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Implementing Support Vector Machine Method for Predicting Urban Area Water Quality 1

Implementing Support Vector Machine Method for Predicting Ur...

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

作者： Al-Jawahry, Hassan M. Madhura, G.K. Ganti, Swathi Sheher Banu, S. Gajbhiye, Pratima The Islamic University of Al Diwaniyah Department of Computers Techniques Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India Dadi Institute of Engineering and Technology Anakapalli India Mvj College of Engineering Department of Electronics and Communication Engineering Bangalore India Lovely Professional University School of Chemical Engineering and Physical Science Phagwara India

ISBN: (纸本)9798350352931

Water quality failure is a long-standing global issue, causing illness, poisoning, spread of diseases, and claiming human lives in urban areas. Urban growth patterns have a substantial impact on stream water quality because they affect pollutant formation, buildup, and wash-off process. To successfully advise urban growth planning and policies, stream water quality must be evaluated and predicted in relation to various urban development patterns. In this work, Support Vector Machine (SVM) is implemented to predict quality of water in urban areas. Initially, data are collected through collected dataset, and the cleaning and normalization techniques are utilized to remove missing values and outliers and normalized the data. Then, SVM method is employed to effectively predict the quality of water. When compared to existing methods like genetic algorithm_extreme gradient boosting (GA_XGBoost), and artificial neural networks (ANNs), implemented SVM achieved high values of 0.953 of R 2,0.03of Root-mean-square error (RMSE), 0.01 of Mean Absolute Error (MAE), 9 of average invalidity percent (aiP), and 99 of average validity percent (AVP) performance result. © 2024 IEEE.

关键词： River pollution

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Block-Chain Assisted Strategies for Power Loss Reduction in Modern Distribution Systems

Block-Chain Assisted Strategies for Power Loss Reduction in ...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICaiQSA 2024

作者： Srinivas, Chodagam Babu, P Sailesh Rao, V Durga Reddy, A Venkateswara Mallikarjuna, G. Chittapragada, Heman Madanapalle Institute of Technology & Science Department of EEE Andhra Pradesh India SRKR Engineering College Department of EEE Andhra Pradesh India Swarnandhra College of Engineering and Technology Department of AI&ML Andhra Pradesh India Sai Rajeswari Institute of Technology Department of EEE Andhra Pradesh Proddatur India Madanapalle Institute of Technology & Science Department of CSE Andhra Pradesh India

ISBN: (纸本)9798331517953

This paper presents a novel approach utilizing Golden Jack Optimization (GJO) algorithm integrated with block-chain technology for minimizing power losses and enhancing voltage profile in distribution systems (DS). The optimization framework incorporates the IEEE 69 bus system as a testbed, employing GJO to iteratively optimize capacitor placements and reactive power compensation. The backward-forward sweep technique is adopted for accurate loss calculation and voltage profile assessment. Key components of the proposed methodology include transparency, trust, decentralization, data integrity, and traceability facilitated by block-chain technology. By recording each iteration's best solution, fitness value, and capacitor placements on an immutable ledger, block-chain ensures transparency and trust among stakeholders. Moreover, the decentralized nature of block-chain prevents unauthorized modifications, enhancing data integrity. The traceability feature enables stakeholders to track the evolution of optimization results, providing insights into the optimization process. Experimental results demonstrate the effectiveness of the proposed approach in minimizing power losses and improving voltage profile, showcasing the potential of block-chain-integrated optimization algorithms for enhancing distribution system performance. © 2024 IEEE.

关键词： block-chain technology data integrity decentralized optimization Golden Jack Optimization (GJO) IEEE 69 bus system power loss minimization traceability transparency voltage profile improvement

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Heart Disease Prediction using Machine Learning

Heart Disease Prediction using Machine Learning

引用

Data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： E.N.Vijaya Kumari Shaik Jakeer Hussain T. Srinivas Reddy M.Ratna Sirisha Manohar Madgi Kora Swetha Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Department CSE (AI & ML) Institute of Aeronautical Engineering Hyderabad Department of ECE Malla Reddy Engineering College Medchal Telangana Department of Computer Science and Engineering (CSE) CVR College of Engineering Hyderabad School of Computer Science and Engineering KLE Technological University Hubballi Karnataka India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

A complex disease, heart disease affects many people worldwide. Fast and accurate cardiac disease detection is crucial in cardiology. This article proposes a machine learning-based cardiac disease diagnosis method that is efficient and accurate. The system utilizes classification algorithms like SVM, LR, ANN, K-nearest neighbor, NB, and Decision tree, as well as feature selection algorithms like Relief, MRMR, LASO, and Local learning to eliminate irrelevant and redundant features. We also suggested a fast conditional mutual information feature selection approach for feature selection. The features selection techniques improve classification accuracy and reduce classification system execution time. Additionally, leave one subject out cross-validation has been utilized to learn model evaluation best practices and hyper parameter tuning. Classifier performance is measured using metrics. Classifier performance was tested on features selected by features selection methods. Experimental results suggest that the proposed feature selection method (FCMIM) with classifier support vector machine can create a high-level intelligent heart disease detection system. More accurate than prior methods, the suggested diagnosis system (FCMIM-SVM) The proposed approach can also be readily deployed in healthcare to detect cardiac problems.

关键词： Heart Support vector machines Thallium Accuracy Pain Cardiac disease Feature extraction Classification algorithms Tuning Diseases

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A Regression Framework for Predicting Cognitive Decline in Frontotemporal Dementia using Recurrent Neural Networks 46

A Regression Framework for Predicting Cognitive Decline in F...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Poonam, Km Guha, Rajlakshmi Chakrabarti, Partha P Iit Kharagpur Centre of Excellence in Ai West Bengal India Iit Kharagpur Centre for the Science of Happiness West Bengal India Iit Kharagpur Department of Computer Science & Engineering West Bengal India

ISBN: (纸本)9798350371499

Frontotemporal dementia (FTD) is a progressive neurodegenerative disorder with a diverse range of symptoms, including personality changes, behavioral disturbances, language deficits, and impaired executive functions. FTD has three main subtypes: behavioral variant FTD, non-fluent variant primary progressive aphasia, and semantic variant primary progressive aphasia. While there has been extensive research on detecting FTD, there is a limited exploration of the progression of FTD severity in patients over time. FTD typically manifests at a younger age, occurring between 40 and 65 years, than any other dementia forms. Therefore, detecting specific FTD subtypes early on becomes more feasible when assessing disease severity in the initial stages. This study aims to forecast an individual's future cognitive status using their neuropsychiatric inventory. This inventory consists solely of neuropsychological test scores related to FTD markers collected from one or more time points. We proposed and applied a regression framework with Encoder-Decoder Long-Short-Term-Memory (ED-LSTM) model to the data from the Frontotemporal Lobar Degeneration Neuroimaging Initiative (FTLDNI/NIFD) comprising longitudinal data of 288 participant's 918 instances. This study represents the first comprehensive exploration of forecasting individuals' FTD markers (cognitive scores) over four years into the future. We compared the performance of the proposed model with two baseline recurrent neural network models (LSTM and Simple RNN). The results indicate that the suggested model outperforms other implemented models when considering mean absolute error and root mean square error performance *** relevance - This study aims to provide valuable insights into the early identification and prognosis of cognitive decline in individuals with FTD. This could contribute to more timely and targeted interventions, improving patient outcomes and enhancing the overall management of FTD. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Segmentation of Liver Tumors in CT Scans Using Active Contour Models and Machine Learning

Segmentation of Liver Tumors in CT Scans Using Active Contou...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Sharma, Rajita Alabdeli, Haider Baghel, Abhishek Singh Jayashankari, J. Maheswari, G. Dhanashree, S. Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Ies University Ies Institute of Technology and Management Department of Computer Science & Engineering M.P. Bhopal India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Hindustan Institute of Technology and Science Department of Information Technology Chennai India Saveetha Institute of Medical and Technical Sciences Department of Ai & Ml Tamilnadu Chennai India

ISBN: (纸本)9798350390759

Liver tumor segmentation in CT scans plays a crucial role in cancer diagnosis and treatment planning. This paper proposes a novel methodology that integrates Active Contour Models (ACMs) with Convolutional Neural Networks (CNNs) for accurate and efficient segmentation of liver tumors. The approach leverages the feature learning capabilities of deep learning and the precise boundary detection of ACMs to achieve state-of-the-art segmentation results. Evaluation on the LiTS dataset demonstrates superior performance in terms of Dice Similarity Coefficient (DSC), Precision, Recall, and computation time. The proposed methodology offers a promising solution for improving clinical decision-making in liver cancer treatment. © 2024 IEEE.

关键词： Convolutional neural networks

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Attention Based Generative Adversarial Networks to Augment Image Data for Rapid Training

Attention Based Generative Adversarial Networks to Augment I...

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International Conference on Parallel Distributed and Grid Computing (PDGC)

作者： Vijayakumar Adaickalam Sadagopan Selvarasu Gowthul Alam M A Suresh Kumar S Annamalai Sangeetha N School of Computer Science and Engineering and Information Presidency University Bangalore Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Chennai School of Computer Science and Engineering JAIN (Deemed-to-be University) Bangalore Department of AI & ML City Engineering College Bangalore

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

In this paper, we develop an Attention based Generative Adversarial Networks (AGAN) to augment image data for the purpose of robust training for efficient processing of the hyper spectral imaging. The AGAN model enables generation of images from the sample images that helps in training the classifier and in this study a fundamental classifier namely a convolutional neural network is used. A robust training is conducted to test the accuracy of detecting the instances effectively using the dataset. The simulation shows that the proposed AGAN-CNN attains improved accuracy after robust training than the existing methods.

关键词： Training Accuracy Training data Imaging Production computer architecture Generative adversarial networks Grid computing Convolutional neural networks Hyperspectral imaging

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An Efficient Approach to Data Clustering Using the K-Means Algorithm in Big Data Analytics

An Efficient Approach to Data Clustering Using the K-Means A...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： S. Sandhiya Vani Ramya P Y. Nagendar P. Arun Prakash Malladi Chanti Athiraja Atheeswaran Department OF Computer Science and Engineering Rajalakshmi Engineering College Chennai Department of Computer Science and Engineering Anna University University College of Engineering Department of School of Computer Science and Artificial Intelligence SR University Hyderabad Telangana Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Computer Science and Engineering Malla Reddy Engineering College (Autonomous) Hyderabad Telangana Department of CSE(AI&ML) Malla Reddy College of Engineering Hyderabad Telangana India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

The large-scale, high-dimensionality of datasets poses major obstacles to effective and precise data clustering in the field of big data analytics. This work focuses on a well-known technique for dividing huge datasets into informative clusters: the K-Means algorithm. This approach to data clustering is efficient. However, because of scalability and computational complexity problems, K-Means is not as effective when managing large amounts of data. In order to reduce processing cost and improve clustering accuracy, this work provides an improved version of the K-Means algorithm that blends dimensionality reduction approaches and efficient initialization methods. Principal Component Analysis (PCA) is used as a preprocessing step in the methodology to reduce the dimensionality of huge datasets. An optimized K-Means algorithm with improved centroid initialization is then applied. These changes lead to shorter iteration durations and higher-quality clusters. Comparing the results to standard K-Means, experiments on many big data benchmarks showed a 15% gain in cluster accuracy and a 25% reduction in execution time. To sum up, the incorporation of optimized initialization techniques and dimensionality reduction greatly improves the scalability and performance of K-Means in big data analytics, with useful implications for sectors that depend on effective data analysis.

关键词： Dimensionality reduction Accuracy Scalability Supervised learning Clustering algorithms Big Data Data models Classification algorithms Standards Principal component analysis

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Artificial Intelligence Based Hierarchical Classification of Frontotemporal Dementia 46

Artificial Intelligence Based Hierarchical Classification of...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Poonam, Km Guha, Rajlakshmi Chakrabarti, Partha P. Iit Kharagpur Centre of Excellence in Ai West Bengal India Iit Kharagpur Centre for the Science of Happiness West Bengal India Iit Kharagpur Department of Computer Science & Engineering West Bengal India

ISBN: (纸本)9798350371499

Frontotemporal dementia (FTD) is a typical kind of presenile dementia with three main subtypes: behavioral-variant FTD (bvFTD), non-fluent variant primary progressive aphasia (nfvPPA), and semantic variant primary progressive aphasia (svPPA). Our aim is to classify brain images of each subject into one of the spectrums of the FTD in a hierarchical order by applying data-driven techniques. Specifically, we took 300 subjects (30 bvFTD, 41 svPPA, 25 nfvPPA, 80 Alzheimer's Disease, and 124 cognitively normal) from the Frontotemporal Lobar Degeneration Neuroimaging Initiative archive to validate our approach. The cortical and subcortical measurements, e.g., cortical thickness and volumetric segmentation, were extracted from MRI images for the experiment. Our proposed model yielded classification accuracy of 87.42%, 83.23%, and 82.44% with Support Vector Machine (SVM), Linear Discriminant Analysis, and Naive Bayes methods, respectively, for five classes. We observed a significant improvement over the flat multi-class model, which has an accuracy of 82.80%, 81.27%, and 80.45%, respectively, for five classes. We also compared our proposed approach with a Convolutional Neural Networks model and ensemble with multi-layer perception as well. The results showed that the hierarchical approach performs almost equally well here as in the SVM model. Moreover, we also tested our hierarchical approach for three classes (CN, Non-FTD, FTD) to compare with state-of-the-art methods and obtained better *** relevance - This study provides a detailed understanding of the heterogeneity within the FTD subtypes;clinicians can better tailor diagnostic and treatment strategies to match each subtype's specific characteristics and progression patterns. © 2024 IEEE.

关键词： Support vector machines

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Transforming Business Intelligence using Data Analytics and Artificial Intelligence 2

Transforming Business Intelligence using Data Analytics and ...

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Jwala, V.Ch. Joshi, Bhavesh Mishra, Pavan Saha, Aahana Goyal, Shweta Ramesh, Bandla Srkr Engineering College Department of Information Technology Andhra Pradesh Bhimavaram India Graphic Era Hill University Scool of Management Bhimtal Campus India Lovely Professional University Department of Computer Science and Engineering Punjab Phagwara India Kr Mangalam University Department of Psychology Gurgaon India Graphic Era Deemed to be University Deptt. of Electrical Engineering Dehradun India St. Martin's Engineering College Department of Ai & Ml Dhullapally500100 India

ISBN: (纸本)9798350364699

A wide range of businesses values generation through decision-making is crucial use business intelligence solutions. Business Intelligence is the process of using existing data to extract, analyse, and forecast business-critical insights. This paper looks at how artificial intelligence and data analytics are revolutionising business intelligence and how this has drastically changed how businesses plan their plans and make decisions. Business need to use ml and business intelligence to obtain an competitive hand and pursue informed choices in the present information driven market. This study takes a gander at how strategies from machine learning can be applied to further develop significant business experiences. An apple dataset was exposed to three unique classifiers: Random Forest, Decision Tree, and SVM. This article shows how machine learning algorithms may revolutionise business intelligence and provide businesses with a competitive edge. Businesses may predict future trends, find hidden patterns, and automate decision-making processes by utilising machine learning algorithms. The importance of selecting the right classifier for predictive modelling is further shown by the fact that Random Forest outperformed all other classifiers evaluated by 92%. This study also emphasises how important it is for business organisations to use machine learning and data analytics in order to improve business insights and drive business success. © 2024 IEEE.

关键词： Adversarial machine learning

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Retrieval Augmented Generation for Relational Mapping of Resume Data for Improved Analysis

Retrieval Augmented Generation for Relational Mapping of Res...

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Machine Learning and Autonomous Systems (ICmlAS), International Conference on

作者： Vazzula Manish Yugandhar Manchala Sudeep Banerjee Chopra Muddam Siddartha Kothi Yashwanth Reddy Dept. of Information Technology Vardhaman College of Engineering Hyderabad India Dept. of Computer Science (AI & ML) Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Analysis of resume data is often a critical and time-intensive process that requires extensive metadata. However, a lack of consistency and scalability often leads to inefficiencies and errors in the recruitment process. Tools like Applicant Tracking Systems (ATS) often struggle with the contextual understanding of resume data, as they evaluate each resume in isolation without considering its relations to other similar resumes. We proposes the implementation of a Retrieval-Augmented Generation (RAG) architecture to create a vector relational mapping of resume data based on several curated parameters, such as skills, experiences, and education. This approach enables the system to understand the contextual relations between different resumes, helping the system evaluate them more effectively within a broader context, and improve recruitment outcomes by reducing biases and increasing efficiency.

关键词： Social networking (online) Soft sensors Resumes Retrieval augmented generation Software as a service Transforms Natural language processing Vectors Recruitment Software development management

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