Titlebook: Data Management, Analytics and Innovation; Proceedings of ICDMA Neha Sharma,Amol C. Goje,Alfred M. Bruckstein Conference proceedings 2024 T

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efficient while giving scientists, planners, and decision-makers access to a variety of valuable data for effective policy and decision-making. Change detection in satellite images is a key application area of the proposed system.

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Forecast of Energy Demand Using Temporal Fusion Transformer, novelty of TFT model lies in combining the strengths of recurrent and convolutional neural networks, ultimately resulting in improved accuracy in forecasting. The dataset used in this study consists of data on power consumption per quarter hour of 370 consumers, over a four-year period. The results

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Mental Health Prediction Using Artificial Intelligence,ms to predict the type of Mental Disorder using Artificial Intelligence, specifically the Random Forest Algorithm which is known for its effectiveness in classification tasks. The motivation for this study is the lack of a model which can accurately predict the type of mental health disorder of any

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VGGish Deep Learning Model: Audio Feature Extraction and Analysis, One of the key challenges in speech recognition systems is the efficient extraction of discriminative features from raw audio signals which directly impacts their performance. This study recommends a distinct method to enhance the precision and resilience of speech recognition systems in which audi

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Stacking Ensemble-Based Approach for Sarcasm Identification with Multiple Contextual Word Embeddingwever, relying exclusively on a singular word embedding approach can hinder our capacity to capture the intricate interconnections between words within texts. To address this issue, this paper introduces a stacking ensemble model for sarcasm detection that harnesses the strengths of multiple context

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