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Efficient Radon Levels Detection in Residential Areas using Cloud-Enabled Decision Trees for Real-Time Analysis 8

Efficient Radon Levels Detection in Residential Areas using ...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Lenin, J. Rohit, V.Robin Nisha, F. Vasudevan, B. Sivakumar, S. Murugan, S. Alliance College of Engineering and Design Alliance University Department of Computer Science & Engineering Bangalore India Presidency University Computer Science & Engineering School of Computer Science & Engineering and Information Science Bengaluru India Cmr University School of Engineering and Technology Computer Science & Engineering Bangalore India Srm Institute of Science and Technology Department of Electronics and Communication Engineering Kattankulathur SRM Nagar Tamil Nadu Chennai India Department of Computer Science Tamil Nadu Perambalur India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376425

This research introduces a new approach to radon detection in homes utilizing Decision Trees (DTs) enabled by the cloud in real-time. High radon levels, a natural radioactive gas, are dangerous to human health. Quick evaluation and mitigation efforts are hindered by the long processes required by traditional detection systems. Using DTs, a machine learning methodology, the proposed method sorts of radon levels according to variables, including building materials, ventilation systems, and geographic location. These trees may handle both numerical and categorical data and provide interpretability. Fast processing of massive data sets and real-time analysis across varied residential areas are made possible by integrating cloud computing, increasing efficiency and scalability. Stakeholders may remotely access crucial information due to the solution's accessibility and flexibility. Results from controlled experiments demonstrate that this method can accurately identify radon levels in the air in real time, allowing for preventative actions to lessen potential health hazards. People living in areas prone to radon may benefit from this practice in terms of their health. © 2024 IEEE.

关键词： Indoor air pollution

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Skin Disease Detection and Diagnosis Using Deep Neural Network 15

Skin Disease Detection and Diagnosis Using Deep Neural Netwo...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kavitha, K. Gopalakrishnan, K. Parvathi, M. Indumathi, M. Kavitha, S. Balaji, S. Department of Computer Science and Engineering Kongu Engineering College Erode India Department of Information Technology Velalar College of Engineering Thindal India Department of Artificial Intelligence and DataScience Nandha Engineering College Erode India Department of Computer Science and Design Erode Sengunthar Engineering College Perundurai Road Erode India

ISBN: (纸本)9798350370249

Deep learning stands poised to revolutionize the field of dermatology, offering innovative solutions to pressing challenges in skin disease management. By harnessing the capabilities of these advanced models, we can address crucial aspects such as lesion classification, severity assessment, and early detection, paving the way for more effective patient care. Consider the scenario of deep learning models trained on extensive repositories of dermatological images. These sophisticated algorithms can meticulously scrutinize skin lesions, examining nuances in color, texture, and vascular patterns with unparalleled precision. This granular analysis empowers the model to categorize lesions accurately, distinguishing between conditions like eczema, psoriasis, and potentially malignant melanomas. Armed with this invaluable information, dermatologists can expedite diagnoses with greater confidence, facilitating prompt intervention for improved patient outcomes. Yet, the application of deep learning extends beyond mere classification, extending to the nuanced evaluation of disease severity. By scrutinizing parameters such as lesion size, color variations, and surrounding tissue health, these models can predict the progression of skin conditions. Such insights not only inform treatment strategies, guiding medication intensity and surgical interventions but also enable clinicians to monitor patient progress, ensuring adaptive and personalized care plans. ©2024 IEEE.

关键词： Dermatology

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Malware Detection in URL Using Machine Learning Approach 14th

Malware Detection in URL Using Machine Learning Approach

引用

12th International Advanced Computing Conference, IACC 2022

作者： Kumar, Rajesh Talwar, Rachit Sharma, Manik Kumari, Suchi Goel, Shivani Malhotra, Kanika Ahmed, Faiz College of Engineering and Design Alliance University Bangalore Bengaluru India Department of CSE Bharati Vidyapeeth’s College of Engineering New Delhi India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9783031356438

The most common technique to host fraudulent or harmful content, such as spam, malicious ads, etc., is using a Uniform Resource Locator (URL). Malicious URLs hold harmful contents that cause loss of information, malware installation, and monetary loss of the victims. Hence, it is necessary to detect such URLs and take some action on such threats. Earlier, one database is maintained to blacklist such URLs and a URL is compared with the available database of blacklisted URLs. If the URL is found in the database then the browser considers the URL suspicious and blocks it. But, this method is ineffective in finding newly discovered URLs. By suggesting a solution based on machine learning, this problem can be resolved. This research aims to investigate how machine-learning techniques can be used to identify harmful URLs. © 2023, Springer Nature Switzerland AG.

关键词： Machine learning

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IoT-Connected Telehealth Environments with Long Short-Term Memory Networks for Precise Time-Series Patient Behavior Analysis

IoT-Connected Telehealth Environments with Long Short-Term M...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Maruthukannan, B. Karpagalakshmi, R.C. Lakshmi, D. Rajapriya, M. Wise, D.C. Joy Winnie Srinivasan, C. Npr College of Engineering and Technology Department of Computer Science and Engineering Tamil nadu Dindugal India Alliance College of Engineering and Design Alliance University Karnataka Bengaluru India St. Joseph's College of Engineering Omr Department of Electronics and Communication Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Management Studies Tamil Nadu Chennai India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350375237

Advanced analytics are becoming more important in telehealth environments as the number of connected devices continues to rise, necessitating the collection of massive amounts of time-series patient data. This system uses Long Short-Term Memory (LSTM) networks in telemedicine that are linked to the Internet of Things (IoT), with an emphasis on accurate longitudinal monitoring of patient behavior. An extensive dataset gathered from IoT devices that track several health metrics in real-time is used in the research. LSTM networks are used to detect patterns and relationships in time-series data using a deep learning technique. In turn, this allows the system to analyze patient behavior with more precision and speed. IoT provides seamless connections for real-time data transfer it offers continuous monitoring within the telehealth framework. The LSTM-based model improves our comprehension of patient behavior, which in turn enables us to spot outliers, forecast health trends, and provide individualized healthcare recommendations. Through its demonstration of LSTM networks' usefulness in the context of IoT-connected telehealth settings, the study makes a valuable contribution to the rapidly growing area of digital health. Accurate time-series analysis has the ability to improve patient monitoring, early intervention, and healthcare outcomes, as shown by the results. © 2024 IEEE.

关键词： Long short-term memory

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Automated CKD Detection via Image Processing Employing Delta-E Metric and CNN

Automated CKD Detection via Image Processing Employing Delta...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Espin Shalo, S.P. Jeeshan, R.J. Gunasekar, S. Pius Agbulu, G. Venkat Ramana, N. Rajalakshmi Engineering College Department of Computer Science and Engineering Tamilnadu Chennai602105 India Rajalakshmi Engineering College Department of Computer Science and Design Tamilnadu Chennai602105 India Chinhoyi University of Technology Department of Mechatronics Engineering Zimbabwe Indian Institute of Technology Department of Electrical Engineering Tamilnadu Chennai India

ISBN: (纸本)9798331510022

Chronic Kidney Disease (CKD) is a global health concern that often progresses silently until advanced stages, making early detection critical. This paper presents a point-of-care system that automates the color comparison between a test strip and a reference strip to detect biomarkers like albumin associated with kidney dysfunction. The system leverages mobile-captured images, applying image processing techniques such as segmentation and color extraction, and compares the test strip's top color with reference colors on the parent strip using the Delta-E metric in the LAB color space. The closest match quantifies the biomarker concentration, providing a reliable diagnostic measure. To enhance diagnostic precision, this research incorporates Convolutional Neural Networks (CNNs) to refine image preprocessing and anomaly detection. The CNN model complements existing methods by mitigating environmental influences like lighting variability and improving feature extraction accuracy. This hybrid approach ensures consistency while adapting to diverse datasets and image conditions. The result is a cost-effective, fast, and accessible diagnostic tool that reduces manual errors and supports early CKD detection in resource-limited settings. Future work will focus on expanding the application of this system to detect other diseases and further optimize the integration of CNNs for enhanced scalability and robustness. © 2024 IEEE.

关键词： Convolutional neural networks

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Unveiling the Enigma: Sudden Cardiac Arrest Among Youth and Uncovering Underlying Causes: A Data Analysis Study 5th

Unveiling the Enigma: Sudden Cardiac Arrest Among Youth and ...

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5th International Conference on Artificial Intelligence and Speech technology, AIST 2023

作者： Rao, Mohit M. Shrivastava, Virendra Kumar Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bangalore562106 India

ISBN: (纸本)9783031751660

This study focuses on analyzing electrocardiogram (ECG) records related to sudden cardiac death in youth and analyze the factors influencing a sudden cardiac arrest. Sudden cardiac arrest poses a severe threat to life and can result in death if not addressed promptly. Recent American research indicates a 13% increase in sudden cardiac arrest cases among individuals aged between 35–45. Furthermore, the Indian Heart Association has reported that heart disease tends to affect Indians at a younger age compared to other demographic groups, often without prior warning. Studies reveal that Indians tend to experience heart diseases approximately a decade earlier than their Western counterparts. This work aims to conduct data collection and surveys to collect ECG records and other relevant information that is crucial for training the machine learning models to achieve the study outcomes. The study's scope encompasses evaluating the feasibility of the task, identifying relevant datasets, conducting data preprocessing, and assessing the performance using K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and random forest algorithm. Among these, both SVM and Random Forest demonstrated superior performance, with the Random Forest algorithm further fine-tuned to align with the dataset's specific requirements. The refined model exhibited 93% accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Support vector machines

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Enhanced Honey Badger Algorithm for Resource Allocation and Task Scheduling in Cloud Environment 4

Enhanced Honey Badger Algorithm for Resource Allocation and ...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Rajagopal, R. Arunarani, A.R. Arivarasi, A. Ingle, Anup Ravichandran, T. Prakash, R. Vijaya Alliance College of Engineering and Design Alliance University Department of Computer Science and Engineering Karnataka Bengaluru India School of Computing College of Engineering and Technology Srm Instutite of Science and Technology Department of Compuational Intelligence Tamilnadu Chennai India Sri Sairam College of Engineering Department of Electronics and Communication Engineering Karnataka Bengaluru India Vishwakarma Institute of Information Technology Department of Electronics and Telecommunications Maharashtra Pune India Karpagam College of Engineering Department of Computer Science and Engineering Tamilnadu Coimbatore India School of Computer Science and Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798350300888

Cloud computing is a technology that offers dynamic resources to the users with enhanced scalability and flexibility. The major concerns in cloud environment that has direct impact on the throughput of cloud system and contentment of cloud users is the problem of task scheduling and resource allocation. The time taken to execute the tasks and cost incurred for the computation are the significant objectives that affect the performance of the cloud system. This work proposes a multi-objective task scheduling and resource allocation technique using meta-heuristic optimization algorithm. Enhanced Honey Badger algorithm (EHBA) is employed to schedule the tasks and allocate computing resources effectively while minimizing the time and cost objectives. The performance of the proposed technique is assessed in a simulation environment, CloudSim, which mimics the settings of real cloud computing system. Various measures such as Time-to-Execute, Cost-to-Compute, Task-to-Resource utilization and Time-to-Respond are used to assess the performance of the suggested EHBA method for efficient task scheduling and resource allocation. The experimental results produced by the proposed method is also compared against the state-of-the-art studies that employ meta-heuristic optimization algorithms. The outcomes revealed that EHBA outperformed other methods by executing the tasks in a minimum time with reduced cost and maximum utilization of the computing resources. © 2023 IEEE.

关键词： Resource allocation

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An overview of thalassemia: A review work

An overview of thalassemia: A review work

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

作者： Begum, Ruqqaiya Suryanarayana, G. Rama, B. V. Saketha Swapna, N. Department of Computer Science and Engineering Vardhaman College of Engineering Telangana Hyderabad India Department of Computer Science Engineering Indian Institute of Information Technology Design and Manufacturing Kancheepuram Tamil Nadu Chennai India Department of Computer Science & Engineering Vijay Rural Engineering College Telangana Nizamabad India

ISBN: (纸本)9781032669663

Thalassemia is a Disease that passes from parents to children through genes. It is a red blood cell disorder caused when the body doesn’t make enough of a protein in the blood called hemoglobin. Three classifications of Thalassemia are Alpha(a), Beta(b), DeltaBeta(db) And Beta thalassemia is the most effective disease compared to other types. Out of 5% of patients 3% comes in Beta Thalassemia. It is first identified in the early 19’s with the symptoms like poor growth, severe anemia, and huge abdominal organs. It is a disease of which most people are deceased due to unawareness, and not getting proper Medication, especially in rural areas as they cannot find symptoms because other disease symptoms differ from thalassemia. Out of 10000 newborns, approximately an average of 4.4 will be affected by thalassemia. Out of 32 districts In India, 10 to 12 thousand children were born with this disease. To date, no research work has shown the exact death rates of thalassemia worldwide. Blood transfusion is the major method used for the prevention of thalassemia-affected patients. Apart from that Bone marrow is another prevention method used for Beta Carriers. In this work, we gathered detailed Information about thalassemia and its types so that people will get aware of this disease. This work provides details of how this disease is getting inherited from parents to children, the depth of the characteristics of every subtype, and the statistics about causes and death rates from this disease. © 2024 The Author(s).

关键词： Blood

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Network Model to Generate Visual Realism images from Textual Data 2

Network Model to Generate Visual Realism images from Textual...

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

作者： Vidyullatha, Pellakuri Unhelkar, Bhuvan Chakrabarti, Prasun Ghantasala, G.S. Pradeep Shaik, Amjan Pyla, Meghana Department of Information Systems and Decision Sciences Muma College of Business University of South Florida South Florida United States Department of CSE Koneru Lakshmaiah Educationn Foundation Vaddeswaram AP Guntur India Department of Computer Science and Engineering Sir Padampat Singhania University Rajasthan Udaipur India Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India Department of Computer Science and Engineering St.Peters Engineering College TS Maisammaguda India

ISBN: (纸本)9798350383867

Generative Adversarial Networks (GANs) have the ability to produce realistic images from random noise vectors and has attracted a lot of attention in recent years. The textual input is discontinuous and requires efficient semantic information capture, extending GANs to create images from text descriptions has special obstacles. In this research, a unique GAN-based text-to-image creation method that makes use of conditional GAN architectures and text embeddings. The generator neural network in our suggested model converts text embeddings into appropriate image representations, whereas the discriminator network separates real images from those produced by the generator. This paper shows the strategies like multi-modal fusion mechanisms and attention mechanisms in the network architecture to help textual descriptions and visual information match. This method uses the benchmark datasets and show that it can produce realistic and varied visuals that correspond with textual descriptions. Both qualitative and quantitative assessments demonstrate our model's capacity to grasp minute details and subtle semantic nuances found in the input text. In addition, we perform user studies to evaluate the fidelity and perceived quality of the generated images relative to ground truth images. Based on our experimental findings, it appears that the suggested framework for text-to-pixel image production provides both textual relevance and image quality. This paper illuminates how our methodology may be utilized in different settings, including virtual universes, PC supported plan, and the production of imaginative substance. We likewise accentuate future bearings and potential for profound learning and normal language handling procedures to further develop text- to-picture creating models' abilities. © 2024 IEEE.

关键词： Generative adversarial networks

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A Convolutional Neural Network Based Prediction Model for Classification of Skin Cancer Images 1st

A Convolutional Neural Network Based Prediction Model for Cl...

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1st EAI International Conference on Intelligent Systems and Machine Learning, ICISML 2022

作者： Saini, Vanshika Rai, Neelanjana Sharma, Nonita Shrivastava, Virendra Kumar Department Indira Gandhi Delhi Technical University for Women Delhi India Information Technology Department Indira Gandhi Delhi Technical University for Women Delhi India Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bangalore562106 India

ISBN: (纸本)9783031350771

There has been an unprecedented rise in the cases of skin diseases since past few decades owing to several factors. Among several skin diseases, skin cancer has also taken a steep rise and resultantly it becomes imperative to devise an efficient model to detect skin cancer. The requirement for automatic detection of skin cancer further grows owing to rise in rate of melanoma skin cancer, its expensive treatment, and its high fatality rate. Treatment of cancer cells frequently necessitates patience and manual inspection. Here, in this work authors propose an image processing and machine learning approach for skin cancer detection. It also uses a feature extraction technique to retrieve the features of the injured skin cells. The proposed model uses convolutional neural network classifier to stratify the extracted data. During the experimental evaluation, it is observed that the proposed system yields an accuracy of 77.03% and a training accuracy of 80% for the datasets available in public domain. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Cancer cells

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