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3rd International Conference on Electrical, Electronics, Information and Communication technologies, ICEEICT 2024

作者： Kavitha, T. Jaswanth, J. Kumar, M. Manoj Supraja, C. Ashokkumar, N. Yatm, Narayana reddy Vel Tech Rangarajann Dr.Sagunthala R&d Institute of Science and Technology Chennai Avadi India Tirupati India Micron Technologies Telangana Hyderabad500081 India

ISBN: (纸本)9798350369083

In recent years, multiple-input multiple-output (MIMO) systems have gained significant attention in wireless communication due to their ability to enhance spectral efficiency and reliability. designing efficient signal processing techniques for MIMO systems is crucial for achieving high data rates and reliable communication. In this paper, we propose a novel approach utilizing autoencoder-based deep neural networks (dNNs) for signal processing in MIMO systems. The autoencoder is a type of neural network that learns to encode input data into a lower- dimensional representation and then decode it back to its original form. By leveraging the inherent dimensionality reduction capabilities of autoencoders, we aim to efficiently capture the spatial characteristics of MIMO channels and mitigate the effects of channel impairments. First, we present the architecture of the proposed autoencoder-based dNN tailored for MIMO systems. The input to the network consists of received signals from multiple antennas, and the output is the estimated transmitted symbols. We design the network layers to perform feature extraction, channel estimation, and symbol detection jointly. Furthermore, we investigate the training methodology for the proposed dNN. We employ a combination of supervised learning with labeled data and unsupervised learning to exploit the abundance of unlabeled data typically available in MIMO systems. We discuss the challenges associated with training deep networks for MIMO channels, including data preprocessing, network initialization, and optimization techniques. To evaluate the performance of the proposed approach, extensive simulations are conducted using realistic channel models and various system configurations. We compare the proposed autoencoder-based dNN with conventional MIMO detection techniques, such as maximum likelihood detection and linear precoding. Performance metrics such as bit error rate (BEr) and spectral efficiency are used to assess the effective

关键词： Channel estimation

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Strategic Workforce Planning in the digital Age using deep Naive Bayes Approach for Hr Professionals 10

Strategic Workforce Planning in the Digital Age using Deep N...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Praveenadevi, d. rajapriya, M. Aparna, dhulipala Anmitha, T.Mani Sri Neha, Koganti Harshita, V. Koneru Lakshmaiah Education Foundation A.P Vaddeswaram India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Chennai Avadi India

ISBN: (纸本)9798350384369

Organizations need to prioritize strategic business planning (SWP) to gain competitive advantage. However, Hr professionals often have difficulty using existing information to predict employees' future needs. This work focuses on separating existing data from a new SWP proposal called deep Naive Bayes (dNB). The research chose the dNB method because it is effective in processing large data sets. This study aims to demonstrate how dNB can better meet future workforce needs by analyzing a variety of data, including public employees, products using technology, and business. The results show that the dNB method outperforms traditional methods in terms of accuracy and scalability. This allows Hr professionals to make informed decisions about workforce planning for the digital age. This research is useful because it provides a new foundation for SWP using advanced machine learning techniques. © 2024 IEEE.

关键词： deep Naive Bayes digital Age Hr Professionals Machine Learning Strategic Workforce Planning

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Influence of nano AL2O3 on compression ignition engine characteristics fuelled with Mahua biodiesel

Influence of nano AL2O3 on compression ignition engine chara...

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作者： Backiyaraj, A. Parthasarathy, M. Murugu Nachippan, N. Senthilkumar, P.B. Kumaran, T. Department of Mechatronics Engineering Agni College of Technology Thalambur Chennai600130 India Department of Automobile Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai600062 India Department of Aeronautical Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai600062 India

Growing energy demand and rising emissions have prompted testing on alternative energy sources for IC engine applications. Alternative energies are commonly extracted from biomass waste, algae and plant seeds. Many environmental difficulties develop as a result of the use of petroleum products in IC engines. As a result, adapting an alternative fuel to an existing engine may necessitate some design changes in order to achieve greater or nearly equal performance of conventional fuels. The primary objective of this experiment is to increase the efficiency of the engine and reduce the concentration of HC, CO2 and NOx. The experimental investigation was assessed in a single cylinder, compression ignition engine fuelled with mahua biodiesel and its diesel blends with AL2O3 nano additive. The engine was analysed with different test fuels such as diesel, 10 % mahua biodiesel + 90 % diesel (B10), 20 % mahua biodiesel + 80 % diesel (B20), 70 % diesel (B30) at different load conditions. From the tested fuel, 20 % mahua biodiesel + 80 % diesel (B20) showed reasonable performance and tail pipe emission compared to other biodiesel blends. The preparation of nano blended biodiesel was based on 80 % of diesel, 20 % of mahua biodiesel and 10, 25, 50 ppm of nano AL2O3. The 50 ppm of nano AL2O3 blended biodiesel shows better thermal efficiency and reduced emissions due to presence of nanoparticle in the blends which enhance the fuel atomization characteristics. BTE of 50 ppm of nano AL2O3 blended biodiesel increased 6.05 %, when compared with the diesel. The emission of HC, CO, NOx, and Smoke by 50 ppm of nano AL2O3 blended biodiesel was reduced by 19.52 %, 29.81 %, 14.48 %, and 23.35 %, respectively when correlated to base fuel. On whole, it was concluded that nano AL2O3 blended mahua biodiesel found to be a better alternative energy for unmodified existing CI engine. © 2022

关键词： Aluminum oxide

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A Method for Predicting Coronary Heart disease Using Machine Learning Approaches 2

A Method for Predicting Coronary Heart Disease Using Machine...

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2nd International Conference on recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： ramathilagam, A. Pradeepha, S. ram, M. Preethi Lakshmi, B. Sankara Kumar, S. Arun Neels Ponkumar, d. david P.S.R. Engineering College Department of Computer Science and Engineering Tamil Nadu Sivakasi India Ramco Institute of Technology Department of Computer Science and Engineering Tamil Nadu Rajapalayam India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350366570

Predicting a patient's heart disease risk is a major difficulty in medical data analysis for preventative measures and mortality reduction. Early diagnosis is essential for timely treatment and continuing monitoring by healthcare professionals, however medical staff's limitations in patient surveillance frequently prevent it. Early cardiac problem detection and medical monitoring reduce fatality rates. The accuracy of heart disease detection is uncertain, and clinicians cannot regularly communicate with patients. Machine learning (ML) may help clinicians identify and forecast heart diseases using global healthcare data. This project will employ many feature selection methods to create a viable ML system for early heart disease prediction. It is possible to correctly forecast cardiac illness using a variety of machine learning methods, including Logistic regression, random Forest, Naïve Bayes classifier, decision Tree, and LrNd classifier. With the help of nearby technology, these algorithms may detect heart problems and provide treatment. © 2024 IEEE.

关键词： Logistic regression

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Mitigating denial of Service Attacks through Machine Learning-based Intrusion detection

Mitigating Denial of Service Attacks through Machine Learnin...

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Hussian, Mohammed Ashraf Vasantha, S. Ala, Sowmya Uma Mageswari, r. Pendam, Keerthi rame, Swetha Vardhaman College of Engineering Department of CSE Hyderabad India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of CSE Chennai India

ISBN: (纸本)9798331539948

This research examines the application of twelve leading Machine Learning (ML) techniques, utilizing the Pycaret module, to effectively analyze distributed denial of Service (ddoS) attacks. The objective is to develop a highly efficient methodology that facilitates the rapid detection, mitigation, and prevention of such cyber-attacks by thoroughly analyzing incoming network traffic and packet patterns. In comparison to conventional ML models like random Forest, decision Tree, and Gradient Boosting, the study's findings highlight that Pycaret, especially when integrated with the XGBoost model, significantly improves the accuracy and speed of ddoS detection. This approach plays a crucial role in preserving the availability, integrity, and confidentiality of cloud services, thereby minimizing financial losses, reputational damage, and strengthening overall cybersecurity in today's fast-paced digital world. © 2024 IEEE.

关键词： Cyber attacks

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Fermatean Neutrosophic Fuzzy Graphs: A Study on the Winner Index with Enhancing Election Analysis

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Neutrosophic Sets and Systems 2025年 80卷 128-148页

作者： Al-Omeri, Wadei Fares Kaviyarasu, M. rajeshwari, M. Department of Mathematics Faculty of Science and Information Technology Jadara University Irbid Jordan Department of Mathematics Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Tamilnadu Chennai600062 India School of Engineering Presidency University Bangalure India

In this article, we discuss the fermatean neutrosophic graph of Wiener index, which is an essential topological index formed according to geodesical distance of vertices. The Wiener index is an important factor to describe the structure of a graph and we defined it in relation to fermatean neutrosophic graphs and computed it for some specific fermatean neutrosophic graph structures including complete fermatean neutrosophic graphs, cycles and trees. Subsequently, the Wiener index is compared with the connectivity index, a core-degree based parameter, using a sequence of theorems. As an application the study responds to the difficulties in election analysis in democratic environments where voter choices are often nuanced an unpredictable and the methods of measurement are not sensitive enough to capture these changes. To improve the modeling of election data, this work employs fermatean neutrosophic graphs (FNGs) and the Wiener index, which distinguish nodes that represent leadership qualities, policy suggestions, and public commitment as well as the relationship between these nodes. This approach manages uncertainty and indeterminacy well and provides a sound method of enhancing the measurability and credibility of analytical techniques in managing complicated events like elections. © 2025, University of New Mexico. All rights reserved.

关键词： Graph theory

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Area and Power Efficient Transposition Buffer design for 2d-dCT Architecture in Video Applications 9

Area and Power Efficient Transposition Buffer design for 2D-...

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9th IEEE International Conference on Smart Structures and Systems, ICSSS 2023

作者： Preethi, d. Sasikala, G. Gnanaprakash, V. Sathyasri, B. Saranya, N. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Ece Chennai India Bannari Amman Institute of Technology Ece Sathy India

ISBN: (纸本)9798350384208

The discrete cosine transform (dCT) in one and two dimensions are implemented in this study using effective area and delay designs. These are supported in lengths ranging from 4 to 32. The various video coding standards for picture compression use dCT blocks. In order to do the calculation, architecture is split into two 1d-dCT calculations employing a transpose buffer for the 2d-dCT Separability Property. Using the 1d-dCT architecture currently in use, folded and parallel forms of 2d-dCT architectures are implemented. The same Transpose buffer is used by each of these structures. Compared to the current transposition buffer, the proposed transpose buffer is faster and takes up less space. As a result, both 2d-dCT designs' size, low power, and delay are minimized. © 2023 IEEE.

关键词： discrete cosine transforms

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Identification of Nutrient Content of Psidium Guajava and Syzygium Cumini Leaves Using Hyperspectral Imaging 2

Identification of Nutrient Content of Psidium Guajava and Sy...

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2nd International Conference on Smart technologies and Systems for Next Generation Computing, ICSTSN 2023

作者： Bakiya, A. Neeli, Venkatesh Priya, dhupam Mohana Lakshmi Pavan, Chapala rama Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai600062 India

ISBN: (纸本)9798350348002

The Nutrient contents of the leaf play a vital role in the plant growth, metabolism, and nutritional values of the fruits, etc. However, the conventional method for identifying the leaf nutrition content is chemical analysis, which is timeconsuming, destructive, and labor-intensive. Therefore, a nondestructive approach technique, namely hyperspectral imaging, has been increasingly used to determine the spectral characteristics of plants. This study investigated hyperspectral imaging to identify the leaf nutrition content of two leaves (Psidium Guajava and Syzygium Cumini). To identify the nutrient content of the leaf, 30 samples were taken from the two leaves with four different conditions (standard leaf, diseased leaf, dried leaf, and pigmented leaf). Further, the spectral signature of the leaves was extracted and fed into the developed regression techniques. The three different types of developed regression algorithms such as Savitzky-Golay Partial Least Squares regression (SG-PLS), Support Vector Machine regression (SVMr), and Partial Least Squares regression (PLS), were performed for the identification of nutrition content in the leaves. The results demonstrated that the SG-PLS could accurately predict leaf nutrition content using hyperspectral imaging data, with root mean squared errors rMSE =0.536 and r}^{2=0.992 for Psidium Guajava and rMSE =1.54 and r}^{2=0.936 for Syzygium cumini leaves in normal leaf condition. Further, the results show that hyperspectral imaging can be a powerful tool for nondestructive, rapid, and accurate measurement of plant nutrient status. Also, the proposed model will identify the difference between the normal, dried, pigmented, and diseased leaves using the rMSE values. © 2023 IEEE.

关键词： Hyperspectral imaging

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Efficient Supply Chain Management System Using ArM COrTEX-M3

Efficient Supply Chain Management System Using ARM CORTEX-M3

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Coumar, S. Oudaya Surender, r. Yazhinian, S. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Avadi Tamil Nadu Chennai600062 India Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798350315127

A supply chain network has services and options for distribution that play out the job for materials acquirement and change from momentary to finish items and the circulation of complete items to clients. All manufacturing organizations have supply chains. This proposed supply chain management system consists of GSM module, GPS module, rFId reader and tags. This vehicle module serves for surveillance, enabling the real-time tracking, monitoring, and delivery of goods. The ArM COrTEX M3 microcontroller collects this data and offers details about the supply of goods. This supply chain management can be used by courier service, food, and automobile processing industries for real time monitoring. © 2023 IEEE.

关键词： Global positioning system

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Evaluation of durability Characteristics on Hybrid Fibre reinforced High-Performance Concrete Incorporated with Graphene Oxide

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Iranian Journal of science and technology - Transactions of Civil Engineering 2024年 1-24页

作者： Anish, V. Logeshwari, J. Samson, S. Department of Civil Engineering Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Tamil Nadu Chennai India

The longevity of concrete structures rely on, the capability of concrete to withstand environmental deterioration, which further enhance the operating life of structure, thereby reducing unforeseen expense for maintenance and rehabilitation. To overcome the issue, a high-performance concrete containing hybrid steel-basalt fibres and graphene oxide was evaluated for their resistance to environmental deterioration. The experimental analysis done in this investigation to determine the concrete, with long lasting and great performance to durability were water absorption, water sorptivity, rapid chloride permeability, acid attack, alkaline attack and sulphate attack tests. Various proportions of high-performance concrete (HPC) with hooked end carbon steel fibres, basalt fibres, hybridization of fibres and graphene oxide were tested for their suitability against various durability tests. Supplementary cementitious components like fly-ash and micro-silica were used to advance the performance of concrete and to reduce the usage of cement, which is responsible for CO₂ emission, abrasion and excessive heat of hydration. Graphene oxide in concrete, helps in attaining a denser microstructure by filling the voids and pores, thereby making it less permeable, with enhanced durability, while the fibre reinforcement in concrete helps in improving the mechanical qualities, by bridging the micro and macro cracks. The water absorption and sorptivity tests on HPC, results with increase in percentage of permeability, which might be because of the voids created due to the reinforcement of fibres, however with inclusion of graphene oxide, the percentage of water absorption and sorptivity was reduced in HPC with fibre reinforcements. rapid chloride permeability tests on concrete specimens were also noticed with reduced permeability in HPC with inclusion of graphene oxide and increased permeability due to the reinforcement of fibres in HPC, however was within the limits. Acid, Alk

关键词： Chemical attack test Hybrid hooked end carbon steel-basalt fibres rapid chloride permeability Water absorption Water sorptivity

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