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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Basthikodi, Mustafa Shetty, Shreya S. Yashmitha, B. Shetty, Shravya R. Prakruthi, P.D. Computer Science and Engineering Department Sahyadri College of Engineering and Management Mangalore India Information Science and Engineering Department Sahyadri College of Engineering and Management Mangalore India

ISBN: (纸本)9798350383867

Drowning is a leading cause of unintentional death worldwide, with a significant number of fatalities occurring in natural bodies of water, swimming pools, and other aquatic environments. Timely and accurate detection of drowning incidents is critical for preventing tragic outcomes. This provides an overview of challenges, methods, and technologies associated with drowning detection. In the proposed work, we have developed a wearable device supplemented by web application that aims to detect drowning incidents effectively. The system incorporates sensors designed to detect distress signals or abnormal movement patterns underwater, enhancing safety by promptly alerting nearby individuals or authorities to the situation. The sensors are utilized to monitor critical parameters such as SpO2, heartbeat, water presence, as well the temperature and humidity levels of the individual. Key components of this system include the ESP8266 NodeMCU and the Arduino Uno board R3, which provide wired connectivity and microcontroller capabilities, respectively. By continuously monitoring these vital signs and environmental factors, the system can accurately identify potential drowning incidents and trigger timely alerts. © 2024 IEEE.

关键词： Wearable sensors

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Cardio Care: A Predictive Model for Heart Disease Detection 3

Cardio Care: A Predictive Model for Heart Disease Detection

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3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Rohith, R. Senthilnayaki, B. Joshua Dayalan, M. Anna University College Of Engineering Department of Information Science and Technology India Anna University College Of Engineering Department of Computer Science and Engineering India

ISBN: (数字)9798350391565

ISBN: (纸本)9798350391565

Heart Disease is still an epidemic global health concern, so trustworthy prediction models are vital for risk assessment and early detection. In this research, we propose a comprehensive predictive framework leveraging ensemble learning techniques alongside traditional Machine Learning (ML) models to increase forecasting of cardio disease accuracy. Utilizing a dataset sourced from Cardiovascular-Disease-Dataset, demographic, clinical, and lifestyle factors were incorporated as features. The performance of Naive Bayes (NB), AdaBoost, Support Vector Classifier (SVC) and Logistic Regression (LR) both individually and within ensemble frameworks, was evaluated using accuracy of 98% and AUROC (Area-Under the Receiver Operating Characteristic Curve) of 99.845% as a primary metric. Our results demonstrate how effective ensemble learning strategies are in obtaining higher accuracy, precision-recall and along with AUROC curve for heart disease prediction compared to individual classifiers. This study underscores the importance of ensemble methods in improving predictive performance and contributing to enhanced clinical decision-making in cardiovascular health management. © 2024 IEEE.

关键词： Contrastive Learning

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An IoT-Driven Performance Evaluation Framework for Secure Healthcare Monitoring Systems 4

An IoT-Driven Performance Evaluation Framework for Secure He...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Aswathy, R.H. Suresh, P. Mani, C. Saveetha School of Engineering Simats Department of Computer Science and Engineering Thandalam Chennai India Nandha Engineering College Department of Computer Science and Engineering Tamil Nadu Perundurai Erode India

ISBN: (纸本)9798331523923

Today, all countries have a socio-economic concern about the healthcare services. Advanced healthcare systems leverage technology, such as the Internet of Things (IoT), to enhance quality and reduce costs. IoT integrates smart devices, medical professionals, and patients into a connected network. The advanced healthcare trend extends the possibilities for doctors to monitor the patient remotely and give medication during emergencies. Since it is a critical application security plays a vital role as the data transferred through the wireless environment and it is maintained digitally as a digital patient record. A major challenge in IoT healthcare includes security, privacy and power consumption. In this proposed work the secure cryptographic based MIAS server is applied for establishing the data transmission and tested over the known security attacks such as DDoS, replay attack, password guessing, sensor node spoofing etc. In contrast to alternative approaches, the proposed scheme is better suited to ensuring security and minimizing power consumption within the limited confines of IoT environments. © 2025 IEEE.

关键词： Electronic health record

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Classification of suspected objects and severity assessment of COVID-19 from chest X-ray images using deep transfer learning

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Research on Biomedical engineering 2023年第3期39卷 705-718页

作者： Verma, Karan Kumar, Ashok Swaraj, Aman Sagar, Aditi Department of Computer Science and Engineering National Institute of Technology Delhi Delhi India Department of Electronics and Communication Engineering Government Mahila Engineering College Rajasthan Ajmer India Department of Computer Science and Engineering Indian Institute of Technology Roorkee India

Purpose: Coronavirus disease 2019 (COVID-19) has infected about 418 million people across the globe. So, the analysis of biomedical imaging accompanied with artificial intelligence (AI) approaches has transpired a vital role in diagnosing COVID-19. Until now, numerous classification approaches have been demonstrated for the detection of COVID-19. The assessment of COVID-19 patients according to severity level is not so far investigated. For this motivation, the classification of COVID-19 chest X-ray (CXR) images according to severity of the infection is presented in this work. Methods: Primarily, the 1527 CXR images are pre-processed to reshape images into unique size, denoised, and enhanced images through median filter and histogram equalization (HE) techniques, respectively. Afterward, reshaped, denoised, and enhanced CXR images are augmented using synthetic minority oversampling technique (SMOTE) to achieve the balanced dataset of 1752 CXR images. After augmentation, a pre-trained VGG16 and residual network 50 (Resnet50) deep transfer learning models with random forest (RF) and support vector machine (SVM) classifiers are utilized for feature extraction and classification of 1752 CXR images into diverse class labels such as normal, severe COVID-19, and non-severe COVID-19. Results: Our proposed ResNet50 model with SVM classifier provides the highest accuracy of about 95% for severity assessment and classification of COVID-19 CXR images as compared to other permutations. For the ResNet50 model with SVM classifier model, the average value of precision, recall, and F1-score are 91%, 94%, and 92%, respectively. Conclusion: The multi-class classification deep transfer learning models are presented to determine the severity assessment and classification of COVID-19 by using CXR images. Out of these proposed models, the ResNet50 model with SVM classifier will be highly favorable for doctors to classify patients according to their severity assessment and detection of COV

关键词： COVID-19

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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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Horizontal Voting Ensemble Based Predictive Modeling System for Colon Cancer

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computer Systems science & engineering 2023年第8期46卷 1917-1928页

作者： Ushaa Eswaran S.Anand Department of Electronics and Communication Engineering Infant Jesus College of EngineeringTuticorinTamil NaduIndia Department of Computer Science and Engineering Infant Jesus College of EngineeringTuticorinTamil NaduIndia

Colon cancer is the third most commonly diagnosed cancer in the *** colon AdenoCArcinoma(ACA)arises from pre-existing benign polyps in the mucosa of the ***,detecting benign at the earliest helps reduce the mortality *** this work,a Predictive Modeling System(PMS)is developed for the classification of colon cancer using the Horizontal Voting Ensemble(HVE)*** different patterns inmicroscopic images is essential to an effective classification system.A twelve-layer deep learning architecture has been developed to extract these *** developedHVE algorithm can increase the system’s performance according to the combined models from the last epochs of the proposed *** thousand(10000)microscopic images are taken to test the classification performance of the proposed PMS with the HVE *** microscopic images obtained from the colon tissues are classified intoACAor benign by the proposed *** prove that the proposed PMS has∼8%performance improvement over the architecture without using the HVE *** proposed PMS for colon cancer reduces the misclassification rate and attains 99.2%of sensitivity and 99.4%of *** overall accuracy of the proposed PMS is 99.3%,and without using the HVE method,it is only 91.3%.

关键词： Colon cancer microscopic images medical image processing ensemble approach computer aided diagnosis texture analysis

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Leveraging user's preference and social circle for personalized recommendation via matrix factorization with sub-linear convergence rate

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Journal of Intelligent and Fuzzy Systems 2024年第1-2期47卷 1-13页

作者： Qiu, Laixiang Zhou, Wang Tian, Ying Ul Haq, Amin Ahmad, Sultan School of Computer and Software Engineering Xihua University Chengdu China Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College LuZhou China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj Saudi Arabia

Recommender systems aim to filter information effectively and recommend useful sources to match users' requirements. However, the exponential growth of information in recent social networks may cause low prediction accuracy for recommendation systems. This article proposes a unified personalized recommendation architecture referred to as PSRec, which incorporates user preference and social relationship into matrix factorization framework. Specifically, PSRec generates two collections for textual reviews and contextual information respectively, and performs preference learning for each user via the Latent Dirichlet Allocation topic model. Moreover, PSRec exploits the inner relations within the social circle for recommendation, including direct trust relationship and indirect trust relationship. Additionally, it's certificated that PSRec can converge at a sub-linear rate via theoretical analysis. Experimental results over DoubanMovie, CiaoDVDs and Yelp demonstrate the superiority of the proposed PSRec, which can achieve significant improvements and provide much better user experience while compared with other benchmark models. © 2024 - IOS Press. All rights reserved.

关键词： Matrix factorization

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Childhood Asthma Disease Prediction Using Classification Algorithms of Supervised Machine Learning 1

Childhood Asthma Disease Prediction Using Classification Alg...

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Kumar, Pawan Jain, Jaishree Jaiswal, Updesh Kumar Yadav, Deepika Solanki, Beerbal Ajay Kumar Garg Engineering College Department of Computer Science & Engineering UP Ghaziabad India Bharati Vidyapeeth's College of Engineering Department of Computer Science & Engineering New Delhi India

ISBN: (纸本)9798350367720

Asthma is a very dangerous lung disease in which our airways are swollen and become narrow and blocked. Air does not flow through narrow airways properly. It is an ongoing disease and does not go away, regular treatment is needed lifelong. According to the survey report, 25 million people in the United States are living with this disease and out of that 5 million are children who are asthma patients. Asthma is a life-threatening disease if don't get treatment timely, can lose your life. Early prediction of this disease among children not only provides a recovery solution for the disease but also helps to increase their lifetime. The proposed designed Intelligent system provides useful resources to the healthcare system to make accurate decisions for preventing asthma in childhood. The research paper aims to design an intelligent artificial-based machine learning classification model to predict asthma disease among children aged less than 10 years. Research also included a comparative analysis of different classification methods of machine learning like Linear regression, KNN Model, Decision Trees, Random Forests and Support Vector machines. It also helps to select the best model by analyzing the performances of these algorithms based on efficiency and accuracy. The outcomes of the research paper to study asthma prediction using different classification models and help doctors and patients to take the right decision timely. © 2024 IEEE.

关键词： Lung cancer

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Effect of pulsed current cathodic protection on pipeline steel API 5L X65 corrosion mitigation: An investigation and machine learning-assisted modeling

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Corrosion Communications 2023年第4期12卷 29-45页

作者： Hosein Eslamian Mehdi Javidi Mohammad Reza Zamani Mohammad Mahdi Dana Eghbal Mansoori Department of Materials Science and Engineering School of Engineering Shiraz University School of Metallurgy and Materials Engineering College of Engineering University of Tehran Computer Science Eng.and IT Dept. School of Electrical and Computer Engineering Shiraz University

Pulsed current cathodic protection(PCCP) could be more effective than direct current cathodic protection(DCCP)for mitigating corrosion in buried structures in the oil and gas industries if appropriate pulsed parameters are chosen. The purpose of this research is to present the corrosion prevention mechanism of the PCCP technique by taking into account the effects of duty cycle as well as frequency, modeling the relationships between pulse parameters(frequency and duty cycle) and system outputs(corrosion rate, protective current and pipe-to-soil potential) and finally identifying the most effective protection conditions over a wide range of frequency(2–10 kHz) and duty cycle(25%-75%). For this, pipe-to-soil potential, pH, current and power consumption, corrosion rate, surface deposits and investigation of pitting corrosion were taken into account. To model the input-output relationship in the PCCP method, a data-driven machine learning approach was used by training an artificial neural network(ANN). The results revealed that the PCCP system could yield the best protection conditions at 10 kHz frequency and 50% duty cycle, resulting in the longest protection length with the lowest corrosion rate at a consumption current 0.3 time that of the DCCP method. In the frequency range of 6–10 kHz and duty cycles of 50%-75%, SEM images indicated a uniform distribution of calcite deposits and no pits on cathode surface.

关键词： Pulsed current cathodic protection Duty cycle Frequency Artificial neural network API X65 pipeline steel

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AI Based Automated Framework for Crime Detection and Crowd Management 2

AI Based Automated Framework for Crime Detection and Crowd M...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Basthikodi, Mustafa Vidya, B. Pinto, Elvicia Miriam Basith, Mohammed Rao, Srinivas A. Computer Science and Engineering Department Sahyadri College of Engineering and Management Mangalore India Information Science and Engineering Department Sahyadri College of Engineering and Management Mangalore India

ISBN: (纸本)9798350383867

Manual monitoring and surveillance methods are often time-consuming and prone to human error, necessitating innovative solutions. This research proposes the integration of AI and ML technologies to address these challenges. AI-based CCTV networks enable real-time data analysis, enhancing crowd management and crime detection across diverse environments. The system utilizes cutting-edge technologies, incorporating im- age and live video feeds for dynamic analysis, anomaly detection, and pattern recognition. Key features include timely security threat notifications and efficient crowd monitoring. Successful implementation requires a significant investment in technology, infrastructure, and robust data management systems. Ethical considerations, particularly privacy, are paramount. The project aims to revolutionize crime detection and crowd management, introducing a modern, accurate, and efficient system aligned with the evolving security landscape. ©2024 IEEE.

关键词： Investments

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