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An Effective machine learning Algorithm for Forecasting the Market Value of a House

An Effective Machine Learning Algorithm for Forecasting the ...

引用

Applied Artificial Intelligence and Computing (ICAAIC), international conference on

作者： S.G. Ancy J. Praveenchandar Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Karunya Nagar Coimbatore Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Karunya Nagar Coimbatore

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

machine learning plays a virtual role in everyday speech commands, product recommendation, and even medical fields. But instead of providing better customer service, it provides safer autonomous vehicle systems. House price prediction using machine learning is a hot topic in data science and AI. With the increasing availability of data and the advancement of machine learning algorithms, accurately predicting house prices has never been easier. This study will not only assist housing developers and researchers to determine the most significant factors affecting housing prices but will also help determine the best machine learning model to use in this research. This model will allow people to allocate resources to a request without relying on a broker. According to the results of this study, the random forest regression is the most accurate. Real estate market analysis is one of the most important tools for predicting changes in house prices and rental income. Several techniques have been developed in recent years to solve this problem, including statistical models and data mining methodologies and machine learning algorithms. However, a few issues, including data quality and availability, needed to be fixed. Missing numbers, outliers, and inconsistent formats are common in housing databases. These issues could impair the performance of the prediction models. To solve these issues, thorough data preprocessing is required. Collaboration with data providers and governmental agencies is also required to ensure data coverage and accuracy.

关键词： Training Accuracy machine learning algorithms Correlation Atmospheric modeling Predictive models Prediction algorithms

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Educational data mining: Using knowledge tracing as a tool for student success 3

Educational data mining: Using knowledge tracing as a tool f...

引用

3rd international Multidisciplinary Information Technology and Engineering conference, IMITEC 2021

作者： Mandlazi, Joel Jadhav, Ashwini Ajoodha, Ritesh University of the Witwatersrand School of Computer Science and Applied Mathematics Johannesburg South Africa University of the Witwatersrand Science Teaching and Learning Unit Faculty of Science Johannesburg South Africa

ISBN: (纸本)9781665417495

Student success is important to institutions of learning. learning institutions offer academic support to their students to ensure success. Knowledge Tracing (KT), the task of modelling student knowledge based on their learning history is a significant topic in the field of Educational data mining and has numerous applications in the development of interactive and adaptive learning technology. KT can be used to understand each student's unique learning behaviour, individual needs, and skill-levels. machine learning classification models were implemented to trace the knowledge state of students. The Support Vector machine outperformed all other models in this article, with Random Forest performing the worst, with accuracy of 76.8% and 70.76%, respectively. One important goal of KT is to personalise study materials in order to assist student's understand learning concepts more efficiently. © 2021 IEEE.

关键词： data mining

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Comparative Analysis of Deep learning Algorithms for Handwritten Digit recognition

Comparative Analysis of Deep Learning Algorithms for Handwri...

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Delhi Section conference (DELCON), IEEE

作者： Padmaja Savaram Sasidhar Bandu Lakshitha B. Dept.of Computer Science Prince Sattam bin Abdulaziz University Al-kharj KSA Dept. of English Prince Sattam bin Abdulaziz University Al-kharj KSA Dept. of Computer Science and Engineering VNRVJIET JNTU Hyderabad India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

Handwritten digit recognition presents a considerable challenge in pattern recognition due to the inherent variability in digit size, stroke width, orientation, and margin alignment. The difficulty is further exacerbated when different individuals write the same digit with varied stylistic nuances. Discriminating between visually similar digits, such as 1 and 7, 5 and 6, 3 and 8, 2 and 5, and 2 and 7, adds an additional layer of complexity. The offline handwritten digit recognition approach discussed in this paper leverages a range of machine learning methodologies. The objective is to establish robust and accurate digit recognition techniques, utilizing several algorithms including Artificial Neural Networks, Convolutional Neural Networks, K-Nearest Neighbors, and Recurrent Neural Networks. These algorithms are evaluated using the Modified National Institute of Standards and Technology (MNIST) dataset and the US Postal Service (USPS) dataset to identify the most effective model. The selection of the optimal machine learning approach is determined by factors such as dataset size, data complexity, and required accuracy. The Convolutional Neural Networks model demonstrated superior performance, achieving an accuracy of 98.58% with 10,000 MNIST images and 94.7% accuracy with 2007 USPS images when compared with other models.

关键词： Measurement Handwriting recognition Accuracy machine learning algorithms Recurrent neural networks Nearest neighbor methods NIST Convolutional neural networks Standards Postal services

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Heart disease Classification Utilising machine learning Techniques

Heart disease Classification Utilising Machine Learning Tech...

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Advancement in Technology (ICONAT), international conference for

作者： Shiv Ashish Dhondiyal Himanshu Verma Rohit Kumar Vikash Sawan Ishita Uniyal Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Dept. of Computer Science and Engineering Haridwar University Roorkee Department of Computer Engineering & Applications GLA University Mathura Department of Electrical Engineering Graphic Era Hill University

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Heart diseases comprise a extensive Various heart conditions and are commonly referred to as cardio vascular diseases. These encompass issues with the rhythm of the heart, blood vessel diseases, and congenital cardiac defects. It has been the world’s biggest cause of death for the last few decades. Finding a reliable and accurate way to automate the process of early disease diagnosis and efficient disease management is urgently needed. data science facilitates the medical sciences in analysing vast quantities of data. Researchers utilize various data mining and machine learning methods to evaluate extensive datasets in order to accurately forecast heart illness. This study examines the supervised learning models of Random Forest, K-NN, and SVM to provide a comparative analysis of their efficiency. It is discovered that, when compared to other algorithms, SVM offers the highest accuracy at 0.994.

关键词： Heart Support vector machines Accuracy machine learning algorithms Simulation Supervised learning Prediction algorithms Classification algorithms Diseases Testing

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A No-Code Approach to Classifying Neurological Disorders Using Orange data mining

A No-Code Approach to Classifying Neurological Disorders Usi...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： Yabez Davidraj P Elavarasan S Vivek T Ramesh Munirathinam Department of Biomedical Engineering Karpagam Academy of Higher Education Coimbatore India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Neurodegenerative diseases are progressive disorders causing neuron damage, leading to cognitive and motor impairments. This study presents a machine learning approach for classifying multiple neurological disorders using MRI data with the Orange data analytics software. A total of 165 MRI images representing conditions such as Alzheimer’s disease, Huntington’s disease, motor neuron disease, and Pick’s disease were sourced from the AANLIB database. Image features were extracted using the VGG-16 deep learning model, and classification was conducted via Support Vector machine (SVM) with polynomial, linear, radial basis function (RBF), and sigmoid kernels. The polynomial and sigmoid kernels achieved the highest classification accuracy of 93.3%, outperforming the linear and RBF kernels. These findings highlight that MRI-based classification using deep learning features and SVM in Orange can effectively differentiate neurological disorders, showing promise for early and accurate diagnosis.

关键词： Support vector machines Neurological diseases Accuracy Magnetic resonance imaging Neurons Motors Polynomials Software Kernel Alzheimer's disease

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Development of an Automatic Speech recognition System for Irula Language: A machine learning Approach

Development of an Automatic Speech Recognition System for Ir...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： M. Krishnaveni P. Subashini R.Janani R.Komalavalli P.Prabhusundhar G. T. Prabavathi Centre for Machine Learning and Intelligence Avinashilingam University for Home Science and Higher Education for Women Coimbatore Tamil Nadu India Gobi Arts & Science College Tamil Nadu India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

The Irula language, which is predominantly spoken by the Irula tribes in southern India, offers specific challenges because of its linguistic features and limited resources. To address these challenges, this research study presents the development of an Automatic Speech recognition (ASR) system for Irula, which accelerates the conversion of spoken language into English text. In the proposed system, the data is collected and pre-processed, features are extracted, and acoustic and language models are developed for efficient translation. The spectral subtraction noise removal method is used to reduce the background noise from collected speech data during the data pre-processing. The Mel Frequency Cepstral Coefficients (MFCC) are then used to extract the features which represent the characteristics of speech signals. Acoustic modelling is implemented using Hidden Markov Models (HMMs) and their fine-tuned version. A pre-trained language model such as GPT is used for accurate translation. Finally, the web application is developed for translating Irula language into English text to increase the accessibility for Irula speakers.

关键词： Translation Noise Hidden Markov models machine learning Linguistics Feature extraction Acoustics Mel frequency cepstral coefficient Intelligent systems Automatic speech recognition

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Tomato Leaf Disease Detection and Identification using machine learning

Tomato Leaf Disease Detection and Identification using Machi...

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international conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Neeraj Kumari Vivek Kumar Neeraj Kumar Pandey Amit Kumar Mishra Deepak Kholiya Computer Science and Engineering Teerthanker Mahaveer University Moradabad India School of Computing DIT University Dehradun India Department of Computer Science & Engineering Graphic Era Hill University Dehradun India School of Agriculture Graphic Era Hill University Dehradun India

Auto-learning machines-based detection and recognition of tomato leaf disease images are a rapidly-growing field. CNN architecture based ResNet50 model used to demonstrate machine learning-based image detection and recognition. Indicator dataused in this study use image pre-processing techniques. Image analysis uses morphological techniques and the Hough transform. The fundamental objective of this work is to detectdiseases in images using the Resnet50 Architecture. The data set used for training the ResNet50 model identifies the leaf disease. Performance measures the efficiency evaluation of the modelfrom the perspectives of exactness, detail, andsensitivity. The findings reveals an overall accuracy of 97.6%, specificity of 95.63, and sensitivity of 98.21, which is an improvement over the existing approaches.

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The Evolutionary Game Research on the Development of New Financial Services Infrastructure under the "Double Cycle" Development pattern 3

The Evolutionary Game Research on the Development of New Fin...

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3rd international conference on machine learning, Big data and Business Intelligence (MLBDBI)

作者： Jing, Luru Feng, Minfu Sichuan Univ Sch Math Chengdu 637000 Sichuan Peoples R China

ISBN: (纸本)9781665417907

"New infrastructure" is a new concept relative to "old infrastructure". As an emerging industry, it has been based on the infrastructure construction of cutting-edge science and technology. This paper takes enterprises and financial institutions as the research objects, and explores the evolutionary game between financial institutions and enterprise groups from the perspective of group game. In this paper, the replication dynamic equation model in evolutionary game theory is constructed, and the parameters are analyzed. The results show that the relationship between the total cost of "actively providing financial services" and the total cost of "passively providing financial services" and the income of "actively participating in financial activities" plays a decisive role in the existence of evolutionary game strategy between the two groups. Finally, according to the model analysis and the conclusion, based on the "double cycle" development pattern, this paper puts forward some countermeasures and suggestions for the construction of new infrastructure market, in order to provide new ideas for the high-quality development of China's economy.

关键词： financial institutions enterprises new infrastructure double cycle

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Benchmarking Neural Networks-Based Approaches for Predicting Visual Perception of User Interfaces 3rd

Benchmarking Neural Networks-Based Approaches for Predicting...

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3rd international conference on Artificial Intelligence in HCI (AI-HCI) Held as Part of the 24th international conference on Human-Computer Interaction (HCII)

作者： Bakaev, Maxim Heil, Sebastian Chirkov, Leonid Gaedke, Martin Novosibirsk State Tech Univ Novosibirsk Russia Tech Univ Chemnitz Chemnitz Germany

ISBN: (纸本)9783031056437;9783031056420

Deep learning techniques have become the mainstream and unquestioned standard in many fields, e.g. convolutional neural networks (CNN) for image analysis and recognition tasks. As testing and validation of graphical user interfaces (GUIs) is increasingly relying on computer vision, CNN models that predict such subjective and informal dimensions of user experience as aesthetic or complexity perception start to achieve decent accuracy. They however require huge amounts of human-labeled training data, which are costly or unavailable in the field of Human-Computer Interaction (HCI). More traditional approaches rely on manually engineered features that are extracted from UI images with domain-specific algorithms and are used in "traditional" machine learning models, such as feedforward artificial neural networks (ANN) that generally need fewer data. In our paper, we compare the prediction quality of CNN (a modified GoogLeNet architecture) and ANN models to predict visual perception per Aesthetics, Complexity, and Orderliness scales for about 2700 web UIs assessed by 137 users. Our results suggest that the ANN architecture produces smaller Mean Squared Error (MSE) for the training dataset size (N) available in our study, but that CNN should become superior with N > 2912. We also propose the regression model that can help HCI researchers to foretell MSE in their ML experiments.

关键词： User Interfaces Aesthetics Visual Complexity Deep learning

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Advancements, Methods, and Obstacles in Emotion recognition with Multimodal Approaches

Advancements, Methods, and Obstacles in Emotion Recognition ...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Arunkumar M Athiban R Selva Jothi M S Pavithra Dept of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Emotion recognition has gained an avid curiosity in the research area due to its broad range of applications in human-computer interaction particularly in real time emotion recognition, sentiment analysis, and affective computing. Deep learning techniques are advancing exponentially, automating feature engineering and transcending traditional machine learning techniques. Appeal of the above models can be highly beneficial but also raise certain technical and ethical concerns that are not often discussed enough in the existing studies. While highlighting the recent advancements, the methods involved and obstacles in emotion recognition, this review also proposes future directions in developing a standard unified framework for multimodal approaches. Categorizing and analysing the recent studies depends upon the model types employed, data modalities used and the type of fusion techniques used, this review was able to gain insights into the effect of recent multimodal approaches addressing technical and ethical concerns. Utilization of fusion techniques in multimodal models is still not reliable but they better convey the connection between different modalities and have a huge potential for development in the future. This review provides insights into the state of art multimodal methods using modalities like facial expressions, body gestures, vocal cues, text, and physiological signals for researchers to understand the trends and future directions in this field.

关键词： Deep learning Emotion recognition Ethics Sentiment analysis Affective computing Reviews Real-time systems Physiology Reliability Standards

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