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
THERMAL AND STRUCTURAL ANALYSIS OF A COOLED TURBINE BLADE USING ANSYS
Gas turbine blades operate under extremely high temperatures that exceed allowable metal limits. Effective internal cooling is therefore essential to maintain structural integrity and prolong service life. This study presents a coupled steady-state thermal and static structural analysis of a turbine blade incorporating ten internal cooling passages. A reduced-order modeling approach is implemented in ANSYS Workbench, where three-dimensional solid elements represent the blade and one-dimensional fluid elements simulate coolant flow. The thermal analysis evaluates temperature distribution in both solid and coolant domains, while the structural analysis determines thermally induced stresses resulting from constrained expansion. Results show significant temperature gradients between the hot external surface (568 K) and cooled internal regions (minimum 338 K). The maximum von Mises stress reaches 2.79 × 10⁹ Pa near critical cooling passages. The study demonstrates that reduced-order thermal-fluid modeling provides computational efficiency while maintaining physical accuracy for preliminary design evaluation.
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THERMAL AND FLOW ANALYSIS OF A PARABOLIC TROUGH COLLECTOR USING CFD WITH NANOFLUIDS
Parabolic Trough Collectors (PTCs) represent one of the most mature and commercially deployed technologies in Concentrated Solar Power (CSP) systems. The thermal performance of PTC systems strongly depends on the thermophysical properties of the working fluid circulating inside the absorber tube. Conventional heat transfer fluids such as water exhibit limited thermal conductivity, which restricts system efficiency. To overcome this limitation, nanofluids have emerged as promising advanced heat transfer media. This study presents a comprehensive Computational Fluid Dynamics (CFD) analysis of a parabolic trough collector using ANSYS Fluent. Water-based nanofluids containing CuO, Al₂O₃, TiO₂, and SiO₂ nanoparticles at 0.2 volumetric concentration were investigated. The finite volume method was used to solve the governing continuity, momentum, and energy equations under steady-state conditions. Solar radiation effects were modeled using the Solar Load Model combined with the Surface-to-Surface (S2S) radiation model.
STRUCTURAL INTEGRITY ASSESSMENT OF A TWO-STAGE DISK WITH PINHOLES UNDER STATIC CONDITIONS
Multistage cyclic symmetry analysis enables efficient simulation of complex rotating structures composed of multiple cyclically symmetric subsystems with differing sector counts. This study presents a static structural assessment of a two-stage rotating disk with stress-relief pinholes using multiharmonic cyclic symmetry analysis in ANSYS Mechanical (Release 2025 R2). The inner stage consists of 11 sectors, while the outer stage contains 16 sectors. Although subjected to axisymmetric centrifugal loading at 100 rad/s, interstage coupling generates non-axisymmetric internal force distributions, requiring inclusion of multiple harmonic indices to ensure displacement continuity and accurate stress prediction. The results demonstrate that single-harmonic (HI = 0) analysis leads to interface discontinuities, while multiharmonic enrichment restores compatibility and produces physically consistent deformation patterns.
STRUCTURAL INTEGRITY ASSESSMENT OF A NUCLEAR PIPING SYSTEM UNDER SEISMIC CONDITIONS
Accurate modeling of pipe elbows is critical in seismic assessment of nuclear piping systems, as curved sections exhibit amplified flexibility, ovalization, and nonlinear stress behavior compared to straight pipes. This study evaluates the performance of ELBOW290 elements in ANSYS Mechanical against detailed SHELL281 shell models for nonlinear seismic analysis. A two-stage global–local methodology is adopted. The global piping system is analyzed using line elements to extract seismic displacement histories, while a critical elbow is studied using both ELBOW290 and SHELL281 elements. Material nonlinearities are modeled using Multilinear Kinematic Hardening (global) and Chaboche Nonlinear Kinematic Hardening (local). Results demonstrate that ELBOW290 accurately captures stress concentration, ratcheting, and ovalization effects with less than 5% deviation from shell models while reducing computational cost by approximately 86%. The study validates elbow element technology as a reliable and efficient solution for seismic qualification of nuclear piping systems.
STRUCTURAL EVALUATION OF HONEYCOMB-BASED WHEEL DESIGN UNDER LOAD CONDITIONS
Lightweight is one of the primary goal for the development of wheels. A new type of automobile wheel is proposed in the present work. The wheel is divided into tread, spoke and wheel hub. The spoke layout is designed based on the honeycomb structure by considering load bearing performance of wheel. The analysis is carried out by the varying material of core wall of honeycomb structure. Structural steel, aluminum alloy, carbon fiber and polyethylene are used as a core material. The mechanical performances are analyzed and results shows that maximum stress and deformation which can meet design requirements like strength and stiffness of the wheel
STRUCTURAL ANALYSIS OF BONDED AND HYBRID JOINTS IN AIRCRAFT FUSELAGE CONNECTIONS
Composite materials are increasingly used in modern aircraft structures due to their high strength- to-weight ratio, corrosion resistance, and design flexibility. Aircraft fuselage structures rely heavily on efficient joining methods for stringer-skin connections and splice joints. Traditional riveted joints, bonded joints, and hybrid joints (bonded + riveted) are commonly used in aerospace applications. This study presents a comparative structural analysis of bonded, riveted, and hybrid joints used in aircraft fuselage splice connections. A Z-stringer model was developed using ANSYS SpaceClaim and analyzed using ANSYS Workbench. Static structural simulations were performed to evaluate total deformation and equivalent (von Mises) stress distribution.
STATIC STRUCTURAL AND MODAL ANALYSIS OF A MULTISTAGE AXIAL COMPRESSOR USING MULTISTAGE CYCLIC SYMMETRY
Multistage cyclic symmetry analysis provides an efficient framework for evaluating complex turbomachinery assemblies composed of multiple cyclically symmetric subsystems with different sector counts. This study presents the static structural, modal, and prestressed modal analysis of a four-stage axial compressor model consisting of three blade rows and a supporting rim. Each stage is modeled as an independent cyclic subsystem and connected using multistage interstage coupling. The structure is subjected to rotational speed, stage-wise pressure loading, and thermal gradients to simulate realistic operating conditions. Static results show stress concentration at blade roots and interstage interfaces, while modal analysis reveals harmonic-index-dependent vibration behavior. Prestressed modal analysis demonstrates centrifugal stiffening effects and frequency shifts under operational loads.
SIMULATION-BASED NONLINEAR PRESTRESSED MODAL ANALYSIS OF A STRUCTURE (IMPELLER PUMP) USING ANSYS
Impeller-type pumps are widely used in fluid-handling systems where structural stiffness and elastic integrity under operational loading are critical. This study presents a linear static structural analysis of a five-component impeller pump assembly subjected to a 100 N belt-equivalent bearing load. The objectives are to verify that impeller deflection remains below the allowable limit of 0.075 mm and to ensure that the Polyethylene pump housing operates within its elastic limit, particularly near the shaft bore where stress concentration is expected.
APPLICATION-BASED THERMAL ANALYSIS OF AN ELECTRONIC SYSTEM USING NATURAL CONVECTION AND RADIATION
Miniaturization of electronic devices has led to increased heat generation per unit volume, creating serious thermal management challenges. Efficient removal of heat from electronic chips is critical to ensure reliability, performance, and longevity. In this study, Computational Fluid Dynamics (CFD) simulations are performed to analyze the thermal behavior of a single electronic chip module. The chip is modeled using ANSYS SpaceClaim and analyzed in ANSYS Fluent by solving governing equations for laminar viscous flow through a channel with obstruction.
Fabrication of MgO nano particles by Solution Combustion Process
In current industry scenario, it is very difficult to understand the behavior of MgO nano particles deposited on Mild steel by any process. Challenge to design the suitable material for various industries like Automotives, ship industries, marine industries, small scale industries and petro chemical industries.
Synthesis and Characterization of ZnO Nano Particles by Sol gel method
Now-a-days, it is very difficult to understand the behavior of ZnO nano particles deposited on Mild steel by any process. Challenge to design the suitable material for various industries like ship industries, marine industries and petro chemical industries
Synthesis and Characterization of ZnO Nano Particles by Solution Combustion Method
Exploring the Mechanical Properties of mild steel by depositing metal oxide nano particles during Arc Welding process
Conventional welding techniques are used to join the conductor metals like copper is a very tedious and costly task as it tends to form oxides during welding which gives a poor weld strength which is not feasible. Alternative means of welding like Arc welding has established itself across a wide range of industries as a cost- effective method for welding these kinds of metals. Welding is commonly used in both automotive and aerospace industries. The objective of this work is to study the strength of the metal before welding and after welding by using different electrodes and depositing metal oxide nano particles during arc welding process. The material chosen for this process is copper of 1.5 mm thickness, mild steel of 4 mm and 5 mm thickness for welding purpose. Hardness of the welded area will be calculated by using Rockwell Hardness Test. Microstructures of the welded area were done by using optical microscope.
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
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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
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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.