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International Conference on Machine Learning and Big Data Analytics, ICMLBDA 2023

作者： Vamsi Kumar Appala, Satya Jagadeesh, S.V.V.D. Durga Satish, M. Sridevi, B. Department of Computer Science B V Raju College Bhimavaram AP Vishnupur India Department of Computer Science & Engineering Vishnu Institute of Technology Bhimavaram Vishnupur India

ISBN: (纸本)9783031513374

Bones play a significant role in human body support, allowing us to use our muscles to walk, ride a bike, and hold a child. A bone fracture is a medical condition that occurs when a bone is cracked or broken due to an accident or trauma. Fractures can range in severity from a small crack in the bone to a complete break, where the bone is broken into two or more pieces. Accidents are one of the most common causes of bone fractures, and they can occur in a variety of ways, such as falls, sports injuries, car accidents, and workplace accidents. The severity of the fracture depends on the amount of force applied to the bone, the location of the fracture, and the age and health of the person. To deal with this problem, it requires an essential need of prediction system for the detection of bone fractures in the human body. The artificial intelligence subfield of machine learning offers distinguished assistance in event prediction and trains its models using data from real-world occurrences. In this chapter, ML and DL algorithms for predicting bone fractures using support vector machine, decision tree, random forest, and convolutional neural network (CNN) algorithms are discussed. Out of these, SVM outperformed with 86% accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Human Affect Recognition Using Deep Learning Approaches

Human Affect Recognition Using Deep Learning Approaches

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Seshaiah, M. Bhargavi, D.S. Hema, H. Mallikashree, N. Manuja, C.R. S J C Institute of Technology Department of Computer Science and Engineering Chickballapur India

ISBN: (纸本)9798350359688

An important advance towards more characteristic human-computer interaction is the automatic detection of human impact. Present-day developments entail the integration of physiological and audiovisual sensors to identify emotions in real-world situations, posing a challenge to standard machine learning algorithms. A thorough assessment of creative writing on human affect recognition from 2010 to 2017 is done in this research, with a centre on deep neural network-based approaches. A distinct trend toward the promotion of deep learning (DL) methods is visible in the classification of 950 studies based on whether they used shallow or deep learning architectures. An extensive assessment of deep neural network appeal in this sector is possible through a more concentrated investigation of a subset consisting of 233 papers that use them. The development of spatial, temporal, and joint feature representations for multi- modal sensor data requires DL. To demonstrate the significant advancements made in this area, the study presents estimable state-of-the-art architectures. For academics working on similar applications, the outcome is identified, and the significance of frameworks in the field of human affect is recognized. © 2024 IEEE.

关键词： Adversarial machine learning

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Electricity Consumption Classification using Various Machine Learning Models

EAI Endorsed Transactions on Energy Web

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EAI Endorsed Transactions on Energy Web 2024年 11卷 1-6页

作者： Paikaray, Bijay Kumar Jena, Swarna Prabha Mondal, Jayanta Van Thuan, Nguyen Tung, Nguyen Trong Mallick, Chandrakant Bhubaneswar India Department of Electronics and Communication Engineering Centurion of University of Technology and Management Odisha India School of Computer Engineering KIIT Deemed to be University Bhubaneswar India Faculty of Engineering Technology Hung Vuong University Viet Nam Quality Assurance Dong A University Viet Nam Department of Computer Science & Information Technology GITA Autonomous College Odisha Bhubaneswar India

INTRODUCTION: As population has increased over successive generations, human dependency on electricity has increased to the point where it has become a norm and indispensable, and the idea of living without it has become unthinkable. OBJECTIVES: Machine learning is emerging as a fundamental method for performing tasks autonomously without human intervention. Forecasting electricity consumption is challenging due to the many factors that influence it;embracing modern technology with its heavy focus on machine learning and artificial intelligence is a potential solution. METHODS: This study employs various machine learning algorithms to forecast power usage and determine which method performs best in predicting the dataset based on different variables. RESULTS: Eight models were tested, including Linear Regression, DT Classifier, RF Classifier, KNN, DT Regression, SVM, Logistic Regression, and GNB Classifier. The Decision Tree model had the greatest accuracy of 98.3%. CONCLUSION: The Decision Tree model’s accuracy can facilitate efficient use of electricity, leading to both conservation of electricity and cost savings, and be a guiding light in future planning. Copyright © 2024 Paikaray, B.K. et al., licensed to EAI. This open-access article is distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transforming, and building upon the material in any medium so long as the original work is properly cited.

关键词： Forecasting

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Novel soft robotic finger model driven by electrohydrodynamic(EHD)pump

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Journal of Zhejiang University-science A(Applied Physics & engineering) 2024年第7期25卷 596-604页

作者： Xuehang BAI Yanhong PENG Dongze LI Zhuochao LIU Zebing MAO Department of Mechanical Automotive and Materials EngineeringFaculty of EngineeringUniversity of WindsorWindsorN9B 3P4Canada Department of Information and Communication Engineering Graduate School of EngineeringNagoya UniversityNagoya464-8601Japan Department of Intelligent Science and Technology College of Computer Science and TechnologyQingdao UniversityQingdao266071China Design and Research Institute of University of Science and Technology Beijing Co.Ltd. Beijing100083China Faculty of Engineering Yamaguchi UniversityYamaguchi755-8611Japan

1Introduction In the field of robotic-human interactions,soft robotics offers enhanced safety and adaptability.A major challenge in this area is the integration of soft actuators with pump systems,which often increases the system volume and *** study presents the development and testing of a robotic finger powered by electrohydrodynamic(EHD)*** leveraging the electric field-induced flow of dielectric fluids.

关键词： robotic complexity. pump

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A Mechanism Used to Predict Diet Consumption and Stress Management in Humans Using IoMT

A Mechanism Used to Predict Diet Consumption and Stress Mana...

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2023 International Conference on Disruptive Technologies, ICDT 2023

作者： Rajkumar, K. Bennyhin, Bapanapalli Muddana, Dharani Sampath Hariharan, U. Department of Computer Science and Engineering Karnataka India Chandigarh University Apex Institute of Technology Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350323887

Overeating and stress-eating are both widespread problems in today's culture. Many people in today's society consume more calories than they require, resulting in a variety of health issues such as heart disease, high cholesterol, obesity, stroke, hypertension, and other conditions. Overeating is a common way to cope with stress, boredom, anxiety, sadness, emotional traumas, and other factors. To avoid stress eating and other health concerns, attention to the quality and quantity of food we consume, as well as the length of our meals, the number of calories required for each body type, calorie expenditure, metabolism, and other factors is needed. As a result, our research focuses on users' calorie consumption and notifies them of how many calories they should consume and if they are stress-eating or eating normally. Because food intake is a major factor in a variety of health concerns, keeping a close eye on our eating habits is critical. To that end, we propose a method for an edge platform that detects, classifies, and notifies us about calories and whether we are doing normal eating or stress-eating because many health problems are largely influenced by food consumption. © 2023 IEEE.

关键词： Food supply

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Analysis of Bio Inspired Based Hybrid Learning Model for Software Defect Prediction

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SN computer science 2024年第7期5卷 825页

作者： Shankar, Sahana P. Chaudhari, Shilpa Shashikant Department of Computer Science and Engineering M.S. Ramaiah Institute of Technology (Affiliated to Visvesvaraya Technological University Belgaum) Bengaluru India Department of Computer Science and Engineering Ramaiah University of Applied Sciences Bengaluru India

The software’s quality can be ensured through software testing, which is one of the critical methods. However, it was found that testing consumes more than half of the project's total expenses. Effective and efficient software testing utilizes minimal software resources to find as many flaws in the software system as possible. This paper presents research on software defect prediction using hybrid machine learning algorithm. The algorithm incorporates the benefits of supervised as well as unsupervised learning methodology that improves the accuracy of defect prediction. The study uses publicly available datasets from the open source PROMISE repository provided by NASA MDP to perform machine learning analysis and maximize accuracy. In particular, the study suggested a hybrid learning model and conducted a comparative analysis among Naïve Bayes, Multilayer Perceptron, Random Forest, CNN, Adaboost, and KNN on four different datasets namely CM1, KC1, KC2 and PC1. With respect to F1-score, accuracy, precision, and recall, the results show that the hybrid method performed better than the other methods. The study achieved an accuracy of 93.67% (BAT + CNN) on PC1 dataset, 92% on (Bat + CNN) for CM1 and KC1 respectively. This indicates that the hybrid learning technique of (BAT + CNN) is more potent than other methodologies in prediction of defects. The research can help software development teams identify potential bugs early on, leading to efficient and effective software creation. One useful approach for predicting software defects is the hybrid machine learning method, which can lead to improved software quality and lower maintenance costs. This aids the software testing phase by predicting the faulty modules with better accuracy thereby reducing the testing effort. The main objectives of the research include applying different machine learning algorithms and methods to the given datasets and determine the most effective ones. © The Author(s), under exclusive licence to

关键词： Bat CNN PROMISE Sparrow Search Squirrel Search Testing

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Design and Implementation of a 4-Antenna Rx Beamforming System with Real-Time Calibration

Design and Implementation of a 4-Antenna Rx Beamforming Syst...

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2024 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2024

作者： Lai, Je-An Lin, Shou-Sheu Department of Computer and Communication Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan

ISBN: (纸本)9798350389210

Real-time calibration for a beamforming receiver is a popular topic. We designed and implemented a 4-antenna Rx beamforming system with real-time calibration. This system sends an out-of-band calibration signal from the transmitter (Tx) side to calibrate the phase error of in-band received signals in real time at the receiver (Rx) side. The system was successfully implemented and validated on the M3force Software Defined Radio (SDR) platforms. It was verified using not only the accuracy of the direction of arrival (DOA) on the beampattern but also the array gain from the error vector magnitude (EVM) of the demodulated signals with beamforming in the receiver. The system achieved 4.60 dB of array gain and 2.9 degrees of the accuracy of DOA with calibration. © 2024 IEEE.

关键词： Direction of arrival

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The Potential of 1D-CNN for EEG Mental Attention State Detection 5th

The Potential of 1D-CNN for EEG Mental Attention State Det...

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5th International Conference on Machine Learning, Image Processing, Network Security, and Data sciences, MIND 2023

作者： Velaga, NandaKiran Singh, Deepak Department of Computer Science and Engineering National Institute of Technology Raipur CG Raipur India

ISBN: (纸本)9783031622168

In cognitive neuroscience, attention detection via electroencephalogram (EEG) signals is a crucial task, especially when looking into brain-computer interfaces (BCIs). However, the current biggest challenge in BCI research is the accurate classification of EEG signals. The old methods, such as visual assessment, were neither standardised or statistically analysed. Because of these limitations, many techniques have been created to quantify and extract meaningful data from EEG signals. It has been shown that machine learning algorithms can reliably classify attention states using EEG data. Support Vector Machine (SVM) and k-Nearest Neighbors (KNN) are two examples of such methods. However, more study is required to ensure these algorithms are as precise and reliable as possible. In light of the challenges presented by EEG signal classification, we have employed state-of-the-art deep learning algorithms to enhance the identification of mental attention states. Specifically, we have used a sophisticated deep learning model that integrates spatial dependency analysis, namely 1D Convolutional Neural Networks (CNN). By leveraging the power of 1D CNN, our approach is able to capture intricate spatial patterns within EEG signals, allowing for robust feature extraction and improved classification accuracy. Our model has undergone rigorous training on a diverse dataset comprising 34 EEG signals from 5 participants across different conditions. The model successfully differentiates between distinct mental states (focused, unfocused, drowsy) by analysing EEG data gathered in the experimental setting, with an impressive accuracy of 98.47% and a loss of 0.0144. This study underscores the importance of leveraging advanced computational approaches to unlock the valuable information contained within EEG signals. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Electroencephalography

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Dynamic Lane Change Coordination and Platoon Management in Vehicular Ad Hoc Networks 3

Dynamic Lane Change Coordination and Platoon Management in V...

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Nayak, Subhasish Hota, Lopamudra Nayak, Biraja Prasad Patel, Sanjeev Kumar, Arun National Institute of Technology Department of Computer Science and Engineering Odisha Rourkela769008 India

ISBN: (纸本)9798350354171

Highway traffic optimization through vehicle platooning presents a promising avenue for improving road capacity and energy conservation. Leveraging Vehicular Ad Hoc Networks (VANETs) technology, particularly in the context of Automated Vehicles (AVs), offers an opportunity to address specific challenges associated with dynamic lane change coordination and platoon management. In this paper, advanced algorithms are developed to enhance traffic flow efficiency and expedite information exchange among platoon members. This research work focuses on the development of specialized rules governing the behavior of vehicles during lane changes within platoons. These rules aim to facilitate smooth transitions and prevent traffic-related issues. Additionally, the study seeks to enable vehicles to swiftly adjust their speed and spacing to ensure seamless operations. The proposed algorithms take into account real-time traffic data, platoon capabilities, and platoon characteristics, thereby optimizing platoon dynamics. To bolster safety, the integration of collision avoidance mechanisms is explored, which helps maintain appropriate inter-vehicle distances during lane changes. By addressing these intricate aspects of VANET-enabled vehicle platooning, this research contributes to the efficient and secure deployment of autonomous vehicles on highways, ultimately advancing the potential benefits of platooning technology for modern transportation systems. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Comparison of Machine Learning Algorithms for Detection of Stuttering in Speech

Comparison of Machine Learning Algorithms for Detection of S...

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International Conference on Machine Learning and Big Data Analytics, ICMLBDA 2023

作者： Gudlavalleti, Sarvagna Devi, P. Sunitha Lakka, Ramyasri Kuchanpally, Rithika Dudekula, Sai Sonali Department of Computer Science and Engineering G Narayanamma Institute of Technology and Science Shaikpet Telangana Hyderabad India Department of Electronics and Telecommunication Engineering G Narayanamma Institute of Technology and Science Shaikpet Telangana Hyderabad India

ISBN: (纸本)9783031513374

Stuttering is a speech disorder characterized by the repetition of sounds, syllables, or words and the prolongation of sounds. An individual who stutters exactly knows what he or she would like to say but has trouble producing a normal flow of speech. This project aims to use machine learning algorithms to detect stuttering behavior in Telugu language speech samples. Stuttering is a speech disorder that affects the flow of speech, making it difficult to communicate with others. The study will collect voice samples from individuals reading the same Telugu script and generate 8- and 16-kHz samples in .wav files. The data will be annotated and coded on a 0–8 scale and will be generated as a dataset. Machine learning algorithms will be used to build a model for detecting stuttering patterns in the audio files. Finally, the model will be integrated with a user interface. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： User interfaces

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