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Optimizing Heart Attack Prediction Through OHE2LM: A Hybrid Modelling Strategy

Journal of Electrical Systems 2024年第1期20卷 66-75页

作者： Mall, Pawan Kumar Srivastava, Swapnita Patel, Mitul M. Kumar, Aniruddh Narayan, Vipul Kumar, Sanjay Singh, P.K. Singh, D.S. GL Bajaj Institute of Technology and Management India Department of Electronics and Communication Engineering Parul Institute of Engineering and Technology Parul University Vadodara India Department of Computer Science and Engineering Galgotias College of Engineering and Technology Knowledge Park-2 Greater Noida India Galgotias University Uttar Pradesh Gautam Buddha Nagar India Rajkiya Engineering College Azamgarh India Computer Science and Engineering Department Madan Mohan Malaviya University Of Technology Uttar Pradesh Gorakhpur273010 India Computer Science and Engineering Department Madan Mohan Malaviya University of Technology Uttar Pradesh Gorakhpur273010 India

Predicting heart attacks stands as a significant concern contributing to global morbidity. Within clinical data analysis, cardiovascular disease emerges as a pivotal focus for forecasting, wherein Data science and machine learning (ML) offer invaluable tools. These methodologies aid in predicting heart attacks by considering various risk factors Just like high blood pressure, increased cholesterol levels, irregular pulse rates, and diabetes, this research aims to enhance the accuracy of predicting heart disease through machine learning *** study introduces a MLdriven approach, termed ML-ELM, dedicated to forecasting heart attacks by analysing diverse risk factors. The proposed ML-ELM model is compared with alternative Utilizing machine learning techniques like Support Vector Machines, Logistic Regression, Naïve Bayes, and XGBoost is a key aspect of this exploration into different approaches for predictive modeling., is part of the research strategy. The dataset utilized for heart disease symptoms is sourced from the UCI ML Repository. The outcomes reveal that our proposed ML-ELM model has demonstrated superior predictive performance among the ML techniques tested. ML models show notable efficiency in identifying heart attack symptoms, particularly with boosting algorithms. Accuracy assessments were employed to gauge the predictive ability, Our suggested model demonstrated an outstanding accuracy rate of 96.77%. © JES 2024 on-line: ***

关键词： Support vector machines

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Privacy BERT-LSTM: a novel NLP algorithm for sensitive information detection in textual documents

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Neural Computing and Applications 2024年第25期36卷 15439-15454页

作者： Muralitharan, Janani Arumugam, Chandrasekar Department of Information Technology St.Joseph’s College of Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering St.Joseph’s College of Engineering Tamil Nadu Chennai India

In this modern digital era, the increasing volume of textual data and the widespread adoption of natural language processing (NLP) techniques have presented a critical challenge in safeguarding sensitive privacy information. As a result, there is a pressing demand to design robust and accurate NLP-based techniques to perform efficient sensitive information detection in textual data. This research paper focuses on the detection and classification of sensitive privacy information in textual documents using NLP by proposing a novel algorithm named Privacy BERT-LSTM. The proposed Privacy BERT-LSTM algorithm employs BERT for obtaining contextual embeddings and LSTM for sequential information processing, facilitating efficient sensitive information detection in textual documents. The BERT with its bidirectional characteristics captures the nuances and meaning of the textual documents, while the LSTM derives the long-range dependencies in the textual data. Moreover, the proposed Privacy BERT-LSTM algorithm with its attention mechanism highlights the important regions of the textual documents, contributing to efficient sensitive information detection. The comprehensive performance evaluation is conducted by employing the SMS Spam Collection dataset in terms of standard performance metrics and comparing it with different state-of-the-art techniques, namely, CASSED, PRIVAFRAME, CNN-LSTM, Conv-FFD, GCSA, TSIIP, and, C-PIIM. The experimental outcomes clearly illustrate that the Privacy BERT-LSTM algorithm demonstrates superior performance in identifying various types of sensitive information by achieving an accuracy of 92.50%, F1-score of 85.02%, and Precision of 89.36%. The proposed algorithm outperforms existing baseline models, providing valuable advancements in sensitive information detection using NLP. Therefore, this research contributes to the advancement of privacy protection in NLP applications and opens avenues for future investigations in the domain of sensitive info

关键词： Classification (of information)

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Brain Tumor Retrieval in MRI Images with Integration of Optimal Features

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Journal of Harbin Institute of technology(New Series) 2024年第6期31卷 71-83页

作者： N V Shamna B Aziz Musthafa Department of Computer Science and Engineering PA College of EngineeringMangaluru 574153KarnatakaIndia Department of Computer Science and Engineering Bearys Institute of TechnologyMangaluru 5754199KarnatakaIndia

This paper presents an approach to improve medical image retrieval, particularly for brain tumors, by addressing the gap between low-level visual and high-level perceived contents in MRI, X-ray, and CT scans. Traditional methods based on color, shape, or texture are less effective. The proposed solution uses machine learning to handle high-dimensional image features, reducing computational complexity and mitigating issues caused by artifacts or noise. It employs a genetic algorithm for feature reduction and a hybrid residual UNet(HResUNet) model for Region-of-Interest(ROI) segmentation and classification, with enhanced image preprocessing. The study examines various loss functions, finding that a hybrid loss function yields superior results, and the GA-HResUNet model outperforms the HResUNet. Comparative analysis with state-of-the-art models shows a 4% improvement in retrieval accuracy.

关键词： medical images brain MRI machine learning feature extraction and reduction content-based image retrieval(CBIR)

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Early Diagnosis of Lung Tumors for Extending Patients’ Life Using Deep Neural Networks

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computers, Materials & Continua 2023年第7期76卷 993-1007页

作者： A.Manju R.Kaladevi Shanmugasundaram Hariharan Shih-Yu Chen Vinay Kukreja Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang Department of Computer Science and Engineering SRM Institute of Science and TechnologyRamapuramChennaiIndia Department of Computer Science and Engineering Saveetha Engineering CollegeChennaiIndia Department of Computer Science and Engineering Vardhaman College of EngineeringHyderabadIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin 64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin 64002Taiwan Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia Department of Computing Science University of AberdeenAberdeenUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh 12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh 11437Saudi Arabia School of Computer&Communication Engineering Changsha University of Science&TechnologyChangshaChina

The medical community has more concern on lung cancer *** experts’physical segmentation of lung cancers is time-consuming and needs to be *** research study’s objective is to diagnose lung tumors at an early stage to extend the life of humans using deep learning ***-Aided Diagnostic(CAD)system aids in the diagnosis and shortens the time necessary to detect the tumor *** application of Deep Neural Networks(DNN)has also been exhibited as an excellent and effective method in classification and segmentation *** research aims to separate lung cancers from images of Magnetic Resonance Imaging(MRI)with threshold *** Honey hook process categorizes lung cancer based on characteristics retrieved using several *** this principle,the work presents a solution for image compression utilizing a Deep Wave Auto-Encoder(DWAE).The combination of the two approaches significantly reduces the overall size of the feature set required for any future classification process performed using *** proposed DWAE-DNN image classifier is applied to a lung imaging dataset with Radial Basis Function(RBF)*** study reported promising results with an accuracy of 97.34%,whereas using the Decision Tree(DT)classifier has an accuracy of 94.24%.The proposed approach(DWAE-DNN)is found to classify the images with an accuracy of 98.67%,either as malignant or normal *** contrast to the accuracy requirements,the work also uses the benchmark standards like specificity,sensitivity,and precision to evaluate the efficiency of the *** is found from an investigation that the DT classifier provides the maximum performance in the DWAE-DNN depending on the network’s performance on image testing,as shown by the data acquired by the categorizers themselves.

关键词： Lung tumor deep wave auto encoder decision tree classifier deep neural networks extraction techniques

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Classification of Healthcare Data Using T-BMSVM in Big Data 13

Classification of Healthcare Data Using T-BMSVM in Big Data

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13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022

作者： Sujitha, R. Paramasivan, B. PSNA College of Engineering and Technology Department of Computer Science and Engineering India Department of Information Technology India

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

With the fast pace in collating big data healthcare framework, a streamlining of machine learning algorithms together with apache spark has been designed for effective classification of images and stages of lung cancer to the greatest extent. We experiment on a combination of binary classification (SVM-non linear SVM with Radial Basis Function RBF) and Multi-class classification (WTA-SVM winner-takes-all with support vector machine) with threshold technique (T-BMSVM) to classify nodules into malignant or benign nodules and also their malignancy levels respectively. We have argued for handling and processing large sizes of data sets as sputum cell images in the field of classification using the map-reduce framework in Pyspark, which works better with Apache spark. The outsourced outcomes reveal that classification works better with Multi-class SVM, therefore, may be considered as a promising tool to diagnose the stages of nodules. Also, Scalability and convergence analysis embed to prove the improving results of multi-class classification than SVM. © 2022 IEEE.

关键词： Classification (of information)

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Anomaly Detection in Image Processing using Artificial Intelligence

Anomaly Detection in Image Processing using Artificial Intel...

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2023 IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2023

作者： Jain, Swati Choudhary, Naveen College of Technology and Engineering Department of Computer Science and Engineering Udaipur India College of Technology and Engineering The Department of Computer Science and Engineering Udaipur India

ISBN: (纸本)9781665474924

Any method that looks for samples that deviate from anticipated patterns is sometimes referred to as anomaly detection. Many outlier finding methods are established on the types of irregularities, various types of data labels that are available, and the applications. The aim of this paper is to give an overview and systematic evaluation of outlier finding research. We believe that the paper contributes to a better understanding of the numerous fields in various types of research done on this subject. The approaches for anomaly detection have been separated into many groups. We discuss the pros and cons of each category and look at how computationally complex the various methods are in terms of real-world application domains. We concluded the paper by providing research gaps and constraints we faced while applying artificial intelligence algorithms in practical applications. This review paper examines several outlier detection and treatment techniques in images, as well as gives better performance of various AI approaches. © 2023 IEEE.

关键词： Anomaly detection

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IoT based deep learning approach for online fault diagnosis against cyber attacks

IoT based deep learning approach for online fault diagnosis ...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Felix, A. Yovan Sharmila, V. Devi, S. Nandhini Deena, S. Yadav, Ajay Singh Jeyalakshmi, K. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering R.M.D. Engineering College Tamil Nadu Kavaraipettai India Department of Computer Science and Engineering VSB Engineering College Tamil Nadu Karur India Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Tamil Nadu Chennai India Department of Mathematics SRM Institute of Science and Technology Delhi-NCR Campus Modinagar Uttar Pradesh Ghaziabad India Department of Physics PSNA College of Engineering and Technology Tamil Nadu Dindigul India

ISBN: (纸本)9781032669663

The Internet of Things (IoT) connects systems, software, cloud computing, activities and could provide new access points for cyber-attacks. Virus attacks and unauthorized downloading are the two main risks to IoT security at the moment. These dangers run the risk of obtaining private information, damaging their reputation and their finances. A frame is employed in this paper’s deep learning to identify threat avoidance in IoT. The cyber security detection systems following database has to be built, after which the benchmark variables are chosen and assessed in order to establish a cyber-security alert system in a large data set. The deep learning strategy to identifying threat avoidance was described for the simulation of this scenario. From the Mailing database, malware samples have been gathered for analysis. The research results demonstrate that the proposed method for assessing risks to cyber security in IoT provides highest accuracy to current approaches. Because of this, Deep Convolution Neural Network (DCNN) is utilized in IoT to detect attack avoidance. © 2024 The Author(s).

关键词： Internet of things

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Hyperparameter optimization: Classics, acceleration, online, multi-objective, and tools

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Mathematical Biosciences and engineering 2024年第6期21卷 6289-6335页

作者： Tan, Jia Mian Liao, Haoran Liu, Wei Fan, Changjun Huang, Jincai Liu, Zhong Yan, Junchi Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China College of Systems Engineering National University of Defense Technology Changsha China

Hyperparameter optimization (HPO) has been well-developed and evolved into a well-established research topic over the decades. With the success and wide application of deep learning, HPO has garnered increased attention, particularly within the realm of machine learning model training and inference. The primary objective is to mitigate the challenges associated with manual hyperparameter tuning, which can be ad-hoc, reliant on human expertise, and consequently hinders reproducibility while inflating deployment costs. Recognizing the growing significance of HPO, this paper surveyed classical HPO methods, approaches for accelerating the optimization process, HPO in an online setting (dynamic algorithm configuration, DAC), and when there is more than one objective to optimize (multi-objective HPO). Acceleration strategies were categorized into multi-fidelity, bandit-based, and early stopping;DAC algorithms encompassed gradient-based, population-based, and reinforcement learning-based methods;multi-objective HPO can be approached via scalarization, metaheuristics, and model-based algorithms tailored for multi-objective situation. A tabulated overview of popular frameworks and tools for HPO was provided, catering to the interests of practitioners. © 2024 the Author(s), licensee AIMS Press.

关键词： Deep neural networks

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Smart Swimming Pool Management System (SSPMS) using IoT

Smart Swimming Pool Management System (SSPMS) using IoT

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2023 IEEE International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2023

作者： Sangeetha, A. Krishnan, R. Santhana Kumar, G. Praveen Julie, E. Golden Ganesh, R. Karthik Robinson, Y. Harold PSNA College of Engineering and Technology Department of Information Technology Dindigul India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Tirunelveli India V.S.B Engineering College Department of Information Technology Karur India Anna University Department of Computer Science and Engineering Regional Campus Tirunelveli India SCAD College of Engineering and Technology Department of Computer Science and Engineering Tirunelveli India Francis Xavier Engineering College Department of Computer Science and Engineering Tirunelveli India

ISBN: (纸本)9798350397208

Swimming has become one of the major routines nowadays among people. People mostly use swimming pools for exercise or recreation. According to statistics one hour of swimming burns 30% more calories than one hour of running. So many people prefer swimming to keep their body fit. But few people use the swimming pool without having proper swimming skills. This leads to loss of their lives. Around the world nearly 42 people drown per hour. The pH level of water in the swimming pool should be between 7.4 to 7.6. The high pH water can cause rashes on human skin and scaling on pool equipment. Low pH causes eye irritation, skin irritation etc. Hence, a smart system is required to monitor the pH level of the water and to track the condition of the swimmers inside the pool. So with the support of few sensors and Arduino, an automatic swimming pool monitoring system, which is capable of sensing the pH level of the water available in swimming pool and condition of the swimmers is developed. The proposed system will also update all the observed status in the cloud with the support of the Internet facility. © 2023 IEEE.

关键词： Swimming pools

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Quadratic and Lagrange interpolation-based butterfly optimization algorithm for numerical optimization and engineering design problem

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Soft Computing 2025年第1期29卷 157-194页

作者： Sharma, Sushmita Saha, Apu Kumar Chakraborty, Sanjoy Deb, Suman Sahoo, Saroj Kumar Department of Computer Science and Engineering Indian Institute of Information Technology Tripura Agartala India Department of Computer Science and Engineering National Institute of Technology Tripura Agartala India Department of Mathematics National Institute of Technology Agartala Tripura Agartala India Department of Information Technology Bir Bikram Memorial College Tripura Agartala India

This work proposes a novel and improved Butterfly Optimization Algorithm (BOA), known as LQBOA, to solve BOA’s inherent limitations. The LQBOA uses Lagrange interpolation and simple quadratic interpolation techniques with adapted parameter settings to enhance the search strategy of BOA and to achieve a better balance of diversification and intensification. LQBOA’s performance was examined on 45 traditional benchmark issues as well as the IEEE CEC 2017 benchmark suites with 10, 30 and 50 dimensions, and the results were compared against popular state-of-the-art and modified algorithms. It was discovered that in more than 85% of cases, the proposed LQBOA outperformed the compared algorithms. The Friedman rank test and Wilcoxon rank test were used to validate the suggested LQBOA’s rank and significance. Additionally, convergence and diversity analyses were performed to investigate its convergence speed and searching behavior, respectively. Furthermore, LQBOA has been successfully deployed to solve twelve real-world issues including several engineering design problems and two multiple gravity assist spacecraft trajectory problems. The results of these problems were compared to those of a wide range of algorithms, demonstrating the superior performance of the proposed LQBOA. In conclusion, LQBOA makes a significant addition to the optimization area, and its application could greatly improve the performance of numerous optimization jobs. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Lagrange multipliers

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