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One of the main factors causing suicide is depression. However, many cases of depression go undiagnosed because they are not correctly diagnosed. An increasing number of people with mental illnesses express their emotions online using tools like social media (SM) and specialized websites. Recently, efforts have been made to use Machine Learning (ML) and deep learning (DL) models to predict depression from SM platforms. However, it is problematic that most ML algorithms now provide no explanation. As a result, this study proposes a novel Deep Learning (DL) model called residual network 50, which includes optimal long short-term memory (RNT-OLSTM) for Depression Detection (DD) on Twitter data. In addition, to address the issue of data imbalance in the Twitter data, a cluster-based oversampling approach is used, which considerably reduces the possibility of bias towards the dominant class (non-depressed).. Finally, the embedding layers are inputted to RNT-OLSTM for DD, in which the hyperparameters of the network are tuned using the Sine Chaotic map and constriction factor-based Coyote Optimization Algorithm (SCCOA) to minimize the prediction loss. The out-comes prove that the proposed system performs better than the existing schemes for the DD of imbalanced Twitter data with higher detection rates.

In our human body bones are the most significant part, which helps people to move and perform several activities. But, the cancer is caused by producing abnormal cell, which is rapidly spread to the whole parts of the body. Bone cancer is one of the critical types due to its malignancy more than other cancers. The approach involves preprocessing and segmentation of input images to remove noise and resize images, followed by feature extraction using a Convolutional histogram of oriented gradients (ConvHisOrGrad). The ROI extraction helps to accurately identify abnormal parts around the cancerous area. The Extreme Deep Convolutional learning machine (Ex‐ConVLM) is used for normal and cancerous bone classification based on the texture properties of bone MRI images. The proposed technique was evaluated using a dataset of 220 bone MRIs for tumor classes classified as necrotic, non‐tumor, and viable‐tumor. Results showed that the proposed technique outperformed existing techniques with the highest accuracy of 97% for the necrotic tumor class, 98.2% for the non‐tumor class, and 98.6% for the viable tumor class. The fine‐tuned model shows promising performance in detecting malignancy in bone based on histological images. In summary, the proposed technique utilizes deep learning architectures and ROI extraction for the accurate identification of abnormal parts in bone MRI images, achieving state‐of‐the‐art performance in the detection and categorization of bone cancers. [ABSTRACT FROM AUTHOR]

Image Captioning is an emergent topic of research in the domain of artificial intelligence (AI). It utilizes an integration of Computer Vision (CV) and Natural Language Processing (NLP) for generating the image descriptions. It finds use in several application areas namely recommendation in editing applications, utilization in virtual assistance, etc. The development of NLP and deep learning (DL) models find useful to derive a bridge among the visual details and textual semantics. In this view, this paper introduces an Oppositional Harris Hawks Optimization with Deep Learning based Image Captioning (OHHODLIC) technique. The OHHO-DLIC technique involves the design of distinct levels of pre-processing. Moreover, the feature extraction of the images is carried out by the use of EfficientNet model. Furthermore, the image captioning is performed by bidirectional long short term memory (BiLSTM) model, comprising encoder as well as decoder. At last, the oppositional Harris Hawks optimization (OHHO) based hyperparameter tuning process is performed for effectively adjusting the hyperparameter of the EfficientNet and BiLSTM models. The experimental analysis of the OHHO-DLIC technique is carried out on the Flickr 8k Dataset and a comprehensive comparative analysis highlighted the better performance over the recent approaches. [ABSTRACT FROM AUTHOR]