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Remote Sensing data-Based Satellite Image Analysis in Water Quality Detection for Public Health data Modelling

Remote Sensing in Earth Systems sciences 2024年第4期7卷 532-541页

作者： Balakrishnan, S. Preetam Raj, P Michael Somasekar, J. Kumar, Kambala Vijaya Amutha, S. Sangeetha, A. Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology (AVIT) Vinayaka Mission’s Research Foundation (Deemed to be University) Chennai India Department of Computer Science and Engineering MLR Institute of Technology Telangana Dundigal Hyderabad 500043 India Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be-University) Karnataka Bengaluru 562112 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Tamilnadu Chennai India Department of Artificial Intelligence and Data Science Kongunadu College of Engineering and Technology TamilNadu Tholurpatti India

One of the most important aspects of environmental sustainability is water quality monitoring. The ecology is impacted by poor water quality in addition to aquatic life. With the volume of data being collected from satellite systems, the issue of effectively and automatically extracting water bodies is growing more pressing. One of the most important things to do for managing and safeguarding water resources is to conduct extensive monitoring of water quality parameters, or WQPs. It is still difficult to monitor optically inactive WQPs in inland waters, such as total nitrogen (TN), ammoniacal nitrogen (AN), and total phosphorus (TP). Water quality (WQ) classification and prediction can be greatly enhanced with the use of artificial intelligence (AI). This research proposes novel method in healthcare detection by water quality analysis based on classification utilising ML model. Input is collected as healthcare dataset based on water quality as well as processed for noise removal and normalisation. The processed data features are extracted and classified utilising reinforcement Gaussian stacked layer network with variational fuzzy Markov basis neural network. In order to suggest best course of action for the water bodies, it is imperative that any study look into the spatiotemporal changes of the dominant water quality parameters (WQPs). WQP concentrations have typically been determined by extensive fieldwork. These findings could help identify and regulate non-point source pollution, offer a large-scale spatial picture of the water quality, and pinpoint vulnerable locations and times of water pollution. Experimental analysis is carried out for various water sample dataset in terms of detection accuracy, mean precision, recall, F-1 score and AUC. The proposed technique obtained 97% of detection accuracy, 95% of recall, 91% of F-1 score, 96% of AUC, and 94% of MAP. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Classification Fuzzy Markov basis Healthcare detection Machine learning model Water quality analysis

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Machine Learning-Enhanced Self-Management for Energy-Effective and Secure Statistics Assortment in Unattended WSNs

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SN Computer science 2025年第2期6卷 1-10页

作者： Biswas, Preesat Mishra, Aishwarya Dixit, Rinku Sharma Dwivedi, Abhishek Choudhary, Shailee Lohmor Tiwari, Shweta Chakravarthi, M. Ayyappa Electronics & Telecommunications Government Engineering College C. G Jagdalpur India Department of Computer Science & Engineering IES College of Technology M.P Bhopal India Department of Artificial Intelligence and Machine Learning New Delhi Institute of Management Delhi India Department of Data Science and Engineering School of AIML IoT & IS CCE DS and Computer Applications Manipal University Jaipur Jaipur Rajasthan India Department of Computer Science and Engineering Gyan Ganga Institute of Technology and Sciences M.P Jabalpur India Department of CSE-Data Science KKR and KSR Institute of Technology and Sciences Guntur India

Unattended Wireless Sensor Networks (UWSNs) operate without human supervision and have limited resources. In a conventional WSN with a fixed sink for data collection, nodes one hop away from the sink consume more energy, causing a hotspot issue. This leads to network fragmentation and reduced sensing efficiency. To address this, a mobile sink is proposed. This study details the development and implementation of Secure MRL, focusing on legitimate message communication to the mobile sink via convex nodes. By using these convex nodes as data collection points, the mobile sink avoids traversing the entire network. We employ an enhanced system to generate keys and authenticate messages, assessing its resilience against various attacks, including sleep deprivation, snooze, network substitution, and modification. Findings indicate that Secure MRL maintains consistent performance across different node counts and significantly reduces energy consumption by 84%, improving the speed of attack detection. This improvement is achieved by monitoring the digital signature and residual energy of nodes. The proposed SVM-based network training establishes a residual energy cut-off, while convex nodes mitigate synchronization issues among other sensor nodes in Secure MRL. Additionally, convex nodes manage the availability checks for the mobile sink. The proposed approach demonstrates a 92% improvement in packet delivery ratio (PDR) and an 89% reduction in end-to-end delay, highlighting its efficacy in enhancing network performance. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Duty-cycled UWSN MRL Sec-MRL SEC-TMP SVM UWSN

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Machine Learning based Human Stress Detection through Sleep Patterns 5

Machine Learning based Human Stress Detection through Sleep ...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Santhosh Krishna, B.V. Mai, Kiran Aswini, S. Biju, J. Mohanraj, S. Kaliappan, S. New Horizon College of Engineering Department of Computer Science and Engineering Bangalore India Bangalore Technological Institute Department of Computer Science and Engineering Bangalore India Bannari Amman Institute of Technology Information Science and Engineering Erode Tamilnadu Sathyamangalam India KGiSL institute of Technology Artificial Intelligence and Data Science Tamilnadu Coimbatore India Lovely Professional University Division of Research and Development Punjab India

ISBN: (纸本)9798331504403

Stress is a critical factor in overall wellbeing, and understanding its patterns during sleep can provide valuable insights into improving sleep quality and managing stress-related health issues. Stress has become an increasingly prevalent issue in modern society, impacting both mental and physical well-being. The ability to accurately detect stress levels in individuals is crucial for timely intervention and management. Sleep patterns serve as a valuable indicator of stress, as disruptions in sleep quality and duration often correlate with heightened stress levels. Leveraging advancements in machine learning techniques, this study proposes a novel linaer regression based approach to detect human stress by analyzing sleep patterns extracted from wearable devices. The proposed system consists of several key components. Firstly, data collection involves the continuous monitoring of sleep parameters such as sleep stages, heart rate variability, and movement using wearable sensors. Next, feature extraction techniques are applied to transform raw sleep data into informative features that capture relevant aspects of sleep patterns indicative of stress. © 2024 IEEE.

关键词： Sleep research

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Brain Tumour Detection from MRI Images Using Deep CNN 8

Brain Tumour Detection from MRI Images Using Deep CNN

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8th IEEE International Conference on science, technology, engineering and Mathematics, ICONSTEM 2023

作者： Josephin Shermila, P. Nishitha, J. Shoba, L.K. R.M.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science Chennai India Arunachala College of Engineering for Women Department of Electronics and Communication Engineering Vellichanthai India Jeppiaar Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798350347791

The brain is one of the most important organs in humans. Abnormal growth in the brain occurs, which may be a tumor. In this world, humansin any age group suffer from brain tumors. Stress is also a cause of brain tumors. Early detection can cure completely, which became the main objective of the work. In this research work, brain tumor detection from its images is investigated. Experiments are done using deep learning, wherein a convolutional neural networkis used. The brain is one of the activeparts of the central nervous system. Detectinga tumor in the brain is very difficult as the size, shape, and location of the tumor differ for each individual. If the brain tumor is identified and detected primarily as early, the chance of patients'treatment is very high. Normally, diagnosis is madeusing magnetic resonanceimaging. Our methodologies predictive models obtainedvery promising visual and quantitative results at real-time speed. © 2023 IEEE.

关键词： Brain

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Generative AI in Agri: Sustainability in Smart Precision Farming Yield Prediction Mapping System Based on GIS Using Deep Learning and GPS 4

Generative AI in Agri: Sustainability in Smart Precision Far...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Senthil, G.A. Suganthi, S.U. Prinslin, L. Selvi, R. Prabha, R. Department of Information Technology Agni College of Technology Chennai India Department of Artificial Intelligence and Data Science Sri Sai Ram Institute of Technology Chennai India Department of Computer Science and Engineering Agni College of Technology Chennai India Assistant Professor Department of Computing Technologies SRM Institute of Science and Technology kattankulathur Chennai India Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India

Agriculture faces many challenges of precision farming, such as the need for sustainable practices, improving yields, ensuring high yields. In resolution to these challenges, the present research provides an AI-based system that enables the use of deep learning, Global Positioning System (GPS), and Geographic Information System (GIS) technologies to create a highly intelligent smart agricultural precision farming system. Its goal is to monitoring crop health and reduce disease risk, which will lead to improved resource utilization and environmentally sustainability techniques. The proposed framework addresses the urgent need for consistency in agricultural practices, especially as global agriculture deals with pressures from climate change, resource shortages, and increasing demand for food. Traditional agricultural methods for predicting and optimizing crop yields due to increasing factors affecting crop performance Not enough generative AI, especially the use of deep learning models, supports agricultural research in many cases, allowing patterns to be identified and future results to be predicted accurately. The integration of GPS and GIS allows for more accurate mapping, real-time analysis, and effective decision-making. Weather forecasting variability, resource constraints, and demand for more food are isolated from environmental influences using deep learning models, especially artificial Neural Networks (ANN). By using large data sets, including historical crop yield performance, soil properties, and weather conditions, the system provides highly accurate crop forecasts. Generative Adversarial Networks (GANs) and You Only Look Once (YOLO) hybrid model is playing a key role in generating crop yield and growth potential under different conditions, adjusting model accuracy over time, and this combination of ANN, GANs and YOLO optimization algorithms ensures that the system continuously enhances its predictive accuracy and overall effectiveness. The proposed gene

关键词： Generative adversarial networks

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Machine Learning Algorithms used in Crop Yield Prediction for Precision Agriculture 4

Machine Learning Algorithms used in Crop Yield Prediction fo...

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

作者： Umamaheswari, R. Shanmugapriya, S. Vimala, P. Ganesan, R. Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech High Tech Dr.Rangarajan Dr. Sagunthala Engineering College Department of Computer Science and Engineering Chennai India Saveetha Engineering College Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331515720

Agriculture is a crucial sector that plays a vital role in producing food for human consumption. However, predicting crop yield accurately can be a challenging task due to various natural factors, such as soil quality, climate, rainfall, and resource availability. In the past, farmers relied on their expertise and judgment to determine crop yield, but their traditional methods have become less effective due to the unpredictable nature of these factors. To address this challenge, researchers have developed various algorithms and models for predicting crop yield based on different parameters, such as soil quality, climate, and image analysis. These algorithms can help farmers make informed decisions about when and where to grow their crops, leading to improved productivity and higher yields. Among these algorithms, neural networks are showing great promise as they can analyze vast amounts of data and identify patterns that may not be immediately apparent to human observers. By training neural networks on historical crop yield data, researchers can develop models that accurately predict crop yield based on a variety of factors. Ensuring accurate crop yield prediction is critical to ensuring food security and preventing famine, particularly in developing countries such as India. As technology continues to advance, we can expect even more advanced algorithms and models for predicting crop yield that will help farmers make better decisions and ultimately lead to increased productivity and better outcomes for agriculture. © 2024 IEEE.

关键词： Agriculture Crop yield prediction (CYP) Machine learning Neural Network (NN)

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Cardiac Affliction Detection Using Improved Grasshopper Optimized CNN

Cardiac Affliction Detection Using Improved Grasshopper Opti...

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2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Shamna, B. Maheswaran, C.P. Noorul Islam Centre for Higher Education Department of Computer Science and Engineering Tamil Nadu Nagercoil India Sri Krishna College of Technology Department of Artificial Intelligence and Data Science Coimbatore641 042 India

ISBN: (纸本)9798350333244

The most significant cause of death globally is cardiovascular disease. Whenever an accurate diagnosis is obtained at the earliest possible stage, cardiovascular problems are avoided. Electrocardiogram (ECG) tests are typically used to screen cardiac conditions, and facilitates healthcare process. Due to the dependence and direction of trained medical professionals, traditional methods of evaluating ECG images require more work and time. As a result, automated methods become necessary and image processing is then used to the medical industry. In this research, a neural network-based, effective computational method for determining heart disease is developed. In general, the input ECG images are first pre-processed using Wiener filter, which calculates the mean and variance and then conducts enhanced smoothing in low-variation circumstances. The K-means clustering method is adopted to perform segmentation on pre-processed image in precise manner. The Grey Level Co-occurrence Matrix (GLCM), which chooses the predominant features is utilized to enable accurate diagnosis of conditions in ECG images, then extracts the pertinent features. Finally, for accurate classification, an Improved Grasshopper Optimized Convolutional Neural Network (IGO- CNN) is used. The obtained outcomes demonstrate that the suggested work assists in the establishment of exact outputs and successfully recognizes the presence of heart disease in ECG images. © 2023 IEEE.

关键词： Electrocardiograms

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Predictive Modeling of Portuguese Student Performance: Comparative Machine Learning Analysis

Predictive Modeling of Portuguese Student Performance: Compa...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Rizk, Faris H. Mohamed, Mahmoud Elshabrawy Sameh, Basant Zaki, Ahmed Mohamed Eid, Marwa M. El-Kenawy, El-Sayed M. Computer Science and Intelligent Systems Research Center BlacksburgVA24060 United States Delta Higher Institute of Engineering and Technology Department of Communications and Electronics Mansoura35111 Egypt Delta University for Science and Technology Faculty of Artificial Intelligence Mansoura35111 Egypt

ISBN: (纸本)9798350351408

Such an analysis of different machine learning methods for predicting the achievement levels of students in Portuguese secondary education makes this essay. The research highlights the importance of accurate expectations of learners' results for education system administrations and respective policymakers. The current study makes use of the 'Student Performance in Portuguese Secondary education' dataset and employs machine learning algorithms, namely MLPRegressor, XGBoost, DecisionTreeRegressor, CatBoost, and KNeighborsRe-gressor, to the corpus. Performance metrics such as mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), etc., are used to judge every model's performance. The conclusion that can be drawn from the data is that the MLPRegressor model leads among the competitors, having an MSE equivalent of 0.0103, which is superior to others. The findings of this study are of great significance for educational institutions and policymakers as they work to make appropriate contact with students' performance prediction. © 2024 IEEE.

关键词： Contrastive Learning

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A Fuzzy C-Means With Support Vector Regression for Calibration and Fault Detection in Modern Instruments 4

A Fuzzy C-Means With Support Vector Regression for Calibrati...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Habelalmateen, Mohammed I. Kasarla, Satish Reddy Sravanthi, Jajimoggala Kavitha, P. Sundaram, S. Meenakshi College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Hyderabad Institute of Technology And Management Department of Electronics and Communication Engineering Hyderabad India Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science and Engineering Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India

ISBN: (纸本)9798350352931

From the past few decades, Calibration and Fault Detection in modern instruments are crucial for ensuring accuracy, reliability, and safety by configuring instruments to provide accurate measurements and identifying anomalies or malfunctions. These processes prevent accidents, reduce downtime, and increase productivity, but also have drawbacks, including being time-consuming and labor-intensive, requiring specialized expertise and equipment, and being expensive, while also being susceptible to environmental factors and requiring frequent recalibration. To overcome these problems, a Fuzzy C-Means with Support Vector Regression (FCM-SVR) is proposed for detecting calibration and fault in modern instruments using Predictive Electrical Fault Analysis (PEFA) dataset. The collected data is undergone preprocessing using Label Encoder (LE) to normalize data without reducing the dimension. Then, Empirical Mode Decomposition and Sample Entropy are considered for feature extraction for robust fault detection and to reduce false alarm. At last, FCM-SVR is employed as classifier for enhancing computational efficiency, flexibility, and overall performance. From the results, it clearly shows that the proposed FCM-SVR obtained higher values in the context of precision, recall, F1 score and accuracy when compared to existing method K-Nearest Neighbor (KNN). © 2024 IEEE.

关键词： Calibration

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NOMA Attack Prediction in Large-Scale Networks Using Non-Linear Chaotic Belief Mechanism

NOMA Attack Prediction in Large-Scale Networks Using Non-Lin...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： K. Sangeetha M. Suguna Jebakumar Immanuel D SA Suje Department of Computer Science and Engineering SNS College of Technology Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data Science Karpagam Institute of Technology Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data Science Nandha Engineering College Erode Tamil Nadu

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Non-Orthogonal Multiple Access (NOMA) systems are becoming relevant in the fast-expanding terrain of large-scale networks because of their efficiency in concurrently managing many users. This is true since NOMA systems let numerous users concurrently be managed. On the other hand, the intricacy of these networks leaves them vulnerable to a wide spectrum of attacks, including the more advanced and erratic NOMA attacks on the network. These strikes could produce major disturbances that would compromise the quality of service and cast questions regarding the general network security. It has been demonstrated that the effective projection of these hazards is limited by standard linear and probabilistic techniques. This is true as contemporary methods fail to adequately capture the basic non-linear dynamics of these large-scale networks. This article offers a novel method for NOMA attack prediction by means of a non-linear chaotic belief process. The results are shown here. To recreate the uncertainty and intricate interactions inside the network, the proposed method which is the logistic map which in turn generates the sequences for ensuring the accurate iterative updates which in turn provides better scalability and precision. This integrates belief networks with chaos theory. More exactly, we capture the random and non-linear aspect of network dynamics by building belief values indicating the likelihood of an attack by use of a chaotic map. After that, the belief values proliferate across the network in search of defects and project the probability of NOMA attacks. Effectiveness of the proposed method is demonstrated by test results on a simulated large-scale network simulation. With a prediction accuracy of 92.7%, the chaotic belief mechanism obtained much above the average accuracy of 78.4% of traditional linear prediction systems. Moreover, the proposed approach lowered the false positive rate to 5.3%, substantially below the rate of 12.8% applied in the standard a

关键词： Training NOMA Accuracy Uncertainty Chaotic communication Prevention and mitigation Network security Iterative methods Computer crime Standards

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