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Performance evaluation of artificial neural networks in sustainable modelling biodiesel synthesis
Biodiesel is a characteristic and inexhaustible homegrown fuel removed from creature fats or vegetable oil and liquor through a transesterification response. The exploration work means to assess the exhibition of biodiesel blend. In this paper, biodiesel was displayed and improved by utilizing a hereditary calculation and Artificial Neural Network (ANN). In AI, hereditary calculations and counterfeit neural organizations assume a significant part in displaying biodiesel blend. To upgrade an excellent arrangement hereditary calculation was created. The mix of ANN and Genetic Algorithm gives the ideal condition as the temperature of methanol molar proportion, impetus fixation. It tentatively decides the exhibition trademark like the Coefficient of determination and Absolute Average deviation (AAD). It predicts the Fatty Acid Methyl Ester (FAME) model productively than Response Surface Methodology (RSM). The exhibition examination is reenacted and hypothetical outcomes are recorded then it is contrasted with constant information to decide the exactness of ANN. 2022 Elsevier Ltd -
Performance evaluation of diesel engine using genetic algorithm
?Abstract: Engine analysis and optimization is not a new approach to the field of automobiles. It has always been a keen focus in the research of experts domestically as well as internationally, the control of Air-Fuel Ratio (AFR) in transient operating conditions of engine. For the last few decades, the industry and economic expansion of developed countries has showed a clean increase in the vehicle production as well as transport volume. Global warming, acid rain, greenhouse effect and air pollution problems related to emission of CO2, NOx, PM, CO and unburned HC, together with the consumption of fossil fuels, unite to create serious problems at a global level. Therefore it is a research study considering all these current issues and taking it to a new level of optimization for the output of a better efficiency, better economy and less pollution. Performance of Diesel Engine is evaluated by parameters like Power, Torque and Specific Fuel Consumption. 2018, Blue Eyes Intelligence Engineering and Sciences Publication. All rights reserved. -
Performance evaluation of machinelearning techniques indiabetes prediction
Diabetes diagnosis is very important at preliminary stage rather than treatment. In todays world devices like sensors are used for detection of diabetes. Accurate classification techniques are required for automatic identification of diabetes disease. In regards to research diabetes prediction with minimal number of attributes (test parameters) is to be identified earlier research states about feature reduction but with less predictive accuracy. In this regards, this work exploits machine learning techniques(methodology) such as Logistic Regression (LR), Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF) and Neural Network (NN) with 10-fold Cross Validation (CV) for classification and prediction of diabetes with Feature Selection Methods (FSMs) using R platform. Above all models enable us to investigate the relationship between a categorical outcome and a set of explanatory variables. The experiment was conducted on PIMA Indian diabetes dataset selected from UCI machine learning repository. From the experimental results it is identified that for full set of diabetes dataset attributes, Classification Accuracy (CA) achieved was 84.25%whereas with reduced set attributes an accuracy of 85.24% is achieved using NN with 10-fold CV technique compared to others which will help in medical application to predict diabetes with minimal features. BEIESP. -
Performance evaluation of Map-reduce jar pig hive and spark with machine learning using big data
Big data is the biggest challenges as we need huge processing power system and good algorithms to make a decision. We need Hadoop environment with pig hive, machine learning and hadoopecosystem components. The data comes from industries. Many devices around us and sensor, and from social media sites. According to McKinsey There will be a shortage of 15000000 big data professionals by the end of 2020. There are lots of technologies to solve the problem of big data Storage and processing. Such technologies are Apache Hadoop, Apache Spark, Apache Kafka, and many more. Here we analyse the processing speed for the 4GB data on cloudx lab with Hadoop mapreduce with varing mappers and reducers and with pig script and Hive querries and spark environment along with machine learning technology and from the results we can say that machine learning with Hadoop will enhance the processing performance along with with spark, and also we can say that spark is better than Hadoop mapreduce pig and hive, spark with hive and machine learning will be the best performance enhanced compared with pig and hive, Hadoop mapreduce jar. Copyright 2020 Institute of Advanced Engineering and Science. All rights reserved. -
Performance evaluation of parallel genetic algorithm for brain MRI segmentation in hadoop and spark /
Indian Journal of Science and Technology, Vol.8, Issue 48, pp.1-7, ISSN: 0974-6846 (Print), 0974-5645 (Online). -
Performance Evaluation of Predicting IoT Malicious Nodes Using Machine Learning Classification Algorithms
The prediction of malicious nodes in Internet of Things (IoT) networks is crucial for enhancing network security. Malicious nodes can significantly impact network performance across various scenarios. Machine learning (ML) classification algorithms provide binary outcomes ("yes" or "no") to accurately identify these nodes. This study implements various classifier algorithms to address the problem of malicious node classification, using the SensorNetGuard dataset. The dataset, comprising 10,000 records with 21 features, was preprocessed and used to train multiple ML models, including Logistic Regression, Decision Tree, Naive Bayes, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). Performance evaluation of these models followed the ML workflow, utilizing Python libraries such as scikit-learn, Seaborn, Matplotlib, and Pandas. The results indicated that the Naive Bayes classifier outperformed others with an accuracy of 98.1%. This paper demonstrates the effectiveness of ML classifiers in detecting malicious nodes in IoT networks, providing a robust predictive model for real-time application. The SensorNetGuard dataset is available on the IEEE data port and Kaggle platform. 2024, Prof.Dr. ?skender AKKURT. All rights reserved. -
Performance evaluation of random forest with feature selection methods in prediction of diabetes
Data mining is nothing but the process of viewing data in different angle and compiling it into appropriate information. Recent improvements in the area of data mining and machine learning have empowered the research in biomedical field to improve the condition of general health care. Since the wrong classification may lead to poor prediction, there is a need to perform the better classification which further improves the prediction rate of the medical datasets. When medical data mining is applied on the medical datasets the important and difficult challenges are the classification and prediction. In this proposed work we evaluate the PIMA Indian Diabtes data set of UCI repository using machine learning algorithm like Random Forest along with feature selection methods such as forward selection and backward elimination based on entropy evaluation method using percentage split as test option. The experiment was conducted using R studio platform and we achieved classification accuracy of 84.1%. From results we can say that Random Forest predicts diabetes better than other techniques with less number of attributes so that one can avoid least important test for identifying diabetes. Copyright 2020 Institute of Advanced Engineering and Science. All rights reserved. -
Performance Evaluation of Refractory Bodies Fabricated from Composite Oxide Powders Beneficiated from Black Al-dross
Aluminum Oxide (Corundum, ?-Al2O3) and Magnesium-Aluminum Oxides (Spinel, MgAl2O4) are highly desired refractory materials due to their ability to withstand high-temperature service conditions without corroding and cracking. They are present in composite form in black Aluminum Dross (Al-dross), a hazardous industrial waste. 1 Kg batch of this composite powder was beneficiated from Al-dross to 98+% purity after removing the hazardous Aluminum Nitride (AlN) by aqueous treatment of Al-dross in an environment-friendly manner. The treated slurry was oven dried, ball milled to fine powder, hydraulically pressed, and sintered at 1500 C/6 h into solid cylinders (50 mm diameter 20 mm height). The structural phase analysis of the sintered product (refractory blocks) revealed a highly crystalline XRD pattern with peaks pertaining to only ?-Al2O3 and MgAl2O4. The blocks with Rockwell Hardness values of 4850 HRC, were subjected to thermal shock cycling by following the guidelines of IS 1528 (heat quench between 1000 C and air at ambient) which successfully withstood > 100 shock cycles without failure. SEM was employed to study the fracture surface in an as-sintered state and after thermal shock cycling, to reveal a fine-grained microstructure with clear grain boundaries in the as-sintered state to a glassy matrix with fine cracks at the end of the thermal shock cycle test. The potential for utilization of Al-dross for refractory applications was thus established. 2023 -
Performance evaluation of ternary blended geopolymer binders comprising of slag, fly ash and brick kiln rice husk ash
The use of industrial and agro-based precursor materials from local sources can achieve desirable properties for geopolymer binders, and thus realize the carbon-efficient sustainable materials in the construction industry. At the same time, the synergy between these precursors can be assessed using the multilevel material investigation, which has not been explored extensively. Moreover, there are limited studies on ternary geopolymer synthesized with rice husk ash from uncontrolled burning source such as brick kilns. Therefore, this study evaluates the performance of ternary blended geopolymer binders comprised of ground granulated blast furnace slag (GGBFS), fly ash (FLA), and brick kiln rice husk ask (BRHA), implementing the multilevel material approach. The experimental program includes assessment and comparative analyses of the properties of geopolymer binders such as setting time, flow, compressive strength, density, water absorption, and efflorescence. Additionally, X-ray diffraction (XRD) and scanning electron microscopy (SEM) analyses examine crystallographic structure and microscopic morphology of the composite binders. The initial setting time ranged from 21 min to 47 min for ternary mixes, in comparison to 21 min to 58 min for binary mixes. GGBFS significantly contribute in setting of binder due to hydration reaction and formation of C-S-H gel. The flow of ternary mixes exhibits standard deviation of 11.42 mm when compared to 20.96 mm of binary mixes. Lower dispersion in flow values suggests improved coaction between GGBFS, FLA, and BRHA. The compressive strength of ternary mixes improved when compared to the binary mixes. The optimum performance of 60 MPa was obtained for G60A40F95R5, which was 25% and 66.67% higher than binary mixes G60F40 and G60R40, respectively. Similarly, ternary mix G70A30F95R5 showed the least water absorption of 2.08% which was 53% and 58.4% lower than the binary mixes G70F30 and G70R30, respectively. The improvement in the properties of ternary mixes was confirmed from XRD analysis, which reveal coexistence of C-S-H along with crystalline SiO2 that positively improve the microstructure of the composite binder. Moreover, SEM analysis showed dense microstructure for ternary mixes when compared to binary mixes, which further validate the improvement in the strength of such binders. The sustainability analysis discloses the enhanced performance of ternary mixes, wherein, G60A40F95R5 showed 19.35% and 46.23% lower carbon dioxide parameter than binary mixes G60F40 and G60R40, respectively. All in all, the multilevel material investigation provides a great avenue to delve in to the best performing ternary mixes which will find desirable applications in construction industry. 2024 The Authors -
Performance investigation of modular multilevel inverter topologies for photovoltaic applications with minimal switches
Introduction. In recent years, a growing variety of technical applications have necessitated the employment of more powerful equipment. Power electronics and megawatt power levels are required in far too many medium voltage motor drives and utility applications. It is challenging to incorporate a medium voltage grid with only one power semiconductor that has been extensively modified. As a result, in high power and medium voltage settings, multiple power converter structure has been offered as a solution. A multilevel converter has high power ratings while also allowing for the utilization of renewable energy sources. Renewable energy sources such as photovoltaic, wind, and fuel cells may be readily connected to a multilevel inverter topology for enhanced outcomes. The novelty of the proposed work consists of a novel modular inverter structure for solar applications that uses fewer switches. Purpose. The proposed architecture is to decrease the number of switches and Total Harmonic Distortions. There is no need for passive filters, and the proposed design enhances power quality by creating distortion-free sinusoidal output voltage as the level count grows while also lowering power losses. Methods. The proposed topology is implemented with MATLAB / Simulink, using gating pulses and various pulse width modulation methodologies. Moreover, the proposed model also has been validated and compared to the hardware system. Results. Total harmonic distortion, number of power switches, output voltage and number of DC sources are compared with conventional topologies. Practical value. The proposed topology has been very supportive for implementing photovoltaic based multilevel inverter, which is connected to large demand in grid. References 12, table 5, figures 23. E. Parimalasundar, N.M.G. Kumar, P. Geetha, K. Suresh. -
Performance investigation of PID controller in trajectory control of two-link robotic manipulator in medical robots
Robot-assisted surgical procedures have gained much coverage in recent years and favored over manually conducted operations. The medical robots are comprised of manipulators arm that is the multi-degree of freedom positioning devices with a highly non-linear nature to perform various surgical tasks. Due to non-linear effects, robots offer a severe challenge to the control system. Therefore, the control techniques are required for controlling the robots that should be fast enough to accommodate the rapid changes in the system parameters. In this article, the Proportional-Integral-Derivative (PID) controllers performance has been investigated in trajectory control of the Two-Link Robotic Manipulator (TLRM) for reliable functioning of these robots. Tracking error and Control input factors have been used to investigate the PID controllers robustness in trajectory control of TLRM. Eulers-lagrange approach has been used for dynamic analysis of TLRM. This work has been accomplished in the MATLAB/ Simulink environment. 2021 Taru Publications. -
Performance investigations of five-level reduced switches count ?-bridge multilevel inverter
Introduction. This research paper describes a simple five-level single-phase pulse-width modulated inverter topology for photovoltaic grid applications. Multilevel inverters, as opposed to conventional two-level inverters, include more than two levels of voltage while using multiple power switches and lower-level DC voltage levels as input to produce high power, easier, and less modified oscillating voltage. The H-bridge multilevel inverter seems to have a relatively simple circuit design, needs minimal power switching elements, and provides higher efficiency among various types of topologies for multi-level inverters that are presently accessible. Nevertheless, using more than one DC source for more than three voltage levels and switching and conduction losses, which primarily arise in major power switches, continue to be a barrier. The novelty of the proposed work consists of compact modular inverter configuration to connect a photovoltaic system to the grid with fewer switches. Purpose. The proposed system aims to decrease the number of switches, overall harmonic distortions, and power loss. By producing distortion-free sinusoidal output voltage as the level count rises while lowering power losses, the constituted optimizes power quality without the need for passive filters. Methods. The proposed topology is implemented in MATLAB/Simulink with gating pulses and various pulse width modulation technique. Results. With conventional topology, total harmonic distortion, power switches, output voltage, current, power losses, and the number of DC sources are investigated. Practical value. The proposed topology has proven to be extremely useful for deploying photovoltaic-based stand-alone multilevel inverters in grid applications. References 18, table 2, figures 15. 2023, National Technical University "Kharkiv Polytechnic Institute". All rights reserved. -
Performance of DSSC with green synthesized and thermodynamically sintered Bi-phase TiO2 with various sensitizers
The production of green and clean energy in the current era is heavily reliant on light harvesting through the use of solar cells. A successful fabrication of any of the components of Dye-sensitized solar cells (DSSC) through an easy, environmental, and economic-friendly method would be an added advantage in promoting the production of green and clean energy. With this in mind, this paper highlights the green synthesis of materials for the preparation of photo-anodes as well as sensitizers. Apart from the routine synthesis method, this paper presents a new perspective that enhances inter-particle connections by providing an optimum calcination temperature (thermodynamic sintering) during the preparation procedure. The best calcination temperature for the preparation of photo-anode material is initially optimized by comparing the device output performance between synthetic and natural dyes. Further improvement in device performance is achieved through TiCl4 (Titanium tetrachloride) post-annealing treatment on the optimized photo-anodes. The improvement in performance of these optimized photo-anodes is checked and confirmed with different natural, synthetic, and cocktail sensitizers. The best natural dye-sensitized solar cell (NDSSC) device showed an efficiency of 4.65 % and the dye-sensitized solar cell (DSSC) device showed an efficiency of 5.78 %. This confirms the suitability of these green-synthesized TiO2 nanopowders as a promising material for photo-anode preparation that could work well for both NDSSC and DSSC. 2024 Elsevier B.V. -
Performance of pradhan mantri fasal bima yojana: Perception of farmers in rural bangalore
Crop insurance is an agricultural development program supporting the sustainability of farmers. PradhanMantriFasalBimaYojana crop insurance scheme was introduced to provide insurance cover, financial stability, innovative and modern methods of agricultural practice. The study primarily focuses on the reasons for enrollment, benefits, challenges and suggestions regarding the PradhanMantriFasalBimaYojana with respect to farmers of Rural Bengaluru. A qualitative thematic analysis using a primary study reveals PMFBY as a source of financial security and financial stability with reduced premium that increases the confidence level among the farmers. 2019 SERSC. -
Performance of second law in Carreau fluid flow by an inclined microchannel with radiative heated convective condition
This investigation addresses the novel characteristics of entropy production in the fully-developed heat transport of non-Newtonian Carreau fluid in an inclined microchannel. The physical effects of Roseland thermal radiation and viscous heating are included in the energy equation. The no-slip boundary condition for velocity and convective type heating boundary conditions for temperature are also accounted. Mathematical modeling included the non-Newtonian Carreau fluid model. The dimensionless two-point boundary value problem acquired from governing equations via dimensionless variables. The nonlinear system is tackled by using the Finite Element Method. A detailed discussion of the significance of effective parameters on Bejan number, entropy generation rate, temperature and velocity is presented through graphs. Our analysis established that the entropy generation is reduced at the left and right phase of the channel while the Bejan number is improved at both phases of the channel and is maximum at the center of channel by the incrementing values of Weissenberg number. 2020 Elsevier Ltd -
Peristaltic mechanism of Ellis fluid with viscous dissipation and thermal radiation induced by cilia wave
Bioheat transfer analysis in tissue has attracted the attention of numerous researchers due to its widespread potential applications in the medical field, mainly in thermotherapy and the human thermoregulation system. Also, temperature regulation of the human body primarily occurs through bioheat transfer. Due to the widespread biomedical applications of bio-heat transfer, we aim to investigate the movement of biofluid and bioheat in human organs with the influences of thermal radiation and ciliary waves. The mathematical model for Ellis fluid flow through a tube includes the metachronal wave of cilia motion and convective conditions. The governing equations are created based on mass, momentum conservation, and energy. The current problem is displayed and exact solutions are managed under long wavelength (? < 1) and low Reynolds number (Re < 1) approximations. An analytical approach is employed to derive expressions for longitudinal velocity, temperature, pressure gradient, and stream function as a function of the parameters of the problem. The physical behavior of the peristaltic motion of the Ellis fluid is explained in detail and illustrated graphically for various parameter values. The results of the current study provide potential information for advancement in the biomedical industry, particularly in the development of biomedical devices and processes. World Scientific Publishing Europe Ltd. -
Personal fableness and perception of risk behaviors among adolescents
Adolescence is a crucial period where one tends to identify who they are as an individual. However, as a teenager is struggling to find his/her place in this world, it is also a time where they are prone to engaging in risk behaviors, which tend to have an extreme psychological impact. The objective was to explore the experiences of an adolescent who engages in risk behaviors and to understand their level of personal fables. The study was a qualitative design with content analysis with semi-structured interviews of ten male adolescents aged 16-18 years. The major findings of the study indicated that adolescents pattern of thinking revolves around the fact that they are invincible and invulnerable. Furthermore, adolescents are aware of the risks they are putting themselves through and how in the process they are hurting others. The implications of the study are to conduct more life skill programs in schools; greater awareness has to be created on the impact and harmful effects of such behaviors. 2018, Indian Journal of Public Health Research and Development. All rights reserved. -
Personality and Psychological Predictors of Instagram Personalized Ad Avoidance
The purpose of this paper is to apply the meta-theoretical model of motivation and personality (3M) of Mowen to study consumers ad avoidance in the context of online personalized advertisements on Instagram. The current study developed a theoretical framework that links personality traits with reactance arousal and ad avoidance behaviours. Based on the data analysis, it was found that consumers with higher general self-efficacy tend to have more reactance arousal (situational level trait) compared to ad irritation, ad skepticism (surface traits), and ad avoidance behaviours towards personalized advertising on Instagram. The findings will help advertisers and marketers in segmenting the market better based on young users efficacy levels, navigational habits, personality traits, functional motives, and demographic variables to effectively reach the targeted consumers. 2023 IGI Global. All rights reserved. -
Perspective of multiple slips on 3D flow of Al2O3TiO2CuO/H2O ternary nanofluid past an extending surface due to non-linear thermal radiation
A mathematical model is constructed under the slip flow of a Newtonian fluid based on certain assumptions. Such a mathematical model of ternary nanofluid flow is handled by invoking similarity solutions for governing equations. The obtained system of nonlinear equations is solved numerically by utilizing the fourth-fifth order RungeKutta-Fehlberg method. The consequences of distinguished parameters on fluid flow are analyzed in detail through the plotted graphic visuals. Physical quantities such as Nusselt number and skin-fraction coefficient are considered numerically by tables. The results indicated that the mixed convection parameter favors the flow whereas the x-direction velocity slip reduces the velocity. Furthermore, it is observed that the temperature of the nanofluid is increased for the higher values of radiation. The presence of a heat source enhances the temperature and that of a heat sink diminishes the temperature. It is found that the heat conduction capability is more in ternary nanofluid than the hybrid and monophase nanofluids. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Perspectives about Illness, Attitudes, and Caregiving Experiences among Siblings of Persons with Schizophrenia: A Qualitative Analysis
Background: Siblings of persons diagnosed with schizophrenia (SPS) are one among the major sources of support for persons with schizophrenia. There is a dearth of psychosocial literature on SPS in India. This qualitative study explored the perspectives about the illness, attitudes, and caregiving experiences of SPS. Materials and Methods: Qualitative audio-recorded interviews were conducted with 15 SPS, purposively selected from a tertiary mental health hospital of Southern India. A general inductive approach was adopted to analyze the qualitative data. Results: Four broad themes were identified from qualitative data analysis. (1) SPS described several explanatory models of mental illness in terms of causal attributions and treatment care. (2) They had expressed emotion toward their ill siblings, such as criticality, hostility, and emotional over-involvement. (3) They experienced objective and subjective burden while caring for their ill sibling. In spite of all these, (4) they were part of their ill siblings' care in terms of ensuring regular follow-ups and drug adherence and supported their livelihood. They coped up with adaptive as well as maladaptive strategies. Conclusion: SPS provide significant support to their affected siblings. However, they do have non-biomedical models of mental illness and negative attitudes toward patients and experience burden. Hence, psychosocial interventions may help SPS while caregiving for their affected siblings. 2019 Indian Psychiatric Society - South Zonal Branch.