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Dynamic Ensemble Machine Learning Classifier Based Credit Card Financial Risk Management and Prediction

Dynamic Ensemble Machine Learning Classifier Based Credit Ca...

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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： G. Kalpana Bhoomika S S Nagella Venkata Ramana N Shilpa Ramy Riad Hussein Department of CSE(AI&ML) Malla Reddy Engineering College for Women Autonomous Hyderabad India Department of CSE Nitte Meenakshi Institute of Technology Bengaluru India Department of Management Studies Madanapalle Institute of Technology & Science Madanapalle India Department of ECE School of Engineering SR University Warangal India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

The goal is to efficiently manage financial market which evaluate the credit card and reduce the risk of financial enterprises. To address systemic risk arising from insufficient credit scoring in existing financial market. Therefore, this research proposes a Dynamic Ensemble Machine Learning (DEml) classifier for credit card financial risk management and prediction. The ensemble algorithm includes Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM) and Artificial Neural Network (ANN). These ensemble classifier handles intricate data patterns effectively and capturing connection thereby reducing overfitting issues. It enhances the prediction stability and robustness which leads better risk assessment and reduced false negatives and positives. Moreover, it manages the imbalanced data in which the common financial risk prediction through aggregating prediction among reliable result. The f-measure, precision, accuracy and recall are taken to estimate DLEM classifier performance. The DELM classifier achieves 98.82% f-measure, 98.37% precision, 99.16% accuracy and 98.73% recall which is better than Deep Belief Network (DBN).

关键词： Support vector machines Accuracy Artificial neural networks Credit cards Prediction algorithms Robustness Classification algorithms Risk management Random forests Overfitting

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Exploring Time Series Techniques in Production Function Modeling: ARIMA and VECM Applications

Exploring Time Series Techniques in Production Function Mode...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Mohammed Ali Shaik Abdul Rahim V Subhalakshmi Dr R Ravi Kumar Raghunadh Pasunuri Dhanraj Verma School of Computer Science & Artificial Intelligence SR University Warangal Telangana India Department of ECE Malla Reddy Engineering College for Women Secunderabad India Department of CSE(AI&ML) Vidya Jyothi Institute of Technology Department of CSE CMR College of Engineering & Technology Hyderabad India Dept Of CSE AIML Malla Reddy Engg. College PIET- AIML Parul University Vadodara Gujarat

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

This paper uses both used ARIMA and VECM models to analyze the production function to determine the interactions between GDP, capital and labor inputs in this analysis. The paper explores short-term dynamics as well as equilibrium behavior of these variables using annual data for the period 1984–2023. The coefficients estimated by the ARIMA (2,0,0) model indicate that current GDP depends on its past levels, and both capital and labor, although they have positive signs, are statistically insignificant. Moreover, Johsen cointegration estimates unfold one co-integrating vector; VECM result also confirms the positive association between capital and GDP but an unexpected negative relation with labor. The above study’s findings reaffirm the status of capital accumulation as a foundation for developing states’ growth policy priorities; the results of labor analysis are inconclusive and may indicate issues such as difficulty in measuring labor or the possibility of structural changes. The policy implications are for the continuous long term orientations, reform of the labor market, improving human capital, and expanding economic diversification. The paper recognizes certain limitations in the type of data and model used and recommends that future research consider non linearities, technological progress to transfer technological improvement to the production function as well as examining sectoral differences for a better understanding of growth paths in fast growing economies.

关键词： Analytical models Economic indicators Sensitivity analysis Biological system modeling Computational modeling Time series analysis Stability criteria Production Vectors Thermal stability

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Diet Recommendation System for Human Health Using Machine Learning

Diet Recommendation System for Human Health Using Machine Le...

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： B. VeerasekharReddy Venkata Nagaraju Thatha G. Uday Kiran Sk.Khaja Shareef N. V. RajaSekharReddy Yallapragada Ravi Raju Department of Information Technology MLR Institute of Technology Hyderabad India Department of Computer Science and Engineering (AI & ML) B V Raju Institute of Technology Narsapur Medak India Department of Computer Science and Engineering Madanapalli Institute of Technology Madanapalli India

In the past and present generation, the change in lifestyle of young people, less number of physical activities and eating fast food led to an alarming increase of diseases especially with college students. The unhealthy diet of students is responsible for unbalanced weight. The body mass index is used to classify a person's mass or weight as underweight, normal weight, over weight and obese based on tissue mass (bone, fat, and muscle) and height. Underweight leads to malnutrition, vitamin deficiencies, decrease in immune system function, growth issues and development issues. Major risk of being overweight leads to type-2 diabetes. Machine learning algorithms can solve many health problems. The project suggests the creation of an electronic college nurse that can perform the same functions as a human college nurse. This e-nurse will track students' health by utilizing BMI measurements, identify health issues like Type-2 diabetes by utilizing machine learning algorithms, and create a customized e-learning program that focuses on healthy habits such as regular exercise and a nutritious diet. These e-learning modules will be incorporated at appropriate intervals during the students' academic journey.

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Contrastive Learning for Improved Abstractive Sentence Summarization

Contrastive Learning for Improved Abstractive Sentence Summa...

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Advanced Computing Technologies (ICoACT), International Conference on

作者： Ulligaddala Srinivasarao Karthikeyan R Mohamed Sithik M Faizal Mukthar Hussain S Ohammed Uveise S A Hariharan Shanmugasundaram Department of CSE GITAM (Deemed to be) University Hyderabad India Department of CSE(AI&ML) Vardhaman College of Engineering Hyderabad India Department of CSE School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of CSE Mohamed Sathak Engineering College Ramanathapuram India Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

Text summarizing aims to overview the material while maintaining essential details concisely. Lately, summary tasks have been included in the contrastive learning process in addition to visual representation. To optimize their similarities, contrastive learning summarizing algorithms currently concentrate on modelling and semantic similarity in all documents, targets, and data summaries. They do not, however, consider the impact that sentence semantics have on the text. In this study, we propose an RNN technique to develop the model for detecting the salient information. The sentence salience is based on the semantic similarity between the summary sentences of the original content, taking into account the distance of the loss of contrast in the form of soft weights functions. Consequently, our proposed model minimizes the semantic similarity given the textual summaries and possible noise and maximizes the semantic words between summaries and relevant information. Three popular datasets—multi news, CNN/Daily Mail, and Booksum datasets achieved the highest accuracy of 0.69% in the news datasets. The experiment outcomes demonstrate that the suggested strategy can considerably raise baseline performance and outperform other contrastive learning techniques.

关键词： computers Visualization Semantics Noise reduction Noise Contrastive learning Encoding Data models Data mining Postal services

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CNN based Optimized Autoencoder for Satellite Image Compression

CNN based Optimized Autoencoder for Satellite Image Compress...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： P. Sowmya Sri S. Karthi M. Kalaiyarasi S. Saravanan Computer Science and Engineering (Data Science) B V Raju Institute of Technology Narsapur Telangana India Department of Computer Science and Engineering V.S.B.Engineering College India Institute of AI & ML Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai India Department of EEE B V Raju Institute of Technology Telangana India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

This paper introduces an innovative Convolutional Neural Network (CNN) based Optimized Autoencoder for the compression of satellite images, with a focus on high resolution Sentinel-2 and Landsat imagery. In this method, the Satellite images are resized and normalized before an optimal autoencoder architecture is implemented. The encoder compresses the image, while the decoder reconstructs it with minimum quality loss. Convolutional and down sampling layers in the encoder and up sampling layers in the decoder preserve image features. MSE, PSNR, SSIM, and CR were used to assess compression performance. The simulation outcomes show that the CNN-based Autoencoder can retain image quality across images with a high compression ratio of 512, an average MSE of 0.0064, an average PSNR of 25.214 dB, and an SSIM of 0.62. This satellite image compression approach is efficient enough for storage and transmission in satellite data applications.

关键词： Image quality Technological innovation Image coding Satellites Autoencoders Memory Real-time systems Satellite images Decoding Convolutional neural networks

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

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

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

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

ISBN: (数字)9798350371314

ISBN: (纸本)9798350371321

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.

关键词： Deep learning Machine learning algorithms Neural networks Predictive models Prediction algorithms Diabetes Public healthcare

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Combining Machine Learning and Handcrafted Image Processing to Identify Common Weeds in Cotton Fields

Combining Machine Learning and Handcrafted Image Processing ...

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Disruptive Technologies (ICDT), International Conference on

作者： Priyanka Ram Krishna Singh Vikas Gupta Janardan Pawar Uma Aruna Devi Illa Meenakshi Sharma Chandigarh University Gharuan Mohali India Department of Computer Science IMS Engineering College Ghaziabad India Department of Computer Science (AI & ML) Raj Kumar Goel Institute of Technology Ghaziabad India Department of Computer Science Indira College of Commerce and Science Pune India Department of Information Technology Swarnandhra College of Engineering and Technology Narsapur Andhra Pradesh India Department of Electronics & Communication Engineering Inderprastha Engineering College Ghaziabad India

Weed management is a critical aspect of agriculture, with weed infestations posing significant challenges to crop yields. Conventional manual methods for weed identification have limitations, emphasizing the need for innovative solutions. This research focuses on the integration of machine learning and handcrafted image processing to identify common weeds in cotton fields, with a specific emphasis on Southern India. The targeted weed species include Palmer Amaranth (Amaranthus palmeri), Pigweed (Amaranthus species), Johnsongrass (Sorghum halepense), and Yellow Nutsedge (Cyperus esculentus), which are prevalent in cotton fields. Through the use of an RGB camera and a developed ImageJ plugin for shape feature extraction, 39 shape features were extracted for each weed species. Three machine learning models, k-Nearest Neighbor (kNN), Random Forest (RF), and Support Vector Machine (SVM), were employed, and the results were compelling. RF exhibited the highest accuracy at 96.1%, demonstrating remarkable precision, recall, and F1- Score values, particularly for Palmer Amaranth. The practical implications of this methodology extend to farmers, producers, and other stakeholders in cotton production. The open-source nature of the tools used makes this approach accessible, cost-effective, and user-friendly. It offers a valuable resource for precise weed identification, timely intervention, resource optimization, and ultimately, improved crop yields. This research underscores the potential for advanced machine learning models in weed management, offering a practical solution for enhancing agriculture's productivity and sustainability.

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Robotics and ai Integration for Automated Surgical Procedures

Robotics and AI Integration for Automated Surgical Procedure...

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Advances in Computing, Communication and Applied Informatics (ACCai), International Conference on

作者： Kasharaju Balakrishna Praveen Kumar Md. Shakil Hossain Ridoy Vasantha Gadipally Shaik Balkhis Banu Joshuva Arockia Dhanraj School of Computer Science and Artificial Intelligence SR University Warangal Telangana India NIET greater noida School of Mechanical and Vehicle Engineering Beijing Institute of Technology Beijing China Mathematics MRECW Dulapally Hyderabad Department of Physiotherapy Fatima college of health sciences Al Ain UAE Department of Computer Science and Engineering (AI&ML) School of Engineering Dayananda Sagar University Devarakaggalahalli Harohalli Kanakapura Road Ramanagara District Bengaluru Karnataka India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

A lot has changed in the field of automated surgical operations since recent advances in robots and artificial intelligence. This research looks into the possibilities, problems, and new findings that might come up when these two parts come together. The research shows that using robots can improve the accuracy and speed of surgery by letting artificial intelligence systems make the most of decisions and make changes across operations in real time. Surgical procedures that use robots are used to do this. Concerns about ethics and the legal frameworks needed to ensure the responsible use of medical tools are also being thought about.

关键词： Ethics Accuracy Law Surgery Real-time systems Artificial intelligence Informatics

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Interactive Hyperspectral Imaging Data Visualization: A Catalyst for Sustainable Horticultural Practices with Artificial Intelligence

Interactive Hyperspectral Imaging Data Visualization: A Cata...

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Disruptive Technologies (ICDT), International Conference on

作者： Minal Bafna Hina Singh Kaushal Kumar Amit Vinay Kumar Singh Jagendra Singh Department of Physics Agrawal P G College Jaipur India Department of CSE Ajay Kumar Garg Engineering College Ghaziabad India Department of Computer Science (AI & ML) Raj Kumar Goel Institute of Technology Ghaziabad India Department of Computer Science IMS Engineering College Ghaziabad India School of Computer Science & Engineering Lovely Professional University Jalandhar India School of Computer Science Engineering and Technology Bennett University Greater Noida India

This work investigates the revolutionary potential of ai-powered interactive visualisation for sustainable horticulture practises. The amount of spectrum data provided by hyperspectral photography on the Earth's surface enables the observation and analysis of various phenomena that are vital to environmentally sustainable land use, agriculture, and research. However, there are numerous obstacles in handling and analysing the massive amounts of complex data provided by hyperspectral sensors. To address these issues, our study culminated in the development of an aidriven interactive visualisation system that enables users to rapidly extract relevant information from hyperspectral data. The framework delivers great dependability in the interpretation of complicated hyperspectral data, with outstanding recall, accuracy, precision, and F1 score values. Furthermore, based on our statistics, user engagement scores show how well the framework engages users. The framework's utility and applicability are proved by the effects it has on decision-making, user happiness, and visual clarity. Our research shows that combining ai and hyperspectral imaging can improve horticulture practises and encourage resource- and environmentally-friendly ways, which has substantial practical implications. By using this strategy, growers and environmental scientists can avoid resource waste, make datadriven decisions, and ameliorate the negative environmental effects of horticulture practises.

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Adaptive System for Human Scenario Forecasting in Intelligent Homes

Adaptive System for Human Scenario Forecasting in Intelligen...

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： P. Kalyani K. V. Prashanth R. Manivannan Raksha Puthran Shruti Jalapur Dhananjaya Babu K AI&ML Department Sree Venkateswara College of Engineering Nellore Andhra Pradesh India Department of Electronics & Communication Engineering Koneru Lakshmaiah Education Foundation Hyderabad Telangana India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology(Deemed to be University) Chennai India Department of MCA NMAM Institute of Technology NITTE (Deemed to be University) Udupi India Department of Computer Science and Engineering Christ University Bangalore India Department of Electrical and Electronics Engineering Annamacharya University Rajampet Andhra Pradesh India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

Due to the rise in energy usage in recent years, there has been a worldwide push to reduce energy use, particularly in the housing sector. When the concept of a "smart house" first emerged, it brought together automation and control via communication network technologies, as well as warmth from many heat source mechanisms and the terminology "process controllers" stated earlier. The Internet of Things, or IoT, is a multibillion dollar industry that shows how overlapping technologies span industries. It does this by utilizing the internet as an objective standard for sharing information that IoT services manage, supervise, or *** home automation systems, big data, and machine learning, together with the Internet of Things, to increase energy efficiency is another exciting topic. This article introduces HEMS-IoT, a comprehensive energy management system designed for the modern home. It leverages big data and machine learning to provide security and convenience in a comprehensive, eco-efficient setting. Weka API and the J48 neural network technology were used to extract user behavior and energy consumption trends. This approach was used to categorize loads depending on *** Mahout with Rule-based language (Ruleml) in a smart house for user comfort and security This is because these technologies are able to predict what consumers would require for energy action and supply it during this consultation because to the aggregation of user preferences. XtraPrinting As they demonstrated how they would monitor the instance, the article offers an experimental way for monitoring intelligent buildings for comfort and safety with the least amount of energy use. Therefore, their approach, which explains the demonstration success rate, is responsible for the test’s high performance.

关键词： Industries Energy consumption Machine learning Big Data Energy efficiency Safety Behavioral sciences Internet of Things Security Monitoring

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