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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： Arumulla Srinayana J. Praveenchandar Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

This research study intends to develop an online quiz system that prioritizes user involvement, enjoyment, and performance. To achieve this, the proposed platform features a diverse array of quiz themes and formats, catering to a wide audience with varied interests and preferences. Unlike traditional platforms that rely on ranking systems, our application fosters a relaxed environment where users can engage with quizzes at their own pace, free from competitive pressure. Methodologically, we conducted user testing, performance monitoring, and comparative analysis with rival algorithms to assess the platform's effectiveness. The research findings indicate high levels of user satisfaction, engagement, and overall performance, along with areas for improvement identified through these evaluations. Notably, we plan to enhance the platform further based on user feedback and experimental learnings. This includes optimizing the quiz creation algorithm, implementing UI enhancements, and exploring gamification and social integration features. Our developed online quiz system thus presents a promising tool for education, collaboration, and entertainment, aiming to cultivate a welcoming and inclusive community where learning is both enjoyable and socially enriching.

关键词： Education Collaboration Entertainment industry User interfaces Green computing Monitoring Testing

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A Genetic Algorithm Approach to Adaptive learning: Enhancing Learner Satisfaction and Performance

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IEEE Access 2025年 13卷 102815-102829页

作者： Anju Kalwar Deepika Shekhawat Sandeep Joshi Amit Kumar Bairwa School of Computer Science and Application JECRC University Jaipur India Department of Computer Science and Engineering Amity University Mumbai Mumbai Maharashtra India Department of Computer Science and Engineering Manipal University Jaipur Jaipur India Department of Artificial Intelligence and Machine Learning Manipal University Jaipur Jaipur India

As educational technologies mature at a growing rate, effective models of instructional delivery systems that can deliver learning experiences based on the individual learner’s learning style have also been created. One of the core elements of such systems is the ability to recommend the right content that improves the learner’s experience. The adaptative learning environment and content recommendation with the help of genetic algorithms are investigated in this paper. GA, based on the principles of Natural Selection, provides stability-based search and solving capacity needed to tackle the inherent messiness and diversity of PBL. In our approach, each learner’s context information, as well as performance metrics, preferences and learning goals, are dynamic and fed into the GA. The algorithm genetically generates a random vote of potential content sequences as the initial population that improves through creation by selection, crossover, or mutation. The fitness function assesses an individual sequence in terms of its conformity to the learner’s profile and, thus, helps to increase an LMS usage rate, select appropriate content, and achieve targeted educational objectives. The proposed system is an application of GA that constantly updates feedback from the learner’s generated performance data and adjusts the recommended content in real time. This dynamic creates a positive learning environment capable of enhancing learner interaction and improving learning achievements. When the proposed GA-based solution is applied in a specific case with an instructional design within an online learning context, enhancement in learner satisfaction and performance are substantial, sufficiently proving the approach’s applicability. Thus, this study expands the existing literature on adaptive learning technologies by including the usage of GA in education. Future work will extend this by investigating how GAs can be mixed with other AI approaches, GA applicability to larger datasets,

关键词： Genetic algorithms Adaptive learning Real-time systems Heuristic algorithms Education Adaptation models Recommender systems Adaptive systems Systematic literature review computer science

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DECLARATIVE NETS THAT ARE EQUILIBRIUM MODELS 10

DECLARATIVE NETS THAT ARE EQUILIBRIUM MODELS

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10th International Conference on learning Representations, ICLR 2022

作者： Tsuchida, Russell Yong, Suk Yee Armin, Mohammad Ali Petersson, Lars Ong, Cheng Soon Data61 CSIRO Canberra Australia Space & Astronomy CSIRO Epping Australia Machine Learning & Artificial Intelligence Future Science Platform

Implicit layers are computational modules that output the solution to some problem depending on the input and the layer parameters. Deep equilibrium models (DEQs) output a solution to a fixed point equation. Deep declarative networks (DDNs) solve an optimisation problem in their forward pass, an arguably more intuitive, interpretable problem than finding a fixed point. We show that solving a kernelised regularised maximum likelihood estimate as an inner problem in a DDN yields a large class of DEQ architectures. Our proof uses the exponential family in canonical form, and provides a closed-form expression for the DEQ parameters in terms of the kernel. The activation functions have interpretations in terms of the derivative of the log partition function. Building on existing literature, we interpret DEQs as fine-tuned, unrolled classical algorithms, giving an intuitive justification for why DEQ models are sensible. We use our theoretical result to devise an initialisation scheme for DEQs that allows them to solve kGLMs in their forward pass at initialisation. We empirically show that this initialisation scheme improves training stability and performance over random initialisation. © 2022 ICLR 2022 - 10th International Conference on learning Representationss. All rights reserved.

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Comparative Analysis of Time Series Forecasting Models for Weather Prediction: ARIMA vs. STL 8

Comparative Analysis of Time Series Forecasting Models for W...

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8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Kamble, Aahash Belsare, Sanika Chaudhari, Purva Gourshettiwar, Palash Gundewar, Swapnil Datta Meghe Institute of Higher Education and Research Department of Artificial Intellegence and Data Science Wardha India Department of Artificial Intellegence and Data Science Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Department of Computer Science and Medical Engineering Wardha India Datta Meghe Institute of Higher Education and Research Department of Artificial Intellegence and Machine Learning Wardha India

ISBN: (纸本)9798350391770

This research compares two well-known models for predicting weather: ARIMA (Auto Regressive Integrated Moving Average) and STL (Seasonal-Trend Decomposition using Loess). Accurate weather forecasts are crucial for businesses and daily life, making strong forecasting methods necessary. ARIMA finds linear patterns and time dependencies in data, while STL splits time series into seasonal, trend, and random parts. Using real-world weather data, we train and test these models. We compare their performances using MSE (Mean Squared Error) and R2 (R-squared) metrics. The research demonstrates that the STL model performs better than the ARIMA model at accurately predicting weather patterns and fitting to the data. This superior performance makes the STL model a more dependable choice for weather forecasting. This study enhances techniques for weather prediction and offers valuable information to those making decisions in weather-sensitive businesses. © 2024 IEEE.

关键词： Prediction models

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An Optimized Elliptical Curve Cryptography based Crypto-Watermarking Model in Smart Healthcare Application 2

An Optimized Elliptical Curve Cryptography based Crypto-Wate...

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

作者： Murthy, G. N. Keshava Manjula, G. Harshitha, P. Achyutha Prasad, N. Siddaganga Institute Of Technology Department Of Electronics And Instrumentation Engineering Karnataka Tumakuru India Bgs College Of Engineering And Technology Department Of Cse Bangalore India 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

ISBN: (纸本)9798350381665

Telemedicine uses current communication and information technology to provide clinical treatment remotely. Given the ubiquitous usage of DWT and DCT are used for watermarking, ECC-FFA is used for encryption. The only cryptographic algorithms used in the current approaches for watermarking are AES, DES, and RSA, and the DCT and DWT are typically used separately. But because we integrated the ECC-FFA cryptographic method with the DWT and DCT watermarking technologies, the recommended solution is more effective than earlier ones.. A strong and lossless patient medical information exchange system employing crypto-watermarking is suggested in order to secure medical data. For the watermarking approximate, the discrete wavelet alter (DWT) and discrete cosine alter (DCT) are combined, and the elliptical curve cryptography (ECC) algorithm is utilized. At the same time, metaheuristic algorithms may be used to solve the optimum key creation issue in cryptography, which is viewed as an optimisation challenge. As a stem, the master nucleus of this masterpiece is on the development of cryptographic encryption for picture security based on metaheuristic optimization algorithms. According to this perspective, this study creates the ECC-FFA model, an effective produce fruit fly optimisation (FFO) technique with ECC for picture protection. The suggested model uses the ECC technique to carry out the encryption and decryption procedure. Various medical photos were used in the experiment, and the peak signal to noise ratio was used to assess how well the suggested method worked. © 2024 IEEE.

关键词： Optimization

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Advancing Vascular Dementia Diagnosis Through Salp Swarm Optimized Convolutional Neural Network 8

Advancing Vascular Dementia Diagnosis Through Salp Swarm Opt...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Mathkunti, Nivedita Manohara Ananthanagu, U. Abdulpur, Vidya N. Mangaiyarkarasi, D. Anitha, S. Alliance School of Advanced Computing Alliance Univerisity Department of Computer Science & Engineering Karnataka Bengaluru India Dr.D.Y Patil Pratishthan's College of Engineering Department of Electrical Engineering Maharastra Kolhapur India Institute of Science and Technology Avadi India Department of Artificial Intelligence & Machine Learning Karnataka Bengaluru562106 India

ISBN: (纸本)9798331505462

Vascular Dementia (VD) is a form of cognitive impairment seen in individuals, restricting from problems with blood vessels supplying the brain. These issues lead to strokes or reduced blood flow and gradually lead to the development of vascular dementia. Since it tends to progress slowly, diagnosis often comes after significant brain damage making prevention is crucial. Early identification of risk factors are vital to mitigate the risk of irreversible cognitive decline associated with vascular dementia. Accordingly, this research proposes an optimized neural network model for predicting vascular dementia at its earlier to improve the quality of life. The initial stage in this research involves preprocessing, where the input data is cleaned and normalized. These steps ensure that the vascular dementia dataset is cleaned of inconsistencies and prepared with normalized features for accurate analysis and modeling. Following preprocessing, relevant data features are selected using the chi-square approach, making the classification task easier. Finally, classification is performed utilizing a Convolutional Neural Network (CNN) and for fine-tuning of CNN parameters Salp Swarm Optimization (SSO) Algorithm is implemented. The execution of proposed work is processed using Python software and the outcome reveals superior performance in terms of accuracy and precision. Thereby the proposed method provides better performance with an accuracy of 94.5%, contrasted to existing methods in predicting VD. © 2024 IEEE.

关键词： Convolutional neural networks

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LSTM based Soft-Sensor for Estimating Nitrate Concentration in Aquaponics pond

LSTM based Soft-Sensor for Estimating Nitrate Concentration ...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： A. Dharshan Purushottam Kumar S. Ravimaran U Srinivasulu Reddy Department of Artificial Intelligence and Data Science Saranathan College of Engineering Trichy India CoE in Artificial Intelligence Machine Learning & Data Analytics Lab National Institute of Technology Trichy India Department of Computer Applications Machine Learning & Data Analytics Lab National Institute of Technology Trichy India

In the field of aquaponics, where fish and plants coexist in a symbiotic environment, closely monitoring nitrate levels in the water is crucial due to their profound impact on aquatic and plant well-being. Traditional nitrate measurement methods are often time-consuming and costly. Various approaches, including first principles, IoT-based sensors, and machine learning-based soft sensors, have been attempted to address this challenge. However, these efforts face challenges such as expensive sensors, infrequent data collection, multistage data processing using limited sensor types, and the need for regular maintenance like cleaning and calibration. Additionally, varied environmental conditions affect sensor suitability for different water environments, and even some machine learning-based soft sensors have proven inaccurate. In response, soft sensors, especially deep learning-based ones, have gained prominence in industrial applications for their adaptability and accuracy. These sensors provide real-time insights into complex processes without requiring expensive hardware. In this study, an innovative solution was introduced using Long Short-Term Memory (LSTM) technology, a neural network architecture in deep learning known for capturing complex temporal patterns. LSTM is well-suited for modeling and predicting nitrate concentration changes in aquaponics, trained with extensive data collected from various aquaponic ponds. Through rigorous evaluation, a remarkable MSE value of 0.00074 and an impressive R-squared score of 0.98 were achieved, holding potential for scaling up to commercial applications, benefiting aquaponics operations, supporting researchers, and enhancing sustainability and productivity in aquaponic systems.

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Speech Emotion Recognition with Web Speech API 9

Speech Emotion Recognition with Web Speech API

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9th IEEE International Conference on Smart Structures and Systems, ICSSS 2023

作者： Anand, M. Vijay Rajasekaran, A. Rajendra Thilahar, C. Bharathiraja, N. Department of Computer Science and Engineering Chennai India Simats Saveetha School of Engineering Department of Artificial Intelligence and Machine Learning Chennai India VelTech MultiTech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Biomedical Engineering Chennai India

ISBN: (纸本)9798350384208

Speech is one of the natural ways that we as humans communicate with one another. We rely on it so much that we have come to understand its significance in electronic communication, including text messages and emails, where we frequently utilize emojis to convey the emotions we are feeling at the time. Since emotions are a major component of communication, it is critical to identify and analyze them in the digital world of remote communication that we live in today. We use the SER technique because emotion detection is a difficult task because emotions are subjective. The goal of Speech Emotion Recognition (SER) is to identify the affective components of speech, independent of its semantic content. The goal of research on automatic emotion recognition systems is to develop effective, real-time techniques for identifying emotions in a range of application domains, such as caller-agent conversation analysis and interactive voice assistants. In this project, Web-Speech API - pre-existing software for analyzingthe nuances and annotations of speech - is used to analyze and identify the emotional aspects of *** we will be recognizing the words we speak using the scores which are predefined in the AFINN database. The outputwill be displayed in the webpage in the form of emojis as we speak in real-time. The approximate accuracy obtained in this project is around 80%-85%.Thus the sentiment in any content published as a speech or a live recording can be analysed through this project. © 2023 IEEE.

关键词： Speech recognition

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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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Smart Finance: An overview of artificial intelligence Integration in Fintech 2

Smart Finance: An overview of Artificial Intelligence Integr...

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

作者： Akre, Parth Dhananjay Pacharaney, Utkarsha Siraskar, Warsha Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Yeshwantrao Chavan College of Engineering Maharashtra Nagpur441110 India

ISBN: (纸本)9798331528713

The role of FinTech in advancing contemporary economies, societies, and technologies is growing. Smart FinTech is the next wave of FinTech that leverages artificial intelligence and data science methodologies to drive major advancements in fields including blockchain, cryptocurrencies, InsurTech, PayTech, BankingTech, TradeTech, LendTech, InsurTech, WealthTech, and RiskTech. An overview of these domains, as well as the artificial intelligence and data science techniques applied, is given in this paper. These techniques include augmentation, optimization, privacy-preserving processing, deep learning, federated learning, and intelligent interactions, complex system methods, mathematical approaches, knowledgeable communication, recognition and responses, and data analytics. This study offers a thorough analysis of smart financial companies, the obstacles they face, the smart FinTech environment, the artificial intelligence and data science methods that make smart FinTech possible, and potential future research areas for the artificial intelligence and data science community. To improve comprehension and analysis, the work makes use of visual aids such as figures and tables. The relevance of data quality, including data augmentation and manipulation, as well as enhancing fairness and decision-support, is emphasized in the study for financial firms. It also covers ethical and explicable FinTech, with a focus on privacy preservation, accountability, openness, and objectivity in FinTech and business designs. The potential for more research and innovation at the heart of analytics, data science, and FinTech is highlighted in the paper's conclusion. © 2024 IEEE.

关键词： Federated learning

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