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2024 International Conference on Recent Innovation in Smart and Sustainable technology, ICRISST 2024

作者： Rangasamy, Gokul Primya, T. Kumar, Gurupriya Sivakumar, Harinath Kanagaraj, G. Sathish Kumar, D. Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Kumaraguru College of Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Vellore Institute of Technology School of Computer Science Engineering and Information Technology Tamil Nadu Vellore India

ISBN: (纸本)9798350309140

Nowadays, the majority of people rely heavily on the internet. Online transactions, online sign-ups, and online purchasing are used by everyone. One kind of threat to websites is phishing, which is an illegal activity that involves obtaining user data, including credit/debit card numbers, passwords, and login credentials. An efficient phishing detection method based on ensemble learning is presented in this study. The technique proposed, which combines three classifiers (K-Nearest Neighbours, KNN, Support Vector Machines, and Gradient Boosting Classifiers, GBC) to achieve the highest accuracy performance, has been shown to yield positive results overall (97.4%). The proposed technique uses feature extraction for processing the Uniform Resource Locator (URL) and predicts the website whether it's phishing or not. Therefore, it is preferable and used to employ the suggested technique to identify phishing using machine learning. © 2024 IEEE.

关键词： Phishing

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Performance Analysis of the Machine Learning Algorithms for the Early Detection of Breast Carcinoma 2

Performance Analysis of the Machine Learning Algorithms for ...

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2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Vanitha, K. Karpagavalli, S.M. Mahesh, T.R. Ali, A. Althaf Sridhar, T. Anitha, K. Faculty of Engineering Department of Computer Science and Engineering Coimbatore India Al Ameen Engineering College Department of Computer Science and Engg Tamilnadu Erode India Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India Madanapalle Institute of Technology & Science Department of Computer Applications Andhra Pradesh India Alliance University Alliance College of Engineering and Design Bangalore India Csi College of Engineering Department of Information Technology Tamilnadu Ooty India

ISBN: (纸本)9798350360523

Breast Carcinoma, generally known as breast cancer, primarily affects women, though men can develop it as well. Because of the existence of breast tissue and exposure to female hormones, notably oestrogen, women are at a higher risk It's critical to diagnose breast tumors early. Several techniques based on machine learning (ML were used in this study to classify breast cancer using a dataset that was made available to the public. F-score, recall, precision, preciseness, and other performance metrics were used to evaluate these ML algorithms. Previous research and experimental findings indicate that Random Forest achieved the highest accuracy, with a remarkable accuracy rate of 99.12%. © 2024 IEEE.

关键词： Hormones

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Subject–Verb Agreement Error Handling Using RNN Architectures 4th

Subject–Verb Agreement Error Handling Using RNN Architectur...

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4th International Conference on Innovations in Computational Intelligence and computer Vision, ICICV 2024

作者： Gautam, Prakhar Thakur, Jitendra Singh Mishra, Ashish Department of Computer Science Engineering Jabalpur Engineering College Jabalpur India Department of Computer Science Engineering Gyan Ganga Institute of Technology and Sciences Jabalpur India

ISBN: (纸本)9789819769919

The domain of natural language processing has reached to a level where various tools are available for automatically identifying and correcting grammatical errors in a wide range of languages text. Though these tools are productive, it is still difficult to develop an effective and accurate grammatical error handling system. In this paper, we examine the efficacy of several neural network architectures with a particular focus on subject–verb agreement, a rule of English grammar. This work investigates the capabilities and performance of deep learning models using an annotated dataset designed specifically for English language subject–verb agreement issues. The strengths and weaknesses of well-known models, including Support Vector Machine (SVM), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), and Transformers (BERT), are assessed through testing. The study sheds light on how the performances of these models differ from one another in detecting subject–verb agreement errors in English language sentences. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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PollX—Polling Assistant 2nd

PollX—Polling Assistant

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2nd International Conference on Computational Intelligence in Machine Learning, ICCIML 2022

作者： Shah, Neel Sapate, Suhas G. Pradnyavant, Ajit Kamble, Vishal Mulla, Anis Fatima Department of Computer Science and Engineering Annasaheb Dange College of Engineering and Technology Maharashtra Ashta416301 India Department of Computer Science and Engineering Sanjeevan Engineering and Technology Institute Maharashtra Panhala416201 India

ISBN: (纸本)9789819979530

India is a sovereign, socialist, and secular country where democracy is a form of government in which citizens exercise power by voting. Polling is the survey of a particular group of people who cast votes in an election. The existing election process is carried out using ballot paper which leads to the use of a huge amount of papers, cutting down of trees, and requires more labor and this process is comparatively slow. Along with this, there are few systems that support online voting, but these applications have few drawbacks and lack of features. The present system of conducting elections and processing polling consumes considerable manpower and time with a lot of dependencies for handling election booths, polling stations, and processes. This article presents a prototype to develop the mobile application for instantaneous communication to display vote count and present graphical analysis of polling data. It will display the male, female, and total vote count of each polling booth to their respective zonal officers through a mobile application for the purpose of saving time and the rapid appearance of data. PollX Assistant is a cross platform application which creates flexibility for officers to access the application on multiple operating systems. It gives simplicity, user-friendly navigation and provides security in the form of confidentiality, integrity, and non-repudiation to the polling data. It shares data faster between different officers, automates various tasks, and generates reports and graphs. This model would help to have a faster and secure communication between officers and to keep proper track of vote counts. PollX Assistant is a fast, secure, and reliable alternative to existing systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Online systems

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HASLB: Huge Page Allocation Strategy Optimized for Load-Balance in Parallel Computing Programs 20th

HASLB: Huge Page Allocation Strategy Optimized for Load-Bal...

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20th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2024

作者： Yue, Lei Wu, Tao Shen, Yang Zhang, Jin Zhang, Wenzhe School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China College of Computer Science and Technology National University of Defense Technology Changsha410073 China

ISBN: (纸本)9789819628292

Equitable resource allocation is currently considered as a key enabler for achieving complete parallelism in parallel computing. Optimized memory management mechanisms are crucial for load balance in parallel tasks. In some cases, the performance of Linux’s huge page management mechanism deteriorates when handling parallel tasks due to the nonhomogeneous allocation of memory resources among parallel processes. This research introduces HASLB, a huge page allocation strategy optimized for parallel programs. HASLB evaluates the status of parallel processes based on the count of huge pages acquired by each process and the number of page faults it encountered. Following the results of the analysis, huge pages are allocated using a method conducive to load balance. The problems in the Linux transparent huge page mechanism are optimized through this strategy, which limits the greedy allocation mechanism in some situations. HASLB achieves a reasonable allocation of memory resources among parallel processes with low overhead, benefiting the load balance of parallel computing programs. In addition, contiguous memory would be protected because of limiting the greedy allocation of huge pages. The parallel benchmark experiments show a maximum performance improvement of 6.4% compared to Linux in the BFS benchmark. In the IS benchmark program, huge page consumption exhibits a 9% reduction. © IFIP International Federation for Information Processing 2025.

关键词： Memory management

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Quantum-Accelerated Hyperparameter Tuning for Dynamic NLP Models 2

Quantum-Accelerated Hyperparameter Tuning for Dynamic NLP Mo...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Ravikumar, S. Raj, Arockia Babu, R. Vijay, K. Ramani, R. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai600062 India Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India Department of Computational Intelligence Faculty of College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Chennai603203 India Department of Computer Science and Engineering Rajalakshmi Engineering College Tamilnadu Chennai602117 India Department of Computer Science and Engineering P.S.R. Engineering College Tamilnadu Sivakasi626140 India

In the rapidly evolving field of natural language processing (NLP), performance optimization of large-scale NLP models is crucial. Through the application of Quantum-Accelerated Hyperparameter Tuning (QAHT), this abstract introduces a novel approach to addressing this issue. Our proposed framework leverages quantum computing capabilities to dynamically optimize NLP model hyperparameters in real-time, catering to the ever-changing character of textual data streams. Traditional hyperparameter optimisation methods usually rely on laborious grid searches or random exploration, which may not be suitable for dynamic NLP jobs. Contrarily, QAHT uses Quantum Neural Network (QNN) architectures that have been specially designed for hyperparameter optimisation. These QNNs improve performance and efficacy by dynamically modifying and improving model configurations. This abstract discusses the key elements of the QAHT architecture, including real-time model deployment, adaptive learning, and continuous data stream processing. In addition to speeding up the hyperparameter optimisation process, QAHT makes sure that NLP models are still flexible and responsive to shifts in the sentiment of the data and its distribution. This method has applications beyond NLP since it provides a foundation for effectively optimising machine learning models in complex, real-time situations. As quantum computing develops, QAHT represents a promising future in machine learning, where quantum-enhanced capabilities satisfy the needs of contemporary data-driven applications. © 2024 Elsevier B.V.. All rights reserved.

关键词： Network architecture

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Leveraging EfficientNetB3 in a Deep Learning Framework for High-Accuracy MRI Tumor Classification

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computers, Materials & Continua 2024年第10期81卷 867-883页

作者： Mahesh Thyluru Ramakrishna Kuppusamy Pothanaicker Padma Selvaraj Surbhi Bhatia Khan Vinoth Kumar Venkatesan Saeed Alzahrani Mohammad Alojail Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India School of Computer Science and Engineering VIT-AP UniversityAmaravati522241India Department of Computer Science&Technology Madanapalle Institute of Technology&ScienceMadanapalle517325India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK School of Computer Science Engineering&Information Systems(SCORE) Vellore Institute of Technology(VIT)Vellore632014India Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh11531Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpuraPunjab140401India

Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient *** different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI *** approaches predominantly rely on traditional machine learning and basic deep learning methods for image *** methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI *** the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor *** approach highlights a major advancement in employing sophisticated machine learning techniques within computer science and engineering,showcasing a highly accurate framework with significant potential for healthcare *** model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification *** successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current *** integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider *** research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.

关键词： Deep learning MRI brain tumor cassification EfficientNetB3 computational engineering healthcare technology artificial intelligence in medical imaging tumor segmentation neuro-oncology

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DeepOCL:A deep neural network for Object Constraint Language generation from unrestricted nature language

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CAAI Transactions on Intelligence technology 2024年第1期9卷 250-263页

作者： Yilong Yang Yibo Liu Tianshu Bao Weiru Wang Nan Niu Yongfeng Yin School of Software Beihang UniversityBeijingChina College of Computer Science and Technology Guizhou UniversityGuiyangGuizhouChina Faculty of Information Technology Beijing University of TechnologyBeijingChina Department of Electrical Engineering and Computer Sciences University of CincinnatiCincinnatiOhioUSA

Object Constraint Language(OCL)is one kind of lightweight formal specification,which is widely used for software verification and validation in NASA and Object Management Group *** OCL provides a simple expressive syntax,it is hard for the developers to write correctly due to lacking knowledge of the mathematical foundations of the first-order logic,which is approximately half accurate at the first stage of devel-opment.A deep neural network named DeepOCL is proposed,which takes the unre-stricted natural language as inputs and automatically outputs the best-scored OCL candidates without requiring a domain conceptual model that is compulsively required in existing rule-based generation *** demonstrate the validity of our proposed approach,ablation experiments were conducted on a new sentence-aligned dataset named *** experiments show that the proposed DeepOCL can achieve state of the art for OCL statement generation,scored 74.30 on BLEU,and greatly outperformed experienced developers by 35.19%.The proposed approach is the first deep learning approach to generate the OCL expression from the natural *** can be further developed as a CASE tool for the software industry.

关键词： deep learning OCL software engineering

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Multivariate Time series Forecasting for Cloud Machine Utilization 3

Multivariate Time series Forecasting for Cloud Machine Utili...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Naik, Archana Sooda, Kavitha Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India Department of Computer Science and Engineering B.M.S. College of Engineering Bangalore India

ISBN: (纸本)9798331527549

The cloud machine utilization prediction using a multivariate time series model is explored in this study. The cloud machine usage data set samples are selected with varied patterns. The long sequence forecasting model Informer is investigated for the forecasting. The model results are benchmarked against LSTM, GRU, Prophet, and XGBoost across the diverse datasets. The performance is evaluated using RMSE. Experimental results show that the Prophet model performs best overall due to its ability to capture both seasonal and irregular patterns accurately. The RMSE value for Informer is 0.13, Prophet is 0.097, XGBoost is 0.096, LSTM is 0.1725, and GRU is 0.1547. © 2025 IEEE.

关键词： Time series

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Covid-19 Vaccine Certificate Verification using Sha Algorithm and IPFS in Blockchain technology 5

Covid-19 Vaccine Certificate Verification using Sha Algorith...

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5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Agrawal, Naitik Singh, Yash Veer Sharma, Vishnu Bedi, Vikas Kumar Chhonker, Sumit Computer Science And Engineering Galgotias College Of Engineering And Technology Greater Noida India Galgotias College Of Engineering And Technology Dept. Of Computer Science And Engineering Greater Noida India

ISBN: (纸本)9798350330861

The COVID-19 pandemic has suddenly arisen as a global health emergency requiring concerted international action. Governments are working to stop the virus from spreading and health organizations rushed to create vaccines that would protect people. Reduce Covid-19 morbidity, reduce strain on healthcare systems, and let economies to expand. The Vaccine Certificate has been adopted as a way forward after the COVID-19 vaccination to assist authorities in formulating policies by restricting cross-border travel. In this work, we employ blockchain technology to construct a safe and verifiable vaccination certificate, which alleviates substantial privacy concerns and eliminates the need for third parties to maintain trust and control the user's data. Different modern technologies, such as smart contracts, are used in our approach. © 2023 IEEE.

关键词： Smart contract

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