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The Internet of Things (IoT) has recently been implemented in various applications. An IoT network is a group of Internet-connected computing devices embedded in everyday objects. Usually, those devices can interact with each other via the Internet by exchanging data. Because of privacy and security requirements, users of IoT-connected objects need measures to secure corresponding data during storage and transmission. This work presents an architecture that integrates IoT devices, blockchain technology, and embedded homomorphic encryption to ensure high computation speed and security levels in IoT systems. The ultralight linear encryption technique enhances computation speed, making it possible to obtain homomorphic addition over the encrypted data. Using this technique, each administrator gains specific access to the encrypted data. Security, stability, traceability, and anonymity are provided through blockchain technology, which is used to store data. The proposed architecture is demonstrated via a use case from the healthcare sector. The experimental analysis shows our technique's effectiveness in energy consumption reduction and privacy preservation with minimal computation and communication costs. By using five fields per record, name, ID, age, gender, and blood glucose level, we achieved an encryption time equals 0,19 ms and decryption time equals 0,98 ms vs. 15,7 and 1,6 respectively in the best comparison technique. In communication cost, we achieved 1KB vs. 5KB. [ABSTRACT FROM AUTHOR]

In the rapidly advancing landscape of digital technologies, clustering plays a critical role in the domains of artificial intelligence and big data. Clustering is essential for extracting meaningful insights and patterns from large, intricate datasets. Despite the efficacy of traditional clustering techniques in handling diverse data types and sizes, they encounter challenges posed by the increasing volume and dimensionality of data, as well as the complex structures inherent in high-dimensional spaces. This research recognizes the constraints of conventional clustering methods, including sensitivity to initial centroids, dependence on prior knowledge of cluster counts, and scalability issues, particularly in large datasets and Internet of Things implementations. In response to these challenges, we propose a K-level clustering algorithm inspired by the collective behavior of fish locomotion. K-level introduces a novel clustering approach based on greedy merging driven by distances in stages. This iterative process efficiently establishes hierarchical structures without the need for exhaustive computations. K-level gives users enhanced control over computational complexity, enabling them to specify the number of clusters merged simultaneously. This flexibility ensures accurate and efficient hierarchical clustering across diverse data types, offering a scalable solution for processing extensive datasets within a reasonable timeframe. The internal validation metrics, including the Silhouette Score, Davies–Bouldin Index, and Calinski–Harabasz Index, are utilized to evaluate the K-level algorithm across various types of datasets. Additionally, comparisons are made with rivals in the literature, including UPGMA, CLINK, UPGMC, SLINK, and K-means. The experiments and analyses show that the proposed algorithm overcomes many of the limitations of existing clustering methods, presenting scalable and adaptable clustering in the dynamic landscape of evolving data challenges. [ABSTRACT FROM AUTHOR]

Artificial intelligence has been involved in several domains. Despite the advantages of using artificial intelligence techniques, some crucial limitations prevent them from being implemented in specific domains and locations. The accuracy, poor quality of gathered data, and processing time are considered major concerns in implementing machine learning techniques, certainly in low-end smart devices. This paper aims to introduce a novel pre-treatment technique dedicated to image text detection that uses the images' pixel divergence and similarity to reduce the image size. Mitigating the image size while keeping its features improves the model training time with an acceptable accuracy rate. The mitigation is reached by gathering similar image pixels in one pixel based on calculated values of the standard deviation σ, where we consider that two pixels are similar if they have approximately the same σ values. The work proposes a new pipeline approach that reduces the size of the image in the input and intermediate layers of a deep learning model based on merged pixels using standard deviation values instead of the whole image. The experimental results prove that this technique significantly improves the performance of existing text detection methods, particularly in challenging scenarios such as using low-end IoT devices that offer low contrast or noisy backgrounds. Compared with other techniques, the proposed technique can potentially be exploited for text detection in IoT-gathered multimedia data with reasonable accuracy in a short computation time. Evaluation of the MSRA-TD500 dataset demonstrates the remarkable performance of our approach, Standard Deviation Network (σNet), with precision and recall values of 93.8% and 85.6%, respectively, that outperform recent research results. [ABSTRACT FROM AUTHOR]

Internet cloud services come at a price, especially when they provide top-tier security measures. The cost incurred by cloud utilization is directly proportional to the storage requirements. Companies are always looking to increase profits and reduce costs while preserving the security of their data by encrypting them. One of the offered solutions is to find an efficient encryption method that can store data in a much smaller space than traditional encryption techniques. This article introduces a novel encryption approach centered on consolidating information into a single ciphertext by implementing Multi-Key Embedded Encryption (MKEE). The effectiveness of MKEE scales in tandem with the volume of information encapsulated within the ciphertext. MKEE substantially reduced the size of the ciphertext, achieving an 88% decrease when incorporating ten plaintext values. To further reduce the size of the ciphertext in our proposal, a Modular Multiplicative Inverse method (MMI) is introduced. MMI experiments were conducted, demonstrating that we achieved a commendable 50% reduction in the ciphertext size. To validate the practicality of the proposed method, a case study was conducted on a diabetes dataset. By integrating MKEE and MMI, this study showed a data storage reduction of 94%. [ABSTRACT FROM AUTHOR]

A corporation or company's production velocity and efficiency are essential. It is necessary to use robotic automation to create a successful organization in a competitive business environment. Recent investments have been made in robots across a wider range of manufacturing sectors. In this era of industry, recent advances in robotics technology are dramatically transforming the business and entrepreneurial landscape. Today, robotics is already well established in many areas of our professional, personal, and even daily lives. This technology can develop and modernize commercial processes, innovative ideas, services, and products and solve complex tasks by producing new results for the massive growth of entrepreneurial activities. This study aims to outline the significance of the impact of investment in robotic automation on business performance and employment at the enterprise level and its further perspectives. Following the review of the most important international articles on exploiting this technology, we conducted a brief study on an Algerian factory to learn about the level of use of robotics technology and its future prospects. [ABSTRACT FROM AUTHOR]

Unconditional security means that knowledge of an encrypted text does not provide any information about the corresponding plaintext: or more. regardless of the number of ciphertexts available to an attacker. no amount of cryptanalysis can break the cipher. Until now. only the One-Time Pad (OTP) method meets this condition with well-defined assumptions. The design of a Homomorphic Encryption scheme that allows operations over the encrypted data is required in current applications to reach the highest possible level of privacy. However. existing symmetric solutions that use OTP have a key management problem: they are not linear encryption. which means that they have high computational complexity. and some of them do not meet all homomorphic properties. This article simulates the 0TP taking into consideration these issues and achieving the maximum resistance to cryptanalysis, even when the attacker has great computing power. The first major advantage of the proposed OTP-based method is that it only uses a single pre-shared key, The key is composed of two sections, a fixed number of bits followed by random bits, the size of each section is dependent on the robustness of the system. Analysis of the proposed technique shows that it provides perfect privacy by using a different key for each message to be encrypted. [ABSTRACT FROM AUTHOR]