International Research Journal of Multidisciplinary Technovation

Harnessing the Power of AI for Accurate Pediatric Pneumonia Detection Pediatric pneumonia is a leading cause of childhood mortality globally. Early and accurate diagnosis is crucial for effective treatment. Our recent research explored the potential of transfer learning and ensemble models to enhance the analysis of chest X-rays for pneumonia detection and classification. Key findings: We developed two transfer learning-based ensemble models. The first model achieved a remarkable accuracy of 98.03% in classifying normal and pneumonia cases. The second model distinguished between bacterial and viral pneumonia with an accuracy of 91.93%. Our proposed custom CNN designs, PneumoNet-v1 and PneumoNet-v2, demonstrated high classification accuracy for pneumonia detection. What this means: This research demonstrates the effectiveness of transfer learning and ensemble models in improving the analysis of pediatric pneumonia using chest X-rays. This has the potential to: Improve diagnostic accuracy and efficiency. Facilitate early intervention and better treatment outcomes. This research is a significant step forward in leveraging AI for improved pediatric healthcare. We are committed to further advancements in this field. Link to the research paper: https://lnkd.in/gr39kfvH #pediatrics #pneumonia #AI #healthcare #chestxray #diagnosis

New Non-invasive Method for Gastric Volume Measurement using GMR Sensors We excited to share a novel approach for non-invasive gastric volume measurement published in the International Research Journal of Multidisciplinary Technovation (IRJMT)! This work introduces the GMR-based Gastric Volume Meter (GVM), a system utilizing Giant Magneto Resistance (GMR) sensors and sophisticated signal processing algorithms. Key Features of GVM: Enables continuous and non-invasive monitoring of gastric volume. Leverages advanced signal processing techniques like Rational-Dilation Wavelet Transform (RaDWT) and Tunable Q-factor Wavelet Transform (TQWT) for accurate volume estimation. Achieves a remarkable 93.4% accuracy in predicting Total Gastric Volume Measurement (TGVM). Benefits of GVM: Offers a significant improvement over existing invasive and non-continuous gastric volume measurement techniques. Provides real-time insights into stomach function. Holds potential for personalized medication dosing in diabetic patients with gastroparesis. Future Directions: Integration of additional sensors for broader applicability. Enhanced noise reduction algorithms for improved sensitivity in real-time situations. Exploration of deep learning models for deeper understanding of gastric signals. Implementation of longitudinal studies to evaluate GVM effectiveness in practical settings. Want to learn more? Read the full article here: https://lnkd.in/g4FgSWrz #GastricVolumeMeasurement #GMRsensors #NoninvasiveTech #DiabetesManagement #Gastroparesis #SignalProcessing #MachineLearning #MedicalTechnology #Research

Diagnosis of COVID-19 in X-ray Images using Deep Neural Networks The global COVID-19 pandemic has presented unprecedented challenges, notably the limited availability of test kits, hindering timely and accurate disease diagnosis. Rapid identification of pneumonia, a common COVID-19 consequence, is crucial for effective management. This study focuses on COVID-19 classification from Chest X-ray images, employing an innovative approach: adapting the Xception model into a U-Net architecture via the Segmentation_Models package. Leveraging deep learning and image segmentation, the U-Net architecture, a CNN variant, proves ideal for this task, particularly after tailoring its output layer for classification. By utilizing the Xception model, we aim to enhance COVID-19 classification accuracy and efficiency. The results demonstrate promising autonomous identification of COVID-19 cases, offering valuable support to healthcare professionals. The fusion of medical imaging data with advanced neural network architectures highlights avenues for improving diagnostic accuracy during the pandemic. Notably, precision, recall, and F1 scores for each class are reported: Normal (Precision = 0.98, Recall = 0.9608, F1 Score = 0.9704), Pneumonia (Precision = 0.9579, Recall = 0.9579, F1 Score = 0.9579), and COVID-19 (Precision = 0.96, Recall = 0.9796, F1 Score = 0.9698). These findings underscore the effectiveness of our approach in accurately classifying COVID-19 cases from chest X-ray images, offering promising avenues for enhancing diagnostic capabilities during the pandemic. #Chest X-rays #COVID-19 #Xceptionmodel #Segmentationmodels #U-Net #Research #MaterialScience #Innovation #Journal #Publication #Scopus #OpenAcess #Environment #Engineering #Scientific #Science #Study #RandD Read more https://lnkd.in/gAk-z_VT                  For more information, please read our recent articles at https://lnkd.in/gqaMY-sW

Green Synthesis of Selenium Nanoparticles: Characterization and Therapeutic Applications in Microbial and Cancer Treatments Selenium is one of these micronutrients that are essential for animals, plants and microorganisms to remain functional. This review is about the green synthesis of selenium nanoparticles and its application in microbial and cancer therapies. Our hypothesis was that Se NPs produced using plant extracts might offer the biocompatibility and environmental friendliness advantages, and hence be a new prospect for medical applications. To test our hypothesis, we conducted a comprehensive analysis of recent literature, exploring various green synthesis conditions and processes for Se NPs. Various characterisation techniques such as spectroscopy, microscopy and physicochemistry were discussed in order to provide insight into the formation and function of green-synthesised Se NPs. Our findings show that Se NPs produced by green chemistry methods have good properties such as uniform size, shape and stability as detailed examples from recent studies reveal. Furthermore, we discussed the therapeutic and theranostic applications of Se NPs produced in this manner: their potential in antimicrobial and anticancer treatments. Through illustrations of cases where Se NPs inhibit microbial growth and cause apoptosis in cancer cells, the practical significance of our findings was underscored. In summary, our review affirms that using green-mediated synthesis Se NPs improves their biocompatibility and therapeutic efficacy, thus opening up new realms for their application in medical research. #Selenium #Nanoparticles #Anticancer #Therapeutic #Antibacterial #Antioxidant #Research #MaterialScience #Innovation #Journal #Publication #Scopus #OpenAcess #Environment #Engineering #Scientific #Science #Study #RandD Read more https://lnkd.in/gRDrDT7e                 For more information, please read our recent articles at https://lnkd.in/gqaMY-sW

Hybrid Power Generation: Experimental Investigation of PCM and TEG Integration with Photovoltaic Systems Global warming and escalating energy consumption have presented pressing issues, catalyzing a pivotal shift towards environmental development worldwide. In recent years, the installed capacity of solar photovoltaic (PV) cells, particularly crystalline silicon cells, has experienced a significant surge. Among the myriad studies aimed at enhancing the efficiency of PV cells' power generation, one prominent avenue involves reducing the internal temperature of these cells. The primary objectives of the present study revolved around augmenting power generation and improving photocell efficiency. This was pursued through the strategic blending of nanoparticles with phase change material (PCM), with variations in insertion percentages to modulate the heat absorption capacity of the PV panel. Additionally, the study sought to evaluate the impact of integrating Thermoelectric Generator (TEG) modules and a water-based nano-fluid cooling system beneath the TEG setup. These measures aimed to effectively monitor the conversion of waste heat into electrical energy. Consequently, the proposed orientation of PV panels – involving PCM adjustment via alteration of insertion percentages, coupled with TEG integration and water-based nano-fluid cooling technology – holds significant promise for enhancing efficiency and mitigating solar cell degradation. #PhotovoltaicPanel #Phasechangematerial #ThermoelectricGenerator #ArtificialNeuralNetwork #Research #MaterialScience #Innovation #Journal #Publication #Scopus #OpenAcess #Environment #Engineering #Scientific #Science #Study #RandD Read more https://lnkd.in/gTJ3xWgc                For more information, please read our recent articles at https://lnkd.in/gqaMY-sW

Early Diagnosis of Lung Infection via Deep Learning Approach The rapid global spread of COVID-19 and RT-PCR tests are insensitive in early infection phases, according to hospitals. To find Covid-19, a fast, accurate test is needed. CT scans have shown diagnostic accuracy. CT scan processing using a deep learning architecture may improve illness diagnosis and treatment. A deep learning system for COVID-19 detection was derived using CT scan features. Using and comparing numerous transfer-learning models, fine-tuning, and the embedding process yielded the best infection diagnostic results. All models' diagnostic effectiveness was assessed using 2482 CT scan images. The optimized model demonstrated encouraging outcomes by significantly enhancing the sensitivity metric (86.26±1.72), a critical factor in accurately detecting COVID-19 infection. Additionally, the resulting model demonstrated elevated values for accuracy (81.15±0.17), specificity (77.90±1.33), precision (76.79±0.80), F1_score (81.24±0.37), and AUC (81.88±0.2). Deep learning methodologies have been effectively employed to detect COVID-19 in chest CT scan images. In the future, the suggested approach may be employed by clinical practitioners to study, identify, and effectively mitigate a greater number of pandemics. #Deeplearning #CT #Transferlearning #Lung #Classification #Diagnosis #Research #MaterialScience #Innovation #Journal #Publication #Scopus #OpenAcess #Environment #Engineering #Scientific #Science #Study #RandD Read more https://lnkd.in/gzxRBFDh               For more information, please read our recent articles at https://lnkd.in/gqaMY-sW

Identification of Optimum Retrofitting Approach for Strengthening RC Beams using CFRP Sheets Recently the formation of disasters like earthquakes, Tsunami, etc., are quite common in all parts of the world. Due to the disasters the existence of loss to property as well as human life is quite common and more to avoid/decrease the damage due to disasters, strengthening a structure is one parameter. Retrofitting is the use of revolutionary technology to reinforce the structural elements to resist the upcoming damage due to disaster. In this paper carbon fiber reinforced polymer strengthening is considered for retrofitting technique. Carbon fiber reinforced polymer sheets of 50 mm width are used and wrapped on the beams with four different orientations like 00, 450, 600 and 900. Experimentally ten beams are casted in which two beams are marked as control beams and in remaining eight beam, every two beams are used for each orientation. The beams are subjected to four-point loading, and the greatest deflections and cracks at the beam center are recorded. The beams are tested for flexural loading and studied different parameters like maximum deflection, maximum load, Initial crack load etc are compared. With an emphasis on RC beams specifically, the goal of this work is to close the current research gap by examining the behavior of fiber reinforced polymer orientation in concrete elements. A beam covered with 50 mm strips at a 45-degree angle produced better results than the remaining beams. #CFRPWrapped #RetrofittedRCCBeam #Actuator #Carbonfiberreinforcedpolymersheets #Research #MaterialScience #Innovation #Journal #Publication #Scopus #OpenAcess #Environment #Engineering #Scientific #Science #Study #RandD Read more https://lnkd.in/g47eSKeM              For more information, please read our recent articles at https://lnkd.in/gqaMY-sW

An Ensemble Classification Model to Predict Alzheimer’s Incidence as Multiple Classes This study introduces an ensemble classification model designed to categorize Alzheimer’s disease (AD) into four distinct classes—mild dementia, no dementia, moderate dementia, and very mild dementia—using Magnetic Resonance Imaging (MRI). The proposed model entitled the Ensemble Classification Model to Predict Alzheimer's Incidence as Multiple Classes (PAIMC) that integrates a six-dimensional analysis of MR images, encompassing entropies, Fractal Dimensions, Gray Level Run Length Matrix (GLRLM), Gray Level Co-occurrence Matrix (GLCM), morphological features, and Local Binary Patterns. A four-fold multi-label cross-validation approach was employed on a benchmark dataset to evaluate the model's performance. Quantitative analysis reveals that PAIMC consistently achieves superior Decision Accuracy, F-Score, Specificity, Sensitivity Recall, and Precision metrics compared to existing state-of-the-art models. For instance, PAIMC's Decision Accuracy and Precision outperform the second-best model by a notable margin across all folds. The model also demonstrates a significant improvement in Sensitivity Recall and Specificity, reinforcing its efficacy in the multi-class classification of AD stages. A novel data diversity assessment measure was developed and utilized, further confirming the robustness of the PAIMC model. The results underscore the potential of PAIMC as a highly accurate tool for AD classification in clinical settings. #MiniMentalStateExamination(MMSE) #PAIMC #MagneticResonanceImaging #Alzheimer’sDisease #MildCognitiveImpairment #SupportVectorMachine #Research #MaterialScience #Innovation #Journal #Publication #Scopus #OpenAcess #Environment #Engineering #Scientific #Science #Study #R&D Read more https://lnkd.in/gUJPivP3            For more information, please read our recent articles at https://lnkd.in/gqaMY-sW

Detect the cardiovascular disease’s in Initial Phase using a Range of Feature Selection Techniques of ML Heart-related conditions remain the foremost global cause of mortality. In 2000, heart disease claimed around 14 million lives worldwide, a number that surged to approximately 620 million by 2023. The aging and expanding population significantly contribute to this rising mortality trend. However, this also underscores the potential for significant impact through early intervention, crucial for reducing fatalities from heart failure, where prevention plays a pivotal role. The aim of the present research is to develop a prospective ML framework that can detect important features and predict cardiac conditions as an early stage using a variety of choice of features strategies. The Features subsets that were chosen were designated as FST1, FST2, and FST3, respectively. Three distinct methods, including correlation-based feature selection, chi-square and mutual information, were used for picking features. Next, the most confident theory & the most appropriate feature selection were identified using six alternative machine learning models: Logistical Regression (LR) (AL1), the support vector Machine (SVM ) (AL2), K-nearest neighbor (K-NN) (AL3), Random forest (RF) model (AL4), Naive Bayes (NB) model (AL5), and Decision Tree (DT) (AL6). Ultimately, we discovered that, with 95.25% accuracy, 95.11% sensitivity, 95.23% specificity, 96.96 area below receiver operating characteristic and 0.27 log loss, the random forest model offered the most excellent results for F3 feature sets. No one has investigated coronary artery disease forecasting in depth; however, our study evaluates multiple statistics (specificity, sensitivity, accuracy, AUROC, and log loss) and uses multiple attribute choices to improve algorithms success for important features. The suggested model has considerable promise for medical use to speculate CVD find in Precursor at a minimal cost and in a shorter amount of time as well as will assist limited experience physician to take right decision based on the results of the used model combined with specific criteria. #MLTechnique #FeatureSelectionMethod #CategorizationandModelling #DataInterpretation #Random Forest #Research #MaterialScience #Innovation #Journal #Publication #Scopus #OpenAcess #Environment #Engineering #Scientific #Science #Study #R&D Read more https://lnkd.in/gVyqUNf2            For more information, please read our recent articles at https://lnkd.in/gqaMY-sW

PV based Systems with Advanced Control Strategies for Load Balancing in Multilevel Inverter In an era driven by sustainable energy solutions, the synergy of photovoltaic (PV) system stands as a beacon of hope for meeting the world's growing energy demands while minimizing environmental impact. This research ventures into the domain of renewable energy integration by seamlessly including a PV system, ingeniously controlled by Chaotic Flower Pollination Optimized Adaptive Neuro Fuzzy Inference System (ANFIS) based MPPT (Maximum Power Point Tracking) controller capable of optimizing the efficiency in the face of ever-changing weather dynamics. The PV system's quest for optimal efficiency receives a substantial boost through the implementation of the High Gain Modified Luo Converter. Designed to achieve an optimal PV output voltage, this converter's prowess finds its true calling in grid applications, where precision and efficiency are paramount. Furthermore, this research extends its purview to incorporate a bidirectional converter linked to an energy storage solution, such as a battery, through a common DC link. The output power is then passed to the Flyback Converter, seamlessly connected to a 31 level Cascaded H Bridge Multi-Level Inverter (31-level CHB MLI) controlled by PI controller. This formidable inverter architecture facilitates the efficient delivery of power to the grid, ensuring a smooth and controlled integration of renewable energy resources. This strategic integration bolsters the system's adaptability, enabling the seamless management of energy flows and grid interactions along with load balancing in MLI. The MATLAB simulation platform is used for confirming the system's overall performance. According to the simulation results, the proposed approach achieves the maximum efficiency with the lowest THD value of 94.5% and 2.5%, respectively. #PVSystem #ChaoticFlowerPollination #OptimizedANFISbasedMPPT #HighGainModifiedLuoconverter #Flybackconverter #31-levelCHBMLI #Research #MaterialScience #Innovation #Journal #Publication #Scopus #OpenAcess #Environment #Engineering #Scientific #Science #Study #R&D Read more https://lnkd.in/g8hqmG5T          For more information, please read our recent articles at https://lnkd.in/gqaMY-sW