A small, affordable computer, the size of a debit card called Raspberry Pi, that uses a regular mouse and keyboard and connects to either a desktop screen or television. It makes it possible for people of all generations to learn about computer devices and various programming languages like Python and Scratch. It has all the characteristics of a desktop computer, including the potential to browse the Internet, view high-definition video, work with spreadsheets, write documents, and play games. In addition, the Raspberry Pi was involved with several digital maker projects, such as song players and climate forecasting. It is therefore effective in communicating with people around the world. In this article, detailed features and information of the Raspberry Pi have been discussed very clearly as well as each version along with its alternative boards. The hardware components utilized, as well as the software needed to program it, will be discussed in this article (Raspberry Pi board). This article will offer a broad overview of the Raspberry Pi and is helpful for individuals who want to learn and do projects using the Raspberry Pi by giving them a proper guide and details regarding the Pi boards. It serves as the comprehensive guide for the basic ideas and uses of the Raspberry Pi boards.

The COVID-19 pandemic made all organizations and enterprises work on cloud platforms from home, which greatly facilitates cyberattacks. Employees who work remotely and use cloud-based platforms are chosen as targets for cyberattacks. For that reason, cyber security is a more concerning issue and is now incorporated into almost every smart gadget and has become a prerequisite in every software product and service. There are various mitigations for external cyber security attacks, but hardly any for insider security threats, as they are difficult to detect and mitigate. Thus, insider cyber security threat detection has become a serious concern in recent years. Hence, this paper proposes an unsupervised deep learning approach that employs an artificial neural network (ANN)-based autoencoder to detect anomalies in an insider cyber security attack scenario. The proposed approach analyzes the behavior of the patterns of users and machines for anomalies and sends an alert based on a set security threshold. The threshold value set for security detection is calculated based on reconstruction errors that are obtained through testing the normal data. When the proposed model reconstructs the user behavior without generating sufficient reconstruction errors, i.e., no more than the threshold, the user is flagged as normal; otherwise, it is flagged as a security intruder. The proposed approach performed well, with an accuracy of 94.3% for security threat detection, a false positive rate of 11.1%, and a precision of 89.1%. From the obtained experimental results, it was found that the proposed method for insider security threat detection outperforms the existing methods in terms of performance reliability, due to implementation of ANN-based autoencoder which uses a larger number of features in the process of security threat detection.

Tamil character recognition is an important field of research in pattern recognition and it is a technical challenge than other languages due to similarity and complexity of characters. Stone inscriptions reveal the details of lavishness, lifestyle, economic conditions, culture, and also of the managerial regulations followed by various rulers and dynasties particular to those regions. However, due to the long history of ancient stone inscription, natural erosion and lack of early protection measures, there are lot of noise in the existing ancient stone inscriptions, which create adverse effects on reading these stone inscriptions and their aesthetic appreciation. The research challenge in recognizing Tamil characters is mainly because of the characters with a number of holes, loops and curves. The number of letters in Tamil language is higher when compared to other languages. Even though there are various approaches provided by the researchers, challenges and issues still prevail in recognition of tamil text in stone inscriptions. In the existing systems, detection algorithms fail to produce desired accuracy and hence stone inscription recognition using transfer learning, a promising method is proposed in this paper. Lion Optimization Algorithm (LOA) is applied to optimize brightness and contrast and then stone inscription images are pre-processed for noise removal and then each character is separated by identifying contours. Characters are recognized using Transfer Learning (TL), a Deep Convolution Neural Network-based multi classification approach. The proposed hybrid model Self-Adaptive Lion Optimization Algorithm with Transfer Learning (SLOA-TL) when implemented in images of stone inscriptions achieves better accuracy and speed than other existing methods. It serves as an efficient design for recognition of tamil characters in stone inscriptions and preserving tamil traditional knowledge.

Background: Segmentation of medical images plays a key role in the correct identification and management of different diseases. In this study, we present a new segmentation method that meets the difficulties posed by sophisticated organ shapes in computed tomography (CT) images, particularly targeting lung, breast, and gastric cancers Method: Our suggested methods, Resio-Inception U-Net and Deep Cluster Recognition (RIUDCR), use a Residual Inception Architecture, which combines the power of residual connections and inception blocks to achieve cutting-edge segmentation performance while reducing the risk of overfitting. Result: We present mathematical equations and functions that describe the design, including the encoding and decoding steps within the UC-Net system. Furthermore, we provide strong testing results that show the effectiveness of our method. Through thorough testing on varied datasets, our method regularly beats current techniques, achieving amazing precision and stability in organ task segmentation. These results show the promise of our residual inception architecture in better medical picture analysis. Conclusion: In summary, our research not only shows a state-of-the-art segment methodology but also reinforces its usefulness through thorough testing. The inclusion of residual inception architecture in medical picture segmentation offers good possibilities for improving the identification and management of disease planning.

Now a days decision-making plays a significant role in various applications and several research. For applications such as diseases, intelligent routing systems, and online shopping carts such as e-commerce sites, recommended systems are developed based on sentiment analysis (SA) and take accurate decision-making based on the predictions and analyze the accurate decisions based on the result analysis. When it comes to practical uses, deep learning (DL) has by far been the most popular. DL becomes an indispensable domain for several tasks in science and engineering. It is very difficult to take decisions based on traditional tests in various research areas such as disease prediction, textual sentiment analysis, and risk prediction of autonomous vehicles due to the lack of accuracy and long time for results. To address this, various approaches are proposed to adopt. Decision-making is based on multi-criticism, which is more useful to solve critical issues in making accurate decisions than previous approaches. In this paper, an improved and augmented decision-making deep learning algorithm is discussed and shows the comparison among the various DL algorithms. The performance is calculated according to the parameters.