Review on Automated Depression Detection from audio visual clue using Sentiment Analysis

Yadav, Uma; Sharma, Ashish K.

doi:10.1109/icesc51422.2021.9532751

Cited by 10 publications

(7 citation statements)

References 23 publications

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“…When it comes to plant pathology, the scale and diversity of database may have an effect on the usefulness of various DL methods. The picture database employed in this study includes photos of a wide range of crops, each of which had different features in terms of the number of samples, illnesses, and environmental changes 56 . The results showed that CNN was the most successful strategy for dealing with plant disease image identification issues, although the recognition quality was also restricted by the quantity of the image collection 57 …”

Section: Discussionmentioning

confidence: 99%

“…The picture database employed in this study includes photos of a wide range of crops, each of which had different features in terms of the number of samples, illnesses, and environmental changes. 56 The results showed that CNN was the most successful strategy for dealing with plant disease image identification issues, although the recognition quality was also restricted by the quantity of the image collection. 57 As can be seen from the above, transfer learning can take models learned in one domain and relate them to another, resulting in high-quality model learning and construction on the basis of limited amounts of data.…”

Section: Discussionmentioning

confidence: 99%

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Recent advances in agricultural disease image recognition technologies: A review

Bondre

Patil

2023

Concurrency and Computation

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In the world of intelligent agriculture, agricultural disease picture detection plays a critical role. Plant disease identification must be efficient if agricultural production is to be increased sustainably. Anomalies in plants affected by viruses, insects, nutritional deficiencies, or poor weather conditions have traditionally been diagnosed by human experts. Deep learning (DL) and transfer learning, two sophisticated machine learning techniques synonymous with the advancement of artificial intelligence technologies in recent years, have started to be used for the identification of agricultural diseases.However, a variety of significant obstacles exist in the way of widespread use of these approaches. This paper looks at DL and transfer learning in particular, and discusses recent developments in using these advanced technologies to recognize agricultural disease images. Many DL architectures have recently been adopted, along with visualization tools, which are critical for identifying signs and classifying plant diseases. The analysis and evaluation of these two approaches show that transfer learning is the better choice given existing agricultural disease data tools. The paper then looks at the key problems that need to be addressed for research in this area to advance, such as image dataset creation, the collection of big data auxiliary domains, and the optimization of the transfer learning process. The construction of technically feasible agricultural disease image recognition systems requires the creation of image datasets collected under real cultivation conditions. This survey aims to learn more about DL capabilities in plant disease detection to increase device efficiency and accuracy in future research.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Recent advances in agricultural disease image recognition technologies: A review

Bondre

Patil

2023

Concurrency and Computation

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“…According to the study, video clips can be used to identify and diagnose prospective depressive patients by recognizing facial expression changes between depressed patients and normal individuals. 65 In this paper, the work described how videos of depression patients and a control group were collected. A person-specific active appearance model was proposed to extract the important facial characteristics from the gathered facial videos.…”

Section: Depression Detection From Video Featuresmentioning

confidence: 99%

Review of automated depression detection: Social posts, audio and video, open challenges and future direction

Yadav

Sharma

Patil

2022

Concurrency and Computation

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Depression is the primary cause of illness and injury in the country, with over 280 million people suffering from it as per the 2021 survey. Depression is one of the most common psychiatric illnesses in the world, affecting millions of people. Early detection of major depression symptoms and treatment with timely intervention can help to prevent the emergence of major depression. This has generated the need for some novel techniques to be utilized for depression detection to help doctors in diagnosing and treating depression effectively. Depression can be investigated through online posts, audio files, facial expressions, as well as through video files. In this regard, the research presents a detailed survey of the existing machine learning methods in depression detection, along with the different datasets available. The research explores automated depression detection strategies and the various ways to detect depression from text, audio, and video, and even examines the various systems and procedures for detecting depression using various criteria. More than 140 related research articles were considered for this review, with 80 of them being processed for comparative analysis based on various key performance indicators. The classifications of various methods used to detect stress, anxiety, and depression are highlighted followed by open research challenges and issues while analyzing depression techniques.

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“…Multi‐modal fusion and deep learning technologies have largely contributed to recent improvements 3 . Automatic detection, like in a clinical interview in which a psychiatrist identifies the patient's mental state based on his words and behavior might be derived from a variety of signals including video, audio, and text 4 . Audio characteristics are the most commonly researched of the three modalities 5 while text elements are rarely investigated on their own 6 .…”

Section: Introductionmentioning

confidence: 99%

“…3 Automatic detection, like in a clinical interview in which a psychiatrist identifies the patient's mental state based on his words and behavior might be derived from a variety of signals including video, audio, and text. 4 Audio characteristics are the most commonly researched of the three modalities 5 while text elements are rarely investigated on their own. 6 The main aim of the current research is to enhance the accuracy and efficiency of detection.…”

Section: Introductionmentioning

confidence: 99%

A novel automated depression detection technique using text transcript

Yadav

Sharma

2022

Int J Imaging Syst Tech

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Review on Automated Depression Detection from audio visual clue using Sentiment Analysis

Cited by 10 publications

References 23 publications

Recent advances in agricultural disease image recognition technologies: A review

Recent advances in agricultural disease image recognition technologies: A review

Review of automated depression detection: Social posts, audio and video, open challenges and future direction

A novel automated depression detection technique using text transcript

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