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Carbon monoxide is one of the most dangerous gas affecting the people travelling in the automobile vehicles with the permissible exposure level of 35ppm. In order to reduce the mortality rate due to Carbon monoxide poisoning, we could implement a system to detect the increase in the intensity level of carbon monoxide inside the cars cabin with the help of carbon monoxide gas sensor. The circuit is constructed using Raspberry Pi and MQ-7 gas sensor. If the Carbon monoxide level reaches 400 parts per million and above, the system could automatically switch off the air conditioner and turn on the fan to control the intensity level of carbon monoxide gas inside the cabin to the permissible exposure level. [ABSTRACT FROM AUTHOR]

To assess water quality standards using a Novel Spatial Analysis approach at the catchment basin level and analyze water pollution while identifying the key pollutant parameters, in order to highlight areas of high pollution risk within the catchment basin. The data were analysed using GIS software while taking into account various chemical factors such as Chemical Oxygen Demand (COD), Total Nitrogen (TN), Total Phosphorus (TP), Nitrate nitrogen (NO3-N), and Nitrite nitrogen (NO2-N). To better understand this, 36 water samples from 16 zones were collected from Karani Mitta Lake Andhra Pradesh, India. To assess the level of water quality, single factor pollution index and comprehensive pollution index approaches have been used, together with the Geographic information system (GIS). A comparison of drinking and irrigation standards was conducted using SPSS software on the mentioned parameters. This illustrates the typical water quality in watersheds that are impacted by different parameters, highlighting the presence of pollutants such as COD, total phosphorus, total nitrogen, and nitrite nitrogen, which exceed standard class III levels at 17, 5.65, 0.140, 0.460, and 0.030, respectively, in mg/L. Drinking water standards are set at lower levels of 10, 1, 0.05, 0.5, 1, and 10 mg/L, while wastewater standards are higher at 20, 2, 0.5, 1, 45, and 20 mg/L. The analysis was conducted with a G-force of 80% and a 95% confidence interval. The SPSS test resulted in a significance value of 0.000 (p<0.05), indicating a significant difference between the two groups compared in this study. The contamination levels within the catchment basin are correlated with a novel spatial analysis technique, which provides valuable information for effective control and treatment strategies to manage contamination in the catchment basin. [ABSTRACT FROM AUTHOR]

Acoustic attenuations coefficients divided by frequency square (α /f2) have been computed for both longitudinal and shear waves propagating along the (100) axis and polarised along (110) direction in the Rubidium halide crystals (RbCl, RbBr and RbI), which are dielectric in nature. These attenuation coefficients are determined for a wide range of temperature between 100 – 400 K. Initially, Second order and Third order elastic constants (SOEC and TOEC) of these dielectric crystals were determined by using the Brugger's definitions for elastic constants and using these the Gruneisen parameters γ i j and 〈 γ i j 〉 were found out. Temperature dependent non-linearity parameter DL and DS, for the longitudinal and shear waves were determined with the help of Gruneisen parameters. The computed attenuation coefficients for the Rubidium halide dielectric crystal are found to be temperature dependent. Attenuation of high frequency acoustic waves is found to have maximum value for RbI crystal and minimum value for RbCl crystal, while, for RbBr crystal it is intermediate between the above two values. Phonon-Phonon interaction is the most prominent cause of the acoustic attenuation of the high frequency waves propagating through the crystals. Longitudinal waves are more attenuated as compared to shear waves. [ABSTRACT FROM AUTHOR]

High frequency acoustic attenuations have been calculated for dielectric crystals of Potassium Halides (KCl, KBr and KI) for a wide range of temperature from 100 – 500 K. Second order and Third order elastic constants of these dielectric crystals are used to obtain the temperature dependent values of longitudinal wave velocity (VL), shear wave velocity (VS), non-linearity parameter DL and DS, thermal relaxation time τ, energy density E0, and using them the attenuation coefficients are calculated. Attenuation in dielectric crystal is found to be temperature dependent. Attenuation of high frequency acoustic waves is found to have maximum value for KI crystal and minimum value for KCl crystal, while, for KBr crystal it is intermediate between the above two values. Phonon-Phonon interaction is the most prominent cause of the acoustic attenuation of the high frequency waves propagating through the crystals. Shear waves are more attenuated as compared to longitudinal waves. [ABSTRACT FROM AUTHOR]

QR codes is the type of matrix bar code. It was invented by Denso Wave in japan.QR code,Due to its quick readability and bigger storage capacity than traditional UPC barcodes. This essay explores the fundamentals of QR codes as well as how they used in daily life. Shopping centres are expanding in size and variety,carrying a bigger variety of goods. To reduce the complexity of shopping frameworks,and various shopping guide mailings, and To make Shopping for customers more convenient,the system has developed.he creation of an easy, quick, and convenient shopping system is a concern for both retailers and customers. Additionally, new approach corrects a number of flaws in the old system. This system generates QR codes for each product using a QR code generator. The test's findings are implemented in a Python software that uses a camera to read QR codes and create a bill for purchases. [ABSTRACT FROM AUTHOR]

The effect of surfactant on the sensitivity of polymer-based heterojunction has been studied at room temperature. To fabricate heterojunction,polypyrrole (thickness∼3.8 µm) were deposited by galvanostatic electrodeposition on stainless steel substrate (SS). An electrochemical bath contains pyrrole monomer with surfactant like Triton-X 100 (TX-100) and having fixed concentration 0.001M. In order to make heterojunction polyaniline films has been deposited using layer by layer chemical oxidation process on the pre-deposited polypyrrole films. Layer by layer process was used to attain maximum thickness 0.72 µm of polyaniline films. Individual structural, optical, electrical, surface morphological properties was studied by X-Ray Diffraction, FTIR, UV-Visible spectroscopy, Electrical conductivity, FE-SEM, and Contact angle measurementfor Polypyrrole (with and without surfactant) and polyanilinefilms of maximum thickness. Fabricate heterojunction and named it as PANI/PPy-NS, PANI/PPy-TX. It was used to study sensing performance LPG at ambient conditions. Different concentration of LPG is passing in the chamber and measured Current (I)-Voltage (V) characteristics at room temperature. Sensitivity (%) of heterojunction sensor is studying in air and LPG environment. A response and recovery time is measured for maximum sensitive heterojunction i.e.PANI/PPy-TX. The average maximum sensitivity is about<43% for 1040 ppm concentration of LPG. 80 sec and 500sec time is for response and recovery respectively. [ABSTRACT FROM AUTHOR]

Adhesive bonded joints are extensively used in the aerospace sector as compared to other joining techniques. In this paper, experimental and numerical analysis of single lap-jointed samples has been carried out with two different adherents such as Al-Al and Carbon fiber reinforced polymer(CFRP-CFRP). The shear strength of AV138/HY998 adhesive is estimated with Al-Al adherents and CFRP-CFRP adherents. Further, the adhesive fracture toughness is estimated experimentally and numerically. To estimate mode I fracture toughness double cantilever specimens are made as per the standards and tested under tensile load. The opening mode fracture toughness is estimated using simple beam theory and the Virtual crack closure (VCCT) technique. [ABSTRACT FROM AUTHOR]

This research presents a real-time method for detecting screen damage on mobile devices using accelerometer data without the use of machine learning. The method utilizes a continuous stream of accelerometer readings to detect changes in the device's motion that indicate screen damage. The proposed system can detect screen damage in real-time, as it occurs, providing an immediate alert to the user. The system was tested on a dataset of real-world devices and found to be highly effective in detecting screen damage with minimal false alarms and without the need for machine learning models. [ABSTRACT FROM AUTHOR]

This study aimed to compare the performance of various existing dryers. The present analysis was based on several parameters, including Build ease, upfront cost, portability, mobility, and thermal characteristics are all important considerations and other parameters that are also essential in determining components of excellence of the dried product. The utilization of photovoltaic panels as a source of electricity for running electrical components has been well established. The factors examined included climatic factors, geometry, lights, heat exchangers, heat pumps, air circulation modes, and changeable-phase materials. The results obtained from the study revealed that all of these factors played a significant role in determining the efficiency of the solar drying system. This combination of technologies has shown promise in providing a sustainable and reliable energy source for various applications. As such, the use of solar dryers based on the convection mechanism holds great promise for sustainable and affordable drying applications. Incorporating the convection mechanism into the existing array of solar dryers is believed to contribute to advancing and unifying solar drying technology. [ABSTRACT FROM AUTHOR]

In today's world which is digitizing increasingly, facial expressions serve as one of the vital indicators of variety of human emotions. With the advancement of technologies like Artificial Intelligence (AI) systems, even machines can process and understand these facial expressions with wide number of applications across various domains. However, the challenge still lies in accounting the human diversity, which leads to distinct characteristics for each individual. And an attempt of creating a single model to fit all such individuals becomes nearly impractical. To tackle this challenge, an organized approach based on inter country facial distinctions could be a feasible solution. This approach works better within the specific country, but the universality is also required which is again a very big challenge. For this as a solution a collaborative methodology is being proposed. The aim of this paper is to prove that it is possible to create a universal model for detecting facial expressions with higher precision and accuracy by combining various datasets from different nationalities to obtain a single dataset with uniform distribution. This paper does so by using datasets of two countries i.e., JAFFE and CK datasets and generating a convolutional neural network-based model by optimizing the trade-offs between model complexity, training time, and required computational resources. [ABSTRACT FROM AUTHOR]

The authors have crafted a comprehensive three step blueprint for planned marketing preparation that harnesses the manifold advantages of artificial intelligence (AI). This framework encompasses three facets of AI: motorized AI for automating boringadvertising tasks, rational AI for data analysis leading to choice making and examining human interactions and emotions. AI plays a pivotal role in this context, involving a strategic integration of AI across various facets, such as market research, strategy development (including subdivision, directing, and positioning, commonly known as STP), and the execution of actionable plans. In the marketing study phase, powered AI handles data collection, rational AI drives market analysis, and feeling AI fosters a deep understanding of customer behavior. Moving into the marketing strategy (STP) phase, mechanical AI assists in segment credit, rational AI provides segment targeting recommendations, and feeling AI contributes to resonating with the positioned segments. Transitioning to the advertisingact stage, motorized AI streamlines standardization processes, thinking AI enhances personalization, and sensation AI enriches efforts towards renationalization. [ABSTRACT FROM AUTHOR]

The proper segmentation of brain tumors is essential in medical image analysis field. Convolutional neural networks (CNNs), which make up the majority of deep learning approaches, have demonstrated encouraging results in automating this process. This research paper presents a novel two-step approach for brain segmenting and detecting tumors. In the first step, we employ a DenseNet121 model with transfer learning, fine-tuning the pre-trained weights obtained from the Image Net dataset, to recognize tumors in the brain. The second step involves utilizing a ResUnet model, which combines the strengths of ResNet50 and Unet models, for accurate segmentation. To validate our approach, we conduct experiments on "The Cancer Imaging account (TCIA)'s dataset "The Cancer Genome Atlas (TCGA)" and compare the results with other models of deep learning such as K-means clustering and VGG16. Our experimental findings demonstrate that our proposed approach achieves superior accuracy and efficiency compared to the alternative models. [ABSTRACT FROM AUTHOR]

Due to its relatively cheap production costs, India became a popular sourcing hub for multinational original equipment manufacturers (OEMs) after globalisation. This influx of capital boosted India's automotive sector. In order to become a part of this worldwide supply chain, Indian auto component makers embraced TQM (Total Quality Management) principles and earned a number of quality certifications, including as ISO 9000, QS 9000, TS 16949, and the Deming award. Even though there is a lot of study on TQM deployment, it doesn't include the key success factors (CSFs) of TQM in the Indian car sector. We don't know how well TQM is implemented in certified and non-certified car component manufacturers. A study instrument was developed using the ten dimensions as necessary success elements and the four dimensions as results. Critical success elements were tested in practice using confirmatory factor analysis (CFA), which was based on theoretical research. As part of a cross-sectional field research, data was collected from 135 certified organisations and 97 non-certified enterprises using a standardised questionnaire. A comparison between certified and non-certified organisations revealed a notable disparity in TQM adoption across all critical success criteria and outcomes. The accredited organisations' superior product design and supplier management helped explain the variation in TQM adoption. [ABSTRACT FROM AUTHOR]

The present study proposes to design, fabricate and test the solid rocket thruster using the KNSU propellant consisting of Potassium Nitrate as the Oxidizer and Sucrose as the fuel. Basic grain geometry like an internal burning tube is chosen to simplify the analysis. This study consists of the Combustion chamber and Nozzle design. Hereby, the thickness and geometry of the chamber is based on the maximum pressure and quantity of the propellant respectively. Nozzle design is based on the pressure ratio of 30 between the chamber and exit. The design of the nozzle is further verified using the Ansys Fluent software. With the above geometry of propellant and nozzle design, the thrust calculated will be around 10N. Fabrication is done using the stainless steel as the material due its properties of availability and higher melting points. The results obtained in the experimental phase are compared with theoretical calculations. [ABSTRACT FROM AUTHOR]

A numerical model is developed for calculating the optical properties of amorphous silicon (a-Si:H) thin films from the spectral response of optical transmission of the films. The method includes predicting the envelopes of upper and lower values of the fringes in transmission spectra, and numerically calculating the film thickness and the spectral responses of refractive index, absorption, and extinction coefficient, which are key optical parameters of the thin film. The procedure was employed on various a-Si:H thin films and obtained reliable accuracy in calculated physical and optical parameters above 98% of the values obtained from spectroscopy ellipsometer technique. Further, the optical model results were used to simulate a-Si:H based solar cell using SCAPS-1D and were in close agreement with the experimental results. [ABSTRACT FROM AUTHOR]

Optoelectronic devices often use external light manipulating optical components which improve the performance of optoelectronic devices without affecting the internal characteristics of the device. In this work, two types of single-unit external light-trapping concentrators are designed using TracePro®, for use in micro-concentrator photovoltaic devices to improve the incident light intensity. The compound concentrator consists of a single standalone light-trapping concentrator placed on top of an optical cage and was designed to be positioned over an individual photovoltaic (PV) cell of < 1 mm dimension. Two types of concentrators have been selected for the investigative studies: hemispherical reflective concentrators and parabolic reflective concentrators. The work also explores the effect of the opening radius of the concentrator that opens to the optical cage. A significantly high light concentration up to ~100X was obtained for the parabolic concentrator. For the PV module type arrangements, where multiple solar cells are connected in series as a strip, the concentrator designs are chosen to be trough type. In trough concentrator case, two designs of cross-sectional shapes were studied: semi-circular and parabolic. The parabolic shaped cross-section of trough concentrator yields ~3 times the light concentration compared with that of the semicylindrical trough concentrator. [ABSTRACT FROM AUTHOR]

In general, the phrase "parking operation system" refers to the specially designed, vicious systems that are mounted in buildings and promenades. The goal of the paper is to create an across-platform smart parking system that will lessen parking issues by giving drivers more time to search for open spaces in parking lots and by providing useful parking space applications. Numerous studies have shown how the smart parking system reduces business issues, promotes corporate expansion, and results in lucrative improvements in that community. However, there are many locations where such priceless outcomes cannot be deployed for a variety of colourful reasons, such as setup requirements and cost. In order to explain empty parking spaces in our council district where automated systems aren't established, this design focuses on creating a parking operation system based on object detection. The camera photos of the parking area are subjected to an object discovery algorithm, which uses the information about the residents to identify virtual locations in the area and direct arriving drivers to available spots based on their location. Reduced complexity machine learning model is used in a smart parking system that comprises of a camera linked to a mobile application that uses cloud computing to identify open spots. YOLOv5 and the real-time custom data set are used to train and evaluate the model. In addition, reserved spaces will be shown via IoT using information retrieved from the model. Cloud services will be used for the project's deployment. [ABSTRACT FROM AUTHOR]

Diabetes is linked to an increased susceptibility to various health complications, encompassing heart disease, kidney issues, strokes, ocular impairments, and nerve afflictions. The current healthcare practice revolves around the execution of multiple diagnostic tests to procure the requisite information for diagnosing diabetes and consequently devising treatment plans based on these diagnoses. The healthcare sector gains significantly from applying Big Data Analytics, offering the capability to dissect vast and intricate datasets to unveil concealed patterns, insights, and knowledge that can ultimately facilitate precise predictive outcomes. In this research endeavour, we focus on the comprehensive analysis of the Pima Indian diabetes database sourced from the UCI repository. Our primary objective is to fashion a robust and efficient model for predicting and diagnosing diabetes. To enhance the accuracy of our model, we integrate the bootstrapping resampling technique and meticulously assess the performance of a range of machine learning algorithms. These encompass the Random Forest, Decision Trees, K-Nearest Neighbours (KNN), Support Vector Machine (SVM), and Artificial Neural Network (ANN). Through these exhaustive analyses, we aspire to pinpoint the algorithm that offers the utmost prediction accuracy for diabetes diagnosis. [ABSTRACT FROM AUTHOR]

The goal of VLSI design is to increase speed while lowering space, power, and delay requirements. One of the key topics of research in VLSI system design is the development of a digital adder with the best possible area and speed. An arithmetic logic unit serves as the fundamental component of microprocessors, microcontrollers, and digital signal processors (ALU). The adder design of an ALU determines its performance. The Carry Propagation Delay (CPD), area, and power are crucial parameters for the adder's structure. The carry choose strategy has been identified as a good cost, performance trade-off in the design of carry propagation adders. However, the conventional carry select adder (CSLA) still takes up a lot of room because of the twin ripple carry adder structure. Numerous authors put up various ideas to lessen the carry select adder's area, power and delay. In this article, we'll contrast the solutions and conclusions of those authors. [ABSTRACT FROM AUTHOR]

In this research, the focus is on creating and producing a sweep-back parachute, which is characterized by a curved leading edge that enhances stability and maneuverability while descending. The process involves developing a mathematical model for the parachute's structure, examining the aerodynamic forces exerted on it, and improving its performance by evaluating the time of flight and glide angle by drop testing. The final product is constructed using top-quality materials and subjected to tests to assess its stability, glide ratio, and deployment time. The findings of this study offer valuable insights into the production and design of sweep-back parachutes, which may have potential applications in various sectors, including military, aviation, and extreme sports. [ABSTRACT FROM AUTHOR]

Mathematical programming techniques determine the optimal functional configuration of a Wireless Sensor Network (WSN). However, the high computational complexity of these techniques has been regarded as a Non-Polynomial problem. Hence, Machine Learning (ML) approaches are used for WSN parameter prediction with improved accuracy and less computing complexity. This study aims to predict the reliability of clustering algorithms in WSNs using supervised ML classification approaches. ML algorithms, namely, decision tree and K-nearest neighbor classifications are implemented on the obtained dataset to predict the reliability of clustering in WSNs on the basis of high packet delivery ratio, low delay, and high residual energy. The reliability parameter accepts binary values, where zero represents the absence of reliability and one represents the presence of reliability metrics. Then the predictions from each classifier are compared with one another. The results show that the accuracy rate of the decision tree is 97.98% and the error rate is 2.02% which shows that the decision tree performs better than the K-nearest neighbor in predicting the reliability of the proposed approach. [ABSTRACT FROM AUTHOR]

Direct Current (DC) systems are in some instances preferred for electricity transmission due to having higher efficiency than Alternating Current (AC) systems, which also have certain interoperability constraints. These constraints include inability of synchronization of AC systems operating at different frequencies. In High Voltage DC (HVDC) systems, electricity is majorly generated as AC, then transmitted over long distances as DC to minimize losses, and is finally reconverted into AC for consumer loads. This paper proposes an HVDC-based power flow method with various power set points and load flow equations with the priority being accurate modeling of control techniques for control of the power converters. The numerical methodology relies on the fast, yet accurate converging of the Newton-Raphson method (NRM) in solving complex synchronized high-voltage AC and DC networks, with power generation and operation disparities put into consideration. A five-bus network is used to illustrate the proposed method and active and reactive power flows compared with and without the HVDC. Greater power transfer capabilities are registered with HVDC implementation. [ABSTRACT FROM AUTHOR]

In this work, it was selected to focus on the development of multi-element organometallic precursors in the Si/C/Zr system. In addition, various polymer structures have been considered to anticipate the shape controls of these precursors and to modify the chemical composition of the final ceramics. Furthermore, the laser pyrolysis spray process was selected to integrate the micro phase sequence powders to gain access to the microstructure of the final compound material. In order to evaluate the compatibility of the original precursors with this formulation process, this work will take up the spray pyrolysis tests. [ABSTRACT FROM AUTHOR]

In this study, multiple estimating methods for longitudinal wave displacements caused by critically refracted waves in a material are compared. When evaluating the residual stress in mechanical items, it is important to determine the precise depth of the measurement. Due to an acoustoelastic effect, longitudinal waves that have been critically refracted are used to quantify residual stress. In this work, we model the displacement of the LCR wave in the material being tested. With the aid of wave propagation equations and the basic principles of the acoustoelastic effect, a finite element model was created. On test material, many cases with various slot depths are created. To learn more about wave travel at different depths, simulations are run both with and without slots. When the slots were created below the frequency's wave propagation limit, they operated as barriers in the way of the waves. To verify the modelling and experiment results, ultrasonic transducers with frequencies of 1MHz, 2MHz, 3MHz, 4MHz, 5MHz, and 10MHz with a 6mm diameter were used. The findings of the tests and numerical simulation vary with a 1.35 percent inaccuracy. The approach employed in this study is easily adaptable to a variety of materials used in industrial applications. [ABSTRACT FROM AUTHOR]

The paper "IOT WEATHER DATA ACQUISITION AND AUTOMATION SYSTEM" deals with presenting a smart device in terms of real-time accumulation of data and automation. The core intention is to make life easier and hassle-free. From agriculture drones to the defense, real-time data is feeding in every sector of industry to make everything automated, more susceptible, and sustainable. Data can be further studied to bring out meaningful insights from them. Right on the track, the prototype is capable of visualization of data through the internet on many platforms in the form of GUI (Graphical User Interface). This prototype uses some sensors like DHT11, MQ135, BMP280, Soil moisture, and Flame sensor. 38% Humidity, 32.3 Temperature reading provide by the DHT11 during the test. BMP280 sensor shows their readings in graphical format. 9% moisture present in the soil, noted during the test through the soil moisture sensor. 8% of Hard Particles are present in the atmosphere and the Lux value was 89.167 noted during the test with the help of MQ135 and Light Intensity sensor. A flame sensor also used in the prototype gives the warning. When it senses the flame, and it increases the safety. All data are represented through interactive graphs and plots that are easy to interpret. In addition, this data is stored in the cloud platform. Which are further fetched into platforms like MATLAB, Simulink, and excel sheets for further analysis. Simply connecting to the internet, can get their data from anywhere in the world through the API ports and are supported in many channels like Thing Speak and Arduino IoT cloud. [ABSTRACT FROM AUTHOR]

This paper is about the design and fabrication of a pick and place robot which can load and unload a workpiece from and to a shaping machine. In addition to this, the vice of the shaping machine will also be tightened and loosened after loading and unloading of the workpiece. The machine is therefore made into two units. The first unit consists of a motor driven by a chain and sprocket drive which will help in performing the tightening/ loosening of the vice after the placement/ before removal of the workpiece. The second unit consists of a robotic arm which can perform the loading and unloading operation. Thus, the coordinated working of the two units results in the process sequence of loading and unloading components getting automated. [ABSTRACT FROM AUTHOR]

Construction companies generate non-biodegradable waste that harms the environment. However, incorporating this waste into concrete can support sustainable and eco-friendly construction practices. The present investigations involve the partial replacement of binder materials such as cement with marble dust powder (MDP), fine aggregate with copper slag (CPS), and coarse aggregate with recycled aggregate concrete (RAC), when substituted for coarse aggregate, to analyze concrete mechanical properties. A constant percentage of marble dust powder was added to replace the binder material (cement). Additionally, copper slag replaced 10%, 20%, and 30% of the fine aggregate, and recycled aggregate replaced 10%, 20%, and 30% of the coarse aggregate. According to the M-30 standard, water/cement ratios in concrete mixtures were kept at 0.4. Superplasticizers reduce water content and provide better strength. The compressive, tensile, and flexural strengths of specimens were evaluated for (7, 14, and 28) days after cure, respectively. These results indicate that incorporating 10% marble dust powder, 30% copper slag, and 10% recycled aggregate into concrete mixes yielded the optimum compression, tensile, and flexural strength after 28 days, at 34.33 N/mm2, 3.65 N/mm2, and 5.68 N/mm2, respectively, which displayed 10.27%, 17.74%, and 14.05% strength improvement from the control specimen. These additives can improve concrete's mechanical characteristics. Thus, these materials have the potential to serve as alternatives to conventional construction materials through partial replacement. [ABSTRACT FROM AUTHOR]

Applications of deep learning to solve complex problems related to computer vision and human level control system has been successfully implemented in real life. On the other hand, deep leaning based systems can also cause harm to the privacy and security of the personnel or a system. One of the threats that is powered by deep learning and AI is deep fake technique which is used to create fake images, video and audios that are not identified by human visual or audio system. This makes it difficult for human to distinguish between the original data and the deep fake data. This paper aims at discussing the techniques or algorithm require to create deepfake and to detect the deepfake in audiodata. [ABSTRACT FROM AUTHOR]

In modern engineering field, data modelling and prediction capability of different techniques are to be evaluated in order to find the most accurate technique. In this work, the prediction capability of RSM (Response Surface Methodology), ANN (Artificial Neural Networks), ANFIS (Adaptive Neuro Fuzzy Inference System) have been compared in case of dry machining of Ti-6Al-4V Alloy. Ti-6Al-4V being one of the difficult to machine material, the machining of such alloys becomes necessary for the aerospace/manufacturing industries. Total 15 experimental runs were finalized and performed by using Response Surface Methodology (Box-Behnken Design). The machining was carried out using carbide inserts and the output cutting forces are measured using Kistler Dynamometer. The above mentioned three techniques have been employed in prediction of cutting force and all had a good predicting capability. However, ANN showed good results than RSM and ANFIS technique. Percentage of error in prediction for RSM, ANFIS and ANN are 10.10, 4.56 and 2.56 respectively. The R2 values of RSM, ANFIS and ANN are 93.86, 95.96, 97.91 respectively. The performance of ANN also depends on the algorithm and parameters used for training. [ABSTRACT FROM AUTHOR]

In the present time, the demand of energy is increasing rapidly. The heavy demand of energy is in the smart cities domain as there are number of sub domains like Smart Homes, Smart Transportation, Smart Healthcare, etc. So, as the result the domain is opening the new research direction for the industrialist, researchers, and scientists. They are attracting towards the domain of smart city for different kinds of research projects. The primary concern of this paper is to focus on the energy consumption of smart homes. In this paper different Machine Learning (ML) and Deep Learning (DL) models are being implemented and the results are analyzed to check the performance of the different models on the dataset of smart home energy consumption. This study found that the Random Forest is predicting the energy consumption with lowest error with 0.6616, 0.4377, 0.3171 RMSE, MSE, and MAE respectively. The performance of the models is being evaluated in the terms of Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE). This study found that the most suited model for energy consumption data in smart city is Random Forest. [ABSTRACT FROM AUTHOR]

Several fuzzy time-series forecasting (FTSF) models have been created throughout the years to deal with the indeterminism and uncertainty in time-series data. In this work, the inherited uncertainty of a time series dataset is represented with three distinct memberships namely, truth, false and indeterminacy, and the approach is known as neutrosophic set (NS) theory. Also, the performance of the forecasting models are highly dependent on the appropriate selection of the number of divisions of universe of discourse (UOD). So, the study adopts a completely data-driven partitioning technique to obtain the optimal number of intervals thus making the model more reliable. The forecasting is made based on a simple arithmetic rule using the score function of the neutrosophic elements which makes the model user-friendly and avoids the complicated computations. To demonstrate the usefulness and applicability of the proposed forecasting approach, it is applied to the University of Alabama time series dataset. The model's performance is assessed using Root mean square error (RMSE), and its low value reflects the model's superiority over other previously existing methodologies. [ABSTRACT FROM AUTHOR]

Friction stir welding (FSW) is regarded as one the energy efficient solid state welding process. FSW is used to produce permanent joints for aluminium alloy, magnesium, copper and many more with numerous advantages over fusion welding processes. It possesses many characteristics which motivates researchers to treasure out the avenues of enhancements in the process. In FSW mechanical and metallurgical changes takes place due to temperature variations during welding. The temperature variations are the main reason of weld quality and weld defects. If the temperature variations can be monitored and record, the weld quality and weld defect can also be controlled to some extent. Infrared thermography (IRT) has become one of the most extensively recognized tools for condition monitoring. It is a real time non-contact type of temperature measurement system. The intention of this is to provide a comprehensive review of the research work undertaken in the field of condition monitoring of welding by infrared thermography. An attempt is made to review the efficacy of Infrared Thermography for Friction Stir Welding so as to understand the relationship between heat generation and weld quality. [ABSTRACT FROM AUTHOR]

The world is gradually becoming digital after the boom of the Internet. Technology has now added a new dimension of the Internet in every business. With the Internet and enhancement in mobile cameras, any news clip can go viral. But unfortunately the formal reporting & coverage of the same can't be done instantaneously. The entire workflow of online News Publication is either manual or semi-automatic including Scripting, Recording, Editing, Mixing and Publishing. The proposed software bot solves this issue by producing News Bulletin-like videos right from scratch and publishes them online on its own. [ABSTRACT FROM AUTHOR]

Football is a famous sport played around the world that involves various actions from defending to attacking the ball to score goals. Various rules like half-side, No hands, Throw-ins Direct and Indirect kicks, etc. are followed. In a game, players might get yellow-cards and red-cards which serves as warning and elimination from that particular game. There are-many factors like shots, possession, pass accuracy, yellow and red cards, etc. that decides which team wins in a match. In the data that we worked on,the Rating is calculated from the parameters which are mentioned. Machine learning regression algorithms have been applied to predict the Rating feature. The goal of this paper is to know which regression algorithm performs better to predict the Rating variable. Evaluation metrics like Root mean square error (RMSE), Mean absolute error (MAE), and R square (R2) are used to identify the best algorithm among the applied algorithms. [ABSTRACT FROM AUTHOR]

Heart diseases are very dangerous for humans, as these are very serious and can even lead to the death of the patient. In today's world, Machine Learning has a vital role in bioscience as well. This paper presents a comparative analysis to detect cardiovascular disease using average Ensemble Learning, Random Forest and extreme Gradient Boosting Algorithms. The Normalization method is used to preprocess the used dataset and then all the techniques mentioned above are used to train the model. By using all three techniques/ methods accuracy, recall, precision, ROC, and F-measure, are evaluated. Boosting ensemble learning method performed best among all with an exactness/accuracy score of 91.2%. [ABSTRACT FROM AUTHOR]

This paper proposes an integrated vision based obstacle detection algorithm based on Background subtraction technique, optical flow, Grey Level Co-occurrence Matrix features, Color Histogram Features and Hu Moments for Micro Aerial vehicles (MAVs). Raspberry Pi 3 camera module is mounted on the MAV to acquire the image frames with obstacles. Obstacle detection algorithm is developed using Python programming language. In the feature extraction phase, Haralick Features, Color Histogram Features and Hu Moments Features were extracted from the training image frames. The extracted features are used to train the SVM classifier. In the testing phase, trained object recognition model is used to detect the object in the test image frame. Support vector machines with various kernels such as the linear kernel (LSVM), Gaussian kernel SVM (GSVM), Sigmoid kernel SVM (SSVM) and Polynomial kernel SVM (PSVM) have been employed. Experimental results demonstrate that the SVM with gaussian kernel-based yields the highest accuracy for the classification of object class and non-object class from the testing images. Finally, dynamic obstacles are detected by using the Optical Flow Lukas Kanade tracking method. [ABSTRACT FROM AUTHOR]

In recent years, a boom in the use of multi-purpose robots has been observed. This has increased the need for developing stable and optimized All-Terrain Robots. These robots have excellent off-roading capabilities that can be useful for disaster relief operations, extra-terrestrial applications, commercial mining activities and safe agricultural practices. The designs for All-Terrain Robots proposed in the literature reports that the eccentric loads used for suspension can deteriorate the elasticity of the spring, also these robots are prone to punctures. Moreover, there is a scope to improve the gripping capabilities, and longevity, to extend the applications and stability of these robots. These challenges motivated the authors to develop a novel design of an all-terrain multipurpose robot for maneuvering through rugged terrain. In this design, the authors investigated multiple suspension systems, such as Rocker-Bogie, CRAB and RCL-e and selected the appropriate suspension mechanism for smooth and stable rover traversal. They also focused on choosing the tires for providing the maximum gripping capabilities, longevity, and resistance to punctures. The authors implemented an electronic subsystem to pilot the robot for maneuvering through rough, slippery, and sloppy terrains by optimizing the motor's torque and rotational speed. They also implemented a hardware-based architecture that includes embedded systems, sensors, circuits, motors, and power distribution for a six-wheeled ATR to visualize safe and efficient connections. Now, the authors evaluated the performance of the proposed robot in terms of vertical drop, stress-strain tests and stability in a simulated environment. Based on the simulation results, they demonstrate the supremacy of the proposed design over the existing designs of robots. [ABSTRACT FROM AUTHOR]

For current work, numerical analysis was carried out for one of the sites in southwest zone of Gujarat. The structure consists of G.F.+ four storey building with 11 m x 21 m footprint. The foundation consists of three rafts of equivalent thickness. For sake of simplicity, the analysis was restricted to one of the rafts supported by under reamed piles. The geometry and dimensions of the under reamed pile were kept as per relevant Indian standard code. Comparison has been made to understand the settlement response for various pile group combination. The FE model consist of (a) 7 x 2, (b) 6 x 2, (c) 5 x 2, (d) 4 x 2, (e) 3 x 2 under reamed pile group with equal height and diameter as well as uniform raft thickness. The soil data where estimated based on the observed SPT N recorded through three boreholes. The results show significant decrease in settlement value was observed for under reamed pile configuration when compared to conventional pile group. [ABSTRACT FROM AUTHOR]

Development of communication technologies has emerged in increasing Wireless Body Area Networks (WBAN) with tiny sensors implanted on human body to monitor vitals regularly. Due to its unique property of less distance communication range and less energy consumption of the nodes leads to the design of WBAN scenarios. Many researchers used conventional and soft computing techniques in order to route the data from patients's device to the end of device i.e. base station without any fault tolerance. In this paper, a hybrid combination of Adaptive Neuro Fuzzy Inference System (ANFIS) and cluster based ANT colony algorithm is proposed in order to determine the link quality, energy of the node and cost function by using the ANFIS technique. The simulation of the routing protocols were done using OMNeT++ and the performance is compared with the hybrid combination of ANFIS and cluster based ANT colony with existing protocols and the performance is compared with the hybrid combination of ANFIS and cluster based ANT colony with existing protocols for routing the data from one device to another device. The proposed hybrid routing protocol algorithm has better performance in network life time and latency. [ABSTRACT FROM AUTHOR]

A critical phase of drug development is determining drug-target interactions (DTIs) and drug repositioning. Discovery of potential processes is necessary because current antibiotic treatments are becoming less effective. Even though traditional in-vivo or in-vitro procedures are still being worked on, financial investment in pharmaceuticals has declined over time. Therefore, developing efficient computational techniques is essential to finding new leads for drug discovery. We propose a machine learning ensemble strategy that makes use to classify protein and drug molecule descriptors. This study demonstrates the conventional machine learning approaches such as k-nearest neighbor, support vector machine, and logistic regression and homogeneous ensemble machine learning approaches such as random forest and adaboost. The heterogeneous ensemble models that make use of all the individual classifiers and applied voting mechanism such as soft voting and hard voting approaches for generating result for the models. Experimental study shows that employing ensemble models rather than conventional machine learning classifiers. [ABSTRACT FROM AUTHOR]

Urbanization and increased population has a severe concern over the problem of waste management. The industrial growth and commercialization also causes high risk of human health due to increased amount of waste and disposal problems. Thus it is a high time to call for an effective waste management practices. Present study focuses on waste handling practices by Municipal Corporation of a Nashik city located in Maharashtra State. About 300 - 350 MT/day of waste is collected in the year 2006, however with increased population and industrialization this waste generation has increased to 400-450 MT/day in the year 2013 and presently it is 550-600 MT/day. Paper presented here includes the various methods of collection and segregation of waste. Limitations of all such practices are presented here with the suggestions of handling and controlling the ill effects of the industrial and commercial waste. It is observed that the recycling initiated by the municipal corporation like fertilizer production, energy generation, bio medical waste handlers etc. has effectively contributed for the environmental protection and human health. Compost generation data from the solid waste was collected from the municipal corporation and a neural network model was developed to predict the compost generation. The study presented here is the first part as prediction of compost generation. Overall objective is to suggest the new IoT based methodology of the waste management. From the present study it is observed that the developed neural network model has a MSE less than 1% and the Regression coefficient (R2) is 0.8788. Since accuracy of the developed ANN is good this model can then further processed for the development of the IoT based waste management process. [ABSTRACT FROM AUTHOR]

Marketing research is focusing more on consumer smartphone purchase trends. This research examines how COVID-19 and LAC concerns have affected Indian smartphone purchases of Chinese brands. We will also investigate how internal and external influences, corporate credibility, and brand attitude affect customers' decisions. Researchers analysed their data using linear regression. We observed that even when every construct, except reluctance to purchase, impacts each other favourably, i.e., when individuals believe that the Chinese smartphone brand delivers excellent features and is worth the money, these two constructions still have a positive association. Hence, when individuals think the Chinese smartphone brand has excellent features and is worth the money, these two constructions are positively related. Due to their nationalism and desire to protect their country, they will continue to deny it. This study will help smartphone firms evaluate the Indian consumer market and other commercial matters. [ABSTRACT FROM AUTHOR]

A solar trough collector is efficient solar energy harnessing equipment towards green energy sources. Parabolic Trough Collectors (PTC) are power generation systems that sometimes work in tandem with other conventional forms of energy, such as coal-fired thermal power plants, effectively reducing the load on an individual system. A PTC itself has various ways of power generation, among the more commonly used methods being Direct Steam Generation (DSG). The present work focuses on improving the performance and quality of Parabolic Trough Collectors using parameters defined after thorough reviewing, with the ending objective being to maximize the efficiency of heating, increase the longevity of the components, and optimizes the heating conditions by virtue of design. Results indicated that the maximum temperature increases by 26.5 K, 29.26K, 32.92 K for single, two, and three finned bodies for 300 K inlet temperature and 2 m/s velocity. It was found during the course of this investigation that the implementation of three fins is the optimal choice for maximizing the thermal efficiency and an R squared value of 98.79%. Furthermore, it has also been observed that with increasing the number of fins, the cost and possibility of errors increases along with the high-pressure losses which negatively affect the system. [ABSTRACT FROM AUTHOR]

The projects focus on the design and development of novel wet curing systems for vertical concrete structures. Due to a lack of moisture in the concrete, the curing process does not take place properly in vertical structures, so this leads to reduced compressive strength development and also increased drying shrinkage due to the reduced levels of cement hydration. The chemical reaction that occurs when cement and water come into contact is known as cement hydration. This is an exothermic reaction, which means that a lot of heat is released throughout the cement hydration process. This project uses polymer lamina as a membrane, having capsules that can hold water, this membrane is placed over the concrete surfaces by providing moisture on the surface and helps in avoiding evaporation loss. This project focus on improving the concrete curing process by providing adequate moisture for the cement hydration to take place effectively and help the concrete to achieve maximum physical and chemical properties. [ABSTRACT FROM AUTHOR]

The performance assessment of students is the most important work as this is directly related to the future of the students. This research paper focuses on structural study, analysis and identification of non academic characteristics and data mining techniques, specifically classification and clustering. These are required for designing a model which collectively gives performance assessment in the form of academic and non-academic characteristics. These characteristics need to be classified separately on the basis of different subcategories in each type of characteristics and data collected from the questionnaire from the stakeholders of this model students, teachers and parents. Identifying and using classification and clustering algorithms suitable to the structure of this assessment leads to required output of the research. [ABSTRACT FROM AUTHOR]