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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Rithani, M. Shabarirajan, K.J. Shivanesh, B. Syamdev, R.S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India New Horizon College of Engineering Department of AI & ML Bangalore India

ISBN: (纸本)9798331518943

Crop disease detection is an important maintenance aspect for the upkeep of agricultural productivity. This paper presents a novel autonomous robot system designed to navigate and monitor crop health. The system was developed within a ROS framework using a camera to capture real-time video feeds of crops and a YOLO v8 model for detecting the presence of three specific plant diseases. Such a configuration resembles a farm in the presence of crops and trees and hence can be termed as a dirty farm. Automated navigation will cover all the area. The detection model processes each frame to detect plant disease. Such an approach has the potential to make improvements in early detection of diseases, thus leading to proper management and informed decisions about precision agriculture. © 2024 IEEE.

关键词： Microrobots

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Detection and Quantification of TB Bacilli in Sputum Smear Images 1

Detection and Quantification of TB Bacilli in Sputum Smear I...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Rithani, M. Jeevanantham Sona, B. Syamdev, R.S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India New Horizon College of Engineering Department of AI \& ML Bangalore India

ISBN: (纸本)9798331518943

Early and precise detection is essential to stop the spread of tuberculosis (TB) and enhance patient outcomes. This research aims to create an automated system that employs cutting-edge image processing and machine-learning approaches to identify tuberculosis (TB) bacilli in sputum smear microscopy pictures. An automated solution is required to increase diagnostic accuracy and efficiency because traditional diagnostic methods are labor-intensive, time-consuming, and prone to human mistakes. In the end, we optimize the best-performing model to reduce processing time while preserving diagnostic accuracy. Our goal is to assess the performance of several models in terms of training time, testing time, and accuracy. By enabling the use of sophisticated diagnostic technologies in environments with limited resources, the suggested method supports international efforts to combat tuberculosis. © 2024 IEEE.

关键词： Convolutional neural networks

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Natural Language Processing-Driven Voice Recognition System for Enhancing Desktop Assistant Interactions

Natural Language Processing-Driven Voice Recognition System ...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Swathi Gowroju Sandeep Kumar Shilpa Choudhary Department of Computer Science and Engineering (AI&ML) Sreyas Institute of Engineering and Technology Hyderabad Telangana India School of Computer Science and Artificial Intelligence SR University Warangal Andhra Pradesh India Department of Computer Science and Engineering (AI&ML) Neil Gogte Institute of Technology Hyderabad Telangana India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

A voice assistant is a software application designed to perform tasks and provide services based on the user's commands. This involves recognizing speech patterns and responding with synthetic speech. With these assistants, users can automate various tasks, including sending emails, managing schedules, and playing media. As technology progresses, people increasingly rely on these tools, particularly computers. Traditionally, commands are input through keyboards, but voice input offers a more accessible and user-friendly alternative. This is particularly beneficial for visually impaired individuals who find it challenging to use a keyboard. Voice assistants enhance the usability of computers by understanding and processing voice commands accurately. This allows for hands-free operation, improving convenience and productivity. Their advanced speech recognition technology involves sophisticated algorithms and machine learning to understand natural language and context. Once commands are processed, the assistant executes the tasks and provides feedback through voice or other means. For visually impaired users, voice assistants are transformative, enabling them to interact with their computers without needing a visual interface. They can perform tasks such as browsing the internet, sending messages, and controlling smart home devices through voice commands alone. Overall, voice assistants represent a significant advancement in making technology more accessible and user-friendly, allowing for a more inclusive environment where users can efficiently complete tasks through voice interaction.

关键词： computers Performance evaluation Visualization Technological innovation Personal voice assistants Keyboards Smart homes Machine learning Speech synthesis Usability

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Chaotic Water Wave Optimization and Support Vector Machine of Role-Based Access Control Policies in Enterprise IAM

Chaotic Water Wave Optimization and Support Vector Machine o...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Priyanka, K. Harshini, Chikka Aluvala, Srinivas Abbas, Haider Mohammed Ram Kumar, Ramaswamy Padmanathan Nitte Meenakshi Institute of Technology Department of Ise Bengaluru India Department of Cse Hyderabad India Sr University Department of Computer Science and Engineering Warangal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Gokaraju Rangaraju Institute of Engineering and Technology Department of Ai&ml Bachupally Hyderabad India

ISBN: (纸本)9798350375442

The access control state of a system is complex, and Machine Learning (ml) based access control decision engines have demonstrated advantages of policies. However, struggle to effectively implement Role-Based Access Control (RBAC) policies because require large enterprises Identity and Access Management (IAM) system with a high volume of users and access logs. This paper, proposed Chaotic Water Wave Optimization (CWWO) algorithm combined with a Support Vector Machine (SVM) to classify users based on selected features, leading to more accurate and secure role to assignment. The dataset used is obtained from amazon and pre-processing is done using Min-max normalization, which scales the feature to a specific range, ensuring that different features are on same scale. Feature selection is performed using the CWWO algorithm, which helps in selecting relevant feature and reduce dimensionality. The classification using SVM for binary classification, which assigns relevant feature and handle large amounts of data, achieving better accuracy. The proposed ensemble model technique for ship motion detection is achieving better outcomes such as high accuracy of 0.98, precision of 0.97, F1-score of 0.96 and recall of 0.98 and respectively. The existing technique such Random Forest (RF) and Decision Tree (DT) are evaluated of proposed method. © 2024 IEEE.

关键词： Random forests

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Transfer Learning-Based Fault Classification Methods for Solar Photovoltaic Modules: A Comparative Analysis 6th

Transfer Learning-Based Fault Classification Methods for Sol...

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6th International Conference on Intelligent Communication, Control and Devices, ICICCD 2024

作者： Supriya Shukla, Ashutosh Manchanda, Mahesh Department of AI and ML COER University Roorkee247667 India Electrical Cluster School of Engineering UPES Dehradun India Department of Computer Science and Engineering GEHU University Dehradun India

ISBN: (纸本)9789819783281

India’s reliance on fossil fuels to meet its energy generation demands has a negative impact on both the environment and public health. In the present energy transition, photovoltaic (PV) technologies have become more widely accepted for commercial use, achieved significant progress in technology, and have a major role to play in reducing the harmful environmental effects of fossil fuel-based power generation. Soiling research on solar PV has notably increased in recent years due to its critical role in reducing the performance of solar PV cells. Therefore, to guarantee the amount of power yield, efficient monitoring of the soiling is crucial. It is difficult to manually analyse soiling severity on the panel images due to the non-uniformity of the type and amount of soiling faults. Deep learning models have been successfully applied to the analysis of solar panel images and soiling categorization;nevertheless, this approach may require a significant amount of training time and may lead to gradient descent. To tackle these training issues, we propose a comparative study of two pre-trained neural network architectures, namely, MobileNetV3small and RestNet50, on a publicly available dataset of 875 images of six solar PV fault classes, namely, clean, dust, bird-drop, electrical damage, physical damage, and snow-covered. The experimental results show that MobileNetV3small achieved a better performance of 98%. The results show that MobileNetV3small obtained a promising result of 11% higher classification accuracy than ResNet50, with an accuracy of 86.6%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electric losses

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BPCPR-FC: blockchain-based privacy preservation with confidentiality using proxy reencryption and ring signature in fog computing environments

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International Journal of Information Technology (Singapore) 2023年第6期15卷 3343-3357页

作者： Gowda, Naveen Chandra Bharathi Malakreddy, A. School of Computer Science and Engineering REVA University Bengaluru India Department of AI and ML BMS Institute of Technology & Management Bengaluru India

The usage of edge devices in distributed fog and cloud computing applications is increasing day by day in the current digital communication world. The edge devices can be mobile devices, smart devices, IoT devices, and vehicular devices. The edge devices produce a variety of data in various applications such as healthcare, educational, vehicular networks, smart systems and industrial IoT. The private data of a user or any generated data at the edge devices will be stored in the centralized cloud server and then can be accessed by any user when required upon permission by the data owner. An increase in the rate of data sharing in current digital communications directly increases vulnerable activities which require security services like access control, privacy, data confidentiality, anonymity, unforgeability, and nonframeability. In this article, a blockchain-based privacy preservation with confidentiality using proxy reencryption and ring signature in fog computing environments (BPCPR-FC) mechanism is proposed to achieve the requirements. The proposed BPCPR-FC is verified using mathematical analysis for a set of security issues and an informal security analysis is carried out for a set of common attacks. Cryptanalysis of the proposed BPCPR-FC scheme is discussed and shows the improvement of encryption and decryption time by 8–22% and 10–23% compared to existing schemes. The blockchain implementation of the proposed BPCPR-FC is carried out in the Truffle suite and discussed the improvement of block preparation time by 7–17% compared to suitable existing works. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Blockchain Cloud computing Confidentiality Fog computing Privacy Proxy reencryption Ring signature

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Fracture Detection Health Network (FDHN): A solution to generate bone fracture insight

Fracture Detection Health Network (FDHN): A solution to gene...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Divyanka Thakur Priya Pal Sobhin Somraj Sarita Bopalkar Sunil Chavan Department of Computer Science Engineering (AI&ML) Smt. Indira Gandhi College of Engineering Navi Mumbai India

This research addresses the critical issue of bone fracture detection in medical imaging using FDHN, emphasizing the need for efficient and reliable techniques to enhance patient care. Leveraging the YOLOv8 real-time object detection model, the study focuses on automating and optimizing the bone fracture detection process in X-ray images. The architecture of YOLOv8, with its balanced performance characteristics, is explored, and a custom dataset of 200 annotated X-ray images is employed for model training and testing. The study demonstrates the impact of training duration on model accuracy, achieving a mean Average Precision (mAP) of 0.886 at the 50 IoU (Intersection over Union) threshold after 100 epochs. The model's ability to detect objects under stricter criteria (50-95 IoU) also significantly improved, reaching a mAP of 0.636. These results underscore the value of extended training in enhancing the model's accuracy, making it a reliable tool for bone fracture detection in medical imagery. The research contributes to optimizing clinical workflows, reducing diagnostic errors, and ultimately improving patient outcomes in the healthcare sector.

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Navigating Challenges in Creating Effective Chatbots with Natural Language Processing 15th

Navigating Challenges in Creating Effective Chatbots with Na...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Sathwik, Abbaraju Sai Rao, A. Koteswara Saiteja, Bheri Madhavi, K. Reddy Doppalapudi, Asutosh Amudha, P. VIT-AP University Amaravati India Department of Computer Science and Engineering Andhra Loyola Institute of Engineering and Technology Vijayawada India AI&ML School of Computing Mohan Babu University Tirupati India Coimbatore Ayya Avinashilingam Nagar641108 India

ISBN: (纸本)9783031889912

A conversational agent, also referred to as a chatbot, is computer software that can communicate with users in natural language. Because of a number of issues, developing a successful chatbot model is still difficult. First, understanding the nuances and context of human language remains a complex task, especially when dealing with slang, dialects, or ambiguous expressions. Second, maintaining engaging and coherent conversations over extended periods can be a hurdle, as chatbots need to adapt to changing topics and user moods. Third, ensuring ethical and responsible behavior in chatbots, such as avoiding biased or harmful responses, requires continuous monitoring and refinement. Lastly, staying up-to-date with the evolving field of NLP and ai is essential to incorporate the latest advancements and provide users with increasingly sophisticated conversational experiences. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Chatbots

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Robust Digital Watermarking based on Machine Learning

Robust Digital Watermarking based on Machine Learning

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： Manish Rai Hemlata Department of Computer Science and Engineering (AI&ML) Manipal University Jaipur Jaipur India

In the generation of big data and systems administration, it is important to build up a safe and strong digital watermarking plan with high computational effectiveness to ensure copyrights of digital works. However, the greater part of the current strategies focuses on robustness and embedding limit, losing a security, or requiring huge computational assets in the encryption procedure. Digital watermarking has become an important method for ensuring the security of digital copyrights and ensuring the authenticity of data because of rapid advancement and broad usage of technologies related to multimedia and network connectivity. which has been used for practical application such as medical application, currency, digital product identification and many more. The success story of digital watermarking techniques deepened on the embedding of the watermark image and present image. In the current decade, the machine learning feature-based watermarking techniques create new milestone concerning security concern. The machine learning and hybrid digital watermarking performances an especially vital role in optimization and selection of features which improves the performance of watermarking process. In this paper, we have presented countless digital watermarking, methods which is based on various machine learning procedures along with their comparison, and limitation.

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A survey on model-based reinforcement learning

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science China(Information sciences) 2024年第2期67卷 59-84页

作者： Fan-Ming LUO Tian XU Hang Lai Xiong-Hui CHEN Weinan ZHANG Yang YU National Key Laboratory for Novel Software Technology Nanjing University Polixir. ai Department of Computer Science and Engineering Shanghai Jiao Tong University

Reinforcement learning(RL) interacts with the environment to solve sequential decision-making problems via a trial-and-error approach. Errors are always undesirable in real-world applications, even though RL excels at playing complex video games that permit several trial-and-error attempts. To improve sample efficiency and thus reduce errors, model-based reinforcement learning(MBRL) is believed to be a promising direction, as it constructs environment models in which trial-and-errors can occur without incurring actual costs. In this survey, we investigate MBRL with a particular focus on the recent advancements in deep RL. There is a generalization error between the learned model of a non-tabular environment and the actual environment. Consequently, it is crucial to analyze the disparity between policy training in the environment model and that in the actual environment, guiding algorithm design for improved model learning, model utilization, and policy training. In addition, we discuss the recent developments of model-based techniques in other forms of RL, such as offline RL, goal-conditioned RL, multi-agent RL, and meta-RL. Furthermore,we discuss the applicability and benefits of MBRL for real-world tasks. Finally, this survey concludes with a discussion of the promising future development prospects for MBRL. We believe that MBRL has great unrealized potential and benefits in real-world applications, and we hope this survey will encourage additional research on MBRL.

关键词： reinforcement learning model-based reinforcement learning planning model learning model learning with reduced error model usage

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