This paper provides an extensive evaluation of YOLO object detection models (v5, v8, v9, v10, v11) by com- paring their performance across various hardware platforms and optimization libraries. Our study investigates inference speed and detection accuracy on Intel and AMD CPUs using popular libraries such as ONNX and OpenVINO, as well as on GPUs through TensorRT and other GPU-optimized frameworks. Furthermore, we analyze the sensitivity of these YOLO models to object size within the image, examining performance when detecting objects that occupy 1%, 2.5%, and 5% of the total area of the image. By identifying the trade-offs in efficiency, accuracy, and object size adaptability, this paper offers insights for optimal model selection based on specific hardware constraints and detection requirements, aiding practitioners in deploying YOLO models effectively for real-world applications.

Benchmarks for large language models (LLMs) often rely on rubric-scented prompts that request visible reasoning and strict formatting, whereas real deployments demand terse, contract-bound answers. We investigate whether such "evaluation scent" inflates measured performance without commensurate capability gains. Using a single open-weights model (GPT-OSS-20B), we run six paired A/B scenarios that hold task content and decoding fixed while varying framing (evaluation-oriented vs. real-world) and reasoning depth (Medium/High): deterministic math, strict code-fix, citation generation, incentive flips (caution vs. competence), CoT visibility, and multilingual (Urdu) headers. Deterministic validators compute accuracy, answer-only compliance, hedging/refusals, chain-of-thought (CoT) length, and schema compliance, with pre-registered deltas and composite indices. Across scenarios, evaluation framing reliably inflates CoT (hundreds to >1000 characters) and reduces answer-only compliance, with limited or inconsistent accuracy gains. In structured outputs, it improves wrappers (e.g., fenced blocks, enumerated lists) but not regex-validated substance. Incentive wording reweights error composition: praising caution modestly improves accuracy at high reasoning and reduces wrong-but-confident errors, whereas praising competence yields terser but riskier outputs. Urdu rubric headers reproduce these signatures and can decrease accuracy at higher reasoning depth, indicating multilingual parity risks. We provide a reproducible A/B framework (prompt banks, validators, per-run scores, scripts; versioned DOI) and practical guidance: neutral phrasing or dual-framing checks, contract-aware grading, style-delta reporting, confidence governance, and multilingual dashboards to ensure that benchmark gains reflect deployable capability.

Large Language Models (LLMs) have revolutionized the field of Natural Language Processing (NLP) by automating traditional labor-intensive tasks and consequently accelerated the development of computer-aided applications. As researchers continue to advance this field with the introduction of novel language models and more efficient training/finetuning methodologies, the idea of prompt engineering and subsequent optimization strategies with LLMs has emerged as a particularly impactful trend to yield a substantial performance boost across diverse NLP tasks. To best of our knowledge numerous review articles have explored prompt engineering, however, a critical gap exists in comprehensive analyses of prompt optimization strategies. To bridge this gap this paper provides unique and comprehensive insights about the potential of diverse prompt optimization strategies. It analyzes their underlying working paradigms and based on these principles, categorizes them into 11 distinct classes. Moreover, the paper provides details about various NLP tasks where these prompt optimization strategies have been employed, along with details of different LLMs and benchmark datasets used for evaluation. This comprehensive compilation lays a robust foundation for future comparative studies and enables rigorous assessment of prompt optimization and LLM-based predictive pipelines under consistent experimental settings: a critical need in the current landscape. Ultimately, this research will centralize diverse strategic knowledge to facilitate the adaptation of existing prompt optimization strategies for development of innovative predictors across unexplored tasks.

The popularity of e-commerce has given rise to fake advertisements that can expose users to financial and data risks while damaging the reputation of these e-commerce platforms. For these reasons, detecting and removing such fake advertisements are important for the success of e-commerce websites. In this paper, we propose FADAML, a novel end-to-end machine learning system to detect and filter out fake online advertisements. Our system combines techniques in multimodal machine learning and automated machine learning to achieve a high detection rate. As a case study, we apply FADAML to detect fake advertisements on popular Vietnamese real estate websites. Our experiments show that we can achieve 91.5% detection accuracy, which significantly outperforms three different state-of-the-art fake news detection systems.

Deep learning has become an extremely powerful tool for complex tasks such as image classification and segmentation. The medical industry often lacks high-quality, balanced datasets, which can be a challenge for deep learning algorithms that need sufficiently large amounts of data to train and increase their performance. This is especially important in the context of kidney issues such as for stones, cysts and tumors. We used deep learning models for this study to classify or detect several types of kidney diseases. We use different classification models, such as VGG-19, (CNNs) Convolutional Neural Networks, ResNet-101, VGG-16, ResNet-50, and DenseNet-169, which can be enhanced through techniques such as classification, segmentation, and transfer learning. These algorithms can help improve model accuracy by allowing them to learn from multiple datasets. This technique has the potential to revolutionize the diagnosis and treatment of kidney problems as it enables more accurate and effective classification of CT-scan images. This may ultimately lead to better patient outcomes and improved overall health outcomes.

A blockchain-based framework for distributed agile software testing life cycle is an innovative approach that uses blockchain technology to optimize the software testing process. Previously, various methods were employed to address communication and collaboration challenges in software testing, but they were deficient in aspects such as trust, traceability, and security. Additionally, a significant cause of project failure was the non-completion of unit testing by developers, leading to delayed testing. This paper integration of blockchain technology in software testing resolves critical concerns related to transparency, trust, coordination, and communication. We have proposed a blockchain based framework named as TestingPlus. TestingPlus framework utilizes blockchain technology to provide a secure and transparent platform for acceptance testing and payment verification. By leveraging smart contracts on a private Ethereum blockchain, TestingPlus can help to ensure that both the testing team and the development team are working towards a common goal and are compensated fairly for their contributions.

A dynamic SBox using a chaotic map is a cryptography technique that changes the SBox during encryption based on iterations of a chaotic map, adding an extra layer of confusion and security to symmetric encryption algorithms like AES. The chaotic map introduces unpredictability, non-linearity, and key dependency, enhancing the overall security of the encryption process. The existing work on dynamic SBox using chaotic maps lacks standardized guidelines and extensive security analysis, leaving potential vulnerabilities and performance concerns unaddressed. Key management and the sensitivity of chaotic maps to initial conditions are challenges that need careful consideration. The main objective of using a dynamic SBox with a chaotic map in cryptography systems is to enhance the security and robustness of symmetric encryption algorithms. The method of dynamic SBox using a chaotic map involves initializing the SBox, selecting a chaotic map, iterating the map to generate chaotic values, and updating the SBox based on these values during the encryption process to enhance security and resist cryptanalytic attacks. This article proposes a novel chaotic map that can be utilized to create a fresh, lively SBox. The performance assessment of the suggested S resilience Box against various attacks involves metrics such as nonlinearity (NL), strict avalanche criterion (SAC), bit independence criterion (BIC), linear approximation probability (LP), and differential approximation probability (DP). These metrics help gauge the Box ability to handle and respond to different attack scenarios. Assess the cryptography strength of the proposed S-Box for usage in practical security applications, it is compared to other recently developed SBoxes. The comparative research shows that the suggested SBox has the potential to be an important advancement in the field of data security.

In this research manuscript, we explore cylindrically symmetric solutions within the framework of modified $f(R)$ theories of gravity, where $R$ representing the Ricci scalar. The study focuses on analyzing the cylindrical solutions within realistic space-time regions and investigates three distinct cases of exact solutions derived from the field equations of $f(R)$ theory of gravity. Moreover, we examine well-known Levi-Civita and cosmic string solutions for the said modify gravity. Furthermore, the manuscript delves into the implications of the obtained results by exploring energy conditions for each case. Notably, a violation of null energy conditions is observed, suggesting the potential existence of cylindrical wormholes. The findings contribute to the understanding of modified $f(R)$ theories of gravity, providing valuable insights into the nature of cylindrically symmetric solutions and their implications in theoretical physics.

Acetylation of lysine residues (K-Ace) is a post-translation modification occurring in both prokaryotes and eukaryotes. It plays a crucial role in disease pathology and cell biology hence it is important to identify these K-Ace sites. In the past, many machine learning-based models using hand-crafted features and encodings have been used to find and analyze the characteristics of K-Ace sites however these methods ignore long term relationships within sequences and therefore observe performance degradation. In the current work we propose Deep-Ace, a deep learning-based framework using Long-Short-Term-Memory (LSTM) network which has the ability to understand and encode long-term relationships within a sequence. Such relations are vital for learning discriminative and effective sequence representations. In the work reported here, the use of LSTM to extract deep features as well as for prediction of K-Ace sites using fully connected layers for eight different species of prokaryotic models (including B. subtilis, C. glutamicum, E. coli, G. kaustophilus, S. eriocheiris, B. velezensis, S. typhimurium, and M. tuberculosis) has been explored. Our proposed method has outperformed existing state of the art models achieving accuracy as 0.80, 0.79, 0.71, 0.75, 0.80, 0.83, 0.756, and 0.82 respectively for eight bacterial species mentioned above. The method with minor modifications can be used for eukaryotic systems and can serve as a tool for the prognosis and diagnosis of various diseases in humans.

In this study, we investigate traversable wormholes within the framework of Einstein-Euler-Heisenberg (EEH) nonlinear electrodynamics. By employing the Einstein field equations with quantum corrections from the Euler-Heisenberg Lagrangian, we derive wormhole solutions and examine their geometric, physical, and gravitational properties. Two redshift function models are analyzed: one with a constant redshift function and another with a radial-dependent function $\Phi=r_{0}/r$. Our analysis demonstrates that the inclusion of quantum corrections significantly influences the wormhole geometry, particularly by mitigating the need for exotic matter. The shape function and energy density are derived and examined in both models, revealing that the energy conditions, including the weak and null energy conditions (WEC and NEC), are generally violated at the wormhole throat. However, satisfaction of the strong energy condition (SEC) is observed, consistent with the nature of traversable wormholes. The Arnowitt-Deser-Misner (ADM) mass of the EEH wormhole is calculated, showing contributions from geometric, electromagnetic, and quantum corrections. The mass decreases with the Euler-Heisenberg correction parameter, indicating that quantum effects contribute significantly to the wormhole mass. Furthermore, we investigate gravitational lensing within the EEH wormhole geometry using the Gauss-Bonnet theorem, revealing that the deflection angle is influenced by both the electric charge and the nonlinear parameter. The nonlinear electrodynamic corrections enhance the gravitational lensing effect, particularly at smaller impact parameters.

Regardless of the adequate descriptions of complexity in distinct alternative gravity theories, its elaboration in the framework of $f(R,\mathcal{L}_{m},\mathcal{T})$ theory remains uncertain. The orthogonal splitting of the curvature tensor yields the complexity factor as suggested by Herrera \cite {herrera2018new}. To commence our study, the inner spacetime is assumed to be spherically symmetric static composition comprised of the anisotropic fluid. In this context, we derive the modified field equations for the considered theory and take into account the established relationship between the conformal and curvature tensors to interpret the complexity. Furthermore, we determine the correspondence of the mass functions with the complexity factor, represented by a specific scalar $Y_{TF}$. Certain solutions complying with the precedent of diminishing $Y_{TF}$ are also evaluated. It is noted that celestial formations having anisotropic and non-uniform compositions of matter assert the utmost complexity. Nevertheless, the spherically symmetric matter distribution may not exhibit complexity in the scenario of vanishing impacts of non-homogenous energy density and anisotropic pressure due to the presence of dark source terms associated with this extended gravity theory.

We study the scattering of J/$\Psi$-J/$\Psi$ mesons using Quadratic and Cornell potentials in our tetraquark ($c$$\bar{c}$$c$$\bar{c}$) system. The system's wavefunction in the restricted gluonic basis is written by utilizing adiabatic approximation and Hamiltonian is used via quark potential model. Resonating group technique is used to get the integral equations which are solved to get the unknown inter-cluster dependence of the total wavefunction of our tetraquark system. T-Matrix elements are calculated from the solutions and eventually the scattering cross sections are obtained using the two potentials respectively. We compare these cross sections and find that the magnitude of scattering cross sections of Quadratic potential are higher than Cornell potential.

This paper explores the dynamics of evolving cluster of stars in the presences of exotic matter. The $f(R)$ theory is used to presume exotic terms for evolution scenario. We use structure scalars as evolution parameters to explore dynamics of spherically symmetric distribution of evolving cluster of stars. We consider Starobinsky model, $f(R)=R+\epsilon R^2$ and study different evolution modes having features like isotropic pressure, density homogeneity, homologous and geodesic behavior. It is concluded that dynamics of these modes of evolution depends upon the behavior of dark matter. The presences of dark matter directly affects the features of cluster like anisotropic pressure, dissipation, expansion, shear as well as density homogeneity. The evolution having homogeneous density and isotropic pressure depends upon conformally flat and non-dissipative behavior of baryonic as well as non baryonic matter. The dissipation factor induces density inhomogeneity in the expanding clusters having shear effects. The non dissipative homologous evolution also be discussed in the presence and absence of shear effects. It is found that high curvature geometry in the presence of shear supports homologous evolution. Expanding clusters are also explored in the presences of dissipation of dark matter and shear effects. By using quasi-homologous conditions the geodesic evolution is studied. It is theoretically showed that geodesic and homologous conditions depends upon each other. Finally we investigate behavior of Starobinsky model for a stellar structure $4U 1820-30$ toward center. It is found that for increasing values of $\epsilon$ the DM behavior is dominant as compare to baryonic matter.

In this work, we develop a new relativistic model for a charged anisotropic compact star in the framework of modified symmetric teleparallel gravity, namely $f(Q)$-Euler-Heisenberg gravity. By employing the MIT bag model equation of state, we establish a relation between the metric potentials, leading to an exact solution of the field equations for an anisotropic fluid configuration coupled with a non-linear electromagnetic source. The interior spacetime is smoothly matched with the exterior geometry calculated from the theoretical setup of $f(Q)$-Euler-Heisenberg gravity using the Darmois-Israel junction conditions, ensuring the continuity of the metric functions and their derivatives at the stellar boundary. The physical viability of the model is examined through regularity, energy, and causality conditions, all of which are satisfied throughout the stellar interior. The study highlights how the pressure anisotropy, the propagation speeds of sound, and the Tolman-Oppenheimer-Volkoff balance condition are interconnected, showing that the star remains in mechanical equilibrium only when the gravitational, hydrostatic, electric, and anisotropic contributions counterbalance one another appropriately. The dynamical stability of the configuration is further supported by the requirement $\Gamma > \tfrac{4}{3}$ for the adiabatic index, indicating resilience against small radial perturbations. The plots of compactness, surface redshift, and the mass--radius profiles confirm that all physical quantities behave regularly and vary smoothly throughout the stellar interior. We graphically plotted the mass-radius curves.

Cyber Security is one of the most arising disciplines in our modern society. We work on Cybersecurity domain and in this the topic we chose is Cyber Security Ontologies. In this we gather all latest and previous ontologies and compare them on the basis of different analyzing factors to get best of them. Reason to select this topic is to assemble different ontologies from different era of time. Because, researches that included in this SLR is mostly studied single ontology. If any researcher wants to study ontologies, he has to study every single ontology and select which one is best for his research. So, we assemble different types of ontology and compare them against each other to get best of them. A total 24 papers between years 2010-2020 are carefully selected through systematic process and classified accordingly. Lastly, this SLR have been presented to provide the researchers promising future directions in the domain of cybersecurity ontologies.

As the use of DevOps practices continues to grow, organizations are seeking ways to improve collaboration, speed up development cycles, and increase security, transparency, and traceability. Blockchain technology has the potential to support these goals by providing a secure, decentralized platform for distributed integration and development. In this paper, we propose a framework for distributed DevOps that utilizes the benefits of blockchain technology that can eliminate the shortcomings of DevOps. We demonstrate the feasibility and potential benefits of the proposed framework that involves developing and deploying applications in a distributed environment. We present a benchmark result demonstrating the effectiveness of our framework in a real-world scenario, highlighting its ability to improve collaboration, reduce costs, and enhance the security of the DevOps pipeline. Conclusively, our research contributes to the growing body of literature on the intersection of blockchain and DevOps, providing a practical framework for organizations looking to leverage blockchain technology to improve their development processes.

Citrus diseases have been major issues for citrus growing worldwide for many years they can lead significantly reduce fruit quality. the most harmful citrus diseases are citrus canker, citrus greening, citrus black spot, citrus leaf miner which can have significant economic losses of citrus industry in worldwide prevention and management strategies like chemical treatments. Citrus diseases existing in all over the world where citrus is growing its effects the citrus tree root, citrus tree leaf, citrus tree orange etc. Existing of citrus diseases is highly impact on economic factor that can also produce low quality fruits and increased the rate for diseases management. Sanitation and routine monitoring can be effective in managing certain citrus diseases, but others may require more intensive treatments like chemical or biological control methods.

Most people around the globe are dying due to heart disease. The main reason behind the rapid increase in the death rate due to heart disease is that there is no infrastructure developed for the healthcare department that can provide a secure way of data storage and transmission. Due to redundancy in the patient data, it is difficult for cardiac Professionals to predict the disease early on. This rapid increase in the death rate due to heart disease can be controlled by monitoring and eliminating some of the key attributes in the early stages such as blood pressure, cholesterol level, body weight, and addiction to smoking. Patient data can be monitored by cardiac Professionals (Cp) by using the advanced framework in the healthcare departments. Blockchain is the world's most reliable provider. The use of advanced systems in the healthcare departments providing new ways of dealing with diseases has been developed as well. In this article Machine Learning (ML) algorithm known as a sine-cosine weighted k-nearest neighbor (SCA-WKNN) is used for predicting the Hearth disease with the maximum accuracy among the existing approaches. Blockchain technology has been used in the research to secure the data throughout the session and can give more accurate results using this technology. The performance of the system can be improved by using this algorithm and the dataset proposed has been improved by using different resources as well.

The rise of Internet of things (IoT) technology has revolutionized urban living, offering immense potential for smart cities in which smart home, smart infrastructure, and smart industry are essential aspects that contribute to the development of intelligent urban ecosystems. The integration of smart home technology raises concerns regarding data privacy and security, while smart infrastructure implementation demands robust networking and interoperability solutions. Simultaneously, deploying IoT in industrial settings faces challenges related to scalability, standardization, and data management. This research paper offers a systematic literature review of published research in the field of IoT in smart cities including 55 relevant primary studies that have been published in reputable journals and conferences. This extensive literature review explores and evaluates various aspects of smart home, smart infrastructure, and smart industry and the challenges like security and privacy, smart sensors, interoperability and standardization. We provide a unified perspective, as we seek to enhance the efficiency and effectiveness of smart cities while overcoming security concerns. It then explores their potential for collective integration and impact on the development of smart cities. Furthermore, this study addresses the challenges associated with each component individually and explores their combined impact on enhancing urban efficiency and sustainability. Through a comprehensive analysis of security concerns, this research successfully integrates these IoT components in a unified approach, presenting a holistic framework for building smart cities of the future. Integrating smart home, smart infrastructure, and smart industry, this research highlights the significance of an integrated approach in developing smart cities.

This study provides Urdu poetry generated using different deep-learning techniques and algorithms. The data was collected through the Rekhta website, containing 1341 text files with several couplets. The data on poetry was not from any specific genre or poet. Instead, it was a collection of mixed Urdu poems and Ghazals. Different deep learning techniques, such as the model applied Long Short-term Memory Networks (LSTM) and Gated Recurrent Unit (GRU), have been used. Natural Language Processing (NLP) may be used in machine learning to understand, analyze, and generate a language humans may use and understand. Much work has been done on generating poetry for different languages using different techniques. The collection and use of data were also different for different researchers. The primary purpose of this project is to provide a model that generates Urdu poems by using data completely, not by sampling data. Also, this may generate poems in pure Urdu, not Roman Urdu, as in the base paper. The results have shown good accuracy in the poems generated by the model.