International Research Journal of Multidisciplinary Technovation
International Research Journal of Multidisciplinary Technovation (IRJMT) (E ISSN 2582-1040) is a peer-reviewed, open-access journal published in the English – language, provides an international forum for the publication of Engineering and Technology Researchers.
Enhancing Diabetes Diagnosis with Confidence-Calibrated Adaptive Weighting and Multi-Model Ensemble Framework
Metabolic conditions like diabetes often show up as high blood sugar levels, or hyperglycemia, which happens when the body doesn't make enough insulin. If this goes unnoticed or untreated, it can cause serious damage to critical organs such as the eyes, kidneys, nerves, heart, and blood vessels. Recently, machine learning and other computational techniques have shown great potential for predicting who might develop diabetes. But there's still room to improve how accurate and reliable these models are. In this work introduce a step-by-step machine learning approach to predict and diagnose diabetes using three different datasets. First, used Hot Deck Imputation (HDeckImp) to fill in missing data and make the classifications more accurate. Next, we apply K-fold cross-validation to test how well the model holds up with different data splits, ensuring robustness. Then, we incorporate multiple classifiers like Random Forest, XGBoost, AdaBoost, and Bagging to strengthen predictions. Finally, we developed a new weighting method called Confidence-Calibrated Adaptive Weighting (CCAW). This method dynamically gives more weight to models that are both accurate and confident, measured through information entropy, so the best models have more influence on the final outcome. Experiments show that this approach, especially with CCAW, reaches a top accuracy of 98.9% on the Frankfurt dataset, beating previous ensemble methods such as ADR-W. This improvement not only enhances prediction accuracy but also makes the model stable, which is essential for practical clinical use.
Confidence-Calibrated Adaptive Weighting (CCAW)
Copyright (c) 2025 Usha V, Rajalakshmi N.R (Author) https://creativecommons.org/licenses/by/4.0
Design and Development of MEMS Based Array Structured Vibrational Energy Harvester
Rapid advances in microelectromechanical structures (MEMS) have enabled the improvement of efficient electricity harvesting equipment, converting ambient vibrational power into usable electric electricity. This newsletter offers the layout and simulation of a MEMS-primarily based array structure vibrational electricity harvester. This is optimized for effective electricity conversion. The proposed harvester ambitions to remove mechanical vibrations from renewable energy resources and convert them into power through piezoelectric effects. It consists of a circular array of rectangular factors, each attached to a heavy weight, to improve the efficiency of electricity incidents. This specific structural configuration ensures surest use of space even as making sure maximum strength harvesting performance. To attain great electricity conversion efficiency, Harvester's PZZ-5H uses piezoelectric substances with excessive electromechanical bonding. The array factors are mainly advanced with prolonged beam lengths and comparatively low widths and thicknesses, which efficaciously make use of low frequency vibrations. The resonant behavior of the structure is analyzed the use of COMSOL Multiphysics software, which provides insight into the mechanical and electric residences of the harvester. The simulation is executed to decide the resonant frequency, ordinary shift, and output voltage under one-of-a-kind boundary conditions. The effects display that the harvester reaches a most output voltage of 1 V whilst it quickens 1 g. This demonstrates its potential as a sensible solution for applications with low strength sources, especially wi-fi sensor networks, biomedical gadgets and IoT-primarily based clever structures. Additionally, through using the available area correctly, the circular array shape considerably increases power density. This study highlights the effectiveness of MEMS-based piezoelectric harvesting in recording environmental vibrations and the conversion into usable electric electricity that contributes to the development of self-successful electronic systems. Future work will cognizance on optimizing material houses and research talents for multi-faceted electricity harvesting to improve normal performance.
Copyright (c) 2025 Pavan Kumar Siddana, Taj Mohammad (Author) https://creativecommons.org/licenses/by/4.0
An Enhanced Extreme Learning Machine Based on a Swarm Intelligence Approach for Heart Disease Detection
Heart disease (HD) is frequently considered the most problematic human disease. Designing a more precise heart disease prediction technique is a challenging task. Recently, several health disease detection problems have been addressed using the extreme learning machine (ELM) approach. The ELM has become increasingly generally employed in various learning domains for prediction and control applications due to its rapid learning rate, straightforward structure, and excessive generalizability. Finding the perfect input weights and hidden bias parameters is the foremost challenge to enhancing the ELM's performance. Randomly selecting these parameters wants more hidden neurons than the traditional gradient learning technique, delaying the prediction response and decreasing the likelihood of finding the ideal output weight. In this study, bacterial colony optimization (BCO) is used to adjust the connection weights and bias of ELM (BCO+ELM) to address the drawbacks above. Additionally, the missing value of the heart dataset is filled with multivariate imputation by chained equation (MICE) and relevant features are selected by using recursive feature elimination (RFE) for obtaining more efficient solution accuracy and enhancing the performance of proposed BCO+ELM. According to the performance measures, BCO+ELM shows excellent prediction performances. The experimental effects indicate that the BCO+ELM creates better outcomes and low computation time with fast convergence time when compared to other approaches. The best-performing BCO+ELM shows consistent gains, although at a somewhat smaller rate (0.4% after imputation, 2.76% after feature selection). This implies that BCO+ELM still gains from the improvements provided by both imputation and feature selection, further optimizing its performance even if it is already highly optimized.
Copyright (c) 2025 Vigneshvaran P, Vijaya Kathiravan A (Author) https://creativecommons.org/licenses/by/4.0
Advanced Computer Vision Model for Lung Tumor Segmentation and Detection in Radiology and Histopathology Images
For effective treatment scheme and initial diagnosis, it is important to segue and detect the lung tumors. Tumor variability, low contrast and overlapping tissue structure are some problems with traditional methods that make it challenging to accurately and detect it. So, a newly advanced computer vision model that uses a Binary Gennet Optimizer Tund Forward Neural Network (BGO-FFNN) method is used to sort tumors. The dataset contains anotate CT scan and histopathology slides that were taken from publicly available repository. Gabber filters are used in preprosauting to get rid of noise and strengthen the opposite, making it easier to see the edges of the tumor. Multi-scale edge-watthers' techniques work together for different tumor areas simultaneously. Local binary pattern (LBP) is used to achieve texture features, which are important to explain the difference between a variety of tumors. BGO is used to improve FFNN, and then classification performance is tested. The results showed that the suggested model improves with recall (94.48%), F1-score (97.21%), accuracy (95.14%), and accurate (99.71%). Results suggest that the proposed advanced computer vision model works better than standard people. This hybrid model creates a major difference on how well the tumor is found and classified. It is very likely to use in the clinic and helps radiologists and pathologists assess and diagnose the exact tumor. These enrichment improves clinical accuracy and streamlines the workflow in clinical settings. As technology is developed, further integration of artificial intelligence in medical imaging can lead to more significant progress in patient results.
Copyright (c) 2025 Sheeja T.S, Arun Chokkalingam (Author) https://creativecommons.org/licenses/by/4.0
Detection of Distributed Denial of Service Attacks Based on Deep Learning Approaches: A Survey, Taxonomy, and Challenges
DDoS attacks are among the most dangerous dangers to the digital world, according to recent theoretical and empirical research. Over time, DDoS attack mitigation strategies have developed to guarantee security. In the past, several traditional techniques, including heuristics and signatures, were employed to detect DDoS attacks encoded with different characteristics. The advanced obfuscation strategies used by new generations of DDoS attackers were too formidable for detection tools designed for traditional DDoS attacks. Since DL-based systems beat traditional DDoS attack detection techniques in discovering novel DDoS attack variations, Deep Learning (DL) is being employed more and more in DDoS attacks. Additionally, DL-based methods offer quick DDoS attack prediction together with superior detection rates and DDoS attack analysis. Thus, this work is interested in examining recently suggested DL-based DDoS attack detection systems and their development. It provides a comprehensive examination of the most current advances in DL-based detection methods. This survey's main objective is to give readers a thorough grasp of the applications of DL for detection. The outcome of this review discusses various DL methods, their strengths and weaknesses, datasets, challenges of recent research work, and future enhancements of present works.
Copyright (c) 2025 Vidhya G, Jagadheeswari M (Author) https://creativecommons.org/licenses/by/4.0
SCCM: A Novel Hybrid Framework for Dual Biometric Authentication for Identity Enhancement
The increasing need for secure identification has led to advancements in biometric authentication. Traditional single-factor methods, such as passwords or tokens, are vulnerable to cyber threats, necessitating more robust alternatives. This study proposes a Dual Biometric Authentication System (DBAS) based on SCCM (SIFT-CNN-MLP), integrating fingerprint and iris recognition to enhance security, precision, and reliability. The system leverages state-of-the-art machine learning and computer vision techniques, including Multilayer Perceptron (MLP), Convolutional Neural Networks (CNN), and Scale-Invariant Feature Transform (SIFT). MLP and CNN models analyze intricate iris patterns, while SIFT and CNN extract distinguishing features from fingerprint ridge-valley structures. By intelligently combining authentication results from both biometrics, DBAS ensures seamless access for authorized users while effectively blocking imposters. The proposed SCCM framework demonstrates high performance in terms of accuracy. For iris authentication, MLP achieved 97.33% accuracy, while CNN outperformed with 97.92% accuracy. Similarly, for fingerprint authentication, SIFT yielded 91.30% accuracy, whereas CNN excelled with 99.23% accuracy. The SCCM-based DBAS significantly enhances authentication accuracy and robustness compared to traditional methods. It is highly effective for computer logins, mobile security, and critical infrastructure protection, making it a novel, future-proof solution for secure authentication.
Copyright (c) 2025 Saurav Verma, Ashwini Rao, Ketan Shah (Author) https://creativecommons.org/licenses/by/4.0
Design and Control of a Dual Inverter-Based Grid-Tied PV System with QB Converter: Comparison of PI and FOPID Controller Performance
This paper presents the design and implementation of a dual inverter-based grid-connected photovoltaic (PV) system incorporating PI (Proportional-Integral) and FOPID (Fractional-Order Proportional-Integral-Derivative) controllers, along with a Quadratic Boost Converter (QBC) for enhanced energy conversion and grid integration. In order to guarantee optimal DC-AC conversion from the photovoltaic array and smooth grid integration, the dual inverter system features cascaded voltage source two level inverters (DI). Enhancing power quality, improving the system's dynamic response, and making sure the injected current satisfies the necessary requirements while lowering harmonic content are the main goals. While the FOPID controller is used to enhance the system's transient response and provide better dynamic performance under changing irradiance and grid disturbances, the PI controller is employed for steady-state control. The Quadratic Boost Converter is a great option for PV applications since it also has a high efficiency, a high step-up voltage conversion, and lower switching losses. The FOPID controller reduces settling time by 43% and overshoot for grid current by 38% when compared to the PI controller, according to a comparative study of the two controllers. Furthermore, the grid current's Total Harmonic Distortion (THD) is decreased from 4.41% (PI) to 4% (FOPID). These outcomes demonstrate how well the twin inverter-based PV system with the QBC works and how the FOPID controller provides better dynamic performance for grid-tied applications.
Cascaded Voltage Source Two Level Inverter (DI)
Copyright (c) 2025 Shaik Masum Basha, Nagaraju K (Author) https://creativecommons.org/licenses/by/4.0
A Novel Multistage Approach for Medicinal Plant Classification with Deep Learning Techniques
Accurate classification of medicinal plant images into high-level categories and specific sub-groups is essential for various applications, including agriculture, plant research, and conservation. This paper proposes a multi-stage deep learning approach to enhance the precision of medicinal plant image classification. In the first stage, known as Broad Classification, CNN and pre-trained models such as VGG16, ResNet50 and EfficientNetB0 are utilized to categorize images into high-level groups, including "Medicinal Plants," "Fruit-Related Plants," and "Flower-Related Plants." The model is fine-tuned using data augmentation techniques to ensure robust learning and generalization. In the second stage, referred to as Detailed Classification, separate models are trained for each high-level group to classify images into specific sub-groups within that category. The architecture of these models is adjusted to accommodate the unique number of classes in each sub-group. Each model undergoes training with optimized hyperparameters and is evaluated based on precision, recall, F1-score, and accuracy. The proposed multi-stage method demonstrates the ability to handle both broad and fine-grained medicinal plant classifications effectively, showcasing an improvement in classification performance over traditional single-stage models. This approach highlights the potential for deep learning to contribute to more precise and practical medicinal plant image classification solutions.
Copyright (c) 2025 Narayana Rao K, Srinivas Kalime, Sujatha P, Dinesh Babu Vunnava, Sushma S, Tulasi Krishna Sajja (Author) https://creativecommons.org/licenses/by/4.0
Development of an Automated Endoscopic Image Segmentation Technique for Accurate Localization of Gastrointestinal Lesions
Automatic detection of gastrointestinal (GI) lesions makes endoscopic diagnosis more accurate, but it is difficult because the lesions can look different. A deep learning (DL) method for specific classification makes things more accurate, but for clinical use, a strong model is needed to make sure the location is correct. This study comes up with a better way to divide things up and solves these problems. The goal is to make a custom partition frame with a Residual Swin Transformer Fusion Network (RN-RSTFN) that has been fine-tuned with a Refined Nutcracker. Gaussian Filtering (GF) was used to reduce noise in the images and make them clearer. Then, Z-Score was used to standardize the distribution of pixel intensity. Function Extraction using a histogram of oriented gradients (HOG) helps to get the wound pattern needed for better partition performance. Give the right location for GI ulcers. Endoscopic image datasets that are available to the public from depot and medical institutions include pictures of different GI lesions. Gaussian Filtering (GF) was used to reduce noise in the images and make them clearer. Then, Z-Score was used to standardize the distribution of pixel intensity. The RN-RSTFN that was suggested combines the hierarchical representation and residual learning of the Swin Transformer to improve the boundaries of lesions. When measured using the Dice score and mIOU, Precision, and Recall measures, the RN-RSTFN model showed a lot of improvement in finding GI lesions. The scores were 0.9458, 0.9383, 0.9634, and 0.9596, respectively.
Refined Nutcracker-tuned Residual Swin Transformer Fusion Network (RN-RSTFN)
Copyright (c) 2025 Swetha Kumari T, Vasuki R (Author) https://creativecommons.org/licenses/by/4.0
Taurolidine, a Sulfonic Amino Acid Derivative, Induces Apoptosis and Cytotoxicity against Cervical Cell Carcinoma an In Silico and in Vitro Approach
Taurolidine, a derivative of endogenous sulfonic amino acid taurine with a chemical formula of C7H16N4O4S2 with antineoplastic, anti-inflammatory, cytotoxic, and antimicrobial characteristics, was subjected to in silico and in vitro studies. The fingerprint plots of the Hirshfeld surface analysis highlight that the O…H/H…O interaction makes a significant contribution with 57.4% of the total crystal packing. The biological interaction of taurolidine with antiapoptotic proteins Bcl-2 and Bcl-XL, characterized by binding energies of -5.54 and -6.92 kcal/mol, confirms its cytotoxic activity. The in vitro experiments were conducted against cervical carcinoma (SiHa) cells to confirm its antiproliferative activity. The MTT assay revealed that the cytotoxicity of taurolidine was observed in a dose-sensitive manner, with an IC50 (half maximal inhibitory concentration) at 204.1 µM, and induced cell cycle halt at the G0/G1 phase. The apoptotic characteristics of taurolidine-treated cells were measured using dual (AO/EtBr) and propidium iodide fluorescence staining. In addition, the mRNA expression analysis of proteins Bax and Bcl-2 confirms that taurolidine promotes apoptosis in cervical carcinoma cells.
Copyright (c) 2025 Mani A, Elaiyaraja R (Author) https://creativecommons.org/licenses/by/4.0
A Hybrid IoT and Machine Learning Framework for Smart Greenhouse Automation in Sustainable Agriculture
Growing demand for green farming has expedited the progress of smart greenhouse technology. This paper presents a smart greenhouse system combining Internet of Things (IoT) platforms with autonomous controls and artificial intelligence (AI) to improve the efficiency of crop production and minimize the utilization of resources. Greenhouse management under traditional technique is usually influenced by ineffective controls and dependency on human intervention, which includes wastage of resources and reduced yields. The newly proposed system provides real-time tracking of climate factors—temperature, humidity, light intensity, and soil moisture—by means of a network of sensors. Irrigation, ventilation, and lighting functions are managed by machine learning (ML) algorithms that predict optimal climatic conditions. The system utilizes cost-effective ESP8266 and ESP32 microcontrollers and MQTT for data transmission at low costs. Experimental validation confirmed a 30% reduction in water consumption and 15% increase in crop yield, and energy efficiency improved to traditional systems. The system is flexible for different scales of greenhouses and types of crops. The results confirmed that the integration of IoT with AI provides a scalable, green solution to green farming and resource management.
Copyright (c) 2025 Venkataramanan V, Mrunalini Pimpale, Vijay Kapure, Pankaj Mishra, Aarya Rokade, Tulsi Bhushan, Jitendra Singh (Author) https://creativecommons.org/licenses/by/4.0
Analysis of machining parameters for EN24 material in CNC vertical machining center by Ti-N coated HSS tool using GRG reinforced RSM technique
The goal of the present study is to evaluate the optimum machining parameters for EN24 material to achieve an increased material removal rate (MRR), low cutting force (CF), and surface roughness (SR). The total number of trial experiments for the three control parameters spindle speed (SS), feed rate (FR), and depth of cut (DOC) varying at three levels are designed based on the Taguchi L27 orthogonal array. To optimize the multiple machining parameters for EN24 steel based on the three experimentally measured responses such as MRR, CF, and SR, a contemporary and hybrid multi-objective optimization technique, grey relational grade (GRG) reinforced response surface methodology (RSM) is employed in the present study. Based on the results obtained from the integrated multi-objective optimization technique, the predicted optimum machining conditions for SS, FR, and DOC are 1383.47 rpm, 106.5 mm/min, and 0.79 mm respectively. The responses MRR – 21.56 mm3/min, CF – 195.56N, and SR- 0.53µm are obtained at optimum level. The percentage of improvement achieved in the preferred responses for milling the EN 24 material after implementing the GRG reinforced RSM technique are MRR - 5.19%, CF - 15.42%, and SR- 19.69%.
Copyright (c) 2025 Murugan R, Senthilkumar G, Vinodkumar V, Rathinasabapathi G, Dhanasakkaravarthi B, Sivaraman V (Author) https://creativecommons.org/licenses/by/4.0
Comparative Analysis of Suitability of Deep Learning Models in Quality Assurance of Fabrics
Industry 4.0 has revolutionized the manufacturing sector in India. The Textile Industry in India is a strong pillar of the Indian economy and leans on deploying Machine learning techniques to overcome its inherent challenges. Identifying defects in the fabric after production is a tedious process. The defect, if tiny, may not catch the attention of human vision. Fabric defect detection can be effectively done using image processing. This work analyses the capabilities of ten cutting-edge pre-trained convolutional neural networks for distinguishing between defective and non-defective fabrics, which is essential for assuring the quality of the fabric produced. For this purpose, we leverage the transfer learning models VGG16, ResNet50, InceptionV3, Xception, InceptionResNetV2, DenseNet121, NasNetLarge, EfficientNetB0, EfficientNetB3, and MobileNetV2. Fabric irregularities influence the quality of the product and consumer satisfaction. Advanced Convolutional Neural Network methods automate the detection process with reduced manual intervention, leading to standardized quality measures. We aim to determine the best-suited model for binary classification to execute the task at hand with maximum performance. This work compared improvised deep learning models by implementing them over a fabric defect dataset. This was done by fine-tuning the different models and incorporating custom layers to cater to the specific datasets. The performance of these models was evaluated using metrics such as F1-score, precision, recall, and accuracy. InceptionResNetV2 was found suitable over both defective and non-defective classes. The results of this work demonstrate the suitability of using deep learning techniques for automating fabric defect detection and, hence, the quality assurance process of fabrics.
Copyright (c) 2025 Malathi S, Aiswarya P.U (Author) https://creativecommons.org/licenses/by/4.0
Enhancement of Mobility of a Tracked Armoured Fighting Vehicle by Introducing an Automatic Track Tensioner
A prototype automatic track tensioner was developed and test evaluated. The automatic track tensioner mitigates the chances of track shedding under dynamic condition in a tracked armoured fighting vehicle and also avoids exposure of crew in battle field. This research paper focuses on virtual performance evaluation of hydro-pneumatic type automatic track tensioner, by fitting its 3D model on a Multi-Body Dynamics model of the vehicle and subjecting for virtual simulation for various road profiles and vehicle speeds. The Multi-Body Dynamics analysis affirms that the automatic track tensioner plays a vital role in enhancing the off-road mobility of armoured fighting vehicle by providing real-time track tension during running of the vehicle in different surface undulations. This paper also covers a lab level experimental evaluation carried out on the actual hardware of the prototype, by subjecting it under an endurance test with various input loads and frequencies, in which the performance of the automatic track tensioner was excellent in the entire duration of the test.
Copyright (c) 2025 Kavivalluvan V, Vijaya Kumar K.R, Sayaan Banerjee, Rajesh Kumar J (Author) https://creativecommons.org/licenses/by/4.0
Analysis of Emotion Detection from Code Mixed or Code-Switched Social Media Text using Deep Learning
Emotion detection plays a vital role in understanding user sentiment specially in this the era of digital communication. Due to exponential growth of internet and social media, social media became a huge source of information which gives insight in varieties of applications. Emotion detection is an emerging field of research. Users express their implicit feelings, thoughts on social media. Analyzing such data becomes challenging due to linguistic diversity, especially in code-mixed or code-switched content involving transliterated Hindi and English. This paper addresses the problem of emotion detection from such complex textual data. We have explored and compared performances of different deep learning models in handling code-mixed social media text. Our experiments demonstrate that among all tested architectures, the hybrid LSTM-CNN model shown the highest mean test accuracy of 79.60% and 0.4216 F1-score without data balancing. For balanced data CNN gives the highest accuracy of 77.79%, while the Bi-LSTM model gives the highest F1-score of 0.4978. This research demonstrates the effectiveness of deep learning for emotion detection in transliterated Hindi-English social media posts.
Copyright (c) 2025 Vinayak Malavade, Virat Giri (Author) https://creativecommons.org/licenses/by/4.0
Secure Firmware over the Air Updates for Vehicles using Blockchain, Signcryption, and Proxy Re-encryption
Modern electric cars with upgraded passenger vehicles experience security risks from wireless firmware updates that allow attackers to threaten the safety of drivers and their passengers. This research develops a distinctive technique that unites blockchain technology with signcryption and proxy re-encryption to ensure vehicle-manufacturer communication and resolve this problem. Through IPFS (Inter Planetary File System) firmware updates can be safely distributed to permissioned vehicles. The proposed method implements identity-based cryptography as a fusion of signcryption with proxy re-encryption to enhance air-based firmware update security. The security evaluation of this method provides evidence about how well the cryptographic update operations function within the firmware procedure. The study performs a simulation investigation with AVISPA through the implementation of OFMC and Cl-AtSe models. The simulation study results demonstrate that the proposed security techniques prove their resistance to both man in the middle and replay attacks. The study investigates vehicle firmware update security weaknesses to develop a framework which protects firmware update integrity and confidentiality.
Copyright (c) 2025 Rachana Y. Patil, Yogesh H. Patil, Deepali Naik, Rupali Gangarde, Aparna Joshi, Aparna Bannore (Author) https://creativecommons.org/licenses/by/4.0
Assessment of Tribological Behaviour of Flax/Bagasse Composites Using GRA Based Statistical Studies
A combined Taguchi-grey Relational Analysis (GRA) has been used to improve the wear resistance of Flax, Bagasse and Epoxy F-B-E composites that have been treated with NaOH. The study investigates Dry sliding wear behaviour using Taguchi's L27 orthogonal array experimental design in compliance with ASTM G99 standards. Additionally, this study assesses the influence of independent variables such as usual load (2N,3N,4N), Sliding velocity (1m/s,2m/s,3m/s) and proportion of fibers (F75-B25, F50-B50, F25-B75) on the wear behaviour of epoxy resin reinforced with flax and bagasse powder composites, adopting a statistical methodology. Composite samples were made by hand layup technique using an epoxy resin mixed with flax fiber and bagasse powder. Dry sliding wear tests were done in a pin-on-disc setup, with adherence to Taguchi’s experimental design L27. This means scheming the “Signal-to-noise” (S/N) ratio and performing analysis of variances (ANOVA) to identify the optimal factors to minimize the wear rate to 98.30%. A better wear resistance rating was obtained from a composite material created by epoxy reinforced with 25% flax and 75% bagasse. Regression analysis's multi-response rate was 98.57%. The results showed that A1 (Load), B1 (Sliding Velocity), and C3 (Fibre Content Percentage) produced the best outcomes for wear rate and co-efficient of friction. The observed composite specimens were the primary variable influencing the wear rate, with sliding velocity and normal load also being major factors. ANOVA on the grey relational grade (GRG) showed that sliding velocity was the most important factor affecting how well the F-B-E composites resist wear.
Copyright (c) 2025 Yerru Kalyana Krishna, Karthikeyan B, Devarakonda Sameer Kumar (Author) https://creativecommons.org/licenses/by/4.0
A Spatially Constrained Density-Weighted Clustering Method for Brain Tumor Segmentation in MRI Images
The article proposes a modified K-means clustering algorithm to improve the accuracy of identifying tumors in brain MRI images, a crucial challenge in diagnosing and treating neurological disorders. The algorithm incorporates iterative refinement with tolerance-based convergence, density-based weighting for centroid adjustment, and spatial constraints to enhance segmentation accuracy. The proposed method introduces new methods to improve the segmentation of brain MRI images. A process of repeating small improvements on cluster centroids is used, helping to achieve low intra-cluster variation and ensuring that the algorithm reaches a good calculation point without repeating unnecessary parts. An important aspect is the added density-based weighting which updates centroid positions based on the local distribution of data points. As a result, the algorithm works better with groups that are uneven in size or shape. In addition, spatial issues are covered so that clustering is influenced by where the pixels are and how bright they are, so as to hold anatomical structures. All these methods make it possible to exactly segment the different and complicated parts of the brain in MRI scans. Results from experiments on benchmark brain scans show that the proposed method gets better results compared to both traditional and advanced segmentation methods. The model shows an average increase of 6.41% in DSC, 10.52% in JI and 6.41% in F1 Score, accompanied by a 38.43% decline in MSE. These outcomes suggest that the algorithm is a promising tool for clinical use in brain MRI analysis.
Copyright (c) 2025 Lakshmi Pravallika R, Pradeep Kumar Reddy R (Author) https://creativecommons.org/licenses/by/4.0
Laravel Technology based Maternal e-Healthcare Systems with Improved Response Time and Stability
Various maternal e-healthcare systems contribute a great deal to providing holistic care, but most of them are rife with technological constraints that make it impossible for them to address the needs of modern healthcare. The constraints consist mostly of obsolete infrastructures, limited and inefficient integrations, with poor large-scale operational management systems. Literature cited the maternal e-health systems are facing challenges in the area of interoperability, scalability, security, and usability. Key performance indicators to consider encompass response time and stability of the system. Mechanisms in the Laravel Framework are used to address the issues raised by the following: module-based architecture, caching, and queue management. It should however be noted that literature has shown an apparent gap of research that has greatly impacted on response time and stability in maternal e-health systems. Additionally, there is a critical lack of slim systems broad integration that's driven by data. To fill these gaps, this research is intended to strengthen the two performance indicators by means of Laravel use. The research enhances the given e-healthcare system, to increase performance outcomes regarding response time and system reliability, through the use of the Laravel framework, on the modules including user authentication, data collection, and report generation. Detailed performance measures to be studied will include load, stress, and endurance testing with JMeter in response time, and throughput, and error rate settings. The assessment of system performance based on the functionality of Laravel focused on the following metrics concerning the predetermined service-level agreement: mean response time, throughput, and error rates. The results from the analyses are merged with the graphs and tables which indicate an improvement in performance.
Copyright (c) 2025 Pradeepkumar C Dhage, Rajesh A. Thakker, Krishna K. Warhade (Author) https://creativecommons.org/licenses/by/4.0
Mustum (extracted grape juice) - Assisted Green Synthesis of Metal Oxide Nanoparticles: Evaluation of Phase, Vibrational, Morphological, and Thermal Properties
Environmentally friendly green chemical techniques for nanomaterial synthesis employing non-toxic chemicals and renewable resources have received interest. The green chemical method was adopted to synthesize metal oxide nanoparticles to study their physicochemical properties. XRD was used for crystallite size, lattice characteristics, and phase purity. XRD analysis confirmed that the metal oxide nanoparticles produced are single-phase cubic (NiO and Co3O4) and monoclinic (CuO) with 25–35 nm crystallite diameters. Fourier-transform infrared spectroscopy (FTIR) has been used to study functional groups and chemical bonding on metal oxide nanoparticle surfaces. A detected peak between 600 and 400 cm-1 indicates Metal-Oxygen in the synthesized metal oxide nanoparticles. FESEM and TEM were used to investigate nanomaterials' surface morphology, particle size, and shape at high resolution. TGA was used to evaluate metal oxide nanoparticle heat stability and degradation. Two large weight losses at 100°C and above 550°C suggest water and other sample constituents are eliminated. The antibacterial study shows good efficacy in Co3O4. The results demonstrate that synthesized nanoparticles can be used in many functional applications.
Copyright (c) 2025 Nilofur Fathima S.J, Arun Paul C, Mathu Metha K, Ranjith Kumar E (Author) https://creativecommons.org/licenses/by/4.0
Integration of Modified Luo Converter and ANFIS MPPT in Grid-Tied Photovoltaic Systems
The sustainable growth of energy solutions depends on the incorporation of renewable energy sources into grid connected systems. This study describes a grid tied photovoltaic (PV) system that uses a modified Luo converter and Maximum Power Point Tracking (MPPT) based on an Adaptive Neuro Fuzzy Inference System (ANFIS). The proposed system integrates a Modified Luo Converter for efficient DC-DC conversion, ensuring stable voltage regulation under varying solar irradiance and temperature conditions. It utilizes an ANFIS based MPPT controller to optimize energy extraction by accurately tracking the PV array's maximum power point, while an LC filter ensures high quality power delivered to the grid in compliance with grid standards. The developed research, executed utilizing MATLAB 2021a Simulink tool, demonstrates exceptional performance with an efficiency of 94.1% and enhanced grid stability.
Copyright (c) 2025 Thivya Prasad D, Anandhakumar R, Balamurugan P (Author) https://creativecommons.org/licenses/by/4.0
Revolutionizing Marshall Mix Design: Sustainable Integration of Recycled Aggregates Partially in Hot-Mix Asphalt Concrete
This study explores the use of recycled aggregates (RA) in Dense Bituminous Macadam (DBM) through the Marshall Mix method. Five mixes were evaluated: 100% natural aggregates (NA) and four blends with 25%, 50%, 75%, and 100% RA. Marshall Properties, stripping, drain down, & indirect tensile strength (ITS) tests were performed. The optimum binder content (OBC) ranged from 4.5% to 4.72%, meeting the MoRTH minimum of 4.5%. Air voids decreased with more RA but stayed within 3%–5%. VMA values were above 13%, and VFB ranged from 69.24% to 75%, meeting limits. All mixes exceeded 9 kN stability, with 50% RA showing the highest at 15.7 kN. Flow values (2.2–3.4 mm) were within the 2–4 mm range. ITS results showed maximum tensile strength ratio (TSR) of 96% at 50% RA. Stripping and drain down tests confirmed RA's suitability. The findings indicate that RA can be used in DBM mixes while meeting MoRTH standards.
Copyright (c) 2025 Prakash J, Manjunath B.H, Neeraj S.N, Chaya D.Y, Sagar H (Author) https://creativecommons.org/licenses/by/4.0
Enhancing Healthcare Monitoring through Wearable Computing and Massive MIMO Technology in 5G IoT Networks
The growing need for continuous, real-time monitoring of vital physiological parameters like heart rate and blood pressure is driven by the demand for enhanced healthcare, particularly in remote or critical scenarios. Traditional healthcare systems often struggle with delays, inaccuracies, and high costs, making timely medical interventions more challenging. This study aims to design and implement a portable health monitoring system that use widely available technology and advanced wireless communication to address these issues effectively. The system integrates various sensors to track key physiological metrics and applies biomedical signal acquisition along with pre-processing and feature extraction techniques to reduce noise and improve signal quality. Wireless transmission enables real-time data transfer, supporting remote monitoring and analysis. To enhance communication efficiency, the system incorporates Massive Multiple-Input Multiple-Output (MIMO) technology within 5G Internet of Things (IoT) networks, ensuring higher data transfer speeds, reduced latency, and reliable connectivity. The findings demonstrate notable improvements in signal clarity and data transmission, reflected in a significant boost in Signal-to-Noise Ratio (SNR) and a reduction in Bit Error Rate (BER), underscoring the system’s effectiveness. By integrating 5G Massive MIMO technology, the performance of Wireless Body Area Networks (WBANs) is substantially improved, leading to more efficient patient monitoring.
Copyright (c) 2025 Venkatesan C, Thamaraimanalan T, Balamurugan D, Sivaramakrishnan A, Umamaheswari R, Ramkumar M (Author) https://creativecommons.org/licenses/by/4.0
Optimization of Ammonia Mixing in Selective Catalytic Reduction (SCR) Systems: A Computational Fluid Dynamics (CFD) Study on Swirl Angle and Mass Flow Rate Effects
The research deals with design a Heat Recovery Steam Generator (HRSG) model through Computational Fluid Dynamics (CFD) to evaluate Selective Catalytic Reduction (SCR) deNOₓ procedures that help maintain sustainable environmental emissions. We study six swirl angles ranging from θ = 10° to θ = 60° at the GT diffuser exit to determine how they affect ammonia (NH₃) mixing before the SCR catalyst bed. The evaluation process occurred under mass flow conditions of 350 kg/s and 700 kg/s. Ammonia mixing uniformity must comply with Industrial standards by keeping the RMS value of mole fraction distribution less than 5% according to manufacturer specifications. The results indicate that a swirl angle of 30° produces the best NH₃ mixing performance with RMS 4.27% and theta = 40° provides the best temperature distribution ranging within ±7.41°C. The research delivers vital information regarding SCR system optimization when operational parameters change.
Copyright (c) 2025 Gutti Lokesh Kalyan, Rajendra Singh Rajput, Sunkara Srinivasa Rao, Mukesh Kumar Pandey (Author) https://creativecommons.org/licenses/by/4.0
Design of Instrumentation System for Piezoelectric-Based Rainfall Power Generation
This research presents the systematic design, implementation, and evaluation of a piezoelectric-based instrumentation system for energy harvesting from precipitation events. Utilizing Lead Zirconate Titanate (PZT) transducers, signal conditioning via an LM741 operational amplifier, and an Arduino Mega2560 microcontroller for data acquisition and control, the system converts the mechanical energy of raindrop impacts into electrical output. Laboratory simulations were conducted under controlled variations in water droplet height (170–270 cm) to evaluate the electrical response in both series and parallel transducer configurations. Experimental results demonstrated peak output voltages of 2.68 V and 2.66 V for series and parallel configurations, respectively. Measurement system evaluation using Gage Repeatability and Reproducibility (Gage R&R) analysis revealed a total system variability of 4.36% (series) and 1.89% (parallel), confirming high measurement precision and robustness. Despite the low-voltage nature of the generated power, the system validates the feasibility of utilizing rainfall as a renewable energy source for low-power applications. Future research will focus on enhancing energy conversion efficiency through advanced piezoelectric materials, optimized circuit topology including DC-DC boost converters, and integration with high-capacity energy storage modules such as supercapacitors. Moreover, real-time performance monitoring via IoT-based platforms and hybridization with solar or wind systems is proposed for broader applicability.
Copyright (c) 2025 Jotje Rantung, Benny Maluegha, Yan Tondok, Gideon David Rantung (Author) https://creativecommons.org/licenses/by/4.0
Molecular Interactions and Spectral Changes of Beta-and Gamma-Irradiated Silicon Clusters with different Time Intervals
Silicon clusters have garnered significant interest due to their unique physical and chemical properties, which make them valuable in fields such as nanotechnology, optoelectronics, and materials science. The purpose of this research is to investigate the effects of beta and gamma irradiation on silicon clusters, with a particular emphasis on gaining a knowledge of how irradiation over varying time intervals contributes to the molecular interactions and spectral features of silicon clusters. The ambient pressure and the two-step acid-base catalysis were both reliant on the manufacturing process. Beta and gamma radiation were used as sources at 2, 4, and 6 hours. The work combines spectroscopic analysis with molecular interaction assessments, and it makes use of FTIR analysis, N2 adsorption-desorption, FESEM, and EDS in order to shed light on the behavior of silicon before and after it has been irradiated in varied concentration clusters across time. Based on the findings, it was determined that the samples that were subjected to gamma radiation did not exhibit any discernible changes in their chemical structure, pore structure, or even surface appearance. Beta irradiation was applied to the samples for a period of six hours, and after that, there was a small alteration in the pore structure of the sample, the surface area decreases from 851.02 before irradiation to 833.03 at Beta Rad. This decrease occurred due to gamma radiation. The reduction was minimal. After irradiation, the pore size decreases from 10.44 to 0.9 and 0.8, while the pore volume, particle size, and pore diameter increase. The FE-SEM images show morphological stability; indicates that its unique porous nanostructure remains largely unaffected under radiative stress. The study comes to the conclusion that silica aerogel is a material that has the potential to be used in radiation isolation.
Copyright (c) 2025 Israa F. Al-sharuee, Imad Ibrahim Ismael (Author) https://creativecommons.org/licenses/by/4.0
Thermal Resistance of Fibre-Reinforced and FRP-Wrapped RC Columns: An Experimental Investigation
Elevated temperatures significantly affect the construction industry, particularly for reinforced concrete (RC) columns. Although concrete resists high temperatures, exposure can cause spalling, cracking, and reduced load-bearing capacity. This study examines the effects of incorporating composite materials, such as steel, polypropylene, and hybrid fibres, into concrete and using fibre-reinforced polymer (FRP) wrapping to strengthen RC columns exposed to high temperatures. Thirty RC column specimens were cast, varying in dimensions, concrete type (control, steel fibres, polypropylene fibres, hybrid fibres reinforced), and FRP wrapping (Glass Fibre Reinforced Polymer (GFRP), Carbon Fibre Reinforced Polymer (CFRP)). The columns were subjected to 200 °C for 6 hours per day over 75 cycles and then tested for ultimate load-carrying capacity (failure load), displacement, secant stiffness, strain, failure modes, and crack patterns. The results showed that steel fibre-reinforced columns had higher failure loads than other fibre-reinforced columns. FRP-wrapped columns experienced a 7.35% to 23.36% decrease in failure load when exposed to high temperatures compared with unheated FRP-wrapped columns. Fibre-reinforced columns displayed lower displacement compared to control columns after exposure to 200 °C, whereas FRP-wrapped columns showed higher displacement. The findings recommended using various types of fibre-reinforced concrete and FRP wrapping to increase the load-carrying capacity and strengthen RC columns, particularly those exposed to high temperatures.
Copyright (c) 2025 Maulik Mistry, Prasad Barve, Piyush Patel (Author) https://creativecommons.org/licenses/by/4.0
Deep Hole Drilling of AISI 316 Steel using CNC Peck Drilling Approach and Parametric Study
Drilling is a crucial machining process in manufacturing, often used for assembly purposes. Assembly challenges develop as a result of inappropriate hole geometry, such as circularity, perpendicularity and cylindricity regardless of whether the tolerance is within the limit. Deep hole drilling is a specialized technique to create holes with high depth to diameter ratio. In various industries, such as oil and gas, shipbuilding, and aerospace, deep hole drilling of stainless steel is performed to make components like drill collars, rotor shafts and fuel injector nozzles. This study investigates the impact of drilling process parameters on the hole quality, using Taguchi design of experiments for AISI 316 drilling optimization. Deep hole drilling experiments were carried out based on a Taguchi L9 array, with 3 different drills (HSS, M35, TiAlN); at 3 different cutting speeds and feed rates. The innovative concept of intermittent drilling and retraction is applied using CNC vertical milling machine and the results are examined for the achievable tolerance on size and geometry. Results indicate that the cutting tool material is the most significant factor influencing roundness, cylindricity, and hole size, followed by feed and speed. Speed has a lesser effect on perpendicularity compared to the cutting tool type and feed. Feed exerts a greater influence on surface roughness than the cutting tool type. Chip morphology indicates HSS drillbit is effective and stable in the process of producing quality holes.
Copyright (c) 2025 Sujan Kumar M, Deivanathan R (Author) https://creativecommons.org/licenses/by/4.0
HDOMLM: Hybrid Dual Optimized Machine Learning Model for Cluster Head Selection in MANET
The selection of cluster heads with efficient energy awareness is a crucial concern in mobile ad hoc networks (MANETs). MANETs' energy efficiency is enhanced when a suitable cluster head (CH) is selected. Clusters are established in Mobile Ad hoc Networks (MANETs) to enhance communication among nodes. Efficient clustering is necessary to provide the rapid and precise transmission of information in the middle of nodes. In this work, we proposed a hybrid dual optimization of the machine learning model (HDOMLM). In this protocol, we are performing two different optimizations for clustering. Initial clustering is performed using Particle Swarm Optimization (PSO) based on node location and mobility as objection function cost. Once the initial cluster is formed, the selection metric is evaluated from the available network topology for the execution period. Delta difference, average distance, related energy, related mobility, and transmission delay are the five selection metrics used as features in the Optimized Machine Learning Model (O-MLM). By using O-MLM, we can classify that the node belongs to a normal node or cluster head node. To evaluate the effectiveness of our proposed HDOMLM, we are performing the simulation in MATLAB tool. Different performance metrics such as Alive nodes, Residual energy, Energy Tax, Average End-to-End Delay, Number of Successful packets received as BS, and Total processing time. The objective of network lifetime improvement is achieved in the proposed HDOMLM by 200 rounds and residual energy of 56% increased than earlier works.
Copyright (c) 2025 Nirmala Bai K.S, Subramanyam M.V (Author) https://creativecommons.org/licenses/by/4.0
Evaluation of FBMC/OQAM performance using Hybrid MGDFT and PTS approaches in terms of BER, Spectral Efficiency, and PAPR
The fast advancement of next-generation wireless communication systems requires effective multicarrier transmission methods that improve performance metrics, including Bit Error Rate (BER), Peak-to-Average Power Ratio (PAPR), and Spectral Efficiency (SE). Filter Bank Multicarrier with Offset Quadrature Amplitude Modulation (FBMC/OQAM) has emerged as a promising contender for 6G systems due to its enhanced SE and resilience to frequency selectivity. Nonetheless, the elevated PAPR and BER in FBMC/OQAM systems persist as significant challenges. This paper proposes a hybrid strategy that integrates the Modified Generalized Discrete Fourier Transform Spreading (MGDFTS) technology with the Partial Transmit Sequence (PTS) scheme to improve the performance of FBMC/OQAM systems. The merged operation of MGDFTS and PTS not only minimizes PAPR but also enhances the performance of BER and spectrum efficiency. We thoroughly assess the proposed hybrid model via extensive simulations and performance evaluations, comparing it with traditional FBMC/OQAM systems and recent optimization methods. Simulation findings indicate that the proposed hybrid MGDFTS-PTS method significantly decreases PAPR while preserving a low BER and enhancing SE. This study highlights the promise of the hybrid approach as an acceptable choice for advanced multicarrier communication systems, aiding in the advancement of high-performance 6G networks.
Copyright (c) 2025 Sivapriya N, Karthik Kumar Vaigandla, Mohandas R, Kirubasankar K (Author) https://creativecommons.org/licenses/by/4.0