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检索条件"任意字段=1st International Conference on Machine Learning Algorithms, ICMLA 2024"

共 3723 条记录，以下是31-40 订阅

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An Approach for Aspect Based Sentiment Analysis Using Multivariate Feature Extraction and machine learning 1

An Approach for Aspect Based Sentiment Analysis Using Multiv...

引用

1st international conference on AIML-Applications for Engineering and Technology, ICAET 2025

作者： Patil, Priyanka Pravin Gupta, Sunil School of Computer & System Sciences JNU Jaipur India

ISBN: (纸本)9798350355611

Aspect-based sentiment analysis (ABSA) is a nuanced field within natural language processing that focuses on extracting sentiment information at a granular level, targeting specific aspects of entities within a text. This paper presents an innovative approach to ABSA by leveraging multivariate feature extraction techniques combined with advanced machine learning algorithms. Our method begins with a comprehensive pre-processing phase that includes tokenization, part-of-speech tagging, and dependency parsing to structure the textual data. We then employ multivariate feature extraction techniques, such as term frequency-inverse document frequency (TF-IDF), word embeddings, and syntactic dependency features, to capture the contextual and semantic richness of the text. These features are fed into machine learning models, including support vector machines (SVM), random forests, and deep learning architectures like recurrent neural networks (RNN) etc. We evaluate the performance of these models using a well-defined benchmark dataset, highlighting the effectiveness of combining multivariate features with machine learning in improving the accuracy of aspect-based sentiment classification. Our experimental results demonstrate that the proposed approach significantly outperforms traditional sentiment analysis methods, providing more precise and insightful sentiment evaluations at the aspect level. This advancement holds promising applications in fields such as customer feedback analysis, social media monitoring, and market research, where understanding specific sentiment nuances is crucial. © 2025 IEEE.

关键词： Semantics

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Leveraging machine learning Models for Proactive Disaster Forecasting 1

Leveraging Machine Learning Models for Proactive Disaster Fo...

引用

1st international conference on AIML-Applications for Engineering and Technology, ICAET 2025

作者： Affan, Daimi Syed Ahmed, Askari Arslaan Maaz, Ahmed Syed Asma, Iqbal Computer Science and Engineering Muffakham Jah College of Engineering and Technology Hyderabad India Electronics and Communication Engineering Deccan College of Engineering and Technology Hyderabad India

ISBN: (纸本)9798350355611

The increasing frequency and intensity of natural disasters, such as earthquakes, tsunamis, floods, and forest fires, necessitate the development of advanced early warning systems. Current disaster prediction systems are hampered by limitations in sensor technology, which is often expensive and primarily effective for large-scale disasters. This project aims to enhance disaster prediction accuracy and timeliness by utilizing real-time data and advanced machine learning algorithms, including XGBoost, Voting classifier, and Random Forest classifier. Additionally, recognizing the long-term health impacts of poor air quality, the project extends its scope to predict the Air Quality Index (AQI). The project seeks to enable proactive risk management and effective disaster response by integrating real-time environmental data and machine learning models. We aim to improve proactive risk management and disaster response by employing real-time data. The outcomes of this project promise significant advancements in disaster preparedness, ultimately safeguarding human lives and infrastructure. © 2025 IEEE.

关键词： Risk management

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Comprehensive Analysis of Attention Deficit/Hyperactivity Disorder using machine learning and Deep learning algorithms 1

Comprehensive Analysis of Attention Deficit/Hyperactivity Di...

引用

1st international conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Sindhu, T.N. Sujatha, S.R. Sri Siddhartha Institute of Technology Department of Computer Science and Engineering Tumakuru India

ISBN: (纸本)9798331529246

Attention deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that affects children from childhood to adulthood. The symptoms of ADHD include attention deficit, impulsive behaviour, hyperactivity, and early diagnosis and treatment are imperative, especially for children as it continues into adulthood. ADHD detection is generally carried out using the existing methods such as machine learning (ML) and Deep learning (DL) methods. The ML methods such as Linear Support Vector machine (SVM), K-Nearest Neighbors (KNN), Linear Discriminative Analysis (LDA), Linear Regression (LR), Random Forest (RF), and Least Square SVM (LS-SVM) are used for the detection and analysis of ADHD. The existing DL methods such as Deep Convolutional Neural Network (DCNN), Graph Recurrent Unit (GRU), Convolutional Long Short-Term Memory (ConvLstM), and Bidirectional LstM are also employed for the detection of ADHD. The ML and DL algorithms enhance ADHD detection and diagnosis by automatically determining large patterns in data, thereby offering higher accuracy. The performance of the existing techniques is evaluated in terms of evaluation metrics: accuracy, precision, sensitivity, and F1-score to assess their effectiveness. © 2025 IEEE.

关键词： Adversarial machine learning

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1st EAI international conference on Computational Intelligence and Generative AI, ICCIGAI 2024

1st EAI International Conference on Computational Intelligen...

引用

1st EAI international conference on Computational Intelligence and Generative AI, ICCIGAI 2024

ISBN: (纸本)9783031766091

The proceedings contain 10 papers. The special focus in this conference is on Computational Intelligence and Generative AI. The topics include: Prediction of Automobile Insurance Claims Using Deep Neural Networks;an IoT-Backed Blockchain System for E-waste Monitoring and Management in Smart Urban Environments;Cross-Silo Federated learning for IDS in IoT Networks;effective Optimized Detection of Cardiovascular Disease by Supervised machine learning Techniques;enhancing Healthcare Data Security with Homomorphic Encryption in Virtual Health Support;investigating Hyperparameter Effects on U-Net for Oral Epithelial Layer Segmentation;serverless Computing and DevOps: A Synergistic Approach to Modern Software Development;revoking Dynamic Access with Enforced Encryption.

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Artificial intelligence as a tool for missing person identification 1st

Artificial intelligence as a tool for missing person identif...

引用

1st international conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Bhardwaj, Samvedna Ahmad, Faizan Mishra, Vaishali Department of AIML Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Department of CSE Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9781032874036

500 missing person cases are unresolved daily in India. Today’s world makes finding a missing person more difficult. Despite the widespread use of social media for information sharing, the process still involves extensive paperwork, consumes a significant amount of time, and offers little assurance of success. The project aims to address the challenges by implementing a technique for enhancing facial recognition searches. CNN is a highly effective deep learning technology for image-based applications. When a missing person’s image is processed, essential information is stored in the back end. Users can save photos of people they find suspicious in a database. The GUI program uses machine learning to compare user-submitted images with those uploaded by the person in charge. The system displays the match and the missing person’s last known location. © 2025 the Author(s).

关键词： Adversarial machine learning

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AdvAIT 2024 - Proceedings of the 1st international Workshop on Advanced Applied Information Technologies with CEUR-WS

AdvAIT 2024 - Proceedings of the 1st International Workshop ...

引用

1st international Workshop on Advanced Applied Information Technologies, AdvAIT 2024

The proceedings contain 26 papers. The topics discussed include: information technologies in IT education as a factor of digitalization of Ukrainian society;information system to enhance agricultural production efficiency based on sustainable development principles;assessment and prognostic models of the efficiency of anilox rollers cleaning process;efficiency of optimization algorithms in the problem of graphic objects placement;machine learning methods’ comparison for land surface temperatures forecasting due to climate classification;mathematical model for digital transformation of business processes in the coordinates of digital marketing;method for analysis and formation of representative text datasets;and formation of an indicator of the information message reliability level by fuzzy logic toolkit.

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machine learning's Effect on Spectral Sensing in CR Vanets to Improve Link stability: A Survey 1

Machine Learning's Effect on Spectral Sensing in CR Vanets t...

引用

1st international conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Gupta, Gaurav Dixit, Amit Saxena, Nishant Quantum University Roorkee India Tula's Institute Uttrakhand Dehradoon India

ISBN: (纸本)9798331518578

VANETs, or vehicle impromptu networks, offer an efficient facility for applications requiring transportation systems with intelligence (ITS). In Communication using VANET, roadside units (RSUs) are the primary element of connections between vehicles and infrastructure. Two major performance problems were identified by analysing cognitive radio (CR)-VANET studies: high latency and energy usage. A vehicle ad hoc network is one way to regulate congestion, provide entertainment, and improve highway security (VANET). VANET's spectrum shortage problems require the Combining cognitive radio (CR), sometimes referred to as CRYANET. CR-VANET's issues have been the subject of numerous research projects. still a difficulty, though, is faster, more robust, and more dependable spectrum sensing. New dimensions of spectrum sharing and SS have been introduced with the application of machine learning (ML) methods. This study surveys several ML-based techniques in the rapidly changing spectrum allocation (DSS) and Collaborative spectrum sensing (CSS) arena, with a focus on the different features that are taken from primary user signals, the many kinds of machine learning algorithms, and the performance metrics that are employed to assess ML systems. © 2025 IEEE.

关键词： Vehicle to roadside communications

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Analysing Effectiveness of Artificial Intelligence on Indian Judicial System 1

Analysing Effectiveness of Artificial Intelligence on Indian...

引用

1st international conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Biradar, Ashwini Shinde, Rucha Honap, Rohini Patil, Seema Naik, Ashutosh TMV's Lokmanya Tilak Law College Pune India

ISBN: (纸本)9798331518578

Artificial intelligence technology has been widely used in the legal system in recent years. Compared to human judges, artificial intelligence is more unbiased, competent, and efficient when used for judicial purposes. Mostly relying on huge data, algorithms, and processing power instead of natural intelligence, it has drawbacks. Artificial intelligence in the judiciary cannot fully replace human judges due to disparities in theoretical framework, application circumstances, and potential and ability. Declaring unequivocally that human judges will always be replaced by judicial artificial intelligence is essential. Investigating legal concerns and the several ways AI affects the judicial system is the goal of the study. Predicting a legal system's conclusion became feasible with the advent of artificial intelligence and related technologies. This study's objective is to provide the most precise legal case prediction possible. Furthermore, because of advances in machine learning (ML), natural language processing (NLP), and processing capacity, these data offer significant opportunities to employ machine-learning models to evaluate and improve judicial systems. DT achieved the lowest accuracy percentage, while KNN achieved the best at 72%. © 2025 IEEE.

关键词： Adversarial machine learning

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Advanced Content Classification and Topic Modelling Techniques for Dark Web Exploration 1

Advanced Content Classification and Topic Modelling Techniqu...

引用

1st international conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Kumar, Jatin Khaneja, Joy Tyagi, Arpit Shalu Manav Rachna University Haryana India Manav Rachna University Haryana Faridabad India

ISBN: (纸本)9798331518578

The Dark Web, an entirely concealed segment of the internet, poses significant challenges in cybersecurity due to its anonymity and the illicit activities it harbors. This study addresses content classification techniques, topic modeling techniques related to Dark Web crawling, and their application roles in finding and analyzing potential harmful content. To understand its application in topic modeling techniques, including text-based and multimedia content classification, and more complex algorithms such as LDA, it would require a detailed literature review. Subsequently, delineate the advantages and disadvantages of various strategies for addressing difficulties specific to the Dark Web, including encrypted data, evolving obfuscation techniques, and the widespread dissemination of disinformation. These findings emphasize that constant innovation in machine learning and NLP techniques is necessary to further increase the effectiveness of Dark Web monitoring. Therefore, this research could have ramifications for both cybersecurity experts and law enforcement agencies by giving them deeper insights into emerging threats and thus providing better strategies to mitigate associated risks. The study indicates potential future research avenues in machine learning, the integration of decentralized technology, and the formulation of ethical frameworks to ensure responsible analysis of Dark Web content. © 2025 IEEE.

关键词： Adversarial machine learning

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Vision-Based Autonomous Car Brake and steering Assistance Using Deep learning 1

Vision-Based Autonomous Car Brake and Steering Assistance Us...

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1st international conference on AIML-Applications for Engineering and Technology, ICAET 2025

作者： Anees, H. Agrawal, Pooja School of Robotics Defence Institute of advanced technology Pune India

ISBN: (纸本)9798350355611

This paper presents a novel approach for enhancing vehicle safety and navigation through an integrated system for lane detection, vehicle alignment, and automatic braking using visual feedback. Our proposed system employs advanced deep learning and computer vision techniques with real-time processing to detect the exact boundaries of lane and ensures precise vehicle movement within the lane. The system continuously analyses lane markings and modifies the vehicle's position to ensure optimal lane adherence by utilizing a combination of machine learning algorithms and camera-based image processing. Additionally, the system incorporates an adaptive braking mechanism that identifies vehicles ahead using visual inputs. Furthermore, the jerks experienced during steering alignment can be greatly reduced by the suggested steering control system. The system's efficiency in various driving conditions is evidenced by its experimental simulation results, which also show improvements in collision avoidance and lane-keeping accuracy. This approach contributes to improved driving convenience and road safety by marking a substantial advancement in autonomous driving technologies. © 2025 IEEE.

关键词： steering

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