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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Dhatterwal, Jagjit Singh Gupta, Sonali Malik, Kiran Kaswan, Kuldeep Singh Bharti, Sunil Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence & Data Science AP Vaddeswaram India Graphic Era Hill University Department of Computer Science & Engineering Dehradun India Graphic Era Deemed to Be University Dehradun India Matu Ram Institute of Engineering and Management Haryana Rohtak India Galgotias University School of Computer Science & Engineering Uttar Pradesh Greater Noida India Galgotias College of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350360165

The spaceless accessibility of data has dramatically changed the environments of decision making in the digital age. 'Big data' technically refers to datasets that are not only large, but also diverse & fast, thus speeding up the process of managing them with traditional procedures & tools. However, with this more abundance of data, making use of these information, and extracting particular utilities from them will have to be explored and studied. Firstly, decisions must be made that give significant insights from such diverse and changing data which move from the level of transactions, consumer comprehension to the network interpersonal. Massive data analytics, which is an implication of large-scale data analysing by firing on huge data collections, brings such value. The objective of this article is to pursue some of the different kinds of analytical methods and software tools that can be used to analyze data, as well as the alternative decision-making facilitation modes which may be achieved through advanced analytics. © 2024 IEEE.

关键词： MapReduce

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An Empirical Analysis of Python Programming for Advance Computing 2

An Empirical Analysis of Python Programming for Advance Comp...

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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Baliyan, Anupam Kaswan, Kuldeep Singh Dhatterwal, Jagjit Singh Chitkara University Chitkara University Institute of Engineering & Technology Punjab India Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence & Data Science AP Vaddeswaram522002 India

ISBN: (纸本)9781665437899

Python is an object-oriented, scripting, and interpretive programming language that may be used for mentoring and real-world applications. This paper focusses primarily on Python software packages used in data science, pattern recognition, and IoT. This paper will first explain Python as a language, then introduce data science, Machine learning, and IOT, describing prominent packages in the data science and Machine learning community, such as NumPy, SciPy, TensorFlow, Keras, Matplotlib. This paper will also demonstrate the significance of Python in the development of the industry. Throughout, we shall utilize many code samples. We review so many research papers to analyze the usage of python in different fields and easily import packages in the programming software. © 2022 IEEE.

关键词： Big data

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Experimental investigation of different unconventional machined surface on hardox steel

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Materials Today: Proceedings 2022年 66卷 1162-1167页

作者： Shanmuganathan, A. Suresh, R. Manirathnam, D. Mohandas Gandhi, A.G. Rajasekar, P. Senthil, T.S. Ramaswamy, Subbiah Department of Mechanical Engineering NIMS University Rajasthan Jaipur India Department of Mechanical Engineering Annamalai University Tamil Nadu Chidambaram India Department of Mathematics Panimalar Engineering College Tamilnadu Chennai India Department of Mechanical Engineering RVS Technical Campus Tamilnadu Coimbatore India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamilnadu Chennai India Department of Mechanical Engineering Panimalar Engineering College Tamilnadu Chennai India Department of Mechanical Engineering Gokaraju Rangaraju Institute of Engineering and Technology Telangana Hyderabad India

Machinability and surface behaviours are playing an essential role in metal forming and manufacturing processes. Hardox 500 steel is machined by ultrasonic machining (USM) and spark erosion machining (SEM) processes. The different input factors are used in both the processes and evaluate the rate of metal removal. Current, voltage and pulse duration are the control factors for spark erosion. Power rating, slurry concentration and grit size are the control factor for USM. Scanning force microscopy (SFM) is used to investigate the surface topography of the hardox steel. The effect of input constrains on rate of metal removal (RMR) is validated by variance analysis. In SEM process, current was the most effective factor on rate of metal removal and it's provided the maximum contribution (79.75%) than other factors such as pulse duration (16.73%) and voltage (3.43%). In USM process, slurry concentration was the most effective factor on rate of metal removal and it's provided the maximum contribution (83.78%) than other factors. The contribution effect of grit size and power rating is 13.15% and 2.53% respectively. Based on surface plot analysis, the machined surface of SEM is better than USM surface. © 2022

关键词： Metals

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Decision Making and Recognition of String Characters using NN-Fuzzy Implication System 3

Decision Making and Recognition of String Characters using N...

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3rd International Conference on Machine Learning and data engineering, ICMLDE 2024

作者： Henge, Santosh Kumar Bhagat, Avinash Mandal, Sanjeev Kumar Viraati, Nikhil Reddy Singh, Ravleen Kedhar, K. Rushi Department of Computer Science and Engineering School of Computer Science and Artificial Intelligence SR University Warangal506371 India School of Computer Applications Lovely Professional University Punjab Phagwara India Bangalore India Department of Data Insights Crisp Shared Services 7160 Columbia Gateway Dr Columbia United States Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation AP Vaddewaram India BE-CSE RV Institute of Technology and Management Bengaluru India

This research proposes the neural network (NN)-fuzzy logic control (FLC)-based methodology, which is designed with two stages of execution. Stage-1 is composed with the NN approach;it takes the input from the scanned image, the input from the classified clustered group, processes it with hidden layers, and generates the final output, which is input to the FLC in stage 2. The input of stage 2 converts the input into fuzzified data (0s and 1s) and trains the FLC system with fuzzified set values. The trained data has been simulated for further stages in the FLC-based decision-making approach and integrated to identify the character patterns based on the trained dataset. FLC-based planting and normalization techniques are integrated for re-training the data. The dataset has been framed with more than 6200 handwritten image characters, which were written by 235 writers. It aligned with the pre-processing, classification, and filtering stages and achieved a 99.8% recognition rate. © 2025 The Author(s).

关键词： Optical character recognition

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Narrow-linewidth and frequency-agile FMCW laser on the Si3N4 -on-SOI photonic platform

Narrow-linewidth and frequency-agile FMCW laser on the Si3N4...

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CLEO: Applications and Technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

作者： Liu, Chuxin Li, Xinhang Guo, Yuyao Lu, Liangjun Chen, Jianping Zhou, Linjie State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab of Navigation and Location Services Shanghai Institute for Advanced Communication and Data Science Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China

We demonstrate a hybrid-integrated self-injection-locked laser on the Si3N4-on-SOI platform to generate FMCW signal with 9 kHz linewidth, 2 MHz repetition rate, and high linearity, supporting FMCW-LiDAR with refresh r... 详细信息

ISBN: (纸本)9781957171395

关键词： Linewidth

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Experimental Evaluation of AI Powered Vehicle Toll Collection System Based on RFID and Internet of Things (IoT) Assistance

Experimental Evaluation of AI Powered Vehicle Toll Collectio...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Pulikanti, Jyothi Jayaraj, R. Purushotham, S. Devi, V. Saranya Antony, A.Santhi Mary Chitra, M.P. Qis College of Engineering and Technology Department of Aimlds Andhra Pradesh Ongole India School of Computing Srm Institute of Science and Technology Department of Data Science and Buisness Systems Kattankulathur Chennai India Sona College of Technology Department of Eee Salem India Qis College of Engineering and Technology Department of Ece AndhraPradesh Ongole India Loyola-ICAM College of Engineering and Technology Department of Eee Nungambakkam Loyola Campus Chennai India Panimalar Engineering College Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331543617

This paper presents the experimental evaluation of an AI-powered vehicle toll collection system, utilizing RFID technology and IoT integration. The system was designed to automate toll collection, reducing transaction time and improving accuracy while minimizing human intervention. The methodology involved RFID-based vehicle identification, real-time data transfer via IoT, and automatic toll deductions from prepaid accounts. The system was evaluated based on transaction speed, accuracy, user satisfaction, and system reliability. Results show that the automated system significantly reduced average transaction times from 32.5 seconds (manual) to 10 seconds (automated) during peak hours. Additionally, the system demonstrated a 100% accuracy rate in detecting unregistered vehicles and low-balance scenarios. User satisfaction was high, with an average rating of 4.6 out of 5. System reliability was also excellent, achieving an average uptime of 99.5% across multiple toll booth locations. The AI-powered toll collection system proved to be highly efficient in automating toll processes, reducing congestion, and improving the user experience. These findings suggest that such systems can effectively modernize toll management, contributing to time savings, reduced operational costs, and enhanced security. © 2024 IEEE.

关键词： Radio frequency identification (RFID)

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Amplitude-Invariant Phase Shifter Based on Carrier-Injection Mechanism for Optical Phased Array

Amplitude-Invariant Phase Shifter Based on Carrier-Injection...

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CLEO: Applications and Technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

作者： Yuan, Qiqi Xu, Weihan Ran, Shihuan Yang, Yunhong Lu, Liangjun Chen, Jianping Zhou, Linjie State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab of Navigation and Location Services Shanghai Institute for Advanced Communication and Data Science Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China

We present an amplitude-invariant phase shifter based on a PIN diode embedded in a Mach-Zehnder Interferometer for optical phased arrays. The dynamic power contrast is lower than 0.42 dB over a p phase shift. © O... 详细信息

关键词： Phase shifters

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Automated Defect Detection in Electronic Components using Convolutional Neural Networks 9

Automated Defect Detection in Electronic Components using Co...

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Shobanadevi, A. Kottu, Sreekanth Kumar, K. R. Senthil Amudha, K. Praveena, K. Venkatesh, R. School of Computing Srm Institute of Science And Technology Department of Data Science And Business Systems Tamil Nadu Chennai600026 India Mallareddy University Department of Computer Science & Engineering Telangana Hyderabad500043 India R.M.K. Engineering College Department of Mechanical Engineering Tamil Nadu Kavaraipettai601206 India Department of Science And Humanities-Physics Tamil Nadu Kavaraipettai601206 India Mohan Babu University Erstwhile SreeVidyanikethan Engineering College Department of Electronics And Communication Engineering Andhra Pradesh 517102 India Department of Physics Tamil Nadu Dindigul624622 India

ISBN: (纸本)9798350365092

This exploration paper explores the operation of convolutional neural networks(CNNs) in automating the discovery of blights in electronic factors. With the rapid-fire advancement of technology, the demand for high- quality electronic factors has surged, emphasizing the need for effective disfigurement discovery styles. using the capabilities of CNNs, this study aims to develop a robust and accurate automated system able to relate colorful types of blights to electronic factors. By training the CNN model on a different dataset of disfigurement images, the system learns to fete subtle variations and anomalies reflective of faults, thereby enhancing examination delicacy and effectiveness. The proposed approach offers promising results in streamlining disfigurement discovery processes, reducing homemade intervention, and ameliorating overall quality control in electronics manufacturing. Results indicate the effectiveness and trustability of CNNs in automating disfigurement discovery, paving the way for enhanced productivity and product trustability in electronic diligence. © 2024 IEEE.

关键词： Quality control

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Incorporating Transfer Learning in Engine Health Assessment for Accurate Remaining Useful Life Prediction 3

Incorporating Transfer Learning in Engine Health Assessment ...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Karpagavalli, C. Nelson, A. Raja, R. Saranya, E. Soudagar, Manzoore Elahi M. Ramaian, Christu Paul Ramco Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Rajapalayam626117 India Panimalar Engineering College Department of Mathematics Tamil Nadu Chennai600123 India Pandian Saraswathi Yadav Engineering College Department of Electronics and Communication Engineering Tamil Nadu Sivagangai630561 India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Tamil Nadu Coimbatore641202 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of VLSI Microelectronics Tamil Nadu Chennai602105 India Hindustan Institute of Technology and Science Department of Automobile Engineering Tamil Nadu Chennai603103 India

ISBN: (纸本)9798350396157

Aircraft engines are critical, therefore any move to make them safer, more reliable, and fuel-efficient is highly encouraged. To solve the challenges of flight safety and maintenance expense during aircraft engine operation, a prognostics and health management system is deployed. This system is primarily concerned with flaw detection, health evaluation, and life prediction. Making decisions about aircraft engine operation and maintenance is strongly reliant on estimating the remaining useful life (RUL). This study provides an ensemble transfer learning model for RUL prediction using the C-MAPSS turbofan engine dataset. Pre-processing procedures such as filtering and normalization are used to improve the dataset. The proposed ensemble model for RUL prediction is then trained on the processed data. Some of the well-known transfer learning models that are compared include Xception, ResNet-50, and VGG-16. The ensemble model outperforms the other models, with the highest R2 value of 0.9901. Furthermore, the ensemble model is very effective;it can predict RUL in just 37 seconds, which suggests that it could be useful for keeping an eye on engine health and making decisions about aircraft. © 2024 IEEE.

关键词： Aircraft engines

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Basketball Win Percentage Prediction using Ensemble-based Machine Learning 6

Basketball Win Percentage Prediction using Ensemble-based Ma...

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6th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2022

作者： Sikka, Dhruv Rajeswari, D. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur603203 India School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur603203 India

ISBN: (纸本)9781665482714

Basketball is a prominent team sport played on a rectangular court between two teams of five players each. The goal is to shoot the ball through the defender's hoop, which is high on a backboard at each end of the court, while stopping the other team from shooting through its own hoop. Predicting how sports will turn out is an interesting sports analytics problem because it gives valuable data for running the sports market. In this work, data from NBA (National Basketball Association) basketball teams is analyzed, and a machine learning model is made that uses advanced statistical measures and performance data to evaluate and predict the winning percentage of different teams. Voting Regression is used to put together algorithms like Random Forest, Decision Trees, Linear Regression, Gaussian Regression, and Gradient Boosting into a single unit that can predict a team's win percentage over the course of a season with 93.3% accuracy. © 2022 IEEE.

关键词： Forecasting

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