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Fully Connected Deep Convolutional Neural Network and Improved SURF for Land Cover Classification

Fully Connected Deep Convolutional Neural Network and Improv...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Baskar, N. Bhoomika, S.S. Al-Farouni, Mohammed Kumar, Gotte Ranjith Deiwakumari, K. Malla Reddy Engineering College for Women Secunderabad India Nitte Meenakshi Institute of Technology Department of Cse Bengaluru India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Sr University School of Computer Science & Artificial Intelligence Warangal India Sona College of Technology Department of Mathematics Salem India

ISBN: (纸本)9798350360660

Developing an accurate Land Cover (LC) mapping for efficient land management is crucial to update cover maps for producing sustainable crops. However, extracting accurate features from the LC maps to identify the regions is a challenging task. To overcome this, a Deep Leaning (DL) approach, Fully Connected Deep Convolutional Neural Network and an Improved Speeded Up Robust Features (FCDCNN-ISURF) is proposed for effective feature extraction and selection. Initially the Sentinel Satellite Imagery (Sentinel-2) dataset is pre-processed using Land Surface Water Index (LSWI) which gives the details about soil moisture level from the images. The features are extracted using ISURF for key points detection and description, which is fast and stable to scaling, rotation and illumination. The extracted features are then classified using FCDCNN which classifies the features by pixel-wise prediction. The experimental results on Sentinel-2 dataset showed better LC classification with 98.23% accuracy and 97.70% precision when compared to the results of existing methods such as Shapely Additive Explanation - Convolutional Neural Network (SHAPs-CNN) and Deep Relearning based Recurrent Neural Network (DRRNN) in LC classification. © 2024 IEEE.

关键词： Convolutional neural networks

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Stress Recognition Using Facial Landmarks and Cnn (Alexnet) 1

Stress Recognition Using Facial Landmarks and Cnn (Alexnet)

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1st International Conference on Applied Mathematics, Modeling and Simulation in engineering, AMSE 2021

作者： Naidu, Ramesh P. Sagar, Pruthvi S. Praveen, K. Kiran, K. Khalandar, K. Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Karnataka Bangalore India

Stress is a psychological disorder that affects every aspect of life and diminishes the quality of sleep. The strategy presented in this paper for detecting cognitive stress levels using facial landmarks is successful. The major goal of this system was to employ visual technology to detect stress using a machine learning methodology. The novelty of this work lies in the fact that a stress detection system should be as non-invasive as possible for the user. The user tension and these evidences are modelled using machine learning. The computer vision techniques we utilized to extract visual evidences, the machine learning model we used to forecast stress and related parameters, and the active sensing strategy we used to collect the most valuable evidences for efficient stress inference are all discussed. Our findings show that the stress level identified by our method is accurate is consistent with what psychological theories predict. This presents a stress recognition approach based on facial photos and landmarks utilizing AlexNet architecture in this research. It is vital to have a gadget that can collect the appropriate data. The use of a biological signal or a thermal image to identify stress is currently being investigated. To address this limitation, we devised an algorithm that can detect stress in photos taken with a standard camera. We have created DNN that uses facial positions points as input to take advantage of the fact that when a person is worried their eye, mouth, and head movements differ from what they are used to. The suggested algorithm senses stress more efficiently, according to experimental data. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Machine learning

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Domain Adaptive based Convolutional Neural Network for Change Detection using Time Series Satellite Imagery

Domain Adaptive based Convolutional Neural Network for Chang...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Jayarajan, K. Alzubaidi, Laith H. Vasanthakumar, G.U. Lande, Jayapal Deiwakumari, K. Malla Reddy Engineering College for Women Secunderabad India The Islamic University Engineering College of Technical Engineering Department of Computers Techniques Najaf Iraq Nitte Meenakshi Institute of Technology Department of Cse Bengaluru India Sr University School of Computer Science & Artificial Intelligence Warangal India Sona College of Technology Department of Mathematics Salem India

ISBN: (纸本)9798350360660

The Change Detection (CD) is the process of observing the transition of the earth's surface which is crucial in urban areas. Existing methods showed various developments in CD using Deep Learning (DL) models, however, they are limited to learn the image features only in one domain. To overcome this, the proposed research developed a Domain Adaptive (DA) based Convolutional Neural Network (CNN) for CD (DA-CNN-CDNet). The time series satellite images from the Sentinel-2 are collected and pre-processed by applying pixel-level and spatial-level transforms for effective training of the model. The features are extracted using Pseudo-Siamese with non-weights and fed to the CNN. The CNN is trained in DA environment to apply the knowledge that CNN has learnt in one domain to one or more other domains which enhances the CD. The experimental results of the proposed DA-CNN-CDNet showed better CD with 99.76% of accuracy, 99.18% of precision, 98.62% of recall, and 97.98% of f1-score when compared to existing methods Cross Layer Convolutional Neural Network (CLNet) and Local Similarity Siamese Network (LSS-Net). © 2024 IEEE.

关键词： Satellite imagery

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Plant Leaf Disease Detection using Novel Machine Learning Approach

Plant Leaf Disease Detection using Novel Machine Learning Ap...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Yogananda, G.S. Ananda Babu, J. Ramya, R. Maheswari, Mamilla Sundaram, S. meenakshi Kalpataru Institute of Technology Department of Electronics and Communication Tiptur India Malnad College of Engineering Department of Information Science and Engineering Hassan India Bannari Amman Institute of Technology Department of Computer Science and Engineering Erode India Malla Reddy Engineering College for Women India Nitte Meenakshi Institute of Technology Department of Ai and Ds Bengaluru India

ISBN: (纸本)9798350313413

Plant leaf disease detection is a critical component of modern agriculture, aimed at early and accurate identification of diseases that can impact crop yields and food security. But the existing detection models attain insufficient and low-quality when utilizing the large amount of data. To overcomes this issue, a novel Machine Learning (ML) algorithm namely, Logistic Regression based Ensemble Classifier (LREC) is proposed for detecting the plant leaf disease. The data are gathered from the leaf image dataset and collected images are pre-processed to remove noise and Histogram equalization is utilized to enhance the quality of the image. Principal Component Analysis (PCA) is performed in feature extraction process and classified through Logistic regression integrated with ensemble classifier. When compared with existing method, the proposed method attains the better performance in detection of plant leaf diseases. The results are outperformed with evaluation metrics such as precision, recall, accuracy and f-measure as respectively. © 2023 IEEE.

关键词： Ensemble classifier Feature extraction Leaf disease detection Machine Learning Principal Component Analysis

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Dynamic Radial Movement Optimization for Throughout Maximization in Energy Harvesting Cognitive Radio Network

Dynamic Radial Movement Optimization for Throughout Maximiza...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Vasantha, B. Kumar, Komuravelly Sudheer Husain, Saif O. Naveen, I.G. Kalpana, Kasapogu Maisammaguda Hyderabad India Sr University School of Computer Science & Artificial Intelligence Warangal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India Gokaraju Rangaraju Institute of Engineering and Technology Department of Ai&ml Hyderabad India

ISBN: (纸本)9798350375442

The Energy Harvesting (EH) and corrperative communication technique and information accessibility of Energy Harvesting Cognitive Radio Network (EH-CRN). However, minimized throughput in EH, leading to longer EH periods where nodes are inactive and multi-use CRN secondary user interfere with each other transmissions overlap in time due to require maximum energy. In this paper, proposed Dynamic Inertia Weight with Radial Movement Optimization (DRMO) approach provides a framework for dynamic management of both energy and spectrum resources, allowing the network to adapt to changing conditions and maintain high throughput. The DRMO improves the utilization of under used spectrum bands by allowing secondary users to opportunistically transmit data while minimizing interference with primary user. The RMO adapts to changing environmental factors such as variations in harvested energy and spectrum access opportunities making it minimized energy for using dynamic wireless network. The DRMO method exhibits superior performance compared to existing method such as Whale Colony Optimization algorithm (WCO) and Particle Swarm Optimization (PSO). The proposed DRMO method achieving maximum throughput of 0.45, energy of 0.25mJ and time of 0.2, respectively. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Machine Learning based Predictive Modelling for Sustainable Product Design

Machine Learning based Predictive Modelling for Sustainable ...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Kumar, Gotte Ranjith Mamatha Bai, B.G. Shnain, Ammar H. Hemalatha, K.L. Alkhafaij, Mahdi Abdulkhudur Sr University School of Computer Science & Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of Cse Bengaluru India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Sri Krishna Institute of Technology Department of Information Science and Engineering Bangalore India Ahl Al Bayt University College of Mlt Karbala Iraq

ISBN: (纸本)9798350360660

Silica Fume (FC) is an eco-friendly concrete which is commonly used in construction industry as a building material. With the growing awareness of environmental issues, developing predictive Machine Learning (ML) models becomes most important for optimizing the use of SF in concrete production. This research aims to develop predictive models for Silica Fume Concrete (SFC) properties to enhance Sustainable Product Design (SPD) through ML techniques. Utilizing a comprehensive dataset of SFC, the research focuses on analysing the importance of input selection, isolating six key variables. The feature selection employs an Improved Binary Cuckoo Search Algorithm (IBCSA) enhanced with a Chi-square filter and chaotic map for optimal feature identification. Predictive modelling is done using Decision Trees (DTs), with ensemble methods like bagging and boosting to mitigate overfitting and improve accuracy. The experimental results are validated on SFC and the proposed ML based SPD achieved better accuracy rate at 98.49%, Mean Square Error (MSE) of 1.04, Mean Average Error (MAE) of 0.73 when compared to the results of the existing ML based predictive models such as Gene Expression Programming (GEP) and DT Regression- MultiLayer Perceptron (DTR-MLP). © 2024 IEEE.

关键词： Concrete industry

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Wavelet Transform based Bidirectional Long Short-Term Memory for Predicting Water Demand 1

Wavelet Transform based Bidirectional Long Short-Term Memory...

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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Habelalmateen, Mohammed I. Palanivel, R. Rana Veer Samara Sihman Bharattej, R. Vijayashaarathi, S. Poonguzhali, I. The Islamic University Najaf Iraq Nitte Meenakshi Institute of Technology Department of Ai & Ds Bengaluru India Saint Leo University St Leo Department of Computer Science United States Sona College of Technology Department of Electronics and Communication Engineering Salem India Panimalar Engineering College Poonamallee Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350352931

The prediction of urban water demand is essential in rational allocation of water and developing smart city. Influenced through multiple factors, the water demand huge frequency noise and difficult patterns, that is due to nostationary of daily data has huge influence on generalization capability of methods. In this research, Wavelet Transform (WT) and Hodrick-Prescott (HP) techniques are utilized to decompose of daily data for resolving an issue of non-stationary. Bidirectional Long Short-Term Memory (BiLSTM), Seasonal Auto Regressive Integrated Moving Average (SARIMA) and Gaussian Radial Basis Function Neural Network (GRBFNN) are introduced to predicting various subseries. Ensemble learning is implemented for enhancing a generalization capability of methods and interval prediction is produced depended on t-test of students is handled along difference of laws in water supply. The coupling and WT techniques correctly predicted the water demand and give optimum 0.19% of Mean Square Error (MSE), 3.37% of Mean Absolute Error (MAE), 0.2 of Mean Relative Error (MRE), 97.25% of Nash-Sutcliffe Efficiency (NSE) and 0.99 of Correlation coefficient. © 2024 IEEE.

关键词： Radial basis function networks

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Optimization of Renewable Energy Using Hybrid Multi-Objective Slime Mould and Simulated Annealing Algorithms 2

Optimization of Renewable Energy Using Hybrid Multi-Objectiv...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Malathy, V. Kalshetty, Jagadevi N. Al-Attabi, Kassem Kamesh, M.R. Das, Abhijeet Sr University Department Of Ece Telangana Warangal India Nitte Meenakshi Institute Of Technology Department Of Computer Science And Engineering Bengaluru India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq Dayananda Sagar College Of Engineering Department Of Mechanical Engineering Bengaluru India Department Of Civil Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350381665

During the past few years, increase in population resulted in increase in energy demand all over the world. The increased energy demand resulting depletion of fossil fuels which are now being replaced with Renewable Energy (RE) systems. However, operating on different energy sources for cost management and energy demand are difficult to handle due to its uncertainties like varying weather, load demand. In order to address these issues, a Hybrid RE System (HRES) is proposed in this study with a Hybrid Multi-Objective Slime Mould Algorithm and Simulated Annealing (HMOSMASA) method. The Simulated Annealing (SA) with advanced local search ability improves the effectiveness of Multi-Objective Slime Mould (MOSMA) exhibiting decentralized behavior and finds optimal solutions efficiently. Primarily, the search space is explored by MOSMA then solutions are exploited for local search by SA to obtain best optimal solution. The HMOSMASA ensures optimal sizing of HRES, load demand effectively and which is trained on historically collected data. The HMOSMA method is evaluated by Annual System Cost, Net Present Cost, Levelized Cost of Energy that achieved optimized results of 6374$, 2109732$, 0.231$/kWh respectively. The HMOSMASA demonstrated best results compared to Particle Swarm Optimization (PSO) and Hybrid Grey Wolf Optimization (GWO). © 2024 IEEE.

关键词： Simulated annealing

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IoT - based Weighted K-means Clustering with Decision Tree for Sedentary Behavior Analysis in Smart Healthcare Industry 2

IoT - based Weighted K-means Clustering with Decision Tree f...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Gudivaka, Rajya Lakshmi Alabdeli, Haider Sunil Kumar, V. Sushama, C. Muthu, BalaAnand Wipro Andhra Pradesh India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Department Of Aiml Bengaluru India Mohan Babu University School Of Computing Dept. Of Cse Tirupati India Tagore Institute Of Engineering And Technology Department Of Computer Science And Engineering Tamil Nadu India

ISBN: (纸本)9798350381665

Sedentary lifestyle has become a very prevalent feature of the modern living brought in different metabolic and cardiovascular problems. A high level of inactivity accompanied by physically unhealthy habits like drinking alcohol, smoking and bad diets may cause some deadly ailments in addition. Thus, it is necessary to identify health, behavior, and environmental anomalies to know the degree of health problems in people suffering from sedentary behavior. In particular, this study distinguishes itself from others with the proposed e-healthcare framework which involves merits of the Internet of Things (IoT) and fog technology for detecting health, behavioral, physical posture and environmental abnormalities of the individual. The 1st stage of data processing steps, weighted K-Mean clustering, is able to identify the faults of fog layer which will assist the network to keep the sensitivity of healthcare enterprise. In addition, the weighted k-mean clustering technique- decision tree (WKMC-DT) assisted hybrid methodology is recommended on the cloud layer for early health severity prediction. Using a multi-tiered decision process besides that will be an added advantage for better targeting of this resource allocation. Performance of irregularity determination will be calculated by tracking 15 individuals in the age group of 32-45 years continuously for 30 days. It can be done with the help of a framework deployed in an indoor laboratory. The experiment outcomes of the framework show that the method is effective to identify the health inconsistencies because it has 98.43% of accuracy, 94.56 % of sensitivity, 96.75% of specificity 96.58%, precision rate and 97.38% of f-measure. © 2024 IEEE.

关键词： Medical applications

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Employee Attrition Prediction based on Light Gradient Boosting Machine with Bayesian Optimization 1

Employee Attrition Prediction based on Light Gradient Boosti...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Pramodhini, R. Obbayed, Saif Manju, K. Swarnalakshmi, G. Purushotham, N. Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Sona College of Technology Department of Electronics and Communication Engineering Salem India New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India Mohan Babu University Department of Computer Science and Engineering Tirupati India

ISBN: (纸本)9798331529246

In recent days, many organizations struggle to retain the valuable employees with severe consequences of Employee Attrition (EA) rate. The existing Random Forest (RF) was utilized to predict the turnover rate of employees in corporate industries. However, the existing model struggled with interpretability and computation cost issues. To overcome these issues, Light Gradient Boosting Machine with Bayesian Optimization (LightGBM-BO) is proposed. The proposed LightGBM-BO reduced computational complexity with gradient boosting and improved interpretability with hyperparameter tuning. Initially, the data is considered and preprocessed with min-max normalization to rescale the data into significant range. Then, features are extracted with Principal Component Analysis (PCA) by utilizing covariance matrix. After that, Recursive Feature Elimination (RFE) is incorporated to select optimal features based on feature importance. Finally, the EA prediction is done with proposed LightGBM-BO and the performance is improved with hyperparameter tuning. From the results, the proposed LightGBM-BO attained better results when compared to existing eXtreme Gradient Boosting (XGBoost) including accuracy (94.93%) and precision (92.84%) respectively. © 2025 IEEE.

关键词： Adaptive boosting

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