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A Novel Machine learning-Based Artificial Voice Box

A Novel Machine Learning-Based Artificial Voice Box

Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE), International Conference on

作者： Naveen Kumar Priya Nandihal Madhumala R B Piyush Kumar Pareek Nikshepa T Sowmya S R Department of Computer Applications (BCA) Dr NSAM First Grade College (Affiliated to Bangalore City University) Bengaluru Dept of Computer Science and Design Dayananda Sagar Academy of Technology and Management Bangalore Department of ISE Dayananda Sagar Academy of Technology and Management Bangalore Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru Department of Information Science and Engineering Dayananda Sagar Academy of Technology and Management Bangalore

ISBN: (纸本)9781665493970

Patients may experience a great deal of discomfort while undergoing rigorous medical procedures for the identification of vocal abnormalities. As a result, there has been a lot of interest in automated speech recognition and disorder detection approaches in recent years, and these methods have shown to be effective. Voice recordings have been acquired from the Saarbruecken Voice Database for the purpose of this study. The signals undergo preprocessing using Hybrid Wiener Filter Discrete Wavelet Transforms in order to de-noise and eliminate any silence that may have been there (HWFDWT). Cat Swarm Optimization is used to extract features, and Mel Frequency Cepstrum Coefficients are taken into account (CSOMFCC). Classification using Modified Optimized Back Propagation Network Disorder voice Classification is then used to sort the features in the end (MOBPNDC). In terms of Accuracy, Precision, Recall, F-Measure, and Time period, the classification scheme beats the current Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN) approaches. The neural speech system is a gadget that enables individuals who are unable to talk to communicate their thoughts and emotions with the outside world. It is a piece of equipment that can record the electric pulses that are generated by the brain and turn them into a synthetic voice. - Provide an overview of the concept or solution that you want to build. The electrical activity of the brain will be recorded and then sent into a synthesiser. The Synthesizer will convert the signal into voice when it has finished decoding it. The voice that has been deciphered is then supplied to an artificial voice box. The brain's electrical activity is used to generate an artificial voice, which is then output via the box.

关键词： Support vector machines Backpropagation Wiener filters Synthesizers Transforms Speech recognition Feature extraction

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Machine learning with Monarch Butterfly Optimization for Prediction of Emergency Patient Admission Status

Machine learning with Monarch Butterfly Optimization for Pre...

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International Conference on Electronics, Communication and Computing Technologies (ICECCT)

作者： S. Vijayarangam S. Vasundhara Nihar Ranjan Beherac Shyamali Das Shanker Chandre R. Rajagopal Department of Computer Science and Engineering Sri Indu College of Engineering and Technology Hyderabad Telangana India Department of Humanities and Mathematics G.Narayanamma Institute of Technology and Science Hyderabad Telangana India Swiss School of Business and Management Geneva Petit-Lancy Switzerland Department of Artificial Intelligence and Machine Learning CMR Institute of Technology AECS layout Bangalore Karnataka India Department of Computer Science & Artificial Intelligence SR University Warangal Telangana India Department of Computer Science & Engineering Alliance College of Engineering & Design Alliance University Bengaluru Karnataka India

Emergency admission is one of the most important resources of healthcare expenditure. The accessibility of predictive methods is support to recognize the admission status of arriving patients and the patient mix that supports managing downstream resources and decreasing overpopulation at emergency department (ED) by restriction boarding delays. In recent times, machine learning (ML) techniques are well enhanced and executed in several applications like health protection, cancer detection, and hospital operations. But, most difficult problem with ML is that each technique has parameter and without optimizing the parameter, attaining a higher accuracy method develops very complex. This study introduces Machine learning with Monarch Butterfly Optimization for Prediction of Emergency Patient Admission Status (MLMBO-PEP A) technique. The presented MLMBO-PEP A technique can predict the admission status of the emergency patient. In the presented MLMBO-PEP A technique, data normalization is initially performed to scale the medical data. For predictive process, the MLMBO-PEP A technique uses Dendritic Neuron Model (DRN). The MBO technique is used to optimize the forecasting outcomes of the DRN model. A widespread simulation study is performed for ensuring the better performance of the MLMBO-PEP A technique. The resultant values implied the improved efficacy of the MLMBO-PEP A technique under different evaluation metrics.

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Ant Colony Optimization based Medical Image Preservation and Segmentation

Ant Colony Optimization based Medical Image Preservation and...

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Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE), International Conference on

作者： Priya Nandihal Piyush Kumar Pareek Victor Hugo Costa De Albuquerque Madhumala R B Ashish Khanna V Sunil Kumar Dept of Computer Science and Design Dayananda Sagar Academy of Technology and Management Bangalore Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Ceara Fortaleza Brazil Department of Teleinformatics Engineering Federal University of Ceara Brazil Department of ISE Dayananda Sagar Academy of Technology and Management Bangalore CSE Dept Maharaja Agrasen Institute of Technology GGSIPU Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru

ISBN: (纸本)9781665493970

Image segmentation is an essential component in many different types of computer vision systems. Image segmentation is used in order to identify objects and boundaries within pictures. It is important to note that the success of recognition algorithms is heavily dependent on the quality of the picture segmentation, and there is yet no technique that can universally segment images. Boundary detection has a long history of being recognised as one of the most difficult challenges in the fields of image processing and pattern analysis, in particular, when it comes to applications in medical imaging. The clarity of the picture is being muddled by more information, which is the source of the problem's complexity. The creation of segmentation algorithms for medical pictures is an active field of study, and segmentation algorithms are meant to account for a variety of factors, including noise, intensity inhomogeneity, poor contrast between the lesion and the surrounding skin, and so on. There is currently no active contour model that combines the locally statistical property of being more noise-resistant, in which the intensities in the transformed domain have less overlapping in the statistics, and the locally image fitting property of extracting the local image information in order to be able to segment images with intensity inhomogeneity. This is because there is no such model. So the proposed work includes Ant colony optimization based double threshold segmentation in medical images. Performance of the proposed algorithm is more efficient, reliable and accurate than the existing algorithm. Performance parameters such as segmentation accuracy, error rate, and Dice co-efficient are used in order to conduct the evaluation of the proposed work.

关键词： Integrated optics Image segmentation Ant colony optimization Biomedical optical imaging Optical design Image edge detection Metaheuristics

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Joint Parking and Power Management for Electric Vehicle Edge Computing: A Bilevel Optimization Approach

Joint Parking and Power Management for Electric Vehicle Edge...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Xumin Huang Weifeng Zhong Jiangtian Nie Jiawen Kang Zehui Xiong Yuan Wu Mohsen Guizani School of Automation Guangdong University of Technology Guangzhou China State Key Laboratory of Internet of Things for Smart City University of Macau Macau China School of Computer Science and Engineering Nanyang Technological University Singapore Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Computer and Information Science University of Macau Macau China Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi UAE

With the vehicle-to-grid and computing capabilities, a parked electric vehicle (EV) has a dual role, namely being an energy prosumer as well as a computing node for accommodating computation-offloading services. This dual-role feature of EVs yields a new computing paradigm named Electric Vehicle Edge Computing (EVEC). To ease the implementation of EVEC, we propose a fine-grained EV management approach to jointly provide parking guidance for EVs and control their charging/discharging power in parking lots. We formulate a bilevel optimization problem where the top-level problem optimizes the matching between EVs and parking lots from the perspective of computation offloading, and the bottom-level problem optimizes the control of EV charging/discharging power from the view of power networks. We transform the bilevel optimization problem into a single-level form, which is a nonconvex mixed-integer nonlinear programming problem, and we further tackle it by linearization techniques. Finally, we provide numerical results to demonstrate the efficiency and effectiveness of our approach.

关键词： Wireless communication Vehicle-to-grid Systematics Power system management Computational modeling Transforms Relaxation methods

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Agent-based Simulation Evaluation of CBD Tolling: A Case Study from New York City

arXiv

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arXiv 2024年

作者： Liang, Qingnan Yao, Ruili Zhang, Ruixuan Chen, Zhibin Wu, Guoyuan The Department of Civil and Urban Engineering New York University New York United States Key Laboratory of Urban Design and Urban Science NYU Shanghai Shanghai China The Department of Electrical and Computer Engineering University of California Riverside RiversideCA United States Shanghai Frontiers Science Center of Artificial Intelligence and Deep Learning NYU Shanghai Shanghai China The Center of Environmental Research and Technology University of California Riverside RiversideCA United States

Congestion tollings have been widely developed and adopted as an effective tool to mitigate urban traffic congestion and enhance transportation system sustainability. Nevertheless, these tolling schemes are often tailored on a city-by-city or even area-by-area basis, and the cost of conducting field experiments often makes the design and evaluation process challenging. In this work, we leverage MATSim, a simulation platform that provides microscopic behaviors at the agent level, to evaluate performance on tolling schemes. Specifically, we conduct a case study of the Manhattan Central Business District (CBD) in New York City (NYC) using a fine-granularity traffic network model in the large-scale agent behavior setting. The flexibility of MATSim enables the implementation of a customized tolling policy proposed yet not deployed by the NYC agency while providing detailed interpretations. The quantitative and qualitative results indicate that the tested tolling program can regulate the personal vehicle volume in the CBD area and encourage the usage of public transportation, which proves to be a practical move towards sustainable transportation systems. More importantly, our work demonstrates that agent-based simulation helps better understand the travel pattern change subject to tollings in dense and complex urban environments, and it has the potential to facilitate efficient decision-making for the devotion to sustainable traffic management. Copyright © 2024, The Authors. All rights reserved.

关键词： Simulation platform

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An Improved Deep learning-Based Intrusion Detection for Reliable Communication in VANET

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IEEE Transactions on Consumer Electronics 2024年

作者： Suman, Preetam Padhy, Sasmita Kumar, Naween Suman, Amrit Singh, Akansha Singh, Krishna Kant Castilla, Angel Kuc Al-Zahrani, Turki Saad S. VIT Bhopal University School of Computing Science and Engineering Bhopal India Bennett University SCSET Greater Noida India Sharda University Department of CSE Greater Noida India Delhi Technical Campus Greater Noida India C/Isabel Torres 21 Engineering Research and Innovation Group Universidad Europea del Atlántico Santander39011 Spain Universidad Internacional Iberoamericana Department of Project Management C.P. Campeche24560 Mexico Universidad Internacional Iberoamericana Department of Project Management Arecibo00613 Puerto Rico University of Jeddah Learning Design and Technology Department Jeddah Saudi Arabia

The research area of Vehicular Ad-Hoc Network (VANET) is rapidly expanding for different reasons. The main factors responsible for this are the unstable topology, fast vehicle movement, and security vulnerabilities. Identifying malicious attacks in vehicular ad-hoc networks is necessary to enhance the security and reliability of communication between all vehicles in the system. The intruders carry out numerous malicious attacks. The focus of this paper is to identify and mitigate attacks like Botnet, Sybil, DoS, Wormhole, PortScan, Blackhole, and BruteForce. The present work proposes an Improved LeeNET (I-LeeNet) architecture to identify and mitigate unidentified attack types. The suggested architecture intelligently blends Convolutional Neural Networks (CNN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to provide real-time solutions to unidentified attacks. The proposed approach includes a module KIDS for known attack detection and another module UIDS for learning and identifying previously unidentified attacks. The KIDS module uses ANFIS classification to identify destructive attacks. ANFIS and CNN use the UIDS module to identify unknown attacks on VANET further. The efficacy of the proposed I-LeeNet is tested on three different datasets, such as i-VANET, ToN-IoT, and CIC-IDS 2017. The results section discusses the testing in a regressive manner. In addition, the experimental results are compared to other advanced methods. The average accuracy achieved by the proposed method is 97.21% on i-VANET, 97.75% on ToN-IoT, and 96.66% on the CIC-IDS 2017 dataset. The analysis suggests the proposed method is promising and can be applied in real-time. © 1975-2011 IEEE.

关键词： Vehicular ad hoc networks

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Exploring the Effect of Virtual Reality with Haptics on Educational Research: A Meta-analysis From 2010 to 2020

Exploring the Effect of Virtual Reality with Haptics on Educ...

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Immersive learning Research Network (iLRN), Conference of the

作者： Xuesong Zhai Yulian Sun Minjuan Wang Fahad Asmi Wenjing Cai Xiaoyan Chu College of Education Zhejiang University Hangzhou China School of electronic and Information Engineering Anhui Jianzhu University Hefei China Learning Design and Technology San Diego State University San Diego USA Department of Science and Technology Communication University of Science and Technology of China Hefei China School of public affairs University of Science and Technology of China Hefei China

Virtual reality (VR) not only expands the learning field and enriches the learning experience, but also provides an effective presentation for scarce educational resources. However, because VR mainly depends on image processing technology, its application in the field of education mostly stays at the level of knowledge observation, and many challenges are gradually exposed, such as untimely interaction and weak sense of existence. In recent years, haptics has gradually become the development frontier of human-computer interaction, breaking through the limitation of single visual stimulation of VR to a great extent. Some researchers began to explore the teaching mode of VR with haptics, in order to mobilize learners’ multi-sensory stimulus responses such as hearing, touch, force and movement, and enhance learners’ on-the-spot experience and flow experience. In order to deeply explore the effectiveness of VR with haptics in teaching and explore the regulatory variables affecting this teaching method, 1646 academic papers published from 2010 to 2020 were screened based on WOS and Scopus databases. After meta-analysis of 50 selected research data, it is found that: (1) from the perspective of overall effect, VR with haptics can significantly improve learners’ learning performance and efficiency; (2) Through the analysis of the effects of three regulatory variables, it is found that VR with touch has a positive impact on learning performance; VR with haptics has a positive impact on learning efficiency, especially for learners who have no previous learning experience and take practical skills as their learning goal for a long time. This discovery provides an important data reference for building an effective “virtual reality with haptics” teaching scene.

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Integrated IoT-Based Smart Home Security System: Real-Time Hazard Detection and Alerts Using Multi-Sensor Fusion

Integrated IoT-Based Smart Home Security System: Real-Time H...

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Intelligent & Innovative Practices in Engineering & Management (IIPEM), International Conference on

作者： A M S V Sushma A Aswini Priyanka A N G Lakshmi Narendra Khatri Harish Sharma Department of Electrical and Electronics Engineering S R K R Engineering College Bhimavaram Andhra Pradesh India Department of Computer Science and Design Engineering S R K R Engineering College Bhimavaram Andhra Pradesh India Department of Electronics and Communication Engineering Sir C.R. Reddy College Engineering Bhimavaram Andhra Pradesh India Department of Mechatronics Manipal Institute of Technology Manipal Acacdemy of Higher Education Manipal India Department of Artificial Intelligence and Machine Learning School of Computer Science and Engineering Manipal University Jaipur Jaipur India

ISBN: (数字)9798350390049

ISBN: (纸本)9798350390056

This paper presents an innovative IoT-based smart home security system that integrates multiple sensors to detect various hazards and provide real-time alerts. Unlike traditional single-function systems, this solution combines motion detection via a PIR sensor, gas leak detection using an MQ-2 sensor, and fire detection through an IR flame sensor into a single, affordable package. The Arduino UNO serves as the central controller, interfaced with a GSM module for instantaneous SMS notifications and supported by an on-site buzzer alarm. Extensive testing in diverse environments demonstrates the system’s exceptional performance, detecting intrusions in under 2 seconds and reporting threats with approximately 89% accuracy, while minimizing false alarms. The integration of smart power management techniques reduces energy consumption compared to conventional systems. This approach not only enhances home security but also offers a scalable and easily deployable solution for both residential and commercial settings. This work addresses the urgent need for an affordable, multi-functional smart home security system and provides a robust framework for future IoT-driven safety solutions.

关键词： Accuracy Fires Smart homes Sensor systems Real-time systems Threat assessment Security Intelligent sensors Gas detectors Testing

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Applying the design thinking model to hands-on mechatronics STEM activities for senior high school students to improve the learning performance and learning behavior

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International Journal of technology and design Education 2023年第4期33卷 1389-1408页

作者： Hsiao, Hsien-Sheng Chang, Yuan-Chen Lin, Kuen-Yi Chen, Jyun-Chen Lin, Chien-Yu Chung, Guang-Han Chen, Jheng-Han Department of Technology Application and Human Resource Development National Taiwan Normal University Taipei City No.162 Sec. 1 Heping E. Rd. Da’an Dist. Taipei106 Taiwan Institule for Research Excellence in Learning Sciences National Taiwan Normal University Taipei City No.162 Sec. 1 Heping E. Rd. Da’an Dist. Taipei106 Taiwan National Taipei University of Education Taiwan Taipei City No.134 Sec.2 Heping E. Rd. Da’an Dist. Taipei106 Taiwan National Academy for Educational Research New Taipei City No. 2 Sanshu Rd. Sanxia Dist. Taipei237 Taiwan National Applied Research Laboratories Taipei City No.106 Sec.2 Heping E. Rd. Da’an Dist. Taipei106 Taiwan Department of Multimedia Design Chihlee University of Technology New Taipei Taiwan

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Intrusion Detection in Cloud Architecture Using Machine learning

Intrusion Detection in Cloud Architecture Using Machine Lear...

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Advances in Computing, Communication Control and Networking (ICAC3N), International Conference on

作者： Govinda Rajulu. G R. Santhoshkumar D. Venkatesan Kalvikkarasi. S P. Santosh Kumar Patra Department of Computer Science and Design St. Martin’s Engineering College Department of Artificial Intelligence and Data Science St. Martin’s Engineering College Department of Artificial Intelligence and Machine Learning Department of CSE St. Martin’s Engineering College Professor in ECE Department Podhagai College of Engineering & Technology St. Martin’s Engineering College

The research of Intrusion Detection Systems is known to be among the most challenging aspects of Cloud Security. In the context of the cloud, security breaches such as denial of service, scanning, injection of malicious code, viruses, worms, and the breaking of passwords are becoming increasingly commonplace. In the event that these attacks are not stopped in a timely manner, the company's reputation may be put in jeopardy, and it may sustain financial harm. As a consequence of this, it is essential to safeguard the cloud against dangers of this kind. In this research, we provide solutions to defend cloud systems by incorporating some of the most successful methods of intrusion detection. Within IDS, our primary focus has been placed on two critical aspects: the effectiveness of the detection techniques and the pace of detection. In order to overcome these obstacles, we suggest utilising machine learning in conjunction with parallelization. Approaches based on machine learning gain information from the data itself, hence diminishing the significance of the human expert. In order to identify intrusions, statistical models, safe system approaches, neural networks, and a variety of other methods are utilised. The purpose of this study is to improve the accuracy of intrusion detection in cloud computing by presenting a new approach to intrusion detection that is based on existing intrusion detection systems and the various architectures that those systems employ.

关键词： Databases Cloud computing security Neural networks Intrusion detection Computer architecture Passwords Decision trees

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