Our proposed approach is well suited to play a significant role in future research.

Machine learning techniques applied to industrial engineering: a multi criteria approach

2019 ◽  
Author(s):  
Fabio De Felice ◽  
Marta Travaglioni ◽  
Giuseppina Piscitelli ◽  
Raffaele Cioffi ◽  
Antonella Petrillo
Keyword(s):  
Machine Learning ◽  
Industry 4.0 ◽  
Technological Development ◽  
Predictive Maintenance ◽  
Industrial Engineering ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
The World ◽  
The Subject ◽  
To Receive

With the Industry 4.0 (I4.0) beginning, the world is witnessing an important technological development. The success of I4.0 is linked to the implementation of enabling technologies, including Machine Learning, which focuses on the machines’ ability to receive a series of data and learn on their own. The present research aims to systematically analyze the existing literature on the subject in various aspects, including publication year, authors, scientific sector, country, institution and keywords. Understanding and analyzing the existing literature on Machine Learning applied to predictive maintenance is preparatory to recommend policy on the subject.

scholarly journals Artificial Intelligence and Business Strategy towards Digital Transformation: A Research Agenda

2021 ◽  
Vol 13 (4) ◽  
pp. 2025 ◽  
Author(s):  
Fotis Kitsios ◽  
Maria Kamariotou
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Theoretical Model ◽  
Business Strategy ◽  
Management Practices ◽  
Corporate Strategy ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Practical Implementation ◽  
Learning Techniques

In the past decade, current literature and businesses have drawn attention to Artificial Intelligence (AI) tools and in particular to the advances in machine learning techniques. Nevertheless, while the AI technology offers great potential to solve difficulties, challenges remain implicated in practical implementation and lack of expertise in the strategic usage of AI to create business value. This paper aims to implement a systematic literature review analyzing convergence of the AI and corporate strategy and develop a theoretical model incorporating issues based on the existing research in this field. Eighty-one peer-reviewed articles were discussed on the basis of research methodology from Webster and Watson (2002). In addition to gaps in future research, a theoretical model is developed, discussing the four sources of value creation: AI and Machine Learning in organizations; alignment of AI tools and Information Technology (IT) with organizational strategy; AI, knowledge management and decision-making process; and AI, service innovation and value. These outcomes lead to both theoretical and managerial viewpoints, with extensive possibilities to generate new methods and types of management practices.

scholarly journals Machine Learning Techniques for Human Age and Gender Identification Based on Teeth X-Ray Images

2022 ◽  
Vol 2022 ◽  
pp. 1-14
Author(s):  
K. C. Santosh ◽  
Nijalingappa Pradeep ◽  
Vikas Goel ◽  
Raju Ranjan ◽  
Ekta Pandey ◽  
...  
Keyword(s):  
Machine Learning ◽  
Digital Images ◽  
Automated System ◽  
Machine Learning Techniques ◽  
Human Beings ◽  
Age And Gender ◽  
X Ray ◽  
Learning Techniques ◽  
Gender And Age ◽  
And Gender

The use of digital medical images is increasing with advanced computational power that has immensely contributed to developing more sophisticated machine learning techniques. Determination of age and gender of individuals was manually performed by forensic experts by their professional skills, which may take a few days to generate results. A fully automated system was developed that identifies the gender of humans and age based on digital images of teeth. Since teeth are a strong and unique part of the human body that exhibits least subject to risk in natural structure and remains unchanged for a longer duration, the process of identification of gender- and age-related information from human beings is systematically carried out by analyzing OPG (orthopantomogram) images. A total of 1142 digital X-ray images of teeth were obtained from dental colleges from the population of the middle-east part of Karnataka state in India. 80% of the digital images were considered for training purposes, and the remaining 20% of teeth images were for the testing cases. The proposed gender and age determination system finds its application widely in the forensic field to predict results quickly and accurately. The prediction system was carried out using Multiclass SVM (MSVM) classifier algorithm for age estimation and LIBSVM classifier for gender prediction, and 96% of accuracy was achieved from the system.

scholarly journals Cervical Cancer: Machine Learning Techniques for Detection, Risk Factors and Prevention Measures

2020 ◽  
Vol 9 (3) ◽  
pp. 158-163
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Cervical Cancer ◽  
Preventive Measures ◽  
Hpv Infection ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Prevention Measures ◽  
Detection Techniques ◽  
Learning Techniques

Cervical Cancer is considered the fourth most common female malignancy worldwide and represents a major global health challenge. As a result, in recent years, various proposals and researches have been conducted. This study aims to analyze the data presented in current researches regarding cervical cancer and contribute to future research, all through the framework of literature review, based on 3 research questions: Q1: What are the risk factors that cause cervical cancer? Q2: What preventive measures are currently established for cervical cancer? and, Q3: What are the techniques to detect cervical cancer? Findings show that detection techniques are complementary since they are categorized under machine learning. Therefore, we recommend that further study be promoted in these techniques as they are helpful in the detection process. In addition, risk factors can be considered for a greater scope in detection, such as HPV infection, since it is the most relevant factor for the development of cervical cancer. Finally, we suggest to conduct further research on preventive measures for cervical cancer.

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