Identity Management System (IDMS) refers to how users or individuals are identified and authorized to use organizational systems and services. Since traditional identity management and authentication systems rely heavily on a trusted central authority, they cannot mitigate the effects of single points of failure. As a decentralized and distributed public ledger in a peer-to- peer (P2P) network, Blockchain (BC) technology has garnered a considerable amount of attention in the field of IDMS in recent years. Through Self-Sovereign Identity (SSI), users can have full authority over their digital identity. Successful implementation of a BC-based IDMS can significantly increase the degree of privacy and security of a user’s SSI. However, the integration of BC-based IMDS to provide a user with SSI is still an unorganized area of research in its early stages of development. This article presents an extensive literature review of state-of-the-art academic publications as well as commercial market offerings regarding the applicability of BC-based SSI solutions. It also provides a detailed preliminary regarding the building blocks of blockchain technology and a progressive roadmap of IDMS solutions. In order to develop an effective BC-based IDMS solution that focuses on securing a user’s SSI, this article outline five essential components of a BC-based IDMS: authentication, integrity, privacy, trust, and simplicity. Furthermore, we perform a security analysis that outlines several types of adversarial threats that can cause potential damage to the BC-based IDMS. We identify and discuss associated issues and challenges by analyzing several notable BC-based IDMS solutions in academic literature. We also highlight potential research gaps and provide future research scope.

The speech emotion recognition system determines a speaker's emotional state by analyzing his/her speech audio signal. It is an essential at the same time a challenging task in human-computer interaction systems and is one of the most demanding areas of research using artificial intelligence and deep machine learning architectures. Despite being the world's seventh most widely spoken language, Bangla is still classified as one of the low-resource languages for speech emotion recognition tasks because of inadequate availability of data. There is an apparent lack of speech emotion recognition dataset to perform this type of research in Bangla language. This article presents a Bangla language-based emotional speech-audio recognition dataset to address this problem. BanglaSER is a Bangla language-based speech emotion recognition dataset. It consists of speech-audio data of 34 participating speakers from diverse age groups between 19 and 47 years, with a balanced 17 male and 17 female nonprofessional participating actors. This dataset contains 1467 Bangla speech-audio recordings of five rudimentary human emotional states, namely angry, happy, neutral, sad, and surprise. Three trials are conducted for each emotional state. Hence, the total number of recordings involves 3 statements × 3 repetitions × 4 emotional states (angry, happy, sad, and surprise) × 34 participating speakers = 1224 recordings + 3 statements × 3 repetitions × 1 emotional state (neutral) × 27 participating speakers = 243 recordings, resulting in a total number of recordings of 1467. BanglaSER dataset is created by recording speech-audios through smartphones, and laptops, having a balanced number of recordings in each category with evenly distributed participating male and female actors, and would serve as an essential training dataset for the Bangla speech emotion recognition model in terms of generalization. BanglaSER is compatible with various deep learning architectures such as Convolutional neural networks, Long short-term memory, Gated recurrent unit, Transformer, etc. The dataset is available at https://data.mendeley.com/datasets/t9h6p943xy/5 and can be used for research purposes.

This paper introduces a newly curated dataset named “BDMediLeaves” that includes a diverse collection of leaf images of ten distinct medicinal plants from various regions in Dhaka, Bangladesh. The ten distinct categories are Phyllanthus emblica, Terminalia arjuna, Kalanchoe pinnata, Centella asiatica, Justicia adhatoda, Mikania micrantha, Azadirachta indica, Hibiscus rosa-sinensis, Ocimum tenuiflorum, and Calotropis gigantea. The dataset contains a total of 2,029 original leaf images, along with an additional 38,606 augmented images. Each original image was meticulously captured under natural lighting conditions with an appropriate background. Experts provided accurate labeling for each image, ensuring its seamless integration into various machine learning (ML) and deep learning (DL) models. This comprehensive dataset holds immense potential for researchers in utilizing various ML and DL methods to make significant advancements in the healthcare and pharmaceutical sectors. It serves as a valuable resource for future investigations, laying the foundation for crucial developments in these domains.

Wildland fires, a natural calamity, pose a significant threat to both human lives and the environment while causing extensive economic damage. As the use of Unmanned Aerial Vehicles (UAVs) with computer vision in disaster management continues to grow, there is a rising need for effective wildfire classification and localization. We propose a multi-stream hybrid deep learning model with a dual-stream attention mechanism for classifying wildfires from aerial and territorial images. Our proposed method incorporates a pre-trained EfficientNetB7 and customized Attention Connected Network (ACNet). This approach demonstrates exceptional classification performance on two widely recognized benchmark datasets. Bayesian optimization is employed for the purpose of refining and optimizing the hyperparameters of the model. The proposed model attains 97.45%, 98.20%, 97.10%, and 97.12% as accuracy, precision, recall, and F1-score, respectively, on the FLAME dataset. Moreover, while evaluated on the DeepFire dataset, the model achieves accuracy, precision, recall, and F1-scores of 95.97%, 95.19%, 96.01%, and 95.54%, respectively. The proposed method achieved a TNR of 95.5% and a TPR of 99.3% on the FLAME dataset, as well as a TNR of 94.47% and a TPR of 96.82% on the DeepFire dataset. This performance surpasses numerous state-of-the-art methods. To demonstrate the interpretability of our model, we incorporated the GRAD-CAM technique, which enables us to precisely identify the fire location within the feature map. This finding illustrates the efficacy of the model in accurately categorizing wildfires, even in areas with less fire activity.