Numerically Unveiling Hidden Chaotic Dynamics in Nonlinear Differential Equations with Riemann-Liouville, Caputo-Fabrizio, and Atangana-Baleanu Fractional Derivatives

In recent years, the use of variable-order differential operators has emerged as a powerful tool in the analysis of nonlinear fractional differential equations and chaotic systems. In finance, the accurate prediction of market trends and the ability to make informed investment decisions is of great importance, and the integration of artificial intelligence and mathematics has greatly improved the accuracy of these predictions. In this study, we displayed an analysis of adaptive equations produced by three fractional derivatives: the Riemann-Lioville, Caputo-Fabrizio, and Atangana-Baleanu fractional derivatives. These fractional derivatives were employed to analyze financial models in order to gain a deeper understanding of the complex dynamics of financial markets. The models studied were the Lorenz system, Rossler system, and Shilnikov cashless model. The results showed that each fractional derivative produced varying outcomes and computation times, highlighting the importance of selecting the appropriate mathematical approach and software for financial modeling. The findings of this study underscore the continued integration of Artificial Intelligence and mathematics in financial analysis and decision-making, driving the future of investment strategies and market predictions.The application of variable-order differential operators in the analysis of nonlinear fractional differential equations and chaotic systems is an important and growing area of research that holds great promise for the field of finance.Comment: Total 11 pages, 15 figure

MatriVasha: A Multipurpose Comprehensive Database for Bangla Handwritten Compound Characters

At present, recognition of the Bangla handwriting compound character has been an essential issue for many years. In recent years there have been application-based researches in machine learning, and deep learning, which is gained interest, and most notably is handwriting recognition because it has a tremendous application such as Bangla OCR. MatrriVasha, the project which can recognize Bangla, handwritten several compound characters. Currently, compound character recognition is an important topic due to its variant application, and helps to create old forms, and information digitization with reliability. But unfortunately, there is a lack of a comprehensive dataset that can categorize all types of Bangla compound characters. MatrriVasha is an attempt to align compound character, and it's challenging because each person has a unique style of writing shapes. After all, MatrriVasha has proposed a dataset that intends to recognize Bangla 120(one hundred twenty) compound characters that consist of 2552(two thousand five hundred fifty-two) isolated handwritten characters written unique writers which were collected from within Bangladesh. This dataset faced problems in terms of the district, age, and gender-based written related research because the samples were collected that includes a verity of the district, age group, and the equal number of males, and females. As of now, our proposed dataset is so far the most extensive dataset for Bangla compound characters. It is intended to frame the acknowledgment technique for handwritten Bangla compound character. In the future, this dataset will be made publicly available to help to widen the research.Comment: 19 fig, 2 tabl

BeyondPixels: A Comprehensive Review of the Evolution of Neural Radiance Fields

Neural rendering combines ideas from classical computer graphics and machine learning to synthesize images from real-world observations. NeRF, short for Neural Radiance Fields, is a recent innovation that uses AI algorithms to create 3D objects from 2D images. By leveraging an interpolation approach, NeRF can produce new 3D reconstructed views of complicated scenes. Rather than directly restoring the whole 3D scene geometry, NeRF generates a volumetric representation called a ``radiance field,'' which is capable of creating color and density for every point within the relevant 3D space. The broad appeal and notoriety of NeRF make it imperative to examine the existing research on the topic comprehensively. While previous surveys on 3D rendering have primarily focused on traditional computer vision-based or deep learning-based approaches, only a handful of them discuss the potential of NeRF. However, such surveys have predominantly focused on NeRF's early contributions and have not explored its full potential. NeRF is a relatively new technique continuously being investigated for its capabilities and limitations. This survey reviews recent advances in NeRF and categorizes them according to their architectural designs, especially in the field of novel view synthesis.Comment: 22 page, 1 figure, 5 tabl

A Trust Management Framework for Vehicular Ad Hoc Networks

The inception of Vehicular Ad Hoc Networks (VANETs) provides an opportunity for road users and public infrastructure to share information that improves the operation of roads and the driver experience. However, such systems can be vulnerable to malicious external entities and legitimate users. Trust management is used to address attacks from legitimate users in accordance with a user’s trust score. Trust models evaluate messages to assign rewards or punishments. This can be used to influence a driver’s future behaviour or, in extremis, block the driver. With receiver-side schemes, various methods are used to evaluate trust including, reputation computation, neighbour recommendations, and storing historical information. However, they incur overhead and add a delay when deciding whether to accept or reject messages. In this thesis, we propose a novel Tamper-Proof Device (TPD) based trust framework for managing trust of multiple drivers at the sender side vehicle that updates trust, stores, and protects information from malicious tampering. The TPD also regulates, rewards, and punishes each specific driver, as required. Furthermore, the trust score determines the classes of message that a driver can access. Dissemination of feedback is only required when there is an attack (conflicting information). A Road-Side Unit (RSU) rules on a dispute, using either the sum of products of trust and feedback or official vehicle data if available. These “untrue attacks” are resolved by an RSU using collaboration, and then providing a fixed amount of reward and punishment, as appropriate. Repeated attacks are addressed by incremental punishments and potentially driver access-blocking when conditions are met. The lack of sophistication in this fixed RSU assessment scheme is then addressed by a novel fuzzy logic-based RSU approach. This determines a fairer level of reward and punishment based on the severity of incident, driver past behaviour, and RSU confidence. The fuzzy RSU controller assesses judgements in such a way as to encourage drivers to improve their behaviour. Although any driver can lie in any situation, we believe that trustworthy drivers are more likely to remain so, and vice versa. We capture this behaviour in a Markov chain model for the sender and reporter driver behaviours where a driver’s truthfulness is influenced by their trust score and trust state. For each trust state, the driver’s likelihood of lying or honesty is set by a probability distribution which is different for each state. This framework is analysed in Veins using various classes of vehicles under different traffic conditions. Results confirm that the framework operates effectively in the presence of untrue and inconsistent attacks. The correct functioning is confirmed with the system appropriately classifying incidents when clarifier vehicles send truthful feedback. The framework is also evaluated against a centralized reputation scheme and the results demonstrate that it outperforms the reputation approach in terms of reduced communication overhead and shorter response time. Next, we perform a set of experiments to evaluate the performance of the fuzzy assessment in Veins. The fuzzy and fixed RSU assessment schemes are compared, and the results show that the fuzzy scheme provides better overall driver behaviour. The Markov chain driver behaviour model is also examined when changing the initial trust score of all drivers

Response of PPR soils to changing groundwater and salinity: a microcosm study of greenhouse gas emissions

Non-Peer Reviewe

Child marriage news coverage in Bangladeshi newspapers to enhance news literacy

Bangladesh has witnessed a dramatic change in media-sphere with a mushrooming growth of media house in a recent decade. The current trend of media monopolization causes media audiences facing more challenges to filter and evaluate the content as well as make informed decisions. However, lack of relevant study in Bangladeshi media context indicates that there is a need for developing news literacy skills that focus language used in media. From this point of view, the study on news literacy has intended to analyses the nature of presenting social issues on a similar media setting to promote and develop critical thinking by identifying multiple aspects of a problem and generate recommendations to upgrade the situation. This case study based content analysis analytically investigates how english and bengali language newspaper in Bangladesh presented the issue of Child Marriage. Finding suggests that both english and bangla language media give less coverage to child marriage issue. Differences are also found between the newspapers to cover the child marriage news, i.e. the statement, tone, frequency, direction, length and source of the report. Some recommendations have been suggested based on the findings to improve the situation. It is hoped that researchers will use this study as a reference to conduct further research as well as enrich the field of news literacy for better understanding

Effectiveness of Transformer Models on IoT Security Detection in StackOverflow Discussions

The Internet of Things (IoT) is an emerging concept that directly links to the billions of physical items, or "things", that are connected to the Internet and are all gathering and exchanging information between devices and systems. However, IoT devices were not built with security in mind, which might lead to security vulnerabilities in a multi-device system. Traditionally, we investigated IoT issues by polling IoT developers and specialists. This technique, however, is not scalable since surveying all IoT developers is not feasible. Another way to look into IoT issues is to look at IoT developer discussions on major online development forums like Stack Overflow (SO). However, finding discussions that are relevant to IoT issues is challenging since they are frequently not categorized with IoT-related terms. In this paper, we present the "IoT Security Dataset", a domain-specific dataset of 7147 samples focused solely on IoT security discussions. As there are no automated tools to label these samples, we manually labeled them. We further employed multiple transformer models to automatically detect security discussions. Through rigorous investigations, we found that IoT security discussions are different and more complex than traditional security discussions. We demonstrated a considerable performance loss (up to 44%) of transformer models on cross-domain datasets when we transferred knowledge from a general-purpose dataset "Opiner", supporting our claim. Thus, we built a domain-specific IoT security detector with an F1-Score of 0.69. We have made the dataset public in the hope that developers would learn more about the security discussion and vendors would enhance their concerns about product security

A Secure Land Record Management System using Blockchain Technology

A land record (LR) contains very sensitive information related to land e.g. owner, buyer, etc. Currently, almost all over the world, the LR is maintained by different governmental offices and most of them maintain the LR with paper-based approach. Some of the works focus to digitalize the existing land record management system (LRMS) but with some security concerns. A blockchain-based LRMS can be effective enough to solve the existing issues. This paper proposes a blockchain-based LRMS that (i) digitalizes the existing paper-based system, (ii) ensures LR privacy using an asymmetric cryptosystem, (iii) preserves LR integrity, (iv) facilitates a platform for trading land through an advertising agency, and (v) accelerates the process of changing ownership that saves time significantly. Besides, this paper also proposes a new way of character to integer mapping named C2I table that reduces around 33% overhead of text to integer conversion compared to ASCII table. The experimental results, analyses, and comparisons indicate the effectiveness of the proposed LRMS over the state-of-the-art systems.Comment: 6 pages, 5 tables, 10 figures, ICCIT 202