Vardhaman College of Engineering

Food-derived biomass residue, such as jackfruit (Artocarpus heterophyllus) peel, offers a sustainable and abundant feedstock for clean energy production due to its high volatile content and continuous generation. Also, the supercritical water gasification (SCWG) presents an environmentally sound and efficient pathway for converting high-moisture biomass into hydrogen-rich syngas while minimizing solid residues and tar. This study investigates the SCWG performance of jackfruit peel at 300, 450, and 600 °C under 25 MPa for 30 min and evaluates the effect of a multifunctional Mo–Cu@N-biochar–LaFeO3 catalyst at 5, 10, and 15 wt Hydrogen generation via SCWG of waste as a green energy route. Studied 300–600 °C and 5–15 wt

Vehicular Ad Hoc Networks (VANETs) in urban environments are pivotal for intelligent transportation but remain highly susceptible to cyber-attacks, which exploit their decentralized and dynamic nature. Existing intrusion detection methods often suffer from excessive computational requirements, an inability to handle novel attacks, and challenges in maintaining real-time performance in dense traffic scenarios. These limitations restrict their practicality in urban VANET architectures. To address these challenges, a novel framework, Dynamic Gravitational tuned Multilayer Perceptron (DG-MLP), is introduced. The approach combines rule-based detection with a machine learning method optimized using Dynamic Gravitational Search Optimization (DGSO). DGSO facilitates efficient feature selection and hyperparameter tuning, ensuring the method remains computationally lightweight while achieving high detection accuracy. The system is evaluated on a diverse and publicly available VANET dataset encompassing multiple attack types and urban mobility scenarios. Preprocessing steps include data cleaning, Min-Max scaling, and feature extraction to standardize and enhance input quality. The hybrid architecture employs rule-based modules for immediate detection of known attack patterns, while the optimized Multilayer Perceptron (MLP) identifies novel and anomalous behaviors in real-time. The DG-LMP framework demonstrates a detection accuracy exceeding 97

The integration of Artificial Intelligence (AI) driven optimization techniques is transforming smart manufacturing in the industry 5.0 landscape leading to sustainable industrial processes. This review comprehensively explores AI-driven optimization methods that enhance efficiency, resilience, and sustainability in modern manufacturing ecosystems. It highlights the role of various AI - based algorithms in optimizing production processes, energy consumption, and supply chains. Along with this, it also presents the significance of AI-driven manufacturing in improving secure production by facilitating real-time monitoring, anomaly detection, and predictive maintenance. In this work, the authors also examine how AI contributes to human-centric manufacturing, addressing challenges such as resource utilization, waste reduction, and adaptive decision-making. Key advancements, limitations, and future research directions are analyzed to provide a holistic view of AI’s transformative potential. The findings underscore the necessity of AI-driven optimization for achieving sustainable, efficient, and flexible manufacturing processes in Industry 5.0. This work serves as a significant reference for researchers, industry professionals, and policymakers seeking to leverage AI for sustainable industrial advancements. This paper presents the comprehensive synthesis of AI-driven optimization techniques represented for the emerging Industry 5.0 model, prioritizing smart sustainable manufacturing. Unlike prior reviews, it systematically compares traditional and AI-based approaches, highlights the transformative synergy of advanced technologies like AI, IoT, digital twins, and blockchain for real-time, human-centric manufacturing, and details hybrid optimization methods integrating AI algorithms. This review uniquely maps the integration of these innovations with sustainability, adaptability, and mass personalization, presenting a roadmap to help industries employ intelligent, data-driven, and eco-friendly optimization solutions for future-ready manufacturing.

This study investigates the low-temperature magnetic behavior and dielectric relaxation mechanism in the compositionally designed spinel ferrite Ni0.33Co0.33Zn0.33Fe2O4 (NCZFO), synthesized using sol-gel auto-combustion techniques. The X-ray diffraction pattern with Rietveld refinement confirms the formation of a single-phase, polycrystalline inverse cubic spinel structure of NCZFO belonging to the space group Fd3(-)m (No. 227), where Ni2+/Co2+/Zn2+ ions occupy the octahedral sites, and Fe3+ ions occupy both octahedral and tetrahedral sites. The Raman-active phonon modes (A(1g), F-2g, and E-g) are detected in Raman spectra, which serve as a signature of the cubic spinel structure of NCZFO. The scanning electron microscopy images demonstrated large, sub-micron-sized, tightly connected grains with irregular lumps and uniform elemental presence and distributions with their stoichiometric atomic and weight percentages. Magnetic ordering, spin dynamics, and intergrain interactions were investigated using magnetic measurements by DC magnetization vs. temperature and magnetization vs. magnetic field loops. An irreversible and freezing/blocking temperature near 358 K and 215 K is revealed by zero-field-cooled (ZFC) and field-cooled (FC) curves and it suggest the presence of surface spin disorder, domain wall pinning and magnetic anisotropy influenced by Ni, Co and Zn ions. As the temperature drops, hysteresis loops at a specific temperature exhibit increasing coercivity and remanent magnetization, which is consistent with temperature-induced reversibility and strong anisotropy at low temperatures. The temperature-dependent dielectric constant (epsilon(r)) and loss (delta) are enhanced due to thermally activated charge carriers, and a weak relaxation plateau similar to 260 K was observed. Non-Debye type dielectric relaxation and the intrinsic permittivity (epsilon(r)) were observed, which is unaffected by conduction or interfacial effects at low temperature. The activation energy (E-a) was calculated to be similar to 0.41 eV for hopping of Fe2+/Fe3+ ions in NCZFO. This investigation provides new insights into the magnetic and dielectric relaxation governing mixed spinel ferrites in spintronic and cryogenic electronics applications.

With the proliferation of digital imaging and readily available editing tools, ensuring the authenticity of digital images has become a critical challenge in many security-sensitive applications. Detecting and localizing these modifications within the image is a crucial issue. In this paper, we present a novel quadruple self-embedding framework for detecting and localizing the tamper. At first, we divide the cover image into 4 major blocks. Further, these four blocks are subdivided into 4 × 4 blocks that are non-overlapping. A watermark is generated from the image itself by choosing one 4 × 4 block from each four major consecutive blocks. We compute Average Arithmetic Intensity (AAI) for each block to facilitate the localization of tamper. This AAI is then converted to binary form to obtain a Bit Vector (BV), that is 8 bits long for each block. The BV of each block is concatenated to obtain an Enhanced Bit Vector (EBV), which is a 32-bit watermark. To ensure the security of this watermark, we encrypt it using Deoxyribonucleic Acid (DNA) encryption. Further, for mapping, we use Arnold mapping, making it an efficient way for localizing the tamper within the image since it does not use any lookup tables. The experimentation reveals that the proposed framework outdoes the existing state-of-the-art techniques (SOTA) by providing higher PSNR and better localization capability. We have evaluated the framework for Kodak, UCID, and Waterloo grayscale image database. Our method obtained an average PSNR of 44.44 dB for a payload of 2 bits per pixel and an average SSIM value of 0.9898. Further, the framework provides better results for False Positive Rates (FPR), False Negative Rates (FNR), and Tamper Detection Rates (TDR), making it a better candidate for authentication of images.