Artificial Intelligence and Machine Learning-Based Advanced Depression Detection System

2022 ◽  
pp. 92-114
Author(s):  
Suvarna Patil ◽  
Neha More ◽  
Animesh Bhawtankar ◽  
Vishal Pratap Jagtap ◽  
Anjali Jadhav
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Detection System ◽  
Quality Life ◽  
Depression Detection ◽  
Depressed Person ◽  
The Stability ◽  
The Mind ◽  
State Depression ◽  
Detection Of Depression

Depression can be called a health issue that majorly affects the stability of the mind. Depression can also be called a mood disorder or mental illness that affects our mental state. Depression can affect a person mentally and physically. According to WHO, 264 million people in the world are affected because of depression. At its worst, depression can lead to suicide. About 8 million people commit suicide every year because of depression. It is not possible for one to live with depression, but if they get proper treatment at a right time, depression can be controlled and cured to help the person to live a quality life. To identify the level of depression of a person, we have to first identify the type of depression the patient is going through. The type of depression plays a very important role in determining the kind of treatment or help a depressed person needs by providing them various treatments. The authors propose a solution for detection of depression type and depression levels using advanced machine learning and artificial intelligence algorithms.

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.

Facial Expression Recognition with Gender Identification

2020 ◽  
pp. 57-63
Author(s):  
admin admin ◽  
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...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Human Face ◽  
Gender Identification ◽  
Artificial Intelligence Techniques ◽  
The Mind

The human facial emotions recognition has attracted interest in the field of Artificial Intelligence. The emotions on a human face depicts what’s going on inside the mind. Facial expression recognition is the part of Facial recognition which is gaining more importance and need for it increases tremendously. Though there are methods to identify expressions using machine learning and Artificial Intelligence techniques, this work attempts to use convolution neural networks to recognize expressions and classify the expressions into 6 emotions categories. Various datasets are investigated and explored for training expression recognition models are explained in this paper and the models which are used in this paper are VGG 19 and RESSNET 18. We included facial emotional recognition with gender identification also. In this project we have used fer2013 and ck+ dataset and ultimately achieved 73% and 94% around accuracies respectively.

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 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 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 Application of Various Machine Learning Techniques in Sentiment Analysis for Depression Detection

2019 ◽  
Vol 8 (10S) ◽  
pp. 292-296
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Mental Disorder ◽  
Human Life ◽  
Geographic Location ◽  
Machine Learning Techniques ◽  
World Health ◽  
Detection Algorithms ◽  
Learning Techniques ◽  
Depression Detection

Depression is the world’s fourth leading disease and will be in the second in 2020 according to the statistics of World Health Organization.Depression affects many people irrespective of their age, geographic location, demographic or social position and more commonly affects females than males.Depression is a mental disorder which can impair many facets of human life. Though not easily detected it has intense and wide-ranging impressions. Although many researchers explored numerous techniques in predicting depression, still there is no improvement and the generations are facing higher rate of depression. It is believed that the depression detection algorithms can be more accurate and their performance can be better if they rely on artificial intelligence. On considering these factors, it is planned to perform a survey on the application of various machine learning techniques that have been used in the domain of sentimental analysis for depression detection.

scholarly journals High-Performance Deep learning to Detection and Tracking Tomato Plant Leaf Predict Disease and Expert Systems

2021 ◽  
Vol 10 (2) ◽  
Author(s):  
Yaser AbdulAali Jasim
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
High Performance ◽  
Plant Disease ◽  
Detection System ◽  
Open Data ◽  
Data Set ◽  
Performance Rate

Nowadays, technology and computer science are rapidly developing many tools and algorithms, especially in the field of artificial intelligence.  Machine learning is involved in the development of new methodologies and models that have become a novel machine learning area of applications for artificial intelligence. In addition to the architectures of conventional neural network methodologies, deep learning refers to the use of artificial neural network architectures which include multiple processing layers. In this paper, models of the Convolutional neural network were designed to detect (diagnose) plant disorders by applying samples of healthy and unhealthy plant images analyzed by means of methods of deep learning. The models were trained using an open data set containing (18,000) images of ten different plants, including healthy plants. Several model architectures have been trained to achieve the best performance of (97 percent) when the respectively [plant, disease] paired are detected. This is a very useful information or early warning technique and a method that can be further improved with the substantially high-performance rate to support an automated plant disease detection system to work in actual farm conditions.

scholarly journals How artificial intelligence and machine learning can help healthcare systems respond to COVID-19

2020 ◽  
Author(s):  
Mihaela van der Schaar ◽  
Ahmed M. Alaa ◽  
Andres Floto ◽  
Alexander Gimson ◽  
Stefan Scholtes ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Patient Management ◽  
Healthcare Systems ◽  
Healthcare Resources ◽  
Global Pandemic ◽  
The World ◽  
Management Plans ◽  
Recent Developments ◽  
The Stability

AbstractThe COVID-19 global pandemic is a threat not only to the health of millions of individuals, but also to the stability of infrastructure and economies around the world. The disease will inevitably place an overwhelming burden on healthcare systems that cannot be effectively dealt with by existing facilities or responses based on conventional approaches. We believe that a rigorous clinical and societal response can only be mounted by using intelligence derived from a variety of data sources to better utilize scarce healthcare resources, provide personalized patient management plans, inform policy, and expedite clinical trials. In this paper, we introduce five of the most important challenges in responding to COVID-19 and show how each of them can be addressed by recent developments in machine learning (ML) and artificial intelligence (AI). We argue that the integration of these techniques into local, national, and international healthcare systems will save lives, and propose specific methods by which implementation can happen swiftly and efficiently. We offer to extend these resources and knowledge to assist policymakers seeking to implement these techniques.

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