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Performance optimization in ddos prediction with ensemble based approach

Multimedia Tools and Applications 2025年第12期84卷 10631-10644页

作者： Dogra, Amit Taqdir Department of Computer Science and Engineering SoET BGSB University Rajouri India Department of Computer Science Engineering and Technology Guru Nanak Dev University Regional Campus Gurdaspur India

Distributed Denial of Service (DDoS) attacks pose a significant threat to network infrastructures, leading to service disruptions and potential financial losses. In this study, we propose an ensemble-based approach for DDoS attack detection, leveraging the strengths of three different classifiers: Adaboost, K-Nearest Neighbors (KNN), and Random Forest. We apply data normalization during pre-processing, utilize a Multi-Layer Perceptron (MLP) for feature extraction, and combine the classifiers using an ensemble approach. The performance of each individual classifier and the ensemble is extensively evaluated, and the results demonstrate the effectiveness of the proposed ensemble in accurately identifying and mitigating DDoS attacks. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Denial-of-service attack

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Medicare: A telemedicine healthcare website 7

Medicare: A telemedicine healthcare website

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Payal, Payal Dey, Raghunath Senapati, Rohan Piri, Jayashree Mohanty, Surajit KIIT University School of Computer Engineering Bhubaneswar India Silicon University Department of Computer Science and Engineering Bhubaneswar India DRIEMS University SOET Department of Computer Science and Engineering Cuttack India

ISBN: (纸本)9798350372816

Despite the advancement of technology, the modern healthcare system still has many limitations. In order to address many unresolved issues, the world is moving toward digitalized systems, including the healthcare system. A telemedicine system would greatly benefit society if developed to be inexpensive, easily accessible, and equipped with substantial, dependable medical resources. This paper explores existing research on telemedicine within healthcare management systems (HMS) and the services it offers. Traditional healthcare has limitations in accessibility and keeps evolving with technology. Telemedicine addresses these limitations by providing convenient, affordable, and dependable medical services. Like other communication technologies, telemedicine is intended for wider adoption and use. Our web application "Medicare", incorporates features related to HMS that will significantly improve the usability and reliability of this technology management system. This research identified similar existing applications, and additional functionalities are implemented to make the telemedicine website more impactful and relevant for future needs. © 2024 IEEE.

关键词： Telemedicine

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Machine Learning for Real-Time Language Translation: A Server-Side Approach to Overcome Cross-Cultural Communication Challenges 4

Machine Learning for Real-Time Language Translation: A Serve...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Elango, S. Mohanaprakash, T.A. Siva, D. Gowtham, A. Sasikumar, A.N. Sunitha, T. Department of Computer Science and Engineering SOET CMR University Bengaluru India Department of Computer Science and Applications SRM Institute of Science and Technology Chennai India Madanapalle Institute of Technology & Science AP India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Artificial Intelligence & Data Science Saveetha Engineering College Chennai India

ISBN: (纸本)9798331515720

The evolution of human civilization has been intrinsically linked to advancements in technology, leading to the development of multiple languages as mediums of communication. However, this linguistic diversity poses significant challenges in cross-cultural communication. Despite numerous efforts and achieving near state-of-The-Art results in language translation, a universal solution remains elusive. This research study proposes an advanced approach to machine translation by utilizing server-side machine learning to enable seamless communication across various languages. By sending GET or POST requests through JQuery, users can efficiently translate text. The process is structured into three key steps: speech recognition (conversion of spoken words to text), translation using machine learning algorithms, and speech synthesis (conversion of translated text to audio). This approach aims to enhance real-Time translation capabilities, offering a more effective and user-friendly solution for overcoming language barriers. © 2024 IEEE.

关键词： Adversarial machine learning

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Data-Driven Personalized Pregnancy Care: Exploratory Data Analysis and Ensemble Learning or Personalized Prediction of Gestational Diabetes 4

Data-Driven Personalized Pregnancy Care: Exploratory Data An...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Loganathan, V. Sreesubha, S. Mohanaprakash, T.A. Ilavarasan, S. Shalinipriya, J. Saveetha Engineering College Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Aiml Chennai India Cmr University Soet Department of Computer Science and Engineering Bengaluru India Saveetha Engineering College Department of Information Technology Chennai India Sri Sairam Engineering College Department of Eee Chennai India

ISBN: (纸本)9798331508845

The disease in endocrine metabolic named as Gestational Diabetes Mellitus (GDM) is found in pregnant womens due to carbohydrate intolerance. The impact of this disease will not be severe if it is detected early. The prediction model deploys machine learning algorithms to identify the cause and factors. If the dataset have no missing values then only machine learning algorithms outperforms well. These challenges must be addressed. It can be implemented through various perspectives of algorithms with different features. The model must adapt mining the features from the data which eliminates the wrong assumptions and constraints. This proposed model helps to construct the missing data and make it complete one. In order to implement this concept, entire dataset is applied as input and Support Vector Machineand K-Nearest Neighbour algorithms based solutions are provided. The dataset is trained mean-square loss function for seismicdata and adam technique to opmize. It also aims to train the dataset in reconstruting the missing values using feature maps and ensemble method using classifers. The performance analysis is made with various other methods for its efficiency. It also addresses the regularly missing data in additionof missing values in the dataset. Finally, the results show that K-Nearest Neighbour algorithm is the state-of-art algorithm in predicting the disease early and addressing missing values in the dataset than Support Vector Machine. © 2024 IEEE.

关键词： Prediction models

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HFai: Deep Networks for Detecting Heart Failure Progression 3

HFai: Deep Networks for Detecting Heart Failure Progression

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Patnaik, Bipasha Kumar Nayak, Debasish Swapnesh Kumar Sahoo, Santanu FET-ITER Siksha 'o' Anusandhan University Department of Electronics and Communication Engineering Bhubaneswar India Centurion University of Technology and Management Soet Department of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798331528201

Heart Failure affects millions worldwide, and early identification is crucial to prevent serious complications. Current diagnostic approaches depend mostly on human interpretation and are frequently inaccurate. Artificial Intelligence models are efficient for estimating heart failure progression since they provide healthcare professionals with more accurate and automated tools. This study aims to train and evaluate DL models (1D-CNN, DNN, ANN) on raw and feature-engineered cardiac arrest detection datasets. The performance of this model was first evaluated using heart disease data. On the raw dataset, ANN outperformed with the highest Accuracy of 80% and AUC of 82.74%. After feature engineering using XGBoost, ANN's performance increased with an Accuracy of 95.08% and an AUC of 93.99%, indicating that feature engineering improves prediction. These findings emphasize the potential based on AI models in developing more reliable and automated diagnosis techniques for cardiac diseases. Future research will integrate real-time patient monitoring data with improved predictive models to detect developing health issues and allow for more accurate early diagnosis. This technique can transform heart failure therapy by providing personalized and more effective medicines, improving clinical decision-making and patient outcomes. © 2024 IEEE.

关键词： Personalized medicine

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DnnARs: An Artificial Intelligence Technique for Prediction of Antimicrobial Resistant Strains in E. coli Bacteria Causing Urine Tract Infection

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SN computer science 2024年第8期5卷 1091页

作者： Nayak, Debasish Swapnesh Kumar Priyadarshini, Arpita Mahanta, Pabani Das, Tejaswini Sahoo, Santanu Kumar Swarnkar, Tripti Department of Computer Science and Engineering FET-ITER Siksha ‘O’ Anusandhan (Deemed to Be) University Bhubaneswar India Department of Computer Science and Engineering SOET Centurion University of Technology and Management Odisha Bhubaneswar India Department of Statistics Utkal University Bhubaneswar India Department of Electronics and Communication Engineering FET-ITER Siksha ‘O’ Anusandhan (Deemed to Be) University Bhubaneswar India Department of Computer Application National Institute of Technology Raipur India

The rise of antimicrobial resistance (AMR) in bacteria that cause infectious diseases poses a significant global health challenge, necessitating advanced prediction technology for effective management and response. This study aims to evaluate the predictive capabilities of several machine learning (ML) and deep learning (DL) models in identifying patterns of antibiotic resistance. We have proposed a comprehensive experimental framework that combines multiple machine learning algorithms and deep learning architectures to evaluate their efficacy and accuracy in predicting antibiotic resistance. We have developed DnnARs, an artificial intelligence pipeline designed to identify antibiotic-resistant strains of E. coli bacteria, which are mostly responsible for urinary tract infections (UTIs). DnnARs utilizes Synthetic Minority Over-sampling Technique (SMOTE) and applies feature engineering using principal component analysis (PCA) and t-distributed Stochastic neighbor embedding (t-SNE) on our dataset to evaluate the effectiveness of various AI models. Our model comparison demonstrates that DL models outperform ML models when the dataset is fine-tuned using SMOTE and feature engineering. Our DnnARs achieve a classification accuracy of 97%, a specificity of 91%, and a remarkable sensitivity of 100%. The DL models that were examined exhibit an average classification accuracy that is 3.66% higher than that of the ML models. The findings demonstrate the capability of deep learning methodology to enhance predictive analytics for antimicrobial resistance, offering a robust and efficient alternative to traditional machine learning techniques. This research aims to enhance diagnostic tools and medications, improve the management of infectious diseases, reduce antibiotic resistance, and integrate deep learning models into clinical workflows to enable immediate decision-making. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Antimicrobial resistance Artificial intelligence Deep learning Infectious disease Machine learning Urine tract infections

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Fog and Edge Computing-Based Smart Health Care System Using Machine Learning 7th

Fog and Edge Computing-Based Smart Health Care System Using ...

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7th International Conference on Advanced Computing and Intelligent engineering, ICACIE 2022

作者： Tripathy, Subhranshu Sekhar Tripathy, Niva Rath, Mamata Hu, Yu-Chen Pattanaik, Swarupa Samantaray, Swarnakanti Department of Computer Science and Engineering DRIEMS Autonomous College Odisha Cuttack India Department of Computer Science and Information Management Providence University Taichung City Taiwan GIET Khurdha Odisha Baniatangi India Nalanda Institute of Technology Odisha Bhubaneswar India School of Computer Engineering KIIT Deemed to be University Bhubaneswar India Department of Computer Science and Engineering SOET Centurion University of Technology and Management Bhubaneswar India

ISBN: (纸本)9789819950140

Data in the medical field include varying degrees of ambiguity and imprecision, which makes it difficult for doctors to improve the health of patients with a group of related suggestive disorders. As a result, doctors use data stored in networks and computing systems to identify a variety of illnesses in patients. The proposed strategy boosts the system’s effectiveness and makes accurate diagnoses and suggestions for treating life-threatening diseases easier. The adaptive prediction systems are assessed, and a machine learning-based model for heart disease detection is trained and compared to the relevant traditional approaches. When compared to other sophisticated classification methods, the cloud-fog-fog architecture was shown to be the most accurate. As a result, the suggested strategy is more accurate in identifying different cardiac issues and suggesting effective treatment options. The performance of the proposed system warrants its application in automated cardiac patient diagnostic and recommendation systems. The Heart Failure Clinical Records repository at UCI provided the dataset for this study, which has 13 characteristics. The experimental findings showed that the CNN model performs better than other deep learning and machine learning models which are proposed by eminent researchers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Internet of things

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Medicare: A telemedicine healthcare website

Medicare: A telemedicine healthcare website

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Payal Payal Raghunath Dey Rohan Senapati Jayashree Piri Surajit Mohanty School of Computer Engineering KIIT University Bhubaneswar India Department of Computer Science and Engineering Silicon University Bhubaneswar India Department of Computer Science and Engineering SOET DRIEMS University Cuttack India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

关键词： computers Technology management Telemedicine Medical services Chatbots Communications technology Reliability

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AI-Driven Student Networking Portal

AI-Driven Student Networking Portal

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International Conference on Inventive Computation Technologies (ICICT)

作者： MohanaPrakash T A Lathashree P V Divyashree A Roshni Reju Sonika V Nandagopal H Department of Computer Science and Engineering R.M.K Engineering College Kavarapettai India Department of IT SOET CMR university Bangaluru India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

In today's constantly changing academic environment, connecting students with career-ready skills, knowledge, and support that they'll need to thrive is more important than ever. That's where Campus Core can help: Campus Core is a smart AI based networking platform designed to connect students in ways that feel natural, useful, and personal. Created for the purpose of bridge the gap between professional networking, academic collaboration, and career development, Campus Core utilizes state of the art AI tools like Market Basket Analysis, Collaborative Filtering, and Content-Based Filtering to match students to the skills, interests, and potential career path they're interested in. Despite being relatively technical, the platform is quite solid and accessible. The frontend is written in HTML, CSS and JavaScript to give it a clean look and feel, but it's built in Python and Flask, who handle everything from the site's login security to a chatbot that answers the user queries. NLP and TF-IDF is used for the search which makes finding the right connections faster and more accurate. And the chatbot that uses Mistral API is always on hand to give real-time career advice or academic support. MongoDB's storage guarantees the system can withstand substantial growth as well. One of the platform's unique features is the Post Section, where students can post job openings, internships, academic events and more-create a space to really feel connected to the college experience. With AI-driven recommendations, real-time search and interactive tools build in, Campus Core solves the common student problems of missed opportunities and fragmented communication: A platform where real people can connect and actually learn, find new roads and build meaningful connections that will help them thrive beyond graduation.

关键词： Accuracy Filtering engineering profession Collaboration Chatbots Solids Real-time systems User experience Security Artificial intelligence

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Enhancing Stock Price Prediction Through Fusion of Deep Learning Models and Technical Indicators

Enhancing Stock Price Prediction Through Fusion of Deep Lear...

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International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES)

作者： Rajesh Kumar Ghosh Bhupendra Kumar Gupta Sunil Kumar Mohapatra Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be) University Bhubaneswar India Department of Computer Science & Engineering SoET Centurion University of Technology and Management Bhubaneswar India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Accurate forecasting of stock price movements is important for traders and investors to handle the unpredictable nature of the financial market. In this study, deep learning models are integrated with Technical Indicators (TI) for stock price prediction using Tata Consultancy Services (TCS) and Infosys Ltd (INFY) over ten years, from 31/07/2014 to 31/07 /2024. We gathered daily stock data on opening, closing, lowest, highest prices and trading volume to monitor the market's daily performance. The opening price was selected as the target variable to understand price movements and overall market behavior. Along with five TI, two deep learning methods, long short-term memory (LSTM) and gated recurrent unit (GRU), were employed. The technical indicators included simple moving average (SMA), exponential moving average (EMA), moving average convergence divergence (MACD), relative strength index (RSI), and bollinger bands (BB). To measure the performance of our model, we used R-squared (R2), root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results show that the proposed LSTM - GRU model, using OHLCV and TI, significantly improves prediction accuracy compared to other models, achieving R2 values of 0.981 for the TCS dataset and 0.976 for the Infosys dataset. Combining deep learning with technical analysis proves more effective for predicting stock prices than using either method alone. This approach helps investors and traders by providing a more accurate tool for understanding the market dynamics and making profitable decisions.

关键词： Deep learning Accuracy Embedded systems Measurement uncertainty Predictive models Signal processing Indexes Forecasting Long short term memory Monitoring

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