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WONGA: the Future of Personal Finance Management – A machine learning-Driven Approach for Predictive Analysis and Efficient Expense Tracking

WONGA: The Future of Personal Finance Management – A Machin...

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Emerging Technology (INCET), international conference for

作者： Uyanahewa M.I.R Jayawardana G.V.H.D Bandara M.B.D.N Hapugala H.A.V.V Buddhima Attanayaka Dasuni Nawinna Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

the financial literacy of Sri Lankans is relatively low, leading to difficulties in managing personal finances. this research presents a smart solution to simplify the complexities associated with money management and assist individuals in managing their finances more efficiently to achieve better financial health without requiring a comprehensive knowledge of money management from the user. the proposed system automates personal finance management with minimal user effort, reducing manual data entry by tracking cash flow by utilizing SMS messages and expense bills to extract bank transaction data and cash expenditures. Each extracted expense will automatically be categorized into the correct expense category. the system also generates a custom budget plan for each user based on spending patterns to help them stay on the budget throughout the month and avoid irrational overspending. Furthermore, the system provides a mechanism to predict future expenses associated with upcoming events based on calendar events, allowing users to devise the most efficient budget plan and avoid facing financially unprepared events in the upcoming month. All these smart solutions are bundled up in the "Wonga" mobile application to help users make better financial decisions to achieve personal financial success.

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Tracing knowledge of student based on academic knowledge with machine learning and deep learning 4

Tracing knowledge of student based on academic knowledge wit...

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2021 4th international conference on computer Information Science and application Technology, CISAT 2021

作者： Hu, Jiayu Li, Mingyi Mao, Huimin Liu, Cheng Northwest University School of Information Science and Technology Xi'an China

Because the number of students not attending school is expanding at an alarming pace, and because of the COVID-19 epidemic, 102 countries have implemented nationwide closures to conduct local shut-downs and temporarily close schools. this slowed down learning possibilities and intellectual growth even more. Every country's equity disparities may widen. As a result, we must restructure our educational system so that students can gain correct knowledge and teachers can track how much each student has learned. As a result, machine learning and Deep learning are the most effective solutions for this type of problem. As a result, we're releasing a method for tackling this challenge using Tabnet, Transformers, LGBM, and a variety of other machine learning approaches for student knowledge tracing. Keywords: Deep learning, PCA, Variance, Self-Attentive Transformers, XGBoost, Catboost, LGBM, Ensembling, Bagging, Boosting and distributions. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 Licence.

关键词： Students

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Construction and application of "AI + RPA + X" Intervention Field for College Students’ learning Adaptation 6th

Construction and Application of "AI + RPA + X" Intervention ...

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6th international conference on Technology in Education, ICTE 2023

作者： Qiu, Yi Peng, Zhiyang Lu, Yi School of Educational Information Technology South China Normal University No. 55 West of Zhongshan Avenue Tianhe District Guangzhou China

ISBN: (纸本)9789819982547

the research focuses on the construction and application of learning adaptation intervention field for college students in the age of intelligence, uses research methods of survey research, literature research, theoretical deduction, and design-based research, under the guidance of field theory, combined with the development direction of digital transformation, proposed the concept of college students’ learning adaptation intervention field construction of factor connectivity, link routing and data integration;applied technologies such as AI and RPA to build an "AI + RPA + X" intervention field that highlights the characteristics of human-machine synergy, and clarified its hierarchical structure and operating mechanism;carried out a one-semester practical application for 49 undergraduate normal students majoring in educational technology in the 2021 grade of H University, and analyzed the application effect of "AI + RPA + X" intervention field. the results show that the "AI + RPA + X" intervention field of college students’ learning adaptation can provide theoretical and practical reference for constructing and optimizing the existing field and interfering with college students’ learning adaptation. © 2024, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data integration

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Synergies of federated learning and blockchain for patient-centric sustainable next generation healthcare through distributed medical internet of things

Synergies of federated learning and blockchain for patient-c...

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4th international conference on Distributed Sensing and Intelligent Systems (ICDSIS 2023)

作者： S. Manna A. Sarkar M. Z. Khan A. Noor Department of Computer Science and Electronics Ramakrishna Mission Vidyamandira Howrah 711202 West Bengal India Department of Computer Science and Information Taibah University Madinah 42353 Saudi Arabia Department of Computer Science College of Computer Science and Engineering Taibah University Madinah 42353 Saudi Arabia

ISBN: (数字)9781837240258

this study investigates how blockchain and Federated learning (FL) may work together to create a patientcentered, long-lasting healthcare ecosystem. the paper presents an innovative method for improving data privacy, security, and interoperability within healthcare systems by utilizing the distributed Medical Internet of things (MIoT). the next generation of healthcare services will be revolutionized by the synergistic combination of FL and blockchain, enabling a decentralized framework for enhanced patient care and data-driven decisionmaking. Centralized machine learning (ML)-based methods lack security of patient's sensitive medical information as all information of patient are trained in central server. To tackle with this problem, this paper offers amalgamation of FL and blockchain technology which enables training of ML models collectively by making use of distributed MIoT data, enhances the accuracy and also guarantees privacy of patient's personal medical record. To preserve privacy over patient's personal medical record, blockchain technology is used. the proposed approach offers a lot of benefits: (1) To predict disease more perfectly and effectively, this paper utilizes AdaBoost, Decision Tree (DT), Extra Tree Classifier (ETC), Linear Discriminant Analysis (LDA) and FL algorithm. (2) Each algorithm is tested against different data splitting, different cross validation and cross validation with feature selection techniques, hyperparameter tuning for model optimization and finding the best model. (3) the proposed methodology ensures collective learning among several healthcare organizations without sending patient's private medical records and thus safeguarding privacy. (4) Accuracy, Markedness (MK), False Omission Rate (FOR), Negative Likelihood Ratio (NLR), Prevalence threshold (PT) are calculated to choose the best algorithm and also to show how well the FL algorithm works. (5) the paper is unique as the best algorithm in terms of different model evaluati

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Conservation and Management of Ancient Stupa Using Digital Twins: A Case Study of Uav-Based 3D Photogrammetric Digitization of Rankoth Wehera Stupa 13th

Conservation and Management of Ancient Stupa Using Digital T...

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ICSBE 2022 - proceedings of the 13th international conference on Sustainable Built Environment

作者： Egodawela, S. Buddika, H.A.D.S. Harischandra, W.A.N.I. Mahmoodian, M. Mathota, M.R.S.N.M. School of Engineering RMIT University 124 La Trobe St. MelbourneVIC3000 Australia Department of Civil Engineering University of Peradeniya Peradeniya20400 Sri Lanka Department of Electrical and Electronic Engineering University of Peradeniya Peradeniya20400 Sri Lanka Central Cultural Fund 4th Floor Sethsiripaya Battaramulla10120 Sri Lanka

ISBN: (纸本)9789819934706

Stupa structures in Sri Lanka are regarded as world heritage sites with rich archaeological and artistic value, some nearing a lifetime of almost 2 millennia. Conservation and management efforts are currently hindered due to the lack of practice of modern Building Information Modelling (BIM) techniques. Recently, Digital Twins (DT) as a tool for Historic Building Information Modelling (HBIM) has gained attraction worldwide. this paper summarizes the initial work that has been carried out to apply modern HBIM techniques for Sri Lankan heritage sites, to manage and preserve these structures for the future. To this end, this article details a case study of Rankoth Wehera in Sri Lanka and the 3D photogrammetric model creation for the application of DT principles. A drone survey of the structure was done with the assistance of the Central Cultural Fund of Sri Lanka to obtain high-resolution images of the location. Following a feature detection and homography calculation step using Scale Invariant Feature Transform (SIFT), the high-fidelity features were fed to an open-source 3D point cloud generation software, Meshroom. A 3D textured mesh was generated to observe the existing structural defects in the stupa dome and the superstructure. Obtained images can be used in conjunction with processing techniques using Image processing and Deep learning fundamentals to automatically detect the defects and pinpoint the locations of the defects on the DT model. Moreover, the generated high-resolution mesh can be fed for Finite Element Modelling (FEM) software to carry out nonlinear analysis when required. With this premiering work, the authors intend to inspire and urge researchers and authorities to integrate intelligent HBIM techniques for the conservation and management of Sri Lankan heritage sites. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Deep learning for Wireless Network Distribution (5G/LTE)

Deep Learning for Wireless Network Distribution (5G/LTE)

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Communications, Information, Electronic and Energy Systems (CIEES), international conference on

作者： Mohammed Khodayer Hassan Al-Dulaimi Omer Mohammed Khodayer Al-Dulaimi Aymen Mohammed Khodayer Al-Dulaimi Maiduc Osiceanu Alexandra Nidal Jihad Dept. Computer Engineering Al-Rafidain University of college Baghdad Iraq Dept. of Telecommunication Engineering University Politehnica of Bucharest Bucharest Romania Dept. of Technical Communication Engineering Al- Farahidi University College Baghdad Iraq Dept. of Director for management of scientific research University Politehnica of Bucharest Bucharest Romania Dept. of Iraqi prime ministers’ council Baghdad Iraq

As a result of using machine learning and deep learning in the network, we were able to more efficiently manage the allocation of network resources and the availability of the network overall. Key performance indicators are used to analyze incoming traffic and make predictions to allocate network resources to unknown devices. High load balancing and efficient use of available resources on current network portions are made possible with intelligent resource allocation. the underlying model is smart enough to determine which piece of the network is most suited for the challenge at present. 5G mobile networks, mobile communications, and the latency of modern and future wireless networks are affected by strict regulations about how long it takes for data transmission to reach its destination, dependability, how much data can be transmitted without a problem, capacity, how much data can be transmitted securely, and speed of user connection. Mobile providers are always looking for new ways to make it possible for multiple, autonomous users to share a single network. Slicing is a method that uses network fragmentation to share the same physical infrastructure among multiple networks that have competing needs. Future communication networks will need to make decisions based on data in order to keep up with the exponential rise in traffic.

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A Review on Radiomics and machine learning Algorithms in Prediction and Progression of Parkinson’s Disease

A Review on Radiomics and Machine Learning Algorithms in Pre...

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Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： Redhya M K. Sathesh Kumar Department of Computer Applications Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu India Department of Computer Science and Information Technology Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu India

Parkinson's disease (PD) is an irreversible neurodegenerative condition that impairs dopamine production in a specific area of the brain. Diagnosis of PD in the early stage is a tedious task as symptoms are not very prominently shown in the first stage. machine learning techniques had been used for the analysis and progression of PD. Radiomics analysis has helped in extracting large amount of quantifiable features from images which can be used as biomarkers for predictive models. this paper gives an idea on different machine learning algorithms and the application of radiomics which has proven its efficacy in the classification and progression of PD. Finally, the limitations and accuracy of the different techniques are also discussed.

关键词： Support vector machines machine learning algorithms Artificial neural networks Production Feature extraction Task analysis Random forests

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AI Based Optimization of Fault Tolerance in Multipath Routing for Medical application

AI Based Optimization of Fault Tolerance in Multipath Routin...

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Smart Electronics and Communication (ICOSEC), international conference on

作者： Lakkakula Hari Prasad G Shobana Anjul Bhardwaj Lakshmi Sireesha Ch. Parikshit Kumar Bais Balakrishna Kothapalli Department of ECE Anurag Engineering College Department of IT Loyola ICAM College of Engineering and Technology UIC - University Institute of Computing Chandigarh University Department of Mathematics CBIT Hyderabad Hyderabad India Computer Application faculty of Engineering LNCT University Bhopal Department of EEE School of Engineering SR University Warangal

Artificial Intelligence (AI) has had a substantial influence on the process of pharmaceutical research and development over the course of the last decade. Several applications that are connected to drug discovery, such as virtual screening and drug design, have made use of a number of AI's varied approaches. One of these applications is drug discovery. In the first section of this study, we are going to present an overview of drug discovery and discuss applications that are relevant to this field. these applications may be divided into two major tasks, which are synthesizing new compounds and making predictions about the characteristics of existing molecules. After that, this study will discuss about the standard data resources, different representations of molecules, and benchmark platforms. As a crucial component of the investigation, the methodologies of artificial intelligence are dissected into a variety of model architectures and learning paradigms. the reviewed papers have been arranged in a chronological manner to demonstrate the development of the technical features of AI in drug discovery throughout the course of time. According to our anticipations, the results of this study should provide a comprehensive analysis of the use of AI in drug development. An additional instructional resource that we make accessible to users is a repository that can be found on Github and is comprised of a variety of publications (and codes, if relevant). this archival storage space is regularly upgraded.

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Healthcare Management System to Improve Data Analytics Using Support Vector machine Algorithm

Healthcare Management System to Improve Data Analytics Using...

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Innovative Practices in Technology and Management (ICIPTM), international conference on

作者： A. Edwin Raj V. Bini Marin C. Balakrishnan K. Elangovan R. Meenakshi M. Muthulekshmi Department of Mathematics Hindustan Institute of Technology and Science Chennai Tamil Nadu India Faculty of Management Studies SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering S.A.Engineering College Chennai Tamil Nadu India Department of Mechanical Engineering Dhanalakshmi Srinivasan Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

Knowledge discovery in databases (KDD) is concerned with progress in data analysis tools. Data mining is a crucial parts of the KDD process. Data mining is a method for discovering and extracting patterns from huge medical datasets. there are a number of benefits to using the Support Vector machine (SVM) algorithm in healthcare systems. SVM is a strong and flexible machine learning algorithm that may excel at certain healthcare analytics applications. SVM is preferred in healthcare systems because of their adaptability, ability to manage imbalanced datasets, feature interpretability, handling of high-dimensional data, handling of classification, predictive modeling tasks, handling of non-linear relationships, resistance to overfitting and provision of a distinct margin of separation. It is essential to carefully consider the unique needs and features of the healthcare application when choosing an algorithm. Healthcare systems that use SVM algorithms do so with the intention of capitalizing on SVM's capabilities to tackle domain-specific problems and tasks. In light of this development, the relevant literature on this topic with regard to approach, SVM algorithm, and outcomes are surveyed. the publications evaluated in this study have been categorized according to their relevance to the disciplines, models, tasks, and approaches discussed. Data mining is the practice of sifting through massive amounts of unstructured data for meaningful patterns and insights. Results show 90% accuracy and 98% precision and F measures of the given medical dataset.

关键词： Support vector machines machine learning algorithms Databases Medical services Prediction algorithms Classification algorithms Data mining

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Establishment of an Evaluation Index System for the Overseas Strategic Climate of Cross-border E-Commerce Based on LightGBM Model 22

Establishment of an Evaluation Index System for the Overseas...

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proceedings of the 2022 4th international conference on E-Business and E-Commerce Engineering

作者： Yi Lei Xiaodong Qiu School of Economics and Management Beijing Jiaotong University China

ISBN: (纸本)9798400700071

China's cross-border e-commerce has witnessed prime time for development When developing cross-border e-commerce, enterprises should first assess the market environment of the target country and reasonably select the target country. this paper establishes an index system for evaluating the strategic climate of China's cross-border e-commerce abroad, uses the LightGBM method in machine learning to train and predict the established index system. Finally, the rationality of the index system is verified, and the weights of each index are also obtained. It provides a reference for cross-border e-commerce enterprises on which indicators and factors they should focus on when selecting target countries.

关键词： Cross-border e-commerce Indicator system machine learning Strategic climate

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