Modeling of Explainable Artificial Intelligence for Biomedical Mental Disorder Diagnosis

Hilal, Anwer Mustafa; ISSAOUI, Im鑞e; Obayya, Marwa; Al‐Wesabi, Fahd N.; Nemri, Nadhem; Hamza, Manar Ahmed; Duhayyim, Mesfer Al; Zamani, Abu Sarwar

doi:10.32604/cmc.2022.022663

Cited by 6 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Generally, the older adults find it easy to use these devices, though they have a few concerns about privacy and understanding of the functions of the devices at certain times. The mobile FD systems can be easily assessed with extreme sophistication since live information can be retrieved about the falls of older adults [8,9].…”

Section: Introductionmentioning

confidence: 99%

Deep Transfer Learning-Enabled Activity Identification and Fall Detection for Disabled People

Eltahir¹,

Yousif²,

Alrowais³

et al. 2023

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

The human motion data collected using wearables like smartwatches can be used for activity recognition and emergency event detection. This is especially applicable in the case of elderly or disabled people who live self-reliantly in their homes. These sensors produce a huge volume of physical activity data that necessitates real-time recognition, especially during emergencies. Falling is one of the most important problems confronted by older people and people with movement disabilities. Numerous previous techniques were introduced and a few used webcam to monitor the activity of elderly or disabled people. But, the costs incurred upon installation and operation are high, whereas the technology is relevant only for indoor environments. Currently, commercial wearables use a wireless emergency transmitter that produces a number of false alarms and restricts a user's movements. Against this background, the current study develops an Improved Whale Optimization with Deep Learning-Enabled Fall Detection for Disabled People (IWODL-FDDP) model. The presented IWODL-FDDP model aims to identify the fall events to assist disabled people. The presented IWODL-FDDP model applies an image filtering approach to pre-process the image. Besides, the EfficientNet-B0 model is utilized to generate valuable feature vector sets. Next, the Bidirectional Long Short Term Memory (BiLSTM) model is used for the recognition and classification of fall events. Finally, the IWO method is leveraged to fine-tune the hyperparameters related to the BiLSTM method, which shows the novelty of the work. The experimental analysis outcomes established the superior performance of the proposed IWODL-FDDP method with a maximum accuracy of 97.02%.

show abstract

Section: Introductionmentioning

confidence: 99%

Deep Transfer Learning-Enabled Activity Identification and Fall Detection for Disabled People

Eltahir¹,

Yousif²,

Alrowais³

et al. 2023

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

show abstract

“…Neutrosophic sets can be utilized to represent the degrees of truth and falsehood associated with the concealed data, ensuring that the embedded information remains Undetectable to unauthorized individuals or systems. [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Robust and Efficient ECG Classification with Privacy Preservation using LSTM+CNN and Adaptive Least Signal Bit

Sakr,

Ahmed,

Rezk

2023

IJCI. International Journal of Computers and Information

View full text Add to dashboard Cite

Maintaining patient data privacy is a critical concern in the medical domain. However, safeguarding crucial information during data transmission presents significant challenges for healthcare professionals and organizations. Additionally, the accurate classification of electrocardiogram (ECG) signals is hindered by the need for supplementary information to ensure precise examinations and accurate diagnoses. This paper introduces a novel hybrid framework that addresses both ECG classification challenges and privacy preservation. The proposed framework consists of two main phases. In the first phase, a combination of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) with an attention gate is employed for ECG classification. This phase enhances the accuracy of ECG signal classification by incorporating deep learning techniques. The second phase of the framework utilizes adaptive least signal bit with neutrosophic logic to conceal critical medical data during transmission. Neutrosophic sets, which represent data as degrees of truth, falsehood, and indeterminacy, are leveraged and passed through an embedding layer. In the sender part, important data is converted into three degrees and embedded within the ECG signal using true and false degrees. An intermediate set acts as a shared dynamic key between the sender and receiver. The receiver can reconstruct the important data using either the shared dynamic key or the intermediate set. The experiments done prove the efficiency of different parts of the framework; the augmentation model archives 14.67 and 37.89 for the inception score and freshet inception distance, respectively. The hybrid approach also achieves 99.01 and 99.12 for dice score and accuracy, respectively.

show abstract

Explainable AI for Autism Classification in Children

Magboo

2022

Smart Innovation, Systems and Technologies

View full text Add to dashboard Cite

Modeling of Explainable Artificial Intelligence for Biomedical Mental Disorder Diagnosis

Cited by 6 publications

References 17 publications

Deep Transfer Learning-Enabled Activity Identification and Fall Detection for Disabled People

Deep Transfer Learning-Enabled Activity Identification and Fall Detection for Disabled People

Robust and Efficient ECG Classification with Privacy Preservation using LSTM+CNN and Adaptive Least Signal Bit

Explainable AI for Autism Classification in Children

Contact Info

Product

Resources

About