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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Kamaruzzaman, Md. Chandra, Abhijit Azharuddin, Md. The Neotia University Dept.of Robotics and Automation Kolkata India Jadavpur University Dept. of Instrumentation and Electronics Engineering Kolkata India Aliah University Dept. of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350382952

In recent years, wireless rechargeable sensor networks (WRSNs) have become more popular because of its diverse array of applications. However, the limited energy resources of individual sensors pose a significant challenge in maintaining network operations over an extended period. To address this challenge, this research paper presents an energy-efficient scheduling scheme based on distance based scheduling for mobile charging vehicle (MCV) in WRSNs. The proposed scheme capitalizes on the MCV, allowing them to dynamically adjust their charging schedules to cater to the energy demands of sensor nodes (SNs). By efficiently allocating charging tasks to MCV, the scheme aims to balance the energy distribution and mitigate the energy imbalance across the network. By conducting comprehensive simulations with various scenarios, the proposed scheme has been compared with some existing schemes in relation to charging path length, total energy received and charging utility. The simulation results manifest significant advantages of the proposed scheme. © 2024 IEEE.

关键词： Energy efficiency

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Care Compass: Monitoring made easy, Care made effective 3

Care Compass: Monitoring made easy, Care made effective

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Manhas, Prabal Chandra, Joydeep Kaur, Ramanjot Sahay, Rashi Chandigarh University Punjab India Chandigarh University Dept.of Computer Science and Engineering Punjab Mohali India Manav Rachna International Institute of Research and Studies Faridabad India

ISBN: (纸本)9798331515911

Traditional healthcare focused on hospitals and clinics proved out to be inadequate, especially during the COVID-19 pandemic and various emergency crisis, Care Compass, is an innovative and affordable smart wearable device aimed at addressing critical health challenges faced by today's youth. The 21st century witnessed a large-scale transformation with the integration of Internet of Things and smart devices ranging from healthcare to manufacturing sector. The device integrates smart IoT devices and other technologies such as cloud computing platforms, health management mobile application with machine learning algorithms, offering real-time personalized health recommendations and continuous monitoring of vital health parameters. Emphasis is placed on accessibility and affordability, making Care Compass a viable solution for a broader demographic. The integration of Care Compass with clinical workflows is facilitated through collaboration with healthcare providers, enhancing the utility and effectiveness of the device in real-world applications. The results from initial testing demonstrate the potential of Care Compass to significantly improve health outcomes and quality of life for young individuals. © 2025 IEEE.

关键词： Electronic health record

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Comparative Analysis of Deep Learning Algorithms for Handwritten Digit Recognition 3

Comparative Analysis of Deep Learning Algorithms for Handwri...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Savaram, Padmaja Bandu, Sasidhar Lakshitha, B. Prince Sattam bin Abdulaziz University Dept.of Computer Science Al-Kharj Saudi Arabia Prince Sattam bin Abdulaziz University Dept. of English Al-Kharj Saudi Arabia Vnrvjiet Jntu Dept. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331518592

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. © 2024 IEEE.

关键词： Contrastive Learning

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An Efficient Machine Learning Model for Identification of Seagrasses through Morphometrics 2

An Efficient Machine Learning Model for Identification of Se...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Dubey, Ashwani Kumar Thanikkal, Jibi G Dilipan, E. Sharma, Puneet Shukla, Manoj Kumar Amity University Uttar Pradesh Aset Dept.of Electronics and Communication Engineering UP Noida India Amity University Uttar Pradesh Aset Dept.of Computer Science and Engineering UP Noida India Saveetha University Center for Marine Omics Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai77 India Sr University School of Computer Science & Ai Telangana Warangal India

ISBN: (纸本)9798350360523

Seagrasses are marine flowering plants capable of completing their life cycle underwater. Globally, 72 species of seagrasses have been identified, including 15 species from India. Artificial intelligence/ Machine learning techniques are gaining wide use in many fields, and their applications in marine sciences are still in the initial stage. Considering this, a study is attempted to identify seagrass species through Artificial intelligence/ Machine learning techniques. Since the characteristics of seagrass vary depending on location and other environmental factors, identifying seagrass species is regarded as a difficult task. In the present study, a known seagrass species is taken as a reference and its leaf morphological features are considered for shape description and deep learning-based identification. Leaf morphometrics of seagrass species are drawn to develop the databases. Morphological feature extraction algorithm, descriptor generation algorithm, and deep learning-based identification are applied for species identification. Existing artificial intelligence/ machine learning models use image pixels as input to the deep learning model. However, this is the first attempt to recognize seagrasses through their morphological properties. Here, the leaf shape property is utilized for seagrass identification. The proposed machine learning model received 60% accuracy during testing. Additional investigation was possible with the same algorithm for future seagrass species identification. Utilizing Artificial intelligence/ Machine learning algorithms may lead to identifying new seagrass species. © 2024 IEEE.

关键词： Learning algorithms

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Comparable Studies of Dos Defense Mechanism 2

Comparable Studies of Dos Defense Mechanism

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2nd International Conference on Digital Society and Intelligent Systems, DSInS 2022

作者： Dai, Shuo Dept. of Computer Science and Engineering University of Central Arkansas 201 Donaghey Vae ConwayAR72035-5000 United States Dept. of Computer Science and Engineering University of Central Arkansas 201 Donaghey Vae CA95555-0345 United States

ISBN: (纸本)9781510663244

Today, hacking attacks have affected countless Internet companies and users. Especially DDos attacks. Such attacks are long-lasting and cheap to launch. Currently, such defense mechanisms are not perfect, so DDos attacks have become one of the biggest threats to the global Internet environment. In response to this situation, researchers have proposed some new DDos defense mechanisms to make up for the previous vacancy. Through the experiments of the researchers, it was concluded that the performance of the newly proposed algorithm and mechanism was better than that of the previous ones. In addition, the researchers also proposed solutions based on human solvers to bypass the captcha problem. According to the research objects and research methods, this paper analyzes and classifies the collected relevant literature, aiming at summarizing and discovering possible innovation points. The experimental analyzes and reports of these literatures are then provided. In the final part of this survey, we summarize all the literature and propose research directions and recommendations. © 2023 SPIE.

关键词： Network security

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Monolithic 3D Printed Micro-Optofluidic Device for Two-phase Flow Monitoring and Bioapplications 13

Monolithic 3D Printed Micro-Optofluidic Device for Two-phase...

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13th IEEE International Conference and Expositions on Electric and Power engineering, EPEi 2024

作者： Saitta, Lorena Cutuli, Emanuela Celano, Giovanni Tosto, G Claudio Guarino, Francesca Cicala, Gianluca Bucolo, Maide University of Catania Dept.of Civil Engineering and Architecture Catania95125 Italy University of Catania Dept. of Electrical Electronic and Computer Science Engineering Catania95125 Italy University of Catania Dept. of Biomedical and Biotechnological Science Catania95125 Italy

ISBN: (纸本)9798350356199

In this work, a biocompatible 3D printed micro-optofluidic (MoF) device designed for monitoring two-phase flows is presented. It was manufactured using the Projection Micro-Stereolithography (PμSL) 3D printing technique combined with a biocompatible resin. This device successfully monitored both an air-water slug-flow and a two-phase mixture of micrometric eukaryotic yeast cells (Saccharomyces cerevisiae) suspended in a phosphate-buffered saline (PBS) solution. Its working principle relies on the absorption phenomenon, since different light transmissions are correlated to either the fluid or the cells interfering with a laser beam in a microchannel section. To accomplish this, the MoF device was designed with two micrometric slots for the insertion of optical fibers to capture the light signal. Both the micro-optical and microfluidic components, each 200 μm in size, were integrated into the MoF device as a monolithic structure. A wide experimental campaign was performed on the MoF device investigating different flow rates and cells concentrations. The optimal operative condition for achieving the most robust working ability were determined using a Design of Experiments (DoE) approach. © 2024 IEEE.

关键词： Two phase flow

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Plagiarism Detection and Similarity Checking Program using Machine Learning and String Matching Algorithm 9

Plagiarism Detection and Similarity Checking Program using M...

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

作者： Bhavana, Munugapati Rao, Koppula Srinivas Koduru, Gagan Kumar Vatsal, K.Vijay Krupa Parvathi, S. Sravani, Mogiligidda Mlr Institute of Technology Affiliated to Jntu Dept. of Computer Science & Information Technology Hyderabad India Mlr Institute of Technology Affiliated to Jntu Department of Computer Science & Engineering Hyderabad India Mlr Institute of Technology Hyderabad India Malla Reddy University Department of Computer Science & Engineering Hyderabad India Mlr Institute of Technology Dept.of Computer Science & Information Technology Hyderabad India Mvsr Engineering College Department of Information Technology India

ISBN: (纸本)9798350377972

This research proposes a system for detecting plagiarism in academic submissions. The system utilizes advanced text comparison algorithms to identify instances of plagiarism, ensuring academic integrity and promoting a culture of originality among students. By providing educators with a tool to efficiently detect plagiarism, this system aims to streamline the evaluation process and enhance the overall learning experience for students. This system has the potential to significantly reduce plagiarism incidents and foster a more ethical and productive academic environment. © 2024 IEEE.

关键词： Contrastive Learning

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Privacy-Preserving Deep Learning on Big Data in Cloud

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China Communications 2023年第11期20卷 176-186页

作者： Yongkai Fan Wanyu Zhang Jianrong Bai Xia Lei Kuanching Li State Key Laboratory of Media Convergence and Communication Communication University of ChinaBeijing 100024China Industrial and Commercial Bank of China Zhuhai Branch Zhuhai 519000China Dept.of Computer Science and Information Engineering Providence UniversityTaichung 43301China

In the analysis of big data,deep learn-ing is a crucial *** data analysis tasks are typically carried out on the cloud since it offers strong computer capabilities and storage ***-ertheless,there is a contradiction between the open nature of the cloud and the demand that data own-ers maintain their *** use cloud resources for privacy-preserving data training,a viable method must be found.A privacy-preserving deep learning model(PPDLM)is suggested in this research to ad-dress this preserving *** preserve data privacy,we first encrypted the data using homomorphic en-cryption(HE)***,the deep learn-ing algorithm’s activation function—the sigmoid func-tion—uses the least-squares method to process non-addition and non-multiplication operations that are not allowed by ***,experimental re-sults show that PPDLM has a significant effect on the protection of data privacy *** with Non-Privacy Preserving Deep Learning Model(NPPDLM),PPDLM has higher computational effi-ciency.

关键词： big data cloud computing deep learning homomorphic encryption privacy-preserving

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Deep Learning Techniques for Speech Emotion Recognition 1

Deep Learning Techniques for Speech Emotion Recognition

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1st International Conference on Futuristic Technologies, INCOFT 2022

作者： Bhavani, R. Muni, T Vijay Tata, Ravi Kumar Narasimharao, Jonnadula Murali, K. Kaur, Harpreet Kings Engineering College Dept.of Information Technology Chennai India Koneru Lakshmaiah Education Foundation Dept.of Electrical and Electronics Engineering Vaddeswaram India Koneru Lakshmaiah Education Foundation Dept.of Computer Science and Engineering Vaddeswaram India CMR Technical Campus Medchal Dept.of Computer Science and Engineering Hyderabad India VijayaInstitute of Technology for Women Dept.of Electronics and Communication Engineering Enikepadu India Lovely Professional University Dept.of Computer Science and Engineering Phagwara India

ISBN: (纸本)9781665450461

The identification of feelings sent via one's voice has evolved from a niche field into an essential part of humancomputer interaction (HCI). These systems seek to facilitate more natural interactions between humans and machines by favouring direct voice interaction over the use of conventional input devices in order to comprehend verbal information and to make it simpler for human listeners to respond. A few examples include the use of emotion patterns derived from speech in medical applications, dialogue systems for spoken languages such as those used in cell phone call centres, onboard driving systems in automobiles, and the usage of such patterns in other contexts. In the research that has been done on speech emotion recognition (SER), a variety of methods, many of which are considered to be industry standards in speech analysis and classification, have been applied to the process of extracting emotions from signals. The most crucial stages of the speech emotion recognition (SER) process are the phases that deal with the extraction of features and the categorization of features. For the purpose of speech processing, researchers have developed a variety of features, including prosodic features, vocal traction features, and other hybrid features. The second step involves the categorization of features through the application of deep learning methods. These methods have just lately been put up as a potential replacement for SER's more conventional methods. © 2022 IEEE.

关键词： Speech processing

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Eye Tracking System Using Convolutional Neural Network 5

Eye Tracking System Using Convolutional Neural Network

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5th International Conference on computer and Applications, ICCA 2023

作者： Iskandar, Shatha Jaara, Zahraa Ali, Mohamad Abou Sbeity, Fatima Kasem, Abdallah Hamawy, Lara Lebanese International University Department of Biomedical Engineering Lebanon University of the Basque Country Dept.of Computer Science & Artificial Intelligence Spain Lebanese University Dept. of Physics & Electronics Lebanon Notre Dame University Dept. of Electrical & Computer Engineering Lebanon

ISBN: (纸本)9798350303254

Several diseases and disabilities, including cerebral palsy (CP), cause patients to lose their ability to communicate easily with the outside world. In this paper we aim to detect the movement of the iris that can be used to move a wheelchair for CP patients, write sentences by simply looking at an interactive screen, or even by moving an artificial arm. Thus, the two-dimensional CNN model and the coding method, we proposed, tracks the eye movement, specifically the movement of the iris, using VGG image annotator software and a dataset of several people's eyes images A dataset of 500 images from several patients, where only the eyes were cropped and studied, was taken to train the model about eye movement and detection. The proposed model achieved a mean squared error drop of about 49,720 from the original we started with, which was 50,000. © 2023 IEEE.

关键词： Neural network models

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