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Expert Clouds and Applications (ICOECA), International Conference on

作者： Dheeraj Hebri S. Kirubakaran R. Rajitha Jasmine Anitha Poosamooper S. Rukmani Devi D Raja Joseph Department of Master of Computer Applications Srinivas Institute of Technology Valachil Mangalore Karnataka India Department of Computer Science Engineering CMR College of Engineering and Technology Hyderabad Telangana India Department of Information Technology R.M.K. Engineering College Kavaraipettai Thiruvallur Tamil Nadu India Department of Computer Applications Vel Tech Ranga Sanku Arts College Chennai Tamil Nadu India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India School of Aeronautical Sciences Hindustan Institute of Technology and Science Padur Chennai Tamil Nadu India

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

Remote sensing is concerned with high-resolution geographical data. Its applications include farming, water resources, ecology, forests, deserts, marine applications, soil types, disaster alerts, and management. To extract the necessary information from satellite images, considerable image processing is required. Image segmentation and categorization into land cover categories is an important stage in satellite image processing. This article describes a method for categorizing and detecting different forms of land cover from satellite data. In this study, we examine satellite images for historical map classification using machine learning algorithms. We acquire Landsat images to complete this tedious task. The images are carefully labeled using the Arcmap software. We categorize areas into five types: forest, building, water, barren, and agricultural land. The images are then sent to the Convolutional Neural Network (CNN) model for feature extraction. The retrieved features were then exposed to three machine Learning (mL) models: Support Vector machine (SVm), Naive Bayes (NB), and Decision Tree (DT). Standard classification measures are used to analyze the outcomes of all three models. When compared to the other two models in satellite image classification, CNN feature extraction with SVm classifier achieves the greatest accuracy of 95%.

关键词： Support vector machines Forests machine learning algorithms Biological system modeling Disasters Land surface Landsat

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Identification of Advanced Breast Cancer by Using Aggregate Deep Learning method

Identification of Advanced Breast Cancer by Using Aggregate ...

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Communication, Computing and Energy Efficient technologies (I3CEET), International Conference on

作者： V. J. Chakravarthy Sangeetha Varadhan Sridevi Sakhamuri R. Vijayabharathi Fathima S. K Saritha K Faculty of Computer Applications Dr. M.G.R. Educational and Research Institute Chennai India Department of Computer Applications Dr. M.G.R. Educational and Research Institute Maduravoyal Department of IoT Koneru Lakshmaiah Education Foundation Green Fields Vaddeswaram Andhra Pradesh Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology(Deemed to be University) Chennai Department of CSE Sona College of Technology Salem Department of Computer Applications Muthoot Institute of Technology and Science Kochi

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

metastasis is the main factor contributing to death in breast cancer patients. Faster and more accurate deep learning algorithms are being researched as potential replacements for the current, labor-intensive techniques used to diagnose metastases from lymph nodes. For testing and training purposes, a total of 220025 whole-slide pictures representing the lymph nodes of two cohorts of patients were identified. In order to detect metastatic cancer, we used a hybrid convolutional neural network model. A total of 57458 unlabeled images were utilized in order to verify the accuracy, sensitivity, specificity, and P-value of our diagnostic method. The DL-based approach was created to automatically and selectively assess and identify metastasizing lymph nodes. In the quantification procedure, accuracy was 98.84%. moreover, the accuracy rates for VGG16 and Recall were 92.42% and 91.25%, respectively. The differentiation levels of metastatic cancer may have an impact on recognition performance, according to later study. Our devised diagnostic complex showed a high degree of efficacy and accuracy for lymph node diagnosis. Patients with breast cancer may find it simpler to perform pathological screening for metastases thanks to our innovative DL-based method.

关键词： Deep learning Training Pathology Accuracy Sensitivity Convolution Breast cancer metastasis Convolutional neural networks Lymph nodes

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Heart Failure Prediction by Feature Ranking Analysis in machine Learning

Heart Failure Prediction by Feature Ranking Analysis in Mach...

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International Conference on Inventive Computation technologies (ICICT)

作者： T P Pushpavathi Santhosh Kumari N K Kubra MS Ramaiah University of Applied Sciences Bengaluru Karnataka India M.Tech in Computer Science and Networking MS Ramaiah University of Applied Sciences Bengaluru Karnataka India

ISBN: (数字)9781728185019

ISBN: (纸本)9781728185026

Heart disease is one of the major cause of mortality in the world today. Prediction of cardiovascular disease is a critical challenge in the field of clinical data analysis. With the advanced development in machine learning (mL), artificial intelligence (AI) and data science has been shown to be effective in assisting in decision making and predictions from the large quantity of data produced by the healthcare industry. mL approaches has brought lot of improvements and broadens the study in medical field which recognizes patterns in the human body by using various algorithms and correlation techniques. One such reality is coronary heart disease, various studies gives impression into predicting heart disease with mL techniques. Initially mL was used to find degree of heart failure, but also used to identify significant features that affects the heart disease by using correlation techniques. There are many features/factors that lead to heart disease like age, blood pressure, sodium creatinine, ejection fraction etc. In this paper we propose a method to finding important features by applying machine learning techniques. The work is to design and develop prediction of heart disease by feature ranking machine learning. Hence mL has huge impact in saving lives and helping the doctors, widening the scope of research in actionable insights, drive complex decisions and to create innovative products for businesses to achieve key goals.

关键词： Heart machine learning algorithms machine learning medical services Predictive models Prediction algorithms Diseases

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Rat Swarm Optimizer with Graph Convolutional Neural Network Based Waste Object Detector and Classification

Rat Swarm Optimizer with Graph Convolutional Neural Network ...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： G. Prabaharan manimegalai L R. Sivaraman Uma maheswari S Kamal Raj T Prakash Kumar P S Department of Computer Science and Engineering Veltech University Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology Avadi Chennai Electronics and Communication Engineering R.M.K. College of Engineering and Technology Department of Mathematics Dwaraka Doss Goverdhan Doss Vaishnav College Arumbakkam Chennai Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Department of Computer Science & Engineering RajaRajeswari College of Engineering Bangalore Department of IT K S R Institute for Engineering and Technology Tiruchengode

Object Detection (OD) is a task of computer Vision (CV) that includes locating and detecting intended object inside a video or an image. The objective of this OD is not just categorizing the objects but also to sketch bounding boxes surrounding the objects for precise identification of their location in the given visual. Deep Learning (DL) based OD is a CV model which utilizes Deep Neural Networks (DNNs) for automatically locating and detecting inside any video segment or image. It is one among the major crucial progression in the CV field that enables broad applications comprising robotics, self-driving cars, surveillance systems, etc. In this study, a novel Rat Swarm Optimizer with Graph Convolutional Neural Network based Waste Object Detector and Classification (RSOGCNN-WODC) technique is presented. The focus of the RSOGCNN-WODC technique lies in object detection and classification using optimal DL models. To obtain this, the RSOGCNN-WODC technique comprises residual network (ResNet50) model for feature extraction process. Afterward, the RSO system can be exploited for the optimum parameter chosen by the ResNet50 model. At last, the GCNN model is utilized for the recognition of the identified objects proficiently. The performance of the RSOGCNN-WODC system was simulated on the OD database. The results signify the improved results of the RSOGCNN-WODC technique compared to recent DL approaches.

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Ain’t How You Deploy: An Analysis of BGP Security Policies Performance Against Various Attack Scenarios with Differing Deployment Strategies

arXiv

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arXiv 2024年

作者： Barrett, Seth Idom, Calvin Villafuerte, German Zavala Byers, Andrew Gulmezoglu, Berk School of Computer and Cyber Sciences Augusta University Augusta United States College of Engineering and Science Louisiana Tech University Ruston United States School of Computer Science Texas A&M University - San Antonio San Antonio United States School of Computing and Augmented Intelligence Arizona State University Tempe United States Iowa State University Ames United States

This paper investigates the performance of various Border Gateway Protocol (BGP) security policies against multiple attack scenarios using different deployment strategies. Through extensive simulations, we evaluate the effectiveness of defensive mechanisms such as Root Origin Validation (ROV), Autonomous System Provider Authorization (ASPA), and PeerROV across distinct AS deployment types. Our findings reveal critical insights into the strengths and limitations of current BGP security measures, providing guidance for future policy development and implementation. © 2024, CC BY.

关键词： Border Gateway Protocol

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Safety Wearable Devices for monitoring and Predicting the Emergency of Civil Workers Using mobile Application

Safety Wearable Devices for Monitoring and Predicting the Em...

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

作者： m. Kiruthiga Devi R. Parthasarathy C. Tamilselvi V. Dharani V. Kaviya Department of Information Technology Dr. M.G.R. Educational and Research Institute Chennai India Department of Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Department of Information Technology Dr. MGR Educational and Research Institute Chennai India Department of Computer Science & Engineering Sathyabama Institute Of Science And Technology Chennai India

In several industries, wearable sensing devices are assisting workers in maintaining their health and safety. However, because WSDs can record precise information that might be regarded as private and personal, workers, particularly in the construction industry, have demonstrated some resistance to their use. Nonetheless, this highly regarded data may offer some crucial insight that management needs in order to plan and enhance worksite safety and assist the implementation of technology in decision-making. This project aims to develop a wearable safety device for construction workers, integrating various sensors such as a flame sensor, max30100 sensor, neo-6m GPS module, and mPU6050 sensor. The wearable device is intended to track the worker's position, monitor heart rate and SpO2, and identify falls and fire mishaps. The mQTT protocol will be used to transfer the sensor data to an Android application, enabling for real-time monitoring and emergency warning. To detect falls at the edge, the device also includes a Raspberry Pi camera module. The ultimate purpose of this initiative is to increase safety for construction workers and lower the likelihood of accidents and injuries occurring on job sites. The findings show the development of wearable technology in construction safety research and shed light to reduce the risk of civil workers. The analysis also provides a dynamic platform for integrating future applications and describes the current state of wearable technology in the field of construction safety.

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An Accurate Prediction of Coronary Heart Disease Using Ensemble Algorithms

An Accurate Prediction of Coronary Heart Disease Using Ensem...

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Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE), International Conference on

作者： Kambham Pratap Joshi m Lakshmi Prasad R Natchadalingam Pundru Chandra Shaker Reddy Saptarshi mukherjee G Charles Babu Department of CSE(DS&CS) MLR Institute of Technology Dundigal Hyderabad India Computer Science and Engineering Institute of Aeronautical Engineering Dundigal Telangana India CSE-Data Science R.L.Jalappa Institute of Technology Bengaluru Karnataka India School of Computer Science and Engineering Lovely Professional University Phagwara Punjab India M. Tech-Bioinformatics Maulana Abul Kalam Azad University of Technology West Bengal India Computing Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad Telangana India

Coronary Heart-disease (CHD) is one the foremost causes of death globally, making the accurate recognition of it vital. machine-learning (mL) and deep-learning (DL) are two of the newest technologies being put to use in the healthcare industry. This paper uses a risk factor method to examine how CHD can be predicted. K-Nearest-Neighbors, Binary-Logistic Classification, and Naïve-Bayes are only few of the predictive methods that are measured by accuracy, recall, and receiver operating characteristic (ROC) curves for evaluation. Ensemble modeling strategies including bagging, boosting, and stacking are contrasted to these foundational classifiers. The efficacy of using ensemble approaches to enhance coronary heart disease prediction was investigated using a comparative analytical methodology. The ‘Cardiovascular-Disease Dataset,’ which includes 80,000 records of patient data for CHD, is used to evaluate the modeling techniques. It is demonstrated that bagged models, on average, are more accurate than their non-bagged counterparts by the margin of 1.99 percentage points. The highest accuracy of all models was 76.5%, but the AUC for the boosted models was 0.78. Accuracy of 75.1% was achieved by the stacked model consisting of KNN, random-forest classifier, and SVm. In addition, data-analytic methods and K-Folds cross-validation were used to verify the tested models' performance.

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Cloud Computing-Enhanced and Raspberry Pi-Powered Solution for Remote Ear, Nose, and Throat Consultations with AI-Driven Diagnostics

Cloud Computing-Enhanced and Raspberry Pi-Powered Solution f...

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Emerging technology (INCET), International Conference for

作者： Giriprasad. S T. Karpagam V. Nalini Nilamadhab mishra N. mohankumar N. malathi Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science R.M.K College of Engineering and Technology Chennai Tamil Nadu India Department of Mathematics Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College Chennai Tamil Nadu India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India Department of Electronics and Communication Engineering B.V. Raju Institute of Technology Telangana India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

The cutting-edge telemedicine program presents a game-changing alternative for patients seeking ENT (ear, nose, and throat) consultations from a distant location. Utilizing the capabilities of Raspberry Pi devices, powerful artificial intelligence, and cloud computing, the system allows otoscopic tests to be performed without interruption while securely transmitting data to the cloud. On a web-based platform, interactive patient-professional consultations are made possible via real-time communication, which is made possible by WebRTC. Cloud services are very important because of their ability to provide scalable storage as well as AI-driven diagnostics for accurate evaluations. The system complies with the requirements for healthcare compliance, emphasizes security and privacy, and features encryption from beginning to finish. This integrated approach promises scalability, reliability, and advanced diagnostic capabilities, and it reshapes the landscape of remote ENT care with accessible and technologically advanced healthcare solutions. With a user-friendly interface for patients and a cloud-hosted application for healthcare professionals, this integrated approach provides patients with advanced healthcare solutions.

关键词： Cloud computing Privacy Telemedicine Scalability Nose medical services Ear

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A Novel and Robust Sensing technique under Cooperative Schemes of IOT Based Industrial WSN in Real Time

A Novel and Robust Sensing Technique under Cooperative Schem...

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Artificial Intelligence and Knowledge Discovery in Concurrent engineering (ICECONF), International Conference on

作者： Vijaya Vardan Reddy S P B. Jaison Resmi N C S. Ashok Sahana. S R. Thandaiah Prabu Department of Electronics and Communication Engineering R.M.K. Engineering College Chennai Department of Computer Science and Engineering R.M.K. Engineering College. Chennai Department of Electronics and Communication Engineering Mar Baselios Christian College of Engineering and Technology Kerala Department of Electronics and Communication Engineering Vel Tech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Chennai

The fast growth of IoT-based wireless technologies across industries in recent years is no secret. As the Internet of Things (IoT) has spread into many real-time applications, the WSN (Wireless Sensor Network) has become an integral part of it. Different critical and non-essential uses of the IoT and WSNs now affect almost every facet of our daily lives. This article is to prove that, while the cooperative scheme is less effective under abnormal conditions, it provides better shielding against the severity of jamming, making the WSN more secure by cutting down on upkeep costs, and placing the groundwork for addressing the issue of jamming. This article also includes an algorithm for real-time jamming detection. In the first place, it looks at the aggressor's specific traits and the harm they inflict. And secondly, it demonstrates how the WSN's inherent resistance to attack. Finally, it takes into account performance metrics, particularly those that have an effect on transmission energy consumption.

关键词： Industries Wireless communication Resistance measurement Wireless sensor networks Knowledge discovery Real-time systems

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An Effective Sensor Filtering model for mobile Devices Based on Long Short-Term memory technique in Sensor Archives

An Effective Sensor Filtering Model for Mobile Devices Based...

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Artificial Intelligence and Knowledge Discovery in Concurrent engineering (ICECONF), International Conference on

作者： A. Jasmine Gilda Suresh G S. Sivamrugan Suresh Kumar. R Sushmitha. K R. Thandaiah Prabu Department of Computer Science and Engineering R.M.K. Engineering College Chennai Department of Electrical and Electronics Engineering Sona College of Technology Salem Department of Artificial Intelligence and Data Science Sri Sairam Engineering College Chennai Department of Electronics and Communication Engineering Vel Tech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Department of Electronics and Communication Engineering R.M.K. Engineering College Chennai Department of Electronics and Communication Engineering Saveetha School of Engineering Chennai

It is expected that the Internet of Things (IoT) would be capable to deliver smart applications by aggregating data through many detectors spread across the natural world. A sensor registration system (SRS) is used by a mobile device in order to provide information obtained from ambient sensors. After this, the mobile device connects directly to get useful data. The Sensor Reporting System (SRS) has to offer information for sensors that have a chance of successfully connecting. This procedure is also known as sensor filtering, and it is dependent on the location of the user. In practice, the GPS may sometimes display an incorrect location, which results in anunable to connect. To improve the completion rate of interactions among mobile devices and environmental detectors, we provide a double collaboration approach that simultaneously collects GPS data and predictions from prior trajectories. Furthermore, we update the procedure for assessing detector filtering in SRS by integrating a monte Carlo-based modeling stream within the procedure of gauging the resource supply rate. Realistic research shows that LSTm-based path forecasting can efficiently remove position irregularities and make up for the lack of position information.

关键词： monte Carlo methods Filtering Detectors Predictive models Knowledge discovery mobile handsets Trajectory

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