Cutting-edge Methodology Research Articles

Transformative Advancements in Fetal Cardiac Health through BILSTM Networks for FPCG Classification

This article unveils a cutting-edge methodology for Fetal Phonocardiogram(FPCG) classification employing Bidirectional Long Short-Term Memory (BILSTM)networks. Acknowledging the pivotal role of fetal heart monitoring inearly anomaly detection, the research delves into the profound insights offeredby FPCG signals concerning fetal cardiac activity. The innovative approachencompasses preprocessing FPCG signals using Mel-frequency cepstral coefficients(MFCC) and spectrogram features, coupled with the strategic applicationof BI-LSTM networks, ensuring a resilient classification framework. The bidirectionalnature of the LSTM architecture elevates the model’s ability to capturetemporal dependencies in both forward and backward directions, facilitating thediscernment of intricate patterns in fetal heart sounds. Remarkably, experimentalfindings demonstrate a remarkable 98% accuracy, reaffirming the effectivenessand precision of the BILSTM approach in fetal PCG classification. This pioneeringresearch significantly advances automated methods for evaluating fetalcardiac health, promising transformative enhancements in prenatal care.

Comprehensive bioinformatics and machine learning analyses for breast cancer staging using TCGA dataset.

Breast cancer is an alarming global health concern, including a vast and varied set of illnesses with different molecular characteristics. The fusion of sophisticated computational methodologies with extensive biological datasets has emerged as an effective strategy for unravelling complex patterns in cancer oncology. This research delves into breast cancer staging, classification, and diagnosis by leveraging the comprehensive dataset provided by the The Cancer Genome Atlas (TCGA). By integrating advanced machine learning algorithms with bioinformatics analysis, it introduces a cutting-edge methodology for identifying complex molecular signatures associated with different subtypes and stages of breast cancer. This study utilizes TCGA gene expression data to detect and categorize breast cancer through the application of machine learning and systems biology techniques. Researchers identified differentially expressed genes in breast cancer and analyzed them using signaling pathways, protein-protein interactions, and regulatory networks to uncover potential therapeutic targets. The study also highlights the roles of specific proteins (MYH2, MYL1, MYL2, MYH7) and microRNAs (such as hsa-let-7d-5p) that are the potential biomarkers in cancer progression founded on several analyses. In terms of diagnostic accuracy for cancer staging, the random forest method achieved 97.19%, while the XGBoost algorithm attained 95.23%. Bioinformatics and machine learning meet in this study to find potential biomarkers that influence the progression of breast cancer. The combination of sophisticated analytical methods and extensive genomic datasets presents a promising path for expanding our understanding and enhancing clinical outcomes in identifying and categorizing this intricate illness.

Multimodal AI/ML for discovering novel biomarkers and predicting disease using multi-omics profiles of patients with cardiovascular diseases

Cardiovascular diseases (CVDs) are complex, multifactorial conditions that require personalized assessment and treatment. Advancements in multi-omics technologies, namely RNA sequencing and whole-genome sequencing, have provided translational researchers with a comprehensive view of the human genome. The efficient synthesis and analysis of this data through integrated approach that characterizes genetic variants alongside expression patterns linked to emerging phenotypes, can reveal novel biomarkers and enable the segmentation of patient populations based on personalized risk factors. In this study, we present a cutting-edge methodology rooted in the integration of traditional bioinformatics, classical statistics, and multimodal machine learning techniques. Our approach has the potential to uncover the intricate mechanisms underlying CVD, enabling patient-specific risk and response profiling. We sourced transcriptomic expression data and single nucleotide polymorphisms (SNPs) from both CVD patients and healthy controls. By integrating these multi-omics datasets with clinical demographic information, we generated patient-specific profiles. Utilizing a robust feature selection approach, we identified a signature of 27 transcriptomic features and SNPs that are effective predictors of CVD. Differential expression analysis, combined with minimum redundancy maximum relevance feature selection, highlighted biomarkers that explain the disease phenotype. This approach prioritizes both biological relevance and efficiency in machine learning. We employed Combination Annotation Dependent Depletion scores and allele frequencies to identify variants with pathogenic characteristics in CVD patients. Classification models trained on this signature demonstrated high-accuracy predictions for CVD. The best performing of these models was an XGBoost classifier optimized via Bayesian hyperparameter tuning, which was able to correctly classify all patients in our test dataset. Using SHapley Additive exPlanations, we created risk assessments for patients, offering further contextualization of these predictions in a clinical setting. Across the cohort, RPL36AP37 and HBA1 were scored as the most important biomarkers for predicting CVDs. A comprehensive literature review revealed that a substantial portion of the diagnostic biomarkers identified have previously been associated with CVD. The framework we propose in this study is unbiased and generalizable to other diseases and disorders.

Spatial Transcriptomics Unravel the Tissue Complexity of Oral Pathogenesis

Spatial transcriptomics (ST) is a cutting-edge methodology that enables the simultaneous profiling of global gene expression and spatial information within histological tissue sections. Traditional transcriptomic methods lack the spatial resolution required to sufficiently examine the complex interrelationships between cellular regions in diseased and healthy tissue states. We review the general workflows for ST, from specimen processing to ST data analysis and interpretations of the ST dataset using visualizations and cell deconvolution approaches. We show how recent studies used ST to explore the development or pathogenesis of specific craniofacial regions, including the cranium, palate, salivary glands, tongue, floor of mouth, oropharynx, and periodontium. Analyses of cranial suture patency and palatal fusion during development using ST identified spatial patterns of bone morphogenetic protein in sutures and osteogenic differentiation pathways in the palate, in addition to the discovery of several genes expressed at critical locations during craniofacial development. ST of salivary glands from patients with Sjögren’s disease revealed co-localization of autoimmune antigens with ductal cells and a subpopulation of acinar cells that was specifically depleted by the dysregulated autoimmune response. ST of head and neck lesions, such as premalignant leukoplakia progressing to established oral squamous cell carcinomas, oral cancers with perineural invasions, and oropharyngeal lesions associated with HPV infection spatially profiled the complex tumor microenvironment, showing functionally important gene signatures of tumor cell differentiation, invasion, and nontumor cell dysregulation within patient biopsies. ST also enabled the localization of periodontal disease–associated gene expression signatures within gingival tissues, including genes involved in inflammation, and the discovery of a fibroblast subtype mediating the transition between innate and adaptive immune responses in periodontitis. The increased use of ST, especially in conjunction with single-cell analyses, promises to improve our understandings of craniofacial development and pathogenesis at unprecedented tissue-level resolution in both space and time.

Nanotechnology-driven Microemulsion Based Intranasal Delivery to Neurotechnology-driven Neuralink: Strategies to Improve Management of Neurodegenerative Disorders.

Neurodegenerative disorder refers to malfunctioning of neuronstheirdegradation leading to death of neurons. Among various neurodegenerative disorders APHD (Alzheimer's, Parkinson's, and Huntington's Disease) are particularly concerning due to their progressive and debilitating nature. The therapeutic agent used for treatment and management of APHD often show unsatisfactory clinical outcome owing to poor solubility and limitedpermeability across blood brain barrier (BBB). The nose-to brain delivery can overcome this BBBchallenge as it can transport drug directly to brain though olfactory pathways bypassing BBB. Additionally, the nanotechnology has emerged as a cutting-edge methodology to address this issue and specifically mucoadhesive micro/nanoemulsion can improve the overall performance of the drug when administered intranasally. Beyond the therapy neurotechnology has emerged as are revolutionary AI-driven BCI (Brain computer interface) aimed to restore independence in patients with function loss due to neuron degeneration/death. A promising BCI Neuralink has been recently explored for clinical trials and results revealed that a quadriplegia bearing person with implanted Neuralink chip was able to perform few normal functions of daily routine such as playing online games, text messaging, reading, and learning foreign languages online through accessing the particular websites. This review will discuss the fundamental concepts of neurodegeneration, application of micro/nanoemulsion through intranasal route and integration of neurotechnology for the management and treatment of APHD.

Shapley visual transformers for image-to-text generation

In the contemporary landscape of the web, text-to-image generation stands out as a crucial information service. Recently, deep learning has emerged as the cutting-edge methodology for advancing text-to-image generation systems. However, these models are typically constructed using domain knowledge specific to the application at hand and a very particular data distribution. Consequently, data scientists must be well-versed in the relevant subject. In this research work, we target a new foundation for text-to-image generation systems by introducing a consensus method that facilitates self-adaptation and flexibility to handle different learning tasks and diverse data distributions. This paper presents I2T-SP (Image-to-Text Generation for Shapley Pruning) as a consensus method for general-purpose intelligence without the assistance of a domain expert. The trained model is developed using a general deep-learning approach that investigates the contribution of each model in the training process. Multiple deep learning models are trained for each set of historical data, and the Shapley Value is determined to compute the contribution of each subset of models in the training. Subsequently, the models are pruned according to their contribution to the learning process. We present the evaluation of the generality of I2T-SP using different datasets with varying shapes and complexities. The results reveal the effectiveness of I2T-SP compared to baseline image-to-text generation solutions. This research marks a significant step towards establishing a more adaptable and broadly applicable foundation for image-to-text generation systems.

The Impact of Remittances on Food Inflation in Bangladesh: An ARDL Approach

Purpose of the study: This study aims to investigate how Bangladesh's food inflation is affected by remittance influx. Methodology: The analysis utilizes secondary data collected from 2005 to 2022. In order to accomplish the goals, this study employs the food inflation rate as an endogenous variable, while workers' remittance, official exchange rate, and broad money supply are used as exogenous factors. The data is sourced from the World Development Index (WDI). In this investigation, co-integration is assessed using the Autoregressive Distributed Lag (ARDL) model to provide an accurate outcome. Findings: The study's findings show that, both in the short and long term, the influx of remittances from migrants has a positive and considerable impact on driving up food inflation. This analysis shows that while having a negative short-term impact, the money supply and exchange rate have a long-term, positive, and considerable influence on food inflation. Application of the Study: This research proposes that promoting agricultural output and motivating recipients of remittances to invest in income-generating activities can effectively reduce their reliance on food consumption. If these recommendations are effectively followed, the inflow of workers' remittances will have a positive impact on the economy and contribute to reducing inflationary challenges. Originality of the study: By employing the most recent data set and cutting-edge methodology, the present research adds to the body of available research by demonstrating a link between food inflation and remittances.

Switchable optical trapping and manipulation enabled by polarization-modulated multifunctional phase-change metasurfaces

Optical trapping, a cutting-edge methodology, is pivotal for contactlessly controlling and exploring microscopic objects. However, it encounters formidable challenges such as multiparticle trapping, flexible control, and seamless integration. Here, we employ a polarization-modulated multi-foci technique for versatile nanoparticle trapping using multifunctional metasurfaces relying on geometric phase. Numerical simulations demonstrate the generation of two focused spots with orthogonal polarization distributions through our metasurfaces when illuminated with linearly polarized light, with their polarization distributions be interchanged by orthogonally switching the incident polarizations. We extend this design to an array of multi-foci metasurface tweezers modulated by polarization, highlighting the versatility and robustness of our approach. Furthermore, we demonstrate the simultaneous generation of two distinct focusing cylindrical vector beams using a monolayer metasurface, showcasing the two vector beams possess the interchange ability of their polarization distributions. By leveraging the Maxwell stress tensor, we assess the distinct contributions of the focused beams to longitudinal and transverse optical forces on SiO2 spheres, validating diverse trapping and manipulation behaviors for nanoparticles with the proposed metasurface designs. By manipulating the phase states of Sb2S3 nanopillars, binary-switchable optical trapping and manipulation are facilitated for all proposed metasurface tweezers. Our work underscores the efficacy of polarization-modulation multifunctional metasurface tweezers in consolidating multiple trapping tasks into a single device, paving the way for innovative lab-on-a-chip optical trapping applications in biophysics, nanotechnology, and photonics.

New click chemistry scheme towards improvement of filler/polymer crosslinking in bio-based polymer composites

ABSTRACT The current study aims to develop a cutting-edge methodology to enhance compatibility between natural fiber and polymeric matrix in bio-composites. To achieve this, an innovative approach for grafting an azide functional group (Az) onto the surface of polypropylene (PP) was successfully developed and confirmed. The Az group contributed to enhancing the interfacial interaction between the polymer matrix and the bio-filler through chemical crosslinking. Crosslinking is achieved through an innovative and efficient chemical process known as click chemistry. In particular, a click reaction is established between the functionalized PP with an azide group and functionalized natural filler with an alkyne group. The effectiveness of the proposed technique is verified using Fourier Transform Infrared (FTIR) spectroscopy, X-ray Photoelectron Spectroscopy (XPS), Thermo-Gravimetric Analysis (TGA), and Scanning Electron Microscopy (SEM). The resulting bio-composite exhibits higher mechanical properties, better thermal stability, and improved biocompatibility compared to conventional materials. Specifically, the maximum tensile strength was achieved at 10% DPP loading, showing a 22% increase over the base material (DPP/PP) and a 7% increase over neat PP. A more significant enhancement was observed in Young’s modulus results across all samples. Therefore, the established method is beneficial for polymer modification, promoting the production of high-performance bio-composite materials.

Intelligent Threat Detection—AI-Driven Analysis of Honeypot Data to Counter Cyber Threats

Security adversaries are rampant on the Internet, constantly seeking vulnerabilities to exploit. The sheer proliferation of these sophisticated threats necessitates innovative and swift defensive measures to protect the vulnerable infrastructure. Tools such as honeypots effectively determine adversary behavior and safeguard critical organizational systems. However, it takes a significant amount of time to analyze these attacks on the honeypots, and by the time actionable intelligence is gathered from the attacker’s tactics, techniques, and procedures (TTPs), it is often too late to prevent potential damage to the organization’s critical systems. This paper contributes to the advancement of cybersecurity practices by presenting a cutting-edge methodology, capitalizing on the synergy between artificial intelligence and threat analysis to combat evolving cyber threats. The current research articulates a novel strategy, outlining a method to analyze large volumes of attacker data from honeypots utilizing large language models (LLMs) to assimilate TTPs and apply this knowledge to identify real-time anomalies in regular user activity. The effectiveness of this model is tested in real-world scenarios, demonstrating a notable reduction in response time for detecting malicious activities in critical infrastructure. Moreover, we delve into the proposed framework’s practical implementation considerations and scalability, underscoring its adaptability in diverse organizational contexts.

EARLY LITERACY FOR YOUNG PEOPLE AND ADULTS: EDUCATORS’ CONTINUING TRAINING

The present focus of my research, extension online courses for teaching early literacy is aimed at eradicating functional illiteracy in Brazil, to form critical readers of texts circulating socially, able of writing what is appropriate to their pragmatic intentions, in different communicative situations. The instrument to achieve this goal is Scliar Early Literacy System (SSA), which contains the proposal foundations, the methodology, the instructions for applying the Units in the classroom, the reading and writing books for both teachers and students and the students' respective activity notebooks. This research goal is the teachers’ continuous training for improving young people and adult education, adopting a cutting-edge methodology, inspired by linguistic, neuroscience, neuropsychology, psycholinguistic and sociolinguistic advances. In this paper, I will deal with how to reach a qualitative leap in early literacy among young people and adults, changing the foundations that guide the mediator’s training, the methodology and the pedagogical material, from a perspective associating human and biological sciences. We cannot separate culture from the biological, from the brain, whose structure and functioning make its emergence possible.

Short-wavelength-infrared upconversion edge enhancement imaging based on a Laguerre-Gaussian composite vortex filter

Edge-enhanced imaging by spiral phase contrast has proven instrumental in revealing phase or amplitude gradients of an object, with notable applications spanning feature extraction, target recognition, and biomedical fields. However, systems deploying spiral phase plates encounter limitations in phase mask modulation, hindering the characterization of the modulation function during image reconstruction. To address this need, we propose and demonstrate an innovative nonlinear reconstruction method using a Laguerre-Gaussian composite vortex filter, which modulates the spectrum of the target. The involved nonlinear process spectrally transforms the incident short-wavelength-infrared (SWIR) signal from 1550 to 864 nm, subsequently captured by a silicon charge-coupled device. Compared with conventional schemes, our novel filtering method effectively suppresses the diffraction noise, significantly enhancing image contrast and resolution. By loading specific phase holograms on the spatial light modulator, bright-field imaging, isotropic, amplitude-controlled anisotropic, and directional second-order edge-enhanced imaging are realized. Anticipated applications for the proposed SWIR edge-enhanced imaging system encompass domains such as artificial intelligence recognition, deep tissue medical diagnostics, and non-destructive defect inspection. These applications underscore the valuable potential of our cutting-edge methodology in furthering both scientific exploration and practical implementations.

Demonstration of a 3D-Assembled Dual-Mode Photodetector Based on Tubular Graphene/III-V Semiconductors Heterostructure and Coplanar Three Electrodes.

3D assembly technology is a cutting-edge methodology for constructing high-performance and multifunctional photodetectors since some attractive photodetection features such as light trapping effect, omnidirectional ability, and high spatial resolution can be introduced. However, there has not been any report of 3D-assembled multimode photodetectors owing to the lack of design and fabrication guideline of electrodes serving for 3D heterostructures. In this study, a 3D-assembled dual-mode photodetector (3DdmPD) was realized successfully via the clever electrical contact between the rolled-up tubular graphene/GaAs/InGaAs heterostructure and planar metal electrode. Arbitrary switching of three coplanar electrodes makes the as-fabricated tubular 3D photodetector work at the unbiased photodiode mode, which is suitable for energy conservation high-speed photodetection, or the biased photoconductive mode, which favors extremely weak light photodetection, fully showing the advantages of multifunctional detection. In more detail, the Ilight/Idark ratio reached as high as 2 × 104, and a responsivity of 42.3 mA/W, a detectivity of 1.5 × 1010 Jones, as well as a rising/falling time (τr/τf) of 360/370 μs were achieved under the self-driven photodiode mode. Excitingly, 3DdmPD shows omnidirectional photodetection ability at the same time. When 3DdmPD works at the photoconductive mode with 5 V bias, its responsivity is extremely high as 7.9 × 104 A/W and corresponding detectivity is increased to 1.0 × 1011 Jones. Benefiting from the totally independent coplanar electrodes, 3DdmPD is much more easily integrated as arrays that are expected to offer the function of high-speed omnidirectional image-sensing with ultralow power consumption than the planar counterparts which share communal bottom electrodes. We believe that our work can contribute to the progress of 3D-assembled optoelectronic devices.

Aerodynamic design optimization of a NACA 0012 airfoil: An introductory adjoint discrete tool for educational purposes

The adjoint method is a powerful tool in high-fidelity aerodynamic shape optimization, providing an efficient means to compute derivatives of a target function with respect to various design variables. This paper delves into the discrete adjoint method. It offers a theoretical exploration of its implementation as an innovative tool for calculating partial derivatives (sensitivities) related to objective functions and design variables, specifically applied to a subsonic NACA0012 airfoil. The study conducts a qualitative evaluation using a designated test case, considering specified Mach number and Reynolds number values of 0.297 and 6,667 million, respectively. The Spalart-Allmaras turbulence model is employed to enhance computational cost efficiency. The results affirm the efficacy of the introduced tool, DAFoam, showcasing its ability to generate optimal geometries. The achieved performance optimization is evidenced by minimizing the drag coefficient value (CD) to an impressive 0.0131. While this research does not delve into the post-processing of sensitivity calculations, it acknowledges the potential for future investigations. The primary objective and novelty of this study is to provide the elementary background of the state of the art test case (NACA0012) within the subsonic regime, introducing the pioneer discrete adjoint aerodynamic optimization methodology (DAFoam) with the potential to explore its higher order capabilities in other aerodynamic related studies. Furthermore, it caters the educational needs of both graduate students and engineers in this exciting field. By presenting this cutting-edge methodology, it contributes to future advancements for the aerodynamicists in terms of optimal solutions.

Making it Short: Shortening the Comprehensive Inventory of Mindfulness Experiences Using Ant Colony Optimization

ObjectivesThe Comprehensive Inventory of Mindfulness Experiences (CHIME) comprises 37 items over eight domains and was validated in German and English languages using Rasch methodology. However, the length of the scale might limit its application due to the response burden it places on participants, especially in studies that examine additional constructs. This study aimed to develop a short but comprehensive CHIME version using cutting-edge methodology.MethodAnt colony optimization was applied to optimize and reduce item content of individual CHIME facets and create 24-item (CHIME-S) and 16-item (CHIME-XS) versions. We used data collected in New Zealand (n = 512), and in the USA, which included two independent samples (n = 605 and n = 210). The construct validity of the newly developed CHIME-S and CHIME-XS was established by correlating its scores with measures of mindfulness, affect, and distress.ResultsOverall, the ant-colony algorithm identified a stable solution which included 3 best fitting items per facet CHIME short form (CHIME-S) and the 2 best-fitting items CHIME-XS (16 items). This solution was successfully replicated across all samples and the scale demonstrated acceptable confirmatory factor analysis fit and good internal consistency, and correlated with measures of mindfulness, distress, and affect in expected directions.ConclusionsThe CHIME-S and CHIME-XS are reliable and valid short-scale versions that can be used for assessment of total mindfulness and its facets in a comprehensive way. Future research efforts are invited to validate the CHIME-S and CHIME-XS across different cultures and sample populations, aiming at establishing robustness of the measure.

Learning-based Integrated Cooperative Motion Planning and Control of Multi-AUVs

This paper introduces a learning-based solution tailored for the integrated motion planning and control of Multiple Autonomous Underwater Vehicles (AUVs). Tackling the complexities of cooperative motion planning, encompassing tasks such as waypoint tracking and self/obstacle collision avoidance, becomes challenging in a rule-based algorithmic paradigm due to the diverse and unpredictable situations encountered, necessitating a proliferation of if-then conditions in the implementation. Recognizing the limitations of traditional approaches that are heavily dependent on models and geometry of the system, our solution ofers an innovative paradigm shift. This study proposes an integrated motion planning and control strategy that leverages sensor and navigation outputs to generate longitudinal and lateral control outputs dynamically. At the heart of this cutting-edge methodology lies a continuous action Deep Reinforcement Learning (DRL) framework, specifically based on the Twin Delayed Deep Deterministic Policy Gradient (TD3). This algorithm surpasses traditional limitations by embodying an elaborated reward function, enabling the seamless execution of control actions essential for maneuvering multiple AUVs. Through simulation tests under both nominal and perturbed conditions, considering obstacles and underwater current disturbances, the obtained results demonstrate the feasibility and robustness of the proposed technique.

Exploration and Practice of English Civics Teaching Design in Colleges and Universities Based on Big Data Analysis

Abstract This study explored the cutting-edge methodology of integrating curricular Civics into teaching specialized disciplines in higher education, an essential trend for the future development of higher education. By applying data-mining algorithms, the article constructs a big-data analysis framework from the dimensions of students’ classroom behavior and performance. The fuzzy C-mean clustering algorithm was optimized using the cuckoo algorithm to effectively classify the characteristics of students’ behavior in the Civics classroom, and the attention-based hybrid encoder-decoder model was used to accurately predict students’ grades. After establishing a complete analytical framework, an innovative college English Civics teaching model was developed following the ASSURE teaching model. It was applied and analyzed for its effect in actual teaching. The study results show that, after the implementation of the English Civics and Politics course based on the ASSURE model, the average grade of the students increased by about 0.5 points. The grades were distributed between 80 and 95 points, significantly different from the pre-test grades (P<0.05). The prediction error was controlled to be less than ±2 points, which indicates that the teaching model had a significant teaching effect. This study not only improves English learning performance but also promotes the improvement of students’ performance in Civics, showing the great potential and value of integrating Civics curriculum into professional teaching.

Algorithm for generating neutrosophic data using accept-reject method

This paper introduces a novel and innovative approach to simulating random variates from two distinct probability distributions, namely the neutrosophic uniform distribution and the neutrosophic Weibull distribution. The primary objective of this research is to present a cutting-edge methodology for generating random variates by leveraging the accept-reject simulation method, particularly in the context of managing and addressing uncertainty. In addition to introducing the simulation methodology, this work will also provide comprehensive algorithms tailored to these proposed methods. These algorithms are essential for implementing the simulation techniques and will be instrumental in their practical applications. Furthermore, this study aims to explore the relationship between the level of indeterminacy and the resulting random variates. By investigating how varying degrees of indeterminacy impact random variates, we gain valuable insights into the dynamics of these distributions under different uncertainty conditions. Preliminary results suggest that random variates exhibit a trend of decreasing as indeterminacy levels increase, shedding light on the intriguing interplay between indeterminacy and random variate generation.

Acoustic Wave Based Forest Fire Extinguisher and Detection Using Machine Learning

Worldwide, forest fires are a serious hazard to people, property, and the environment, needing creative approaches to early detection and prompt control. In order to establish an intelligent forest fire control system, this research introduces a cutting-edge methodology that blends conventional fire detection techniques with machine learning and cutting-edge technology. To find fires in a forest, we use flame sensors. The flame sensor informs a microcontroller when a fire is found. We offer a revolutionary form of fire suppression using Sonic Fire Extinguisher technology, which puts out flames by emitting high-frequency sound waves, as opposed to traditional fire suppression methods that use water or carbon dioxide. This strategy offers a safer and more environmentally friendly fire suppression technique. The technology incorporates machine learning to improve the speed and accuracy of fire detection. A SD card for sound file storage is included with the ARDUINO UNO microcontroller. In addition, the microcontroller is wired up with an amplifier and speaker. The microcontroller uses machine learning techniques to analyses the characteristics of the fire, such as size and intensity, and chooses the best sound wave frequency for suppression when the flame sensor detects a fire. This device can put out fires more effectively than conventional techniques and can adjust to various fire circumstances. The device can tailor the produced sound waves for effective fire suppression by fusing real-time data from the flame sensor with machine learning insights. This novel strategy improves the safety from forest fires while simultaneously minimizing the negative ecological effects of fire management activities. A promising approach to protecting priceless natural resources, people's lives, and property from the destructive impacts of forest fires is the combination of machine learning and Sonic Fire Extinguisher technology. This research Endeavour marks a significant advancement in the practice of finding fires and extinguishment, allowing a more environmentally responsible and sustainable method of managing forest fires.

3D MODEL VISUALIZATION FUNCTION FOR RESPONSIVE WEB DESIGN

Except for critical basic implementations seen in historical records, the sheer quantity of similar frameworks, platforms, and apps available may be deceptive. Striking a balance between 3D certified displays and incorporated complicated features continues to be difficult. Geometrically precise point clouds and meshes are the result of cutting-edge methodology (computer vision, reverse engineering, and digital photogrammetry), cutting-edge technology (LiDAR, laser scanners, and unmanned aerial vehicles), and random research approaches. They are a by-product of the procedure. As the precision, size, and complexity of these things rise, the capabilities of present technology to manage them deteriorate. In this paper we propose a method for Development of a low-cost 3d visualization platform, capable of interactively perform tasks. Moreover, we seek to verify the methodological aspect of implementing steps and simplified algorithms in a virtual reality.