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4th IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Pandey, Trilok Nath Vasudev, Anirudh Sagayanathan, Dennis Anjan, Govind Arshad, Danish Patra, Sudhansu Shekhar School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Applications KIIT Deemed to Be University Bhubaneswar India

ISBN: (纸本)9798350306118

Customers' propensity for brand loyalty, recurrent business, and positive word-of-mouth are directly influenced by the degree to which their needs are met. E-commerce happens to be one of the biggest online industries in the world which till date is not completely understood from a customer's standpoint. Brazil being a developing country is the 12th largest market for e-commerce in the world and accounts for 30 % of e-commerce in Latin America and remains largely unexplored. In this study, we used machine learning (ML) techniques such as LSTM, XGBClassifier, Logistic Regression, Neural Network, and Random Forest to predict customer satisfaction using a retail dataset from Brazil. We contrasted the efficacy of these models using a variety of evaluation criteria, such as precision, recall, and Fl-score. The LSTM model had the highest accuracy with a score of 0.89, followed by the XGBClassifier with 0.83, Naive Bayes with 0.73, Logistic Regression with 0.74, Support Vector Classifier with 0.74, and Random Forest with 0.78. The superior performance of the LSTM model over other models suggests that it has superior predictive abilities. © 2023 IEEE.

关键词： Customer satisfaction

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An efficient speech emotion recognition based on a dual-stream CNN-transformer fusion network

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International Journal of Speech Technology 2023年第2期26卷 541-557页

作者： Tellai, Mohammed Gao, Lijian Mao, Qirong Department of Computer Science and Communication Engineering Jiangsu University Jiangsu Zhenjiang212013 China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications Jiangsu Zhenjiang212013 China

The use of machine learning and artificial intelligence enables us to create intelligent systems. Speech emotion recognition system analyzes the speaker’s speech to determine his/her emotional state. Speech emotion recognition is a challenging pattern recognition task. This paper proposes a new robust and lightweight speech emotion recognition system based on a dual-stream CNN-Transformer fusion network that effectively captures spatial and temporal information disentangled in long-distance raw features by exploring both MFCCs and Mel-spectrograms in a parallel mechanism. Experiments are performed on the widely used emotional benchmark datasets. Our approach has shown to be quite efficient. It outperforms the best-known state-of-the-art models with an accuracy of 97.64%, 99.42%, and 97.53% on RAVDESS, TESS, and EMO-DB datasets respectively. The results demonstrate the significant advantages of the proposed model and the ability of this architecture to learn to recognize emotional data features accurately. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Emotion Recognition

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Design of a Lightweight Compressed Video Stream-Based Patient Activity Monitoring System

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computers, Materials & Continua 2024年第1期78卷 1253-1274页

作者： Sangeeta Yadav Preeti Gulia Nasib Singh Gill Piyush Kumar Shukla Arfat Ahmad Khan Sultan Alharby Ahmed Alhussen Mohd Anul Haq Department of Computer Science&Applications Maharshi Dayanand UniversityRohtak124001India University Institute of Technology Rajiv Gandhi Proudyogiki VishwavidyalayaBhopal462033India Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl Majmaah11952Kingdom of Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl Majmaah11952Kingdom of Saudi Arabia

Inpatient falls from beds in hospitals are a common *** falls may result in severe *** problem can be addressed by continuous monitoring of patients using *** advancements in deep learning-based video analytics have made this task of fall detection more effective and *** with fall detection,monitoring of different activities of the patients is also of significant concern to assess the improvement in their *** computation-intensive models are required to monitor every action of the patient *** requirement limits the applicability of such ***,to keep the model lightweight,the already designed fall detection networks can be extended to monitor the general activities of the patients along with the fall *** by the same notion,we propose a novel,lightweight,and efficient patient activity monitoring system that broadly classifies the patients’activities into fall,activity,and rest classes based on their *** whole network comprises three sub-networks,namely a Convolutional Neural Networks(CNN)based video compression network,a Lightweight Pose Network(LPN)and a Residual Network(ResNet)Mixer block-based activity recognition *** compression network compresses the video streams using deep learning networks for efficient storage and retrieval;after that,LPN estimates human ***,the activity recognition network classifies the patients’activities based on their *** proposed system shows an overall accuracy of approx.99.7% over a standard dataset with 99.63% fall detection accuracy and efficiently monitors different events,which may help monitor the falls and improve the inpatients’health.

关键词： Fall detection activity recognition human pose estimation accuracy

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IoT based Illness Prediction System using Machine Learning 2

IoT based Illness Prediction System using Machine Learning

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Lakshmi, B. Naga Joel, M. Robinson Department of Computer Applications Thanjavur India Kings Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798350346640

The adoption of wearable technology will increase and its integration into daily life will improve, particularly in the healthcare sector. The emergence of mobile medicine, the development of new technologies like smart sensing, and the adoption of customised health ideas have all contributed to the rapid growth of smart wearable technology in recent years. The study was primarily focused on the use of wearable technology in office situations with the goal of daily health and safety monitoring of employees. In order to perform data classification and data labeling, a machine learning model is constructed. This research work has proposed a novel framework for processing data with text-related properties using machine learning techniques. Further a data analysis process has been carried out by using a Machine Learning (ML) framework. In the proposed study, machine learning classifiers are used. This study has analyzed the outcomes by considering accuracy as a performance indicator after applying the algorithms to the datasets. After analyzing the accuracy, it is evident that the machine learning algorithms like K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are effective on processing the text datasets. © 2023 IEEE.

关键词： Support vector machines

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GIS and ML Integrated Techniques for Detection of Landslide-Prone Areas 2

GIS and ML Integrated Techniques for Detection of Landslide-...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Sul, Astha Patil, Sanjay Vellore Institute of Technology School of Computer Science and Engineering Vellore India Maharashtra Remote Sensing Applications Centre Maharashtra Pune India

ISBN: (纸本)9798350368413

Landslides are a common natural disaster that can result in substantial casualties. One of the region's most vulnerable to landslides in India is Western Ghats. The risk casualty is reduced with the use of the landslide susceptibility map (LSM) and prediction of landslide points. The aim of this research is to explore and compare the 4 ML algorithms (K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR)) for generating the most promising LSM. This study focuses on 12 Landslide conditioning factors (LCFs) such as Elevation, Slope, Lithology, LULC, Distance to Road, Topographic Wetness Index (TWI), Stream Power Index (SPI), Topographic Roughness Index (TRI), Drainage Networks, NDVI, Rainfall, Sediment Transport Index (STI). It has been found that the slope of the ground, the distance to the road, and the annual rainfall all have a significant impact on the occurrence of landslides. The models made using different approaches have been analysed and compared using the following metrics: prediction accuracy, precision, recall, F1-score, and True Skill Statistics (TSS). Overall accuracy for LR, SVM, KNN, RF are 82.30, 84.00, 82.50, 87.20 respectively. The findings of the study show that there are more landslide-prone locations in Bhor, Velhe, Ambegaon, and Junnar in Pune District, Maharashtra. © 2024 IEEE.

关键词： Landslides

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Optimization of Speech Emotion Recognition Through Advanced Ensemble Learning Techniques 9

Optimization of Speech Emotion Recognition Through Advanced ...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Prathyakshini Prathwini Keerthana Department of Information Science and Engineering Nitte Karkala India Department of Master of Computer Applications Nitte Karkala India Nitte Karkala India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377972

Recognition of emotion in speech (RES) is generating significant interest due to its promise to improve human-computer interaction through precise identification of emotions from speech signals. This work investigates how well deep learning methods advance RES accuracy. The study makes it possible to analyze a variety of speech patterns, emotional expressions, and demographic information all of which are crucial for developing trustworthy RES models. According to the study, CNNs demonstrated their ability to capture spatial characteristics from voice spectrogram representations with an amazing accuracy rate of 93%. RNNs performed exceptionally well at modeling temporal dependencies and capturing the sequential nature of speech, with an accuracy rate of 90%. Both models performed well, but some emotions such 'angry' and 'surprise' were more accurately detected than others, including 'disgust' and 'happy,' suggesting room for improvement. © 2024 IEEE.

关键词： Adversarial machine learning

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Comparison of Faster RCNN and YOLO V3 for Video Anomaly Localization

Comparison of Faster RCNN and YOLO V3 for Video Anomaly Loca...

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2023 International Conference on Power, Instrumentation, Control and Computing, PICC 2023

作者： Anoopa, S. Salim, A. Nadera Beevi, S. College of Engineering Trivandrum Dept. of Computer Science and Engineering Kerala India Government Engineering College Thrissur Dept. of Computer Science and Engineering Kerala India Tkm College of Engineering and Technology Dept. of Computer Applications Kerala India

ISBN: (纸本)9798350334463

Video Anomaly Localization techniques are interesting and emerging tasks in computer vision that is used to locate the position of an anomalous object with bounding boxes. Convolutional neural network-based object localization methods have been developed which detect and classify objects with high precision and accuracy. Many algorithms have been developed for object detection and localization with excellent outcomes. These algorithms are fast RCNN, faster RCNN, and YOLO. In this paper, a comprehensive analysis of faster RCNN and YOLO V3 algorithms for anomaly localization is performed based on the parameters FPS, mAP, Precision, Recall and F1 Score. © 2023 IEEE.

关键词： Convolutional neural networks

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Novel Framework for Quality Crop Predictions Using Data Mining and Soft Computing Techniques

Novel Framework for Quality Crop Predictions Using Data Mini...

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Maurya, Rajesh Kumar Jain, Himani Sharma, Tarun Kumar Sharma, Surbhi Dublish, Mani Abes Engineering College Department of Computer Applications NH-9 Near Crossing Republic Ghaziabad India

ISBN: (纸本)9798350322309

Data mining (DM) and Soft Computing (SC) are a vital computational approach that offers good competence of flexible agricultural data processing systems to solve farmer's problems. Recently, soft computing has emerged as a powerful technique for solving and analyzing complex real-world problems. This article suggests an approach of smart crop predictions is presented through DM &SC in the field of agricultural quality crop prediction. A five-level framework is proposed namely 1) Collection of data from different repositories, 2) Pre-processing of data, 3) Appropriate Classifier Selection, 4) Prediction and Estimation 5) Draw AUC & ROC curve. Method proposed here focuses on analyzing agricultural yield, soil for crop, rainfall required based on chemical property of soil. Agricultural data analysis and cataloging is one of the best applications of new computing tools such as soft Computing, Machine Learning (ML) approach, became a burning area for the reason that of the massive development of farming data. DM&SC approaches for accomplishing applied research will give effective answers for this type of problem. ML is a working tool to study multiple learners and combine their assessments for accomplishing greater forecasting accurateness. In this investigation, we had recapitulated ML methods which can be applied as an essential tool by the farmer and agriculture scientist for timely prediction of crop production. © 2023 IEEE.

关键词： Crops

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Kidney Disease Prediction using ML techniques 4

Kidney Disease Prediction using ML techniques

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Saha, Bishnu Pada Dash, Satya Ranjan Rout, Nikunj Kishore Patnaik, Aswini Prasad Mishra, Manoj Ranjan Kiit University School of Computer Engineering Bhubaneswar India Kiit University School of Computer Applications Bhubaneswar India Kalinga Institute of Medical Sciences Department of Nephrology Bhubaneswar India

ISBN: (纸本)9798350349856

Over the last two years there has been a wide progress in detecting causes of diabetic nephropathy, understanding its complications and preventing it. So far, techniques such as SVM, Decision Tree, KNN and ANN trained predictive models were used by researchers to monitor the progression of DN using T1- and T2-Weighted Imaging. We have discussed the importance of various Machine learning, Deep learning and artificial intelligence techniques used in curing diabetic nephropathy. Further a prospective of precise diagnosis of diabetic kidney disease is discussed to throw light on integrated bio-informatics analysis and machine learning algorithms. We also compare and analyze the research findings and solutions along with their challenges for a summarized view of the topic. In this research work, we have discussed about the Kidney diseases and the ML techniques wherever applicable. © 2024 IEEE.

关键词： Decision trees

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Instantaneous Interpretation into Sign Language for the Hearing Impaired 1

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3rd International Conference on Universal Threats in Expert applications and Solutions, UNI-TEAS 2024

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Kothari, Paras Sheikh, Anisha School of Computing Graphic Era Hill University Dehradun India Department of Information Technology Guru Ghasidas Vishwavidyalaya Bilaspur India Department of Computer Engineering and Applications GLA University UP Mathura India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India Department of Computer Applications Geetanjali Institute of Technical Studies Rajasthan Udaipur India

ISBN: (数字)9789819738106

ISBN: (纸本)9789819738090

Because they are unable to convey their thoughts to others who can hear, deaf persons may have a difficult time controlling their emotions and may find that their routines are often interrupted. This is the primary impetus behind the progression of things. These individuals would be helped by this system, and it would make it possible for them to convey their ideas in a manner that is on par with that of persons who do not have any kind of physical impairment. Recent advances in artificial intelligence have made it possible to design a system that is capable of resolving this problem, which was previously thought to be impossible. The purpose of this research is to develop, for the benefit of the deaf community, a system that can translate speech into text. Because it’s possible that some individuals won’t comprehend only the written word, the speech will also be translated into international sign language (ISL). In this method, persons who are deaf or hard of hearing will have their sign language translated into spoken language. In order to develop the most accurate model possible, we will integrate NLP with a variety of ML and AI techniques. For the purpose of prediction, convolutional neural networks (CNN) will be used since lip movements are continuous and rapid, making them challenging to capture. CNN and attention-based long and short-term memory (LSTM) approaches will function since they are both effective at predicting visual input. In order to get more desirable results, we will be using several techniques for augmenting the data. TensorFlow and Keras are going to be the Python libraries that are employed in order to do the voice-to-text translation. At the moment, there is an abundance of software accessible;however, all of them require the use of a network. This piece of hardware can function very well without being connected to the internet. Using the suggested technique, we were able to attain a 100% accuracy rate in sign language prediction and a 96% a

关键词： Forecasting

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