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An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier

International Journal of Cognitive Computing in engineering 2021年 2卷 40-46页

作者： Kumari, saloni Kumar, Deepika Mittal, Mamta Department of Electronics and Communication Engineering Bharati Vidyapeeth's College of Engineering New Delhi India Department of Computer Science & Engineering Bharati Vidyapeeth's College of Engineering New Delhi India Department of Computer Science & Engineering G B Pant Govt. College of Engineering New Delhi 110020 India

Diabetes is a dreadful disease identified by escalated levels of glucose in the blood. Machine learning algorithms help in identification and prediction of diabetes at an early stage. The main objective of this study is to predict diabetes mellitus with better accuracy using an ensemble of machine learning algorithms. The Pima Indians Diabetes dataset has been considered for experimentation, which gathers details of patients with and without having diabetes. The proposed ensemble soft voting classifier giv.s.binary classification and uses the ensemble of three machine learning algorithms viz. random forest, logistic regression, and Naive bayes for the classification. Empirical evaluation of the proposed methodology has been conducted with state-of-the-art methodologies and base classifiers such as Adaboost, Logistic Regression,support vector machine, Random forest, Naïve bayes, bagging, Gradientboost, XGboost, Catboost. by taking accuracy, precision, recall, F1-score as the evaluation criteria. The proposed ensemble approach gives the highest accuracy, precision, recall, and F1_score value with 79.04%, 73.48%, 71.45% and 80.6% respectively on the PIMA diabetes dataset. Further, the efficiency of the proposed methodology has also been compared and analysed with breast cancer dataset. The proposed ensemble soft voting classifier has given 97.02% accuracy on the breast cancer dataset. © 2021

关键词： classification Diabetes ensemble Machine Learning soft voting

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Heart Disease Prediction using Logistic Regression with PCA-MFsA Feature Extraction Technique and Multidimensional scaling (MDs) Pre-processing Approach 3rd

Heart Disease Prediction using Logistic Regression with PCA-...

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3rd International Conference on Advancements in smart Computing and Information security, AsCIs 2024

作者： Priscila, s. silvia sakthivanitha, M. sadhana, C. Lavanya, M. Poornima, v. Department of Computer Science Bharath Institute of Higher Education and Research Tamil Nadu Chennai India Department of Information Technology Vels Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India Department of MCA Prathyusha Engineering College Tamil Nadu Chennai India Department of Computer Applications S.D.N.B Vaishnav College for Women Tamil Nadu Chennai India Department of Computer Science Vels Isntitute of Science Technology and Advanced Studies Tamil Nadu Chennai India

ISBN: (纸本)9783031862922

Heart disease prediction is critical because it has the potential to save lives, increase patient outcomes, and save the cost of healthcare. An important task in medical diagnostics is the forecasting of heart disease. In this article, we investigate the efficacy of integrating Multidimensional scaling (MDs) preprocessing with various feature extraction methods for precise LR (Logistic Regression) heart disease prediction. The feature extraction methods under investigation include Independent Component Analysis (ICA), Principal Component Analysis (PCA) and Mean Fisher score-based Algorithm (PCA-MFsA). PCA with MFsA emerges as the most effective method, consistently producing superior outcomes in the forms of accuracy rate, precision, and recall value compared to ICA and PCA. This underscores the significance of combining PCA with the specialized feature selection process offered by PCA-MFsA to enhance the LR model’s discriminatory power. Moreover, The incorporation of MDs preprocessing with PCA-MFsA results in a significant improvement in accuracy rate, precision value, and recall. From the result obtained PCA-MFsA produces accuracy of 91%, precision of 0.90 and recall of 0.86 respectively. © The Author(s), under exclusive license to springer Nature switzerland AG 2025.

关键词： Logistic regression

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Fungal disease detection in maize leaves using haar wavelet features 3rd

Fungal disease detection in maize leaves using haar wavelet ...

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3rd International Conference on Information and Communication Technology for Intelligent systems, ICTIs 2018

作者： Deshapande, Anupama s. Giraddi, shantala G. Karibasappa, K.G. Desai, shrinivas D. Department of Computer Science and Engineering B.V. Bhoomaraddi College of Engineering and Technology Hubli India School of Computer Science and Engineering KLE Technological University Hubli India

ISBN: (纸本)9789811317415

Agriculture is the backbone of Indian economy. Diseases in crops are causing huge loss to the economy. Only early detection can reduce these losses. Manual detection of the diseases is not feasible. Automated detection of plants diseases using image processing techniques would help farmers in earlier detection and thus prevent huge losses. Maize is an important commercial cereal crop of the world. The aim of this study is the detection of common fungal diseases, common rust, and northern leaf blight in maize leaf. The proposed system aims at early detection and further classification of diseases into common rust, northern leaf blight, multiple diseases, or healthy using first-order histogram features and Haar wavelet features based on GLCM features. Two classifiers, namely, k-NN and svM are considered for the study. The highest accuracy of 85% is obtained with k-NN for k = 5 and accuracy obtained with svM-based classification is 88%. © 2019, springer Nature singapore Pte Ltd.

关键词： Classification (of information)

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MPPT Control using Modified sepic Converter in Pv Modules Connected DC Micro Grid systems 7

MPPT Control using Modified Sepic Converter in PV Modules Co...

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7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： varatharaju, v.M. senthilkumar, b. Manivannan, R. Mahalakshmi, s. Geetha, C. Gomathi, s. University of Technology and Applied Science Ibra Oman V.S.B Engineering College Karur India St.Joseph's Institute of Technology Chennai India R.M.K. Engineering College Chennai India Saveetha School of Engineering Simats Chennai India

ISBN: (纸本)9798350397284

This research study applies the predictive control method to implement a distributed maximum power point tracking algorithm, which enhances the energy-extracting capabilities of a cascaded power optimizer-based system in variable environmental conditions. Two control loops are used in the developed method, one for each converter to implement a model predictive control loop based on tracking the maximum power point and the other for power optimization across all cascaded Pv modules. Closed loop response of FOPID and ANN controllers simulation results are compared and verified with the prototype. The experimental findings support high energy output, rapid dynamic response, and almost distinct oscillations near the MPP. © 2023 IEEE.

关键词： Maximum power point trackers

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Deep-Q-based Reinforcement Learning Method to Predict Accuracy of Atari Gaming set Classification

Deep-Q-Based Reinforcement Learning Method to Predict Accura...

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2023 International Conference on Data science, Agents and Artificial Intelligence, ICDsAAI 2023

作者： Gobinathan, N. Ponnusamy, R. V.R.S. College of Engineering and Technology Department of Computer Science and Engineering India Chennai Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350348910

A pivotal acquisition model designed to formulate control strategies directly from high-level perceptual data using reinforcement learning is introduced by us. The training of this model, a convolutional neural network, was done using a different Q-learning algorithm. An output is produced in the form of a value function that indicates anticipated future earnings after processing raw image inputs. Recent demonstrations of cutting-edge performance in reinforcement learning tasks have been made by these neural networks, which leverage Deep Q-Networks (DQNs) based on the Q-Learning algorithm. The planning process for DQNs is significantly different from that of standard deep neural networks, resulting in significant challenges in specific reinforcement learning scenarios. © 2023 IEEE.

关键词： Reinforcement learning

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Relaxation effects in the photoionization time delay near the 3s Cooper minimum of Ar

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Physical Review A 2025年第5期111卷 052805-052805页

作者： A. Ganesan s. banerjee s. baral J. Jose P. C. Deshmukh s. T. Manson Department of Physics J.B.A.S College for Women Chennai Tamil Nadu India Center for Free-Electron Laser Science (CFEL) Deutsches Elektronen-Synchrotron DESY Notkestr. 85 22607 Hamburg Germany Department of Physics Indian Institute of Technology Patna Bihta Bihar 801103 India Center for Research and Exploration in Space Science and Technology NASA/GSFC Greenbelt Maryland 20771 USA Center for Atomic Molecular and Optical Sciences and Technologies (CAMOST) Joint Initiative of IIT Tirupati and IISER Tirupati Tirupati Andhra Pradesh India School of Computer Science and Engineering RV University Bengaluru India Department of Physics and Astronomy Georgia State University Atlanta Georgia 30303 USA

The discrepancy in the attosecond time delay between theory and experiment near the 3s Cooper minimum (CM) of Ar is addressed. It is found that inclusion of the relaxation effects in the time delay difference between Ar 3s and 3p photoionization near the 3s CM dramatically alters the 3s time delay and brings the theory into substantial agreement with experiment using the relativistic-random-phase approximation with relaxation. The key point is that 3s photoionization is dominated by interchannel coupling in this region and the interchannel coupling matrix elements were found to be considerably altered by the inclusion of relaxation. This implies that relaxation should be important for any atomic or molecular transition that is dominated by interchannel coupling.

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Nanobiochar: A sustainable solution for environmental remediation

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Environmental Nanotechnology, Monitoring and Management 2025年 23卷

作者： Jadhav, vikram Ahire, bajirao Pawar, Asha Roy, Arpita Kumar, Ashish sharma, Kuldeep Raj, swetha verma, Rajan Department of Chemistry M. V. P. Samaj's K. K. Wagh Arts Science And Commerce College Pimpalgaon (B.) Maharashtra Nashik 422209 India Post Graduate Department of Chemistry N. V. P. Mandals Arts Science And Commerce College Lasalgaon Maharashtra Nashik 422306 India Department of Applied Science SNJB College of Engineering Chandwad Maharashtra Nashik 423101 India Department of Biotechnology Sharda School of Bioscience & Technology Sharda University Greater Noida 201310 India Centre for Phytochemical Research Sharda University Greater Noida India Department of Mechanical Engineering Institute of Aeronautical Engineering Hyderabad India Division of Research and Development Lovely Professional University Phagwara India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura 140401 India Center for Global Health Research Saveetha Medical College and Hospitals Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai India Chitkara Center for Research and Development Chitkara University HP 174103 India

NbC, a nanoform of biochar, shows unique potential due to higher reactivity with an increased surface area and surface-active side modifications. This paper presents a complete overview of the subject and identifies its potential role in the eco-friendly elimination of several kinds of environmental contaminants. It is usually prepared by pyrolyzing biomass at the nanoscale;this process can be modified to improve the material's catalytic and surface properties. The physicochemical properties of NbC, which govern its interaction with pollutants, are analyzed through characterization techniques. Adsorption, absorption, and catalysis are the several modes of NbC that may remove contaminants because of their larger surface area and different functional group applications of NbC in Environmental Cleanup. NbC has been shown to have numerous applications in environmental cleanup, such as air filtration, soil decontamination, and wastewater treatment. Recent studies suggest that NbC extracts heavy metals, organic contaminants, and other hazardous materials, often surpassing traditional biochar and other nanomaterials. Its production process can be designed to minimize environmental impact. NbC should be integrated with other remediation techniques using a synergistic approach in complex ecological systems. synthesis of nanobiochar focuses on following principles from green chemistry and sustainability in the development stages for a global solution in problem-solving ecological management. © 2025 Elsevier b.v.

关键词： Environmental remediation Green chemistry Nanobiochar Pollutant adsorption sustainable technology

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survey on Parts of speech Tagger Techniques

Survey on Parts of Speech Tagger Techniques

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2018 International Conference on Current Trends towards Converging Technologies, ICCTCT 2018

作者： Kanakaraddi, suvarna G. Nandyal, suvarna s. Compter Science Engineering Department B v Bhoomaraddi College of Engineering Technology Hubli Karnataka India Computer Science Engineering Department P D A College of Engineering Kalaburagi Karnataka India

ISBN: (纸本)9781538637012

In Natural Language processing (NLP) the POs tagger plays an important role. Many of the NLP applications such as speech recognition, machine learning information processing, information retrieval, question answering, word sense disambiguation and so on are using Parts Of speech (POs) tagging as a part of the application processing. POs tagger is an essential tool, which assigns parts of speech tags for each word of the sentence. Designing efficient POs tagger is a challenge because of the ambiguities in the language. This paper discusses the survey of different types of POs taggers designed by the researchers and organizations. Different taggers will have their own tag set. Tagging in NLP is similar to tokenization in computer languages. Choosing the proper tag for the context is a challenge for POs taggers. To resolve this ambiguity various researches have tried to provide the solution. © 2018 IEEE.

关键词： surveys

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Enhanced Framework for Authentic and Anonymous Data sharing Over Cloud storage service

Enhanced Framework for Authentic and Anonymous Data Sharing ...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence systems, IACIs 2024

作者： Naik, Archana Anjali, M. Ushashree, P. sooda, Kavitha Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India Geethanjali College of Engineering and Technology Department of Computer Science and Engineering Hydrabad India B.M.S. College of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350360660

This study introduces the concept of anonymous data exchange in banking applications using cloud computing. by intertwining innovative methodologies, the proposed framework bolsters security while introducing novel dimensions to the authentication landscape. Key to the system's architecture is the integration of RsA and sHA algorithms within the framework. In the contemporary digital landscape, the banking sector confronts escalating challenges in establishing robust authentication and security measures to safeguard sensitive customer data and transactions. Color-coded passwords offer a pioneering approach, enhancing traditional authentication methods with added security. Furthermore, the system advocates for integrate multi-factor authentication techniques, thereby reinforcing the authentication process. This integration ensures the confidentiality, integrity, and authenticity of sensitive data transmitted between clients. The performance of this integrated algorithm is compared with other encryption algorithms, and it is observed that the proposed mechanism encryption time is 10% faster. The encrypted message was tested on an online simulator to attempt deciphering and could not be decrypted, confirming its security. © 2024 IEEE.

关键词： Authentication Protocol

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sign Language Recognition: Advancing Human-computer Interaction through Machine Learning

Sign Language Recognition: Advancing Human-Computer Interact...

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2024 IEEE International Conference on Emerging Technologies in computer science for Interdisciplinary Applications, ICETCs 2024

作者： Akarsh, Aditi sinha, Arnav singh, bhavya Divyanshu sheetal, v.A. B.M.S College of Engineering Department of Computer Science and Engineering Karnataka Bangalore560019 India

ISBN: (纸本)9798350372502

sign language is an essential tool for communication among individuals with hearing *** recent progress in computer vision and machine learning has significantly advanced the development of systems that recognize and interpret sign language, contributing to their *** systems play a vital role in bridging communication gaps by automatically identifying and interpreting sign language gestures, facilitating seamless and effective communication. This paper reviews and analyzes sign language recognition systems, covering methodologies and potential applications, including data collection, hand and gesture tracking, feature extraction, and classification algorithms. The goal is to promote inclusivity and equal opportunities by fostering communication between sign language users and the broader community. © 2024 IEEE.

关键词： Feature extraction

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