A novel automated depression detection technique using text transcript

Concurrency and Computation

2023

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.

Section: Discussionmentioning

confidence: 99%

“…Second, most DL systems are complicated and incompatible with transfer learning, requiring a greater emphasis on lightweight models for transfer learning. 61…”

Section: Discussionmentioning

confidence: 99%

Recent advances in agricultural disease image recognition technologies: A review

Bondre

Concurrency and Computation

2023

“…As a result, a technique for recognizing plant diseases using a small training dataset is required. A CNN for plant disease recognition has been constructed in this research, and it can distinguish numerous species and diseases (Yadav & Sharma, 2023). To overcome the overfitting issue brought on by the small training dataset, a GAN‐based method is proposed (Yadav et al., 2023).…”

Section: Related Workmentioning

confidence: 99%

Crop disease identification segmentation algorithm based on Mask‐RCNN

Bondre

2023

Agronomy Journal

Leaf diseases include various diseases like abscission, gummosis, Alternaria solani, vesicatoria, and Pucciniales. Farmers from all around the world are constantly concerned about the health of various plants. Our proposed technology identifies various leaf diseases at an early stage, alerting farmers and allowing them to take necessary control measures. The collection contains 25,362 photos of normal leaves, bacterial spot, rust, early blight, and other disease‐infected leaves in Solanum tuberosum, Solanum lycopersicum, Malus, Zea mays, and Piper nigrum. In this study, a transfer learning ResNet 50 algorithm and a mask regional convolutional neural network are used to segment leaf diseases. An improved faster mask regional‐convolutional neural network (IFMR‐CNN) method is proposed to find the affected areas in the plant. The experiment first gathers photos of leaf disease for preprocessing, then utilizes a VGG annotator to create labels for the data sets, which are split into a test set and training set. The proposed IFMR‐CNN approach is capable of localizing and classifying the disease with 96% accuracy. Accuracy is calculated based on the proportion of properly identified leaf samples in a dataset out of all the leaf samples. Accuracy of disease localization in plant leaves can be measured by the percentage of correctly identified regions of interest in the leaf image that contain the disease symptoms.

“…The entire implementation would involve working on two phases–Audio and Text analysis. The implementation will be carried out using 2D CNN model and a BGRU model respectively 101 . Finally, the results of the above two phases will be combined and input into two FC networks to classify the depression and severity of depression.…”

Section: Further Improvement and Rationalementioning

confidence: 99%

“…The implementation will be carried out using 2D CNN model and a BGRU model respectively. 101 Finally, the results of the above two phases will be combined and input into two FC networks to classify the depression and severity of depression. This will not impose any limitations on the types of questions that can be asked in an interview and even allows for more generalizations.…”

Section: Further Improvement and Rationalementioning

confidence: 99%

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

Yadav

Sharma

Concurrency and Computation

2022

Self Cite

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.