An Attention-Based Mood Controlling Framework for Social Media Users

Ghosh, Tapotosh; Banna, Md. Hasan Al; Angona, Tazkia Mim; Nahian, Md. Jaber Al; Uddin, Mohammed Nasir; Kaiser, M. Shamim; Mahmud, Mufti

doi:10.1007/978-3-030-86993-9_23

Cited by 28 publications

(3 citation statements)

References 22 publications

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“…Yan ZHAO et al [21] presents an attention-based Long Short-Term Memory (LSTM) model for depression detection to make full use of the difference between depression and non-depression between timeframes. Ghosh T. et al [22] proposed an emotion detection-based mood control framework that reorganizes the social media posts to match user's mental state based on Attention mechanism, Bidirectional Long Short Term Memory (BiLSTM), and Convolutional Neural Network (CNN).…”

Section: Related Workmentioning

confidence: 99%

illustration of attention mechanism in LSTM algorithm for analyzing its performance

Bhuvaneswari

Praba²

2022

ijhs

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The advent of technologies and equipments in Information Technology and Communication has increased the use of smart phones and social media by all people irrespective of age and gender. Many times people find social media posts as the outlet for draining all their emotions. Depression is a common mental health problem that ranges from low mood and loss of interest to persistent feeling of sadness and self-destruction. Mental Health Experts are interested in analyzing the physical and social media behavior of patients to diagnose depression. Attention Mechanism imitates the human cognitive of considering only the most relevant data needed for a particular analysis. LSTM is a state-of-the-art Recurrent Neural Network architecture used for depression analysis. In this paper we are further improving the LSTM Algorithm by proposing an Attention Mechanism that makes the model robust and improved. This major role of our Attention Mechanism is identifying the Prime Words and Supporting words that are relevant for the analysis. Distance is calculated between non-prime words and prime words, weights are assigned based on this calculation, higher weight being given to most relevant words. Making use of Attention helps us to focus only on the relevant data and neglect the irrelevant data.

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Section: Related Workmentioning

confidence: 99%

illustration of attention mechanism in LSTM algorithm for analyzing its performance

Bhuvaneswari

Praba²

2022

ijhs

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“…Diagnosis of DR can be performed through either manual examination by an ophthalmologist or by utilising an automated system. With the advancements in Artificial Intelligence (AI) techniques, automated system development has been facilitated in many application areas including anomaly detection [ 3 ], brain signal analysis [ 4 ], neurodevelopmental disorder assessment and classification focusing on autism [ 5 , 6 , 7 ], neurological disorder detection and management [ 8 ], supporting the detection and management of the COVID-19 pandemic [ 9 ], cyber security and trust management [ 10 , 11 , 12 , 13 ], various disease diagnosis [ 14 , 15 , 16 , 17 ], smart healthcare service delivery [ 18 , 19 ], text and social media mining [ 20 , 21 ], understanding student engagement [ 22 , 23 ], etc. As can be seen in the literature, automated systems for early disease detection have been a major area of development.…”

Section: Introductionmentioning

confidence: 99%

Classification of Diabetic Retinopathy Disease Levels by Extracting Spectral Features Using Wavelet CNN

Sundar,

Subramanian,

Mahmud

2024

Diagnostics

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Diabetic retinopathy (DR) arises from blood vessel damage and is a leading cause of blindness on a global scale. Clinical professionals rely on examining fundus images to diagnose the disease, but this process is frequently prone to errors and is tedious. The usage of computer-assisted techniques offers assistance to clinicians in detecting the severity levels of the disease. Experiments involving automated diagnosis employing convolutional neural networks (CNNs) have produced impressive outcomes in medical imaging. At the same time, retinal image grading for detecting DR severity levels has predominantly focused on spatial features. More spectral features must be explored for a more efficient performance of this task. Analysing spectral features plays a vital role in various tasks, including identifying specific objects or materials, anomaly detection, and differentiation between different classes or categories within an image. In this context, a model incorporating Wavelet CNN and Support Vector Machine has been introduced and assessed to classify clinically significant grades of DR from retinal fundus images. The experiments were conducted on the EyePACS dataset and the performance of the proposed model was evaluated on the following metrics: precision, recall, F1-score, accuracy, and AUC score. The results obtained demonstrate better performance compared to other state-of-the-art techniques.

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“…disorder detection and management [2,[33][34][35][36][37][38][39][40][41][42][43][44], supporting the detection and management of the COVID-19 pandemic [45][46][47][48][49][50][51][52], elderly monitoring and care [53][54][55][56], cyber security and trust management [57][58][59][60][61][62], various disease diagnosis [63][64][65][66][67][68][69], smart healthcare service delivery [70][71][72], text and social media mining [73][74][75][76], personalised learning [77][78][79][80], earthquake detection…”

mentioning

confidence: 99%

Explainable Artificial Intelligence in Alzheimer’s Disease Classification: A Systematic Review

Viswan,

Shaffi,

Mahmud

et al. 2023

Cogn Comput

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The unprecedented growth of computational capabilities in recent years has allowed Artificial Intelligence (AI) models to be developed for medical applications with remarkable results. However, a large number of Computer Aided Diagnosis (CAD) methods powered by AI have limited acceptance and adoption in the medical domain due to the typical blackbox nature of these AI models. Therefore, to facilitate the adoption of these AI models among the medical practitioners, the models' predictions must be explainable and interpretable. The emerging field of explainable AI (XAI) aims to justify the trustworthiness of these models' predictions. This work presents a systematic review of the literature reporting Alzheimer's disease (AD) detection using XAI that were communicated during the last decade. Research questions were carefully formulated to categorise AI models into different conceptual approaches (e.g., Post-hoc, Ante-hoc, Model-Agnostic, Model-Specific, Global, Local etc.) and frameworks (Local Interpretable Model-Agnostic Explanation or LIME, SHapley Additive exPlanations or SHAP, Gradient-weighted Class Activation Mapping or GradCAM, Layer-wise Relevance Propagation or LRP, etc.) of XAI. This categorisation provides broad coverage of the interpretation spectrum from intrinsic (e.g., Model-Specific, Ante-hoc models) to complex patterns (e.g., Model-Agnostic, Post-hoc models) and by taking local explanations to a global scope. Additionally, different forms of interpretations providing in-depth insight into the factors that support the clinical diagnosis of AD are also discussed. Finally, limitations, needs and open challenges of XAI research are outlined with possible prospects of their usage in AD detection.

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An Attention-Based Mood Controlling Framework for Social Media Users

Cited by 28 publications

References 22 publications

illustration of attention mechanism in LSTM algorithm for analyzing its performance

illustration of attention mechanism in LSTM algorithm for analyzing its performance

Classification of Diabetic Retinopathy Disease Levels by Extracting Spectral Features Using Wavelet CNN

Explainable Artificial Intelligence in Alzheimer’s Disease Classification: A Systematic Review

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