International Journal of Mechanical Engineering Research and Technology
Editor- In-Chief: Yakiv Beygelzimer Title proper: International Journal of Mechanical Engineering Research and Technology Journal Initials: IJMERT ISSN: 2454-535X Subject: Mechanical Engineering Publisher: Unicorn Publications Dates of publication: 2008 Frequency: quarterly Language: English Country: India Medium: Online Indexed by: CROSSREF Indexed by: ROAD Indexed by: THE KEEPERS Title DOI of the Journal : http://doi.org/10.62651/issn.2454-535X
A UAV THAT HARMS FROM BIOLOGY AND USES FALCON AND ALBATROSS ELEMENTS FOR SURGICAL ENVIRONMENTAL MONITORING AND DATA COLLECTION
This study presents a bio-inspired UAV that combines the exceptional flying skills of albatrosses and falcons, providing a versatile option for high-tech data collecting and monitoring of the environment. Falcons are perfect for traversing complex and dynamic situations, like as thick woodlands or urban areas, because to their fast dives, agility, and nimble movements. Albatrosses, on the other hand, are great at traversing huge areas like deserts and seas because they glide smoothly over great distances with little exertion. By fusing these two distinct flying traits, the Falcon-Albatross Hybrid UAV is able to glide for extended periods of time and move quickly and precisely, making it suitable for a variety of habitats. The UAV can gather high-quality environmental data across vast regions for long periods of time and perform thorough ecological monitoring thanks to its sophisticated sensors and hybrid propulsion system. Not only does the bio-inspired design help with conservation and sustainable management of the environment, but it also opens up new avenues for monitoring ecosystems and detecting environmental changes in a variety of settings. Using simulations in Autodesk CFD and Autodesk Fusion 360, this research compares the UAV's flying efficiency, stability, and data gathering capabilities in detail. It shows that there are considerable improvements in energy efficiency and adaptability compared to traditional UAV designs. Exploring the World of Bio-Inspired Unmanned Aerial Vehicles (UAVs) with a Focus on Environmental Monitoring and Energy Efficiency
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
Using diesel and blends of turmeric leaf oil to reduce emissions and improve the efficiency of variable compression ratio engines
A growing need for renewable energy sources has resulted from research into possible replacement fuels for internal combustion engines. This research examines the effects of using turmeric leaf oil (TLO) as a biofuel in a variable compression ratio (VCR) engine with the goal of increasing efficiency and decreasing exhaust emissions. The experiments used a single-cylinder, four-stroke diesel engine that operated on different compression ratios, from 19.5:1 to 18.5:1, and under different loading situations, from 5 to 9 kg. We measured the brake thermal efficiency, volumetric efficiency, and specific fuel consumption (SFC) of diesel blends with varying amounts of TLO (B50, B60, and B70) to see how well they performed. When the engine load increases, the results show that TLO integration somewhat reduces volumetric efficiency but significantly increases brake thermal efficiency.
Exhaust Emissions, Biofuels, Volumetric Efficiency, Brake Thermal Efficiency, and Variable Compression Ratio
MECHANICAL PROPERTIES & APPLICATIONS OF NATURAL FIBRE-BASED COMPOSITE MATERIALS
The majority of the research focuses on characterizing natural fibers and comparing their mechanical behavior and application performance to that of traditional composites. A wide variety of natural fibres exist, each with its own set of characteristics that determine which commercial uses for each are most appropriate. Because these materials are naturally occurring, their characteristics may vary greatly depending on the harvesting site and other factors; this makes it challenging to choose the best fiber for a given task. This study presents a thorough evaluation of the characteristics of natural fibers used to reinforcing composite materials in an effort to chart the relative positions of the various fiber types with respect to a number of attributes. Also included is the most recent research on emerging fiber kinds. Here we provide the results of a bibliometric analysis of papers on the use of composites made of natural fibers. There is also a discussion and presentation of a prospective analysis of the tendencies in the future of natural fiber applications and the necessary advances to expand their uses. Green composites made of natural fibers have many potential uses in the automotive and industrial sectors.
mechanical properties, chemical modification
MAGNUM MARANGONI FLUID FLOW ACROSS A CIRCULAR SURFACE: THE ROLE OF THERMAL RADIATION AND PRESSURE
Taking into account the pressure work and surface tension, computational work has been carried out on a magneto Marangoni boundary layer that is thermally radiative on a heated circular surface. A system of ordinary differential equations describing various physical characteristics constitutes the numerical model of the present investigation, which was evolved from the governing partial differential equations with appropriate boundary conditions. Increasing the buoyancy parameter raises the magnitudes of the velocities, according to the numerical study of the effective variables, but the magnetic parameter and the ratio of the surface tension had the reverse impact. As the magnetic effect was amplified, the temperature magnitudes rose. The results showed that the Nusselt number is improved by the Marangoni ratio number, the Prandtl number, and the buoyancy parameter.
PRIME HYDRODYNAMIC PERFORMANCE AND ACOUSTIC PROFILE IN UNDERWATER VEHICLES: OPTIMIZING PROPELLER GUARDS PARAMETERS
The development of propulsion systems for underwater vehicles, including AUVs and ROVs, that optimize thrust while minimizing noise pollution caused by humans is becoming more important because to the wide range of uses for these vehicles. Little is known about how to optimize the propeller guards of underwater vehicles, despite the fact that these components change hydrodynamics and affect thrust and noise. There is a lack of research on the effects of guarded propellers on the propulsion efficiency of underwater vehicles and a missed chance to improve environmental outcomes since much of the existing literature focuses on surface vehicles and open propeller designs. In order to find the best design, this study used parametric computational fluid dynamics (CFD) to examine 75 different propeller guard designs. A custom-built equipment was used to measure thrust generated and noise created in order to confirm these findings. Six guard configurations were chosen at random from the parametric research spanning three guard lengths. When it came to measuring thrust, the experimental findings were in agreement with the model-based expectations. Assuming the thrust findings are correct, any parametric study configuration that produces more thrust than the open propeller would be even better. Out of the six experimental guards, four showed noise reductions below the open propeller of 0.01 dB to 10.29 dB. A 5.86% increase in thrust and a 1.08 dB reduction in noise were achieved by the experimental propeller guard design with the highest efficiency. Optimizing propeller guards may improve underwater vehicle performance and reduce noise pollution, according to this research. Research into other testable criteria, improvements to the testing equipment, and assessment of guarded propellers for use in actual underwater vehicles are all potential next steps. Underwater Vehicles, Propeller Guards, Propeller Noise, and Propeller Thrust
Underwater Vehicles, Propeller Guards, Propeller Noise, and Propeller Thrust
A Crop Cutter for Pearl Millet Made with the Help of the Scotch Yoke Mechanism
This study outlines the proposed design of a crop cutter for pearl millet that makes use of the Scotch Yoke mechanism for accurate and efficient cutting. To slice millet stalks with ease and consistency, the Scotch Yoke mechanism transforms rotating action into reciprocating motion. To achieve efficient cutting with little energy input, the design optimizes critical variables such as blade curvature, bevel angle, and material selection. An affordable and practical alternative for small-scale millet harvesting, the design prioritizes simplicity, cost-effectiveness, and ergonomic usage. With any luck, this effort will help mechanize millet harvesting, which will increase productivity while decreasing the need for human labor. Scotch Yolk Mechanism, Millet Cutter
EMERGENCY MEDICAL SUPPLY AUTOMATIC DRONE EVALUATION: A COMPLETE REVIEW
A revolutionary step forward in disaster response and healthcare logistics would be an autonomous drone system that could transport medical supplies in an emergency. The autonomous drone uses cutting-edge technology like AI, GPS navigation, and superior obstacle recognition to deliver life-saving supplies precisely when they need them. Blood products, vaccinations, medicines, and other life-saving equipment can be swiftly delivered to locations that are inaccessible, devastated, or otherwise damaged by natural disasters, where conventional means may encounter long delays. The resilience of the drone's construction guarantees its dependability in a wide range of environments, including those with extreme weather. The technology decreases operational hazards in high-stakes situations and shortens reaction times by reducing the requirement for human involvement. Revolutionizing humanitarian relief and emergency medical response on a worldwide scale, this invention fills critical gaps in healthcare delivery during crises and offers a scalable, cost-effective, and life-saving solution. The following terms are used interchangeably: autonomous drone, healthcare logistics, medical supplies, artificial intelligence, geographic information system navigation, obstacle detection, environmental conditions, operational risks, healthcare delivery, humanitarian aid, and remote areas.
operational risks, healthcare delivery, humanitarian aid, and remote areas.
Aerodynamic Characteristics of the Design of Liam Propeller Blades: An Investigation
By looking at new design changes, this study examines how well Low-Induction Axial Momentum (LIAM) propeller blades function aerodynamically. By comparing blade designs that include airfoil geometries and wave patterns along the edges, the research hopes to boost UAV performance and assist humanitarian operations. Results on lift, drag, and total efficiency under different operating circumstances may be gleaned using ANSYS simulations. This design adjustment has a considerable influence on aerodynamic performance, according to early-stage studies. It provides a platform for additional optimization and refining. Aerodynamics, Unmanned Aerial Vehicle Propellers, LIAM Blades, ANSYS Models, Airfoil Templates, Wave Formations
MACHINE LEARNING BASED IRIS RECOGNITION MODERN VOTING SYSTEM
Based on the iris recognition system and related technologies, one of the primary outcomes of the validation system is the fingerprint-based system. The whole biometric procedure is much more genuine and distinct than the other kinds of validation procedures and recognition systems. This has given people creative ideas for their everyday lives. In general, the multimodal biometric process has used a variety of applications to appropriately address the most important and relevant shortcomings of the "unimodal biometric system." In general, the complete process has been incorporated, taking into account the appropriate noise sensitivity, population coverage regions, situations of variability involving both intra- and inter-class concerns, vulnerability involving potential hacking, and non-universality criteria. The machine learning system with a deep learning orientation has been the primary topic of the whole research article. Convolutional neural network (CNN) technology has been primarily used in the fingerprint-based iris recognition system to provide accurate human validation. The iris recognition system has mostly been used in relation to the "high security protection system with actual fingerprints" in the current data validation procedure. The optimal uniqueness, reliability procedure, and appropriate "validity of the iris biometric validation system" for the real goal of person identification have been briefly discussed throughout the whole text.
validity of the iris biometric validation system
Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-nd/4.0
PREDICTING BEHAVIOR CHANGE IN STUDENTS WITH SPECIAL EDUCATION NEEDS USING MULTIMODAL LEARNING ANALYTICS
Students with special education needs (SEN), whose behavior and learning are particularly sensitive to their bodily conditions and surrounding surroundings, have new options due to the availability of educational data in innovative methods and forms. In order to elucidate the underlying educational insights, multimodal learning analytics (MMLA) collects student and learning environment data in a variety of modalities and analyzes it. In this study, we used MMLA to predict how SEN kids would behave after they get applied behavior analysis (ABA) treatment. ABA therapy is a kind of special education intervention that tries to cure behavioral issues and promote positive behavior changes. Here, we demonstrate how our deep neural network and machine learning models can optimally predict the behavior change of SEN kids with 98% accuracy and 97% precision by importing multimodal educational data. We also show how predictive models with merely standard educational data may perform statistically much better when combined with environmental, psychological, and motion sensor data. Since 2020, the Integrated Intelligent Intervention Learning (3I Learning) System in Singapore and Hong Kong has improved intense ABA therapy for more than 500 SEN kids thanks to our efforts.
HYBRID FEATURE BASED PREDICTION OF SUICIDE RELATED ACTIVITY ON TWITTER
Globally, the number of suicide deaths rises year, which is a troubling public medical concern. Through this experiment, casual, inactive individuals were organically disengaged from online life on Twitter and from sharing self-destructive ideas. Initial emotional evaluation of the idle locations was followed by a thorough comparison with random factors suggested by space experts. With the increasing normalcy of long-distance interpersonal communication platforms, clients have come to rely on these spaces for very sensitive conversations, including thoughts of suicide. The tweets are important for research because of the high frequency of information appearing in them and the considerable capacity and time constraints that computations using them must meet. Emoticons and synonyms features can now be distinguished, and the ngram model—a combination of Unigram, Bigram, and Trigram with half breed word reference—is used to calculate scores. Using machine learning methods, this model uses the informal points to predict how sincere the postings will be. In this study, we also contrast several methodologies such as SVM, NB, and RF.
EPILOTS: A SYSTEM TO PREDICT HARD LANDING DURING THE APPROACH PHASE OF COMMERCIAL FLIGHTS
A go-around might have saved more than half of all commercial aircraft operating accidents. The total accident rate in the aviation business may be lowered by making the choice to do a go-around maneuver in a timely manner. In this study, we report on the development of a deployable machine learning system for the cockpit that facilitates go-around decision-making by the flight crew in the case of a hard landing. This paper provides a hybrid technique for hard landing prediction that feeds a neural network with features modeling the temporal interdependence of aircraft characteristics. The findings demonstrate that our technique has an average sensitivity of 85% and an average specificity of 74% at the go around point, based on a large dataset of 58177 commercial flights. Thus, our method—a cockpit-deployable recommendation system—performs better than previous methods.
European Union Aviation Safety Agency (EASA)
LIVER DISEASE PREDICTION USING SVM AND NAÏVE BAYES ALGORITHMS
When it comes to automated illness detection and prediction, data mining is a crucial component. The process uses data mining algorithms and methods to examine health records. One of the leading causes of death in a number of nations is liver disease, which has been on the rise in recent years. Predicting liver disease using classification models built from datasets of liver patients is the goal of this thesis. This thesis improved the prediction accuracy of Indian liver patients in three stages by constructing feature models and comparing them. The first step is applying the min max normalization technique to the original datasets of liver patients obtained from the UCI repository. During the second step of liver dataset prediction, a subset of the normalized liver patient dataset is produced using PSO feature selection. This subset contains just the most important features. In the third step, the data set is subjected to categorization methods. In the last stage, we'll figure out how accurate it is by calculating the root mean square and root mean error values. After using PSO feature selection, the J48 method is thought to be the superior algorithm in terms of performance. The assessment is concluded by looking at the accuracy values.
UNSUPERVISED MACHINE LEARNING FOR MANAGING SAFETY ACCIDENTS IN RAILWAY STATIONS
Railroad operations must be reliable, easily accessible, well-maintained, and safe (RAMS) in order to move both passengers and freight. An important everyday safety issue in many metropolitan areas is the danger of accidents at train stations. In addition to casualties, public worry, and financial losses, accidents wreak havoc on market reputation. Stations like this are feeling the heat from increased demand, which is putting a strain on infrastructure and making safety a top administrative priority. The use of unsupervised topic modeling to better understand the contributors to these severe incidents is advised for the purpose of analyzing them and using technology, such as AI approaches, to increase safety. This study aims to optimize Latent Dirichlet Allocation (LDA) using textual data collected from RSSB, which includes 1000 accidents that occurred at UK railway stations, with the purpose of reducing mortality accidents at these locations. To improve station safety and risk management, this study details the use of a machine learning topic approach to systematic detect accident characteristics, and it offers advanced analysis. The research assesses the effectiveness of text mining from accident records in obtaining information, lessons learned, and a comprehensive understanding of the risks associated with evaluating accidents that result in deaths on a large and long-term scale. Predictive accuracy for important accident data, including hotspots at railway stations and underlying causes, is shown by this Intelligent Text Analysis. In addition, the advancement of big data analytics allows for a better understanding of the nature of accidents compared to restricted domain analysis of accident reports, which was previously impossible without a large quantity of safety history. A new age of useful and widespread artificial intelligence applications in railway sector safety and other domains for safety applications has dawned, made possible by this technology's high level of accuracy.
STRESS DETECTION IN IT PROFESSIONALS BY IMAGE PROCESSING AND MACHINE LEARNING
The primary goal of our study is to identify IT workers who are experiencing stress by using advanced methods in machine learning and image processing. Our system is an improvement over previous stress detection systems that did not include live detection or personal counseling. Instead, our system includes both of these features, as well as periodic analysis of employees to detect levels of physical and mental stress and to provide them with appropriate remedies for stress management through the use of survey forms. The primary goals of our system are stress management, creating a healthy and spontaneous work atmosphere, and optimizing the performance of workers when they are on the clock.
PREDICTION THE PRICE OF USED CAR USING MACHINE LEARNING
Due to the obvious level of expertise and effort required, the prediction of automobile prices has been a popular topic of study. In order to make a solid and precise forecast, a large number of different characteristics are researched. In order to construct a model that can foretell the value of pre-owned automobiles. Machine learning techniques such as logistic regression, Random Forest, Support Vector Machine, and linear regression were used. But the aforementioned methods were used in conjunction with one another to do regressions and ensemble work. The information that went into making the forecast was gathered Then, in order to determine which method is most suited to the given data set, their respective results were compared. Predicting the optimal percentage and revealing the outcome with the appropriate algorithms are both tasks that need the use of algorithms.
HAND GESTURE USING CONVOLUTIONAL NEURAL NETWORKS
The goal of Sign Language Recognition (SLR) is to enable deaf-mute individuals to communicate with the general public by translating sign language into text or voice. Despite the wide-ranging societal effects, the intricacy and wide-ranging hand motions make this work very difficult. Current state-of-the-art SLR approaches construct classification models using manually-crafted characteristics that characterize motion in sign language. Nevertheless, trustworthy features that can adjust to the wide variety of hand movements are challenging to build. In order to tackle this issue, we present a new convolutional neural network (CNN) that can automatically, and without human intervention, extract discriminative spatial-temporal characteristics from unprocessed video streams. Convolutional neural networks (CNNs) are trained to improve performance by feeding them multi-channel video feeds that include color information, depth clues, and the locations of the body's joints. By comparing it to more conventional methods that rely on manually created features, we show that the suggested model outperforms the former on a real-world dataset acquired using Microsoft Kinect.
MACHINE LEARNING FOR FAST AND RELIABLE SOURCE LOCATION ESTIMATION IN EARTHQUAKE EARLY WARNING
Our goal in creating this random forest (RF) model is to help earthquake early warning (EEW) systems make quick decisions when it comes to earthquake location. Utilizing P-wave arrival times at the first five seismic stations, this technique calculates the relative arrival times of each station in relation to a reference station, which in this case is the first recording station. In order to determine the approximate position of the epicenter, the RF model categorizes these differential P-wave arrival timings and station locations. The suggested technique is trained and tested using a Japanese earthquake database. With a Mean Absolute Error (MAE) of 2.88 km, the RF model generates very accurate earthquake location predictions. Notably, the suggested RF model may acquire sufficient knowledge using only 10% of the information and far fewer recording stations (i.e., three) while still attaining acceptable outcomes (MAE<5 km). An effective new tool for quick and trustworthy source-location prediction in EEW is provided by the method, which is accurate, generalizable, and responds quickly.
MODELING AND STATIC MODAL ANALYSIS OF HEAVY VECHICLE CHASSIS
One of the most crucial components of an automobile is the chassis. The internal frame serves as the vehicle's support throughout operation, allowing it to withstand the specified circumstances. The core of any automobile is the supporting framework that houses the axle assembly and engine body. It is thus the most crucial part that gives the car strength and stability in a range of conditions. The majority of these chassis are steel frames that hold up a car's body and engines. The frame or fundamental structure of a car is referred to as the "chassis". Investigating the design and statistical structural analysis of a large vehicle chassis with different alloy materials at different ideal loads is the aim of this effort. The heavy vehicle chassis will be developed in CATIA V5, Fusion 360, and Ansys Workbench for structural analysis. The goal of the project is to construct a better chassis that, in the best of situations, lowers the overall weight of the vehicle while boosting its performance in large vehicles.
DESIGN AND STATIC STRUCTURAL, IMPACT ANALYSIS ON TYPE IV HYDROGEN FUEL TANK USING ANSYS SOFTWARE
Using ANSYS software for static structural and impact analysis, this research investigates the design and structural integrity of a Type IV hydrogen fuel tank. In order to maximise safety and performance in automotive applications, the tank has a unique dual-layer composition consisting of a 3mm outer shell made of carbon fiber-reinforced polymer (CFRP) with epoxy resin based on bisphenol A and bisphenol F and an inner layer made of high-strength aluminium alloy (7068-T6511). The static structural study, which ensured compliance with strict safety criteria, evaluated stress distribution and deformation in the tank under high-pressure circumstances using finite element analysis (FEA). The impact study verified the structural integrity and longevity of the combined materials by running simulated scenarios and further examining the tank's resistance to collision forces. The results show that although the CFRP exterior layer greatly adds to the tank's lightweight and improves its impact resistance, the aluminium inner layer efficiently retains the hydrogen at high pressures. These results highlight how these composite materials may improve the sustainability, safety, and effectiveness of hydrogen storage systems. According to the study's findings, the usage of high-strength aluminium alloy in conjunction with epoxy resin and CFRP not only satisfies but surpasses the performance standards set now for hydrogen fuel tanks, which has encouraging implications for the development of hydrogen-powered vehicles in the future.
DESIGN AND STATIC MODAL ANALYSIS OF ARTIFICIAL KNEE PROSTHESIS USING FEM
The generally acknowledged effective surgical method for treating osteoarthritis and other degenerative disorders of the knee is total knee arthroplasty (TKA). Nonetheless, current data on low patient satisfaction and survivability highlights the need of receiving the best possible endoprosthetic treatment. Artificial components are used to replace the natural joint surfaces in an effort to alleviate pain, restore function, and enhance the quality of life associated with health. Lightweight, high-load-bearing construction is the main need for this. The project's goal is to develop and evaluate a flexible prosthetic knee joint and identify options and strategies to meet the need for a well-thought-out replacement for a lightweight, readily operable prosthetic knee joint. Ansys software is used to analyse the knee dimensions, which are derived from measurements of the human body. This study examines the suitability of materials with characteristics such as low cost, great lifespan, and no maintenance for use in prosthetic knees. The improvement of knee prostheses over the last ten years has been significant, allowing individuals to continue engaging in numerous activities even after experiencing age or injury. The human knee-joint is a complicated system with a complexly structured femoral condoyle that glides over the complexly curved meniscus of the tibia bone, absorbing different critical stresses during different gait, movement, and sitting activities. Since the beginning of orthopaedic surgery, metal alloys have been the preferred materials. TI-6AL-4V, TI-6AL-7NB, ABS, and stainless steel are the materials that are most often used as biomaterials for knee implants. This project aims to create a 3D CAD model of prosthetic knee joint implants by allocating load combinations of biomaterials to the femoral and tibia components and analysing the distribution of von-misses stresses, total deformation, shear stress, and strain. 3D modelling programs ANSYS 15 finite element analysis software and Catia are used to simulate knee implants in three dimensions and determine the best material for knee prostheses.
BLOCKCHAIN AND MACHINE LEARNING-BASED REAL TIME ATTENDANCE MONITORING SYSTEM
In the period of validation, facial recognition innovation is utilized in numerous areas. Image processing headways like OpenCV have expanded society's reliance on facial acknowledgment as the data age requests quick and safe confirmation. This study presents a blockchain-got Participation Checking Framework involving OpenCV for facial recognition. This innovation smoothes out participation and lifts security. Using OpenCV to assess live camera feeds will recognize understudy faces and consequently produce participation records with entrance timings. Critically, blockchain gets and seals participation information. This extraordinary innovation further develops productivity and guarantees participation record honesty and openness over the dispersed blockchain network.[32]
DEVELOPMENT AND EVALUATION OF AN EXPLAINABLE AI MODEL FOR EARLY CHRONIC KIDNEY DISEASE DIAGNOSIS
The examination creates and tests a logical AI model for early CKD finding. Logic ensures that the model's forecasts are clear and justifiable, a vital calculate medical care AI adoption. Chronic Kidney Disease is a worldwide medical problem. Early ID is significant to forestall kidney harm and diminish progressed CKD medical care consumptions. The review perceives CKD's more extensive impacts and looks for proactive cures. The model adjusts arrangement precision and reasonableness utilizing an improvement structure. This technique ensures that the artificial intelligence model makes accurate expectations and makes sense of them. The streamlining method works on model execution. The review utilizes an extreme gradient boosting classifier, a modern ML strategy, to analyze CKD utilizing hemoglobin, explicit gravity, and hypertension. The model's focus on clinical signs makes these qualities significant for early CKD location. The drive offers a practical early CKD indicative answer for immature countries. The idea makes distinguishing CKD in asset compelled settings practical and successful by underlining cost reserve funds, further developing medical care availability and moderateness. Our framework was more accurate and versatile on the grounds that we utilized a gathering way to deal with total expectations from many models. We utilized progressed outfit strategies like the Stacking Classifier to get 100 percent accuracy.
A HYBRID LEXICON-BASED MACHINE LEARNING FRAMEWORK FOR POLITICAL SECURITY THREAT PREDICTION
The internet is pivotal to public safety today. The US Knowledge People group positions digital dangers close by illegal intimidation and other key difficulties. Safeguarding a country is more diligently now. With such a lot of information, data on the online, and fake news, there's a never-ending chance of impelling scorn and undermining public safety. Project joins emotions and perspectives to country dangers. Negative emotions in internet data could undermine public safety. Specialists should rapidly recognize and address these opinions. Emotions and public safety concerns are connected, but there is little assessment and structure investigation of feelings and their measurements. Review have for the most part characterized human emotions , not their significance to public safety risks or how to figure their ascent. This idea proposes foreseeing political risks utilizing web news feelings. It centers around political security, an essential public safety issue. The examination utilizes word investigation and ML to connect information holes and show their adequacy utilizing genuine web news information. The exploration utilizes complex troupe picking up utilizing a stacking classifier, Random Forest-Decision Tree models, and a standalone Random Forest model. This group method further develops framework expectation, conveying intimidation forecast more strong. Client testing is empowered through an easy to use Flask framework with SQLite network and basic information exchange and signin. It increments model execution and gives a sensible stage to genuine client collaborations, giving a total framework ease of use and viability assessment.
MACHINE LEARNING FOR IOT DEVICE ANOMALY DETECTION ATTACK CLASSIFICATION
The theoretical underscores the security risks of Internet of Things (IoT) gadgets from programmers and assailants. IoT gadgets are defenseless against inconsistency assaults because of their interconnectedness. Support Vector Machine (SVM) and Random Forest (RF), stacking classifier, and voting classifier are utilized to recognize odd attacks in IoT gadgets in the Task. Every strategy is picked for its discovery and component determination abilities. The review explores different avenues regarding the arff NSL-KDD dataset. The suggested calculations RF and stacking classifier have great accuracy. Center around misleading positive rates shows a low rate in all cases. Featuring the technique's promising outcomes, quite Random Forests' preferable exactness over past writing. The stacking classifier and Random Forest exhibit promising exactness, review, and accuracy for recognizing and relieving IoT abnormal dangers. Troupe approaches like Voting Classifier (RF + AB) and Stacking Classifier (RF + MLP with LightGBM) consolidate various model expectations to make a more vigorous and precise forecast. Voting Classifier had 100 percent accuracy, Stacking Classifier 100 percent accuracy, and we made the front end involving flagon for client testing and IoT anomaly detection with client verification.[16]
BLOCKCHAIN-BASED STATE GOVERNMENT FUND ALLOCATION AND TRACKING SYSTEM
This study proposes utilizing blockchain innovation to assist with expressing state governments oversee plots and disperse installments. With a few divisions offering various plans, a solitary framework to safely oversee applications, endorsement situations with, authorized sums is required. Unchanging nature, agreement methods, and cryptographic encryption safeguard information against unlawful access and changes in blockchain innovation. Individual exchange costs are decreased by pooling exchanges prior to adding them to the blockchain. Blockchain can assist governments with further developing proficiency, client experience, and other managerial issues by coordinating new innovation. This study proposes utilizing blockchain to further develop government transparency, security, and efficiency.
MACHINE LEARNING HEART DISEASE PREDICTION WITH EFFECTIVE FEATURE ENGINEERING
Early diagnosis is vital for heart disease, which influences millions around the world. Principal Component analysis is utilized to find and work on the main qualities in an original element designing technique. The examination utilizes AI to immediately figure Heart Disease condition and make a move. Incorporate undertaking utilizes a Stacking Classifier to coordinate RF, MLP, and LightGBM expectations. This methodology synergistically utilizes model qualities to make a powerful and exact figure with 100 percent accuracy. PCHF-based highlights were utilized to produce the model, and the Stacking Classifier was prepared for front-end arrangement. Flask framework with client validation makes client testing easy and safe, advancing the ML based heart disease prediction system.
DEEP CNN-BASED MULTI-CLASS RETINAL DISEASE DETECTION WITH LOW MEMORY CONSUMPTION
The examination fosters a memoryproficient convolutional neural network (CNN) model to recognize and characterize retinal issues. U-Net Division, utilized for retinal sickness characterization, utilizes a great deal of memory and central processor. The recommended model tends to these challenges. This model is tried on EyeNet, a benchmark dataset with 32 retinal sickness types. Trial results uncover that the proposed model beats existing techniques in memory the executives and exactness. Precision, recall, and accuracy measures are assessed utilizing various ages and step times. On the EyeNet dataset, the proposed strategy groups multi-class retinal sicknesses with phenomenal exactness. We utilized mobilenet, densenet, and hybridways to deal with further develop accuracy. MobileNet accomplished 97% accuracy, Xception 100 percent, and A hybrid. For protected and effective client connections, a Flask front-end with validation is made.[38]
MACHINE-LEARNING-BASED CLOUD INTRUSION DETECTION
Capacity, information the executives, handling power, applications, and more are accessible on request with cloud computing. These assets are effectively available and usable. The task utilizes ML to further develop cloud security with interruption discovery. The primary objective is to screen and dissect cloud assets, administrations, and organizations to identify and forestall cyberattacks. This interruption discovery technique utilizes ML, especially the Random Forest (RF) strategy. Multi decision tree outfit learning approach Random Forest further develops expectation accuracy. Highlight designing is essential to demonstrate advancement. It incorporates picking and advancing dataset ascribes for the ML model. Powerful element designing assists the model with perceiving patterns and attacks. The model consistently screens cloud assets, administrations, and organizations to further develop cloud security. The model purposes ML procedures to recognize odd digital assault patterns, further developing cloud foundation security. The model's exhibition is approved utilizing Bot-IoT and NSL KDD. These datasets are interruption discovery benchmarks. Contrasted with ongoing endeavors, the model distinguishes interruptions with high exactness, recommending its value and reliability in spotting security chances. For further developed cloud discovery, the undertaking's Voting Classifier with RF + ADaBoost and Stacking Classifier with RF + MLP with LightGBM accomplished almost 100% and 100 percent accuracy for Kdd-Cup and Bot-IoT information.
GRAPH CONVOLUTIONAL NETWORK AND TENSOR DECOMPOSITION FOR CLASSIFYING FAKE NEWS
With social media disinformation on the ascent, this study offers a clever classification strategy utilizing graph neural networks (GNN). Our methodology groups misleading news by assessing sentence association designs in reports, in contrast to different strategies. We work on context oriented mindfulness by diagramming news things and utilizing GNN to record sentence associations. We fabricate weight networks utilizing a third-request co-event tensor and canonical polyadic (CP) disintegration to reflect nearby word co-event data precisely. SVM, LSTM, CNN, BERT GCN, and GCN with CP were looked at, with GCN accomplishing close to 100% accuracy. Troupe approaches consolidate model forecasts to further develop execution. Outfit approaches like BERT GCN LSTM and LSTM + GRU might accomplish 100 percent accuracy. This thorough system ought to upgrade false news recognizable proof and decrease its social repercussions.[42]