G. Yedukondalu

Koneru Lakshmaiah Education Foundation

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Mahesh Kotha

CMR University

Ravindra Changala

Guru Nanak Institutions

M. Srinivasa Rao

GITAM University

J. Anand Chandulal

Koneru Lakshmaiah Education Foundation

Annapurna Gummadi

Institute of Management Technology

Adapa Gopi

Koneru Lakshmaiah Education Foundation

Karnati Durga

CMR University

Brinder Kumar

Guru Gobind Singh Medical College and Hospital

Guna Santhoshi

K. Sri Vasavi

Cooperative Institutions

Koneru Lakshmaiah Education Foundation

Vignan's Foundation for Science, Technology & Research

GITAM University

CMR University

Jawaharlal Nehru Technological University, Hyderabad

Chaitanya Bharathi Institute of Technology

Advanced Numerical Research and Analysis Group

Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology

SRM Institute of Science and Technology

Institute of Science and Technology

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Framework for Virtualized Network Functions (VNFs) in Cloud of Things Based on Network Traffic Services

International Journal on Recent and Innovation Trends in Computing and Communication (2023)

G. Yedukondalu Yasmeen Yasmeen G. Vinoda Reddy Ravindra Changala Mahesh Kotha

The cloud of things (CoT), which combines the Internet of Things (IoT) and cloud computing, may offer Virtualized Network Functions (VNFs) for IoT devices on a dynamic basis based on service-specific requirements. Although the provisioning of VNFs in CoT is described as an online decision-making problem, most widely used techniques primarily focus on defining the environment using simple models in order to discover the optimum solution. This leads to inefficient and coarse-grained provisioning since the Quality of Service (QoS) requirements for different types of CoT services are not considered, and important historical experience on how to provide for the best long-term benefits is disregarded. This paper suggests a methodology for providing VNFs intelligently in order to schedule adaptive CoT resources in line with the detection of traffic from diverse network services. The system makes decisions based on Deep Reinforcement Learning (DRL) based models that take into account the complexity of network configurations and traffic changes. To obtain stable performance in this model, a special surrogate objective function and a policy gradient DRL method known as Policy Optimisation using Kronecker-Factored Trust Region (POKTR) are utilised. The assertion that our strategy improves CoT QoS through real-time VNF provisioning is supported by experimental results. The POKTR algorithm-based DRL-based model maximises throughput while minimising network congestion compared to earlier DRL algorithms.

Provisioning

Virtual network

10.17762/ijritcc.v11i11s.8068

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Intrusion Detection System Framework Using Machine Learning

2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA) (2021)

G. Yedukondalu G. Hima Bindu Jacopo Pavan Gopalakrishnan Venkatesh A. SaiTeja

Intrusion Detection System (IDS) is one of the most important security tool for many security issues that are prevailing in today's cyber world. Intrusion Detection System is designed to scan the system applications and network traffic to detect suspicious activities and issue an alert if it is discovered. So many techniques are available in machine learning for intrusion detection. The main objective of this project is to apply machine learning algorithms to the data set and to compare and evaluate their performances. The proposed application has used the SVM (Support Vector Machine) and ANN (Artificial Neural Networks) Algorithms to detect the intrusion rates. Each algorithm is used to detect whether the requested data is authorized or contains any anomalies. While IDS scans the requested data if it finds any malicious information it drops that request. These algorithms have used Correlation-Based and Chi-Squared Based feature selection algorithms to reduce the dataset by eliminating the useless data. The preprocessed dataset is trained and tested with the models to obtain the prominent results, which leads to increasing the prediction accuracy. The NSL KDD dataset has been used for the experimentation. Finally, an accuracy of about 48% has been achieved by the SVM algorithm and 97% has been achieved by ANN algorithm. Henceforth, ANN model is working better than the SVM on this dataset.

Data set

Feature (linguistics)

10.1109/icirca51532.2021.9544717

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Inbuilt Framework Using IoT and Computer Vision Technique for Automatic Visitor Management

2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA) (2023)

G. Yedukondalu Nichenametla Rajesh Brinder Kumar G. Sudha Gowd R. Suguna

The Visitor Monitoring (VM) concept is based on the use of systems and gadgets to improve occupant comfort, energy efficiency, privacy, and safety of its inhabitants. Visitor monitoring system is a system through which we can monitor the people who are entering into the workplaces or offices in a digital way. These highly developed electronic dwellings are multifaceted, intricate systems that require research to improve upon those procedures. However, there is lack of consensus in the existing VM concept. So, there is a requirement to research and innovate more effective VM systems to countermeasure new and emerging processes. This research work proposes an automated visitor management tool to manage and coordinate the visitors by using IoT and computer vision techniques. Experiments were carried out on 100 visitors by using putty platform and observed noticeable result.

Visitor pattern

10.1109/iceca58529.2023.10395247

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Video Search Reranking using Multimodel fusion Technique

Ravi Regulagadda G. Yedukondalu

Analyzing click-through data from a huge search engine log information shows that users are usually interested in the top-ranked portion of returned search results. So, it is crucial for search engines to achieve high accuracy on the top-ranked documents. While many methods exist for enhancing video search performance, they either pay less attention to the above factor or encounter difficulties in practical applications. In this thesis, we present an easy and quality reranking method, called CR-Reranking, to increase the retrieval effectiveness. To offer high accuracy on the top-ranked results, CR-Reranking employs a cross-reference(CR) strategy to fuse multimodal cues. Specifically, multimodal features are first utilized separately to rerank the initial returned results at the cluster level, and then all the ranked clusters from different modalities are cooperatively used to infer the shots with high relevance. After the fusion process the results shows that the search quality, especially on the top-ranked results, is improved significantly.

Relevance

Fuse (electrical)

Source

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MOCF: A Multi-Objective Clustering Framework using an Improved Particle Swarm Optimization Algorithm

International Journal on Recent and Innovation Trends in Computing and Communication (2022)

G. Yedukondalu K. Samunnisa M. Bhavsingh I S Raghuram Addepalli Lavanya

Traditional clustering algorithms, such as K-Means, perform clustering with a single goal in mind. However, in many real-world applications, multiple objective functions must be considered at the same time. Furthermore, traditional clustering algorithms have drawbacks such as centroid selection, local optimal, and convergence. Particle Swarm Optimization (PSO)-based clustering approaches were developed to address these shortcomings. Animals and their social Behaviour, particularly bird flocking and fish schooling, inspire PSO. This paper proposes the Multi-Objective Clustering Framework (MOCF), an improved PSO-based framework. As an algorithm, a Particle Swarm Optimization (PSO) based Multi-Objective Clustering (PSO-MOC) is proposed. It significantly improves clustering efficiency. The proposed framework's performance is evaluated using a variety of real-world datasets. To test the performance of the proposed algorithm, a prototype application was built using the Python data science platform. The empirical results showed that multi-objective clustering outperformed its single-objective counterparts.

Centroid

Python

10.17762/ijritcc.v10i10.5743

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Citations (9)

Design and analysis of a lemon cutting robot

AIP conference proceedings (2023)

N. Rishikanth L. Siddiramulu G. Surya Prakash Rao G. Yedukondalu Subramanyam Pavuluri

Lemon juice

10.1063/5.0159251

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A Framework for Fast Host-Based Intrusion Detection System using Data

International Journal of Modern Trends in Engineering and Research (2015)

G. Yedukondalu M. Srinivasa Rao J. Anand Chandulal

Source

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An effective investigation of chatter prediction system on Al6061 alloy in an end milling process

Journal of Engineering and Applied Science (2024)

Trivikrama Raju C Jakeer Hussain S G. Yedukondalu Murahari Kolli

Abstract Visual examination of the surface topography, in conjunction with the other sensors, may confirm the existence of chatter. Online chatter detection during real machining operations is possible with the use of sensors, and the presence of noise in their output and restricted bandwidth are the major drawbacks of these sensors. Productivity drops and manufacturing costs go up when there is a lot of chatter in the machining process. In the present paper, an integrated spindle tool system is modeled using finite element method with Timoshenko beam theory including rotational and shear deformation effects. To maximize the average stable depth of cut in an end milling process while simultaneously minimizing the chatter vibration levels, real time and offline strategies have been investigated. Machining experiments performed on Al6061-alloy specimens provide an empirical confirmation of the stability boundaries. The surface topography methods such as scanning electron microscope (SEM) and optical microscope images along with vibration levels are considered, to identify chatter marks under various machining conditions, which helps to assure cutting process stability. Stability lobe diagrams are plotted with these derived conditions and observed at an incremental level in the axial depths of the cut. The methodology shown in this paper improves the machining stability of the end milling with the reduction in the tool tip vibrations.

End milling

10.1186/s44147-024-00489-5

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Host-Based Intrusion Detection System Using File Signature Technique

Lecture notes in networks and systems (2017)

G. Yedukondalu J. Anand Chandulal M. Srinivasa Rao

Jaccard index

Signature (topology)

10.1007/978-981-10-3818-1_25

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Comparison of methods of prediction of compressive strength of concrete using multiple linear regression in microsoft excel and artificial neural networks in RStudio

AIP conference proceedings (2021)

Mohammed Hussain G. Yedukondalu Y. Kamala Raju V. Kamakshi Prasad

Compressive strength (CS) of concrete is a key quality factor that is being monitored continuously in all construction projects which use huge quantity of concrete throughout the world. Engineers need to be open minded with the attitude of lifelonglearning to upskill with the ever-evolving softwareand hardware technologies. Seven day and twenty-eight-day compressive strengths of concrete samples with varied amounts of cement, blast furnace slag, fly ash, water, super plasticizer, coarse aggregate, and fine aggregate are studied. Multiple Linear Regression (MLR) models are fitted for this data in Microsoft Excel. Artificial Neural Networks (ANNs) in RStudio are developed for this data. The performances of both methods are compared. This paper takes care of Goal 12 of United Nations Sustainable Development of ensuring sustainable consumption and production patterns as environmentally degrading materials (flyash and blast furnace slag) are used.

Slag (welding)

Microsoft excel

10.1063/5.0058070

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Citations (1)