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检索条件"机构=Artificial Intelligence and Machine Learning Engineering"

共 2036 条记录，以下是361-370 订阅

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An Optimized Hybrid Deep learning and IoT-Enabled Health System in Retina Image Analysis for Blindness Detection 2

An Optimized Hybrid Deep Learning and IoT-Enabled Health Sys...

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2nd International Conference on Self Sustainable artificial intelligence Systems, ICSSAS 2024

作者： Yamini, J. Kanimozhi, T. Jenefa, A. Bagyalakshmi, K. Aarthi, K. Dhivya, P. Kalaignar Karunanidhi Institute of Technology Department of Artificial Intelligence and Machine learning Coimbatore India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India Ppg Institute of Technology Department of Computer Science and Engineering Coimbatore India Sns College of Technology Department of Mathematics Coimbatore India Kalaignar Karunanidhi Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Bannari Amman Institute of Technology Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798350368413

Early signs of blind identification and retina disorder in retina image has become prominent. Deep learning techniques are used to improve the accuracy in detecting pathological conditions from the retina images. However, the models have some challenges such as suboptimal feature extraction and inefficient filtering methods. Most of the traditional methods often result in overfitting and under fitting, thus decreasing the generalizability and robustness of the model. The proposed hybrid approach combines bilateral filtering with Convolutional Neural Networks (CNN). In this approach, bilateral filtering acts as a pre-processing technique to enhance image quality by reducing noise and preserving edges for the correct extraction of features. The CNN is applied for feature extraction and it is used to self-learn complex patterns from filtered images. The proposed approach gives an accuracy of 95.65% with a loss of 0.18 without overfitting and underfitting. © 2024 IEEE.

关键词： Convolutional neural networks

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Humans Psychological Traits Prediction from their Spending Patterns using machine learning Algorithms 2

Humans Psychological Traits Prediction from their Spending P...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Arpitha, C.N. Sunitha, M.R. Department of Information Science and Engineering Adichunchanagiri Institute of Technology Karnataka Chikkamagaluru India Visvesvaraya Technological University Belagavi590018 India Department of Artificial Intelligence and Machine Learning Adichunchanagiri Institute of Technology Karnataka Chikkamagaluru India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383867

Analyzing human behavioral data produced by new technology has gained popularity in recent years. Spending behavior is one kind of digital footprint that is ubiquitous worldwide but has gotten very little attention up to this point. In this research, the analysis of the extent to which psychological traits at the individual level may be deduced from bank transaction data, utilizing the transaction records of over 100 bank customers is performerd. Using (i) overall spending behavior (i.e., total number and total amount of transaction), (ii) category-related spending behavior (i.e., variety, persistence, and turnover), more extensive feature space is considered and these features are used to investigate how they relate to certain psychological traits, examine how well each feature family performs, and then attempt to determine how much psychological information can be deduced from expenditure records. Most popular supervised algorithms like, Random Forest, Support vector machine (SVM), Decision Tree and Logistic regression algorithms are used for classifying the humans different personality traits and their performance analysis are represented. And the experimantal results claims that better accuracy is attained by Random Forest and Decision tree approaches for categorization. © 2024 IEEE.

关键词： Support vector machines Logistic regression Privacy machine learning algorithms Psychology Prediction algorithms Classification algorithms Performance analysis Decision trees Random forests

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An Effective machine learning Algorithm for Forecasting the Market Value of a House 3

An Effective Machine Learning Algorithm for Forecasting the ...

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3rd International Conference on Applied artificial intelligence and Computing, ICAAIC 2024

作者： Ancy, S.G. Praveenchandar, J. Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Karunya Nagar Coimbatore641114 India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Karunya Nagar Coimbatore641114 India

ISBN: (纸本)9798350375190

machine learning plays a virtual role in everyday speech commands, product recommendation, and even medical fields. But instead of providing better customer service, it provides safer autonomous vehicle systems. House price prediction using machine learning is a hot topic in data science and AI. With the increasing availability of data and the advancement of machine learning algorithms, accurately predicting house prices has never been easier. This study will not only assist housing developers and researchers to determine the most significant factors affecting housing prices but will also help determine the best machine learning model to use in this research. This model will allow people to allocate resources to a request without relying on a broker. According to the results of this study, the random forest regression is the most accurate. Real estate market analysis is one of the most important tools for predicting changes in house prices and rental income. Several techniques have been developed in recent years to solve this problem, including statistical models and data mining methodologies and machine learning algorithms. However, a few issues, including data quality and availability, needed to be fixed. Missing numbers, outliers, and inconsistent formats are common in housing databases. These issues could impair the performance of the prediction models. To solve these issues, thorough data preprocessing is required. Collaboration with data providers and governmental agencies is also required to ensure data coverage and accuracy. © 2024 IEEE.

关键词： Forecasting

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Titanic Survivors Prediction Analysis Using machine learning Algorithms 1

Titanic Survivors Prediction Analysis Using Machine Learning...

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1st International Conference on Computational intelligence for Security, Communication and Sustainable Development, CISCSD 2024

作者： Devamane, Shridhar B. Abhijith, B.H. Purushotham, Bhushan Ravindranath, R.C. Lakshmikantha, G.C. Udayaprasad, P.K. Global Academy of Technology Department of Computer Science and Engineering Bangalore India Global Academy of Technology Department of Artificial Intelligence and Machine Learning Bangalore India Global Academy of Technology Department of Ai and Ml Bangalore India

ISBN: (纸本)9798350365405

machine learning algorithms are computational models that allow computers to acquire knowledge and improve performance on a task by automatically learning patterns and rules from input data provided by the user. In this work we are predicting the Titanic disaster survivors based on a particular dataset. Based on characteristics such as age, sex, ticket class, etc., we applied the Support Vector machine, K-Nearest Neighbors, Decision Tree, and Random Forest algorithm to identify which passenger would survive. © 2024 IEEE.

关键词： Adversarial machine learning

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Email Spam Detection using Graph based Convolutional Neural Network with MALO 2

Email Spam Detection using Graph based Convolutional Neural ...

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2nd International Conference on Data Science and Information System, ICDSIS 2024

作者： Murthy, G. N. Keshava Nayana, C.P. Harshitha, P. Sandhya, M. Siddaganga Institute Of Technology Department Of Electronics And Instrumentation Engineering Karnataka Tumakuru India East West Institute Of Technology Department Of Computer Science And Engineering Bengaluru India Nitte Meenakshi Institute Of Technology Artificial Intelligence And Machine Learning Bengaluru India

ISBN: (纸本)9798350381665

Every day, millions of individuals and organizations rely on email as a fundamental tool for communication, business transactions, and information exchange. However, the pervasive issue of spam email has escalated in recent years, paralleling the exponential growth in email usage. This surge in spam poses a significant challenge for both individuals and businesses, hindering their ability to effectively manage and filter incoming emails. To confront the complexities inherent in spam email classification, such as the inability of conventional models to adequately capture pertinent information due to document length constraints and the lack of subject matter expertise leading to suboptimal classification outcomes, a novel approach is proposed. This study advocates for the integration of a knowledge graph and a graph convolutional neural network (GCNN) as the foundation for a robust classification model. Utilizing Bidirectional Encoder Representations from Transformers (BERT), the text data is encoded, ensuring a comprehensive representation of semantic meaning. The graph network is then initialized with vectors containing dense semantic information for each word. By leveraging a trained entity-relationship extraction model, the interdependencies between entities and other syntactic information are extracted and utilized as edges within the GCNN. Moreover, the model's weights are optimized using the principle of Maximum A Posteriori (MAP) in Latent Optimization (MALO). The incorporation of graph structures enables the model to discern intricate semantic relationships between words, enhancing its ability to comprehend the underlying context of the text. This innovative technique transcends traditional email spam detection methods by amalgamating knowledge-driven characteristics with data-driven features, thereby advancing the efficacy of classification endeavors. © 2024 IEEE.

关键词： Classification (of information)

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A Survey of Advanced Algorithms and Experimental Approaches for Kidney Abnormality Detection in Ultrasound Imaging 3

A Survey of Advanced Algorithms and Experimental Approaches ...

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

作者： Chitra, N. Thulasi Gogula, Sreenivasulu Satapathy, Sukla Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Telangana Hyderabad500075 India Telangana Hyderabad501218 India Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence and Machine Learning Telangana Hyderabad500075 India

ISBN: (纸本)9798331509675

Kidney abnormalities, such as cysts, tumors, and stones, are critical health concerns requiring early and precise detection. This study reviews advanced algorithms and experimental approaches for kidney abnormality detection in ultrasound imaging, emphasizing deep learning (DL) methodologies and explainable AI (XAI) techniques. Contributions include exploring CNN-based segmentation frameworks like SDFNet and U-Net, multi-scale feature fusion, and transfer learning to enhance diagnostic accuracy. Data augmentation and adversarial training are employed to address dataset bias, while Grad-CAM and CAManim provide interpretability, fostering clinician trust. Results demonstrate significant improvements in diagnostic performance, with sensitivity and specificity exceeding 95% and Dice coefficients surpassing 95% on diverse clinical datasets. The findings highlight the transformative potential of DL to automate kidney abnormality detection, reducing dependency on operator expertise while improving accuracy and efficiency. Challenges such as dataset scarcity and computational complexity persist, but future research on multimodal imaging, CNN optimization, and emerging technologies like quantum machine learning presents promising opportunities. This study serves as a guide for advancing diagnostic tools, paving the way for next-generation solutions in clinical applications. © 2025 IEEE.

关键词： Ultrasonic imaging

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Preserving Correlation in Multi-Modal Data: Challenges and Strategies

Preserving Correlation in Multi-Modal Data: Challenges and S...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Koria, Vaishali Bhatt, Nikita Bhatt, Nirav Prajapati, Purvi Cspit Charusat U & PU. Patel Department of Computer Engineering Anand India Cspit Charusat Department of Artificial Intelligence and Machine Learning Anand India Cspit Charusat Department of Information Technology Anand India

ISBN: (纸本)9798350373783

With the exponential growth of social media, an immense volume of data is generated across diverse modalities, each exhibiting distinct statistical properties. Despite these differences, the modalities often convey semantic similarities, representing the same concepts or topics. Preserving these semantic connections poses a significant challenge but holds immense potential for applications such as cross-modal retrieval, recommendation systems, and fraud detection. This paper explores various correlation methods like Canonical correlation analysis (CCA), kernel Canonical correlation analysis (KCCA) and deep Canonical correlation analysis (DCCA), which preserves the semantic similarities between various modalities of data. Experiments are performed on well-known Multi-Modal datasets like MIRFLICKR-25K, NUS-WIDE-10k and XMediaNet. The experiments conclude that DCCA outperforms CCA and KCCA as DCCA has the capability to preserve complex non-linear relationship exhibit in the Multi-Modal data. Consequently, DCCA emerges as a robust choice for applications involving multi-modal data, ensuring the preservation of correlations. © 2024 IEEE.

关键词： Recommender systems

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Deep learning for Enhanced Spectrum Sensing in Cognitive Radio Networks with Blockchain-based Security 3

Deep Learning for Enhanced Spectrum Sensing in Cognitive Rad...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Solanki, Surendra Brahma, Banalaxmi Manipal University Jaipur Department of Artificial Intelligence & Machine Learning Rajasthan Jaipur India Dr. B. R Ambedkar National Institute of Technology Department of Computer Science and Engineering Punjab Jalandhar India

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354348

The growing need for wireless communication requires the optimum use of spectrum. Cognitive Radio (CR) technology enables the utilization of unused frequency channels through the identification and exploitation of these bands. Nevertheless, conventional spectrum sensing approaches are plagued by drawbacks such as instances of missed detections and false alarms. This study presents a new framework that combines deep learning and blockchain technology to improve the accuracy of spectrum sensing and ensure safe spectrum management in cognitive radio networks. Deep learning models are highly proficient at acquiring intricate signal characteristics, which allows them to attain exceptional performance in spectrum sensing. Blockchain technology, characterized by its decentralized recordkeeping system, inability to be altered, and methods for reaching agreement, promotes confidence and thwarts tampering with spectrum sensing data and user access privileges. Our platform utilizes deep learning models on CR devices to achieve precise spectrum sensing. Additionally, it leverages the secure data storage and access control features of blockchain. This integration improves the accuracy of spectrum sensing, guarantees secure and transparent management of the spectrum, and encourages efficient utilization of the spectrum in cognitive radio networks. The evaluation technique and anticipated results are well defined, setting the stage for future investigations in this field. © 2024 IEEE.

关键词： Radio systems

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AI Powered Aadhaar Authentication for Streamlining Loan Waiver Processes with Smart Vision

AI Powered Aadhaar Authentication for Streamlining Loan Waiv...

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artificial intelligence and machine learning Applications Theme: Healthcare and Internet of Things (AIMLA), International Conference on

作者： Anita C S Akhil S Deepak C Mohamed Shaheen Hameed A Department of Artificial Intelligence and Machine Learning R.M.D. Engineering College Chennai India

ISBN: (数字)9798331538965

ISBN: (纸本)9798331538972

In the Indian subcontinent, Aadhaar is a quite familiar document, which consists of a unique identification number generated by UIDAI, an official authority. Aadhaar is serving either as proof of identity or proof of address in various government reforms and financial institution services such as LPG subsidy, opening bank accounts, loan waiver schemes, and so on. Now, this paper concentrates on the artificial intelligence based Aadhaar authentication system for preventing forged instances in supporting documents by verifying the demographic details submitted by loan waiver applicants through three-factor validation with the deployment of an eminent YOLOv11 model. The OCR algorithm induce the performance of the system on a greater scale with simplistic infrastructure and provide an affordable cost solution. In addition, the authorization is strengthened with the involvement of the Verhoeff algorithm. Creating a paradigm to identify genuine borrowers with respect to the loan waiver scheme turns out to be a feasible answer in the AI realm.

关键词： Smart cards machine learning algorithms Optical character recognition Authentication Medical services machine learning Network security Internet of Things Jamming Public finance

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RTGBO based Recurrent Neural Network for Sarcasm Recognition using Twitter Data 4

RTGBO based Recurrent Neural Network for Sarcasm Recognition...

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4th International Conference on Emerging Research in Electronics, Computer Science and Technology, ICERECT 2022

作者： Madhura, G.K. Pareek, Piyush Kumar Parameshachari, B.D. Nitte Meenakshi Institute of Technology Department of Artificial Intelligence & Machine Learning Bengaluru India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Nitte Meenakshi Institute of Technology Department of Electronics and Communications Engineering Bengaluru India

ISBN: (纸本)9781665456357

Increases in popularity of social media stages have been meteoric in fresh years. Through social media, a massive quantity of information is being released to the public. This massive amount of freely accessible data has several potential uses. This work aims to discourse issues with the social media dataset, such as its short text nature (with just 140 to 160 characters of text data), its continuous cyclosis nature, its use of abbreviations and slang, and the growing prevalence of sarcasm in online communications. Sentiment analysis and sarcasm recognition are two of the most important jobs in NLP. To communicate sarcasm, one must provide an outside element that reverses the positive and negative emotions involved. There is a risk that data mining efforts may be misclassified if they are fed sarcastic tweets. In this study, we provide a Recurrent Neural Network-based technique for analysing the tone of tweets (RNNs). The proposed approach combines a memory cell-level and network-level localization system for identifying sarcastic and non-sarcastic tweets with an attention mechanism designed to improve the network's performance. It uses an algorithm called Ring-Toss-Game-Based Optimization (RTGBO) to fine-tune the hyper-parameters. The primary focus of RTGBO is to provide a simulation of player behaviour and rules for the ring toss game. One distinguishing feature of the RTGBO procedure is that it has no tuning knobs. Since newspaper headlines often include sarcasm in an effort to draw in readers, the newspaper sarcasm dataset is used for this research. Existing methods are used to verify the suggested classifier in terms of a number of metrics. The model works quite well and accurately identifies which sentences are sarcastic and which are not. © 2022 IEEE.

关键词： Data mining

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