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Intelligent & Innovative Practices in engineering & Management (IIPEM), International Conference on

作者： Kamalesh Thoppaen Suresh Babu Bhagyashree Ashok Tingare Vijayshri Khedkar Tarun Dhar Diwan P. William Abhishek Badholia Globallogic Inc Santa Clara CA USA Department of Artificial Intelligence and Data Science D Y Patil College of Engineering Akurdi Pune Department of Computer Science & Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India Assistant Professor and Controller of Examination (COE) Atal Bihari Vajpayee University Bilaspur India Deaprtment of Information Technology Sanjivani College of Engineering Kopargaon Amity University Dubai UAE Department of Data Science Shri Shankaracharya Institute of Professional Management and Technology Raipur

ISBN: (数字)9798350390049

ISBN: (纸本)9798350390056

Deep learning has revolutionized data processing by recognizing complicated patterns using multi-layered neural networks. Encoding complex data like hyperspectral photos may help deep learning models perform better. Encoding shapes raw data for compression, dimensionality reduction, and feature extraction. These methods matter. This research found that higher-dimensional encodings can improve deep learning model accuracy and convergence. Complex encoding algorithms like FE-16D and BLOSUM-20D outperform simpler ones in validation accuracy and AUC. To prove this, extensive testing and comparison were done. The study emphasizes the necessity to combine deep learning frameworks with improved encoding techniques to improve machine learning and large-scale data processing.

关键词： Deep learning Dimensionality reduction Machine learning algorithms Computational modeling Neural networks Feature extraction Data processing Encoding Data models Hyperspectral imaging

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Facemask detection ontology with GBML as meta classifier

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AIP Conference Proceedings 2023年第1期2754卷

作者： B. J. D. Kalyani S. Neelima Kopparthi Praneeth Sai Kopparthi Bhanu Prashanth Associate Professor Department of Computer Science and Engineering Institute of Aeronautical Engineering Dundigal Hyderabad India 500043 Associate Professor Department of Computer Science and Engineering Priyadarshini Institute of Science and Technology for Women Khammam India 507002 Process Executive Wipro Limited Hyderabad India 500032 Vardhaman College of Engineering Hyderabad India 501218

World Health Organization (WHO) recommend usage of mask as an operative prevention to avoid spread of Covid 19. Various reports present that wearing face masks top virus transmission and reduces the risk of transmission. This paper focus on cloud-based AI methodology for the detection of face mask. A mask detection data base with two categories of information includes with mask and without mask images that are stored in cloud data Centre via webcam. The proposed work uses Full Convolution Neural Network (FCNN) and Gradient Boost Machine Learning (GBML) model for classification and detection mask. This paper focus on the objective of to recognize whether the person on image or video stream is wearing a mask or not and not wear properly.

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The use of modeling in the teaching of geometry and graphic disciplines at the University 2

The use of modeling in the teaching of geometry and graphic ...

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2nd International Scientific and Practical Conference on Information Technologies and Intelligent Decision Making Systems, ITIDMS 2021

作者： Vanshina, Ekaterina A. Shevchenko, Olga N. Malikova, Olga N. Staselko, Oksana L. Shusharina, Irina V. Associate Professor of Department of Descriptive Geometry Engineering and Computer Graphics FSBEI Orenburg State University Pobedy Ave. 13 Orenburg460018 Russia Department of Descriptive Geometry Graphics FSBEIHE Industrial University of Tyumen UL. Lunacharskogo 2 Tyumen625001 Russia

1. Introduction: In connection with the introduction of new Federal state educational standards of higher education (FSES) to teachers of geometry graphic departments there was a problem with the revision of the contents and methodology of teaching disciplines "descriptive geometry", "engineering and computer graphics", foundations of engineering education aimed at formation of basic knowledge for studying of the subsequent disciplines used graphic representation of information. The analysis of special literature has indicated that, despite the significant number of scientific publications on the research topic, the use of modeling in teaching geometric-graphic disciplines requires separate research. 2. Materials and methods: Study and analysis of scientific, educational and special literature on the research topic, comparison, systematization, synthesis, generalization. 3. Results: To implement modern teaching methods in the educational process in the geometric-graphic disciplines, the author applied the functionality of the system KOMPAS-3D and developed by: electronic slides based on the modeling included in the evaluation funds for engineering and computer graphics;set a clear three-dimensional (3D) models of twenty-assemblies products intended for assignments on practical and laboratory lessons on the computer in the system KOMPAS-3D to create an Assembly and working drawings;set of individual tasks designed to practical assignments on the computer in this system to create 2D-models of assemblies (Assembly drawings) and 2D models details (working drawings), associated with their three-dimensional models. 4. Discussion and Conclusions: Design and creation of didactic materials based on three-dimensional and flat modeling tools, methods and algorithms of computer graphics in the system KOMPAS-3D, their use in teaching geometric-graphic disciplines, the inclusion in the evaluation funds on descriptive geometry, engineering and computer graphics, contributes to the

关键词： Geometry

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Learning analytics using deep learning techniques for efficiently managing educational institutes

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Materials Today: Proceedings 2022年 51卷 2317-2320页

作者： Ravi Kishore Veluri Indrajit Patra Mohd Naved Veduri Veera Prasad Myla M. Arcinas Shehab Mohamed Beram Abhishek Raghuvanshi Aditya Engineering College(A) Surampalem India An Independent Researcher PhD from NIT Durgapur West Bengal India Department of Business Analytics Jagannath University Delhi-NCR India Associate Professor Behavioral Science Department De La Salle University Philippines Research Scholar Department of Computing and Information Systems Sunway University Malaysia Department of Computer Engineering Mahakal Institute of Technology Ujjain India

Increasing numbers of higher education institutions see themselves as service providers, catering primarily to the needs of its students. The improvement of student performance is a top priority for universities. It is critical to first assess the present situation of the students before designing a program to improve their performance. Higher education administrators face a significant problem in predicting a student's future success. The goal of this study is to learn what factors influence college students' decision on a major. It will be possible to forecast students' behavior, attitudes, and performance with the use of predictive tools and procedures. Predicting student performance ahead of time makes it possible to take proactive measures to raise achievement levels. To obtain a high education standard, several attempts have been made to forecast student performance. However the accuracy of these predictions falls short of the desired level of excellence. Machine learning approaches including Artificial Neural Network, Nave Bayes, and SVM are being studied. A University Data Set from UCI Machinery is used in the experimental investigation.

关键词： Learning analytics Deep learning Educational data mining Machine learning Student performance Classification Prediction

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Reinforcement learning: A novel approach towards drug discovery

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AIP Conference Proceedings 2023年第1期2477卷

作者： Nishath Ansari Amjan Shaik Patluri Harika Boga Varasree K. Purnachand Saikumar Tara Assistant Professor Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Telangana India Professor Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Telangana India Assistant Professor Department of Computer Science and Engineering Institute of Aeronautical Engineering Dundigal Hyderabad Telangana India Assistant Professor Department of Information Technology Institute of Aeronautical Engineering Dundigal Hyderabad Telangana India Associate Professor Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Telangana India Associate Professor Department of Electronics and Communication Engineering CMR Technical campus Hyderabad Telangana India

Reinforcement learning has been giving new strategies to ad-lib in the fields of medication and the drug industry. It is probably the best strategy among all the AI strategies. Reinforcement learning has been compelling to discover strategies that could treat the immense number of deadly infections. In this paper, a detailed new methodology about what Reinforcement Leaning and what is drug revelation and what are various techniques utilized in Reinforcement learning, how Reinforcement learning could be utilized in different utilization of medication plan such as target identification, hit discovery, hit to lead, lead optimization. The consequence of the paper illustrates the summarized data of different approaches for the development and discovery of drugs. A clear illustration of various work done in the field of drug discovery, discuss different pros and cons of Reinforcement learning as well emphasize primary challenges need to handle and how to defeat them in the future.

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Recent developments of load balancing in cloud computing: A review

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AIP Conference Proceedings 2025年第1期3233卷

作者： Srishti Agarwal Jitendra Singh M. Izharul Hasan Ansari Department of Computer Science & Engineering SRM Institute of Science &Technology NCR Campus Ghaziabad India Professor Department of Computer Science & Engineering Meerut Institute of Technology Meerut India

Large-scale distributed and parallel computing is denoted as cloud computing. Based on the user needs, it deals with shared resources, storage, and information. A lot of people are becoming interested in cloud computing; thus it is growing quickly due to its user-satisfying services. There are several open research issues and load balancing is one of the challenging task in cloud computing. It has been observed that load-improved cloud system balancing algorithms surge performance through better resource utilization by dispersing the workload among the system’s many nodes in a dynamic manner. This review article examines virtualization, load balancing, problems, cloud computing architectures, and several load balancing techniques that are currently in use.

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Optimized fuzzy C-means clustering algorithm for cotton fibre quality analysis

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Materials Today: Proceedings 2021年 45卷 2082-2086页

作者： R. Dhanapal D. Selvapandian Assistant Professor Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore 638107 India

Exact determination of cotton fibre quality is a huge help for turning factories to purchase cotton as per the least prerequisites. The nature of cotton fibre is decided on numerous elements, for example, staple length, development, finesses, neatness, tenacity , and quality, to make reference to. The essential target of the framework is to accomplish the institutionalization at various levels in the cotton production network the board in the conveyed mechanical systems condition with exact estimation and characterization procedures. The current framework is executed in information mining procedures to foresee the nature of cotton fibre and to establish the most noteworthy connection between's short fibre file and yellowness. The proposed framework incorporates a bunching strategy to group cotton quality fibre. The proposed optimized principal component analysis (OPCA), and Fuzzy C-means bunching in huge dataset. These proposed calculations are executed inside the Map-Reduce method and cuckoo searching technique is applied for reduces the time complexity it is implemented in Hadoop stage to anticipate dampness content, micronaire, extension, and group quality.

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Analysis and visualization of proxy caching using LRU, AVL tree and BST with supervised machine learning

Analysis and visualization of proxy caching using LRU, AVL t...

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International Conference on Computations in Materials and Applied engineering, CMAE 2021

作者： Kushwah, Jitendra Singh Gupta, Deepak Shrivastava, Anurag Ambily Pramitha, P. Abraham, John T. Lunagaria, Munindra Department of Computer Science and Engineering Institute of Technology and Management Madhya Pradesh Gwalior India Principal and Professor ECE Lakshmi Narain College of Technology and Science Madhya Pradesh Indore India Bharathiyar University Tamil Nadu Coimbatore India Bharata Mata College Thrikkakara Kerala Kochi India Computer Engineering Department Marwadi University Gujarat Rajkot India

Proxy cache access speeds and decreases load time. There is an uncertainty regarding cache level 2 which you neglect. This research shall investigate the L1 cache, primary cache, and L2 cache as a secondary proxy server cache. LRU is typically utilised instead of cache. LRU for cold cache removal used to be a time-consuming process, but it isn't particularly efficient today. The performance of the cache L1 used LRU and L2 used LRU_AVL has risen with these solutions. The output is the LRU_AVL among other ways that utilize LRU tables and graphs. proxy cache LRU, LRU_AVL, and LRU_BST have average time of access calculated. Median access time is estimated using Python tools including Pandas, MatPlotLib as well as LRU, LRU_AVL, and LRU_BST. This research will anticipate the average period of usage of LRU, LRU_AVL, and LRU_BST cache algorithms. © 2021 Elsevier Ltd. All rights reserved.

关键词： Proxy caches

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Analysis and visualization of proxy caching using LRU, AVL tree and BST with supervised machine learning

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Materials Today: Proceedings 2022年 51卷 750-755页

作者： Jitendra Singh Kushwah Deepak Gupta Anurag Shrivastava P. Ambily Pramitha John T. Abraham Munindra Lunagaria Department of Computer Science and Engineering Institute of Technology and Management Gwalior Madhya Pradesh India Principal and Professor ECE Lakshmi Narain College of Technology and Science Indore Madhya Pradesh India Bharathiyar University Coimbatore Tamil Nadu India Bharata Mata College Thrikkakara Kochi Kerala India Computer Engineering Department Marwadi University Rajkot Gujarat India

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Optimizing Mobile Advertising with Reinforcement Learning and Deep Neural Networks

Optimizing Mobile Advertising with Reinforcement Learning an...

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Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), International Conference on

作者： Divya Nimma Chamandeep Kaur Gunjan Chhabra V. Selvi Divya Tyagi A. Balakumar University of southern Mississippi Data Analyst in UMMC USA Department of Computer Science & Information Technology Jazan University Jizan Kingdom of Saudi Arabia Department of CSE Graphic Era Hill University Dehradun Adjunct Professor Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science Mother Teresa Women's University Kodaikanal India Sanskaram University Jhajjar Haryana India Department of ECE K. Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

Mobile advertising is also closely associated with contemporary digital marketing channels, where the projective targeting is critical in regard to businesses’ potential performance. This research makes use of a combination of machine learning and reinforcement learning (RL) to determine the best place to post an ad considering the users’ demographic and behavior. Deepening the neural networks (DNNs) in this regard, the method is expected to advance the efficiency of mobile advertising while delivering more relevant content to the target user. Purchasing tendency predictions in the report are based on Python-based models through a dataset from Kaggle that has incorporated user demographics (Gender, Age, Estimated Salary) and purchasing patterns. The missing values are first handled using the different techniques then normalization of the numerical data and one hot coding of the features such as the Device Type and the Location. Automatic feature are extracted with the help of DNNs which obviate the process of feature extraction. This approach proposed yielded an accuracy level of 98% a higher level as compared to the CNN and LSTM models. For decision-making reinforcement learning is used with the help of Deep Q-Network (DQN) and Policy gradient. DQNs predict Q-values for the actions (ad displays) and choose the best action, while Policy Gradient methods directly learn the policy by modifying the probabilities related to the actions based on the users’ behavior. The training techniques used on both models include backpropagation and stochastic gradient descent, which enables the RL agent in the continuous implementation of better ad placement strategies. Here they used DNN to train the features and RL to optimize the training and then improve the on average click through rates and conversion rates which provides a better strategy towards advertising.

关键词： Training Quantum computing Stochastic processes Reinforcement learning Predictive models Feature extraction Numerical models Remuneration Advertising Long short term memory

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