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Photovoltaic Power Forecasting Using Support Vector Machine and Adaptive Learning Factor Ant Colony Optimization 3

Photovoltaic Power Forecasting Using Support Vector Machine ...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Alabdeli, Haider Rafi, Shaik Naveen, I.G. Rao, Deepak Dasaratha Nagendar, Y. College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Qis College Of Engineering And Technology Department Of Mechanical Engineering Ongole India Nitte Meenakshi Institute Of Technology Department Of Ece Bengaluru India Indian Institute Of Technology Department Of Cse Patna India School Of Computer Science And Engineering Sr University Telangana Warangal India

ISBN: (纸本)9798350318609

Due to its flexible and clean nature, distributed Photovoltaic (PV) power is essential for solving energy and operational coordination challenges in integrating solar energy with other power sources. However, with rapid development complexities have emerged including in climate prediction, resulting in longer training times than those for the signal model. The Adaptive Leaning Factor Ant Colony Optimization (ALFACO) is used to optimize the ability to predict PV power values by linearly decreasing the weight and involved an adaptive learning factor to enhance the optimizing capability of the ACO. The proposed method, a combination of ALFACO and Support Vector Machine (SVM) can improve the robustness of the model, making it less likely to get stuck in local optima and making the SVM more capable of handling noisy or complex datasets. The proposed method, ALFACO-SVM achieves superior performance metrics including Root Mean Square Error (RMSE) of 0.0635, Coefficient of Determination R2 of 0.8920, Mean Absolute Error (MAE) of 0.0091, Median Absolute Error (MedAE) of 0.5620, Mean Squared Error (MSE) of 0.0045 and Explain Variance Score (EVS) of 0.0095, compared to existing methods such as Long Short-Term Memory (LSTM), Artificial Neural Network (ANN), LSTM and Bias Compensation LSTM (BCLSTM). © 2024 IEEE.

关键词： Support vector machines

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Automatic Diagnosis and Grading of Diabetic Retinopathy using Bat Optimization Algorithm-Refined Deep Residual Network

Automatic Diagnosis and Grading of Diabetic Retinopathy usin...

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

作者： Lakshmi, B. Haritha Kaneti, Venkata Ramana Soundariya, R.S. Nayak, Sanjib Kumar Rajesh, N. Malla Reddy Engineering College for Women It Department Hyderabad India Vnr Vignana Jyothi Institute of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Bannari Amman Institute of Technology Department of Computer Technology Sathyamangalam India Hyderabad India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

Diabetic Retinopathy (DR) is one of the most severe sight-threatening disorders resulting from diabetes, and can eventually lead to blindness and visual impairment. Early detection and medical therapy can assist in controlling and preventing DR progression. However, manual grading is extremely difficult and time-consuming because of the retina's complicated structure. In this research, the Bat Optimization Algorithm- Refined Deep Residual Network (BOA-RDRN) is proposed for automatic diagnosis and grading of DR. Initially, the DIARETDB0 dataset is employed for the proposed method, and then pre-processing is performed for image denoising which eliminates noise. The Attention-based Fusion Network (AFU-Net) is used to segment the lesion region. The Grey-Level Co-occurrence Matrix (GLCM) and Local Ternary Pattern (LTP) are used to extract the features and BOA is employed to find an optimal subset of features in retinal images. Finally, the RDRN is utilized to classify the anomalies as normal or abnormal. The BOA-RDRN achieves an accuracy of 99.42% compared to the existing methods such as Tunicate Swarm Spider Monkey optimization-based Refined Deep Residual Network (TSSMO-RDRN), Weighted Kernel Fuzzy C-Means Clustering and Dilation-Based Function (WKFM-DBF), Improved Harris Hawk Optimization-based Convolution Neural Network (IHHO-CNN), and Enhanced Fuzzy C-Means Clustering-Resnet 152 respectively. © 2023 IEEE.

关键词： Bat Optimization Algorithm Diabetic Retinopathy Grey-Level Co-occurrence Matrix Local Ternary Pattern Refined Deep Residual Network

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Customer Churn Prediction based on Embedding Language Model with Chi Square Test 1

Customer Churn Prediction based on Embedding Language Model ...

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

作者： Fallaah, Mohhamied Husaein Ajay, Shilpa Nandhini, A.P. Koilraj, Thomas Uma, S. The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Management Studies Bengaluru India Bannari Amman Institute of Technology Department of Humanities Erode India New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science and Engineering Mamallapuram India

ISBN: (纸本)9798331529246

In recent years, customer churn prediction in subscription based-business is more essential for improving retention policies. Moreover, various traditional methods are failed to address difficulties such as data quality and struggled to understand customer behavior dynamics. To resolve these issues Embedding Language Model with Chi Square Test (ELMo-CST) is proposed to predict customer churns in subscription based-business. Initially, data imputation and z-score normalization techniques are used to preprocess data for improving the performance of the model. Further, Term Frequency-Inverse Document Frequency (TF-IDF) is employed to extract Call Detail Record (CDR) features by estimating the important terms in the customer feedback. Then, the proposed ELMo-CST is used for predicting the customer churns effectively where ELMo is used to create feature embeddings for contextual representation by capturing sematic meanings on the basis of context. Moreover, CST is utilized for evaluating the relationships among CDR features and churn status by assessing the difference among the categorical variables for efficient prediction. From the results, proposed ELMo-CST established better results including accuracy (ACC) of 98.52%, precision (PR) of 93.86%, recall (RE) of 90.15% and F1-score of 0.93 respectively than existing Random Forest (RF). © 2025 IEEE.

关键词： Sales

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Hierarchical Federated Learning-Based Method for privacy-preserving in the Healthcare Environment

Hierarchical Federated Learning-Based Method for privacy-pre...

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

作者： Manoj Kumar, D.P. Babu. J, Ananda Nagasireesha, Jaladi Venkatesh, Bhattu Siddiqua, Ayesha Kalpataru Institute of Technology Department of Computer Science and Engineering Tiptur India Malnad College of Engineering Department of Information Science and Engineering Hassan India Malla Reddy Engineering College for Women Autonomous India Hyderabad India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

The COVID-19 pandemic has led to increase the demand for medical equipment with internet of things (IoMT) capabilities forcing doctors to diagnose and treat patients remotely. This has increased awareness of health, leading to a surge in purchasing of IoMT-capable medical equipment. However, medical data is sensitive and valuable on the dark web, making it vulnerable to cybercriminals. Due to limited storage and processing capabilities of network devices, system administrators struggle to enhance security measures. This work aims to investigate rapidly spreading attacks before they compromise wellness systems. Hierarchical federated learning is (HFL) proposed using a model built on Dew-cloud, which offers improved data privacy due to the increased availability of IoMT critical applications. Dew servers with distributed architecture and a cloud computing-backed backend implement hierarchical long-term memory (HLSTM) method. The implemented HFL-HLSTM method's performance is evaluated using NSL-KDD dataset. Implemented method achieved better performance than the existing intrusion detection methods including MiTed, LS2DNN, and MetaCID and the metrics are 98.62% of f-measure, 99.53% of accuracy, 99.21% of precision, and 99.10% of recall. © 2023 IEEE.

关键词： and Intrusion detection Data privacy Dew computing Federated Learning Hierarchical Long Short-Term Memory Internet of Medical Things (IoMT)

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A Machine Learning Based Methodology for Fault Diagnosis in Rotating Machine

A Machine Learning Based Methodology for Fault Diagnosis in ...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Madhukar, G. Boosi, Shyamala Adhoni, Zameer Ahmed Bhaskar, K Aruna Naik, Ashitha V Department of CSE CMR Technical Campus Kandlakoya medchal India Department of Computer Science & Engineering GITAM School of Technology GITAM University India India Department of Computer Science & Engineering K L University Vadeeshwaram Vijayawada India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (纸本)9798350398465

Over the course of the past sixty years, a significant amount of effort has been put into investigating the challenge of pinpointing the reasons why spinning machinery breaks down. The intricacy of rotating machinery design and analysis has resulted in an increased focus in recent times on the troubleshooting of faulty rotating machinery. Within the scope of the current research, we investigated a number of potential issues that could occur within these rotating components. Our goal was to identify the feature-classifier combination that would provide the most accurate fault prediction using machine learning. Two distinct phases of the experiment were carried out: in the first, the shaft, rotor, and bearing were the only components taken into account;in the second, the gear component was included. In order to determine which component of a spinning machine was malfunctioning, a microphone and accelerometer were utilised to take recordings of the machine's noise and vibration levels. When it was applied to vibration signals, SVM was able to obtain a maximum efficiency of classification of 99.52% for a total of 12 fault classes. When it came to the categorization of audio signals, SVM scored exceptionally well for issues including 12 classes, but it fared poorly for problems requiring 24 classes, achieving a classification effectiveness of less than 52% in those cases. The use of a sound-based fault diagnosis for a significant number of components or fault classes suggests that an SVM is not an appropriate technique. © 2023 IEEE.

关键词： Efficiency

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Optimizing Inventory Management using Demand Forecasting based Ensemble Stacking Model with Hyperparameter Tuning 1

Optimizing Inventory Management using Demand Forecasting bas...

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

作者： Pavithra, V. Abbas, Haayder M. Manjula, B.M. Chidambaram, P.K. Jothilakshmi, S. Srm Institute of Science and Technology Faculty of Science and Humanities Department of Computer Science and Applications Ramapuram Chennai India The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Information Technology Mamallapuram India

ISBN: (纸本)9798331529246

In recent days, the effective inventory management relies on accurate demand forecasting for effective management of business balance stocks. However, the traditional forecasting methods often struggled with different complexities such as variability, overstocking and wasted resources. In this research, the Ensemble Stacking Model with Hyperparameter Tuning (En-Stacking-HT) is proposed for effective optimized inventory management. Initially, the data is collected from M5 accuracy completion dataset and further preprocessed with data imputation, z-score normalization. These preprocessing techniques handles the missing values in data and standardizes the data in significant range. Then, Seasonal and Trend Decomposition (STL) is employed for extracting seasonality and trend features from preprocessed data by using Locally Estimated Scatterplot Smoothing (LOESS). Finally, the proposed En-Stacking-HT is utilized for inventory management prediction and optimized through hyperparameter tuning for predicting effective optimized inventory management for demand forecasting. From the results, the proposed En-Stacking-HT attained better results in terms of Mean Absolute Error (MAE) (16.82), Root Mean Square Error (RMSE) (22.85) and Mean Absolute Percentage Error (MAPE) (4.97%) when compared to existing Random Forest (RF). © 2025 IEEE.

关键词： demand forecasting ensemble stacking model hyperparameter tuning optimized inventory management seasonality and trend decomposition

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Modified Salp Swarm Optimization for Mobile Secure Routing in Intrusion Detection System for Wireless Sensor Network 2

Modified Salp Swarm Optimization for Mobile Secure Routing i...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Hussein, Layth Kumar, K Sudheer Trisheela, S. Rana Veer Samara Sihman Bharattej, R. Sravanthi, Jajimoggala The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Sr University School of Computer Science & Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of Cse Bengaluru India Saint Leo University Department of Computer Science St Leo United States Gokaraju Rangaraju Institute of Engineering and Technology Department of Cse Hyderabad India

ISBN: (纸本)9798350372892

Mobile secure routing is crucial for protecting wireless networks from potential threats and ensuring the confidentiality of transmitted data. Challenges such as dynamic topology changes and susceptibility to attacks underscore the need for robust algorithms. This paper introduces the Modified Salp Swarm Algorithm (MSSA) to optimize mobile secure routing within an Intrusion Detection System (IDS) for Wireless Sensor Networks (WSNs). MSSA, leveraging swarm intelligence, enhances network resilience through dynamic path optimization, effectively countering security risks. Numerical validation attests to MSSA's, achieving an energy consumption of 0.08 J and 12% of average end-to-end delay, 89 kbps of throughput, and 95% of detection rate. Comparative analysis against established methods such as Ant Colony Optimization (ACO), Mobile Agent-based Dynamic Intrusion Tracing (MADIT), Bacterial Foraging Optimization Algorithm (BFOA), and Trust-Based Secure and Energy-Efficient Routing (TBSEER) consistently positions MSSA as the superior solution, underscoring its effectiveness in addressing the complexities of mobile secure routing in wireless networks. © 2024 IEEE.

关键词： Network intrusion

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Fault Classification based on Deep Learning for Photovoltaic Systems 2

Fault Classification based on Deep Learning for Photovoltaic...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Kelin, A. R. Darshika Sudhakar, K. Surekha, Pothireddy Palanivel, R. Al-Jawahry, Hassan M. Karunya Institute of Technology and Sciences Data Science and Cyber Security Karunya Nagar Coimbatore India Department Electronics and Communication Engineering Telangana Hyderabad India Griet Department of Computer Science and Engineering Telangana Hyderabad India Nitte Meenakshi Institute of Technology Department of Ai and Ds Bengaluru India The Islamic University Najaf Iraq

ISBN: (纸本)9798350382952

In general, high power electronic converters such as Multilevel Inverters (MLI), that find their use in many applications, create difficulties of augmented switch count, capacitor flow current and reverse current flowing from inductive loads. above problems, in turn, result in faults, without which MLI reliability is unattainable. Therefore, fault classification and tolerance strategies are of paramount contribute to keeping reliability of inverters. Herein we present a deep learning (DL)-based fault classification approach for 15-level CHB Multi-level Inverter. proposed system includes pre-processing based on Adaptive Hilbert-Huang filtering, segmentation by a Partial Differential Equation (PDE), feature extraction utilizing Scale-Invariant Feature Transform (SIFT), and fault signal classification being performed by a Convolutional Neural Network (CNN) trained with a Honey Badger Optimization Algorithm (HBOA). MATLAB was applied to our method of identifying a set of four complex fault types for purpose of measurement. results of HBOA-optimized CNN have demonstrated highest accuracy rate of 99.91%. © 2024 IEEE.

关键词： Electric inverters

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A Survey Analysis on Dental Caries Detection from RVG Images Using Deep Learning 1

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8th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2023

作者： Nageswari, P. Pareek, Piyush Kumar Suresh Kumar, A. Aditya, Pai H. Guru Prasad, M.S. Kandasamy, Manivel Department of Mathematics Mahendra Institute of Technology Tamil Nadu Namakkal637503 India Computer Science and Engineering NITTE Meenakshi Institute of Technology Bangalore560064 India Karnataka Bengaluru562112 India Unitedworld School of Computational Intelligence Karnavati University Gujarat Gandhinagar382422 India

ISBN: (数字)9789819976331

ISBN: (纸本)9789819976324

A rapid developing area of study in the healthcare industry is medical imaging. Medical imaging is crucial for the early identification, diagnosis, and treatment of disorders. Ultrasound, magnetic resonance, computed tomography, X-rays, and other imaging techniques fall under this category. Dental caries is one of the most common conditions affecting people of all ages globally. Using radiovisiography (RVG) or dental X-ray images, it could be challenging to spot dental caries in its early stages. Practically, all medical fields use deep learning to predict or identify certain diseases. This research work has proposed and investigated a customized convolution neural network (CCNN) for caries detection from dental X-ray images. Few datasets are available for dental image processing. A total of 1300 X-ray images, 500 intra-oral photographic images, and 150 OPG images were collected from two medical practitioners with ground truth and used as a dataset for dental disease detection and image segmentation. This research work has demonstrated that image enhancement techniques play an important role in improving the quality of dental radiograph images. This research work explores different image augmentation techniques, which contribute to increasing the size of the dataset and improving accuracy. Image segmentation plays an important role in further classification and dental disease detection. Various deep learning models, including AlexNet, EfficientNetB0 and B7, Inception3, VGG-16, and the proposed CCNN model, are used to train the dataset. With more accuracy, a comparison between the deep learning model and the suggested CCNN model has been conducted. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image enhancement

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Predictive Maintenance in Autonomous Systems using Improved Support Vector Machine 1

Predictive Maintenance in Autonomous Systems using Improved ...

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

作者： Mamatha Bai, B.G. Divya, N. Sree Hamedshnain, Ammar Kumar, S. Senthil Vasumathi, T. Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Mahatma Gandhi Institute of Technology Department of Information Technology Hyderabad India The Islamic University Department of Computers Techniques Engineering College of Technical Engineering Najaf Iraq New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India Gojan School of Business and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331529246

In recent years, rapid development in the domain of Predictive Maintenance have resulted in improved equipment lifetime span, decrease maintenance costs and reduced the unplanned downtime. Predictive maintenance is an approach that addresses any equipment or component failures in the systems proactively. However, integration of this approach in automated systems has several challenges such as ensuring the accuracy of fault predictions, management of large-scale data and maintenance of system reliability under various operational conditions. To overcome these challenges Improved Support Vector Machine (ISVM) is proposed. Initially, the data is collected from the Predictive Maintenance Dataset (PMD) and the preprocessing is performed through Inter Quartile Range (IQR) which enhances the quality of data and modeling efforts. Further relevant features are extracted using Recursive Feature Elimination (RFE). Finally, the prediction is performed using ISVM which achieved better outcomes compared to existing a One-Class Support Vector Machine (SVM) in terms of accuracy as well as recall by achieving 90.58%, 80.79% respectively. © 2025 IEEE.

关键词： Predictive maintenance

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