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Synthesis and Characterization of Magnetic Nano Particle Materials (Fe2O3 & Fe3O4) Synthesized by Arc Discharge Method

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Journal of The Institution of Engineers (India): Series D 2025年 1-12页

作者： Junaidi, Md. Abdul Raheem Ranjitha, P. Kumar, K. V. Pradeep Rajanish, M. Reddy, Maramreddy Raghu Tilak Manjulaiah, Hareesha Thanuj Kumar, M. Nagaraj, P. Revanna, K. Raghavendra, N. Udayashankar, S. Mechanical Engineering Department Muffakham Jah College of Engineering & Technology Telangana Hyderabad500034 India Mechanical Engineering Department Dayanand Sagar College of Engineering Karnataka Bangalore560078 India Mechanical Engineering Department Ramaiah Institute of Technology Karnataka Bengaluru560054 India Mechanical Engineering Department Dayananda Sagar Academy of Technology and Management Karnataka Bengaluru560082 India Mechanical Engineering Department New Horizon College of Engineering Karnataka Bangalore560103 India Department of Mechanical Engineering Malnad College of Engineering Hassan573202 India Visvesvaraya Technological University Karnataka Belagavi590018 India Department of Robotics and Automation Rajarajeswari College of Engineering Bengaluru560074 India Department of Chemistry Yenepoya Institute of Technology Karnataka Mangalore574225 India Department of Mechanical Engineering Government Engineering College Krishnarajapet571426 India Department of Mechanical Engineering BNM Institute of Technology Karnataka Bangalore560070 India Department of Mechanical Engineering VTU Karnataka Belgaum590018 India

This research work thoroughly describes the process of obtaining and characterizing iron oxide (Fe3O4) nanoparticles as well as emphasizes how different pH levels, reaction environments and synthesis methods can respectively control its size or activity. Characterization of the crystalline structure, morphology and size of the nanoparticles was carried out. It describes key methods of synthesis such as arc discharge or electrode configuration in electrolyte solutions emphasizing on applied voltage or processing time, which resulted in good quality of nanoparticles, concluding with comparing particle diameters and prepared form of nanoparticles in mentioned methods. In this proposed synthesis mechanism, ferrous and ferric ions are hydroxylated in a controlled anaerobic process to yield Fe3O4 precipitates. Synthesis of the iron oxide (Fe3O4) nanoparticles was found to highly depend on pH parameters, and pH 11 was optimized as the favorable pH parameter based on the results acquired, as it well controls the growth and stability of the particles. The findings highlight the promise of Fe3O4 nanoparticles for various biomedicine and environmental remediation applications where particle stability, magnetic properties and cheap synthesis are crucial. © The Institution of Engineers (India) 2025.

关键词： Magnetic nanoparticles

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De-noising of Low Dose CT Liver Images Using Improved Discrete Wavelet Transform 7th

De-noising of Low Dose CT Liver Images Using Improved Discre...

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7th IFIP TC 12 International Conference on Computational Intelligence in Data Science, ICCIDS 2024

作者： Maria, H. Heartlin Kayalvizhi, R. Evangeline, I. Keren Princess, T. George Mangalya, T. Rashmika Prakash, J. Shakthi Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Chennai603203 India Department of Robotics and Automation SRM Easwari Engineering College Chennai India Department of Medical Electronics SRM Valliammai Engineering College Kattankulathur India Department of Artificial Intelligence and Datascience St. Joseph’s Institute of Technology Chennai India

ISBN: (纸本)9783031699818

CT scans have become increasingly common in recent years and are essential for diagnosing various medical conditions. But the most significant element influencing a CT image’s quality is noise, which frequently taints low-dose scans. The radiologists’ choice may then be impacted by this. Therefore, in order to enhance visibility and increase the clarity of the image, LDCT images must be removed of noise before being utilized for diagnosis. This work is one such attempt to remove noise from low dose Computed Tomography (CT) liver images. In the present work, the noise from the CT liver images is eliminated using a modified discrete wavelet transform (DWT). The scheme system is used in conjunction with the DWT. The lifting approach improves the wavelet transform’s accuracy, simplicity, efficiency, and flexibility. Furthermore, the noisy wavelet coefficients are denoised via Bayesian shrinkage. The de-noised image PSNR, MSE, SSIM, and SNR values are computed, and a comparison with the traditional de-noising methods is carried out. The quantitative study reveals that the suggested approach has produced noteworthy outcomes when compared to the efficacy of conventional procedures. Furthermore, as the results and discussion section demonstrate, the suggested lifting technique in conjunction with the DWT minimizes the memory needs while simultaneously reducing complexity. © IFIP International Federation for Information Processing 2024.

关键词： Discrete wavelet transforms

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Implementing Machine Learning Approaches to Identify Fabricated Profiles

Implementing Machine Learning Approaches to Identify Fabrica...

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2024 International Conference on Science, Technology, engineering and Management, ICSTEM 2024

作者： Princess, George T. Kothai, G. Maria, Heartlin H. Rajadurai, Kayalvizhi Ajai, M.R. Department of ADS St. Joseph’s Institute of Technology Chennai India KPR Institute of Engineering and Technology Coimbatore India Department of ECE Rajalakshmi Engineering College Chennai India Dept.of Robotics and Automation Easwari Engineering College Chennai India Department of IT St. Joseph’s Institute of Technology Chennai India

ISBN: (纸本)9798350376913

The increase of fake news is a serious challenge in today’s digital world. During the period of COVID-19 there is a lot of fake news about the COVID-19 virus has been spreading around the world, and it has also been believed among a lot of people, so a lot of conspiracy theories about the virus-like has been intentionally spread by the laboratory and some news is about supporting the zoonotic origin of the virus, likely originating from bats. Some of the news is about False Cures and Treatments, There were various claims about supposed cures for COVID-19, such as drinking bleach, consuming certain herbs, or taking specific vitamins. Health experts and authorities consistently warned against such unproven remedies. Thus it is really necessary to differentiate the fake profile from the genuine profile. This fake news is mostly spread through fake profiles. So it is necessary to differentiate fake profiles from genuine users. This paper introduces a method for the detection of fake profiles using machine learning techniques. The proposed system uses a diversity of features, to distinguish genuine profiles from fake. A dataset is employed to train and evaluate the machine learning algorithm. This dataset includes labeled examples of both pseudo (fake) and real profiles. This paper aims to increase the efficiency in the detection of genuine news from fake. This proposed system mainly focuses on stopping the spread of malicious activities through out the internet, since these activities is mostly spread through the fake account on the social media platforms. © 2024 IEEE.

关键词： Social networking (online)

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A Model of the Contribution of Interneuron Diversity to Recurrent Network Oscillation Generation and Information Coding 16th

A Model of the Contribution of Interneuron Diversity to ...

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16th International Conference on Brain Informatics, BI 2023

作者： Lorenz, Gabriel Matías Martínez-Cañada, Pablo Panzeri, Stefano Istituto Italiano di Tecnologia Rovereto Italy Department of Pharmacy and Biotechnology University of Bologna Bologna Italy Hamburg Germany Department of Computer Engineering Automation and Robotics University of Granada Granada Spain

ISBN: (纸本)9783031430749

Spiking neural network models that have studied how oscillations are generated by recurrent cortical circuits and how they encode information have been focused on describing the encoding of information about external sensory stimuli carried by feed-forward inputs in a two-population circuit configuration that includes excitatory cells and fast-spiking interneurons. Here we extend these models to explore the contribution of different classes of cortical interneurons to cortical oscillations. We found that in our extended model, the feed-forward stimulus is still mainly encoded in the gamma frequency range, consistent with earlier works using a single interneuron type. However, we also found that the information carried by different regions of the gamma frequency range was larger than the sum of the information carried by the two individual frequencies. This shows that the power values at different frequencies carried information about the feedforward input in a synergistic way. This is in contrast to previous models with only one interneuron type, which mainly led to redundant information between frequencies in the gamma range. These results suggest that interneuron diversity has useful properties for enriching the encoding of information in the gamma frequency range. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Neural network models

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Machine Learning in Academic Performance Prediction: Analyzing Attendance and Marks to Forecast Future Results

Machine Learning in Academic Performance Prediction: Analyzi...

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2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Kanmani Divakar, Divyesh Keshaveni, N. Ramesh, E. Prathibha, M. Canara Engineering College Mangaluru Visvesvaraya Technological University Department of Artificial Intelligence and Machine Learning Belagavi India Mangaluru Visvesvaraya Technological University Department of Robotics & Automation Engineering SCEM Belagavi India Canara Engineering College Mangaluru Visvesvaraya Technological University Department of Electronics & Communication Engineering Belagavi India Canara Engineering College Mangaluru Visvesvaraya Technological University Department of Computer Science and Engineering Belagavi India

ISBN: (数字)9798331517892

ISBN: (纸本)9798331517892

In the realm of higher education, predicting student performance is crucial for enhancing educational outcomes and institutional efficiency. This project aims to develop a predictive model to forecast students upcoming semester performance based on their past academic records and attendance. This paper makes use of machine learning regression techniques, including Random Forest, Gradient Boosting, and Voting Regressor, to analyze students' scores and attendance data. The dataset comprises marks and attendance records for analytical, theoretical, and practical subjects, with target grades categorized into seven classes: A+, A, B+, B, C, P and F. To evaluate model performance, continuous predictions were discretized into these grade categories, allowing for the calculation of Precision, Recall, F1-Score, and the generation of confusion matrices. The results indicate the efficacy of different models in accurately predicting student grades. Visualizations such as bar charts and heatmaps were used to present and compare the performance metrics across various models and subject types. The insights from this project are expected to assist educators in identifying at-risk students and improving educational strategies, ultimately contributing to enhanced student success and institutional reputation. © 2024 IEEE.

关键词： Prediction models

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Diagnosis of Faults in Edge-PLC using Optimized Deep Learning models in Industrial IoT 9

Diagnosis of Faults in Edge-PLC using Optimized Deep Learnin...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Kumar, A. Kishore Rajesh Kumar, T. Balamanigandan, R. Kumar, A. Sasi Mahaveerakannan, R. Department of Robotics and Automation Sri Ramakrishna Engineering College Tamilnadu Coimbatore641022 India SIMATS Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Srinivas University Institute of Engineering & Technology Department of Cloud Technology & Data Science Surathkal India

ISBN: (纸本)9798350397376

Industrial Internet of Things (IIoT) may allow for the state of equipment to be monitored and defects to be detected before they become serious, helping smart factories keep up with the rising demands for safety and efficiency brought on by worldwide competition. In the case of Cloud Computing, data-driven approaches implemented in the cloud are used to first train the model using a vast quantity of raw data, and then end-machines send their real-time readings for processing at the cloud data centre. It is possible, however, that this will impose a significant delay and a high computational cost. In this study, we take into account a hierarchical structure in which edge-PLCs are used to collect sensed data locally and lessen the burden on long-distance connections. Due to the fact that a single responsibility may be associated with multiple swaying topographies, we aim to first reduce the sum of features required to identify a fault, and then identify the smallest possible set of edge-PLCs that still accounts for all of the most important ones, all while keeping deployment costs to a minimum. To address this, we present an Enhanced Harris Hawkins Optimization (EHHO) approach to tackling the features selection issue, followed by the assortment of edge-PLCs through the set coverage problematic. The model named Residual Attention Neural Network (RANN) is proposed the in this study to forecast the fault detection procedure. Residual Neural Network (ResNet) is used by RANN to help diagnose problems, alongside the knowledge of experts. We evaluate the performance of our technique in comparison to existing AI-based approaches, such as the lregression model and its expansions, by simulating on a real data trail. The findings confirm competence and presentation of the suggested approach, which meets or even surpasses the correctness of approaches employing the entire collection of data. © 2023 IEEE.

关键词： Internet of things

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Nano porous Structure formation on Multicrystalline Silicon Surface by using Chemical Etching method. 5

Nano porous Structure formation on Multicrystalline Silicon ...

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5th International Conference on Devices for Integrated Circuit, DevIC 2023

作者： Barua, Sangeeta Bandyopadhyay, Sikha Chatterjee, Sayan The Neotia University Robotics and Automation Engineering Department Kolkata India Durgapur Women's College Department of Physics Durgapur India Jadavpur University Department of E.T.C.E Kolkata India

ISBN: (纸本)9798350347265

Present article exhibits a method, formation of Nano porous silicon by metal-Assisted chemical etching (MAE). Nano porous structure is formed on the top of the multicrystalline Silicon by using HF and H2O2 etching solution with Nickel salt at 600 C temperature for the reaction time of 60 minutes. Metal (Ni) nanoparticles deposited on the silicon from the solution. Metal salt act as a catalyst here for silicon etching whereas HF and H2O2 contribute as oxidizing species. Reflectance measurement obtained from nano porous silicon provides degree of reflection of\sim5% to 8% over a spectra range of (220 to 1050 nm). Metals used in MAE process generally have positive standard electrode potentials (EO) (e.g. for Ag, EO =+0.8V) but Ni has negative standard electrode potential (EO =-0.25V). © 2023 IEEE.

关键词： Nickel

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A Sensorless Speed Control of High-Speed BLDC Motor Using Variable Slope SMO

A Sensorless Speed Control of High-Speed BLDC Motor Using Va...

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作者： Ok, Sangjae Xu, Zhenyao Lee, Dong-Hee Department of Mechatronics Engineering Kyungsung University Busan48434 Korea Republic of State Key Laboratory of Robotics Shenyang Institute of Automation The Chinese Academy of Sciences Shenyang110016 China

Low-power and high-speed BLDC(Brushless DC) motor is widely used in vacuum cleaners. This paper presents a sensorless speed control scheme for a high-speed BLDC motor of a hand-stick vacuum cleaner using a variable slope sliding mode observer(SMO). The SMO using the signum function is very effective for the sensorless estimation of the BLDC motor. However, the chattering effect on the slide surface from the signum function decreases the sensorless performance in the wide speed range. The sigmoid function instead of the signum function can reduce the chattering effect. However, the convergence is inversely proportional to the reduced chattering effect. In order to reduce the chattering effect and keep the proper convergence performance, the proposed method uses a variable acceleration slope of the sigmoid function. In the low-speed range, the acceleration slope of SMO is decreased to reduce the chattering. And, it is increased to improve the convergence in the high-speed region. The acceleration slope of the sigmoid function is designed as a function of the estimated speed to keep the constant convergence with reduced chattering in the sensorless speed and position estimation. In order to verify the proposed sensorless control scheme, 115 krpm speed 540W BLDC motor for a vacuum cleaner is tested with various SMO methods. In the experimental results, the proposed method shows better sensorless speed control performance in the wide speed range. © 2023 IEEE.

关键词： Sensorless control

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Microhardness and Wear Rate Analysis on Laser Cladded Composites of AZ91D Alloy with SiC by Grey Technique

Microhardness and Wear Rate Analysis on Laser Cladded Compos...

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International Conference on Mechanical engineering: Researches and Evolutionary Challenges, ICMech-REC 2023

作者： Karthick, L. Loganathan, Ganesh Babu Jagadish, C.A. Somasundaram, B. Kandavalli, Sumanth Ratna Chithambaranathan, P. Department of Mechanical Engineering Hindusthan College of Engineering and Technology Coimbatore641032 India Department of Robotics and Automation Rajalakshmi Engineering College Chennai602105 India School of Mechanical Engineering REVA University Bengaluru India Department of Mechanical Engineering Tandon School of Engineering New York University 6 Metro Tech Center BrooklynNY11201 United States Department of Mechanical Engineering Rajalakshmi Engineering College Thandalam Chennai602105 India

ISBN: (纸本)9789819722488

Owing to increments in heavy vehicles for transporting the materials and reducing the vehicle weight, magnesium alloys play the mandatory role in various fabricating industries. The Mg alloy alone does not satisfy their necessities without adding the strengthening particles;therefore, this investigation stated the silicon carbide (SiC) was the reinforcing into AZ91D Mg alloy to compose the coating metal matrix composites through the laser cladding process. This method is unique when appropriated processing parameters are chosen and compared to laser melting process;laser cladding is the significant one. The laser cladding processing parameters are scanning speed (3–5 m/s), SiC proportions (10–30%) and laser output power (0.6–0.8 kW), respectively. Then the composed coating AZ91D Mg alloy with SiC specimen is accounted for experimentation of microhardness and wear rates. The microhardness and wear rates are improved at increasing of weight percentage from 20 to 30%, maximum of scanning speed at 5 m/s and 0.7 kW of laser output power parameters. The grey relational multi-task optimization method was utilized to recognize the single responses from various laser cladding processing parameters. Finally, the SEM with worn surfaces was taken on the optimal specimen composite. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Silicon carbide

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Novel Digital Assessment System for Upper-Limb Movement in Stroke Patients Using Markless-Sensing Technology and Deep Learning Algorithms

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Journal of Shanghai Jiaotong University (Science) 2024年 1-13页

作者： Sheng, Bo Lei, Ximin Cheng, Jian Xie, Qiurong Tao, Jing Chen, Yujie School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai200444 China Financial Technology Department Shanghai Rural Commercial Bank Shanghai201201 China Department of Rehabilitation Medicine Fujian University of Traditional Chinese Medicine Fuzhou350122 China School of Mechanical Engineering Xi’an Jiaotong University Xi’an710049 China

Traditional motor function assessment for stroke patients involves subjective scoring by rehabilitation physicians, a process that is time-consuming, expensive, and subject to variability. By utilizing sensors (markers) and machine learning algorithms, digital assessment systems offer the potential for more objective clinical decision support. Nevertheless, these algorithms often rely on feature extraction, which opens up opportunities for improving diagnostic accuracy and reliability. Therefore, this study proposed a novel assessment approach based on markless-sensing technology and deep learning algorithms, which can perform contactless data measurement and nonfeature-based digital assessment. Specifically, the movement data of stroke patients were collected via the Microsoft Kinect V2 with a customized motion tracking system. The raw dataset was then processed by the Savitzky-Golay filter and long short-term memory-attention-based assessment model. A total of 25 volunteers (15 stroke patients and 10 healthy subjects) were recruited for experimental validation by conducting the commonly used clinical scale (wolf motor function test-functional ability scores, WMFT-FAS). The experiments showed that the proposed novel digital assessment system could achieve favorable results: an average accuracy of 91.7%, average precision of 87.4%, average recall of 87.3%, and average F1-score of 87.3%. In summary, the proposed system can provide objective and reasonably accurate assessment outcomes, which might have the potential to offer essential clinical information for rehabilitation physicians to make reasonable clinical intervention plans. (Figure presented.) © Shanghai Jiao Tong University 2024.

关键词： Learning algorithms

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