Gamma spectroscopy is an important analytical technique across various fields. Gamma spectroscopy uses the energy spectra of emitted gamma rays to examine the type and quantity of isotopes that exist in samples. Like any other analytical technique, traditional gamma spectroscopy encounters challenges that in some cases make the analysis uncertain. Machine Learning (ML) algorithms have been proposed as an approach for enhancing the precision and robustness of gamma spectroscopy. The current study introduces the basics behind ML and illustrates how they are applied in gamma spectroscopy using case studies. Major findings discussed herein demonstrate the developmental capability that ML has in improving gamma spectroscopy. Radioisotope identification, optimizing detector performance, and simplifying environmental monitoring processes have been the main areas in which ML algorithms have been deployed for improvement. These include the ability to predict and provide real-time spectrometry, among others, even though these opportunities come with their shortfalls such as the necessity for huge training datasets. This review explains that integrating ML into gamma spectroscopy marks a major change from current analytic techniques, with possible further developments in radiation detection and environmental science. It serves as very useful material for those studying or practicing AI and gamma spectroscopy and want to know more about it or need guidance on what is happening so far regarding AI integrated into gamma spectroscopy analysis.

Topic modeling is a data analysis technique that has become increasingly popular in recent years due to the growing availability of large datasets. It enables researchers to uncover underlying themes and topics within large and complex datasets by identifying patterns and relationships among data points. This article overviews the topic modeling process, including the algorithms used, tools, evaluation metrics, and applications across various fields. It also discusses potential areas for future research, such as integrating other machine learning techniques and incorporating temporal and contextual information. Overall, topic modeling represents a powerful tool for data analysis that has the potential to unlock new insights and understanding in a variety of domains

This paper delves into the realm of large language models (LLMs), which are potent tools within the domain of artificial intelligence, facilitating comprehensive comprehension and utilization of human language by computers. LLMs, exemplified by GPT-3, exhibit remarkable proficiency across various linguistic tasks, ranging from writing to language translation and customer service. However, despite their efficacy, these models are not without drawbacks; they often manifest biases, possess opaque workings, and consume substantial energy resources. Tracing the evolution of LLMs from rudimentary models to sophisticated constructs capable of intricate language processing, this paper explores their learning mechanisms, diverse applications, and associated concerns, including the imperative of fairness and privacy preservation. Looking towards the future, it advocates for research endeavors aimed at enhancing the environmental sustainability, comprehensibility, and impartiality of LLMs. The overarching objective is to harness these models effectively and ethically, cognizant of their transformative potential across technological and societal landscapes. In addition, it should be noted that the author extensively reviewed and studied over 40 articles within a span of 2 months to comprehensively acquaint themselves with the subject matter, ensuring the most effective presentation of large language models (LLMs) to the interested audience.

Air pressure systems play an essential role in Scania trucks, so that the correct operation of the braking system and gear shifting system of Scania cars depends on the health of the air pressure system. The presence of the central control unit (ECU) in the air pressure system has caused the collection of different information about its different status, which can be stored and checked in the form of different data sets.

The proliferation of data and text documents such as articles, web pages, books, social network posts, etc. on the Internet has created a fundamental challenge in various fields of text processing under the title of "automatic text summarisation". Manual processing and summarisation of large volumes of textual data is a very difficult, expensive, time-consuming and impossible process for human users. Text summarisation systems are divided into extractive and abstract categories. In the extractive summarisation method, the final summary of a text document is extracted from the important sentences of the same document without any modification. In this method, it is possible to repeat a series of sentences and to interfere with pronouns. However, in the abstract summarisation method, the final summary of a textual document is extracted from the meaning and significance of the sentences and words of the same document or other documents. Many of the works carried out have used extraction methods or abstracts to summarise the collection of web documents, each of which has advantages and disadvantages in the results obtained in terms of similarity or size. In this work, a crawler has been developed to extract popular text posts from the Instagram social network with appropriate preprocessing, and a set of extraction and abstraction algorithms have been combined to show how each of the abstraction algorithms can be used. Observations made on 820 popular text posts on the social network Instagram show the accuracy (80%) of the proposed system.

Today, the web has become a mandatory platform to express users' opinions, emotions and feelings about various events. Every person using his smartphone can give his opinion about the purchase of a product, the occurrence of an accident, the occurrence of a new disease, etc. in blogs and social networks such as (Twitter, WhatsApp, Telegram and Instagram) register. Therefore, millions of comments are recorded daily and it creates a huge volume of unstructured text data that can extract useful knowledge from this type of data by using natural language processing methods. Sentiment analysis is one of the important applications of natural language processing and machine learning, which allows us to analyze the sentiments of comments and other textual information recorded by web users. Therefore, the analysis of sentiments, approaches and challenges in this field will be explained in the following.