scholarly journals A machine learning-based system for detecting leishmaniasis in microscopic images

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Mojtaba Zare ◽  
Hossein Akbarialiabad ◽  
Hossein Parsaei ◽  
Qasem Asgari ◽  
Ali Alinejad ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Visual Detection ◽  
Detection System ◽  
Cost Effective ◽  
Leishmania Parasite ◽  
Parasitic Disease ◽  
Integral Image ◽  
Parasite Detection ◽  
Image Creation

Abstract Background Leishmaniasis, a disease caused by a protozoan, causes numerous deaths in humans each year. After malaria, leishmaniasis is known to be the deadliest parasitic disease globally. Direct visual detection of leishmania parasite through microscopy is the frequent method for diagnosis of this disease. However, this method is time-consuming and subject to errors. This study was aimed to develop an artificial intelligence-based algorithm for automatic diagnosis of leishmaniasis. Methods We used the Viola-Jones algorithm to develop a leishmania parasite detection system. The algorithm includes three procedures: feature extraction, integral image creation, and classification. Haar-like features are used as features. An integral image was used to represent an abstract of the image that significantly speeds up the algorithm. The adaBoost technique was used to select the discriminate features and to train the classifier. Results A 65% recall and 50% precision was concluded in the detection of macrophages infected with the leishmania parasite. Also, these numbers were 52% and 71%, respectively, related to amastigotes outside of macrophages. Conclusion The developed system is accurate, fast, easy to use, and cost-effective. Therefore, artificial intelligence might be used as an alternative for the current leishmanial diagnosis methods.

scholarly journals A Machine Learning-Based System for Detecting Leishmaniasis in Microscopic Images

2021 ◽  
Author(s):  
Mojtaba Zare ◽  
Hossein Akbarialiabad ◽  
Hossein Parsaei ◽  
Qasem Asgari ◽  
Ali Alinejad ◽  
...  
Keyword(s):  
Processing Speed ◽  
Visual Detection ◽  
High Sensitivity ◽  
Leishmania Parasite ◽  
Parasitic Disease ◽  
Integral Image ◽  
Viable Option ◽  
High Processing Speed ◽  
Image Creation ◽  
High Processing

Abstract Background: Leishmaniasis, a disease caused by a protozoan, causes numerous deaths in humans each year. After malaria, Leishmaniasis is known to be the deadliest parasitic disease globally. Currently, direct visual detection of Leishmania parasite through microscopy is the “gold standard” for the diagnosis of this disease. However, this method is time-consuming and subject to errors. This study was aimed to develop an artificial intelligence-based algorithm and image processing algorithms for the automatic diagnosis of Leishmaniasis.Methods: The Viola-Jones algorithm was used in this study due to its high recognition speed. This algorithm performs in four stages: detection of Haar-like features, integral image creation, Adaboost training, cascade architecture.Results: A 65% recall and 83% precision was concluded in the detection of macrophages infected with the Leishmania parasite. Also, these numbers were 52% and 35%, respectively, related to amastigotes outside of macrophages.Conclusion: The results contain a fairly high sensitivity, with the specificity being less satisfactory. High processing speed, ease of work, and low expenses are advantages of the presented method compared to other procedures. By adding a few adjustments, this method could be considered a viable option.

scholarly journals An analytical survey on the role of machine learning algorithms in case of intrusion detection

2020 ◽  
Vol 5 (19) ◽  
pp. 32-35
Author(s):  
Anand Vijay ◽  
Kailash Patidar ◽  
Manoj Yadav ◽  
Rishi Kushwah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Handling Mechanism ◽  
The Impact

In this paper an analytical survey on the role of machine learning algorithms in case of intrusion detection has been presented and discussed. This paper shows the analytical aspects in the development of efficient intrusion detection system (IDS). The related study for the development of this system has been presented in terms of computational methods. The discussed methods are data mining, artificial intelligence and machine learning. It has been discussed along with the attack parameters and attack types. This paper also elaborates the impact of different attack and handling mechanism based on the previous papers.

Tu1990 ARTIFICIAL INTELLIGENCE-ASSISTED POLYP DETECTION SYSTEM FOR COLONOSCOPY, BASED ON THE LARGEST AVAILABLE COLLECTION OF CLINICAL VIDEO DATA FOR MACHINE LEARNING

2019 ◽  
Vol 89 (6) ◽  
pp. AB646-AB647 ◽  
Author(s):  
Masashi Misawa ◽  
Shinei Kudo ◽  
Yuichi Mori ◽  
Tomonari Cho ◽  
Shinichi Kataoka ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Detection System ◽  
Video Data ◽  
Polyp Detection

scholarly journals Artificial Intelligence and Machine Learning in 5G and beyond: A Survey and Perspectives

2021 ◽  
Author(s):  
Abdelfatteh Haidine ◽  
Fatima Zahra Salmam ◽  
Abdelhak Aqqal ◽  
Aziz Dahbi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Mobile Networks ◽  
Network Architecture ◽  
Mobile Network ◽  
Cost Effective ◽  
Mobile Internet ◽  
Network Function ◽  
New Radio ◽  
4G Lte

The deployment of 4G/LTE (Long Term Evolution) mobile network has solved the major challenge of high capacities, to build real broadband mobile Internet. This was possible mainly through very strong physical layer and flexible network architecture. However, the bandwidth hungry services have been developed in unprecedented way, such as virtual reality (VR), augmented reality (AR), etc. Furthermore, mobile networks are facing other new services with extremely demand of higher reliability and almost zero-latency performance, like vehicle communications or Internet-of-Vehicles (IoV). Using new radio interface based on massive MIMO, 5G has overcame some of these challenges. In addition, the adoption of software defend networks (SDN) and network function virtualization (NFV) has added a higher degree of flexibility allowing the operators to support very demanding services from different vertical markets. However, network operators are forced to consider a higher level of intelligence in their networks, in order to deeply and accurately learn the operating environment and users behaviors and needs. It is also important to forecast their evolution to build a pro-actively and efficiently (self-) updatable network. In this chapter, we describe the role of artificial intelligence and machine learning in 5G and beyond, to build cost-effective and adaptable performing next generation mobile network. Some practical use cases of AI/ML in network life cycle are discussed.

scholarly journals Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence

2021 ◽  
Vol 12 ◽  
Author(s):  
José T. Moreira-Filho ◽  
Arthur C. Silva ◽  
Rafael F. Dantas ◽  
Barbara F. Gomes ◽  
Lauro R. Souza Neto ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Drug Discovery ◽  
Cost Effective ◽  
Neglected Tropical Disease ◽  
Parasitic Disease ◽  
Drug Candidates ◽  
Single Drug ◽  
Computer Aided ◽  
Prevalent Disease ◽  
Effective Drugs

Schistosomiasis is a parasitic disease caused by trematode worms of the genus Schistosoma and affects over 200 million people worldwide. The control and treatment of this neglected tropical disease is based on a single drug, praziquantel, which raises concerns about the development of drug resistance. This, and the lack of efficacy of praziquantel against juvenile worms, highlights the urgency for new antischistosomal therapies. In this review we focus on innovative approaches to the identification of antischistosomal drug candidates, including the use of automated assays, fragment-based screening, computer-aided and artificial intelligence-based computational methods. We highlight the current developments that may contribute to optimizing research outputs and lead to more effective drugs for this highly prevalent disease, in a more cost-effective drug discovery endeavor.

scholarly journals Dependable Fire Detection System with Multifunctional Artificial Intelligence Framework

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2025 ◽  
Author(s):  
Jun Hong Park ◽  
Seunggi Lee ◽  
Seongjin Yun ◽  
Hanjin Kim ◽  
Won-Tae Kim
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Transfer ◽  
Detection System ◽  
Smart Cities ◽  
Fire Detection ◽  
Machine Learning Algorithms ◽  
Detection Accuracy ◽  
Detection Systems ◽  
Fire Detection System

A fire detection system requires accurate and fast mechanisms to make the right decision in a fire situation. Since most commercial fire detection systems use a simple sensor, their fire recognition accuracy is deficient because of the limitations of the detection capability of the sensor. Existing proposals, which use rule-based algorithms or image-based machine learning can hardly adapt to the changes in the environment because of their static features. Since the legacy fire detection systems and network services do not guarantee data transfer latency, the required need for promptness is unmet. In this paper, we propose a new fire detection system with a multifunctional artificial intelligence framework and a data transfer delay minimization mechanism for the safety of smart cities. The framework includes a set of multiple machine learning algorithms and an adaptive fuzzy algorithm. In addition, Direct-MQTT based on SDN is introduced to solve the traffic concentration problems of the traditional MQTT. We verify the performance of the proposed system in terms of accuracy and delay time and found a fire detection accuracy of over 95%. The end-to-end delay, which comprises the transfer and decision delays, is reduced by an average of 72%.

Criticalities and Advantages of the Use of Artificial Intelligence in Research

2022 ◽  
pp. 161-175
Author(s):  
Jessica Camargo Molano ◽  
Jacopo Cavalaglio Camargo Molano
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Public Schools ◽  
School System ◽  
Detection System ◽  
Rapid Development ◽  
Academic Research ◽  
The Face ◽  
The Impact ◽  
Machine Learning Models

In recent years, artficial intelligence, through the rapid development of machine learning and deep learning, has started to be used in different sectors, even in academic research. The objective of this study is a reflection on the possible errors that can occur when the analysis of human behavior and the development of academic research rely on artificial intelligence. To understand what errors artificial intelligence can make more easily, three cases have been analyzed: the use of the IMPACT system for the evaluation of school system in the District of Columbia Public Schools (DCPS) in Washington, the face detection system, and the “writing” of the first scientific text by artificial intelligence. In particular, this work takes into consideration the systematic errors due to the polarization of data with which the machine learning models are trained, the absence of feedback and the problem of minorities who cannot be represented through the use of big data.

scholarly journals Combining machine learning and nanopore construction creates an artificial intelligence nanopore for coronavirus detection

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Masateru Taniguchi ◽  
Shohei Minami ◽  
Chikako Ono ◽  
Rina Hamajima ◽  
Ayumi Morimura ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
High Speed ◽  
Simple Procedure ◽  
Rna Extraction ◽  
Disease Outbreaks ◽  
High Sensitivity ◽  
Cost Effective ◽  
Rt Pcr ◽  
Emerging Viruses

AbstractHigh-throughput, high-accuracy detection of emerging viruses allows for the control of disease outbreaks. Currently, reverse transcription-polymerase chain reaction (RT-PCR) is currently the most-widely used technology to diagnose the presence of SARS-CoV-2. However, RT-PCR requires the extraction of viral RNA from clinical specimens to obtain high sensitivity. Here, we report a method for detecting novel coronaviruses with high sensitivity by using nanopores together with artificial intelligence, a relatively simple procedure that does not require RNA extraction. Our final platform, which we call the artificially intelligent nanopore, consists of machine learning software on a server, a portable high-speed and high-precision current measuring instrument, and scalable, cost-effective semiconducting nanopore modules. We show that artificially intelligent nanopores are successful in accurately identifying four types of coronaviruses similar in size, HCoV-229E, SARS-CoV, MERS-CoV, and SARS-CoV-2. Detection of SARS-CoV-2 in saliva specimen is achieved with a sensitivity of 90% and specificity of 96% with a 5-minute measurement.

Sign in / Sign up

Export Citation Format

Share Document