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Evaluation of Incremental Conductance MPPT Algorithm under Varying Conditions 21

Evaluation of Incremental Conductance MPPT Algorithm under V...

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21st International Multi-Conference on Systems, Signals and Devices, SSD 2024

作者： Nezam, Ahmad M. Mahmood, Musaria K. Karim, Sulaiman M. Addawe, Abdirahim Ankara Yildirim Beyazit University Dept. of Energy Systems Engineering Ankara Turkey Jannat Iraq College Dept. of Medical Instr. Tech. Eng. Ramadi Iraq Al-Hadba'a University College Dept. of Computer Technology Engineering Mosul Iraq Karabük Üniversitesi Dept. of Computer Engineering Karabük Turkey

ISBN: (纸本)9798350374131

Photovoltaic-based renewable energy production is constantly developing and becoming an increasing part of mod-ern energy markets. The power efficiency of a PV system is one of the most challenging problems, especially in changing climate conditions. Tracking the Maximum Power Point (MPP) to achieve optimal power production is one of the most important requirements of any PV-based system. This article explores the functionality of the Incremental Conductance (INC) algorithm as a highly effective approach for Maximum Power Point Tracking (MPPT). Simulations are conducted to investigate the effectiveness of INC across diverse operational case studies, considering both Standard Test Conditions (STC) and Partial Shading Conditions (PSC). In this investigation, key factors including system power efficiency, settling time, and steady-state power oscillation are taken into account. Simulation reveals rapid tracking and reduced oscillation under STC, meanwhile, it occasionally demonstrates inadequacy in accurately tracking the MPP under PSC in the case of multi-panel systems. © 2024 IEEE.

关键词： Solar panels

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A Heart Disease Prediction Model using Merged XGBoost-SVM Classifier and Particle Swarm Optimization 5

A Heart Disease Prediction Model using Merged XGBoost-SVM Cl...

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5th International Conference on Mobile Computing and Sustainable informatics, ICMCSI 2024

作者： Majumder, Annwesha Banerjee Gupta, Somsubhra Majumder, Sourav Singh, Dharmpal JIS College of Engineering Dept. of Information Technology Kalyani India Swami Vivekananda University Dept. of Comouter Science Kolkata India Capgemini Kolkata India JIS College of Engineering Dept. of Computer Science and Engineering Kalyani India

ISBN: (纸本)9798350395235

This study aims to develop an advanced heart disease prediction model using machine learning techniques to address the global health challenge of early identification and accurate diagnosis, especially considering the projected deficits in healthcare by 2030. The proposed model integrates two powerful algorithms, XGBoost and Support Vector Machine Classifier, leveraging their complementary strengths to achieve effective classification. To enhance the model's performance, feature selection has been conducted using the LASSO method, which identifies the most relevant features, while outlier removal based on the Z score ensures data quality. StandardScaler has been utilized to standardize the data, mitigating any biases caused by differing scales. Furthermore, Particle Swarm Optimization has been employed to optimize the model's performance by fine-tuning its parameters. The reported accuracy of 91.8% underscores the model's potential to accurately predict heart disease, offering valuable support in prevention efforts. These findings highlight the significance of technological advancements in healthcare, particularly in addressing the growing demand for accurate and timely diagnoses. © 2024 IEEE.

关键词： Support vector machines

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Modified Deep Neural Network Approach to Identify Heart Disease using IoMT: Artificial Neural Networks or Convolutional Neural Networks!

Modified Deep Neural Network Approach to Identify Heart Dise...

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2024 International Conference on Intelligent Computing and Sustainable Innovations in technology, IC-SIT 2024

作者： Nayak, Debasish Swapnesh Kumar Priyadarshini, Arpita Mahanta, Pabani Panigrahi, Soumyarashmi Meher, Sushanta Swain, Satyananda Centurion University of Technology and Management Department of Computer Science and Engineering Bhubaneswar752050 India Utkal University Department of Statistics Bhubaneswar India University Dept. of Computer Science & Engineering Bhubaneswar752050 India

ISBN: (纸本)9798350369175

Unconventional methods of predicting and treating heart disease (HD) are required because HD is still the top mortality on a global scale. This study delves into the potential of combining deep learning (DL) methods with IoMT devices to improve HD prediction and early diagnosis. Despite their usefulness, traditional risk assessment approaches can misrepresent the intricate relationship between cardiovascular risk factors. On the other hand, DL and ML algorithms can sift through mountains of data related to lifestyle, genetics, demographics, and clinical conditions to find complex relationships. To increase generalization performance across varied patient demographics and datasets, our study is focused on designing a novel DL architecture that is specialized for HD prediction. We optimize its design and hyperparameters. With feature engineering, when selecting features, we use linear discriminant analysis (LDA). In our work, two different DL techniques neural network and convolutional neural network (CNN) for HD identification are studied. We then evaluate the studied model's efficacy with measures including recall, accuracy, precision, F1-score, and Matthews Correlation Coefficient (MCC). By attaining 96% accuracy, 97% precision, 95% specificity, 97% F1-score, and 93% MCC on chosen features, the experimental findings show that a one-dimensional (1-D) CNN performs better than other models. Our suggested method, which combines DL and IoMT, offers a powerful answer to the problems of real-time health monitoring and HD risk assessment, which in turn improves early intervention and tailors treatment plans to each patient. The findings of this study highlight the promise of cutting-edge AI-driven approaches to revolutionizing cardiovascular healthcare. © 2024 IEEE.

关键词： Convolutional neural networks

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Implementing a Novel Blockchain Algorithm for Enhanced Data Integrity in Cloud Systems 2

Implementing a Novel Blockchain Algorithm for Enhanced Data ...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Balaji, V. Ambika, S. Varshadevi, S. Rajasekaran, R. Thalapathi Sangeetha, J. Chennai India E.G.S.Pillay Engineering College Department of Information Technology Tamil Nadu Nagapattinam India Grt Institute of Engineering and Technology Dept. of Computer Science and Engineering Tamil Nadu Tiruttani India Saveetha School of Engineering SIMATS Dept. of Computer Science and Engineering Chennai India Sri Venkateswara College of Engineering and Technology Thiruvalluvar Tirupachur India

ISBN: (纸本)9798350384277

Keeping data safe and sound in cloud systems is important. So, new ways need to be found so private information can't get changed or seen without permission by wrong people. This research gives a new blockchain method to make data reliability better in cloud systems. We show how our suggested method is better and more useful than others in keeping data safe. We do this by doing a deep study and comparing with other ways that are currently used. When we look at how many transactions a system can handle, the time it takes for things to happen and its ability to grow bigger without problems, our suggested method does better than other solutions in Comparing Performance. It handles 1000 transactions each second, with a time delay of only 10 milliseconds. It can work well for up to one hundred nodes on the network while still showing it's efficient and able to keep growing bigger over time. The Data Comparison shows that our suggested way is very good at finding out false information, and it can find 98% of this. Besides, it quickly recovers data with a downtime of only 2 hours. This makes it different from old ways by making sure data can't be changed and there are strong checks on who did what. Security Check shows our new plan works well. It stops people spending money twice, stands up against cheating tactics and keeps a strong Byzantine Fault Tolerance (BFT) at 99.9%. The Proof of Stake agreement is both easy on energy and safe. Security of smart contracts is also talked about, to make them safer. © 2024 IEEE.

关键词： Cloud security

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Estimation of Clinical Parameters for Glaucoma Detection Using Image Processing and Deep Learning Techniques from the Biomedical Human Eye Images

Estimation of Clinical Parameters for Glaucoma Detection Usi...

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2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Guru Prasad, M.S. Chandrappa, S. Dhasmana, Gulshan Naveen Kumar, H.N. Mathpati, Shilpa Gangodkar, Ninad Dept. of Computer Science and Engg. Dehradun India Dept. of Computer Science and Engneering Bengaluru India Vidyavardhaka College of Engineering Department of ECE Mysuru India School of Computing and Information Technology REVA University Bengaluru India

ISBN: (纸本)9798350376470

The technique for distinguishing the essential glaucoma is being exhibited in this segment utilizing a changed neural system with the back spread calculation ideas. The morphological administrators ideas are being utilized for the handling of the cup and disc, in the sense, the cup and the disc zones are discovered from which the proportion is registered, from where the illness can be recognition seeing the proportion. Utilizing an updated highlight extraction process, the highlights of the disc portion and the cup can be discovered, which could be utilized in the idea of cup to plate apportion (cup to disc ratio) recognition, regardless of whether the given test image is glaucomatic or not. Classification is also done using artificial neural networks (ANN) along with the training process. Simulations have been done in the Matlab environment by developing. m codes. Once the developed program is run, the simulation results are observed. From the simulation results, it can be observed that using the computed values of the cup to disc ratio, the severity of the disease can be computed (whether it is normal or moderate or severe glaucoma). Databases have been taken from the hospitals and on-line standard websites. The quantitative results are tabulated in a table. Finally, it was found that after taking a survey of 50 images, 16 were affected with normal, 16 were affected with moderate and the rest were affected with severe case of glaucoma. The Matlab results of the simulation done using coding demonstrated the profoundness with respect to the strategy proposed or developed in this exploration write-up using the concept of neural networks. © 2024 IEEE.

关键词： MATLAB

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Identification of Children with ADHD from EEG Signals Based on Entropy Measures and Support Vector Machine 11

Identification of Children with ADHD from EEG Signals Based ...

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11th European Workshop on Visual Information Processing, EUVIP 2023

作者： Maniruzzaman, Md. Al Mehedi Hasan, Md. Suzuki, Taro Shin, Jungpil The University of Aizu School of Computer Science and Engineering Aizuwakamatsu965-8580 Japan Rajshahi University of Engineering and Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350342185

Attention deficit hyperactivity disorder (ADHD) is one of the major psychiatric and neurodevelopment disorders that affects 11% of children worldwide. Moreover, the prevalence of ADHD has rapidly increased over time worldwide. According to DSM-V, three types of symptoms such as inattention, hyperactivity, and combined type (inattention with hyperactivity). So, it is necessary to use a simple, non-invasive, and automatic detection system for identifying children with ADHD at an early stage. The objective of this study was to propose a machine learning (ML)based ADHD-combine type (ADHD-CT) detection system from electroencephalogram (EEG) signals. EEG signals were recorded from nineteen ADHD-CT children and fourteen healthy children. We extracted five entropy-based features such as approximate-based entropy, Shanon-based entropy, permutation-based entropy, sample-based entropy, and singular value decomposition (SVD)-based entropy from each signal. The subset of the most informative and discriminative features was selected for ADHDCT using sequential forward floating selection (SFFS). Following that, support vector machine (SVM) was implemented with leave-one-out cross-validation for the identification of ADHD-CT children and assessed its performances based on classification accuracy. Our results illustrated that SVM with polynomial kernel provided 96.87% classification accuracy to discriminate children as ADHD-CT and healthy children. Our findings showed that our proposed system can be used to detect children with ADHD-CT. © 2023 IEEE.

关键词： Support vector machines

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A Review on Iris Recognition Using Machine Learning Techniques 3

A Review on Iris Recognition Using Machine Learning Techniqu...

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3rd International Conference on Advanced Computing Technologies and Applications, ICACTA 2023

作者： Ingle, Yashwant S. Mahalle, Parikshit N. Khonde, Shraddha R. Bhagwat, Suvarna Diwakar, Mandar P. Vishwakarma Institute of Information Technology Dept. of Ai and Ds Pune India Mescoe Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350348347

An iris is round in shape and colored part of a person's eye. Iris's characteristics are unique in every person, and therefore, it distinguishes one person from others. Several recognition systems exist in the biometric verification system, like face, voice, Iris, and finger. But Iris has a crucial role in authenticating or recognizing a human being. Generally, the iris recognition arrangement is associated with specific top expensive applications where security is paramount and uses a delicate infrared illumination camera to capture images. Even in the same person, the texture of the left and right eye is not similar. It differs even among the twins' eyes, making it an extra secure way to automatically identify a person than other recognition systems. In the review paper, we highlight several existing iris datasets on iris recognition. Moreover, image preprocessing processes and segmentation techniques can be practically used to iris datasets and feature extraction methods, which will be used as input for any machine learning technique to identify the individuals efficiently. © 2023 IEEE.

关键词： Feature extraction

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Simplified Micropayment Mechanism to Eliminate the Risk of Double Payment in E-Commerce

Simplified Micropayment Mechanism to Eliminate the Risk of D...

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2023 International Conference on Advances in Intelligent Computing and Applications, AICAPS 2023

作者： Chahar, Narendra Kumar Singh, Krishan Pal Hussain, Muzzammil Central University of Rajasthan Dept. of Data Science and Analytics Ajmer India Malaviya National Institute of Technology Dept. of Computer Science and Engineering Jaipur India Central University of Rajasthan Dept. of Computer Science and Engineering Ajmer India

ISBN: (纸本)9798350333817

In recent years, the emergence of the Internet and E-commerce has steered significant growth in digital transactions. Businesses today need mobile wallets, credit and debit cards, and e-cash to digitize payments. Digital payment systems are in transition and promise amazing advancements, but they also pose many risks and as the number of online transactions is increasing tremendously, we need a security system that follows all security norms. In this paper, we study digital transaction systems and evaluate various components of E-commerce plat-forms to address the security of these services. We evaluate the attributes that affect the security of digital payment processes and identify several barriers that hinder their performance to propose a simplified payment mechanism for micro-payments that eliminates the double payment problem. © 2023 IEEE.

关键词： Online systems

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Enhancing Power Grid Resilience: Advanced Strategies for Detecting and Analyzing Faults in Three-Phase Transmission Lines

Enhancing Power Grid Resilience: Advanced Strategies for Det...

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2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Ahmed, Khandakar Rabbi Shayer, Khandokar Shadman Shuvo, Jahidul Islam Haque, F.M. Mahmudul Salam, Towfika Miraz, Shafiun Noman, Abdullah Al Sobuj, Md. Shohanur Islam Arafat, Md. Yaqub Rial, Md Istiak Hasan Miyan Research Institute International University of Business Agriculture and Technology Uttara Model Town Dhaka Bangladesh Dept. of Mechatronics Engineering Kazla Rajshahi6204 Bangladesh American International University-Bangladesh Dept. of Electrical & Electronic Engineering Dhaka Bangladesh Ahsanullah University of Science and Technology Dept. of Electrical & Electronic Engineering Dhaka Bangladesh Dept. of Computer Science and Engineering Rajshahi Bangladesh Jahangirnagar University Dept. of Institute of Information Technology Saver Dhaka1342 Bangladesh Hajee Mohammad Danesh Science and Technology University Dept. of Electrical and Electronic Engineering Dhaka Bangladesh International University of Business Agriculture and Technology Dept. of Mechanical Engineering Dhaka Bangladesh Dept. of Computer Science and Engineering Andhra Pradesh Kakinada533291 India

ISBN: (纸本)9798350382693

Power system fault analysis is a process that establishes the bus voltages and line currents when various fault types occur. Three-phase balanced faults and unbalanced faults are two categories of power system faults. Single line-to-ground, line-to-line, and double line-to-ground faults are three different types of unbalanced faults that can occur on transmission lines for power systems. Fault analysis is used to choose and set the proper protective devices and switchgear. Determining the bus voltages and line currents is critical while investigating power system issues. Numerous mathematical calculation techniques are shown in the process, which is challenging to do by hand. The calculation can be performed quickly on a computer using a Simulink created using MATLAB. However, an error occurs when inverting an extensive bus system's Y-bus to Z-bus in the conventional short circuit study;this paper offers a solution that is solved using 'MATLAB SIMULINK' to reduce error. © 2024 IEEE.

关键词： Power system analysis

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Image based and Point Cloud based Methods for 3D View Reconstruction in Real-time Environment 14

Image based and Point Cloud based Methods for 3D View Recons...

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2024 IEEE 14th Annual Computing and Communication Workshop and Conference, CCWC 2024

作者： Agrawal, Arya Sharma, Teena Verma, Nishchal K. Vasikarla, Shantaram Vellore Institute of Technology School of Computer Science and Engineering Vellore India Indian Institute of Technology Mehta Family School of Data Science and Artificial Intelligence Guwahati India Indian Institute of Technology Dept. of Electrical Engineering Kanpur India California State University Dept. of Computer Science NorthridgeCA United States

ISBN: (纸本)9798350360134

3D view reconstruction in a real-time unknown environment poses several challenges to vision-based applications. Herein, this paper outlines real-time experimental results and analysis of 3D view reconstruction methods. Since real-time experimentation requires inputs from different sensors, this paper outlines the results of methods from the following categories: image based methods and point cloud based methods. This paper explains the detailed steps involved in 3D view reconstruction followed by an explanation of the methods. It provides detailed discussions, advantages, limitations, and empirical results, along with presenting differences between algorithms. For analysis of experimental results, two benchmarked datasets i.e., KITTI 3D benchmark suite and ShapeNet are considered. Furthermore, the paper explores potential future scopes, including the utilization of generative models, unsupervised learning, advanced sensors, and many more. © 2024 IEEE.

关键词： Network coding

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