This article presents a method for detecting disinformation in news texts based on a combination of classic machine learning algorithms and deep learning models. The proposed approach was tested on the corpus of Ukrainian- and English-language news with the "fake/truth" classes marked. Before modelling, detailed data pre-processing was performed: deletion of duplicates, cleaning of HTML tags, links and special characters, normalisation of texts, unification of labels, class balancing, and tokenisation. A hybrid approach was used for vectorisation: frequency features (TF-IDF) were combined with contextual vector representations based on the IBM Granite multilingual model. Logistic regression is chosen as a classifier, which allows a balance to be achieved between quality and interpretation of results. Standard metrics are used to assess performance, such as Accuracy, Precision, Recall, F1-score, and ROC-AUC. According to the results of experiments, the model showed an Accuracy in the range of 0.91–0.93, a Precision of 0.89, a Recall of 0.92, an F1-score of 0.90, as well as an ROC-AUC over 0.94. The obtained values demonstrate the balanced ability of the system not only to accurately classify news, but also to minimise false positives, which is especially important in the conditions of information warfare. Priority is given to Recall's high scores, as the omission of fake messages can have critical consequences for information security. Thus, the proposed approach makes a scientific contribution to the field of automated disinformation detection by combining transparent and reproducible data processing with a hybrid text representation. The uniqueness of the study lies in the adaptation of NLP and machine learning methods to the Ukrainian-language information space and the context of modern hybrid warfare, which allows you to effectively identify the sources and routes of spreading fake news.

Information systems and information technology have become indispensable in modern business operations, serving as critical tools for enhancing efficiency, streamlining processes, and supporting strategic decision-making. To ensure these technologies effectively meet organizational needs, enterprise architecture design plays a key role in aligning business goals with IT systems. This alignment not only improves operational efficiency but also lays the foundation for long-term organizational success. This study employs The Open Group Architecture Framework (TOGAF), focusing on its Architecture Development Method (ADM), to design an enterprise architecture tailored for PT Perkebunan Kaltim Utama. TOGAF ADM offers a structured, iterative approach to architecture development, encompassing phases from the initial planning stage to the final design and implementation analysis. Each phase is designed to integrate business processes with IT systems, enabling a cohesive and adaptive framework. PT Perkebunan Kaltim Utama, a company specializing in palm oil mill maintenance, faces significant operational challenges due to its reliance on manual processes and lack of integration. These inefficiencies hinder productivity and affect the company’s ability to meet strategic goals. This research systematically identifies the functional and technological requirements for PT Perkebunan Kaltim Utama’s business activities, laying the groundwork for an integrated solution. The proposed architecture design addresses these inefficiencies by providing a comprehensive blueprint for implementing a unified system. This system will not only enhance PT Perkebunan Kaltim Utama’s operational performance but also support its strategic objectives, enabling the company to remain competitive and responsive to industry demands. By integrating TOGAF ADM into its processes, PT Perkebunan Kaltim Utama can ensure a more effective alignment of business and IT, paving the way for sustainable growth and improved decision-making capabilities.

The development of upper limb prostheses poses a significant challenge in providing amputees with sensory feedback. This paper presents a novel approach by proposing a biomimetic circuit specifically designed to replicate the behavior of slowly adapting (SA-I) afferents, which are responsible for encoding sustained indentation and offering crucial sensory feedback. The circuit has been meticulously designed and simulated using Cadence Virtuoso software, a powerful tool for circuit design and optimization. To validate the functionality and performance of the biomimetic circuit, a grid of mechanoreceptors is simulated and tested, providing realistic inputs for the circuit. The circuit successfully emulates the response of SA-I afferents to sustained indentation, exhibiting a slowly adapting discharge that linearly correlates with the depth of indentation. This ability to replicate the natural behavior of SA-I afferents represents a significant advancement in the field of providing sensory feedback for upper limb prostheses.
The biomimetic circuit holds great promise in addressing the crucial need for sensory feedback in upper limb prosthetics. By integrating this circuit into upper limb prostheses, amputees can experience more intuitive and realistic sensations during interactions with their environment. The replication of SA-I afferent behavior provides users with vital information about the magnitude and duration of applied forces, enhancing their overall perception and control of the prosthesis. 
The findings of this study contribute to the ongoing progress in the field of prosthetics, particularly in the development of more sophisticated and advanced upper limb prostheses. The successful implementation and simulation of the biomimetic circuit demonstrate its potential as a viable solution for providing amputees with enhanced sensory feedback, ultimately improving their quality of life and reintegrating them into daily activities more seamlessly. The new approach emphasizes the development of a biomimetic circuit tailored to replicate SA-I afferent behavior. The proposal addresses the challenge of providing sensory feedback in upper limb prostheses. The study utilizes Cadence Virtuoso software for precise design, layout, and simulation, offering a practical solution for realistic sensory feedback. By accurately emulating the response of SA-I afferents to sustained indentation, the circuit holds the potential to significantly enhance amputees' quality of life and integration into daily activities. The proposed circuit contributes to the advancement of upper limb prosthetics and represents a significant leap forward in achieving more intuitive and authentic sensory experiences for prosthesis users.

Quantum computing is a rapidly developing field with faster computational capabilities than classical computing. The popularity of quantum computing has reached the field of image processing, particularly with a breakthrough method known as Quantum Hadamard Edge Detection. This approach represents a significant advancement in edge detection techniques using quantum computing. Quantum Hadamard Edge Detection is a method that can detect image edges more quickly than classical methods with exponential acceleration. This paper explains the Quantum Hadamard Edge Detection method in detail, including how it is implemented, a time complexity explanation, some experiments, and future research directions. Our experiments utilize a quantum computer simulator and employ four measurement metrics: Structural Similarity Index, Figure of Merit, Entropy, and a Proposed Metric with radius-based features, to detect simple binary images, MNIST images, and the Berkeley Segmentation datasets. We recognize the potential of quantum computing and believe that image processing with quantum representation will make processing more efficient and significantly valuable in the future.

The extreme volatility of Bitcoin markets makes accurate price prediction notably difficult. This paper proposes a new hybrid deep learning model that incorporates a Gated Recurrent Unit (GRU), a Bidirectional Long Short-Term Memory (Bi LSTM) model, and a Multi Head Attention mechanism to permit the model to utilize both historical price data and sentiment information from Twitter. We constructed the model utilizing a two-stage transfer learning approach: we first pretrained the model on data from 2017−2019 to learn lower-level fluctuation behaviors, then we fine-tuned the model on data from 2021−2023 in order to be sensitive to recent market behaviors. The model performed exceptionally well against multiple state-of-the-art baselines using root mean square error (RMSE) and mean absolute error (MAE) metrics, reporting RMSE values of 679.61 and MAE of 452.95, achieving considerable improvement over the baseline models. Our experimental results show that leveraging Twitter sentiment greatly improved trend prediction. In addition, our benchmarks showed that our method performed better than the existing methods. Furthermore, our ablation studies illustrated how each particular feature performed. Overall, our results demonstrate that multi-scale temporal modeling combined with social media sentiment integration produces a scalable and resilient solution to combat the challenges of volatility to forecast cryptocurrency prices accurately and efficiently.

Efficient comparison of heterogeneous tabular datasets is difficult when sources are unknown or weakly documented. We address this problem by introducing a unified, type-aware framework that builds compact data represen- tations (CDRs)—concise summaries sufficient for downstream analysis—and a corresponding similarity graph (and tree) over a data corpus. Our novelty is threefold: (i) a principled vocabulary and procedure for constructing CDRs per variable type (factor, time, numeric, string), (ii) a weighted, type-specific similarity metric we call Data Information Structural Similarity (DISS) that aggregates distances across heterogeneous summaries, and (iii) an end-to-end, cloud-scalable real- ization that supports large corpora. Methodologically, factor variables are summarized by frequency tables; time variables by fixed-bin histograms; numeric variables by moment vectors (up to the fourth order); and string variables by TF–IDF vectors. Pairwise similarities use Hellinger, Wasserstein (p=1), total variation, and L1/L2 distances, with MAE/MAPE for numeric summaries; the DISS score combines these via learned or user-set weights to form an adjacency graph whose minimum-spanning tree yields a similarity tree. In experiments on multi-source CSVs, the approach enables accurate retrieval of closest datasets and robust corpus-level structuring while reducing storage and I/O. This contributes a repro- ducible pathway from raw tables to a similarity tree, clarifying terminology and providing algorithms that practitioners can deploy at scale.

Predicting attitudes towards people with tuberculosis is a solution for preserving public health and a means of strengthening social ties to improve resilience to health threats. The assessment of attitudes towards the sick in general is essential to understand the educational level of a given population and to measure its resilience in contributing to the health of all within the framework of community life. The case of tuberculosis is chosen in this study to highlight the need for a change in attitudes, particularly due to the preponderance of this disease in Africa. While it is clear that attitudes influence the organization of individuals and community life, it remains a challenge to put in place an effective mechanism for evaluating the metrics that contribute to determining the attitude towards people with tuberculosis. Knowledge of attitudes towards any disease is very important to understanding collective values on this disease, hence the need to predict attitudes in the case of tuberculosis in favor of health education for all social strata while targeting areas of training not yet explored or requiring capacity building among populations. Changing attitudes towards tuberculosis patients will contribute to preserving public health and will help reduce stigma, improve understanding of the disease and encourage supportive and preventive behaviors. Achieving these changes involves dismantling stereotypes, improving access to care, mobilizing the media and social networks, including people with TB in society and strengthening the commitment of public authorities. The approach adopted consists of assessing the state of attitude towards tuberculosis patients at a given time and in a specific space based on the characteristics of the different social strata living there. An analysis of several metrics provided by machine learning algorithms makes it possible to identify differences in attitudes and serve as a decision-making aid on the strategies to be implemented. This work also relies on the investigation and analysis of historical trends using machine learning algorithms to understand population attitudes towards tuberculosis patients.

The spreading of misinformation in Online Social Networks (OSNs) is quite similar to the spreading of infection in biological diseases. As the biological virus spreads and makes one infected as well as those who come into contact with the infected person, the nature and behavior of the spread of misinformation in an online social network is similar. So in order to understand the functioning of misinformation and to control over epidemic outbreak of misinformation in OSNs, epidemic models can be quite handy. The introduction of Bifurcation theory in the epidemic model explains the qualitative behavior of the system with changes in parameters. In this paper, we have introduced the Susceptible, Infectious, and Protected (SIP) model with Bifurcation for the propagation of misinformation in OSNs. Here Bifurcation is due to the limited number of users who are ready to adopt the Security and Privacy Policies of Social Network Sites. We define the threshold number for the system of equations and explain the stability of an Infection Free Equilibrium (IFE), representing the absence of misinformation. We have discussed about the endemic equilibrium point and bifurcation conditions along with its nature and stability at those points. Also, we have shown global stability at the endemic Equilibrium of the system. Finally, numerical simulation has been used to show the existence of bifurcation in the system with a change in the value of parameters.

This research presents a novel approach to evaluating student academic performance at Nalla Narasimha Reddy Group of Institutions (NNRG) by implementing a Student Training Based Optimization (STBO) algorithm. The proposed method draws inspiration from the structured training and adaptive learning behavior of students, simulating their progression through knowledge acquisition, skill refinement, and performance enhancement phases. The STBO algorithm is applied to optimize academic performance assessment by identifying key parameters such as attendance, internal assessments, learning pace, participation, and project outcomes. By modelling student development as a dynamic optimization process, the algorithm effectively predicts academic outcomes and recommends personalized strategies for improvement. Experimental evaluation on real academic datasets from NNRG CSE, CSE (Data Science), and CSE (AIML) Students demonstrates that the STBO algorithm achieves higher prediction accuracy and adaptive feedback generation when compared to traditional statistical and machine learning techniques. This approach also facilitates early identification of at-risk students and promotes data-driven decision-making for faculty and administration. Overall, the STBO-based framework not only enhances performance assessment but also contributes to academic excellence by aligning learning strategies with individual student needs.

One of the key aspects of 5G networks is the implementation of massive MIMO (Multiple Input Multiple Output) technology combined with adaptive beamforming. This study explores the use of a linear array antenna to manage and reduce unwanted signals such as jamming, interference, and noise, while also boosting the signal strength towards the intended user or device. The main challenge lay in optimizing the weights of the antenna elements, which was tackled by employing adaptive algorithms like LCMV (Linearly Constrained Minimum Variance) and RLS (Recursive Least Squares). To simplify the optimization process, two soft computing techniques—Particle Swarm Optimization (PSO) and Genetic Algorithm (GA)—were utilized. The performance of the beamforming weights and radiation patterns was assessed in terms of minimizing unwanted signals and maximizing the desired signal. To check how well the proposed methods work, some commonly used algorithms like MVDR (Minimum Variance Distortionless Response) and LCMV are also applied. The outcomes were compared to those from other algorithms. A Differential Beamforming method is applied to examine how effectively the system can focus the signal in the target direction while minimizing unwanted interference from other directions. Additionally, the fminsearch algorithm, which is a basic local search method, is used to compare how well it can adjust the beamforming weights compared to the more advanced global optimization techniques. The results indicate that PSO and GA produce highly similar performance levels.