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machine learning Based Model for Prediction of Loan Approval 3

Machine Learning Based Model for Prediction of Loan Approval

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3rd international conference on Intelligent Engineering and Management, ICIEM 2022

作者： Lohani, Bhanu Prakash Trivedi, Mayank Singh, Ridhik Jeet Bibhu, Vimal Ranjan, Shiv Kushwaha, Pradeep Kumar Amity University Amity School of Engineering and Technology Greater Noida India Amity University Amity Business School Greater Noida India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

Banks are vital to financial management and controlling the economy of a country. Banks and financial institution distribute loans and these loans act as the core business part of almost every banks. The profits are earned from the loans distributed by the banks. The prime goal is to invest their assets in safe hands. The success of bank depends on the decision-making capability to evaluate risk of lending loan to the customer. Checking manually individual consumer's credibility for the loan approval is difficult, time consuming and risky. Thus, the banks aim to minimize the credit risks of defaulting. In this study we have applied logistic regression as a tool to predict whether an applicant is eligible for the loan or not. The data is collected from the Kaggle for studying and prediction. © 2022 IEEE.

关键词： data mining

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Adaptive machine learning Model for Complex data Processing

Adaptive Machine Learning Model for Complex Data Processing

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data Analytics, Computing and Artificial Intelligence (ICDACAI), international conference on

作者： Rui Ma Xiuzhuo Wei Yuzhu Yang Ruoyan Wan Huinan Zhao Changchun Humanities and Sciences College Changchun China

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

With the rapid development of information technology today, data is emerging at an unprecedented speed and scale, with diverse forms of existence and extremely wide sources. Especially in the medical field, the complexity of data is particularly prominent, including multi-source, incremental, high-dimensional, and significant noise interference. To address these challenges, this article delves into adaptive machine learning (ML) models for complex data processing and focuses on their application in the field of medical diagnosis. ML technology, with its excellent data processing and pattern recognition capabilities, has opened up new paths for the precision and intelligence of medical diagnosis. This paper proposes a medical diagnosis model based on multimodal data fusion and ML to address the complexity of medical data. This model integrates multiple types of data from different sources and utilizes advanced fusion techniques to achieve comprehensive information extraction and deep fusion. The experimental results show that the medical diagnosis model proposed in this paper exhibits excellent performance in various disease diagnosis tasks, significantly improving the accuracy and efficiency of diagnosis.

关键词： Adaptation models Analytical models Accuracy Computational modeling data integration machine learning data models Complexity theory Medical diagnosis Medical diagnostic imaging

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Efficient Temporal data mining Technique Using Dynamic Time Warped LSTM for E-Commerce Recommendation Systems 3rd

Efficient Temporal Data Mining Technique Using Dynamic Time ...

引用

3rd international conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Rajassekharan, Dinesh Subramanian, Jayasundar Lazar, Arokia Jesu Prabhu Pichuka Veera Venkata Satya, Srinivas Ray, Samrat Ammasai, Viswanathan Sengan, Sudhakar Faculty of AI Computing and Multimedia Lincoln University College Petaling Jaya47301 Malaysia Department of Computer Science and Engineering Idhaya Engineering College For Women Tamil Nadu Chinnasalem606201 India Department of Computer Science and Engineering CMR Institute of Technology Hyderabad501401 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Dean Head of International Relations IIMS Pune411033 India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627451 India

ISBN: (纸本)9789819783540

The rapid surge of the electronic commerce (Ecomm) industry has ushered in intensified competition, with platforms vying for customer attention and loyalty. Many Ecomm sites have been employing various tactics to stand out, with Recommendation Systems (RS at the forefront of their strategies. However, traditional RS faces challenges, mainly when interactions occur at irregular intervals. This article introduces the DTW + LSTM framework, a methodology combining the merits of Long Short-Term Memory Networks and Dynamic Time Warping to improve this work. This method requires consideration of DTW's time series data alignment capabilities to account for interaction interval variability. The LSTM is then designed to accurately record the key elements of user interactions in sequential order. Combining the outcomes of these two methodologies in the DTW + LSTM model presents a more flexible and extensive approach to generating online shopping recommendations. The design guarantees that forecasts continue to be reliable and accurate despite the inherent temporal deviations that can be observed in user data. This paper investigates the context of this approach, its benefits, and prospective developments, demonstrating its possibilities in the dynamic Ecomm market. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Accuracy CNN E-commerce F1-score GRU LSTM machine learning

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Organ classification Using Quantum Convolution Network 3

Organ classification Using Quantum Convolution Network

引用

3rd IEEE international conference on Quantum Computing and Engineering (QCE)

作者： Gohel, Prashant Chakraborty, Amit Rao, Kameshwar J., V HCL Technol COE AI Bengaluru India HCL Technol COE AI Hyderabad Telangana India

ISBN: (数字)9781665491136

ISBN: (纸本)9781665491136

Automated body part recognition is an essential part of medical image analysis. It is one of the core aspects of any medical image identification or segmentation. It reduces search time with increased accuracy in detecting an organ of interest. Here, we present a quantum machine learning based organ classification model for X-RAY data sets of hand, ankle, knee, and chest. We have used deep quanvolutional neural network. Quanvolutional neural network is a hybrid neural network where input data are encoded into quantum encoding, and then it is processed with a classical neural network. It is also called HQCNN (Hybrid Quantum Convolutional Neural Network) [3]. Our results show that HQCNN based implementation gives 92.5% validation accuracy with fast convergence, while classical CNN gives 93% validation accuracy.

关键词： Quantum machine learning Quanvolutional quantum circuits image classification

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Facial Biometrics-Driven Feature Encoding and Deep learning Integration for Secure Password Retrieval

Facial Biometrics-Driven Feature Encoding and Deep Learning ...

引用

Disruptive Technologies (ICDT), international conference on

作者： Akash Immanuel Solomon A Ashwin M Swaminathan K Dept. of Computer Science St. Joseph's College of Engineering Chennai India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

The proposed system includes face recognition-based systems designed for secure identity verification and decentralized data storage. The working is supposed to be done in three phases: data capture, training of the machine learning model, and decentralized data storage. Collection in the first phase will include face images of individuals and government-issued identification details, such as Aadhar and PAN. A machine learning model will be trained in the second phase for improving the accuracy in the face recognition process. The third phase ensures secure storage of the recognized faces and associated personal data through a decentralized approach similar to blockchain technology. Security, privacy, and resilience against tampering are further developed in this design. The proposed system will reliably meet the application requirements that involve strong authentication processes and protection of data, therefore making the sectors using these systems include government services and secured personal data management.

关键词： Training Privacy Face recognition Government Memory Passwords Reliability Protection Secure storage Resilience

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Self-Supervised Contrastive learning In Spiking Neural Networks

Self-Supervised Contrastive Learning In Spiking Neural Netwo...

引用

Iranian machine Vision and Image Processing (MVIP)

作者： Yeganeh Bahariasl Saeed Reza Kheradpisheh Departement of Computer and Data Sciences Shahid Beheshti University Tehran Iran

Spiking neural networks (SNNs), inspired by the biological neural processing of the brain, are vastly growing due to their higher potential to handle spatiotemporal patterns with lower energy consumption, especially, if implemented on neuromorphic devices. In this study, we propose self-supervised contrastive learning (SSL) for SNNs to learn informative latent representations from a large set of unlabeled data. The proposed SSL pre-trained SNN is then fine-tuned on a small set of labeled samples of a downstream supervised task. To evaluate the proposed method, we trained convolutional SNNs using SSL on MNIST and CIFAR10 datasets with 80% of images as unlabeled samples, then fine-tuned the networks on the remaining 20% images. The proposed SSL-based SNNs could reach 94.23% and 62.24% recognition accuracies on testing sets of MNIST and CIFAR10, respectively.

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Financial Time Series data Analysis Based on data mining 25

Financial Time Series Data Analysis Based on Data Mining

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Proceedings of the 2025 3rd international conference on Communication Networks and machine learning

作者： Yang Long School of Business China West Normal University Nanchong Sichuan China

ISBN: (纸本)9798400713231

Based on the in-depth study of data mining, great likelihood estimation, sequential parameter learning and other models, the study designs a sequential Bayesian learning method to estimate the asymmetric generalized autoregressive conditional heteroskedasticity (GARCH) model for the analysis of financial time-series data, aiming to improve its analytical assessment and forecasting performance. The study fully considers the leverage effect, accurately describes the negative correlation between return and volatility, and successfully solves the complex numerical problems in the estimation of stock simulation models. Through comprehensive simulation analysis, the results show that the model can excellently simulate the stock volatility and price trends with remarkable effectiveness, which provides a powerful support for the research and decision-making in the financial market.

关键词： data mining

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Ensemble learning Approaches to Strategically Shaping Learner Achievement in Thailand Higher Education 3rd

Ensemble Learning Approaches to Strategically Shaping Learne...

引用

3rd international Semantic Intelligence conference, ISIC 2023

作者： Bussaman, Sittichai Nasa-Ngium, Patchara Nuankaew, Wongpanya S. Sararat, Thapanapong Nuankaew, Pratya Faculty of Science and Technology Rajabhat Maha Sarakham University Maha Sarakham44000 Thailand School of Information and Communication Technology University of Phayao Phayao56000 Thailand

ISBN: (纸本)9789819773558

Thailand faces a severe problem of students dropping out of the higher education system. Therefore, this research has three critical objectives: (1) to study the context of students’ academic achievement in science and technology at the higher education level, (2) to assemble a model to predict the risk of students dropping out of higher education, and (3) to evaluate a model for predicting the risk of a student dropping out from higher education. The population and research sample were 2361 students’ academic achievements from five educational programs of the Faculty of Science and Technology at Rajabhat Maha Sarakham University during the 2010–2022 academic year. The research tool utilized data mining and supervised machine learning techniques: Decision Tree, Naïve Bayes, Neural Networks, Gradient Boosting, Random Forest, and Majority Voting. Model performance was evaluated using the cross-validation approaches and confusion matrix techniques, with four indicators: Accuracy, Precision, Recall, and F1-Score. The results showed that learners’ context in science and technology had various learning achievements. The educational program that needs to be monitored is the Bachelor of Science Program in Computer Science. This research successfully developed a predictive model for student dropout risk with an accuracy of 88.14% and an S.D. equal to 1.04. Therefore, this research dramatically benefits the public and stakeholders of Rajabhat Maha Sarakham University, who should be encouraged and encouraged to continue this research. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Contrastive learning

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Hybrid Classification Model Development for Enhancement the data Driven Based Human Falling Detection Performance 3rd

Hybrid Classification Model Development for Enhancement the ...

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3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Ahmed, Saadaldeen Rashid Fadel, Hala Adnan. Ismael, Omar Ayad Sulaiman, Sazan Kamal Mahdi, Nidhal Raoof Ahmed, NoorUlhuda S. Abubaker, Brwa Abdulrahman Hussain, Abadal-Salam T. Artificial Intelligence Engineering Department College of Engineering Alayan University Nasiriyah Iraq Department of Laser Optoelectronic Engineering/Al-Ma’moon University College/Al-Washash Baghdad Iraq Department of Computer Science College of Sciences University of Diyala Diyala Iraq Department of Computer Engineering College of Engineering Knowledge University Erbil44001 Iraq Medical Laboratories Techniques Al Hikma University College Baghdad Iraq College of Medicine University of Al-Ameed PO Box 198 Karbala Iraq Computer Science Department Bayan University Kurdistan Erbil Iraq Department of Medical Instrumentation Techniques Engineering Technical Engineering College Al-Kitab University Altun Kupri Kirkuk Iraq

ISBN: (纸本)9789819624676

It is thus important to identify the cause of a fall in order to be able to protect the lives of people, especially the elderly and hospitalized patients who are most likely to fall. Three fall detection models are compared in this paper: These are the auto-encoder (AEC), the feed forward neural network (FFNN), and the particle swarm optimization-feed forward neural network (PSO-FFNN). The idea is to assess how well these models identify falls, perform, and are suitable for deployment in real-life scenarios. This study was based on data from 17 candidates, and the four classes of falls that were considered here include: Based on this dataset, the models were trained and evaluated with the emphasis made on the correct classification of falls and their differentiation from other movements. From the results presented, all the three models show encouraging signs in fall detection. The PSO-FFNN model, with 98% of accurate optimal rate, however, gave the highest accuracy level. By this result, it is clear that the PSO-FFNN model is highly reliable and accurate in detecting falls. The objective of this research was to develop more precise and credible forecasts of various types of motion turbulence employing the autoencoder’s unsupervised learning and FFFNN’s partitioned classification approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Unsupervised learning

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Pumpkin Leaf Disease Detection: Convenience of CNN Over Traditional machine learning in Terms of Image Classification 3rd

Pumpkin Leaf Disease Detection: Convenience of CNN Over Trad...

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3rd international conference on Smart Systems - Innovations in Computing (SSIC)

作者： Mithu, Mosaddek Ali Momo, Shampa Islam Hasan, Md Mehedi Rahman, Kazi Motiour Sattar, Abdus Daffodil Int Univ 102 Mirpur Rd Dhanmondi Dhaka 1207 Bangladesh

ISBN: (纸本)9789811628771;9789811628764

Bangladesh having a growing agricultural economy quick detection of plant leaf diseases is a primary requirement. Disease discovery with the existing diagnostic procedures demands a longer time. Therefore, growers frequently miss the best time for stopping and treating diseases. Further, early identification and classification of pumpkin leaf diseases extremely needed. This paper proposes to discover the pumpkin leaf diseases by utilizing a modern image processing procedure convolutional neural network (CNN). CNN applied for image classification and recognition because of its high accuracy. Besides, a comparison of traditional machine learning algorithms like support vector machines (SVM), K-nearest neighbor (KNN), decision tree, and Naive Bayes with the performance of CNN is demonstrated in our work. Tensorflow library was adopted to implement the CNN algorithm and Scikit-learn used in terms of utilizing the above-mentioned traditional machine learning algorithms. Finally, we detected the pumpkin leaf diseases by the algorithm that exhibits an assuring accuracy to our suggested approach.

关键词： Classification Convolutional neural network (CNN) data analysis Detection Image processing Leaf disease machine learning

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