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AI-Based Desktop VIZ: A Voice-Activated Personal Assistant-Futuristic and Sustainable technology 10

AI-Based Desktop VIZ: A Voice-Activated Personal Assistant-F...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Subi, Martin Rajeswari, M. Rajan, J. Jerlin Sri Harshini, S. Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Karunya School of Management Coimbatore India Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350353068

Modern technology has led to the development of advanced technologies that employ a variety of cutting-edge techniques, including speech recognition, machine learning, artificial intelligence, and OpenAI. A popular example of these technologies is Voice-Activated personal assistant This is one of the most often used examples of speech recognition and machine learning technologies. Using OpenAI, these models may be adjusted and their exploratory potential can be enhanced. This AI-powered desktop has been configured to pull data that the user requests using OpenAI. Additionally, it makes use of the AutoGUI package in Python to control the mouse and click on windows in other programs. While the existing model used a standard user interface to present the user with stored outputs, this model makes use of OpenAI to deliver more precise and comprehensive results. Furthermore, Auto GUI facilitates automatic user window interaction. This model is an excellent illustration of OpenAI, GUI, and speech recognition combined into one. © 2024 IEEE.

关键词： Speech recognition

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AI-Enhanced Farming: A Real-Time Monitoring App powered by IoT 5

AI-Enhanced Farming: A Real-Time Monitoring App powered by I...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Gayathri, R. Raaj, R. Sathesh Balaanand, M. Kumar, J. Arun Sasikala, N. Rajesh, K. Psna College of Engineering and Technology Department of Computer Science and Engineering Dindigul India Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Tagore Institute of Engineering & Technology Department of Computer Science and Engineering Salem India Dayananda Sagar University Department of Computer Science and Engineering Karnataka Harohalli India Ssm Institute of Science and Technology Department of Electronics and Communication Engineering Dindigul India

ISBN: (纸本)9798331504403

The Constant challenge faced by the farmers is to maintain their crops and enhance productivity. Despite various methods and solution evolving, still farmers can't upgrade to it. Farmers are not aware of the modern agricultural methods. The major cause is that the software and solution developed are of higher complexity. To upgrade them, technology is the foremost important tool to stand up in this modern world. To help them with their problem and adapt to the modern world, the development of software with inbuilt AI and user-friendly features (in agriculture) is needed. This study proposes a novel Agri-tech app, which is a IoT based app used by the farmers for weather forecasting, soil moisture and nutrient content analysis. The developed application also recommends the crop type that can be cultivated in the soil by using the database of soil moisture and nutrient content. IoT sensors fixed in the field are used to detect the moisture level in the soil and notify the farmers to irrigate the land. It recommends the farmer to add manures and fertilizers to soil by calculating the amount of nitrogen, phosphorous, and oxygen contents present in the soil. Using these technical innovation farmers can monitor their fields over a system or smart phone. Moreover, the proposed approach can promote crop diversity and rotation for healthier soil, encourage sustainable practices like organic farming. © 2024 IEEE.

关键词： Soil moisture

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An Intensive Review of Brain Tumor Detection from MR Images using Machine Learning and Deep Learning Networks 15

An Intensive Review of Brain Tumor Detection from MR Images ...

引用

15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Rajat Rani, Seema Kumar, Sanjeev Chaba, Yogesh Department of Computer Science and Engineering Guru Jambeshwar University of Science and Technology Haryana Hisar125001 India

ISBN: (纸本)9798331300579

The use of Magnetic Resonance Imaging (MRI) scan techniques in the field of Brain tumor detection is the main topic of this review study, which explores the changing field of brain tumor *** tumors are a serious medical problem that require early and precise discovery in order to effectively *** introduction highlights the frequency of brain tumors and highlights the critical role that MRI scans serve in providing non-invasive, fine-grained soft tissue *** paper critically addresses the challenges inherent in brain tumor detection, ranging from tumor size and location variability to limitations associated with conventional MRI *** study devotes a significant amount of space to the amalgamation of automated image processing and machine learning methodologies, specifically stressing the application of diverse machine learning models for automated identification of brain tumors in magnetic resonance imaging *** investigation includes a review of datasets that are often used in research as well as approaches that have been adopted by more recent *** evaluation of several models and algorithms reveals the advantages, disadvantages, and performance indicators of *** study concludes with an investigation of possible future paths, considering new technology and multimodal imaging strategies that may improve the precision and effectiveness of MRI scans in the identification of brain *** conclusion, this comprehensive review not only synthesizes existing knowledge but also underscores the imperative for ongoing research to advance the field and improve outcomes for individuals grappling with brain tumors. © Grenze Scientific Society, 2024.

关键词： Magnetic resonance imaging

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Sentiment Analysis on Multilingual Data: Hinglish

Sentiment Analysis on Multilingual Data: Hinglish

引用

International Conference on Data Analytics and Management, ICDAM 2023

作者： Kumar, Ashish Mangotra, Abhishek Ailawadi, Ayush Jain, Rachna Arora, Monika School of Computer Science Engineering and Technology Bennett University Uttar Pradesh India Department of Information Technology Bhagwan Parshuram Institute of Technology Delhi India

ISBN: (纸本)9789819965465

In this study, sentiment analysis was done on a collection of bilingual Hindi and English tweets. Before performing sentiment analysis, we first preprocessed the dataset to improve its quality by removing hashtags, URLs, and mentions. To classify the tweets as good, negative, or neutral, we used a range of machine learning models, such as logistic regression, multinomial Naive Bayes, support vector machines, random forests, and decision trees. We compared the results and utilized the metrics of accuracy and precision to assess the performance of the models. The investigation showed that the hybrid model, which included logistic regression with support vector machines, produced the highest accuracy and precision scores. Hyperparameter adjustment was used to improve the logistic regression model’s performance even more. The best model was then used to predict sentiments on a new dataset of unlabeled Hindi-English tweets. The empirical findings demonstrated that the hybrid model outperformed the other machine learning models utilized in this study, achieving an accuracy of 61.7% and a precision of 62.1%. When compared with all forecasts that were truly positive, the precision score showed the percentage of true positives, while the accuracy score showed the percentage of accurate predictions. The results showed that a highly accurate and exact solution for sentiment analysis on Hindi-English tweets is a hybrid model combining logistic regression and a support vector model with hyperparameter tweaking. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Sentiment analysis

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Extended Kalman Filter with Sliding Window For Vehicle Tracking in Urban Area

Extended Kalman Filter with Sliding Window For Vehicle Track...

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2024 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2024

作者： Hao, Miin-Jong Su, Kai-Yuan Chang, Shun-Hsyung Department of Computer and Communication Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Microelectronics Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan

ISBN: (纸本)9798350389210

For intelligent transportation system (ITS) in 5G, the range between road-side unit (RSU) and vehicle user equipment (VUE), as well as the degree of arrival (DOA), are collected by the RSU as primary measurements. These measurements, including time and azimuth, usually suffer the influences of clock offset and noise in real-world situations where the statistical properties of the signal change over time. This paper proposes the Sliding Window Extended Kalman filter (SWEKF) to enhance the accuracy of vehicle position estimation and tracking. The slide windowing technique breaks measured signals into smaller segments. A window of a fixed size is moved over the signal, and calculations are performed on each segment, making it useful for dealing with non-stationary signals. The algorithm of SWEKF is developed in details, and simulations are performed. Simulation results show that our scheme outperforms the conventional EKF in both position estimation and tracking sensibility. © 2024 IEEE.

关键词： Extended Kalman filters

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An Ensemble Machine Learning Approach with Hybrid Feature Selection Technique to Detect Thyroid Disease 2nd

An Ensemble Machine Learning Approach with Hybrid Feature S...

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2nd International Conference on Big Data, IoT and Machine Learning, BIM 2023

作者： Roy, Priyanka Srijon, Fahim Mohammad Sadique Hasan, Mahmudul Bhowmik, Pankaj Nitu, Adiba Mahjabin Department of Computer Science and Engineering Hajee Mohammed Danesh Science and Technology University Dinajpur5200 Bangladesh

ISBN: (纸本)9789819989362

Thyroid disease is a prevalent health problem that requires early detection for effective treatment. However, there is no universal model for detecting thyroid abnormalities efficiently. This study proposes a three-layer thyroid disease detection framework that utilizes different feature engineering techniques to explore various thyroid datasets, improving model performance. We propose a hybrid framework to identify the most relevant features utilizing feature selection techniques contributing significantly to the model’s performance. We evaluate the proposed bagging XGBoost ensemble model’s performance against K-nearest neighbors, extreme learning machines, and random forest classifiers. It surpassed all with 98.44% accuracy with only 53.57% of the dataset’s features. The proposed model outperformed other classifiers with 98.65% accuracy during cross-validation on the second dataset. Stress testing with train–test split ratios of 70–30 and 30–70 produced 94.92% and 94.68% accuracy rates, respectively, that also outperform the benchmark ML models. These findings have significant implications for improving thyroid disease diagnosis using machine learning techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Feature Selection

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A Disaster Prediction Ensemble Classifier Using Artificial Intelligence

A Disaster Prediction Ensemble Classifier Using Artificial I...

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2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Bharathi, V. Saranya, A. Shukla, Garima Shekhawat, Deepika Awasthi, Vanshaj Kumar, C.N.S. Vinoth Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Amity University Amity School of Engineering and Technology Department of Computer Science and Engineering Mumbai India Amity University Department of Computer Science and Engineering Mumbai India Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur Chennai India

ISBN: (纸本)9798331533311

Natural and man-made disasters may have severe impacts, but disaster prediction can help lessen such effects. The natural catastrophes that strike India on a regular basis cause immense devastation in terms of human lives and material possessions. Predicting the start and development of disasters in real-time is essential for reducing their effect. For more precise and trustworthy catastrophe predictions, this research presents an AI-powered catastrophe Ensemble B-Classifier network (DE BCN). The DPEC ensemble technique improves forecast accuracy over individual models by integrating various predictive outputs from numerous machine learning models that specialise in distinct kinds of disasters. This is where the data was originally pulled from Kaggle. To finish off the preprocessing, the norma clash filter was used. The next step is to use the optimisation method known as mustard twin swarm to extract characteristics that are connected to the tragedy. With any luck, the calamity Ensemble-B Classifier network will be able to forecast the calamity. Python and the Kaggle database were the environments used to carry out the investigation. According to the results of the simulations, the suggested approach is better than the current standard for predicting the catastrophic state. © 2024 IEEE.

关键词： Prediction models

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A Hybrid Approach for Visual Recognition of Object States 39

A Hybrid Approach for Visual Recognition of Object States

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Gouidis, Filippos Computer Science Department University of Crete Heraklion Greece Institute of Computer Scicence Foundation for Research and Technology Heraklion Greece

ISBN: (纸本)157735897X

The basic objective of my research work is to address the challenging problem of recognizing object states in a visual context by integrating data-driven and symbolic approaches. In particular, I focus on the Zero-shot variation of this task. Key contributions include the development of novel methods that exhibit state-of-the-art (SOTA) performance, the creation of a new object states dataset, the formulation of novel problems, the successful integration of low-level and high-level approaches, and comprehensive analyses that highlight the specific challenges posed by the problem. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Protecting the Privacy of IoMT-Based Health Records Using Blockchain technology

Protecting the Privacy of IoMT-Based Health Records Using Bl...

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International Conference on Network Security and Blockchain technology, ICNSBT 2023

作者： Swetha Priya, T.C. Sridevi, R. Department of Computer Science and Engineering JNTUH University College of Engineering Science and Technology Telangana Hyderabad India

ISBN: (纸本)9789819944323

Internet of medical things (IoMT) has become one of the revolutionary technology nowadays due to wide spread use of IoT in healthcare applications. IoMT improves the human–machine interaction enhancing the instantaneous monitoring of patient’s health and also involves patients in making decisions. So, the current medical devices have to be transformed into IoMT-based medical devices to collect patient information instantaneously. The patient’s health-related information can be observed from remote location without any need to go to hospital, and this data can be processed and is sent to mediators for further use. So, we are in need of technology that secures the confidentiality of the health information of patients. Blockchain is one such technology that helps in securing privacy of patient’s health-related data. Blockchain technique uses a sequence of blocks comprising the block ID, previous block ID, and the transactions. The preceding block ID can be used to maintain a link between subsequent blocks. These links continue until the first block is reached. This ID contains hash in blocks, and this hash is appended to subsequent blocks in chain. Any modifications to the patient information in a block will change the subsequent blocks in the chain also. So, changes to the data made by a hacker will become impossible. It uses public key cryptography for performing transactions between nodes. So, use of such type of cryptographic hash technique and chain formation for the blocks guarantees the security and privacy of the patient’s health information or records. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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On Applying Preprocessing Techniques on Gallbladder Ultrasound Images 8

On Applying Preprocessing Techniques on Gallbladder Ultrasou...

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8th IEEE International Conference on Information and Communication technology, CICT 2024

作者： Suji, R. Jenkin Godfrey, W. Wilfred Madhav Institute of Technology and Science Department of Electronics Engineering Gwalior India ABV-Indian Institute of Information Technology and Management Department of Computer Science and Engineering Gwalior India

ISBN: (纸本)9798331531539

Ultrasound imaging is a common and non-invasive method for diagnosing gallbladder diseases, including gallbladder cancer (GBC). However, the inherent challenges of ultrasound images-such as noise, low contrast, and variable image quality-necessitate the use of preprocessing techniques to enhance image clarity and improve diagnostic accuracy. In this study, we apply a series of preprocessing methods, including median filtering, Gaussian filtering, bilateral filtering, adaptive histogram equalization, and wavelet transforms, to gallbladder ultrasound images. We evaluate the effectiveness of these techniques using a variety of quality metrics such as sharpness, signal-to-noise ratio (SNR), entropy, structural similarity index (SSIM), and perceptual image quality evaluator (PIQE). A detailed analysis of the preprocessing methods was conducted, where the performance of each technique was systematically compared. Additionally, we highlight the statistical variations in these metrics across different preprocessing approaches to identify the most suitable methods for enhancing GBC detection. The results show that specific preprocessing methods improve image quality and feature clarity, aiding accurate gallbladder disease detection models. This work is an initial groundwork for enhancing the diagnostic value of ultrasound images for GBC. It suggests future directions for integrating these methods into machine-learning pipelines for automated disease detection. © 2024 IEEE.

关键词： Wavelet transforms

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