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

作者： Gera, Sujini Melingi, Sunil Babu Vignan's Foundation for Science Technology and Research Department of Computer Science and Engineering Guntur India

ISBN: (纸本)9798350369083

By using an improved vote classifier model, this study seeks to improve coronary heart disease (CHD) early detection. The study aims to improve the model's accuracy and efficiency by adjusting model parameters and using an enhanced loss function called Focal Loss (FL). The model's performance was assessed using measures including accuracy, precision, recall, and F1-score, utilizing data from the Framingham Heart Study. The voting classifier that was optimized showed remarkable predictive ability, with an accuracy rate of around 98% and almost 100%. These findings point to the model's potential for early cardiac disease identification, which has important ramifications for clinical use. The research highlights how this sophisticated model might lead to better patient outcomes, earlier diagnosis, and lower treatment costs. © 2024 IEEE.

关键词： Contrastive Learning

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Empirical Investigation on Similarity Measures used in Recommendation Systems 15

Empirical Investigation on Similarity Measures used in Recom...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Chaparala, Pushya Muppa, Lakshmi Durga Shaik, Asma Vignan's Foundation for Science Technology and Research Department of Computer Science and Engineering Guntur India

ISBN: (纸本)9798350370249

Similarity measures play a crucial role in recommendation systems for quantifying the likeness between users or items within the system. These measures are fundamental in collaborative filtering, a widely used approach for generating personalized recommendations. This study analyses 29 distinct similarity measures including common one like Cosine, Jaccard, using metrics like recall, precision, and diversity on datasets like MovieLens and Bookcrossing. Findings from the result revealed that certain measures, such as PIP (Proximity Impact Popularity), CPCC (Constrained Pearson Correlation Coefficient), NHSM (New Heuristic Similarity Model), and COD (Adjusted Cosine Measure), consistently outperform others, particularly in aligning with user preferences due to consideration of rating scale differences, integration of multiple similarity aspects, and normalization of shared ratings. This paper underscores the critical role of selecting appropriate similarity measures to enhance recommendations and user satisfaction in collaborative filtering - based systems. © 2024 IEEE.

关键词： Collaborative filtering(CF) Content-based filtering(CB) Diversity Hybrid filtering(HF) Precision Recall Recommendation systems

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Extraction of Text from Images using Document Understanding Transformer (Donut) 5

Extraction of Text from Images using Document Understanding ...

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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Babu, Panthagani Vijaya Reddy, Mandadi Srinivas Kumar, Bonam Praveen Irshad, Shaik Vignan's Foundation For Science Technology And Reserach Department of computer science and Engineering Guntur India

ISBN: (纸本)9798350367171

Comprehending visual document images, like bills, is a challenging task that necessitates text extraction and a thorough comprehension of the document's contents. This is addressed by visual document understanding (VDU), which extracts text using readily available optical character recognition (OCR) engines and concentrates on interpreting the output. Although OCR-based methods have shown encouraging results, they have drawbacks such expensive processing, rigid models, and the possibility of errors extending to later phases. This paper presents the Document Understanding Transformer (Donut), a novel OCR-free VDU model. This study describes a basic Transformer architecture with a pre-training aim of cross-entropy loss as part of the initial phase of OCR-free VDU research. Donut is a simple and efficient solution. Numerous tests and analyses show that Donut, a straightforward OCR-free VDU model, works faster and more accurately than other models for particular VDU tasks. To facilitate versatile model pre-training across several languages and areas, we offer a generator of synthetic data. © 2024 IEEE.

关键词： Optical character recognition

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Efficient Intelligent Network Intrusion Detection for SDN Using XGBoost 15

Efficient Intelligent Network Intrusion Detection for SDN Us...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Rao, G. Balu Narasimha Kumar, Nitish Kumar, Amit Raj, Prem Department of Computer Science & Engineering Vignan’s Foundation for Science Technology and Research Guntur India

ISBN: (纸本)9798350370249

Networking defined by software (SDN) has arisen as an encouraging path for the internet's future expansion, providing enhanced flexibility and transparency to centrally managed networks. However, these advantages come with inherent vulnerabilities, posing significant risks such as network disruptions, system paralysis, and cybercrimes like online banking fraud and theft. Addressing these challenges is paramount for organizations, enterprises, and economies. In recent times, the incorporation of clever machine learning techniques goes into systems for detecting intrusions (IDS) through SDN has garnered considerable interest. In this manuscript, we introduce a fresh IDS called XGBoost tailored to SDN environments. Initially, a hybrid feature selection and extraction algorithm are employed to reduce data dimensionality and acquire an optimal feature subset. This approach involves utilizing a correlation-based feature extraction (CFE) algorithm followed by random forest recursive feature elimination. Subsequently, the XGBoost algorithm is employed for the detection and classification of varied forms of attacks. Experimental findings conducted on the dataset NSL-KDD illustrate that the suggested system surpasses current methods in F-measure, recall, accuracy, and precision. ©2024 IEEE.

关键词： Network intrusion

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Crowd Distance Induced Multi Objective Binary Salp Swarm Optimization Algorithm for Mining High Frequency and Utility Itemsets

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SN computer science 2025年第2期6卷 1-14页

作者： Budaraju, Raja Rao Jammalamadaka, Sastry Kodanda Rama Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur 522302 India Department of Electronics and Computer Science Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur 522302 India

Extracting valuable itemsets concerning towards high frequent and high utilization from a transactional database is a crucial objective in the field of data mining. This research examines both objectives namely frequency and utility of an itemset in a unified framework, using multi-objective optimization algorithm. More precisely, the activity of extracting frequent and high utility itemsets is formulated as a multi-objective problem. Subsequently, a novel algorithm namely Crowd distance based multi-objective binary Salp Swarm Optimization algorithm (CD-BSSOA) is proposed. This algorithm uses the crowding distance to compute the similarity between the solutions thus balances between exploration and exploitation. This algorithm has the capability to provide numerous itemset recommendations for decision-makers like market basket analysis, where identifying frequent and high-utility itemsets can help optimize product recommendations, enhance customer targeting, and improve inventory management. The algorithm was tested on a variety of real-world datasets including transactional data, classification datasets, and demographic datasets such as Chess, Connect, OnlineRetail, and Mushroom, representing different levels of complexity and item distributions. The interpreted results shows that the proposed algorithm has the efficiency to provide high number of non-dominant solutions on an average of 13.2% higher than the existing methods. In terms of hypervolume and coverage the proposed CD-BSSOA shows significant improvements with 9.2% and 11.6% respectively. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Binary High utility itemset Multi objective Salp swarm optimization

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Stock Price Forecasting by Time Series Analysis

Stock Price Forecasting by Time Series Analysis

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Surekha, Reshma Gupta, Divya Jha, Adarsh Kumar Bhargavi, Maridu Sai, Poorna Department of Computer Science and Engineering Vignan's Foundation for Science Technology and Research Guntur India

ISBN: (纸本)9798331543617

The technological advancement and mountains of financial data now available, more than ever it is crucial for investors, financial institutions, and decision-makers to make an informed choice by obtaining accurate stock price forecasting. Traditional techniques of forecasting often fail in today's complex market dynamics. This paper reveals a number of techniques of machine learning that can be applied to the field of stock price forecasting, de-emphasizing building up the accuracy and relia- bility of such predictions.[2] The main key algorithms presented are ElasticNet for feature selection and XGBoost, CatBoost, and an ensemble Voting Regressor. Its ability to analyze series data and identify strong, non-trivial patterns from the time series of stock price data made this model the best choice. Results show a performance that indicates just how well the ensemble Voting Regressor performed over the rest, with accuracy 99.6% far beyond any model from the bunch, promising much sharper resolution in financial forecasting. XGBoost and CatBoost also had very comparable accuracy that assured the productive use of machine learning in applying it for financial analytics. The research is not only a glorification of benefits from application of developed models of machine learning for predicting stock prices but also introduces points for further refinement of methods of prediction[4]. © 2024 IEEE.

关键词： Prediction models

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A novel AES–DES with improved Cheetah optimisation algorithm for secured medical data transmission in cloud environment

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Journal of Medical engineering and Technology 2024年第3期48卷 100-117页

作者： Joel, Gunde Manikanta Kasyap, Chaturvedula V. Narasimha Sidiq, Shaik Abubakar Riyaz, Jammimanu Santhi Sri, T. Sreedevi, Pogula Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra PradeshVaddeswaram Guntur India Department of CSE Rajeev Gandhi Memorial College of Engineering and Technology Nandyal India

In recent years, transmitting medical data has been a regular process. Although strong, safe, and dependable encryption techniques are necessary for medical data, cryptography is largely a computational process. The research presents a selective encryption approach for the transfer of sensitive data. This study proposes a novel technique for selecting the optimal keys to offer more security to medical data. Initially, the medical data is encrypted using the hybrid AES–DES technique. To make an efficient encryption method, the most optimal keys are selected utilising an improved Cheetah optimisation algorithm (ICO). Finally, the keys are optimised, and the input medical data is safely kept in the cloud system according to the established model. As a result, the proposed approach utilises the Python tool to evaluate the results. The simulation results show that the proposed method outperforms others in terms of encryption time 96 s, decryption time 92 s, memory usage (16), and latency (0.006). © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Advance encryption standard (AES) data encryption standard (DES) improved Cheetah optimisation (ICO) hybrid AES–DES sub bytes transformation mix columns transformation

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Visual Object Tracking Based on Modified LeNet-5 and RCCF

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computer Systems science & engineering 2023年第7期46卷 1127-1139页

作者： Aparna Gullapelly Barnali Gupta Banik Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationDeemed to be UniversityHyderabad500075TelanganaIndia

The field of object tracking has recently made significant ***,the performance results in both deep learning and correlation filters,based trackers achieved effective tracking ***,there are still some difficulties with object tracking for example illumination and deformation(DEF).The precision and accuracy of tracking algorithms suffer from the effects of such *** this situation,finding a solution is *** research proposes a new tracking algorithm to handle this *** features are extracted by using Modified LeNet-5,and the precision and accuracy are improved by developing the Real-Time Cross-modality Correlation Filtering method(RCCF).In Modified LeNet-5,the visual tracking performance is improved by adjusting the number and size of the convolution kernels in the pooling and convolution *** high-level,middle-level,and handcraft features are extracted from the modified LeNet-5 *** handcraft features are used to determine the specific location of the target because the handcraft features contain more spatial information regarding the visual *** LeNet features are more suitable for a target appearance change in object *** experiments were conducted by the Object Tracking Benchmarking(OTB)databases like OTB50 and *** experimental results reveal that the proposed tracker outperforms other state-of-the-art trackers under different *** experimental simulation is carried out in *** overall success rate and precision of the proposed algorithm are 93.8%and 92.5%.The average running frame rate reaches 42 frames per second,which can meet the real-time requirements.

关键词： Object tracking correlation filters feature extraction experimental results semantic information

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Construction of Cascaded Deep Neural Network with Optimization-Based Feature Selection in CT Images for Detecting Laryngeal Cancer 5th

Construction of Cascaded Deep Neural Network with Optimizati...

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5th International Conference on Innovative Product Design and Intelligent Manufacturing Systems, ICIPDIMS 2023

作者： Naga Padmavathi, P. Sree Devi, M. Maheswara Rao, V.V.R. Gopala Gupta, Amara S. A. L. G. Department of Computer Science and Engineering Amrita Sai Institute of Science and Technology AP Paritala India Department of Computer Science and Engineering Shri Vishnu Engineering College for Women AP Bhimavaram India Department of Computer Science and Engineering KoneruLakshmaiah Education Foundation AP Vaddeswaram India

ISBN: (纸本)9789819767311

A sin laryngeal carcinoma is the most common kind of head and neck cancer to damage the soft tissues of the larynx. To prevent further medical difficulties and to provide better patient care, early stage laryngeal cancer identification is essential. The primary objective of this research is to provide computer-aided cancer diagnosis driven by deep learning. In order to do this, we create a brand-new Optimization-Based Cascaded Deep Neural Network (OCDNN) that distinguishes between good and unhealthy/cancerous tissues. Additionally, we also provide cascaded patch-based CNN (CP-CNN) picture segmentation. The models are assessed using accepted measures that are unique to the biomedical field. When compared to other models, the performance of our suggested OCDNN architecture was exceptional. Additionally, it fared better than other cutting-edge CNN models applied to medical domain challenges. The findings suggest that the suggested method is quite successful in identifying early indications of laryngeal cancer. The results demonstrate that the suggested strategy may help doctors identify laryngeal carcinoma early and precisely. The suggested OCDNN is discovered to attain 98.4% accuracy, 94% precision, 97.5% recall, and 98% *** paragraph of about 100 words to give a brief introduction to your work. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： computerized tomography

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AI-Driven Resource and Communication-Aware Virtual Machine Placement Using Multi-Objective Swarm Optimization for Enhanced Efficiency in Cloud-Based Smart Manufacturing

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computers, Materials & Continua 2024年第12期81卷 4743-4756页

作者： Praveena Nuthakki Pavan Kumar T. Musaed Alhussein Muhammad Shahid Anwar Khursheed Aurangzeb Leenendra Chowdary Gunnam Department of Computer Science&Engineering Koneru Lakshmaiah Education FoundationGuntur522302India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of AI and Software Gachon UniversitySeongnam-Si13120Republic of Korea Department of Electronics and Communication Engineering SRM UniversityAmaravati522502India

Cloud computing has emerged as a vital platform for processing resource-intensive workloads in smart manu-facturing environments,enabling scalable and flexible access to remote data centers over the *** these environments,Virtual Machines(VMs)are employed to manage workloads,with their optimal placement on Physical Machines(PMs)being crucial for maximizing resource ***,achieving high resource utilization in cloud data centers remains a challenge due to multiple conflicting objectives,particularly in scenarios involving inter-VM communication dependencies,which are common in smart manufacturing *** manuscript presents an AI-driven approach utilizing a modified Multi-Objective Particle Swarm Optimization(MOPSO)algorithm,enhanced with improved mutation and crossover operators,to efficiently place *** approach aims to minimize the impact on networking devices during inter-VM communication while enhancing resource *** proposed algorithm is benchmarked against other multi-objective algorithms,such as Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),demonstrating its superiority in optimizing resource allocation in cloud-based environments for smart manufacturing.

关键词： Resource utilization smart manufacturing efficiency inter VM communication virtual machine placement cloud computing multi-objective optimization

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