Rajkiya Engineering College Azamgarh

Real‐time medical image classification is a complex problem in the world. Using IoT technology in medical applications assures that the healthcare sectors improve the quality of treatment while lowering costs via automation and resource optimization. Deep learning is critical in categorizing medical images, which is accomplished by artificial intelligence. Deep learning algorithms allow radiologists and orthopaedic surgeons to make their life easier by providing them with quicker and more accurate findings in real time. Despite this, the classic deep learning technique has hit its performance limits. For these reasons, in this research, we examine alternative enhancement strategies to raise the performance of deep neural networks to provide an optimal solution known as Enhance‐Net. It is possible to classify the experiment into six distinct stages. Champion‐Net was chosen as a deep learning model from a pool of benchmark deep learning models (EfficientNet: B0, MobileNet, ResNet‐18, and VGG‐19). This stage helps choose the optimal model. In the second step, Champion‐Net was tested with various resolutions. This stage helps conclude dataset resolution and improves Champion‐Net performance. The next stage extracts green channel data. In the fourth step, Champion‐Net combines with image enhancement algorithms CLAHE, HEF, and UM. This phase serves to improve Enhance‐performance. The next stage compares the Enhance‐Net findings to the lightness order error (LoE). In Enhance‐Net models, the current study combines image enhancement and green channel with Champion‐Net. In the final step, radiologists and orthopaedic surgeons use the trained model for real‐time medical image prediction. The study effort uses the musculoskeletal radiograph‐bone classification (MURA‐BC) dataset. Classification accuracy of Enhance‐Net was determined for the train and test datasets. These models obtained 98.02 percent, 94.79 percent, and 94.61 percent accuracy, respectively. The 96.74% accuracy was achieved during real‐time testing with the unseen dataset.

A planar waveguide consisting of three layers is considered. The guiding layer is assumed of exponentially graded index of refraction. The cover layer is a nonlinear material of Kerr-type. The refractive index distribution of the film layer changes as an exponential function from the guiding layer to the substrate. The solutions of Helmholtz equation are found. They are written in terms of three parameters a, b and V. The solutions in the guiding layer and substrate are found as Bessel functions of order Vb. The characteristic equation is derived and the dispersion curves are plotted and analyzed. A set of attracting features are found such as there is no cut-off thickness corresponding to a symmetric waveguide structure. The b-values do not exceed unity. This means the dispersion curves refer to guided modes.

In this work, a bimetallic (Cu-Ni) prism-based surface plasmon resonance (SPR) sensor is presented. To enhance the interaction of bio-analytes with the sensing surface, a 2-D nanomaterial black phosphorous (BP) is used as it exhibits high biomolecule adsorption on its surface. Our investigation includes an assessment of key performance parameters such as sensitivity (S), full width at half maximum (FWHM), detection accuracy (DA), figure of merit (FoM), and penetration depth (PD). We meticulously optimized the thicknesses of the Copper, Nickel, and TiO2 layers to achieve optimal sensor performance. Our findings demonstrate that the highest sensitivity (S) is achieved with a configuration comprising a 25 nm layer of Cu, a 20 nm layer of Ni, a 1 nm layer of TiO2, and a monolayer of BP, resulting in a remarkable sensitivity (S) of 516°/RIU, with remarkable DA, FWHM, and FoM of 0.20/°, 6.15°, and 83.59/RIU, respectively. The incorporation of the TiO2 layer between the Ni and BP layers contributes to the enhanced sensitivity. Additionally, our proposed sensor configuration is well-suited for the detection of biomolecules within the refractive index (RI) range of 1.330–1.335. We further assessed the sensor’s capabilities in detecting the SARS-CoV-2 coronavirus, which exhibits RI values falling within the considered range (1.3348 and 1.3398). After optimizing the thickness of the metal layers, our sensor achieves an optimal sensitivity of 502°/RIU for SARS-CoV-2 virus detection. This configuration also maintains excellent DA, FWHM, and FoM values of 0.20/°, 4.9°, and 100.56/RIU, respectively.

Longer-term projections indicate that today’s developing and rising nations will account for roughly 60% of the global GDP by 2030. There is tremendous financial growth and advancement in developing countries, resulting in a high demand for personal loans from citizens. Depending on their needs, many people seek personal loans from banks. However, it is difficult for banks to predict which consumers will pay their bills and which will not since the number of bank frauds in many countries, notably India, is growing. According to the Reserve Bank of India, the Indian banking industry uncovered INR 71,500 in the scam in the fiscal year 2018–2019. The average lag time between the date of the occurrence and its recognition by banks, according to the statistics, was 22 months. This is despite harsher warnings from both the RBI and the government, particularly in the aftermath of the Nirav Modi debacle. To overcome this issue, we demonstrated how to create a predictive loan model that identifies problematic candidates who are considerably more likely to pay the money back. In step-by-step methods, we illustrated how to handle raw data, remove unneeded portions, choose appropriate features, gather exploratory statistics, and finally how to construct a model. In this work, we created supervised learning models such as decision tree (DT), random forest (RF), and k-nearest neighbor (KNN). According to the classification report, the models with the highest accuracy score, f-score, precision, and recall are considered the best among all models. However, in this work, our primary aim was to reduce the false-positive parameter in the classification models’ confusion matrix to reduce the banks’ non-performing assets (NPA), which is helpful to the banking sector. The data were graphed to help bankers better understand the customer’s behavior. Thus, using the same method, client loyalty may also be anticipated.

In this study, different combinations of Hemp, Bamboo and Coir fiber are considered as reinforcement materials, while AW106 epoxy resin and HV953 are used as hardeners. The composite lamina was prepared and tested using the hand layup technique with short and random fibers. The tensile and compressive test performed for the analysis of mechanical properties shows that 15% hemp fiber composite is better than bamboo and coir fiber composite. With the increase in fiber volume fraction mechanical properties increases up to a certain limit and a further increase in fiber volume ratio shows adverse effect. Numerical modeling of 15% hemp fiber composite, 15% bamboo fiber composite and 15% coir fiber composite has been done for the calculation of natural frequency and damping factor of natural fiber composite. Numerical modelling was performed using ANSYS 2021 R1 and the specimen was considered in the form of a cantilever beam with a size 200 mm × 20 mm × 4 mm. The damping factor was calculated with the help of the half-power bandwidth method. Numerical analysis reveals that natural frequency and damping values of hemp fiber composite are better than bamboo and coir fiber composite. The worst results were obtained for coir fiber composite. The nondimensional frequency response of hemp fiber composite is better than bamboo and coir fiber composite.

In this work, a surface plasmon resonance (SPR) plasmonic photonic crystal fiber (PCF) biosensor embedded with a rectangular open channel (ROC) is proposed, enabling precise detection and discrimination between healthy and tumorous brain tissues. Healthy and tumorous tissues are considered liquid tissues, each possessing its own distinctive refractive index (RI). The ROC is coated with gold (Au) to generate surface plasmons. To facilitate ample biomolecules, a thin Ti <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> Tx-MXene layer is functionalized over gold. A thin TiO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> layer is coated on the ROC surface to strongly hold the Au nanoparticles, ensuring improved sensing performance. Healthy, cancerous, and tumor tissue samples exhibit unique resonance wavelengths, allowing for their diagnosis through the measurement of shifts in their respective resonance wavelengths. The essential performance parameters, including sensitivity (S), full width at half maximum (FWHM), and figure of merit (FoM) are evaluated. The computed sensitivities for normal and abnormal tissues <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., gray matter, cerebrospinal fluid and oligodendroglioma are 12352.94 nm/RIU, 2030.45 nm/RIU, and 672.26 nm/RIU, measured with respect to white matter and the wall of a solid brain. And, for tumorous tissues (cancers and tumors) such as glioblastoma, lymphoma and metastasis, the sensitivities are 800 nm/RIU, 774.9 nm/RIU, and 643.26 nm/RIU, measured with respect to low grade glioma (Benign). Additionally, the proposed biosensor’s resolution (R) ranges from 1.25 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> to 8.09 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-6</sup> RIU along with the maximum FoM of 126.05 RIU <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> . Hence, this biosensor is poised to excel in the detection of tumor and cancer tissues, making it a promising candidate for advancing medical diagnostics.

Wireless sensor networks (WSNs) encounter a critical challenge of ‘Network Security’ due to extreme operational constraints. The origin of the challenge begins with the entry of worms in the wireless network. Just one infected node is enough to spread the worms across the entire network. The infected node rapidly infects the neighbouring nodes in an unstoppable manner. In this paper, a mathematical model is proposed based on epidemic theory. It is an improvement of the Susceptible-Infectious-Recovered-Susceptible (SIRS) and Susceptible-Exposed-Infectious-Susceptible (SEIS) model. We propose Susceptible-Exposed-Infectious-Recovered-Susceptible (SEIRS) model that overcomes the drawbacks of existing models. The proposed ameliorated model includes a finite communication radius and the associated node density. We obtain basic reproduction number which determines the local and global propagation dynamics of worm in the WSNs. Also, we deduce expression for threshold for node density and communication radius. We investigated the control mechanism against worm propagation. We compare the proposed model with various existing models and evaluate its performance on the basis of various performance metrics. The study confirms melioration in the vital aspects (security, network reliability, transmission efficiency, energy efficiency) for WSNs. The proposed SEIRS model provides an improved technique to restraint worms’ transmission in comparison to the existing models.

In the past few years, the blockchain emerged as peer-to-peer distributed ledger technology for recording transactions, maintained by many peers without any central trusted regulatory authority through distributed public-key cryptography and consensus mechanism. It has not only given the birth of cryptocurrencies, but it also resolved various security, privacy and transparency issues of decentralized systems. This article discussed the blockchain basics overview, architecture, and blockchain security components such as hash function, Merkle tree, digital signature, and Elliptic curve cryptography (ECC). In addition to the core idea of blockchain, we focus on ECC's significance in the blockchain. We also discussed why RSA and other key generation mechanisms are not suitable for blockchain-based IoT applications. We also analyze many possible blockchain-based applications where ECC algorithm is better than other algorithms concerning security and privacy assurance. At the end of the article, we will explain the comparative analysis of ECC and RSA.

Abstract Inadequate sewage treatment plant (STP) capacity, limited power supply, and discharge of partially treated and raw sewage create a significant sanitation problem in Varanasi city, India. This problem becomes severe during the lean period of the river. To reduce the burden on STPs, sewage can be treated and filtered in a naturally occurring sand bed at the convex bank side of the river. In the present study, a 7-km stretch of the sand bed of River Ganga at Varanasi has been selected. This stretch is divided into three zones: entrance, middle, and exit zones. The objective of this research is to assess the filtration potential of selected sections in respective zones and to find out the most suitable zone, out of the three, for wastewater filtration. Seven basic parameters such as dissolved oxygen, biological oxygen demand, electrical conductivity, total dissolved solids, salinity, pH, and temperature were measured before and after filtration, through the sand bed of the three zones of River Ganga. Of the three selected zones of the river bend, filtration length and the amount of available sand were found to be maximum in the middle zone. Experimental results and survey work show that the sand bed in the middle zone of the river bend is best suited for wastewater disposal and filtration.

Nowadays, finding genetic components and determining the likelihood that treatment would be helpful for patients are the key issues in the medical field. Medical data storage in a centralized system is complex. Data storage, on the other hand, has recently been distributed electronically in a cloud-based system, allowing access to the data at any time through a cloud server or blockchain-based ledger system. The blockchain is essential to managing safe and decentralized transactions in cryptography systems such as bitcoin and Ethereum. The blockchain stores information in different blocks, each of which has a set capacity. Data processing and storage are more effective and better for data management when blockchain and machine learning are integrated. Therefore, we have proposed a machine-learning-blockchain-based smart-contract system that improves security, reduces consumption, and can be trusted for real-time medical applications. The accuracy and computation performance of the IoHT system are safely improved by our system.

Abstract A photonic crystal fiber (PCF) containing circularly organized square-shaped air holes in the cladding region is investigated. The fiber core is perforated with four circular air-filled holes to instate high nonlinearity and large negative dispersion. The numerical analysis is done with a finite element method based COMSOL Multiphysics tool to investigate different optical properties of the propounded PCF. The simulation outcome verifies a high nonlinear coefficient value of 85 W −1 Km −1 at telecommunication window 1.55 μ m which is, the highest ever achieved value on comparing with the other existing literature without using any nonlinear materials or liquids to the best of the authors’ knowledge. In parallel, the large negative value of dispersion −597 ps nm −1 km −1 is achieved for S/Λ equals 0.70 at the same communication window. However, the highest achieved nonlinearity and negative dispersion are 300 W −1 Km −1 and −1689 ps/nm/km. Moreover, birefringence, numerical aperture, and propagation loss are also measured as 2.40 × 10 −3 , 0.59, and 4.12 × 10 −11 dB m −1 respectively along with an extremely high core power fraction of 99.98%. Hence, the propounded PCF is suitable for residual dispersion compensation, supercontinuum generation, and high bitrate transmission.

In the present scenario, it is vital for any organization, especially the financial organizations, to understand customers and their financial dealings better. KYC is a process to verify identity and related details of corresponding customers. The current KYC mechanism has a severe concern in financial institutions as it requires separate ledger for the separate financial organizations. Every institution has its KYC process, which sometimes may include third-party, which may cause increased maintenance cost, time and redundancy. There is considerable wastage of costs in the form of opportunity cost, maintenance cost, customer verification cost and many more of around $27 million according to an economic survey. The current KYC process is very time-consuming, and it decreases the user experience. We have proposed an enhanced KYC system using blockchain technology to improve the existing KYC system. An inherent feature of the DLT is used to remove the third-party involvement, and smart contracts are used to build our logic in the mobility of the data. Blockchain technology has various types of cryptographic security which provide a safer place to transact over an unsecured channel. Using the facility of DLT, cryptography and consensus mechanism of blockchain, the proposed model of KYC process can optimize storing, updating, sharing of data and accessing operations along with enhanced security, transparency and privacy. It also enhances customer ownership and improves customer experience. It not only reduces the time duration and document update problem but also saves opportunity cost, aggregation, cost, maintenance cost and many more costs, which can affect the performance of any organization.

As radio access technologies, processing speeds, and multimode interfaces of low-powered portable devices continue to advance, the future of wireless communication is envisioned to offer pervasive network coverage, high data rates, and a wide spectrum of services while maintaining high mobility. High data rates, wide range of services, huge connectivity, capacity, and good geographic coverage are being provided by the ultra-dense deployment of small base stations (BSs) in heterogeneous wireless networks (HWN). But dense deployment of small BSs, high mobility, network heterogeneity, imbalanced traffic, and dynamic user preferences lead to frequent handover. Network overhead, excessive energy consumption, and a decrease in service quality and user satisfaction can be due to frequent handover. So, handover management is one of the crucial challenges in the implementation of 5G and beyond in HWNs for ensuring seamless connectivity, energy efficiency, and the required quality of services and experiences. The effectiveness of handover decisions in HWNs relies on the implementation of a suitable network selection mechanism. Multi-attribute decision-making (MADM) is being used to model and analyze appropriate network selection complexities by considering a broad spectrum of intricate and conflicting decision criteria for efficient handover decisions in HWN. This article extensively explores, compares, and analyzes vital MADM techniques utilized for modeling appropriate network selection strategies in terms of algorithmic strategies, cardinality, types and significance of decision attributes, and network utilities. This article also examines, analyzes, and recognizes the recent mobility management challenges and trends in utilizing MADM strategies to tackle network selection issues in high-speed HWNs.

Abstract The protection of wireless sensor networks (WSN) against malware attacks is crucial. The paper discusses the issue of malware attacks in WSN, which are commonly used for monitoring and surveillance in various applications. Due to resource constraints, sensor nodes in WSN are vulnerable to malware attacks, which can spread rapidly and paralyze the network. The development of new technologies such as IoT, Industry 4.0 has increased the importance of WSN, and it has become essential to address the challenges posed by the resource‐constrained nature of sensor nodes and security concerns. In this paper, a model is considered with two exposed states to investigate the behaviour of malware spreading in WSN, and a SE 1 E 2 IR (Susceptible—Exposed State 1 ‐ Exposed State 2 ‐ Infectious—Recovered) model is proposed. The model is formulated as a system of differential equations, and its equilibrium and stability are examined. The basic reproduction number (R 0 ) is also calculated as a key parameter that characterizes the spread of malware in the network. This parameter helps to identify the conditions under which the network will remain malware‐free or when it will experience an outbreak of malware. The proposed model provides a mechanism for the earlier detection of malware occurrences in WSN, and also discusses the effect of connectivity and coverages on the propagation of malware in the network. The paper also includes a comparative study of the proposed model with existing models; extensive theoretical study and computation analysis are performed to validate the proposed model.

Network security poses a challenge to wireless sensor networks (WSNs) achieving its true potential. It is hard to tackle due to operational constraints of networks. Worms have become an emergent threat to the wireless networks. The spread of worms in the network is epidemic in nature. This article proposes a novel mathematical model with pre-vaccination and quarantine for study of worm propagation dynamics in WSN that is based on epidemic model. Further, the authors have devised an expression to determine threshold communication radius and node density. The objective of this proposed model is to study the propagation dynamics of worms in wireless sensor networks. Through the model, investigate the stability condition of networks in the presence of malicious codes. The experimental studies indicate that the proposed model outperforms in terms of security and energy efficiency over other existing models. It is a leap toward worm-controlling mechanisms in sensor networks. Finally, the control mechanism and performance of the proposed model is validated through extensive simulation results.

Due to entry of body fluid like saliva, blood, etc. in the dental implant assembly lowers the preload value, thus dental implant abutment tightening torque loses. In this article a novel chitosan-reinforced bamboo and nano bio-silica-reinforced five composite materials (CP, CF, C1, C2, and C3) are fabricated using the hand layup method, and their mechanical, biocompatible, and moisture absorption properties are observed and discussed. The present study examines the impact of friction and Young's modulus on the correlation between torque and starting load in dental implant abutment screws, utilizing the attributes of a bio-composite material. C2 bio-composite composite material exhibits the highest tensile strength (139.442 MPa), flexural strength (183.571 MPa), compressive strength (62.78 MPa), and a minimum value of 1.35% absorption of water. C3 is tested with no cytotoxicity, while C3 and CF exhibit weak biofilm resistance against S. aureus gram-positive bacteria. The C2 bio-composite material demonstrated a maximum initial load of 20 N with a tightening torque of 20 N-cm, under both 0.12 and 0.16 coefficients of friction. The simulated results were compared with several theoretical relations of torque and initial load and found that the Motos equation holds the nearest result to the obtained preload value from finite element analysis. Overall, the experimental findings suggest that the C2 bio-composite material holds significant potential as a prominent material for dental implants or fixtures.

The malware spreading in Wireless Sensor Network (WSN) has lately attracted the attention of many researchers as a hot problem in nonlinear systems. WSN is a collection of sensor nodes that communicate with each other wirelessly. These nodes are linked in a decentralised and distributed structure, allowing for efficient data collection and communication. Due to their decentralised architecture and limited resources, WSN is vulnerable to security risks, including malware attacks. Malware can attack sensor nodes, causing them to malfunction and consume more energy. These attacks can spread from one infected node to others in the network, making it essential to protect WSN against malware attacks. In this paper, we focus on the analysis of a novel fractional epidemiology model, specifically the fractional order SEIVR epidemic model in the sense of Caputo’s fractional derivative of order <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0{ &lt; {\alpha }{\leq }}1$ </tex-math></inline-formula> with the goal of examining the efficacy of vaccination strategies and the heterogeneity of a scale-free network on epidemic spreading. First, using the next-generation technique and obtain the basic reproduction number of the proposed epidemic model, which is essential for determining both the locally asymptotically stable equilibrium point of the worm-free system and the unique existence of the endemic equilibrium point. To numerically solve the model, the Adam-Bashforth-Moulton predictor-corrector (ABM) method is applied. The fractional calculus enables us to deal directly with the “memory effect” of numerous phenomena, taking into account the system’s dependence on previous stages. This method provides the results of a complex system. Additionally, research demonstrates that vaccine treatments are quite effective at preventing the spread of malware. The outcome of the study reveals that the applied ABM predictor-corrector method is computationally strong and effective to analyse fractional order dynamical systems in the SEIVR epidemic model for malware propagation in WSN. The results show that the order of the fractional derivative has a significant effect on the dynamic process. Also, from the result, it is obvious that the memory effect is zero for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\alpha }$ </tex-math></inline-formula> = 1. When the fractional order <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\alpha }$ </tex-math></inline-formula> is decreased from 1, the memory effect appears, and its dynamics vary according to the time. This memory effect points out the difference between derivatives of fractional and integer orders. The theorems and their proofs are presented to validate the validity of the proposed model. To validate the proposed model, extensive theoretical study and computational analysis have also been applied.

The aim of this research is to synthesise a cost-effective biodegradable green composite for various low- and medium-load applications. The tensile and flexural results reveal that the rice bran composition in green composite enhances the stiffness of composite, while strength and hardness decrease. The highest values of tensile strength 27 MPa and flexural strength 25 MPa were obtained for 15/85 treated rice bran composites, while the highest value of young modulus 2958 MPa was obtained for the 35/65 composite combination. The highest value of hardness, i.e., 11 HRF was obtained for 15/85 treated rice bran composite. The water absorption test reveals the hydrophilic nature of rice bran and the hydrophobic nature of PLA. Results also reveal better water-absorbing properties of the green composite due to the surface treatment of rice bran. The lowest density of 1.001 g/cm3 found for the 50/50 composite combination means the addition of rice bran makes the composite light in weight. The thermogravimetric analysis performed on the composite to analyse its thermal behaviour shows that major weight loss occurs approximately in the temperature range of 80–350° Celsius. The response surface methodology (RSM) and design of experiment (DOE) optimization model were developed to find that the optimum condition for maximum weight loss reveals two desirable conditions, i.e., 500° Celsius and 424.85° Celsius. ANOVA analysis reveals that the obtained results are significant.

In this study, four distinct composite samples (Samples A, B, C, and D) were fabricated using varying compositions of biochar, rice bran, coconut coir, and epoxy matrix. Sample A, serving as the baseline with 90% epoxy and 10% biochar, exhibited moderate mechanical properties. Sample B, with 80% epoxy and 20% biochar, demonstrated significantly higher tensile and flexural modulus values, indicative of improved stiffness. Sample C, incorporating 10% rice bran alongside 80% epoxy and 10% biochar, displayed reduced mechanical properties compared to Sample B, potentially due to the lower strength of rice bran particles. Sample D, comprising 80% epoxy, 10% biochar, and 5% coconut coir, demonstrated weaker tensile properties but higher flexural modulus, suggesting enhanced resistance to bending forces. Mechanical testing, water absorption analysis, Fourier Transform Infrared (FTIR) spectroscopy, and SEM imaging provided comprehensive insights into the mechanical and chemical characteristics of the composites, underscoring their potential for diverse applications in sustainable materials development.

Corrosion is a major issue affecting the durability and performance of copper in industrial applications, particularly in high-salinity environments. Traditional methods for corrosion inhibition often involve toxic chemicals, posing significant health and environmental risks. In response to these challenges, this study explores an innovative, eco-friendly alternative by using watermelon seed extract as a natural corrosion inhibitor for copper in brine. This research is among the first to investigate watermelon seed extract in this context, offering a sustainable substitute for synthetic inhibitors. Scanning electron microscopy (SEM) paired with EDAX analysis reveals the extract' s uniform coverage on the copper surface, with elemental analysis showing notable amounts of carbon (76.4%) and oxygen (17.2%), providing valuable insights into the extract' s interaction at the microstructural level. Additionally, FTIR analysis confirms the presence of carbon-carbon (C-C) and carbon-hydrogen (C-H) bonds, which impart hydrophobic properties crucial for corrosion resistance. The corrosion inhibition performance is evaluated using multiple electrochemical techniques such as electrochemical impedance spectroscopy (EIS), open circuit potential (OCP), and Tafel polarization curves (TC), demonstrating how inhibition efficiency is influenced by the concentration of the extract. The study identifies an optimal concentration of 200μ L for maximum protection, highlighting the potential for fine-tuning natural extracts for industrial corrosion prevention. By demonstrating the efficacy of watermelon seed extract, this research promotes green chemistry principles, providing a safer, more environmentally friendly solution to corrosion inhibition while advancing sustainability in industrial applications.