Digital Implementation of the Softmax Activation Function and the Inverse Softmax Function

S, Raghuram; Bharadwaj, Anirudh S; Deepika, S K; Khadabadi, Mridula S; Jayaprakash, Aditya

doi:10.1109/i4c57141.2022.10057747

Cited by 12 publications

(3 citation statements)

References 10 publications

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“…Following the Bi-LSTM units, a SoftMax layer has been added. The SoftMax function normalization enables us to interpret the output of the Bi-LSTM units as a probability distribution over musical notes [12] . Model predictions for the current time step, denoted as t Y ∃ , are obtained through sampling.…”

Section: Bilmmentioning

confidence: 99%

A music generation model based on Bi-LSTM

bai

2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

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The unidirectional LSTM based music generation model does not take into account the influence of future information when generating music. It solely focuses on learning the dependencies of the current moment on past information, resulting in music with poor stability and subpar quality. To address this issue, we have developed a music generation model based on bidirectional LSTM. During the training phase, this model effectively captures musical information from both past and future time steps, resulting in a probability distribution of musical elements that closely approximates real-world music. This, in turn, leads to enhanced structural stability and improved music quality in the generated compositions. Finally, we conducted validation experiments on our proposed approach, and the results unequivocally demonstrate its effectiveness.

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Section: Bilmmentioning

confidence: 99%

A music generation model based on Bi-LSTM

bai

2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

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“…These two layers were repeated a second time in the model structure. The last layer is a SoftMax activation [75,76]…”

Section: Layersmentioning

confidence: 99%

Advancing Medical Assistance: Developing an Effective Hungarian-Language Medical Chatbot with Artificial Intelligence

Simon,

Hartveg,

Dénes-Fazakas

et al. 2024

Information

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In recent times, the prevalence of chatbot technology has notably increased, particularly in the realm of medical assistants. However, there is a noticeable absence of medical chatbots that cater to the Hungarian language. Consequently, Hungarian-speaking people currently lack access to an automated system capable of providing assistance with their health-related inquiries or issues. Our research aims to establish a competent medical chatbot assistant that is accessible through both a website and a mobile app. It is crucial to highlight that the project’s objective extends beyond mere linguistic localization; our goal is to develop an official and effectively functioning Hungarian chatbot. The assistant’s task is to answer medical questions, provide health advice, and inform users about health problems and treatments. The chatbot should be able to recognize and interpret user-provided text input and offer accurate and relevant responses using specific algorithms. In our work, we put a lot of emphasis on having steady input so that it can detect all the diseases that the patient is dealing with. Our database consisted of sentences and phrases that a user would type into a chatbot. We assigned health problems to these and then assigned the categories to the corresponding cure. Within the research, we developed a website and mobile app, so that users can easily use the assistant. The app plays a particularly important role for users because it allows them to use the assistant anytime and anywhere, taking advantage of the portability of mobile devices. At the current stage of our research, the precision and validation accuracy of the system is greater than 90%, according to the selected test methods.

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“…Finally, the output layer (Dense) comprises 7 neurons, corresponding to the number of classes in the classification task. To produce a probability distribution over the classes, we utilize the softmax activation function [4]. This distribution represents the model's confidence for each class.…”

Section: Figure 3: Incorporated Modelmentioning

confidence: 99%

Detect Melanoma Skin Cancer Using An Improved Deep Learning CNN Model With Improved Computational Costs

Ganesh

Somasundaram

2023

Preprint

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Melanoma cancer has been considered as one of the most deadliest cancer. Melanoma is a highly malignant form of skin cancer that originates from melanocytes, the cells responsible for producing skin pigment. It is characterized by the uncontrolled proliferation of abnormal cells, which have the potential to invade surrounding tissues and spread to distant parts of the body. In this work, we aim to classify the skin disease into 7 classes. Our objective is to propose a deep learning CNN model to improve the accuracy of melanoma detection by customizing the number of layers in the network architecture and activation functions followed by reducing the computational cost. A comparitive study is made between the improved model and the use of pre-trained Models like Resnet, DenseNet, Inception, VGG and DenseNet-II which has been giving impeccable accuracy. The HAM10000 dataset is used for research and we have got better results for the proposed model. Also, graphical results have been obtained for the same.

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Digital Implementation of the Softmax Activation Function and the Inverse Softmax Function

Cited by 12 publications

References 10 publications

A music generation model based on Bi-LSTM

A music generation model based on Bi-LSTM

Advancing Medical Assistance: Developing an Effective Hungarian-Language Medical Chatbot with Artificial Intelligence

Detect Melanoma Skin Cancer Using An Improved Deep Learning CNN Model With Improved Computational Costs

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