Category Archives: Uncategorized

Abstract-Brain tumour disease arises from the uncontrolled growth of cells. Detecting brain tumours early is crucial for successful treatment. Many current diagnostic methods are cumbersome, demand significant manual input, and yield less-than-ideal results. The EfficientNet-B0 architecture was utilized to diagnose brain tumours through magnetic resonance imaging (MRI). This refined architecture was applied to classify four distinct stages of brain tumours from MRI images. The fine-tuned model achieved 99% accuracy in identifying four different brain tumour classes: glioma, no tumour, meningioma, and pituitary. The proposed model excelled in detecting the pituitary class, with a precision of 0.95, recall of 0.98, and an F1 score of 0.96. It also performed exceptionally well in identifying the no-tumour class, with precision, recall, and F1 score values of 0.99, 0.90, and 0.94, respectively. The precision, recall, and F1 scores for the Glioma and Meningioma classes were also notably high. This proposed solution holds significant potential for improving clinical assessments of brain tumours.

Abstract-An advanced career guidance recommendation sys- tem is an innovative algorithm that differentiates itself from traditional recommendation algorithms, which work through a link to provide relevant recommendations and are heavily dependent on user behavior. These algorithms are trained to understand various aspects relating to an applicant—their skills, qualifications, work experience, and preferences—and utilize these data points to create customized recommendations based on their profile and career aspirations. Additionally, these intelligent talent recommendation systems continuously evolve, refining their suggestions by analyzing user feedback and performance insights. This study introduces an innovative approach to talent rec- ommendation specifically designed for college students who are preparing to enter the emerging job sector based on a Rank- Based Sequential Deep Learning (RBS-DL) Model. By studying students’ skills, qualifications, interests, and career aspirations, the algorithm intends to offer personalized recommendations. The effectiveness of the recommendation algorithm enhanced by RBS-DL is assessed through both simulated experiments and empirical verifications. Results show significant improvement in recommendation accuracy and relevance over traditional approaches. For the RBS- DL algorithm, students showed a higher job offer acceptance rate by 40% and around 30% increment in job satisfaction levels. This algorithm also learns from user interactions, adjusting its recommendations over time based on real-time user feedback.

Abstract-Transport management is a critical component of supply chain logistics, influencing cost efficiency, operational effectiveness, and overall business performance. This study explores the role of transport management in optimizing supply chains by examining the key challenges, technological advancements, and sustainability initiatives Key findings indicate that the integration of digital tools, such as transport management systems (TMS) and artificial intelligence (AI), enhances route planning, cost optimization, and supply chain visibility. Additionally, sustainable transport practices, including alternative fuel sources and electric vehicles, contribute to cost savings and environmental benefits. The study concludes that businesses must adopt modern transport strategies to remain competitive and resilient in a rapidly evolving logistics landscape.

Abstract-We have Developed a Pneumatic Sheet Bending Machine, it is a cost-effective, efficient, and automated solution for bending thin metal sheets using pneumatic power, addressing the challenges of laborintensive manual methods and expensive hydraulic systems. It utilizes a pneumatic cylinder that applies controlled pressure to bend metal sheets with precision, speed, and consistency, reducing manual effort and the need for multiple operators. The machine is designed to be compact, portable, and easy to operate, making it ideal for small to medium-scale workshops, metal fabrication units, and the automotive industry. Its high-speed operation enhances productivity while maintaining accuracy, making it a valuable tool for producing brackets, enclosures, and sheet metal components. However, the machine is limited to bending thin sheets and requires an air compressor, which may not always be available in smaller setups. Future advancements could improve its capacity for thicker sheets, integrate automated controls for enhanced precision, and utilize advanced pneumatic technologies for greater efficiency. Overall, this innovation provides a practical and scalable alternative to traditional bending methods, significantly improving productivity, reducing labor costs, and making metal fabrication more accessible to small industries.

Abstract-Image inpainting is a crucial task in computer vision that focuses on reconstructing missing or corrupted parts of an image while maintaining structural consistency and visual realism. Traditional inpainting methods, such as diffusion-based and exemplar-based techniques, often struggle to restore fine textures and complex structures, leading to blurry and unrealistic results. The advent of Generative Adversarial Networks (GANs) has significantly enhanced image inpainting by learning to generate plausible image content. However, conventional GAN-based models emphasize global image coherence while neglecting finer local details, causing inconsistencies in high-texture regions. To overcome these limitations, PatchGAN-based inpainting evaluates image realism at the patch level rather than analyzing the entire image as a whole. This technique employs multi-scale discriminators that ensure improved texture synthesis and structural continuity at different spatial resolutions. Experimental studies reveal that PatchGAN-based models outperform conventional GAN-based methods in terms of perceptual quality, Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM), producing sharper and more realistic image restorations. This review explores the advancements in PatchGAN-based inpainting, highlighting its benefits, architectural components, and future research directions to further enhance image reconstruction quality.

Abstract-The rapid growth of e-commerce has brought convenience to consumers but has also led to increasing cyber threats and data breaches. Traditional security measures, primarily reliant on centralized identity management, pose critical vulnerabilities that can be exploited by malicious actors. To address these concerns, this research proposes an advanced security model utilizing Zero-Trust Architecture (ZTA) combined with Decentralized Identity (DID) within a MERN stack-based e-commerce platform. This approach ensures that every access request is verified, significantly reducing unauthorized access risks. Furthermore, blockchain-backed DID solutions offer a tamper-proof identity verification system, empowering users with greater control over their credentials while eliminating the dependency on third-party identity providers. This paper explores the implementation, benefits, and real-world applicability of this security model, highlighting its ability to enhance trust and improve cybersecurity in modern e-commerce platforms.

Abstract-Promptify represents a novel approach to AI-driven content creation by offering a centralized online mar- ketplace for buying and selling creative and technical prompts. The platform bridges the gap between prompt creators and consumers, streamlining the process of ac- cessing high-quality prompts across diverse categories such as creative writing, technical tasks, and artistic projects. Existing platforms lack centralized resources, quality moderation, and secure payment mechanisms, leaving users with fragmented solutions. Promptify addresses these challenges by providing secure trans- actions via Stripe, user shop management systems, and community engagement features. The platform uses modern technologies like Next.js, MongoDB, and Prisma ORM to ensure scalability, security, and perfor- mance optimization. The paper details Promptify’s ar- chitecture, implementation, and testing methodologies, emphasizing its potential to democratize access to qual- ity prompt resources. Future work explores AI-based prompt recommendations, blockchain-based ownership verification, and multilingual support.

Abstract-Internal combustion engines produce exhaust gases at high temperature and pressures. As these hot gases passes through the exhaust valve ,temperatures of the valve ,valve seat, and the stem increase .To avoid any damage to the exhaust valve assembly , heat is transferred from the exhaust valve from different parts ,Especially the valve seat insert during the opening and closing cycle as they come into contact each other . In this article, A Finite-Element method is used for modeling the transient thermal anaiysis of an exhaust valve .The temperature distribution and resultant thermal stresses at each opening and closing time are obtained. Detailed analyses are performed to estimate the boundary conditions of an internal combustion engine. The modeling includes exhaust valve, seat, guide, and spring. The analysis continues until a steady state condition is obtained. In this study ANSYS is employed for modeling and analysis of the exhaust valve. A Methodology is developed for transient thermal analysis of the exhaust valve.

Abstract-A secure bootloader plays a critical role in ensuring the integrity and authenticity of firmware during system startup. Traditional bootloaders lack robust security mechanisms, making them vulnerable to tampering and malware injections. Public Key Cryptography (PKC) provides a strong foundation for secure bootloaders by enabling digital signatures and authentication mechanisms to verify firmware integrity before execution. This research explores the design and implementation of a secure bootloader using Public Key Cryptography to prevent unauthorized modifications and ensure a trusted execution environment. The proposed bootloader employs asymmetric encryption to verify signed firmware images using RSA or ECC (Elliptic Curve Cryptography). Additionally, hash-based verification techniques such as SHA-256 are used to ensure firmware integrity. Experimental evaluations demonstrate that the proposed bootloader effectively prevents unauthorized code execution, ensuring a secure boot process with minimal performance overhead. This paper discusses the system architecture, cryptographic implementation, evaluation metrics, and future research directions for enhancing bootloader security.

Abstract-Micro turbines are becoming widely used for combined power generation and heat applications. Their size varies from small scale units like models crafts to heavy supply like power supply to hundreds of households. Micro turbines have many advantages over piston generators such as low emissions less moving parts, accepts commercial fuels. Gas turbine cycle and operation of micro turbine was studied and reported. Brief description on CAD software and CATIA studied and reported. Different parts (Inlet. Storage, Nozzle, Rotor, coupling, outlet, clip, housing) of turbine are designed with the help of CATIA (Computer Aided Three Dimensional Interactive Analysis) software. Then they were assembled to a single unit and coupled to a generator to produce power. The turbine is of Axial input and axial output type.