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检索条件"机构=Computer Science with Artificial Intelligence and Machine Learning"

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Hybrid Approach of CNN & RNN for Breast Cancer Detection and Segmentation

Hybrid Approach of CNN & RNN for Breast Cancer Detection and...

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Smart Technologies and Systems for Next Generation Computing (ICSTSN), International Conference on

作者： P. Manju Bala S. Priyadharshini S. Usharani A. Balachandar Department of Artificial Intelligence and Data Science IFET College of Engineering (An Autonomous Institution) India Department of Computer Science and Engineering IFET College of Engineering (An Autonomous Institution) India Department of Artificial Intelligence and Machine Learning IFET College of Engineering (An Autonomous Institution) India

ISBN: (数字)9798350391565

ISBN: (纸本)9798350391572

Breast cancer is a serious worldwide health concern, and advanced diagnostic tools are needed for an accurate and timely identification of the illness. In order to classify breast cancer through ultrasound pictures, this method suggests a hybrid strategy that combines Convolutional Neural Networks (CNNs) with Recurrent Neural Networks (RNNs). CNN integration makes it possible to extract spatial data from ultrasound pictures and identify complex patterns that may be signs of cancer. Concurrently, RNNs are used to record temporal relationships between successive ultrasound frames, which enables the model to identify changing features of possible anomalies. By combining the advantages of both CNNs & RNNs, the suggested hybrid architecture improves the model's capacity to identify minute, dynamic patterns that are essential for precise diagnosis. In comparison to standalone CNN or RNN models, the hybrid model performs better in classification, as evidenced by experimental assessments carried out on a variety of ultrasound picture datasets. The findings highlight the hybrid approach's potential to improve breast cancer diagnostic skills and present a viable path for raising the precision and dependability of ultrasound-based tumour classification techniques.

关键词： Ultrasonic imaging Recurrent neural networks Accuracy Three-dimensional displays Breast cancer Spatial databases Planning

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An Enhanced Traffic Incident Detection using Factor Analysis and Weighted Random Forest Algorithm 5

An Enhanced Traffic Incident Detection using Factor Analysis...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Kanna, P. Rajesh Vanithamani, S. Karunakaran, P. Pandiaraja, P. Tamilarasi, N. Nithin, P. Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamil Nadu Erode India M. Kumarasamy College of Engineering Department of Master of Computer Applications Tamil Nadu Karur India Nandha Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Erode India Vel Tech Rangarajan Dr. Sagunthala Department of Computer Science and Engineering R&D Institute of Science and Technology Tamil Nadu Chennai India Nehru Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Tamil Nadu Erode India

ISBN: (纸本)9798331518097

Efficient and precise traffic incident detection is essential to reduce casualties and property damage. To address the issue of unbalanced event data, this work offers a novel methodology known as FA-WRF (Factor Analysis and Weighted Random Forest). This approach combines dimensionality reduction through factor analysis with classification using weighted random forests, data preparation through the Synthetic Minority Over-Sampling Technique (SMOTE), and data preparation with SMOTE. The included feature of severity detection is highlighted, as are the performance metrics of the FA-WRF model using well-established metrics such as detection level rate, number of false count alarm rate, classification type rate, and area specification under the receiver operating characteristic curve (AUC). The superiority of the FA-WRF model is illustrated using real-world expressway traffic data represented by imbalanced incidents through thorough comparisons with various machine learning-based algorithms such as Support Vector machine, k-nearest neighbours algorithms, Logistic Regression algorithms, and decision-making trees. In addition to advancing incident detection, this technique has encouraging prospects for enhancing traffic management procedures and well-informed decision-making procedures in transportation networks. © 2024 IEEE.

关键词： Dimensionality reduction

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AI-Powered Advertisement Design: A PIL-Based Approach for Quality and Performance Analysis 3

AI-Powered Advertisement Design: A PIL-Based Approach for Qu...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Chilupuri, Harshitha Ramesh, G. Praveen, J. Gude, Venkataramaiah Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Artificial Intelligence and Machine Learning Hyderabad India Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science and Engineering Hyderabad500090 India Gokaraju Rangaraju Institute of Engineering and Technology Department of Electrical and Electronics Engineering Telangana Hyderabad500 090 India Gp Technologies Llc United States

ISBN: (纸本)9798331515911

The paper presents the development of a Data science solution that applies generative AI models in order to create high-quality images to be used in visual marketing and advertising. Using the Python library PIL (Pillow) along with machine learning frameworks, the solution combines predefined product images with the selected backgrounds, enhancing image synthesis techniques for more appealing and contextually relevant visuals. This leads to improved efficiency and effectiveness in the creation of content by automatically placing product images onto the background images used in marketing campaigns. The approach demonstrated a 98.06% SSIM (Structural similarity Index) score, indicating significant visual quality improvement. Initial results suggest a 15% enhancement in visual appeal compared to traditional image creation methods. © 2025 IEEE.

关键词： Data science

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Smart Locker 2.0: Leveraging IoT and machine learning for Secure, User-Friendly Public Storage

Smart Locker 2.0: Leveraging IoT and Machine Learning for Se...

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2025 International Conference on artificial intelligence and Data Engineering, AIDE 2025

作者： Chandrappa, S. Prithviraj Aditya Pai, H. Rajesh, I.S. Manjunath Sargur Krishnamurthy, R. Sneha Shetty, R. Dept. of Computer Science and Engineering Bengaluru India NMAM Institute of Technology Nitte Dept. of Computer Science and Engineering India MIT Art Design and Technology University Department of CSE Pune India BMS Institute of Technology and Management Dept. of AI & ML Bengaluru India JP Morgan & Chase Co Houston United States NMAM Institute of Technology Dept. of Artificial Intelligence and Machine Learning Nitte India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527518

Smart locker systems are a revolutionary technology, combining advanced automation and connectivity to address increasingly demanding requirements for secure, efficient, and convenient storage solutions in numerous scenarios. These intelligent lockers use the latest technologies, such as the Internet of Things (IoT) and mobile applications, to revolutionize the way people store their belongings. Existing Smart Locker System lacks measurable results. To sustain its claims, we include key findings. For security, the system utilizes RFID authentication and biometric security, which reduces unauthorized access by 98% and blocks spoofing attacks. For convenience, a study with 50 users found 92% satisfaction, with 87% preferring it over traditional lockers due to faster access, averaging 3 seconds. For environmental benefits, the system cut power use by using sleep mechanism and saved paper usage by switching to cloud logs. Real-World tests achieved 98% user satisfaction with the university system proving its efficiency, security, and sustainability. This system expounds on multifaceted benefits and functionalities in smart lockers. First, it covers how these lockers provide better security compared to old lock-and-key systems. Using access controls reduces the risk of theft and illegal access to one's valuables and sensitive belongings by a great degree. It has an easy reservation, access, and management of personal lockers via a remote means;this proves to be seamless without needing any physical keys and contactless interactions, ideal for modern spaces considering hygiene and efficiency is top priority. Moreover, it goes on to analyze the flexibility of smart locker systems. From corporate offices to co-working spaces, universities, shopping malls, and public transportation hubs, these lockers cater to diverse user needs, creating flexible and scalable solutions adaptable to a variety of environments. © 2025 IEEE.

关键词： Authentication

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Comparative Analysis of Air Quality Forecasting Using machine and Deep learning Based Approaches

Comparative Analysis of Air Quality Forecasting Using Machin...

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artificial intelligence and Data Engineering (AIDE), International Conference on

作者： Sowmya HK Koyal Chandresh Jesy Janet Kumari J. Sonia Maria D’Souza New Horizon College of Engineering Artificial Intelligence and Machine Learning Bangalore India Department of Computer Science and Engineering The Oxford College of Engineering Visvesvaraya Technological University Belagavi Bangalore India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

Many emerging nations are experiencing severe air pollution on account of the growing urbanization and industry. Because air pollution has an impact on global human health and sustainable development, governments and it is becoming source of concern in society. Anticipating air pollution thus became essential. Prediction of air quality has gained popularity by virtue of the volume and a range of data that air quality surveillance systems have collected. This paper uses a Python framework to analyze the air quality, implementing historical air quality data analysis for Bangalore city and predicting its quality exercising a number of machine and deep learning (D-learning) models. In the proposed work, 5 distinct machine learning(M-learning) models including Random Forest regression (RFR), Support Vector Regression (SVR), Linear regression (LR), Decision Tree (DT), and Long Short- Term Model of D-learning Technique (D-LSTM) have been accustomed to compute the Air Quality Index (AQI) of Bangalore city. This strategy has been put into practice in Google Collab with GPU run time. As stated by the measures including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), D-learning- based LSTM model has demonstrated better reliability than other M-learning models. Performance metrics values for LSTM is superior compared to the rest of M-learning models

关键词： Deep learning Analytical models Atmospheric modeling Computational modeling Urban planning Predictive models Air pollution Nitrogen Indexes Long short term memory

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Investigating Youtube’s Educational Videos: Patterns, User Conduct and Suggestion Techniques

Investigating Youtube’s Educational Videos: Patterns, User ...

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Electronic Systems and Intelligent Computing (ICESIC), International Conference on

作者： Koushik Sundar Eugene Berna I Vijayakumar R V.D. Ambeth Kumar Vijay K Citibank NA Jersey City NJ USA Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode India Department of Computer Science and Design Rajalakshmi Engineering College Chennai India Department of Computer Engineering Mizoram University Aizawl Mizoram India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India

ISBN: (数字)9798331522988

ISBN: (纸本)9798331522995

This research voyage drew us far into the world of instructional videos on YouTube, a platform that promotes learning and discovery. Our goal was to identify patterns and trends in instructional films so that we might improve the user experience by suggesting better content. We conducted an analysis of data that we directly gathered from YouTube using certain technologies. The varied lifespans of trending videos across nations were one fascinating discovery. Certain videos were more well-known for a longer period of time than others. We were captivated by the cultural diversity found in video lifespans. We also looked at likes, dislikes, views, and comments to find robust user involvement worldwide. Through engagement data, different countries demonstrated their preferences for expressing oneself. It was discovered that a video’s popularity was greatly influenced by the timing of the content’s release. Content producers can stay relevant by keeping in mind this correlation. Sentiment analysis also uncovered emotional overtones connected to video tags, offering perceptions into what appeals to viewers. We also identified the optimal time, based on user involvement and behavior, to watch instructional videos. Ultimately, we used recommendation algorithms to tailor the YouTube learning experience by fusing content-based and collaborative filtering. All things considered, clarifies the complex world of instructional content and offers advice to users and producers on this dynamic platform. Our research will continue to influence the direction of more interesting instructional content as YouTube develops.

关键词： Sentiment analysis Video on demand Films Watches Market research User experience Timing Web sites Cultural differences Videos

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machine learning with Monarch Butterfly Optimization for Prediction of Emergency Patient Admission Status 5

Machine learning with Monarch Butterfly Optimization for Pre...

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5th IEEE International Conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Vijayarangam, S. Vasundhara, S. Beherac, Nihar Ranjan Das, Shyamali Chandre, Shanker Rajagopal, R. Sri Indu College of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India G.Narayanamma Institute of Technology and Science Department of Humanities and Mathematics Telangana Hyderabad India Swiss School of Business and Management Geneva Petit-Lancy1213 Switzerland CMR Institute of Technology AECS Layout Department of Artificial Intelligence and Machine Learning Karnataka Bangalore India SR University Department of Computer Science & Artificial Intelligence Telangana Warangal India Alliance College of Engineering & Design Department of Computer Science & Engineering Alliance University Karnataka Bengaluru India

ISBN: (纸本)9781665493604

Emergency admission is one of the most important resources of healthcare expenditure. The accessibility of predictive methods is support to recognize the admission status of arriving patients and the patient mix that supports managing downstream resources and decreasing overpopulation at emergency department (ED) by restriction boarding delays. In recent times, machine learning (ML) techniques are well enhanced and executed in several applications like health protection, cancer detection, and hospital operations. But, most difficult problem with ML is that each technique has parameter and without optimizing the parameter, attaining a higher accuracy method develops very complex. This study introduces machine learning with Monarch Butterfly Optimization for Prediction of Emergency Patient Admission Status (MLMBO-PEP A) technique. The presented MLMBO-PEP A technique can predict the admission status of the emergency patient. In the presented MLMBO-PEP A technique, data normalization is initially performed to scale the medical data. For predictive process, the MLMBO-PEP A technique uses Dendritic Neuron Model (DRN). The MBO technique is used to optimize the forecasting outcomes of the DRN model. A widespread simulation study is performed for ensuring the better performance of the MLMBO-PEP A technique. The resultant values implied the improved efficacy of the MLMBO-PEP A technique under different evaluation metrics. © 2023 IEEE.

关键词： Forecasting

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Adaptive Ant Selection for Pheromone Update in Ant Colony Optimization

Adaptive Ant Selection for Pheromone Update in Ant Colony Op...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wang, Biao Duan, Dan-Ting Yang, Qiang Zhao, Xiao-Yan Li, Tao Liu, Dong Zhang, Jun College of Computer and Information Engineering Henan Normal University Xinxiang China Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province Xinxiang China Communication University of China Key Laboratory of Media Audio & Video Ministry of Education Beijing China School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China College of Artificial Intelligence Nankai University Tianjin China Hanyang University Department of Electrical and Electronic Engineering Ansan Korea Republic of

ISBN: (纸本)9781665410205

Ant selection for updating the pheromone is one most crucial operation in ant colony optimization (ACO). In this direction, this paper designs an adaptive ant selection strategy (AAS) to adaptively and dynamically select ants to update the pheromone for ACO. Therefore, a new ACO, called AAS-ACO is developed. Specifically, AAS-ACO first assigns a non-linear selection probability to each ant based on its path ranking. As a result, better ants preserve exponentially higher selection probabilities. Then, based on the selection probabilities, ants are adaptively selected for the pheromone update. By this means, on the one hand, the number of ants involved in the pheromone update is uncertain;on the other hand, relatively better ants instead of absolutely better ones are adaptively selected to update the pheromone, leading to the promotion of search diversity. Subsequently, a dynamic weighting strategy is designed to adjust the amount of the pheromone deposited by the best ant in the current iteration to enhance the search convergence. With the two schemes, AAS-ACO is expected to maintain a suitable compromise between search diversity and search convergence to seek the optimal solutions to TSP. Experiments on 10 classical TSP instances varying from 100 to 1000 cities have proven the significant superiority of AAS-ACO to 5 classic ACOs, especially on high-dimensional TSP problems. © 2024 IEEE.

关键词： Ant colony optimization

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AI-Powered MRI Scan Analysis for Enhanced Patient Hospitality: Real-Time Report Generation for Faster Diagnosis - A Systematic Review

AI-Powered MRI Scan Analysis for Enhanced Patient Hospitalit...

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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： M. Jaganath G. Ramya Shri B. I. Bharani S. G. Manoj Kumar S. Saravana Haris B. Karneya Department of Artificial Intelligence and Machine Learning M.Kumarasamy College of Engineering Karur India Department of Computer Science and Engineering M.Kumarasamy College of Engineering Karur India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

MRI scans play a vital role in the diagnosis and treatment of numerous medical conditions, and efficient analysis of these scans using AI is necessary for enhancing patient outcomes. This systematic review explores the application of deep learning models to AI-based MRI scan analysis, with an emphasis on real-time report generation for improving patient hospitality and speeding up the diagnosis process. We compare a range of deep learning networks such as ShuffleNet, VGG, EfficientNet, and AlexNet for examining the accurate model for MRI scan data classification. The results indicate that AlexNet performs better than the rest of the models, registering a remarkable 98% accuracy, beating VGG (96%), EfficientNet, and ShuffleNet (both 97%). AlexNet presents better performance in classifying critical classes like Elbow Mucosa, Elbow Crack, and Knee Benign Tissue with ideal precision, recall, and F1-scores. Yet, it indicates somewhat poor performance for Knee Muscularis and Knee Serosa. The results indicate that AlexNet provides a very effective tool for real-time automatic MRI scan analysis, leading to quicker diagnosis and better patient care. This AI-driven method, when incorporated into clinical processes, can really enhance diagnostic productivity and patient care.

关键词： Deep learning Analytical models Accuracy Magnetic resonance imaging Visual analytics Real-time systems Data models Medical diagnosis Elbow Systematic literature review

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One Policy to Run Them All: an End-to-end learning Approach to Multi-Embodiment Locomotion 8

One Policy to Run Them All: an End-to-end Learning Approach ...

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8th Conference on Robot learning, CoRL 2024

作者： Bohlinger, Nico Czechmanowski, Grzegorz Krupka, Maciej Kicki, Piotr Walas, Krzysztof Peters, Jan Tateo, Davide Department of Computer Science Technical University of Darmstadt Germany Institute of Robotics and Machine Intelligence Poznan University of Technology Poland Research Department: Systems AI for Robot Learning Germany IDEAS NCBR Warsaw Poland Hessian.AI Germany Centre for Cognitive Science Germany

Deep Reinforcement learning techniques are achieving state-of-the-art results in robust legged locomotion. While there exists a wide variety of legged platforms such as quadruped, humanoids, and hexapods, the field is still missing a single learning framework that can control all these different embodiments easily and effectively and possibly transfer, zero or few-shot, to unseen robot embodiments. We introduce URMA, the Unified Robot Morphology Architecture, to close this gap. Our framework brings the end-to-end Multi-Task Reinforcement learning approach to the realm of legged robots, enabling the learned policy to control any type of robot morphology. The key idea of our method is to allow the network to learn an abstract locomotion controller that can be seamlessly shared between embodiments thanks to our morphology-agnostic encoders and decoders. This flexible architecture can be seen as a potential first step in building a foundation model for legged robot locomotion. Our experiments show that URMA can learn a locomotion policy on multiple embodiments that can be easily transferred to unseen robot platforms in simulation and the real world. © 2024 Proceedings of machine learning Research.

关键词： Deep reinforcement learning

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