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检索条件"机构=Artificial Intelligence and Data Science Faculty of Engineering And Technology"

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Machine Learning for Real-Time Language Translation: A Server-Side Approach to Overcome Cross-Cultural Communication Challenges 4

Machine Learning for Real-Time Language Translation: A Serve...

引用

4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Elango, S. Mohanaprakash, T.A. Siva, D. Gowtham, A. Sasikumar, A.N. Sunitha, T. Department of Computer Science and Engineering SOET CMR University Bengaluru India Department of Computer Science and Applications SRM Institute of Science and Technology Chennai India Madanapalle Institute of Technology & Science AP India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Artificial Intelligence & Data Science Saveetha Engineering College Chennai India

ISBN: (纸本)9798331515720

The evolution of human civilization has been intrinsically linked to advancements in technology, leading to the development of multiple languages as mediums of communication. However, this linguistic diversity poses significant challenges in cross-cultural communication. Despite numerous efforts and achieving near state-of-The-Art results in language translation, a universal solution remains elusive. This research study proposes an advanced approach to machine translation by utilizing server-side machine learning to enable seamless communication across various languages. By sending GET or POST requests through JQuery, users can efficiently translate text. The process is structured into three key steps: speech recognition (conversion of spoken words to text), translation using machine learning algorithms, and speech synthesis (conversion of translated text to audio). This approach aims to enhance real-Time translation capabilities, offering a more effective and user-friendly solution for overcoming language barriers. © 2024 IEEE.

关键词： Adversarial machine learning

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Investigation of Red Fox Algorithm for Solving Task Assignment Problem in Heterogeneous Unmanned Aerial Vehicle Swarm 3

Investigation of Red Fox Algorithm for Solving Task Assignme...

引用

3rd International Conference on artificial intelligence For Internet of Things, AIIoT 2024

作者： Manaswitha, K. Issac, Titus Silas, Salaja Terance, J Sebastian Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798350372120

In recent years, there have been significant advancements in Unmanned Aerial Vehicles (UAVs), leading to their integration into everyday life. UAVs are constrained by limited energy, communication, and localization. The various challenges in UAV research include localization, routing, navigation, and task assignment. Recently, UAV swarms have been employed across a wide range of applications. This study focuses on solving task assignment problems within UAV swarms, considering the unique multi-objective resource constraints of UAVs. The work explores the feasibility of adopting the Red Fox Algorithm to address the task assignment problem in UAV swarms. The investigation examines two primary input parameters: Task Energy Demand and distance. The evaluation includes energy utilization and shortest distance as performance metrics. Notably, the proposed approach outperforms the Particle Swarm Optimization Algorithm (PSO), resulting in a 17.47% lower energy utilization while maintaining a 17.83% reduction in the shortest distance travelled. By leveraging the Red Fox Algorithm, the work aims to enhance the efficiency and effectiveness of task allocation in UAV swarms, contributing to their broader adoption across various applications. © 2024 IEEE.

关键词： Energy utilization

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Optimization of Path Planning for Automated Vehicles Using SLAM Algorithm 1st

Optimization of Path Planning for Automated Vehicles Using S...

引用

1st International Conference on data engineering and Machine intelligence, ICDEMI 2023

作者： Prinslin, L. Selvi, R. Revathi, B. Yuvabharathi, E. Sharmila, L. Department of Computer Science and Engineering Agni College of Technology Chennai India Department of Artificial Intelligence and Data Science Dhanalakshmi College of Engineering Chennai India Department of Information Technology Jeppiaar Institute of Technology Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9789819776153

SLAM (simultaneous localization and mapping) is a technique for automated vehicles that enables simultaneous map building and vehicle localization. The vehicle is able to map out uncharted terrain owing to SLAM algorithms. Engineers use the map information to carry out tasks such as path planning and obstacle avoidance. An autonomous machine needs the ability to plan the motion of a high-level command. For example, if a task is given to explore a particular area, the bot has to plan accordingly to complete the required task. To achieve this level of accomplishment, the path works on an algorithm that solves SLAM and path planning problems. The machine is designed in such a way that it will work as a grass cutter will complete the task in the given frame. The grass-cutting machine creates its own path in the frame and detects obstacle coming in the path. In this way, the SLAM robot is utilized in the application of agricultural field. In the current work, we have designed a 3-D model of a SLAM robot and its various components like the LIDAR Camera, Jetson Nano, Lithium-Ion Battery, Motor, and Body design machine. This model is capable of performing grass-cutting operations using a cutting blade and collection of waste in a sack attached to the machine. This machine works on automation based on path planning algorithms using ROS, this eliminates human interference. Various kinds of research papers were reviewed to compare different kinds of mechanisms and various components were analyzed for the final model *** check and verify that the author names are correctly recognized and presented in the correct sequence order, which is [given name, middle name/initial, family name] for the author(s). In addition, please verify that the name(s) and respective affiliation(s) shown on the metadata page are valid and make any necessary amendments if ***, have edited the affiliation of the last author. © The Author(s), under exclusive license to Springer Nat

关键词： SLAM robotics

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A Novel Energy Efficient Sensor data Encryption in Fog Based IoT Networks

A Novel Energy Efficient Sensor Data Encryption in Fog Based...

引用

2023 IEEE International Conference on Next Generation Electronics, NEleX 2023

作者： Manicka Raja, M. Manoj Kumar, S. Kiruba, B. Sri Krishna College of Engineering and Technology Department of Computer Science and Business Systems Coimbatore India KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350319088

Smart devices equipped with embedded systems, including CPUs, sensors, and communication hardware, utilize web connectivity to gather and relay data from their environment, forming a segment of the Internet of Things network. Sensors in an IoT network are resource-constrained devices, but traditional data security techniques use complicated security mechanisms with long processing and reaction times, which reduce the network's overall lifespan. As a consequence, we proposed an EELWEP (energy efficient light weight encryption process) system to keep the data acquired by each sensor node private. When using this strategy, the secret key is exchanged using the Diffie-Hellman method using the Pretty Good Privacy (PGP) program, and a large portion of the operations is carried out via the use of symmetric cryptography. The energy usage and calculation time on the sensor network are significantly reduced as a result of this method. The suggested system is simulated, and their results are analysed using a variety of parameters in comparison to existing benchmark schemes. Comparing the suggested technique to the current approaches reveals that it outperforms the alternatives in the vast majority of situations studied. © 2023 IEEE.

关键词： Internet of things

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Machine Learning and Deep Learning Driven Approaches for Energy Efficient Medical Image in Thyroid Diagnosis 5

Machine Learning and Deep Learning Driven Approaches for Ene...

引用

5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Priyadharshini, S. Karthikeyan, V.V. Kumar, N. Rathina Revathi, K. SNS College of Engineering Department of Artificial Intelligence and Data Science Tamil Nadu Coimbatore India SNS College of Technology Department of Electrical and Electronics Engineering Tamilnadu Coimbatore India SNS College of Engineering Department of Information Technology Tamil Nadu Coimbatore India

ISBN: (纸本)9798331530013

Any machine learning method's main goal is to define or create an algorithm that mainly helps in automatic prediction and analysis. Mainly the model, which is created using machine learning approaches, is expected to find out some hidden patterns in the data collections and use these patterns for the decision making or predictions automatically. This research goal is to create predictions about Thyroid Diagnosis. This research work will use machine learning approaches to analyses the variables. By incorporating these changes into model, it will be able to predict a person's future risk of disease. First step is collecting a database containing details on people with and without a diagnosis of Thyroid Diagnosis. This configuration file will form the basis for planning the actions. Next, need to clean and prioritize the message to remove unnecessary information and then separate it into planning and testing phases. Then select the appropriate machine learning approaches to prepare for the research work. This may include different techniques. Prepare the code to improve its ability to accurately predict Thyroid Diagnosis by using training data and changing its parameters. Overall, this research goal is to provide perfect and reliable predictions of Thyroid Diagnosis that help in early detection and prevention of the disease, ultimately leading to better health outcomes for people at risk of Thyroid Diagnosis. © 2024 IEEE.

关键词： Adversarial machine learning

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A Performance Analysis of Supervised Machine Learning Models for Thyroid Disease Detection 4

A Performance Analysis of Supervised Machine Learning Models...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Gaurkhede, Sanskruti Burle, Rushikesh Puri, Chetan Datta Meghe Institute of Higher Education and Research Computer Science and Design Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Mahartashtra Wardha442001 India

ISBN: (纸本)9798331523923

Thyroid disease is a major causes of the main reasons for medical diagnosis and prediction is thyroid illness, whose growth is a interesting topic in medicinal study. One of our body's utmost dynamic body part is the thyroid secreter. Thyroid hormone issues are in control for adaptable metabolism. Two predominant thyroid disorders that issue thyroid hormones to controller the body's breakdown are thyrotoxicosis and hypothyroidism. Thyrotoxicosis and hypothyroidism are two mutual thyroid situations that impair the thyroid's capacity to generate thyroid hormones and regulate the build's absorption. data cleaning actions remained utilised to kind the data simple sufficient for analytics to demonstration the likelihood of affected role having thyroid illness. Thirteen clinicopathologic features from the dataset occupied from UCI machine learning source are employed In order to prediction the reappearance of well-differentiated thyroid cancer, this article analyses and classifies thyroid disease models. Machine learning is a key component in the sickness prediction process. For complex knowledge activities, like medical diagnostic and prediction jobs, it is crucial to take a tough information base that can be combined and used as a cross model. We too optional a quantity of machine learning methods for thyroid preventative diagnosis in this work. Machine learning methods such decision trees, logistic regression, random forest, and gradient boosting remained used to evaluate the likelihood that a patient will acquire thyroid disease. © 2025 IEEE.

关键词： Endocrinology

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Dimensionality Reduction for Classification Using Divide-and-Conquer Based Genetic Programming 13

Dimensionality Reduction for Classification Using Divide-and...

引用

13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Wang, Peng Xue, Bing Liang, Jing Zhang, Mengjie School of Electrical and Information Engineering Zhengzhou University Zhengzhou China Center for Data Science and Artificial Intelligence School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand School of Electrical Engineering and Automation Henan Institute of Technology Xinxiang China

ISBN: (纸本)9798350308365

Dimensionality reduction (DR) is to obtain meaningful low-dimensional representation concealed within high-dimensional data. Genetic programming (GP) has been used to achieve DR for classification because of the flexible representations and promising performance. However, one drawback of many current GP-based DR methods is the high computational demands, potentially limiting their utility in high-dimensional scenarios. To tackle this issue, this work develops a new GP algorithm to achieve DR for classification. A divide-and-conquer strategy based on feature clustering is proposed to split the original feature set into multiple small feature groups. Each tree in the multi-tree GP is used to learn one high-level feature from each feature group. The performance of the proposed method is examined on 10 classification datasets with varying difficulty. The results indicate that the new GP method achieves significantly better classification performance than commonly used feature construction and feature selection methods. More importantly, the proposed GP method uses fewer nodes but achieves better or comparable classification accuracy than the baseline multi-tree GP method. © 2024 IEEE.

关键词： Dimensionality reduction

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artificial Neural Network-Based Inverter Control and MPPT for Enhanced Performance of Hybrid Microgrids With PV and Battery Storage 15

Artificial Neural Network-Based Inverter Control and MPPT fo...

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15th International Renewable Energy Congress, IREC 2025

作者： Rajab, Z. Elrashid, M. Elhashane, M. Gomez-Romero, J. Khalil, A. Seliman, A. Mohamed, F. Benghazi University Faculty if Engineering Electrical and Electronic Department Libya University of Granada Department of Computer Science and Artificial Intelligence Spain Technical University of Denmark Dtu Engineering Technology Denmark Omar Al-Mukhtar University Faculty of Engineering Electrical Department Libya Authority of Natural Science Research and Technology Tripoli Libya

ISBN: (纸本)9798331516772

Today's power system network has become more complex, with greater responsibilities and challenges in providing a secure, reliable, and high-quality energy supply to communities. A smaller entity of the electrical network, known as a Microgrid (MG), has become more popular in recent times to enhance the reliability and security of energy supply during any energy crisis in the utility network. Microgrids (MG) can provide clean energy through renewable resources such as photovoltaic (PV) systems, wind, and energy storage, offering an effective solution for power supply in both remote and urban residential communities. To ensure the stable operation of a standalone microgrid based on a photovoltaic system, it is essential to implement various mechanisms and control strategies simultaneously for optimal efficiency. This paper introduces a complete model of a hybrid microgrid consisting of a PV source, an artificial Neural Network (ANN)-based Maximum Power Point Tracking (MPPT) system, a storage unit, a DC to AC converter with an ANN controller, and both DC and AC loads. The performance of the controller under various ambient conditions and load variations is tested. The ANN controller demonstrates good performance in different situations. © 2025 IEEE.

关键词： Hybrid power

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A Review on Applications of IoT in Healthcare 3

A Review on Applications of IoT in Healthcare

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Thakare, Vedant Verma, Prateek Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798331509675

This article examines how the Internet of Things (IoT) is rapidly transforming the medical field. IoT technologies are revolutionizing patient care, increasing productivity, and boosting results in a variety of ways, from wearable technology and telemedicine to smart hospitals and personalized medicine. Predictive analytics, remote patient monitoring, and the use of AI and machine learning for more precise diagnosis and treatment regimens are important applications. The future of IoT in healthcare is extremely bright, with developments in 5G, AI, and other cutting-edge technologies set to further transform the sector and enhance human health, even while issues like data security and privacy still need to be resolved. © 2025 IEEE.

关键词： Electronic health record

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The Utilization of Google Cloud in the Healthcare Industry 2

The Utilization of Google Cloud in the Healthcare Industry

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Kapse, Rishikesh Gourshettiwar, Palash Faculty of Engineering and Technology Dept. of Artificial Intelligence and Data Science Maharashtra Wardha India Faculty of Engineering and Technology Dept. of Computer Science And Medical Engineering Maharashtra Wardha India

ISBN: (纸本)9798331505745

This essay's primary focus is on Google Cloud's use in healthcare and how it affects data management, teamwork, and cost-effectiveness. With a focus on Google Cloud, which improves the processing, storing, and accessibility of medical data, the study examines the trends, advantages, and persistent challenges that come with the increasing usage of cloud computing technology. These consist of remote patient monitoring, 'artificial intelligence,' and 'machine learning' applications in diagnostics, diagnostics, and personalized medicine. data privacy, security issues, and concerns with the HIPAA incident are among the problems that are discussed along with all the significant elements that could facilitate the cloud migration of healthcare. © 2025 IEEE.

关键词： Theranostics

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