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Self Sustainable artificial intelligence Systems (ICSSAS), International Conference on

作者： Soham Ajit Dahanukar Dr Tatwadarshi P. Nagarhalli Shruti Sunil Pawar Rujuta Vartak Dept of Artificial Intelligence and Data Science Vidyavardhini’s College of Engineering and Technology Mumbai India Artificial Intelligence and Data Science Vidyavardhini’s College of Engineering and Technology Mumbai India

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

White blood cells (WBCs) must be accurately classified in order to be used in medicine for both therapeutic and diagnostic purposes. The performance of two domains that are very efficient in classifying blood cells are examined in this work, with particular emphasis on the models’ robustness and accuracy of classification. The development of Convolutional Neural Networks (CNNs) and, more recently, Large Image Models (LIMs) has drastically changed the landscape of image recognition. This study examines how well these two architectures perform in comparison when used for image-based tasks with uncommon datasets. Although CNNs have proven to be exceptionally powerful in certain fields, LIMs hold the potential to achieve more broad picture understanding. To do this, this research study compares a pre-trained CNN and a cutting-edge LIM. These results clarify the classification of each strategy, showing that CNNs can potentially beat LIMs at jobs requiring specialized expertise. This work adds to the current discussion on model selection in image analysis by highlighting the significance of dataset properties in choosing the best architecture. This research study intends to direct the creation of more efficient image recognition systems for a variety of applications by comprehending the trade-offs between generalization and specialization.

关键词： White blood cells Analytical models Accuracy Image recognition Image analysis Computer architecture Robustness Convolutional neural networks Stress Medical diagnostic imaging

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System for IoT Agriculture Using LoRaWAN

System for IoT Agriculture Using LoRaWAN

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artificial intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Meher Langote Arpit Chandankhade Munabber Waghade Shizhan Zade Priyanshu Lokhande Artificial Intelligence and Data Science Faculty of Engineering and Technology Sawangi Maharashtra India

The Internet of Things (IoT) has acquired increasing significance in agriculture and food production in recent years, expanding its reach beyond smart city applications. In this comprehensive study, we present a pioneering, exceptionally scalable IoT system specifically tailored for agricultural purposes. This innovative system leverages the Low-Power Wide-Area Network (LoRaWAN) for long-distance, energy-efficient data transmission from sensor nodes to cloud-based services. This IoT solution is underpinned by a highly extensible cloud computing framework that effectively harnesses data streams for in-depth analytics. In the study, we introduce an innovative, highly scalable Internet of Things agricultural system. For long-distance and low-power data transfer from the sensor nodes to the cloud services, this system is built on the LoRaWAn network. Our highly scalable cloud computing solution makes use of data streams for analytics. In this case study, we display some early findings for a grape vineyard.

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Wildlife Recognition in Trap Images: Assessing the Efficiency of YOLO Frameworks

Wildlife Recognition in Trap Images: Assessing the Efficienc...

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2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Srinithi, K. Sushma, S. Fernando J, Aron Nambikai Srivarshini, S. Kalpana, B.N. Karthi, M. St. Joseph's College of Engineering Computer Science and Engineering Chennai India St. Joseph's Institute Of Technology Artificial Intelligence and Data Science Chennai India St. Joseph's Institute of Technology Department of Information Technology Chennai India

ISBN: (纸本)9798331541170

Identifying wildlife in their natural habitats is vital for ecological research, conservation, and biodiversity monitoring. Camera traps, which are remotely deployed devices designed to capture images of wildlife with minimal human interference are commonly used tools for this purpose. However, accurately detecting and identifying animals in these images is challenging due to factors such as camera angle, animal pose, background clutter, and lighting conditions. Object detection models, particularly those in the You Only Look Once (YOLO) family, have shown promise in addressing these challenges. YOLO models are known for their balance of speed and accuracy, making them well-suited for real-time applications. This study evaluates the efficacy of three YOLO model versions - YOLOv8, YOLOv9, and YOLOv10 for wildlife detection in camera trap images. The dataset consists of 16,489 images categorized into seven distinct wildlife species. The study aims to provide a comprehensive comparative analysis of these models based on key performance metrics: precision, recall, F1 Score, and mean Average Precision (mAP). The results reveal that YOLOv10 outperforms the other versions, achieving a precision of 0.956, a recall of 0.902, and a mAP of 0.675. These findings suggest that YOLOv10 is highly effective for detecting wildlife in this dataset. The study's results could significantly enhance wildlife monitoring and conservation strategies by improving the automated identification of animals in camera trap images. © 2024 IEEE.

关键词： Invertebrates

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Real Time Image Processing for Autonomous Vehicles

Real Time Image Processing for Autonomous Vehicles

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Chandrashekhar, Mhamane Sanjeev Bachute, Bhagyashri Amol Gadgoli, Amruta K. Rankhamb, Dinesh Dattatraya Madri, Shrinivas Department of Electronics and Telecommunication Engineering Shree Siddheshwar Women's College of Engineering Maharashtra Solapur413002 India Department of Artificial Intelligence & Data Science VVP Institute of Technology Maharashtra Solapur413008 India

ISBN: (纸本)9798350374933

Autonomous vehicles require real-time image processing to improve their capabilities by allowing them to understand and respond appropriately to their environment. This paper examines the present state of real-time image processing for self-driving vehicles, including the techniques employed, challenges, and advancements. This article investigates methods such as semantic segmentation, object recognition, categorization, and depth estimation, with an emphasis on enhancing vehicle perception, navigation, and decision-making. The article delves into the implementations of significant algorithms on embedded platforms, their computational efficiency, and their deployment in real-world scenarios. In conclusion, the report investigates prospective avenues for additional research to improve the reliability and efficiency of the real-time image processing systems of autonomous automobiles. © 2024 IEEE.

关键词： Semantic Segmentation

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Exploring the Potential of IoT in Healthcare: Challenges and Opportunities 3

Exploring the Potential of IoT in Healthcare: Challenges and...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Verma, Navneet Rana, Sumit Gaba, Shivani Nagpal, Shally Paniapt Institute of Engineering and Technology Dept. of Computer Science & Engg. Haryana Panipat India Paniapt Institute of Engineering and Technology Dept. of Artificial Intelligence and Data Science Haryana Panipat India

ISBN: (纸本)9798350342338

Due to the propagation of IoT technologies Healthcare cannot remain outside of this paradigm. In latest years, there has been a lot of interest in the Internet of Things (IoT) technology to relieve the strain on healthcare systems brought on by the ageing population and the rise in persistent illnesses. One of the main obstacles preventing advancement in this field is standardization or global connectivity between the Internet of Things (IoT) and the medical surroundings. All from electrical engineers to data engineers have a significant problem in having to integrate everything in a global setting. The difficulties that healthcare IoT confronts, such as security, privacy, and less-energy procedures, are discussed, and suggestions are provided for future research paths. This paper is written to gather data on various Internet of Things architectures now used in Healthcare and analyse how well different academics have applied it. © 2023 IEEE.

关键词： Architecture Connectivity Healthcare Internet of Things Security

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A Corpus-Based Auto-encoder-and-Decoder Machine Translation Using Deep Neural Network for Translation from English to Telugu Language

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SN Computer science 2023年第4期4卷 354页

作者： Mahanty, Mohan Vamsi, Bandi Madhavi, Dasari Department of Computer Science and Engineering Vignan’s Institute of Information Technology (A) Visakhapatnam 530046 India Department of Artificial Intelligence and Data Science Madanapalle Institute of Technology and Science Madanapalle 517326 India Department of Computer Science and Engineering Sridevi Women’s Engineering College Hyderabad 500075 India

There is a huge demand for machine translation to design automated auto-encoders that can convert English to the Telugu language. Neural machine translation (NMT) is a new and effective technology that has significantly benefited traditional machine translation strategies. As a result, the primary goal of this work is to propose an auto-encoder–decoder-based deep neural network for translating English to the Telugu language. Deep neural networks are used for learning, where input language is supplied to the neural network following data processing and analysis. The neural network then does auto-tuning, which helps to improve the translation quality. This proposed work produces target sentences in the Telugu language, resulting in a higher Bilingual Evaluation Understudy (BLEU) Score and a lower Word Error Rate (WER). The BLEU finds the similarity of the translated text by Machine Translation models with high-quality references and WER finds better accuracy in identifying the target sentence. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Deep neural network Encoder-and-decoder English-to-Telugu Neural machine translation

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Energy Efficient Clustering and Sink Mobility Protocol Using Hybrid Golden Jackal and Improved Whale Optimization Algorithm for Improving Network Longevity in WSNs

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China Communications 2025年第3期22卷 16-35页

作者： S B Lenin R Sugumar J S Adeline Johnsana N Tamilarasan R Nathiya Department of Electronics and Communications Engineering Sri Manakula Vinayagar Engineering CollegePondicherryIndia Department of CSE FET-Jain UniversityBengaluruKarnatakaIndia Department of Artificial Intelligence and Data Science Sri Indu College of Engineering and TechnologyHyderabadTelanganaIndia Department of Electronics and Communications Engineering Sri Indu College of Engineering and TechnologyHyderabadTelanganaIndia Department of Electronics and Communications Engineering Dhanalakshmi Srinivasan UniversitySamayapuramTrichyIndia

Reliable Cluster Head(CH)selectionbased routing protocols are necessary for increasing the packet transmission efficiency with optimal path discovery that never introduces degradation over the transmission *** this paper,Hybrid Golden Jackal,and Improved Whale Optimization Algorithm(HGJIWOA)is proposed as an effective and optimal routing protocol that guarantees efficient routing of data packets in the established between the CHs and the movable *** HGJIWOA included the phases of Dynamic Lens-Imaging Learning Strategy and Novel Update Rules for determining the reliable route essential for data packets broadcasting attained through fitness measure estimation-based CH *** process of CH selection achieved using Golden Jackal Optimization Algorithm(GJOA)completely depends on the factors of maintainability,consistency,trust,delay,and *** adopted GJOA algorithm play a dominant role in determining the optimal path of routing depending on the parameter of reduced delay and minimal *** further utilized Improved Whale Optimisation Algorithm(IWOA)for forwarding the data from chosen CHs to the BS via optimized route depending on the parameters of energy and *** also included a reliable route maintenance process that aids in deciding the selected route through which data need to be transmitted or *** simulation outcomes of the proposed HGJIWOA mechanism with different sensor nodes confirmed an improved mean throughput of 18.21%,sustained residual energy of 19.64%with minimized end-to-end delay of 21.82%,better than the competitive CH selection approaches.

关键词： Cluster Heads(CHs) Golden Jackal Optimization Algorithm(GJOA) Improved Whale Optimization Algorithm(IWOA) unequal clustering

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A Novel Approach for Heart Disease Classification using Cascaded XGBoost Algorithm 3

A Novel Approach for Heart Disease Classification using Casc...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Rajitha, M. Megala, S. Santhana School of Computer Studies RVS College of Arts and Science Sulur Coimbatore India Artificial Intelligence and Data Science SNS College of Engineering Coimbatore India Department of Computer technology Hindustan College of Arts and Science Coimbatore India

ISBN: (纸本)9798331532420

Heart disease remains a leading cause of mortality worldwide. Accurate and timely diagnosis is crucial for effective treatment and prevention. This research proposes a novel approach using a cascaded XGBoost model to improve the accuracy and efficiency of heart disease diagnosis. With gradient boosting, the cascaded XGBoost model effectively handles complex interactions within the dataset, leading to superior performance compared to traditional machine learning techniques like logistic regression, support vector machines, and decision trees. The proposed model was evaluated on a standard heart disease dataset, demonstrating significant improvements in terms of accuracy, precision, and recall. Furthermore, the model's ability to identify key factors contributing to heart disease provides valuable insights for clinical decision-making. This research highlights the potential of machine learning techniques, particularly cascaded XGBoost, in advancing the field of heart disease diagnosis and improving patient outcomes. © 2024 IEEE.

关键词： Contrastive Learning

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Collaboration of Mixed Reality for Interactive Visualization of Ocean Mapping 2

Collaboration of Mixed Reality for Interactive Visualization...

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2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023

作者： Rithish, Ramamoorthy Sathya Vaishali, V. Balavedhaa, S. Sushmita, V. Sabitha, B. Ug Mechatronics Engineering Kumaraguru College of Technology Coimbatore India UG Artificial Intelligence and Data Science Kumaraguru College of Technology Coimbatore India Mechatronics Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (纸本)9798350306811

Technological advancements in recent times have led to the combination of artificial intelligence (AI) and Mixed Reality (MR), making it a powerful tool to address major challenges in a variety of fields. Ocean mapping includes a wide network of research associations which collect ocean data for analysis. This information about ocean floors is vital for various industrial and education fields. A few machine learning algorithms and data pre-processing techniques help to collect, clean, and manipulate data faster and at a lower cost due to the increased speed and efficiency it offers. This allows us to create MR infographics which can be used by research associates to visualize and view the data in a better way. This immersive technology enhances the method of visualization and makes it easier for researchers to conclude a final decision from the analysis. This paper includes an introduction of key terminologies, followed by the current ocean mapping methods i.e., Bathymetry and Multibeam Mapping, along with their respective visualization techniques, and how MR can be used for visualizing the collected data for further study purposes. This paper also briefs about how the implementation of MR in ocean mapping can be advantageous to a wide range of industries. © 2023 IEEE.

关键词： Mixed reality

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Advanced Power and Voltage Regulation in a DC Microgrid with Solar Energy and Battery Storage 15

Advanced Power and Voltage Regulation in a DC Microgrid with...

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15th International Renewable Energy Congress, IREC 2025

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

ISBN: (纸本)9798331516772

This research investigates the modeling, design, and simulation of a microgrid with advanced energy management controller, developed using MATLAB/Simulink. This paper presents a comprehensive analysis, modeling, and simulation of a smart controller for a boost converter, using an artificial neural network (ANN) for maximum power point tracking (MPPT) to enhance power output. The study begins with a comparison between traditional MPPT techniques and the ANN-based approach. These MPPT strategies are compared in terms of their response to dynamic weather conditions (such as temperature and irradiance variations) and their ability to minimize oscillations around the maximum power point. The simulation results demonstrate that the ANN-based MPPT method outperforms the conventional P&O and IC methods, providing faster response times and higher output power. Additionally, the system's performance was evaluated under conditions of power shortage and during battery charging and discharging cycles, showing the effectiveness of the smart controller in maintaining system stability and optimizing energy management. © 2025 IEEE.

关键词： MATLAB

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