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2nd International Conference on Edge Computing and applications, ICECAA 2023

作者： Bhushanam, P. Naga Selva Kumar, S. School of Computer Science and Engineering VIT-AP University Andhra Pradesh India

ISBN: (纸本)9798350347579

Unconstraint video analytics are important in visual learning. Unconstrained videos contain complex content with various artifacts, variable lengths, and different operating environments. Human activity plays an important role in the video, abundant in archives and becoming more prevalent on the Internet. Various methods are employed for action recognition under constrained conditions, but huge attention to complex actions and realistic applications is substantially needed. Complex movements consist of sequences of simple movements that have long temporal structures. Complex actions in the same class exhibit large variations in class-interior appearance and behavior due to complex temporal structures, confusing backgrounds, changes in viewpoint, and changes in movement speed. Thus, feature representation and classification of complex motions are determined to be challenging in unconstrained video analysisdue to complex temporal structures. This article presents a comprehensive analysis to solve this problem using deep learningapproaches. © 2023 IEEE.

关键词： Deep learning

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Predictive Insights for Enhancing Customer Retention in Banking: Integrating Power BI and Random Forest 3rd

Predictive Insights for Enhancing Customer Retention in Bank...

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3rd International Conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Sumanasri, A. Vyshnavi, M. Manogna, R. Nikhila Keerthi, Kelam Kesavarshini, A. Pande, Sagar Dhanraj School of Computer Science Engineering VIT-AP University Andhra Pradesh Amaravati India Department of Computer Science and Engineering School of Engineering and Technology Pimpri Chinchwad University Maharashtra Pune India

ISBN: (纸本)9789819743582

The main goal of the study is to solve the problem of losing customers or clients in various financial organizations, especially in the banking sector. It investigates the use of Random Forest algorithm and the Power BI dashboard which helps to forecast churn and improve retention strategies. Historical data—transactions, demographics, and usage patterns—is collected and a thorough analyzation is done with the help of Power BI to provide a dynamic dashboard that provides insights in real-time. Combining data analytics technologies and predictive skills with their retention initiatives can help banks increase their efficacy and efficiency. Economic loss and operational difficulties are brought on by customer attrition. The outcome of this study suggests a method for forecasting customer attrition in banks that combines Power BI’s amazing data visualization techniques with Random Forest’s highly accurate predictive skills. The Random Forest algorithm integrates multiple decision trees to make predictions in a precise and expandable way. Moreover, it has the capability of handling large data sets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sales

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A Comprehensive Review of Machine Learning and Deep Learning Techniques for Addressing Class Imbalance Issues in Network Intrusion Detection Systems 6

A Comprehensive Review of Machine Learning and Deep Learning...

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6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Saikam, Jalaiah Koteswararao, Ch. VIT-AP University School of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9798350394702

Nowadays, a lot of information is shared between various connecting devices due to the quick development of internet technologies and the growing reliance on online services. Security systems are one of the most important scientific topics in today's computer network, so data should be transmitted safely between connected devices. The intrusion detection system is crucial for network security because it can identify and stop malicious activities. IDS still has trouble identifying novel intrusions due to data imbalance problems and a high false alarm rate. Many different methods have been applied to either prevent or detect intrusions. We discussed several research problems as well as potential future directions for NIDS. This study investigates ML-based and DL-based intrusion detection strategies and dataset selection. © 2023 IEEE.

关键词： Intrusion detection

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Traffic Sign Recognition for Autonomous Vehicle Using Optimized YOLOv7 and Convolutional Block Attention Module

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computers, Materials & Continua 2023年第10期77卷 445-466页

作者： P.Kuppusamy M.Sanjay P.V.Deepashree C.Iwendi School of Computer Science and Engineering VIT-AP UniversityAndhra Pradesh522237India School of Creative Technology University of BoltonManchesterBL35ABUK

The infrastructure and construction of roads are crucial for the economic and social development of a region,but traffic-related challenges like accidents and congestion *** Intelligence(AI)and Machine Learning(ML)have been used in road infrastructure and construction,particularly with the Internet of Things(IoT)*** detection in computer Vision also plays a key role in improving road infrastructure and addressing trafficrelated *** study aims to use You Only Look Once version 7(YOLOv7),Convolutional Block Attention Module(CBAM),the most optimized object-detection algorithm,to detect and identify traffic signs,and analyze effective combinations of adaptive optimizers like Adaptive Moment estimation(Adam),Root Mean Squared Propagation(RMSprop)and Stochastic Gradient Descent(SGD)with the *** a portion of German traffic signs for training,the study investigates the feasibility of adopting smaller datasets while maintaining high *** model proposed in this study not only improves traffic safety by detecting traffic signs but also has the potential to contribute to the rapid development of autonomous vehicle *** study results showed an impressive accuracy of 99.7%when using a batch size of 8 and the Adam *** high level of accuracy demonstrates the effectiveness of the proposed model for the image classification task of traffic sign recognition.

关键词： Object detection traffic sign detection YOLOv7 convolutional block attention module road sign detection Adam

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An Innovative approach Towards Efficient Diabetes Mellitus Prediction Using Federated Forest

An Innovative Approach Towards Efficient Diabetes Mellitus P...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Madhu, Gifsy De, Anurag Pal, Gautam School of Computer Science and Engg. VIT-AP University AP Amaravati India Tripura Institute of Technology Computer Science & Engineering Dept. Tripura Narsingarh India

ISBN: (纸本)9798331540821

In this study, we discuss a method for diagnosing diabetes mellitus that combines the Random Forest algorithm with Federated Learning. The global model preserves data privacy while guaranteeing accurate and dependable diagnostics by combining local models rather than data. The diabetes dataset, sourced from Mendeley Data, is preprocessed to address class imbalance through the Synthetic Minority Oversampling Technique (SMOTE). The dataset is further augmented using Conditional Generative Adversarial Networks (CGAN) to introduce non-lID variations while splitting the data. Our global model is evaluated and compared across different node configurations, with the optimal performance achieved using six nodes. The results show impressive performance characteristics with 98.18% accuracy, 99.09% specificity, 98.18% sensitivity, and an ROC AUC of 1.00. These findings indicate that our Federated Forest based approach significantly enhances the diagnostic capability for Diabetes Mellitus while maintaining data privacy and integrity. © 2024 IEEE.

关键词： Federated learning

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Digital Twins and Cyber-Physical Systems:A New Frontier in computer Modeling

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computer Modeling in engineering & sciences 2025年第4期143卷 51-113页

作者： Vidyalakshmi G S Gopikrishnan Wadii Boulila Anis Koubaa Gautam Srivastava Department of Computer Science and Engineering SRM University APAmaravati522240India School of Computer Science and Engineering VIT-AP UniversityAmaravathi522240India Robotics and Internet-of-Things Laboratory Prince Sultan UniversityRiyadh12435Saudi Arabia Department of Math and Computer Science Brandon UniversityBrandonMB R7A6A9Canada Research Centre for Interneural Computing China Medical UniversityTaichung40402Taiwan

Cyber-Physical Systems(CPS)represent an integration of computational and physical elements,revolutionizing industries by enabling real-time monitoring,control,and optimization.A complementary technology,Digital Twin(DT),acts as a virtual replica of physical assets or processes,facilitating better decision making through simulations and predictive *** and DT underpin the evolution of Industry 4.0 by bridging the physical and digital *** survey explores their synergy,highlighting how DT enriches CPS with dynamic modeling,realtime data integration,and advanced simulation *** layered architecture of DTs within CPS is examined,showcasing the enabling technologies and tools vital for seamless *** study addresses key challenges in CPS modeling,such as concurrency and communication,and underscores the importance of DT in overcoming these *** in various sectors are analyzed,including smart manufacturing,healthcare,and urban planning,emphasizing the transformative potential of CPS-DT *** addition,the review identifies gaps in existing methodologies and proposes future research directions to develop comprehensive,scalable,and secure CPSDT *** synthesizing insights fromthe current literature and presenting a taxonomy of CPS and DT,this survey serves as a foundational reference for academics and *** findings stress the need for unified frameworks that align CPS and DT with emerging technologies,fostering innovation and efficiency in the digital transformation era.

关键词： Cyber physical systems digital twin efficiency Industry 4.0 robustness and intelligence

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A Harmonic approach to Handwriting Style Synthesis Using Deep Learning

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computers, Materials & Continua 2024年第6期79卷 4063-4080页

作者： Mahatir Ahmed Tusher Saket Choudary Kongara Sagar Dhanraj Pande Seong Ki Kim Salil Bharany School of Computer Science and Engineering VIT-AP UniversityAmaravatiAndhra Pradesh522237India School of Engineering&Technology Pimpri Chinchwad UniversityPuneMaharashtra412106India Department of Computer Engineering Chosun UniversityGwangju61452Republic of Korea Independent Researcher AmritsarPunjab143001India

The challenging task of handwriting style synthesis requires capturing the individuality and diversity of human *** majority of currently available methods use either a generative adversarial network(GAN)or a recurrent neural network(RNN)to generate new handwriting *** is why these techniques frequently fall short of producing diverse and realistic text pictures,particularly for terms that are not commonly *** resolve that,this research proposes a novel deep learning model that consists of a style encoder and a text generator to synthesize different handwriting *** network excels in generating conditional text by extracting style vectors from a series of style *** model performs admirably on a range of handwriting synthesis tasks,including the production of text that is *** works more effectively than previous approaches by displaying lower values on key Generative Adversarial Network evaluation metrics,such Geometric Score(GS)(3.21×10^(-5))and Fréchet Inception Distance(FID)(8.75),as well as text recognition metrics,like Character Error Rate(CER)and Word Error Rate(WER).A thorough component analysis revealed the steady improvement in image production quality,highlighting the importance of specific handwriting *** fields include digital forensics,creative writing,and document security.

关键词： Recurrent neural network generative adversarial network style encoder fréchet inception distance geometric score character error rate mixture density network word error rate

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Analyzing Artificial Intelligence Based Intrusion Detection System in Detecting DDoS Attack 4th

Analyzing Artificial Intelligence Based Intrusion Detection ...

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4th International Conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Kurra, Sai Suchith Rayana, Likhitha Yoosuf, Mohamed Sirajudeen School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Department of Mathematics and Computer Science Modern College of Business and Science Muscat Oman

ISBN: (纸本)9789819751990

With the increasing complexity of the cyber-attacks, traditional intrusion systems have become inadequate in effectively detecting and preventing network attacks. Modern machine learning techniques have the capability of preventing sophisticated and aggressive cyber-attacks and have emerged as a promising approach to enhance the accuracy and efficiency of the intrusion detection. This research paper provides a comprehensive analysis and evaluation of the integration of machine learning and deep learning algorithms into intrusion detection system with respect to the Distributed Denial of Service (DDoS) attack. The study begins by presenting an overview of intrusion detection systems, highlighting their importance and significance in safeguarding network infrastructure. Subsequently, it explores supervised machine learning algorithms and deep learning models, such as Random Forest tree, Support Vector Machines (SVM), ResNet and TabNet. The paper presents the pros and cons of existing IDS models with respect to real-time processing, scalability, false positive and false negative results, and IDS vulnerabilities. The above-mentioned algorithms have been trained and evaluated with CICIDS-2017 dataset. The performance metrics used to evaluate the IDS models are accuracy, precision, F1 score, training time of each model, and adaptability to evolving attack patterns. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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Skin Cancer Segmentation Based on Triangular Intuitionistic Fuzzy Sets

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SN computer science 2023年第3期4卷 228页

作者： Namburu, Anupama Mohan, Senthilkumar Chakkaravarthy, Sibi Selvaraj, Prabha School of Computer Science Engineering VIT-AP University Amaravati Andhra Pradesh Guntur 522237 India School of Computer Science Engineering VIT Vellore Andhra Pradesh Guntur 522237 India

Malignant Melanoma is a dangerous form of skin cancer, and its detection is a challenging task as it appears in numerous ranges of size, shape, and shading with various skin tones. Also, artefacts like hairs, outlines, blood vessels, and boils add further complexity. A simplified clustering method is proposed in this paper to improve melanoma detection while reducing time *** triangular membership function (TMF) is used to detect the initial regions for obtaining initial centroids. These initial centroids are used to apply intuitionistic fuzzy c-means clustering. The TMF helps in identifying the initial clusters and regions and reduces the number of iterations needed for segmentation. The proposed method effectively detects skin cancer regions with an average accuracy of 90% and performs well. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Fuzzy sets Image processing Intuitionistic fuzzy sets Medical image segmentation Skin cancer

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Analytical Comparison for the Diagnosis of Chronic Kidney Disease applying Intelligent ML-based Systems 7

Analytical Comparison for the Diagnosis of Chronic Kidney Di...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Durga, P. Sudhakar, T. VIT-AP University School of Computer Science and Engineering Andhra Pradesh Amaravati India

ISBN: (纸本)9798350397253

Diseases like Chronic Kidney Disease (CKD) develop gradually over time and then stick around for a very long time. At its most advanced stage, it is fatal, and the only treatment option is a transplanted kidney or ongoing dialysis, which is an artificial filtering mechanism. Even though there have been improvements in surgical care and treatment, CKD remains a Serious Threat to Health. As CKD has become more common in recent years, scientists throughout the world have been increasingly invested in finding effective ways to diagnose, treat, and prevent the disease. By studying the features that cause the problem, it is possible to improve performance. Healthcare providers can learn more about a patient's condition before a visit to the clinic by analyzing the patient's medical records. Although an early diagnosis of CKD is crucial for providing the curative or at least manageable treatments it requires, the effectiveness of current systems remains to be improved. A clever categorization and prediction model is proposed in this paper. Classification techniques such as Artificial Neural Networks (ANNs), XGBoost, and K-NN are used to provide predictions about kidney illness. Based on the results of the evaluation, the suggested model outperforms the state-of-the-art methods for predicting CKD by 98.33 percent. Analyzing these outcomes demonstrates that cutting-edge ML learning approaches are useful for clinical decision-making and can aid in the early prediction of CKD and its related phases, which can help to slow the worsening of kidney damage. Algorithms for the dataset obtained from the default UCI repository have been compared using the alternative metric. This study also emphasizes the significance of statistical analysis and domain understanding of the features when extrapolating from clinical data to predict CKD. © 2023 IEEE.

关键词： Diagnosis

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