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Levy Flight Adopted Particle Swarm Optimization-based Resource Allocation Strategy in Fog Computing

Chinese Journal of Electronics 2023年

作者： Sharmila Patil(Karpe) Brahmananda S H Research Scholar Department of Computer Science and Engineering GITAM School of Technology GITAM Deemed University Professor Department of Computer Science and Engineering GITAM Bengaluru

The prevalence of the Internet of Things(Io T) is unsteady in the context of cloud computing, it is difficult to identify fog and cloud resource scheduling policies that will satisfy users’ Qo S need. As a result, it increases the efficiency of resource usage and boosts user and resource supplier profit. This research intends to introduce a novel strategy for computing fog via emergencyoriented resource allotment, which aims and determines the effective process under different parameters. The modeling of a non-linear functionality that is subjected to an objective function and incorporates needs or factors like Service response rate, Execution efficiency, and Reboot rate allows for the resource allocation of cloud to fog computing in this work. Apart from this, the proposed system considers the resource allocation in emergency priority situations that must cope-up with the immediate resource allocation as well. Security in resource allocation is also taken into consideration with this strategy. Thus the multi-objective function considers 3 objectives such as Service response rate, Execution efficiency, and Reboot rate. All these strategies in resource allocation are fulfilled by Levy Flight adopted Particle Swarm Optimization(LF-PSO). The evaluation is performed to determine whether the developed strategy is superior to numerous traditional schemes. The cost function attained by the adopted technique is 120, which is 19.17%, 5%, and 2.5%greater than the conventional schemes like GWSO, EHO,and PSO, when the number of iterations is 50.

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Analyze the impact of feature selection techniques in the early prediction of CKD

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 66-77页

作者： Hema, K. Meena, K. Pandian, Ramaraj Department of Computer Science and Engineering GITAM School of Technology GITAM University Bangalore India Netapp San JoseCA United States

Background: Chronic renal disease, often known as Chronic Kidney Disease (CKD), is an illness that causes a steady decline in kidney function. As per the World Health Organization survey, the incidence of CKD may increase from 10% to 13% by 2030. Because of the lack of symptoms in the initial phase, diagnosing CKD early on may be difficult. The key objective of this study is to develop a forecasting model for the early detection of chronic renal disease. Methods: In medical science, Machine Learning (ML) Techniques play a significant role in disease prediction despite numerous studies conducted to categorize CKD in patients using machine learning tools. Most researchers need to analyze the impact of feature selection techniques, yielding high-quality and reliable results. The efficiency of any Techniques/Algorithms depends on feature selection, feature extraction, and classifiers. In this work, the impact of feature selection is experimented with using the Exhaustive Feature Selection (EFS) method. For the early prediction of CKD, a comparative examination of machine learning classifiers, including Gradient Boost (GB), XGBoost, Decision Tree (DT), Random Forest (RF), and KNN (k-nearest neighbors), are utilized. Results: Two types of datasets, standard (New Model) & real-time data sets collected from the dialysis unit of a reputed hospital in Chennai, are used to carry out extensive experiment analysis. Various metrics, including Accuracy, Precision, Recall, and F1-score, are used to tabulate the results of experiments conducted to measure the performance of the proposed approach for various combinations of test and training data. Conclusion: CKD is an irreversible and silent disease;it might have a high impact on many people and begin to manifest themselves at an early age in life. This research paper analyses the effect of feature selection techniques on early CKD prediction. © 2023 The Authors

关键词： Forecasting

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Federated Approach for Privacy-Preserving Traffic Prediction Using Graph Convolutional Network

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Journal of Shanghai Jiaotong university(science) 2024年第3期29卷 509-517页

作者： LONARE Savita BHRAMARAMBA Ravi Pimpri Chinchwad College of Engineering Pune 411044India Department of Computer Science and Engineering GITAMViskhapatnam 530045India

Existing traffic flow prediction frameworks have already achieved enormous success due to large traffic datasets and capability of deep learning ***,data privacy and security are always a challenge in every field where data need to be uploaded to the *** learning(FL)is an emerging trend for distributed training of *** primary goal of FL is to train an efficient communication model without compromising data *** traffic data have a robust spatio-temporal correlation,but various approaches proposed earlier have not considered spatial correlation of the traffic *** paper presents FL-based traffic flow prediction with spatio-temporal *** work uses a differential privacy(DP)scheme for privacy preservation of participant's *** the best of our knowledge,this is the first time that FL is used for vehicular traffic prediction while considering the spatio-temporal correlation of traffic data with DP *** proposed framework trains the data locally at the client-side with *** then uses the model aggregation mechanism federated graph convolutional network(FedGCN)at the server-side to find the average of locally trained *** results of the proposed work show that the FedGCN model accurately predicts the *** scheme at client-side helps clients to set a budget for privacy loss.

关键词： federated learning(FL) traffic flow prediction data privacy graph convolutional network(GCN) differential privacy(DP)

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A Learning-Based Ensemble Algorithm with Optimal Selection for Outlier Detection

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第14期49卷 171-192页

作者： Ginni, Girish Reddy Lade, Srinivasa Chakravarthi Computer Science Engineering GITAM University Gandhi Nagar Rushikonda Andhra Pradesh Visakhapatnam530 045 India Department of Computer Science and Engineering College GITAM University Gandhi Nagar Rushikonda Andhra Pradesh Visakhapatnam530 045 India

In this paper, we propose a Learning-based Ensemble Method with Optimal selection strategy (LbEM-OSS), which presents a new outlier detection algorithm that captures only outstanding ones of constituent models. Using KNN to define local regions and Pearson correlation to evaluate the detectors makes the ensemble robust. Our method can adapt and generalize better across different high-dimensional datasets by generating pseudo-ground truths with average and maximum aggregation strategies. On a wide range of benchmark datasets, LbEM-OSS outperformed both statistics-based and neural ensemble methods, which achieved state-of-the-art ROC-AUC as high as 97.78% in the best-case and 4-8% AUC improvements over existing methods on average. These results portray its potential for noise, different dimensionality, and heterogeneous data nature. Moreover, it is highly scalable and accurate, which makes it an essential application in practical fields like fraud detection, network security, and healthcare. This research highlights the need for dynamic selection approaches within ensemble methods, providing the groundwork for future developments in sound outlier detection. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Optimal detection

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Drug and Vaccine Extractive Text Summarization Insights Using Fine-Tuned Transformers

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Journal of Artificial Intelligence and Technology 2024年第4期4卷 351-362页

作者： Rajesh Bandaru Y.Radhika Department of Computer Science Engineering GITAM(Deemed to be University)VisakhapatnamIndia

Text representation is a key aspect in determining the success of various text summarizing *** using pretrained transformer models has produced encouraging *** the scope of applying these models in medical and drug discovery is not examined to a proper *** address this issue,this article aims to perform extractive summarization based on fine-tuned transformers pertaining to drug and medical *** research also aims to enhance sentence *** the extractive text summarization aspects of medical and drug discovery is a challenging task as the datasets are ***,this research concentrates on the collection of abstracts collected from PubMed for various domains of medical and drug discovery such as drug and COVID,with a total capacity of 1,370 abstracts.A detailed experimentation using BART(Bidirectional Autoregressive Transformer),T5(Text-to-Text Transfer Transformer),LexRank,and TexRank for the analysis of the dataset is carried out in this research to perform extractive text summarization.

关键词： BART BERT extractive text summarization LexRank TexRank

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Enhanced Ransomware Detection using a Hybrid Quantum-Classical Convolutional Neural Network with Boosted Sooty Tern Optimization 4

Enhanced Ransomware Detection using a Hybrid Quantum-Classic...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Chaithanya, B.N. Patil, Renuka R. Gitam School of Technology Gitam University Department of Computer Science and Engineering Nagadenahalli Bengaluru India

ISBN: (纸本)9798331523923

Ransomware attacks threaten organizations by encrypting files or locking systems and keeping them inaccessible unless a ransom is paid. Early detection of ransomware attacks helps organizations avoid financial losses, theft of data, and harm to their reputations. This early detection will also reduce the risks related to malicious activities. Detection by signature-based or heuristic approaches tends to be traditional and fails when evolvement in ransomware attacks is considered. Thus, a need for innovative techniques of deep learning and optimization algorithms can be observed. The method proposed in this study focuses on developing a novel multi-stage framework for detecting ransomware attacks. Firstly, correlation coefficient-based min-max normalization (CCMMN) for data preprocessing is used since it makes the data elements more uniform and makes the detection system more efficient. Feature selection is then carried out and optimized using the hybrid Osprey optimization algorithm with an enhancing sparrow search algorithm (OOA-ESSA) to arrive at the most relevant feature for classification. Last, this paper applies a hybrid quantum-classical convolutional neural network with boosted sooty tern optimization (HQCCNN-BSTO) in ransomware detection. The classic model is replaced by a quantum and hybrid classical neural network that may be complemented by an optimization algorithm to increase the speed and efficiency of the detection. The method that has been proposed here offers a flexible and efficient approach to the problems associated with the detection of ransomware threats in the practical environment. Thus, high effectiveness is proved by the 99% accuracy rate of the suggested method. This exceptional outcome proves the robustness of the approach and its ability to adapt to various patterns of dependence and data changes. © 2025 IEEE.

关键词： Convolutional neural networks

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Peer Collaboration in DBLP Using Graph Convolutional Network

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SN computer science 2025年第1期6卷 1-7页

作者： Kumar, Chintoo Dar, Showkat Ahmad Batchu, Charishma Singamaneni, Kundan Sai Panguluri, Venkata Lakshmi Alekhya Department of Artificial Intelligence and Data Science GITAM Karnataka Bengaluru 562163 India Department of Computer Science and Engineering GITAM Karnataka Bengaluru 562163 India

The increasing popularity of Graph-based neural network architectures plays a pivotal role in providing promising results in applications, viz., Friendship networks, Co-authorship networks, Product recommendations, etc. Many machine learning tasks can be addressed when dealing with graph representation, i.e., graph classification, node classification, link prediction, graph embedding, etc. The traditional rule-based methods have many drawbacks in comparison to graph representation learning, such as it requires manual feature engineering and struggles with unseen cases (generalizability), etc., in the case of rule-based methods. At the same time, graph representation learning overcomes those issues profoundly. The advent of graph-based deep neural network architectures inspired us to complete a graph classification problem to explore clusters of researchers who worked together in academic publications. We built the model by using a graph convolutional network to solve a multi-classification problem. We conducted a comparative study to check the efficacy of graph representation learning by GCNs on the DBLP dataset. A comparative study shows that the built model outperforms the baseline models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： DBLP Graph convolutional network Graph mining Social networks

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An effective intrusion detection system based on feature selection and regularized long short-term memory classifier

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Multimedia Tools and Applications 2025年 1-24页

作者： Chikkalwar, Sudha Rani Garapati, Yugandhar Department of Computer science and Engineering GITAM Deemed to be University Rudraram Hyderabad India Department of Computer science and Informatics Mahatma Gandhi University Telangana Nalgonda India

In recent days, the expansion of Internet of Things (IoT) and the quick advancement of computer system applications contribute to the current phenomenon of data growth. The field of intrusion detection has expanded considerably as a result of the need to protect networks from various attacks. Previously, security has been provided by combining conventional intrusion monitoring systems with a high number of security measures. However, the ability of combined systems to analyze enormous amounts of data has been restricted. Therefore, in this research, open-source and freely available datasets known as NSL-KDD and CSE-CIC-IDS2018 datasets are analyzed. Those datasets contain a class column that splits the attack detection into two classes (normal and anomalous) and four categories (DoS, probe, R2L, and U2R). Then, feature encoding and scaling are used for pre-processing. After that, a SelectPercentile-based feature selection algorithm is suggested to identify the numerous various intrusion attacks and choose the best features. Later, the Regularized Long Short-Term Memory (LSTM) is employed to classify the data. The result analysis verified that Regularized LSTM surpasses the existing models in the NSL-KDD based on accuracy (99.95%), false alarm rate (0.05%), attack detection rate (99.82%), and f1-measure (99.94%). While in the CSE-CIC-IDS2018 dataset, the Regularized LSTM method achieves better accuracy (99.33%), false alarm rate (0.07%), attack detection rate (99.96), and f1-measure (99.95%). © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Network intrusion

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Comparative Analysis of Neurodegenerative disease detection using Artificial Intelligence

Comparative Analysis of Neurodegenerative disease detection ...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Vidya, J. Yendapalli, Vamsidhar Gitam University Gitam School of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350359688

Neurodegeneration is one of the features of several debilitating diseases that are rising rapidly. As they are not curable and progressive, early detection may help the patients and the caretakers to maintain a good quality of life. Advancements in computational methods have made it possible to diagnose and classify NDD (neurodegenerative diseases) using the automated procedure, which will be more effective than manual detection. This paper aims to comprehensively describe the neurodegenerative disease classification using ML and DL techniques that may help the community working on early detection of the disease. © 2024 IEEE.

关键词： Contrastive Learning

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An efficient missing value imputation and evaluation using GK-KH means and HTR-RNN

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International Journal of Business Intelligence and Data Mining 2023年第1期24卷 25-46页

作者： Syavasya, C.V.S.R. Muddana, A. Lakshmi Department of Computer Science and Engineering Gitam Deemed University Rudraram Hyderabad502329 India

The accuracy of the data mining (DM) outcomes might be affected by mining and analysing incomplete datasets with missing values (MV). Thus, a complete dataset is created by the imputation of MV, which makes the analysis easier. An effectual missing values imputation (MVI) is proposed and evaluated utilising Gaussian kernel-K harmonic means (GK-KH means) and hyperbolic tangent radial-recurrent neural networks (HTR-RNN) to combat this issue. At first, preprocessing is performed on the input data as of the CKD dataset wherein the duplicate form of the data gets eradicated. Next, the missing data are handled by ignoring them;and utilising GK-KH means, the MV is imputed. Next, the data are rationalised into a structured format. Then, SDRM-DHO selects the most optimal features as of the extracted features. Lastly, the HTR-RNN classifier accepts these chosen features as input. Proposed work performed well in more accurate missing value imputation. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Recurrent neural networks

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