Deep neural networks (DNNs) have proven explicit contributions in making autonomous driving cars and related tasks such as semantic segmentation, motion tracking, object detection, sensor fusion, and planning. However, in challenging situations, DNNs are not generalizable because of the inherent domain shift due to the nature of training under the i.i.d. assumption. The goal of semantic segmentation is to preserve information from a given image into multiple meaningful categories for visual understanding. Particularly for semantic segmentation, pixel-wise annotation is extremely costly and not always feasible. Domain generalization for semantic segmentation aims to learn pixel-level semantic labels from multiple source domains and generalize to predict pixel-level semantic labels on multiple unseen target domains. In this survey, for the first time, we present a comprehensive review of DG for semantic segmentation. we present a comprehensive summary of recent works related to domain generalization in semantic segmentation, which establishes the importance of generalizing to new environments of segmentation models. Although domain adaptation has gained more attention in segmentation tasks than domain generalization, it is still worth unveiling new trends that are adopted from domain generalization methods in semantic segmentation. We cover most of the recent and dominant DG methods in the context of semantic segmentation and also provide some other related applications. We conclude this survey by highlighting the future directions in this area.

Cardiovascular disease is now one of the leading causes of morbidity and mortality. Electrocardiogram (ECG) is a reliable tool for monitoring the health of the cardiovascular system. Currently, there has been a lot of focus on accurately categorizing heartbeats. There is a high demand for automatic ECG classification systems to assist medical professionals. But there is a big issue in obtaining original data extensively in medical domains in rare diseases, so it is essential to have a robust solution adopting this challenge. So, we need a solution that can address the problem of tackling data insufficiency, which is a major concern nowadays for medical applications. Without having, significant training samples the overall output can be demised. But the recent works on ECG classification did not address the challenge of solving the data insufficiency label problem. To overcome this issue, we developed a new generative adversarial network-based deep learning method called HeartNet for tackling the data insufficiency problem. The proposed deep learning method is compressed by a multi-head attention mechanism on CNN architecture. The main challenge of insufficient data labels is solved by adversarial data synthesis by adopting a generative adversarial network (GAN) with generating additional training samples. It drastically improves the overall performance of the proposed method by 5-10% on each insufficient data label category. Since the training samples are increased. We evaluated our proposed method utilizing the MIT-BIH dataset. Our proposed method has shown $99.67\pm {0.11}$ accuracy and 89.24± 1.71 MCC trained with adversarial data synthesized dataset. However, we have also utilized two individual datasets as Atrial Fibrillation Detection Database and PTB Diagnostic Database to see the performance and generalization of our proposed model on ECG classification. The effectiveness and robustness of the proposed method are validated by extensive experiments, comparison, and analysis. Later on, we also highlighted some limitations of this work.

Artificial intelligence is a spectacular part of computer engineering that has earned a compelling diversion in the field of medical data classification due to its state-of-art algorithmic strength and learning capabilities. Machine Learning is a major sub-domain of artificial intelligence, where it has become one of most promising fields in computer science. In recent years, there is a large spectrum of healthcare and biomedical data has been growing intensely. Due to the huge labeled or unlabeled data, it is important to have a compact and robust machine learning solution for classification. Several optimizers have been deployed to improve the inclusive performance of machine learning models. The classification of machine learning models depend on several factors. This comprehensive review paper aims to insight the current stage of optimized machine learning success on medical data classification. Increasing number of unstructured medical data has been utilizing in machine learning algorithms to predict intuitions. But it is difficult to inherent immense intuition from those data. So machine learning researchers have utilized state-of-art optimizers and novel feature selection techniques to overcome and emend the performance accuracy. We have highlighted some recent literatures, which exhibits the robust impact of optimizers and feature selection on machine learning techniques on medical data characterization. On the other hand, a clean-cut introduction on machine learning and theoretical outlook of widely utilized optimization techniques like genetic algorithm, gray wolf optimization, and particle swarm optimization are discussed for initial understanding to the optimization techniques.

Preserving linguistic dialects is paramount for preserving cultural identity, fostering diversity, and maintaining cultural prestige. In the context of Bengali, the language exhibits religious distinctions through its two major dialects: the West Bengal (Hindu majority) and Bangladesh (Muslim majority) dialects, deeply rooted in the historical evolution of Bangla. Understanding and respecting these dialect-based nuances is crucial for Human-AI collaborative tools to generate culturally appropriate content and effectively assist human writers without limiting their creativity. To address these challenges, we introduce DiaFrame, a comprehensive framework tailored for understanding and tuning dialects in human-AI collaborative creative writing spaces, focusing on Bengali. It integrates advanced components such as active learning, real-time processing of human feedback, memory management, and contextual understanding to enhance user experience and ensure culturally sensitive content generation. Our contributions include providing a holistic end-to-end solution that enables the AI model to actively learn, adapt, and generate content aligned with user preferences and dialectical variations, ultimately fostering effective collaboration between humans and AI in creative writing endeavors.

Electrocardiography (ECG)-based arrhythmia classification intends to have a massive role in cardiovascular disease monitoring and early diagnosis. However, ECG datasets are mostly imbalanced and have regularization to use real-time patient data due to privacy concerns. Traditional models do not generalize on unseen cases and are also unable to preserve data privacy. Which incentivizes performance degradation in existing models with privacy limitations. To tackle generalization and privacy issues together, we introduce the framework SF-ECG, a source-free domain adaptation approach for patient-specific ECG classification. This framework does not require source data during adaptation, which solves the privacy issue during adaptation. We adopt a generative model (GAN) that learns to synthesize patient-specific ECG data in data-inefficient classes to make additional source data for imbalanced classes. Then, we use the local structure clustering method to strongly align target ECG features with similar neighbors. After seizing clustered target features, we use a classifier that is trained on source data with generated source samples, which makes the model generalizable in classifying unseen data. Empirical results under different experimental conditions in various interdomain datasets prove that the proposed framework achieves 0.8% improvements in UDA settings, along with preserving privacy and generalizability.