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An IoT and data mining-Based Tool for Early Identification of Speech Disorders in Children Using Advanced Algorithms 3rd

An IoT and Data Mining-Based Tool for Early Identification o...

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3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

作者： Usha, M. Department of Information Technology KG College of Arts and Science College Tamil Nadu Coimbatore India

ISBN: (纸本)9783031862892

Early detection of speech disorders in children is critical for effective intervention and treatment. This paper presents an innovative tool leveraging the Internet of Things (IoT) and advanced data mining techniques to identify speech disorders in children at an early stage. This paper explores the integration of IoT and data mining technologies for the early detection of speech disorders in children by applying advanced algorithms. The aim is to develop an efficient tool for early diagnosis and intervention, enhancing speech therapy outcomes through real-time monitoring and analysis. The system integrates IoT-enabled devices for real-time data collection, capturing various speech parameters such as pitch, tone, and articulation. These data are processed using advanced algorithms, including machine learning and deep learning techniques, to analyze patterns and anomalies indicative of speech disorders. The proposed paper offers a user-friendly interface for parents, educators, and healthcare professionals, providing insights into a child′s speech development and highlighting potential areas of concern. The system′s effectiveness is evaluated through extensive testing with a diverse dataset, demonstrating its accuracy and reliability in identifying various speech disorders, such as stuttering, dysarthria, and apraxia. By facilitating early diagnosis, this tool aims to improve the quality of life for children with speech disorders through timely and targeted interventions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Performance Enhancement of DBSCAN Density-Based Clustering Algorithm in data mining

Performance Enhancement of DBSCAN Density-Based Clustering A...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： Neha Tyagi Rashmi Mishra Sitanshu Kumar Patel Somraj Karki Department of Computer Science and Engineering Amity University Noida India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

In order to facilitate better data analysis, clustering is a prominent data mining approach which separates data into separate categories based on their similarities and distinctions. It’s a method of unsupervised learning which may generate a model with a training dataset. Throughout this study, the Euclidian distance and EPS (Epsilon) value has been determined using the DBSCAN technique based on the similarities as well as disparity for the input data. Moreover, Euclidian distance is dynamically determined using the back propagation method; a simulation study is carried out that demonstrates both speed and precision improvements. DBSCAN, or spatial clustering based on density of application with noise, is a common clustering technique for geographic datasets that can locate clusters of any shape and automatically detect noise points. To deal with the problem, an improved DBSCAN technique based on the selection of features (FS-DBSCAN) has been suggested. However, the algorithm proved unstable while handling huge dimensional data. Many simulation via real-world datasets testify the algorithm’s efficiency. Comparison of the proposed algorithm to the DBSCAN algorithm demonstrates its benefits. Structures within the collections may be identified by dividing of collections of products into classes and clusters, with a focus on increasing the resemblances of objects within each cluster and lowering the similarities of things belonging to different clusters.

关键词： Backpropagation Training machine learning algorithms Shape Noise Clustering algorithms Spatial databases data mining Object recognition Unsupervised learning

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Real-Time Anomaly Detection in Telecommunications: Advanced data mining Techniques for Fraud Identification

Real-Time Anomaly Detection in Telecommunications: Advanced ...

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Disruptive Technologies (ICDT), international conference on

作者： Khalid Hafiz Mir Ravin Kumar Sangeeta Rai Abass Hassan Tiyas Sarkar Bhimasen Moharana School of Computer Science and Engineering Lovely Professional University Phagwara Punjab India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Recently, the telecom fraud issues are so intense that it has become difficult for industry professionals, not just because of monetary loss but because it tarnishes the reputation of the firm as well. In any case, real time analysis and monitoring systems are critical in curbing such transactions and activities. The main purpose of this paper is to analyze how mining processes can help in detecting telecommunication fraud from the interactions of the users, which generate significant amounts of data. In this paper, different methods of machine learning are applied: clustering, decision trees and anomaly detection to identify unusual call patterns, unauthorized use, and impersonation. To detect suspicious actions, a combination of time and frequency data from various mobile operators is used for model training and testing. The goal of this study is to improve the accuracy of fraud detection systems by applying Naive Bayes, Support Vector machines (SVM), and deep learning networks. Furthermore, this paper indicates the possibilities of incorporating these models into the current framework of network security architecture to measure the level of scalability of such solutions in an advanced fraud environment. Along with many other findings, these results reveal that data mining can handle monitoring and prevention of frauds in real-time. This observation shows the importance of updating the fraud detection mechanisms within the telecommunication industry.

关键词： Support vector machines Industries Training Prevention and mitigation Network security Real-time systems Communications technology Fraud Anomaly detection Monitoring

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ClipXpert: Automated Clip mining from Video data for High-Demand Content

ClipXpert: Automated Clip Mining from Video Data for High-De...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： Rajdeep Chatterjee Sudip Chakrabarty Pappu Bishwas School of Computer Engineering KIIT Deemed to be University Bhubaneswar India School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

ClipXpert is a new system that automates the extraction of relevant clips from YouTube videos using user-defined keywords or frequently used word or comment analysis. The system uses advanced transcription models to search a pre-existing database for lines associated with the provided keywords. If the database doesn’t contain the keywords, it dynamically identifies and stores relevant content for future use. ClipXpert also automates identifying frequently used nouns, adjectives, and adverbs to generate focused content highlights without user intervention. It also performs sentiment analysis on YouTube comments to understand audience engagement and reception. ClipXpert enhances video content extraction efficiency and accuracy, catering to the growing demand for targeted, high-value clips in the digital media landscape.

关键词： Sentiment analysis Video on demand Accuracy machine learning Media Real-time systems Web sites data mining Intelligent systems Videos

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A Study on machine learning Algorithms: Forecasting Crop Prices 3rd

A Study on Machine Learning Algorithms: Forecasting Crop Pri...

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3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

作者： Hirpara, Jignesh Doshi, Milan Patel, Amit Choudhury, Koushik Marwadi University Rajkot-Morbi Road Gujarat Rajkot360003 India Department of C.S. & I.T Atmiya University Rajkot Gujarat India

ISBN: (纸本)9783031862984

In this era, with the technology used in every field, the agriculture market also generates a huge amount of revenue every day. Computer technology plays a vital role in managing and finding meaningful information about it. To manage large datasets, a field that integrates data mining, machine learning, mathematics, computer applications, and artificial intelligence is used. Crop price forecasting is a demanding and interesting method for the agricultural sector because it is dependent on future crop production worldwide. The maximum crop price for the current session or the next year is something that the entire agriculture community is interested in knowing. Currently, crop price analysis dominates the study instead of agriculture crop amount forecast. In comparison to other nations, crop yield in India is quite uneven when measured by the agriculture fraternity. It is insufficient as a base rate for any chosen crop price if an appropriate MSP is not provided. There may be a decrease in Indian poverty when these farmers receive or determine fair crop prices. A vast amount of commodity data is produced in agriculture today. A significant amount of commodity data is produced by the agriculture sector, but sadly, most of this data is not utilized to uncover hidden information. machine learning algorithms are used to make a machine think like a human, comprehend the data sets that are currently available, and compare them to similar events that have happened in the past. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Multiple Manifold Regularization Based on Non-Negative Matrix Factorization for Multi-View Clustering

Multiple Manifold Regularization Based on Non-Negative Matri...

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Disruptive Technologies (ICDT), international conference on

作者： Mutyala Sirisha Ghufran Ahmad Khan Jalaluddin Khan Hemlata Pant Kaneez Zainab Zulfikar Ali Ansari Shamshul Haq Ansari Department of Computer and Applications Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur Uttar Pradesh India Department of Computer Science and Engineering B.N. College of Engineering and Technology Lucknow Uttar Pradesh India Symbiosis Institute of Technology Pune Campus Symbiosis International (Deemed University) (SIU) Lavale Pune India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

In recent years, non-negative matrix factorization (NMF) cluster analysis of multi-view data has shown outstanding results in data mining and machine learning. Multi-view data typically encompasses complementary elements from multiple perspectives. Finding a consensus solution that works for all the different views is a difficult task. This study proposes a novel NMF-based clustering method that utilizes various manifold regularizations for multi-view data to address the previously mentioned problem. In this method, the NMF will decompose the input data into the two non-negative matrices. Next, we employ the manifold scenario to preserve the geometric structure within the data. Lastly, we design the novel objective function by combining all the earlier mentioned terms and then optimize it using the iterative strategy method to achieve the optimal solution. The empirical analysis of real-world datasets indicates that the proposed method outperforms numerous existing techniques in clustering efficiency.

关键词： Manifolds Encyclopedias machine learning Linear programming Internet Performance analysis Online services Iterative methods Matrix decomposition Optimization

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Exploring Advanced Ensemble learning Strategies in machine learning and data mining for Predictive Modeling of Marathon Running Time 3rd

Exploring Advanced Ensemble Learning Strategies in Machine L...

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3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

作者： Panwala, Brijal Buch, Sanjay College of Computer Applications SDJ International College Vesu Surat India College of Computer Applications Bhagwan Mahavir University Surat India

ISBN: (纸本)9783031862922

This study explores state-of-the-art advanced ensemble learning methodologies for predictive modeling in marathon running times. The research emphases on enhancing the precision and reliability of marathon time predictions by utilizing a varied array of machine learning methods, given the multifaceted nature of influencing factors, including historical performance data. The process initiates with the deployment of individual machine learning models, followed by the application of normalization techniques to standardize the dataset, ensuring uniform feature scaling. To further refine model performance, ensemble methods such as bagging, boosting, and stacking are employed, leveraging the collective advantages of multiple models to generate more robust predictions. Comprehensive experimental analysis and comparisons reveal that ensemble learning significantly improves predictive accuracy and uncovers the intricate relationships between variables influencing marathon outcomes. This research contributes meaningfully to the evolving domain of sports analytics, providing valuable insights and tools for athletes, coaches, and sports scientists aiming to optimize training regimens and race strategies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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machine learning Based Fault Distance Estimation of Underground Transmission Using Impedance Relay

Machine Learning Based Fault Distance Estimation of Undergro...

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

作者： Sri Chaithanya Mathi Pooja Naidu Tammisettti Shaik Rohid Akthar Manitha P.V. Department of Electrical and Electronics Engineering Amrita School of Engineering Bengaluru Amrita Vishwa Vidyapeetham India Department of Electronics and Communication Engineering Amrita School of Engineering Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Fast development of electric transmission infrastructure necessitates efficient methods of transmission and distribution for reliable cost effectiveness. Between the two main types of transmission, Overhead Transmission Lines involve extensive maintenance cost, use of extensive protection schemes, earthing, and lightning protection. In contrast, Underground Transmission Lines with their insulated and securely laid design are highly resistant to external factors such as lightning, wind, and open conductor faults, showing higher reliability and efficiency. However, faults such as symmetrical and unsymmetrical faults can occur due to conductors being bound together. The traditional fault distance estimation techniques are often inefficient for high-power transmission when using impedance-based fault distance relays. The constant variation in fault characteristics leads to impedance fluctuations, and therefore, fault distance calculations become inaccurate. The current research introduces a machine learning-based Fault Distance Estimation system for Underground Transmission Lines using Impedance Relays to address the above-mentioned challenges. Through data fed into the training machine learning model based on various fault scenarios, data mining is extracted for predicting precise impedance in faulty states and accurately determining fault locations by utilizing trained models with high-performance K-Nearest Neighbour (KNN), Decision Trees (DT), Support Vector Regression (SVR), Random Forest (RF), Long Short-Term Memory (LSTM), and Artificial Neural Networks (ANNs). Through these algorithms and approaches, implementations that emphasize optimal preprocessing, effective feature selection and tuning of specific hyperparameters determine their respective modeling efficiencies. The results show that the models generate very accurate distance estimations, allowing for more accurate fault locations and quick rectification. Not only does it bypass the deficiency of the method

关键词： Power transmission lines Costs Estimation Predictive models Fault location Conductors data models Impedance data mining Long short term memory

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Design and Application of English Pronunciation Correction Algorithm Based on SVM+LDA

Design and Application of English Pronunciation Correction A...

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Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： Huizhong Yu Ting Zhou Jiangsu College of Tourism Yangzhou Jiangsu China

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

For the common pronunciation errors of English learners in the pronunciation process, an English pronunciation error correction algorithm based on SVM (Support Vector machine) and LDA (Latent Dirichlet Allocation) is introduced. The algorithm aims to accurately identify and correct the problems in learners' pronunciation, thereby improving their pronunciation accuracy and fluency. By combining the feature extraction of speech signals with machine learning methods, different types of pronunciation errors can be effectively distinguished and corrected. First, the pronunciation data of English learners are collected and preprocessed to extract speech features, including MFCCs (Mel-Frequency Cepstral Coefficients), formants, pitch, etc. Secondly, LDA is used to reduce the dimension of the extracted features to reduce the complexity of the feature space and enhance the ability to distinguish pronunciation errors. Then, SVM is used for classification training to train a pronunciation error model. Finally, the trained model is used to predict the learner's pronunciation, and corresponding pronunciation correction suggestions are made according to the classification results. This method can effectively identify different types of pronunciation errors and provide personalized correction feedback according to the nature of the error. When the pronunciation error correction algorithm based on SVM+LDA is used for testing, the learners' error rate is 31.9% on average before error correction, and drops to 26.3% after error correction. The English pronunciation error correction algorithm based on SVM+LDA introduced in this study can effectively improve the pronunciation accuracy of English learners. This method has strong practicality and scalability, and can provide strong support for English pronunciation teaching and speech recognition technology.

关键词： Support vector machines Training Accuracy machine learning algorithms Systematics Speech recognition Feature extraction Error correction Classification algorithms data mining

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Analyzing Handwriting Features for ADHD Detection in Children with Autism via machine learning

Analyzing Handwriting Features for ADHD Detection in Childre...

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Disruptive Technologies (ICDT), international conference on

作者： Ch. Prathima Anitha. T P Jayachandra reddy Yekkaluri Susmitha P Soni Reddy Nakkarasupalli Charitha Dept.of DS Mohan Babu University Tirupati India Department of CSE K.S.R.M College of Engineering Dept of CSE Annamacharya institute of technology & sciences Dept. of IT Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Attention deficit hyperactivity disorder (ADHD) develops in children's brains, portrayed by symptoms of inattentiveness, hyperactivity, and impulsivity. Early identification is very much essential, but traditional diagnostic methods might be subjective in practice and consuming time. Hence this work intends to introduce an end-to-end hybrid deep-learning system which fuses the techniques CNN and Bi-LSTM Networks to develop a system using handwriting to detect ADHD in children. The Bi-LSTM layers capture the sequential dependencies that exist in the handwriting data, capturing the temporal irregularities, whereas the CNN layers extract spatial features such as stroke patterns and pressure variations. The training and evaluation of the proposed model was done by synthetically balancing the dataset with accuracy, Confusion matrix, ROC curve and AUC for assessing performance. A method that has surpassed an accuracy of 90% in classification, this proposed system has been able to properly point out ADHD-related traits by hand. This method is scalable as it approaches to do fewer computations for the detection, thus making it a real data-driven alternative to traditional methods. Future work: Add attention mechanisms to increase the model's ability toward generalization, expand available datasets, and improve generalized capabilities.

关键词： Deep learning Training Hands Accuracy Attention mechanisms Fuses Feature extraction data models data mining Disruptive technologies

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