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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Tripathi, Mukesh Kumar Ananya, Meka Murthy, V.N.L.N. Shashank, Tumma Reddy, Jale Subash Attaluri, Kaushal Vardhaman college of Engineering Department of Computer Science & Engineering Hyderabad India Vardhaman college of Engineering Department of Information Technology Hyderabad India Pegasystems Worldwide India Pvt Ltd Machine Learning/AI Team Hyderabad India

ISBN: (纸本)9798331540364

Breast cancer remains a significant health concern for women, and accurate prediction of recurrence is vital for effective treatment and improved outcomes. This research investigates the application of advanced ensemble learning techniques to enhance the reliability of breast cancer recurrence predictions. By combining Deep Neural Networks (DNN) and Artificial Neural Networks (ANN) with traditional machine learning methods, the proposed approach demonstrates significant improvements in accuracy, precision, sensitivity, specificity, F1-scores, and areas under the curve (AVCs). The study utilizes two breast cancer relapse datasets, UMCIO and WPBC, to evaluate the performance of the ensemble learning models. The results highlight the effectiveness of the proposed approach in accurately predicting breast cancer recurrence. This research contributes to the advancement of breast cancer diagnosis and treatment by providing a valuable tool for clinicians to assess the risk of recurrence and tailor treatment plans accordingly. © 2024 IEEE.

关键词： Prediction models

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Bridging Horizons in Diabetes Prediction: A Comparative Exploration of Machine Learning and Deep Learning Approaches in Pima Indian Women 2

Bridging Horizons in Diabetes Prediction: A Comparative Expl...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Chandra Sekhar Reddy, L. Gottipalli, Monikadevi Sravanthi, P. Rajanikanth, J. Yalamarthi, Ganesh Gurrapu, Neelima Hyderabad India Sir C R Reddy College of Engineering Department of CSE Eluru India CMR College of Engineering & Technology Department of CSE Hyderabad India S R K R Engineering College Department of CSE Bhimavaram India SIR C R Ramalinga Reddy Polytechnic College Department of AI&ML Eluru India SR University Department of computer science and Artificial intelligence Warangal India

ISBN: (纸本)9798350371314

This study has demonstrated that diabetes prediction models will require powerful machine learning and anomaly detection methodologies in practice. Researchers are exploring novel outlier identification algorithms to enhance predictions. This study has addressed various concerns outlined in prior research. According to this study, diagnostic records, when combined with powerful machine learning such as Adaboost and XGBoost, can offer a precise forecast of the likelihood of diabetes. RNNs also improved deep learning, according to experts. Experts use IQR for the straightened subspace to detect outliers. The stand appears to have a highly positive impact on prediction models. IQR-boosted RNNs outperform XGBoost and AdaBoost in terms of prediction and efficiency. This suggests that combining outlier detection with advanced machine learning could potentially improve diabetes diagnosis and care. The identification of IQR outliers, using RNNs cultivated through iteration with a different line of machine learning, may improve diabetes prediction outcomes. RNN produces better results, with 93% accuracy. The study demonstrated that enhanced machine learning and outlier detection led to improvements in diabetes prediction algorithms. © 2024 IEEE.

关键词： Recurrent neural networks

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Resource Scheduling using Enhanced Teaching–Learning-based Optimizer in Cloud Environment 2

Resource Scheduling using Enhanced Teaching–Learning-based ...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Karunakaran, S. Khan, Nikhat Raza Porkodi, K. Hemalatha, S.M. Raj Balaji, D. Srimathi, S. Department of Computer Technology Kongu Engineering College Perundurai Erode India Department of Computer Science & engineering IES College of Technology Bhopal India Computer Science Rathinam College of Arts and Science Coimbatore India AI&DS M Kumarasamy college of engineering India Department of Computer Science Rathinam College of Arts and Science Coimbatore India Department of Bio Technology Saveetha School of Engineering SIMATS Tamilnadu Chennai India

ISBN: (纸本)9798350340235

Resource Scheduling (RS) in cloud environment is a significant task targetedat effective management andallocation of cloud resources in order to meet the changing user demands and applications while maximizing the system efficiency and utilization. Effective RS has gainedmore importance in the cloud environment for meeting the dynamic user demands, enhancing overall system performance, and optimizing resource utilization. In this research study, metaheuristicoptimizing techniques have proved efficiency in tackling the RS convolution issues. This study introduces a new Resource Scheduling using Enhanced Teaching–Learning-Based Optimizer (RS-ELTBO) algorithm in Cloud Environment. The primary goal of the RS -ELTBO technique lies in the process of optimizing resource allocationand performance metrics. Applying ETLBO for resource scheduling in cloud environments can lead to improved resource utilization, reduced costs, and enhanced overall performance. The ETLBO algorithm makes use of an objective function to determine the fitness of considerable resource allocation andemploys a teaching phase, learning phase, and exploration strategy to iteratively refine the solutions. The performance study of the RS -ELTBO system was tested on various aspects. The achieved values stated that the RS-ELTBO system reached improved scheduling results over other approaches. © 2023 IEEE.

关键词： Cloud Computing Metaheuristics Resource Scheduling Resource Utilization TLBO Algorithm

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Enhanced Efficiency in Fog Computing: A Fuzzy Data-Driven Machine Selection Strategy

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International Journal of Fuzzy Systems 2024年第1期26卷 368-389页

作者： Zavieh, Hadi Javadpour, Amir Ja’fari, Forough Sangaiah, Arun Kumar Slowik, Adam School of Economics and Statistics Guangzhou University Guangzhou510006 China Harbin Institute of Technology Shenzhen China Department of Computer Engineering Sharif University of Technology Tehran Iran International Graduate School of AI National Yunlin University of Science and Technology Douliu Taiwan Department of Electronics and Computer Science Koszalin University of Technology Koszalin Poland ADiT-Lab Electrotechnics and Telecommunications Department Instituto Politécnico de Viana do Castelo Porto4900–347 Portugal Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

With the rapid proliferation of IoT and Cloud networks and the corresponding number of devices, handling incoming requests has become a significant challenge. Task scheduling problems have emerged as a common concern, necessitating the exploration of new methods for request management. This paper proposes a novel approach called the Fuzzy Inverse Markov Data Envelopment Analysis Process (FIMDEAP). Our method combines the strengths of the Fuzzy Inverse Data Envelopment Analysis (FIDEA) and Fuzzy Markov Decision Process (FMDP) techniques to enable the efficient selection of physical and virtual machines while operating in a fuzzy mode. We represent data as triangular fuzzy numbers and employ the alpha-cut method to solve the proposed models. The paper provides a mathematical optimization model for the proposed method and presents a numerical example for illustration. Furthermore, we evaluate the performance of our method in a cloud environment through simulations. The results demonstrate that our approach outperforms existing methods, namely PSO + ACO and FBPSO + FBACO, in terms of key metrics, including energy consumption, execution cost, response time, gain of cost, and makespan. © The Author(s) 2023.

关键词： Markov processes

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Customer Churn Prediction Using Hierarchical Clustering and Ensemble Learning

Customer Churn Prediction Using Hierarchical Clustering and ...

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Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： Konda Avinash Varala Aditya Ram Seguru Rithvik Reddy Bharath Kumar Nangunuri M.A. Jabbar P. Swetha Computer Science Engineering(AI&ML) Vardhaman College of Engineering Shamshabad Hyderabad Information technology Vardhaman College of Engineering Shamshabad Hyderabad

The capacity to forecast which consumers are most likely to discontinue their use of a service or leave is known as customer churn prediction. This prediction holds significant importance for many companies, as acquiring new customers often proves to be more costly than retaining existing ones. Once customers at risk of churn are identified, it becomes crucial to determine the most effective marketing efforts for each individual customer in order to maximize their likelihood of staying. This entails recognizing that customers exhibit different behaviors, preferences, and reasons for canceling their subscriptions. Therefore, actively engaging with each customer is essential in order to maintain them on the customer list. Understanding which marketing activities are most effective for individual customers, as well as the optimal timing for these activities, is typically achieved through training a supervised machine learning model. This model was trained on historical data, which may have included customer service interactions, account activity, and purchase history, and it reached an extremely impressive accuracy of 87 percent. The model is then applied to new data to forecast things like whether a new customer would churn within a given period of time.

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Towards Enabling Data Security: NFT Generator Automation for Blockchain User Experience

Towards Enabling Data Security: NFT Generator Automation for...

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Advancement in Technology (ICONAT), International Conference for

作者： Makadiya Kishan Shukla Madhu Vaghela Krunal Department of Computer Engineering Marwadi University Rajkot India Department of AI ML and DS Marwadi University Rajkot India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Non-fungible tokens (NFTs) have become increasingly more popular in recent years as a manner to represent particular virtual assets on the blockchain. However, the creation of NFTs can be a complicated and technical process that calls for an understanding of blockchain technology and programming. To make the procedure more accessible to a wider target audience, NFT turbines have been developed to enable customers to create their very own NFTs without the want for technical knowhow. This paper pursues to discover the consumer experience of NFT mills and their capability effect on the adoption of NFTs. We conducted a literature evaluation to study present studies on NFTs and case studies of NFT generator structures and remarks from popular NFT generator systems to gain insights into the person revealed. Our analysis revealed that NFT turbines can substantially simplify the procedure of making and selling NFTs. By supplying person-pleasant interfaces and automation of technical duties, NFT turbines can allow a much broader audience to take part in the NFT marketplace. There also are demanding situations that want to be addressed to make sure a fine user enjoy. These encompass issues around the protection, transparency, and authenticity of NFTs. We identified numerous key layout concerns for NFT turbines, which include ease of use, customization alternatives, and assistance for a couple of blockchains. we discovered that Mintable gives a streamlined and consumer-friendly revel in for developing and selling NFTs, with several customization options and aid for multiple blockchains. NFT generators will possibly play an increasingly vital function in permitting users to take part in this emerging economy. Overall, this research highlights the significance of personal experience within the design of NFT generators and gives insights and hints for designers looking to create NFT generator systems that are consumer-friendly, on-hand, and secure.

关键词： Industries Automation Art Smart contracts Programming Generators User experience Nonfungible tokens Protection Turbines

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Analyzing P300 Signals to Assess Undergraduate Attention Span Using Impulse Neiry BCI

Analyzing P300 Signals to Assess Undergraduate Attention Spa...

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2024 International Conference on Brain computer Interface and Healthcare Technologies, iCon-BCIHT 2024

作者： Sooda, Kavitha Indiramma, M. Kubakaddi, Sanjeev B.M.S. College of Engineering Department of Computer Science and Engineering Bangalore India B.M.S. College of Engineering Department of AI and DS Bangalore India Itie Knowledge Solutions Bangalore India

ISBN: (纸本)9798331540067

The study on brain signals to understand working memory in undergraduate students has yielded promising results. However, the challenge remains in accurately assessing different brain activities. This work seeks to measure concentration in cognitive performance, offering a detailed dataset that tracks how individuals respond to cognitive tasks. A virtual reality-based solution has proven effective in capturing the P300 signal, providing a more focused and accurate measurement platform. The study aims to evaluate the efficacy of P300 signals as indicators of attention and cognitive engagement in educational settings. A brain-computer interface system, Impulse Neiry, captured and analyzed P300 signals from undergraduate students while performing various cognitive tasks. User attention span was evaluated by measuring parameters such as session duration and interaction frequency with the software, offering insights into the system's usability. The results indicated that participants experienced about 35% deeper relaxation, and their attention spans increased by 18.97% compared to baseline engagement levels. © 2024 IEEE.

关键词： Virtual reality

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Simplex Social Spider Optimization with Decision Tree Consumer Behavior Patterns

Simplex Social Spider Optimization with Decision Tree Consum...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Mohammed I. Habelalmateen S. Sindhuja C. Rajan M. Hemamalini R. Navin Kumar Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science and Engineering (AI&ML) Sri Eshwar College of Engineering Coimbatore India Department of Computer Science and Engineering (AI&ML) KSR College of Technology Tiruchengode India Department of Tamil New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Computer Applications Nandha Engineering College (Autonomous) Erode India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

The population growth and the economic development are growing rapidly as a result of this makes more understanding the population behavior towards buying things. Understanding consumer behavior plays a major role for businesses in order to enhance the product quality and to provide a satisfactory product for consumers. The consumer behavior is considered in various factors based on age, their income, and their previous purchase history b considering all this factors a chart known as Customer Relationship Management (CRM) is made in order to maintain all the details of the customers of that particular company. In this study we proposed Simplex Social Spider Optimization (SSSO)-Decision Tree of the consumer behavior. This technique uses each customer information as a spider web and splits the data based on the feature values. The results indicated that specific features, such as purchase frequency, average transaction value, and product preferences, significantly influenced customer classification outcomes. A decision tree is used for the classification of the consumer behavior. This enhances the predicting and classifying the customer's attitude while purchasing the products. This research highlights the potential of predictive analytics in tailoring marketing strategies and improving CRM. The proposed method have achieved the accuracy of 92.42 % The findings suggest that businesses can leverage these insights to optimize targeted promotions and enhance customer experience, ultimately driving sales and fostering brand loyalty.

关键词： Consumer behavior Accuracy Customer relationship management Product design Quality assessment Decision trees Optimization Standards Random forests Business

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Application of computer Software in Chemical Data Processing using Spider Monkey Optimization Algorithm 2

Application of Computer Software in Chemical Data Processing...

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

作者： Rana Veer Samara Sihman Bharattej, R. Rupavath, Rana Veer Samara Sihman Brahmatej Hussein, Abbas Hameed Abdul Anjaneyulu, Madugula Upendra Roy, B.P. Saint Leo University Department of Computer Science St Leo United States Indiana Wesleyan University Marion United States Ahl Al Bayt University College of Pharmacy Karbala Iraq Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Department of Ai&ml Hyderabad India Channabasaveshwara Institute of Technology Gubbi Department of Electronics and Communication Engineering Tumkur India

ISBN: (纸本)9798350372892

The development of chemical-based products and functional materials is crucial for modern technologies, yet it often entails significant costs and time delays. Artificial intelligence and machine learning present novel approaches to leverage data for addressing these challenges;however, they encounter specific hurdles in the chemical domain. To address these issues, a software platform has been introduced to facilitate training and development of Recurrent Neural Networks (RNN). This platform incorporates the Spider Monkey Optimization (SMO) algorithm to optimize model's hyperparameters, including neurons number, rate of learning, and activation function. This software package goes beyond neural network training and development by encompassing design and operating-parameter optimization, essential aspects of chemical engineering processes. Within this framework, the weights that were optimized and bias obtained from neural network were stored and utilized in other SMO process for optimizing important process variables, like swiftness and temperature, according to user specifications. © 2024 IEEE.

关键词： Software packages

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Electrohysterogram Data Augmentation Using Generative Adversarial Network for Pregnancy Outcome Prediction

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IEEE Access 2025年 13卷 92468-92482页

作者： Cheema, Muhammad Omar Din, Zia Mohy Ud Imran, Azhar Khan, Fawad Salam Al-Rawi, Mahmood Basil A. El-Meligy, Mohammed Gul, Jahan Zeb Air University Department of Biomedical Engineering Islamabad44000 Pakistan Air University Faculty of Computing & AI Department of Creative Technologies Islamabad44000 Pakistan King Saud University College of Applied Medical Sciences Department of Optometry Riyadh Saudi Arabia Amman Arab University College of Computer Science and Informatics Amman Jordan Maynooth University Department of Electronic Engineering Ireland

There are many difficulties in managing and detecting preterm pregnancies, especially in the early stages. Analyzing electrohysterogram data, which show the electrical activity of uterine muscles, is a promising non-invasive method for classifying term and preterm pregnancies. However, the effectiveness of machine learning classifiers is hampered by the inherent class imbalance in datasets. In order to overcome this constraint, this study uses a Generative Adversarial Network to produce high-quality synthetic EHG data while maintaining the statistical properties of actual signals. By successfully reducing class imbalance, the enhanced dataset makes it possible for machine learning models to be trained with resilience. Real and synthetic features did not differ significantly, according to statistical validation using a t-test (p > 0.05). With this method, the Random Forest classifier obtained a ROC-AUC of 0.99 and a 10-fold cross-validation accuracy of 98%. The suggested GAN-based method’s simplicity and efficacy demonstrate its promise for resolving class imbalance and developing reliable classification systems for diagnostic applications. © 2013 IEEE.

关键词： Generative adversarial networks

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