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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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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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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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A Quantum Algorithm for Evaluating the Hamming Distance

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computers, Materials & Continua 2022年第4期71卷 1065-1078页

作者： Mohammed Zidan Manal G.Eldin Mahmoud Y.Shams Mohamed Tolan Ayman Abd-Elhamed Mahmoud Abdel-Aty Department of Artificial Intelligence Hurghada Faculty of Computers and Artificial IntelligenceSouth Valley UniversityEgypt Faculty of Engineering King Salman International UniversitySouth SinaiEgypt Department of Mathematics and Computer Science Faculty of ScienceBeni-Suef UniversityBeni-SuefEgypt Department of Machine Learning and Information Retrieval Faculty of Artificial IntelligenceKafrelsheikh UniversityEgypt Mechanical Department Faculty of Technology and EducationSuez UniversitySuezEgypt Faculty of Technological Industries King Salman International UniversitySouth SinaiEgypt Faculty of Engineering at Mataria Helwan UniversityCairoEgypt Department of Mathematics Faculty of SciencesSohag University82524 SohagEgypt

We present a novel quantum algorithm to evaluate the hamming distance between two unknown oracles via measuring the degree of entanglement between two ancillary *** particular,we use the power of the entanglement degree based quantum computing model that preserves at most the locality of interactions within the quantum model *** model uses one of two techniques to retrieve the solution of a quantum computing problem at *** the first technique,the solution of the problem is obtained based on whether there is an entanglement between the two ancillary qubits or *** the second,the solution of the quantum computing problem is obtained as a function in the concurrence value,and the number of states that can be generated from the Boolean *** proposed algorithm receives two oracles,each oracle represents an unknown Boolean function,then it measures the hamming distance between these two *** hamming distance is evaluated based on the second *** is shown that the proposed algorithm provides exponential speedup compared with the classical counterpart for Boolean functions that have large numbers of Boolean *** proposed algorithm is explained via a case ***,employing recently developed experimental techniques,the proposed algorithm has been verified using IBM’s quantum computer simulator.

关键词： Quantum computing quantum algorithm quantum circuit

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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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Liver Tumor detection and segmentation using Cascaded FCNN

Liver Tumor detection and segmentation using Cascaded FCNN

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2024 International Conference on Computational intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Valarmathy, G. Rashme, M. Umapathi, N. Saai Srivathsan, R. Sri Dhivya Krishnan, K. Chennai India Sri Sairam Institute of Technology Department of Computer and Communication Chennai India Jyothishmathi Institute of Technology and Science Department of Electronics and Communication Karimnagar India Sri Sairam Engineering College Department of Artificial Intelligent & Machine Learning Chennai India

ISBN: (纸本)9798350381887

One of the leading causes of death in humans is liver tumors. Ignorance at an early age leads to serious health issues and may even be fatal. The earlier a tumor is identified and treated, the lower the chance of death. Manual procedures require a lot of time to find tumors. Even if there are numerous approaches for this procedure, automatic tumor detection will solve this issue, yet it still has certain limitations, such as inaccurately identifying larger cells and not being able to detect tumors in their early stages. This paper presents a deep learning approach to the identification and segmentation of liver cancer. To prevent these limitations, a fully Cascaded Convolutional Neural Network (FC-CNN) is proposed. From this FCCNN technique will improve the accuracy to 84% and the time consumption also was reduced. © 2024 IEEE.

关键词： Convolutional neural networks

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Harnessing NLP and Deep learning to Decode Disease–Gene Interactions in Lung Cancer

Harnessing NLP and Deep Learning to Decode Disease–Gene Int...

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Women in Computing (InCoWoCo), International Conference for

作者： K. Mary Sudha Rani V. Kamakshi Prasad Department of Artificial Intelligence and Machine Learning Chaitanya Bharathi Institute of Technology Hyderabad India Department of Computer Science and Engineering Jawaharlal Nehru Technological University Hyderabad Hyderabad India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

Lung cancer remains a critical global health challenge, necessitating advanced predictive models to enhance diagnostic and prognostic accuracy. This study employs natural language processing (NLP) and deep learning to advance personalized lung cancer prognosis and treatment response prediction. Using a synthetic dataset, our novel neural network model integrates clinical data with patient narratives to extract meaningful insights, correlating patient transcripts with existing datasets. The model interprets lung cancer histology types, gene sets, and genetic mutations, achieving 85% accuracy in predicting treatment responses, with a sensitivity of 0.88 and specificity of 0.83. By merging clinical and transcript-based data, this research provides clinicians with a reliable tool to support personalized medicine and informed decision-making. The results highlight the potential of AI-driven approaches to improve disease-gene association analysis, paving the way for innovative computational methodologies in lung cancer research.

关键词： Deep learning Ethics Accuracy Neural networks Genetic mutations Lung cancer Predictive models Natural language processing Data models Synthetic data

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Vehicle Type Detection and Classification Using Improved Relief Feature Selection Algorithm and Bidirectional Gated Recurrent Unit 2

Vehicle Type Detection and Classification Using Improved Rel...

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

作者： Monica, K.M. Al-Farouni, Mohammed Madhura, G.K. Ghamya, K. Geethalakshmi, M. Vellore Institute Of Technology School Of Computer Science And Engineering Chennai India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Department Of Artificial Intelligence And Machine Learning Bengaluru India Tirupati India Kongunadu College Of Engineering And Technology Department Of Electronics And Communication Engineering Trichy India

ISBN: (纸本)9798350381665

Nowadays, Traffic density estimation can be utilized to control traffic light signals for managing the traffic effectively. Vehicle Type Classification (VTC) is used in this traffic density. Because of the factors like minimal interclass variance and maximum intraclass variation, the VTC is challenging which is handled by proposed Improved Relief Feature Selection Algorithm and Bi-directional Gated Recurrent Unit (IRFSA-Bi-GRU) method. Bi-directional Gated Recurrent Unit (Bi-GRU) model is proposed for enhancing Vehicle Type Classification (VTC) within Intelligent Transport Systems. Multiview In Out Traffic Complexity Database (MIOTCD) is used for data collection then a camera response model is used for enhancing visual data quality. Kalman filtering and Gaussian mixture model techniques are utilized for vehicle recognition and tracking. Local Binary Pattern (LBP), Steerable Pyramid Transform (SPT), and Dual Tree Complex Wavelet Transform (DTCWT) are used for feature extraction and Improved Relief Feature Selection Algorithm (IRFSA) is used for feature selection. Finally, the Bi-directional Gated Recurrent Unit (Bi-GRU) model is used for classifying 11 vehicle categories on MIO-TCD. The obtained results exposed that the IRFSA-BiGRU method outperformed the existing methods by achieving an accuracy of 99.02%, 98.34% F-score, 98.78% MCC,99.93% specificity, 99.32% NPV, 98.75% precision, 98.14% recall on the MIO-TCD database. © 2024 IEEE.

关键词： Classification (of information)

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