Authors - Ritesh Kumar, S. Rajaprakash Abstract - The society we live in today has seen the accumulation of knowledge via social media platforms such as Twitter and Facebook, which are expanding at a tremendous rate on a daily basis. The victims of these social media platforms are users who tweet or post on a variety of issues from any location in the globe via the usage of the internet. Tweets are used to assess both positive and negative mes-sages in order to produce polarity scores and also to have the ability to anticipate future trends. Twitter is a source from which these polarity scores may be collected; nevertheless, the information about polarity scores is kept confidential. The information will be easily compromised, and the fluctuations in the score will result in incalculable consequences, such as affecting the global economic position, the brands of corporations, and therefore the reputations of businesses. The installation of Salsa, which offers faster encryption due to the district round and greater data security, was something that Daniel Bernstein intended to do in order to address these issues. In this work, a fresh approach is provided by changing the Salsa20/4 algorithm in order to further strengthen the security of the polarity scores, which is a vital necessity in the society that we live in today. The proposed method is RRCF has two encryption stages. Stage 1 is comprised of column operations, whereas stage 2 is comprised of four procedures. Finding the greatest common factor of the pain text that has been provided is the initial step in the procedure. Identifying the time period in the pain text is the second step in the procedure. The outcome of the second step is used in the third phase, which is to create a pair of values. The application of the pair values and the swapping of the cell values in the given matrix is the fourth step. In comparison to the Salsa20/4 technique, the suggested methodology has a much higher level of security.
Authors - Boago Seropola, George Anderson Abstract - When it comes to healthcare, the implementation of machine learning (ML) and deep learning models requires a shift away from blackbox methodologies and toward frameworks that are transparent and auditable. This is necessary in order to guarantee ethical governance and patient safety. In this study, an integrated XAI-CRISP-DM framework is proposed. This methodology incorporates post-hoc Explainable Artificial Intelligence (XAI) into the iterative stages of the Cross-Industry Standard Process for Data Mining (CRISP-DM). Additionally, the research places an emphasis on continual post-deployment oversight. Within the context of HIV/AIDS risk classification in Botswana, we analyse the interpretability of non-linear decision boundaries in LightGBM and Multi- Layer Perceptron (MLP) models. This evaluation is carried out with the assistance of SHAP and LIME. For the purpose of quantitatively validating the clinical significance of socio-demographic characteristics using the publicly available dataset, the fifth Botswana AIDS Impact Survey 2021 (BAIS V), this study makes use of impact score and explanatory specificity. Results demonstrate that LIME and SHAP effectively decompose complex model outputs into human-readable feature weights, identifying key drivers such as sexual-activity-before-15 and condom use. Through the tracking of model integrity and concept drift in dynamic clinical situations, the incorporation of a monitoring stage guarantees that continuous accountability is maintained. The results of this study suggest that the XAI-CRISP-DM framework is an essential methodological standard for resource-constrained environments such as Botswana. This framework ensures that data-driven healthcare solutions are not only high-performing but also statistically reliable, equitable, and ethically sound.
Authors - Ana Laura Lezama Sanchez, Mireya Tovar Vidal Abstract - In this paper, we present the automatic classification of autoimmune skin diseases using deep convolutional neural networks. Hence in this study we conducted within the context of supervised classification of dermatological images, with the objective of designing and implementing a model capable of distinguishing among five clinical classes like lupus, psoriasis, vitiligo, lichen planus and healthy skin. Therefore, a deep convolutional neural network-based system, trained and evaluated on a labeled dataset of clinical images, is proposed. The model was evaluated using the metrics precision, recall, F1 and accuracy. The results obtained indicated that the accuracy was 70%, demonstrating the model’s ability to learn relevant discriminattive features. The best performance was observed in the healthy skin and vitiligo classes, with F1 of 0.82 and 0.80, respectively, indicating high identification capacity. On the other hand, the psoriasis and lichen planus classes showed moderate performance, with F1 values of 0.63 and 0.58, respectively. The lupus class exhibited the lowest performance, with an F1 of 0.46, reflecting the complexity of its visual variability and its similarity to other conditions.
Authors - Katherine Garcia-Velez, Daniel Maldonado Abstract - This paper examines the trajectory of digital transformation in Ecuador's public sector between 2021 and 2025. Its objective is to analyze how the country moved from an initial strategic orientation to a legal and public policy consolidation of digital transformation. Methodologically, the study adopts a qualitative approach based on documentary analysis and diachronic comparison of official instruments: the Digital Agenda 2021-2022, the Digital Transformation Agenda 2022-2025, the Organic Law for Digital and Audiovisual Trans-formation (2023), and the Digital Transformation Public Policy 2025-2030. The analysis is grounded in the idea of the progressive institutionalization of digital reform and compares the evolution of these instruments in terms of their nature, scope, and institutional implications. The findings show a four-phase sequence: agenda setting, strategic coordination, legal consolidation, and programmatic consolidation. The study concludes that Ecuador progressed from strategically oriented instruments toward a binding legal framework and a national public pol-icy. However, the existence of this institutional architecture does not, by itself, imply homogeneous results in terms of performance or service quality, so its effective implementation remains an open empirical field.
Authors - C. Bagath Basha, S. Rajaprakash, K. Karthik, Panjala Vijay Goud, Macharla Rakesh, M Ramana Kumar Abstract - The privacy and security of sensitive information is a major concern in the social assistance sector due to the widespread use of Internet of Things technologies. This study presents a secure sharing algorithm for IoT social assistance data based on blockchain and smart contracts. It aims to address the inherent hazards of conventional centralized administration, such as data loss and manipulation. This paper propose a security method and this method has four process. There are four steps to the new method. First, change the text from plain text to “ASCII code” (A). The second step is to take the A values and make pairs. Then, swap the cells in the matrix so that the even pair numbers start from the 0th cell value and go to the end of the matrix. The third step is to use the “ASCII code” as C to find the prime number. To do the fourth step, you need to use Equation 1. Take the numbers from A1 and pair them up. Then, change the cells in the matrix The message is ultimately received in its original format via the process of decryption, which is thought of as the inverse of this conversion. The proposed technique provides a higher level of security when compared to more conventional encryption methods.
Authors - Rory Lewis Abstract - This work presents a formal supervisory framework for detecting and intervening in large-scale AI misbehavior using neuromorphic sentience, supported by probabilistic guarantees. As generative and adaptive artificial intelligence systems become foundational to human decision-making, scientific discovery, and national infrastructure, ensuring their reliable and safe operation has emerged as a critical challenge. Existing approaches rely primarily on external, reactive monitoring and are insufficient for models operating at machine speed. More fundamentally, closed computational systems cannot reliably represent or act upon their own epistemic limits, creating an inherent blind spot in autonomous operation. To address this limitation, a control architecture is introduced in which an independent neuromorphic module supervises internal AI dynamics through event-driven processing and dendritic integration. Operating without a global clock, the system continuously monitors activation patterns, attention shifts, and inter-module interactions in real time, enabling low-latency and energy-efficient detection of transient and distributed signatures of instability that are inaccessible to conventional approaches. Using probabilistic inference over these signals, the framework identifies early indicators of hallucination, instability, and unintended coordination prior to output generation. Formal lemmas establish mathematical bounds on the probability of undetected misbehavior under realistic operating conditions. Finally, the framework is integrated with a human governance model in which democratically defined thresholds regulate the balance between AI capability and societal safety.
