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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Gunturu, Vijaya Padma Priya, V. Savithri Abishek B, Ebenezer Shetty, Shreya Buddhi, Dharam Shukla, Surendra Kumar SR University Department of ECE Telangana Warangal India Geethanjali College of Engineering and Technology Telangana Hyderabad India Department of Electronics and Communication Engineering Chennai India NMIMS Deemed-to-be-University Department of School of Technology Management and Engineering Maharashtra Mumbai India Uttaranchal Institute of Technology Uttaranchal University India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9781665487320

Corporate IT architecture benefits greatly from cloud services, which is supported by virtual machine, which makes resource centralization simple and reduces cost, space, and administrative work. Nevertheless, a lot of clients are still hesitant to Due to worries about the security of sensitive and confidential data, companies are making the shift from their traditional internal IT infrastructure to a cloud storage service. The possibility of corporate data breaches and privacy losses is increased by the methods for physically locating the popularity of vulnerability assessment exploits, cloud-based virtual machine instances, and bridge assaults across virtual servers. This research offers a strategy for reducing such risks and fostering client confidence in business cloud computing. Even with well designed software systems, new flaws are found on a daily basis, and hacking methods are evolving over time. This situation makes it seem unlikely that a corporation computer processing will be completelysecure;software companies in the internet are prone to security breaches. A well-designed attack contingency plan is the practical answer to the security issues. On the plus side, if an adequate defensive mechanism is in place, cloud computing's communal infrastructure may be exploited to successfully neutralize threats. We suggest such a cloud-based threat mitigation architecture. Various software flaws in the cloud have varying degrees of severity and effects on security measures (confidentiality, integrity, and availability). We continually track and assess the risk of penetration in various security variables using the Markov model (For instance: a change in the potential to undermine the confidentiality of the data.) Our methodology would make it easier for tenants to determine their ability to deploy a dynamic mitigation approach if there is a Meanwhile to Security Flaw (MTTSF) a drastic shift in risk. This architecture adds an additional layer of safety to thediffe

关键词： Virtual machine

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Various RZ percentage duty cycle coding with NRZ modulation technique signature on the fiber system performance

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Journal of Optical Communications 2024年第0期45卷 s1525-s1538页

作者： Zaki Rashed, Ahmed Nabih Reddy, Mittamidi Madhusudhan Snehith, Namgiri Kak Anwer, Twana Mohammed Ahammad, Shaik Hasane Inthiyaz, Syed Amzad Hossain Shaker, Fatma Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of VLSI Microelectronics Institute of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Tamilnadu Chennai India Department of Civil Engineering Institute of Aeronautical Engineering Dundigal Hyderabad India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur India Department of Physics College of Education Salahaddin University-Erbil Kurdistan Region Erbil Iraq Department of ECE Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Faculty of Electrical Engineering and Information Technology Institute of Theoretical Electrical Engineering Ruhr University Bochum Bochum Germany Department of Electrical and Electronic Engineering Jashore University of Science and Technology Jashore Bangladesh

This study has clarified various RZ Percentage Duty Cycle Coding with NRZ modulation Technique Signature on The Fiber System Performance. The total form lighted/electrical power after linear measured multimode fiber/FSO channel measured based different RZ percentage duty cycle coding with the assistance of NRZ modulation techniques is performed. The electrical/lighted form measured power after APD receiver with linear multimode fiber/FSO channel measured based different RZ percentage base band form duty cycle coding with the assistance of NRZ modulation techniques is demonstrated. The lighted based band form can be used for the modulation of the based band electrical signal form based on the RZ percentage duty cycle coding with the assistance of NRZ for both linear measured multimode fiber and FSO measured channel. © 2023 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Multimode fibers

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Investigation of agricultural waste and ZrB2 nanoparticles with reinforcement in aluminum alloy matrix

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Measurement: Sensors 2024年 33卷

作者： Rathish, C.R. Parthasarathy, P. Gopinath, S. Thanga Mariappan, L. Ashok, P. Arun Kumar, U. Department of Computer Engineering New Horizon College of Engineering Karnataka Bengaluru560103 India Department of Electronics and Communication Engineering CMR Institute of Technology Karnataka Bengaluru560037 India Department of ECE Karpagam Institute of Technology Tamilnadu Coimbatore641105 India Department of Smart Computing School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamilnadu Vellore India Lavale Maharashtra Pune India Tamil Nadu Coimbatore641021 India

Agricultural waste has the potential to serve several purposes, such as reinforcement. In recent times, researchers have incorporated agricultural waste into various materials with the aim of enhancing their qualities. By acting as a kind of reinforcement, it has the potential to decrease the quantity of agricultural waste that needs further processing. This work enhances the area of advanced materials by examining the characteristics of Al 7175 alloy reinforced with zirconium diboride (ZrB2) nanoparticles and rice husk ash, which is a byproduct of agriculture. Understanding and improving the wear resistance of composites is crucial in several industrial applications. This study involves the incorporation of ZrB2 nanoparticles into the Al 7175 alloy at three distinct concentrations, with the addition of 2.5 % rice husk. This study examines the correlation between wear parameters and responses (Cf), including friction force (FF), wear rate (Sf), and friction coefficient. Sliding distance, load, composition, and speed are among the wear parameters. The aforementioned combinations result in a load of 25.2 N, a rotational speed of 400 rpm, a sliding distance of 71.2 m, and a composition of 5 %. Taguchi signal-to-noise ratio (SNR) analysis is used to identify optimal combinations for minimizing Cf, Sf, and FF. Confirmation tests are conducted in order to ensure that the results align with the expected optimal combinations. Data scientists may also use an Artificial Neural Network (ANN) model as a valuable tool. It utilizes input data to forecast replies. The ANN model demonstrates strong performance throughout testing, validation, and training, with an overall prediction rate of 87.873 %. The study's results indicate that reducing the load and speed, while increasing the concentration of ZrB2 nanoparticles, may effectively decrease the wear rate. In addition, the use of rice husk enhances the wear resistance of the base alloy by 28 %. The alloy Al 7175 may experience a r

关键词： Wear resistance

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Fault Classification in Photovoltaic Arrays Using Convolutional Neural Networks with L2 Regularization

Fault Classification in Photovoltaic Arrays Using Convolutio...

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Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： Pradeep Kumar S. L. Umasankar Muntather Almusawi Pothireddy Surekha Srinivas Aluvala Department of ECE Nitte Meenakshi Institute of Technology Bengaluru India Electrical and Electronics Engineering S.A.Engineering College Poonamallee Tiruvallur India The Islamic university Najaf Iraq Department of Computer Science and Engineering GRIET Hyderabad Telangana India School of Computer Science and Artificial Intelligence SR University Warangal India

Fault detection in photovoltaic (PV) arrays is of paramount importance, because the output power is maintained at its highest value and the efficiency of the system is prolonged. In the past, there were issues of output accuracy of the detection method in complex situations such as PS and high impedance faults. This paper proposes a method for diagnosing PV failures based on the combination of Hybrid Transformer- Convolutional Neural Network (CNN) Architecture framework that is used for the purpose. Proposed method implies an extension of transformers that is capable to capture long-range dependencies of images, and CNNs to extract spatial features. This is because it performs in a higher way which is to achieve better fault classification performance. A contrast with the extant techniques that deal with limited few scenarios of faults, we have faced up to five different faulty scenarios, PS included and have devised the maximum power point tracking (MPPT). The results of this experimental work proved that the implemented hybrid method is much better than the previous methods regarding fault detection, and succeeded in having the accuracy of 88.4% compared to the other methods; and it is a promising one for betterment of the fault diagnosis in the photovoltaic systems.

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Internet of Medical Things (IoMT): Opportunities and Security Challenges

Internet of Medical Things (IoMT): Opportunities and Securit...

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electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： Ashokkumar. N Narayana Reddy Yatam Chandra. I Anita Christaline. J. R. Bhairavi Thiruveni. M Department of Electronics and Communication Engineering Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering college) Tirupati AndraPradesh Micron Technologies Hyderabad Telangana Rajalakshmi Institute of Technology Chennai School of Computer Science and Engineering Vellore Institute of Technology Chennai campus Chennai Department of ECE SRM Institute of Science and Technology Chennai Tamilnadu Department of ECE PSNA College of Engineering and Technology Dindigul Tamilnadu

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

The IoMT is nothing but a technology phase breakthrough in the healthcare industry that uses wearables, and other low-level digital data collection devices for better patient care and operational efficiency. Through this research, we seek to bring together the opportunities and security threats related to IoMT with a special focus on machine learning algorithms as a predictive tool for health assessment. Empirical experiments were conducted to determine the performance of four machine learning techniques (Random Forest, Support Vector Machine, K-Nearest Neighbors, and Deep Learning Neural Network) using different set of data covering medical sensor data and patient's health records. The prediction results show that the DNN-based model had higher accuracy than other algorithms, its deep learning producing the right outcomes of 0. 88, precision of 0. 89, recall of 0. With more than eighty-seventy-five percent recall rate and F1-score of 0. 88. The comparison will enlighten the advantages and shortcomings of various algorithms in using the IoMT data to predict the health outcomes, showing the prospect of the DNN as one of the better algorithms in the space. Along with this, this study brings to the foreground the significance of focusing on strong cybersecurity measures to ensure that patients' data remains protected and IoMT ecosystems are free from flaws.

关键词： Deep learning Support vector machines Data privacy Machine learning algorithms Reviews Telemedicine Internet of Medical Things Prediction algorithms Wearable devices Random forests

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Implementation of Human Robot Interaction with Motion Planning and Control Parameters with Autonomous Systems in Industry 4.0 2

Implementation of Human Robot Interaction with Motion Planni...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Chandramowleeswaran, G. Choubey, Chandan Pendem, Sireesha Pal, Shaunak Verma, Amit Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Commerce and Business Administration Tamilnadu Chennai India Ims Engineering College Department of Electronics & Communication Engineering Uttar Pradesh Ghaziabad India Cmr Technical Campus Kandlakoya Village Department of Ece Telangana Hyderabad India Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Vellore632014 India Graphic Era Hill University Department of Physics Dehradun India

ISBN: (纸本)9798350325799

By merging autonomous systems and human-robot interaction (HRI), Industry 4.0 has caused a paradigm shift in manufacturing and production processes. To maximize productivity and ensure smooth machine-human collaboration in this environment, motion planning, and control parameters must be effectively integrated with autonomous robots. With the help of hybrid deep learning models including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Recurrent Neural Networks (RNNs), the implementation of HRI with motion planning and control parameters in autonomous systems is proposed in this research. To increase productivity, safety, and flexibility in industrial settings, the suggested approach intends to enable robust and adaptive interaction between humans and robots. CNNs are used to analyze and comprehend the surroundings by the autonomous system through object identification and visual perception. LSTM and RNN models are used to combine this perception with the temporal components of motion planning and control. The suggested method allows the autonomous system to learn from sizable datasets of human-robot interactions, enabling the real-time prediction of human intentions and behavior. The models' ability to respond to changing settings and modify the motion planning and control parameters as necessary makes it easier to complete tasks quickly and work together. By solving important issues with human-robot collaboration, the adoption of this hybrid deep learning-based HRI system advances Industry 4.0. The suggested method improves the general effectiveness and safety of industrial operations by seamlessly integrating motion planning and control parameters with autonomous systems. Additionally, the hybrid deep learning models' adaptive nature enables ongoing learning and performance improvement over time. © 2023 IEEE.

关键词： Motion planning

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Analysis of software-defined radio for optical networking

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Journal of Optical Communications 2025年

作者： Kumar, Arun Sarveswara Rao, T.D.N.S.S. Chandrashekhara, K.T. Chakravarty, Sumit Gaur, Nishant Nanthaamornphong, Aziz Department of Electronics and Communication Engineering New Horizon College of Engineering Bengaluru India ECE Department Sri Vasavi Engineering College AP Tadepalligudem534101 India Department of CSE/ISE BMS Institute of Technology & Management Bangalore India Department of Electrical Engineering and Computer Engineering Kennesaw State University KennesawGA United States Department of Physics JECRC University Jaipur India College of Computing Prince of Songkla University Phuket Thailand

Software defined networking (SDN) is a communications methodology that centralizes network management and programmability resulting in more flexible network administration. This project aims to configure and simulate a software-defined network to implement customized, dynamically-assigned flow control rules between network nodes - replacing static routes. We consider virtual network embedding (VNE) in development of network routing algorithms. Through this project, we seek to demonstrate the feasibility and benefits of SDN-based network control for diverse systems, enhancing adaptability and efficiency in managing communication between diverse network nodes. The proposed work is crucial for advancing adaptive, efficient, and scalable communication systems, enabling dynamic resource management, enhanced interoperability, and supporting next-generation technologies like six generation (6G) and IoT seamlessly. © 2024 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Internet of things

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Fault Detection and Classification in Semiconductor Manufacturing for Sensor Screening Using Multi-Layer Deep Neural Network

Fault Detection and Classification in Semiconductor Manufact...

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Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： S S Mohan Reddy K Rakesh Srinivas Aluvala G. Bindu Ahmad Husseen Department of ECE SRKR Engineering College Bhimavaram Andhra Pradesh India Electronics and Communication Engineering Department Mallareddy Engineering College for Women (Autonomous) Hyderabad Telangana India School of Computer Science and Artificial Intelligence SR University Warangal India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh India The Islamic university Najaf Iraq

The fault detection and classification are a prediction model that employs the sensor equipment data to detect if each wafer is healthy or faulty which is significant to attain a greater yield. However, the manufacturing of semiconductors has difficulties in maintaining high-quality production because of potential defects. Hence, effective fault detection and classification is essential to overcome this issue. In this research, the multi-layer Deep Neural Network (DNN) is proposed for fault detection and classification in semiconductor manufacturing for sensor screening. Initially, the image is acquired from a benchmark dataset of Printed Circuit Board (PCB) dataset. The median filter is employed to eliminate the noise in the acquired image. Then, the feature extracted is established using a Gray Level Co-occurrence Matrix (GLCM). Finally, the multi-layer DNN is used to detect and classify as healthy or faulty in semiconductors effectively. The existing techniques like Convolutional Neural Network-VGG16 (CNN-VGG16), Global Contextual attention augmented You Only Look Once (GCC-YOLO), DL-based Path Aggregation Network (PANet), and improved fully CNN are used to compare with proposed technique. When compared to this existing technique, the multi-layer DNN achieves a better accuracy of 99.89% respectively.

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Wireless Sensor Network-based Intrusion Detection Technique using Deep Learning Approach of CNN-GRU 8

Wireless Sensor Network-based Intrusion Detection Technique ...

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8th International Conference on Communication and electronics Systems, ICCES 2023

作者： Sagar, Anita Anushkannan, N.K. Indumathi, G. Vasant Muralidhar, Nikale Dhamotharan, K.A. Malini, P. Patliputra -University College of Commerce Arts & Science Department of Physics Bihar Patna India Kathir College of Engineering Department of Ece Tamilnadu Coimbatore India Srm Institute of Science and Technology Cse Ramapuram Tamilnadu Chennai India Ayat Shikshan Sanstha's Dada Patil Mahavidyalaya Karjat Department of Physics Karjat Maharashtra Ahmednagar India Erode Sengunthar Engineering College- Autonomous Computer Science and Engineering Erode Tamilnadu Perundurai India K. Ramakrishnan College of Engineering Department of Electronics and Communication Engineering Trichy Tamil Nadu Samayapuram India

ISBN: (纸本)9798350396638

In a WSN, sensors are always collecting information and transmitting it to other nodes. Their primary uses are in fields like defense, smart city development, and agricultural monitoring. The WSN needs to perform at a premium for these uses. Yet, there are many potential security concerns that could compromise a WSN's performance. Any interference with the WSN could severely degrade its functionality and lead to catastrophic failures. As a result, having the ability to quickly identify and stop intrusions is crucial. The goal of this proposed is to use a GRU-CNN model for quick detection and prevention of any intrusion. After receiving the input, the proposed method uses normalization and discretization for preprocessing the data, PSO (Particle Swarm Optimization) for feature extraction, CFS for feature selection and finally training the model by CNN-GRU. To better detect intrusions in wireless sensor networks, a GRU-CNN hybrid neural network model was presented;although the CNN module is responsible for extracting the feature vector from other high-dimensional data, the GRU module is responsible for from time sequence data to extract the feature vector. © 2023 IEEE.

关键词： Wireless sensor networks

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Fuzzy Control and Simulink Model of Boiler Water Level 23

Fuzzy Control and Simulink Model of Boiler Water Level

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Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Neeraj Gupta Lalit Rai Devendra Kumar Somwanshi Prashant Kumar Rashmi Gupta Anil Kumar ECE Amity University Haryana India ECE J.C. Bose University of Science & Technology YMCA Faridabad India121006 India Electronics & Communication Engineerring Poornima College of Engineering India CSE Amity University Haryana India Department of Computer Science and Engineering Poornima Institute Of Engineering and Technology India

ISBN: (纸本)9798400709418

In this paper, simulation analysis of water level control of boiler is presented with the help of fuzzy controller. PID controller parameters proportional, integral and differential gain (Kp, Ki, Kd) are tuned with the help of Fuzzy Logic. The ranges of Kp, Ki and Kd are the fuzzy rules for the controller. Triangular geometric function-related type is used as fuzzy. The Simulink model for water level of boiler system controlled by Fuzzy Logic is developed. The Fuzzy Logic behavior in the surface form of the Simulink model of Boiler is also described. The Fuzzy Logic Control of a boiler system has been explained and the settling point has been achieved as 208s.

关键词： Defuzzification

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