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Enhanced AI Based Diabetic Risk Prediction Using Feature Scaled Ensemble learning Technique Based on Cloud Computing

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SN Computer Science 2024年第8期5卷 1-11页

作者： Manivannan, K. Ramkumar, K. Krishnamurthy, Revatthy Department of Information Technology V.S.B. Engineering College Tamilnadu Karur India Department of Computer Science and Engineering Rajalakshmi Institute of Technology Tamilnadu Chennai India Department of Artificial Intelligence & amp Machine Learning Rajalakshmi Engineering College Tamilnadu Chennai India

Diabetes causes many health problems including microvascular disease, macrovascular abnormalities and neuropathy. Diabetes is one of the costliest diseases from an economic perspective, and most adults through diabetes live in small- and intermediate-revenue countries, creating additional financial burdens and problems for these *** healthcare industry is one ofvast industries in the comprehensivebudget with revenue and investment increasing every year. Early and exactanalysis of diabetes, especially in the initial stages of the disease, can be difficult for medical professionals. To resolve the issue, we deploy the AI based ensemble learning method. To attain the objective, we perform three stagesfor instance preprocessing, feature selection, and classification. In the first phase, we preprocess the diabetes dataset by using numeric normalization method. This method is used for the diabetes dataset improves the validity of subsequent ML progression and produces more reliable and descriptive results. After preprocessed the dataset we go to select the features based on Correlation-based feature selection (CFS) method. CFS aims to select a feature subset with strong predictive power while minimizing redundancy by estimating the correlation between features and target variables and cross-correlation between features. The proposed method needs to select the important features for classification. At last, we classify the diabetes dataset on the basis ofreviewed ensemble learning methodologies to progress prediction accurateness. A comparison of the review methods for diabetes risk prediction includes K-Nearest Neighbor (K-NN), Whale Optimization and Fuzzy Neural Network (WO-FNN), Decision Tree (DT), and Support Vector machine (SVM). In this paper, we use K-NN, WO-FNN and DT as the base models and SVM as the meta-model. In the ensemble learning methodology we examine that SVM is the best method for forecasting the diabetes at premature stages by the diabetes dat

关键词： Accurate diagnosis CFS Clinical data Diabetes Healthcare industry Numeric normalization method SVM

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Exploring Cybersecurity Research Areas, Categories, and Limitations Utilizing an Interdisciplinary Approach 10

Exploring Cybersecurity Research Areas, Categories, and Limi...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer engineering, UPCON 2023

作者： Mittal, Vinayak Martolia, Mamta Chhabra, Payal Srivastav, Arun Pratap College of Engineering Department of Cyber Security Roorkee India Krishna Institute of Engineering and Technology Artificial Intelligence and Machine Learning Ghaziabad India Lloyd Institute of Engineering & Technology Greater Noida India

ISBN: (纸本)9798350382471

Rapid technological advances have brought unprecedented levels of connectivity and security, but also new and complex cybersecurity challenges The purpose of this paper is to provide an in-depth insight into current cybersecurity research areas, divided into specific areas and defined boundaries in each Poetry, Cryptography, Law, Psychology, It also integrates ideas from other relevant fields In the face of increasing cyber threats, this paper provides a comprehensive insight into the multifaceted aspects of cybersecurity. Computer science, cryptography, law, psychology, research in related fields, applying central topic vision, studying current security research fields, qualifying on the side in human areas, establishing each class, major research boundaries and selection, technology, agricultural line, The conclusions broaden the light of the complex interpersonal contribute to smaller understandings of interdisciplinary cybersecurity, which build emphasizing the need for collaborative and adaptive solutions to meet the changing cyber environment © 2023 IEEE.

关键词： challenges cryptography cybersecurity information security technology threats

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Predicting Soil Types and Crop Suitability through Comprehensive Soil Analysis

Predicting Soil Types and Crop Suitability through Comprehen...

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Sivakumar, S. Dharani, U. Hephzibah, Dineep Srm Institute Of Science And Technology Department Of Computer Science And Engineering Tiruchirapalli India Srm Institute Of Science And Technology Dept. Of Comp. Sci. And Engineering Specialization In Artificial Intelligence And Machine Learning Tiruchirapalli India

ISBN: (纸本)9798350352689

The analysis of soil nutrients and water levels holds utmost significance in the realms of agriculture. Determining the soil type and identifying the crop suitable for that specific soil are critical factors in optimizing agricultural yield. These considerations play a pivotal role in precision agriculture, ensuring that the chosen crops align with the soil's characteristics. This strategic alignment enhances the efficiency of cultivation practices, promotes sustainable agricultural methods, and ultimately contributes to increased crop yields. In this research paper, we systematically identify key soil properties, including NPK concentrations, temperature, humidity, and soil pH. The collected dataset, encompassing these crucial soil attributes, is utilized to train machine learning models. The primary goal is to forecast the soil type utilizing the gathered data, and subsequently, undertake further analyses, to ascertain the relevant crop types that are most suitable for the identified soil characteristics. This comprehensive approach involves the integration of data-driven insights and machine learning techniques to enhance our understanding of soil composition and facilitate informed decision-making in agriculture. © 2024 IEEE.

关键词： Soil testing

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DBROA based Convolutional Auto Encoder for European Credit Card Fraud Detection 2

DBROA based Convolutional Auto Encoder for European Credit C...

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

作者： Pareek, Piyush Kumar Mohan, B.R. Vidya, E.V. Zanke, Pankaj Navyashree, K.S. Nitte Meenakshi Institute Of Technology Artificial Intelligence And Machine Learning Bengaluru India East West Institute Of Technology Department Of Computer Science And Engineering Bengaluru India Sapiens International Corporation United States

ISBN: (纸本)9798350381665

Numerous studies using bad neuronic nets (DNNs) for the objective of detecting recognition card fraud have concentrated on enhancing point prediction accuracy and limiting unwelcome biases by developing various network designs or learning models. It is crucial to reckon uncertainty together with point estimate of the training data has a significant impact on how well machine learning algorithms function, therefore data imbalance is not a minor problem. In general, the data only shows a tiny portion of false communications. The effectiveness of simple machine acquiring classifiers is significantly impacted by this. This research studies several data augmentation strategies to handle the unbalanced data problem for credit card fraud detection in order to deal with the shortage of false events. The balanced data is then sent to a Convolutional Auto Encoder (CAE) for classification, and a Developed Battle Royale optimisation algorithm (DBROA) is used to determine the model's weight in the best possible way. For the purpose of recognising credit card fraud involving European cardholders, each model includes stratified K-fold cross-validation and a real-world dataset. The results show that the suggested model is more accurate than current models. © 2024 IEEE.

关键词： Convolution

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A Review on machine learning Assisted Drying Behaviour Prediction of Agricultural Seeds and Fruits 2

A Review on Machine Learning Assisted Drying Behaviour Predi...

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2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Jawre, Sandip Verma, Prateek Kothare, Chandrakant B. Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha India Shri Shankarprasad Agnihotri College of Engineering Department of Mechanical Engineering Maharashtra Wardha India

ISBN: (纸本)9798331528713

machine learning approaches have been more popular in recent years across many scientific and engineering fields, with the potential to improve drying behavior and thermal property predictions in terms of accuracy and efficiency. Agricultural seeds and fruits must be handled carefully after harvest because drying has a considerable impact on their quality, shelf life, and overall worth. To optimize drying processes and guarantee product quality, drying kinetics and thermal characteristics must be accurately predicted. This review article outlines the basic principles of drying, emphasizes their significance in determining the ideal drying conditions, and provides a thorough overview of the application of machine learning methods for predicting the drying kinetics and thermal properties of agricultural seeds and fruits. Then, in the context of their applications to drying prediction, a summary of several machine learning approaches, including artificial neural networks, support vector machines, and decision trees have been discussed. © 2024 IEEE.

关键词： Fruits

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Enhancing the Security by Analyzing the Behavior of Multiple Classification Algorithms with Dimensionality Reduction to Obtain Better Accuracy 2

Enhancing the Security by Analyzing the Behavior of Multiple...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Nandini, Sai Murthy, Sreenivasa Praksha, K.P. Prakash, U. Suriya Prakash, J. Department of CSE Bengaluru India Rajarajeswari College of Engineering Department of ISE Bengaluru India Rajarajeswari College of Engineering Dept of Artificial Intelligence Machine Learning Bengaluru India Sri Eshwar College of Engineering Department of Information Technology Coimbatore India

ISBN: (纸本)9798331519056

Intrusion detection in network traffic is a crucial component of modern cyber security strategies, aimed at identifying and mitigating malicious activities that threaten the integrity and availability of computer networks. With the proliferation of internet-enabled devices and the increasing sophistication of cyber threats, the importance of effective intrusion detection systems (IDS) cannot be overstated. This abstract provides an overview of the literature survey on intrusion detection in network traffic, which explores various techniques, algorithms, and methodologies employed in detecting and responding to cyber threats. Overall, this literature survey provides valuable insights into the complexities of intrusion detection in network traffic, offering readers a comprehensive overview of this critical aspect of cybersecurity. By understanding the latest trends, techniques, and challenges in intrusion detection, organisations can better protect their networks and mitigate the risks posed by cyber threats. Github link: https://***/pandhini13/intrusion-detection-and-network-traffic- © 2024 IEEE.

关键词： Network intrusion

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Empirical techniques for effort estimation in designing effective ML models

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International Journal of Computers and Applications 2025年第2期47卷 226-234页

作者： Padmanabha Reddy, Y.C.A. Kosuru, Sai Sathwik Sirisalla, Nageswara Rao Vivekananda, G.N. Perala, Venkata Akanksha Department of Artificial Intelligence and Machine Learning Chaitanya Bharati Institute of Technology Telangana Hyderabad India Department of CSE B V Raju Institute of Technology Telangana Narsapur India Department of Computer Science and Engineering K.S.R.M. College of Engineering AP Kadapa India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

machine learning Research often involves the use of diverse libraries, modules, and pseudocodes for data processing, cleaning, filtering, pattern recognition, and computer intelligence. Quantization of Effort Required for the above cumulative processes is rarely discussed in the existing works and The time to reach the desired level of the model's functionality is essential to gauge the environment training for the model training and pre-deployment testing. In this study, we empirically defined the manual-cum-computational effort required for the model development in terms of 2-time factors: time-to-live (time until 1st code modification) and time-for-modification (time between 2 codebase changes) taken together in a mathematical model to compute Effort Factor(quantitative measure of determining effort needed to design ML algorithms). The study is novel in terms of how the effort required to create ML models is calculated with a particular focus on the manual effort direction. The results and the findings obtained can be used for determining the total time needed to synthesize ML models and frameworks in terms of code change cycles and implementation strategies marking a 25% performance increase in the current method above the Standard Pipeline. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Pipeline codes

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Optimal Renewable Energy Wireless Power Management System for Electric Vehicles Using Predictive Analytics 2

Optimal Renewable Energy Wireless Power Management System fo...

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

作者： Kumar, Narendra Shivakumarswamy, P.M. Nikhil, N. Moger, Nagaraj Raghav, M.Vijay Kavya, B.M. Yuvaraj, N. Mahendra, H.N. Mallikarjunaswamy, S. Rns Institute of Technology Artificial Intelligence and Machine Learning Bengaluru India Jss Academy of Technical Eduction Dept of Electronics and Communication Engineering Bengaluru India Ks Institute of Technology Dept of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350372892

This paper introduces the Optimal Renewable Energy Wireless Power Management System (OREWPMS) for electric vehicles (EVs), designed to significantly enhance charging efficiency and sustainability by integrating innovative wireless charging technology with renewable energy sources. In contrast to conventional EV charging methods, which predominantly rely on direct electrical connections involving cables and plugs prone to wear and environmental damage, and heavily dependent on non-renewable energy, the proposed system capitalizes on an integrated network of solar and wind energy. This renewable grid powers a series of strategically placed wireless charging pads in parking areas and along roadways, offering a more adaptable and less intrusive solution. The OREWPMS utilizes resonant inductive coupling for efficient and low-interference power transfer, and it is governed by a predictive analytics algorithm that dynamically adjusts power distribution based on real-time vehicle demand, weather forecasts, and renewable energy availability. Preliminary testing has shown that the OREWPMS improves charging efficiency by approximately 0.40% and increases the utilization of renewable energy sources in EV charging by up to 0.65% compared to these conventional wired charging systems. Additionally, it reduces overall energy costs by about 0.30%, presenting a substantial improvement in both environmental impact and operational costs, thereby supporting a more sustainable and economically feasible transition to electric vehicle usage. © 2024 IEEE.

关键词： Charging stations

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Crop Monitoring System with Water Moisture Level using Arduino 5

Crop Monitoring System with Water Moisture Level using Ardui...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Sowmia, K.R. Prithi, S. Vijay, K. Berna, I. Eugene Bhuvaneswaran, B. Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Computational Intelligence School of Computing Srm Ist Kattankulathur Campus India Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9781665474672

Many factors-including sunlight, water, soil, and pest control-need to be considered when cultivating crops. The farmer has other responsibilities besides keeping an eye on the seedlings. In this way, the productivity of farming is enhanced by a machine that tends to all the plants with minimal human intervention. The amount of water a plant receives should be proportional to the amount of sunlight it gets. This research work employs the idea of IoT to connect the monitored plant to the network and transmit data. The microcontroller is an Arduino UNO, and components include a photoresistor for light detection and a moisture sensor for measuring the amount of moisture in the soil. The information is transmitted over a network, and the result is viewed using a mobile application. The data that has been collected is displayed in the app. It warns the user when the plant is in danger of drying out due to a lack of water or sunlight. If the crop is dry, a motordriven water pump is used to saturate the soil, and if it's too wet, a motor-operated shutter opens to let the water out. This project intends to make farming as mechanized and labor-free as possible. © 2023 IEEE.

关键词： Crops

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Apriori Analysis of Deep learning Models on a Multi-class Data Set 4th

Apriori Analysis of Deep Learning Models on a Multi-class Da...

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4th International Conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Kosuru, Sai Sathwik Padmanabha Reddy, Y.C.A. Akanksha, Perala Venkata Chaitanya, Kotagiri Parsi, Snehaja Chunduri, Madhu Babu Department of Computer Science and Engineering B V Raju Institute of Technology Telangana Medak Narsapur502313 India Department of Artificial Intelligence & Machine Learning Chaitanya Bharathi Institute of Technology Ganidept Hyderabad500075 India

ISBN: (纸本)9789819751990

Deep learning techniques have large scopes in terms of applications and resource requirements, hence calling for a deep analysis of their behavior. Many studies focused on the application part of these models and there is a relatively lesser discussion about the unit-wise behavior. A multi-class dataset was taken and three different models were trained where their performances of training processes are described in terms of runtime characteristics like learning rate, times, etc. An apriori analysis was conducted where the runtime characteristics are investigated for their similarity with pre-defined knowledge of their architectures, as opposed to the posterior analysis which particularly focuses on observations. The work also serves as a framework for defining the logistics of the model training processes and fine-tunes the parameters devised in future for the maximum accuracy of the model. The results were further converted into graphical representations for enhanced delivery of insights. The study is also novel where, as opposed to previous works, in which performances are only tested, the model is evaluated in a design-specific manner dealing with only architectures and the parameters defining them. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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