The field of low-resource sentiment analysis has seen significant developments in recent years. This research review SLR evaluates the approaches and data sources utilized in low-resource sentiment analysis by deep learning. The primary aim is to discover suitable approaches for future sentiment analysis in low-resource. Our studies explore various languages, models, and data sources expressing a desire to create effective approaches. Our emphasis lies in the critical evaluation of the approaches and the datasets utilized, to identify areas where further research is needed. Our analysis study adds to the existing body of literature reviews, encompassing multilingual low-resource sentiment analysis research spanning from 2018 to 2023. The findings indicate that the transfer learning approach is the most frequently used, followed by word embedding learning and machine translation systems. Additionally, the study shows that social media is the most used platform for data collection, followed by product reviews, movies, and hotels. There has been a significant surge in the adoption of pre-trained transformers, indicating a growing interest in exploring the potential of these models for low-resource languages within the natural language processing (NLP) community. This trend is largely attributed to the novel nature of these models and their feature of being non-labour intensive. However, the scarcity of annotated datasets for such languages remains a major hurdle. finally, these research findings are relevant and informative for any researcher working in the field of low-resource multilingual sentiment analysis. The study introduces a conceptual framework for performing sentiment analysis in low-resource. The study provides a valuable resource for future researchers.

Hydraulic fracturing has made the production of gas more economical. Shale gas possesses the potential to arise as a main natural gas source worldwide. It has been assessed that the top 42 countries, including the U.S., are predicted to own 7299 trillion cubic feet (tcf) of technically recoverable shale gas resources. The main goal of this paper is to serve as a guide of different shale gas extraction methods. The significance of these methods and possible pros and cons are determined. Each technique was explained with the support of literature review. Specifically, this paper revealed that some fracking methods such as pulsed arc electrohydraulic discharges (PAED), plasma stimulation and fracturing technology (PSF), thermal (cryogenic) fracturing, enhanced bacterial methanogenesis, and heating of rock mass are at the concept stage for conventional and other unconventional resources. Thus, these found to be significant for stimulating natural gas wells, which provides very good production results. This paper also discovered that fracking remains the recommended technique used by the oil and gas industries.

Large Language Models (LLMs) have transformed Natural Language Processing (NLP) and Software Engineering by fostering innovation, streamlining processes, and enabling data-driven decision-making. Recently, the adoption of LLMs in time-series analysis has catalyzed the emergence of time-series LLMs, a rapidly evolving research area. Existing reviews provide foundational insights into time-series LLMs but lack a comprehensive examination of recent advancements and do not adequately address critical challenges in this domain. This Systematic Literature Review (SLR) bridges these gaps by analysing state-of-the-art contributions in time-series LLMs, focusing on architectural innovations, tokenisation strategies, tasks, datasets, evaluation metrics, and unresolved challenges. Using a rigorous methodology based on PRISMA guidelines, over 700 studies from 2020 to 2024 were reviewed, with 59 relevant studies selected from journals, conferences, and workshops. Key findings reveal advancements in architectures and novel tokenization strategies tailored for temporal data. Forecasting dominates the identified tasks with 79.66% of the selected studies, while classification and anomaly detection remain underexplored. Furthermore, the analysis reveals a strong reliance on datasets from energy and transportation domains, highlighting the need for more diverse datasets. Despite these advancements, significant challenges persist, including tokenization inefficiencies, prediction hallucinations, and difficulties in modelling long-term dependencies. These issues hinder the robustness, scalability, and adaptability of time-series LLMs across diverse applications. To address these challenges, this SLR outlines a research roadmap emphasizing the improvement of tokenization methods, the development of mechanisms for capturing long-term dependencies, the mitigation of hallucination effects, and the design of scalable, interpretable models for diverse time-series tasks.

Smart Grid (S.G.) is a digitally enabled power grid with an automatic capability to control electricity and information between utility and consumer. S.G. data streams are heterogenous and possess a dynamic environment, whereas the existing machine learning methods are static and stand obsolete in such environments. Since these models cannot handle variations posed by S.G. and utilities with different generation modalities (D.G.M.), a model with adaptive features must comply with the requirements and fulfill the demand for new data, features, and modality. In this study, we considered two open sources and one real-world dataset and observed the behavior of ARIMA, ANN, and LSTM concerning changes in input parameters. It was found that no model observed the change in input parameters until it was manually introduced. It was observed that considered models experienced performance degradation and deterioration from 5 to 15% in terms of accuracy relating to parameter change. Therefore, to improve the model accuracy and adapt the parametric variations, which are dynamic in nature and evident in S.G. and D.G.M. environments. The study has proposed a novel adaptive framework to overcome the existing limitations in electrical load forecasting models.

The novelty of Federated Learning (FL) has emerged as a promising alternative to centralized machine learning systems in the context of Anomaly-Based Intrusion Detection Systems (AIDS) deployed on Internet of Things (IoT) devices. Unlike traditional centralized models, FL allows on-device model training and updates, reducing privacy concerns and issues like single points of failure and high false alarm rates (FAR). This approach, termed ’Fed-AIDS,’ offers a more secure and efficient solution. However, the development of Fed-AIDS models faces challenges related to limited training data and the diverse nature of IoT datasets. Additionally, FL’s decentralized nature introduces weight divergence issues arising from non-Independently and Identically Distributed (non-IID) clients. To address these challenges and optimize Fed-AIDS modeling, interdisciplinary research efforts are vital. The primary objective of this study is to conduct an up-to-date review by adopting a Systematic Literature Review (SLR) approach to analyze existing studies of Fed-AIDS modeling procedures for IoT devices. Data from the published studies were retrieved from Scopus database, which covered major publishers like IEEE, Elsevier and others. Specifically, our review conducted from the following Fed-AIDS perspectives: workflow and tools, training dataset, complexities of non-IID data in Fed-AIDS models, classification tasks, aggregation tasks, and model validation metrics. Based on the research findings, the study highlights a series of challenges and proposes potential solutions to stand in future research in Fed-AIDS modeling, aiming to advance the field of IoT device security.