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Science, Technology, engineering and Management (ICSTEM), International Conference on

作者： Gautam Solaimalai J. Maria Shanthi Sampath Kumar S Priyanka Khabiya K. Geetha Gokul Talele US. Bank Software Engineering Manager Georgia United States of America Department of Artificial Intelligence and Machine Learning J B. Institute of Engineering and Technology Hyderabad Telangana India Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Computer Science Engineering Mandsaur University Mandsaur Madhya Pradesh India Department of Computer Science G. T.N Arts College Dindigul Tamil Nadu India Department of Data Science Organization name Accenture Pune Maharashtra India

ISBN: (数字)9798350376913

ISBN: (纸本)9798350388473

The proliferation of virtual sidekicks in colorful disciplines has prodded a swell of interest in advancing Natural Language Processing(NLP) ways to enhance their effectiveness. This paper provides a comprehensive review of the current approaches and challenges encountered in integrating NLP into virtual sidekicks. It begins by outlining the foundational generalities of NLP and its vital part in enabling mortal- suchlike relations with virtual sidekicks. latterly, it delves into a discussion of the different methodologies employed in NLP for understanding and generating mortal language, ranging from rule-grounded systems to deep literacy models. likewise, the paper highlights the crucial challenges such as nebulosity resolution, environment understanding, and handling different verbal variations that stymie the flawless functioning of virtual sidekicks. By synthesizing perceptivity from recent exploration trials, this paper offers precious perspectives on the state-of-the-art NLP ways and directions for unborn exploration to overcome the challenges and propel the development of further intelligent and intuitive virtual sidekicks.

关键词： Reviews Virtual assistants Propulsion Natural language processing Distance measurement

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Customer Segmentation Using Clustering Analysis

Customer Segmentation Using Clustering Analysis

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Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), IEEE International Conference on

作者： Moses Makuei Jiet Aahash Kamble Chetan Puri Prajyot Yesankar Prateek Verma Rajendra Rewatkar Department of Computer Science & Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India Department of Artificial Intelligence & Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India Department of Artificial Intelligence & Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India Department of Biomedical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

This research focuses on the crucial role of the clustering technique in data mining, specifically in market forecasting and planning. The study presents a comprehensive report on utilizing the k-means clustering technique and data visualization tools to develop a real- time system for a supermarket, predicting sales across various annual cycles. An open-source dataset of a mall with n = 200 customers divided into k = 3 groups, demonstrated high accuracy in predicting day-to-day sales statistics when compared with actual results. The research highlights the effectiveness of the clustering technique in real-time sales prediction for supermarkets.

关键词： Sociology Refining Decision making data visualization Prediction algorithms Real-time systems Planning

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Artificial Intelligence-Driven Protein Folding System Employing Alpha Fold

Artificial Intelligence-Driven Protein Folding System Employ...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： Sowmiya R M. Pravin Kumar T. M. Sathish Kumar Rasika P M. Poornima Devi S. Priya Dharshini Department of Artificial Intelligence and Data Science SNS College of Engineering Coimbatore Tamil Nadu India Department of Medical Electronics Velalar College of Engineering and Technology (Autonomous) Erode Tamil Nadu India Department of Electronics and Communication Engineering K.S.R. College of Engineering Namakkal Tamil Nadu India Department of Pharmaceutics SNS College of Pharmacy and Health Sciences Coimbatore Tamil Nadu India Department of Artificial Intelligence and Machine Learning SNS College of Technology Coimbatore Tamil Nadu India Department of Pharmaceutics Periyar College of Pharmaceutical Sciences Tiruchirappalli Tamil Nadu India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Artificial Intelligence (AI) is a main role to solve the lot of real-live issues. One of the significant challenges in the bioinformatics that researchers have been identified recently is understand the protein folding. First of all, people want to give a big hand off to the advancements because, day to day activities is performed in 3-dimensional basis that is formed based on a sequence of amino acids using protein folds. The folding of proteins is mostly related to three major issues. The first issue is the lack of a specific folding code. There is a folding mechanism in the second one. The ability to ascertain the 3-dimensional structure of proteins presents the last challenge. The structural components and microscopic machinery that make up human cells are called proteins. They provide vital tasks include storing oxygen, breaking down food, and creating scaffolding to support cells in maintaining their structure. Here, Amino acids is formed based on a single chain mechanism because amino acids folds safely and it performs a unique structure that is incorporated here. All living things, including viruses, are composed of proteins, including every element of human body and every other organism. The computational approach can be used to predict the structure of minuscule proteins. In this research work explained about how artificial intelligence is incorporated in predictions of protein structure and artificial intelligence give the solution for the problem of problem folding.

关键词： Proteins Hands Computer viruses Microscopy Amino acids Organisms Artificial intelligence machinery Collaborative intelligence Periodic structures

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Implicit Regularization for Tubal Tensor Factorizations via Gradient Descent

arXiv

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arXiv 2024年

作者： Karnik, Santhosh Veselovska, Anna Iwen, Mark Krahmer, Felix Department of Mathematics Northeastern University Boston United States Department of Computational Mathematics Science and Engineering Michigan State University East Lansing United States TUM School of Computation Information and Technology Munich Data Science Institute Technical University of Munich Garching Germany Munich Center for Machine Learning Munich Germany Department of Mathematics Michigan State University East Lansing United States

We provide a rigorous analysis of implicit regularization in an overparametrized tensor factorization problem beyond the lazy training regime. For matrix factorization problems, this phenomenon has been studied in a number of works. A particular challenge has been to design universal initialization strategies which provably lead to implicit regularization in gradient-descent methods. At the same time, it has been argued by [7] that more general classes of neural networks can be captured by considering tensor factorizations. However, in the tensor case, implicit regularization has only been rigorously established for gradient flow or in the lazy training regime. In this paper, we prove the first tensor result of its kind for gradient descent rather than gradient flow. We focus on the tubal tensor product and the associated notion of low tubal rank, encouraged by the relevance of this model for image data. We establish that gradient descent in an overparametrized tensor factorization model with a small random initialization exhibits an implicit bias towards solutions of low tubal rank. Our theoretical findings are illustrated in an extensive set of numerical simulations show-casing the dynamics predicted by our theory as well as the crucial role of using a small random initialization. © 2024, CC BY-SA.

关键词： Matrix factorization

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Development of Modern Forecasting Models 16

Development of Modern Forecasting Models

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16th International Conference Management of Large-Scale System Development, MLSD 2023

作者： Ivanyuk, Vera Shuvalov, Konstantin Akhobadze, Gurami Malekova, Victoria Mikhailov, Alexey Levchenko, Kiril Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia Bauman Moscow State Technical University Department of Higher Mathematics Moscow Russia V.A.Trapeznikov Institute of Control Sciences of Ras Laboratory of Management of Large-Scale System Development Moscow Russia V. A.Trapeznikov Institute of Control Sciences of Ras Laboratory of Network Systems Control Moscow Russia Financial University under the Government of the Russian Federation Department of Financial Markets and Financial Engineering Moscow Russia Financial University under the Government of the Russian Federation Department of Mathematics Moscow Russia

ISBN: (纸本)9798350337907

关键词： Forecasting

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Cloud Based Intelligent Nutrient Management System for Precision Agriculture Using CNN

Cloud Based Intelligent Nutrient Management System for Preci...

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： M. Shafiya Banu Arshadh Ariff Mohamed Abuthahir R. Mohandas D. Antony Joseph Rajan B. Meenakshi N. Mohankumar Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai Tamil Nadu India Riazulhameed Architect - Data Engineer Blackstraw Technologies Pvt Ltd Chennai Tamil Nadu India Department of Electronics and Communication Engineering Chenai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Sri Sairam Engineering College Chennai Tamil Nadu India Symbiosis Institute of Technology Symbiosis International (Deemed University) Nagpur Maharashtra India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Precision agriculture struggles with scalability, data integration, and decision-making, prompting innovative solutions. Recent advancements like CNNs and cloud computing provide interesting answers but confront challenges. data quality, processing needs, interoperability, and real-time decision-making are issues. These difficulties demand innovative data synchronization, model dependability, and system scalability methods. This project uses sophisticated analytics and cloud technology to improve nutrient management systems. Improving scalability, accuracy, and efficiency while presenting unique data fusion and user-friendly interfaces are goals. Clear problems and goals guide the study's scope and purpose. Wireless soil nutrient sensors can capture real-time data on important soil factors such as pH levels, the amount of nitrogen, phosphate, and potassium present, and more. This information is then sent to a platform hosted in the cloud, where it is processed using sophisticated data analytics and machine learning algorithms, in addition to previous crop performance data and agronomic models. The program will create tailored suggestions for nutrient management, including topics such as the kinds of fertilizer, application rates, and the best time. These proposals give farmers more agency, allowing them to make educated decisions via an easily navigable interface. Because of the system's ability to react to changing circumstances and its continual monitoring, nutrient management techniques are kept in sync with those changes throughout the crop's life cycle.

关键词： Precision agriculture Cloud computing Scalability Decision making Soil Nutrients Real-time systems

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Cross-Store Next-Basket Recommendation

Cross-Store Next-Basket Recommendation

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IEEE International Conference on data Mining (ICDM)

作者： Liang-Chen Ma Ya Li Zi-Feng Mai Fei-Yao Liang Chang-Dong Wang Min Chen Mohsen Guizani School of Computer Science and Engineering Sun Yat-sen University Guangzhou China School of Electronics and Information Guangdong Polytechnic Normal University Guangzhou China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi UAE

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Next-basket recommendation (NBR) infers a set of items that a user will interact with in the next basket. Existing methods often struggle with the data sparsity problem, particularly when the number of baskets is significantly large due to diverse user behaviors. Cross-domain recommendation (CDR) can effectively alleviate this problem in NBR by transferring knowledge across different domains. Nevertheless, these methods often rely on the similarities of overlapping users, which leads to the negative transfer problem and ignores the overlapping items that are general in real-world scenarios like chain stores. In this paper, we provide a clear symbolic definition of cross-store recommendation (CSR) and distinguish it from CDR. We also propose a novel CSNBR model for cross-store next-basket recommendation task. To fully model the transferable collaborative information between two stores, we learn the embeddings of users, baskets, and items by two intra-store bipartite graphs, and use an inter-store unified bipartite graph to transfer the previously learned knowledge. Furthermore, to alleviate the negative transfer problem, we propose to reconstruct the inter-store unified bipartite graph by utilizing user embeddings obtained from the transfer layer and the disentanglement layer. We also employ two sequence encoders to model the historical sequential information at basket-level and item-level. Extensive experiments conducted on real-world datasets demonstrate the effectiveness of the CSNBR model.

关键词： Neural networks Collaboration data models Bipartite graph data mining

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The Spatial-Temporal Evolution and Influencing Factors of Public Attention to 5G: Empirical Analysis Based on Baidu Index from 2011 to 2021

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Mathematical Problems in engineering 2023年第1期2023卷

作者： Liu, Pan Wang, Guanyu Liu, Kai Li, Junke Chen, Yanlin School of Information Engineering Suqian University Jiangsu Suqian223800 China School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun558000 China Key Laboratory of Machine Learning and Unstructured Data Processing of Qiannan Guizhou Duyun558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun558000 China

The development of 5G (fifth-generation wireless systems) determines the future direction of technology and economy and has received extensive public attention. Studying the changing rules of public attention to 5G can provide an important guiding significance for the sustainable development of 5G. This paper takes Baidu Index as the measurement index of 5G public attention and analyzes the spatial and temporal evolution characteristics and influencing factors of public attention to 5G from 2011 to 2021 by using the elasticity coefficient, Gini coefficient, geographical concentration index, and panel data model. The results of the study show the following. (1) The public concern to 5G is generally on the rise, but the heat has declined in the past two years. (2) The public’s 5G attention shows a seasonal effect, with the highest attention in March and June. (3) The spatial difference of 5G public attention is obvious. The eastern region has a high degree of attention, the internal differences between the eastern and western regions are obvious, and the central region is relatively balanced. (4) The factors such as local economic level, education level, Internet development, and media attention have significantly affected the public focus on 5G. Also, some suggestions are made for the sustainable development of 5G and the planning of 6G (sixth-generation wireless systems). Copyright © 2023 Pan Liu et al.

关键词： Sustainable development

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Optical Character Recognition (OCR) in Handwritten Characters Using Convolutional Neural Networks to Assist in Exam Reader System

Optical Character Recognition (OCR) in Handwritten Character...

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Advancement in Computation & Computer Technologies (InCACCT), International Conference on

作者： P L Lekshmy S. Velmurugan Indra Kumari S. Kayalvili B. Teja Sree P. Karthik Kumar Department of Computer Science and Engineering LBS Institute of Technology for Women Kerala India Department of Electronics and Communication Engineering T.J.S. Engineering College Chennai Tamil Nadu India Department of Machine Learning Data Research Applied AI Korea National University of Science and Technology (UST) Daejeon South Korea Department of Artificial Intelligence Kongu Engineering College (Autonomous) Erode Tamilnadu India Department of Information Technology S.R.K.R. Engineering College Chinaamiram Bhimavaram Andhra Pradesh India Department of Computer Science and Engineering (Cyber Security) Karpagam College of Engineering Coimbatore India

ISBN: (数字)9798350371314

ISBN: (纸本)9798350371321

This work aimed to develop a character recognition method to facilitate the correction of answer cards in the Multiprova software through the development of a response card analysis flow that would culminate in the recognition of letters written by students and automatic correction of the tests. To isolate and identify the answers written on the answer cards, image segmentation techniques were used based on fixed marks printed on the cards. To recognize the letters and numbers written on the cards, trained three convolutional neural networks (for digits, letters and true or false). The results achieved (98.84% accuracy for digit CNN, 98.38% accuracy for letter CNN and 99.89% accuracy for true or false CNN) point out a great average success rate.

关键词： Training Image segmentation Image recognition Codes Optical character recognition Neural networks Streaming media

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Achieving Linear Speedup with Network-Independent learning Rates in Decentralized Stochastic Optimization

Achieving Linear Speedup with Network-Independent Learning R...

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IEEE Conference on Decision and Control

作者： Hao Yuan Sulaiman A. Alghunaim Kun Yuan Center for Machine Learning Research Peking University Beijing P. R. China Dept. Electrical Engr. Kuwait University Safat Kuwait AI for Science Institute Beijing P. R. China National Engineering Labratory for Big Data Analytics and Applications Beijing P. R. China

Decentralized stochastic optimization has become a crucial tool for addressing large-scale machine learning and control problems. In decentralized algorithms, all computing nodes are connected through a network topology, and each node communicates only with its direct neighbors. Decentralized algorithms can significantly reduce communication overhead by eliminating the need for global communication. However, existing research on the linear speedup analysis of decentralized stochastic algorithms is limited to the condition of network-dependent learning rates, which rarely holds in practice since the network connectivity is typically unknown to each node. As a result, it remains an open question whether a linear speedup bound can be achieved using network-independent learning rates. This paper provides an affirmative answer. By utilizing a new analysis framework, we prove that D-SGD and Exact-Diffusion, two representative decentralized stochastic algorithms, can achieve linear speedup with network-independent learning rates. Simulations are provided to validate our theories.

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